Search results for: publicly traded corporations

Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Aditya Sharma

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Profits earned from relative strength strategy of zero-cost portfolio i.e. taking long position in winner stocks and short position in loser stocks from recent past are termed as momentum profits. In recent times, there has been lot of controversy and concern about sources of momentum profits, since the existence of these profits acts as an evidence of earning non-normal returns from publicly available information directly contradicting Efficient Market Hypothesis. Literature review reveals conflicting theories and differing evidences on sources of momentum profits. This paper aims at re-examining the sources of momentum profits in Indian capital markets. The study focuses on assessing the effect of fundamental as well as behavioral sources in order to understand the role of investor behavior in stock returns and suggest (if any) improvements to existing behavioral asset pricing models. This Paper adopts calendar time methodology to calculate momentum profits for 6 different strategies with and without skipping a month between ranking and holding period. For each J/K strategy, under this methodology, at the beginning of each month t stocks are ranked on past j month’s average returns and sorted in descending order. Stocks in upper decile are termed winners and bottom decile as losers. After ranking long and short positions are taken in winner and loser stocks respectively and both portfolios are held for next k months, in such manner that at any given point of time we have K overlapping long and short portfolios each, ranked from t-1 month to t-K month. At the end of period, returns of both long and short portfolios are calculated by taking equally weighted average across all months. Long minus short returns (LMS) are momentum profits for each strategy. Post testing for momentum profits, to study the role market risk plays in momentum profits, CAPM and Fama French three factor model adjusted LMS returns are calculated. In the final phase of studying sources, decomposing methodology has been used for breaking up the profits into unconditional means, serial correlations, and cross-serial correlations. This methodology is unbiased, can be used with the decile-based methodology and helps to test the effect of behavioral and fundamental sources altogether. From all the analysis, it was found that momentum profits do exist in Indian capital markets with market risk playing little role in defining them. Also, it was observed that though momentum profits have multiple sources (risk, serial correlations, and cross-serial correlations), cross-serial correlations plays a major role in defining these profits. The study revealed that momentum profits do have multiple sources however, cross-serial correlations i.e. the effect of returns of other stocks play a major role. This means that in addition to studying the investors` reactions to the information of the same firm it is also important to study how they react to the information of other firms. The analysis confirms that investor behavior does play an important role in stock returns and incorporating both the aspects of investors’ reactions in behavioral asset pricing models help make then better.

Keywords: investor behavior, momentum effect, sources of momentum, stock returns

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Authors: Niklas Ravn-Boess, Nainita Bhowmick, Takamitsu Hattori, Shohei Koide, Christopher Park, Dimitris Placantonakis

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Background: Glioblastoma (GBM) is the most common and deadly primary brain malignancy in adults. Tumor propagation, brain invasion, and resistance to therapy critically depend on GBM stem-like cells (GSCs); however, the mechanisms that regulate GSC self-renewal are incompletely understood. Given the aggressiveness and poor prognosis of GBM, it is imperative to find biomarkers that could also translate into novel drug targets. Along these lines, we have identified a cell surface antigen, CD97 (ADGRE5), an adhesion G protein-coupled receptor (GPCR), that is expressed on GBM cells but is absent from non-neoplastic brain tissue. CD97 has been shown to promote invasiveness, angiogenesis, and migration in several human cancers, but its frequency of expression and functional role in regulating GBM growth and survival, and its potential as a therapeutic target has not been investigated. Design: We assessed CD97 mRNA and protein expression in patient derived GBM samples and cell lines using publicly available RNA-sequencing datasets and flow cytometry, respectively. To assess CD97 function, we generated shRNA lentiviral constructs that target a sequence in the CD97 extracellular domain (ECD). A scrambled shRNA (scr) with no predicted targets in the genome was used as a control. We evaluated CD97 shRNA lentivirally transduced GBM cells for Ki67, Annexin V, and DAPI. We also tested CD97 KD cells for their ability to self-renew using clonogenic tumorsphere formation assays. Further, we utilized synthetic Abs (sAbs) generated against the ECD of CD97 to test for potential antitumor effects using patient-derived GBM cell lines. Results: CD97 mRNA expression was expressed at high levels in all GBM samples available in the TCGA cohort. We found high levels of surface CD97 protein expression in 6/6 patient-derived GBM cell cultures, but not human neural stem cells. Flow cytometry confirmed downregulation of CD97 in CD97 shRNA lentivirally transduced cells. CD97 KD induced a significant reduction in cell growth in 3 independent GBM cell lines representing mesenchymal and proneural subtypes, which was accompanied by reduced (~20%) Ki67 staining and increased (~30%) apoptosis. Incubation of GBM cells with sAbs (20 ug/ ml) against the ECD of CD97 for 3 days induced GSC differentiation, as determined by the expression of GFAP and Tubulin. Using three unique GBM patient derived cultures, we found that CD97 KD attenuated the ability of GBM cells to initiate sphere formation by over 300 fold, consistent with an impairment in GSC self-renewal. Conclusion: Loss of CD97 expression in patient-derived GBM cells markedly decreases proliferation, induces cell death, and reduces tumorsphere formation. sAbs against the ECD of CD97 reduce tumorsphere formation, recapitulating the phenotype of CD97 KD, suggesting that sAbs that inhibit CD97 function exhibit anti-tumor activity. Collectively, these findings indicate that CD97 is necessary for the proliferation and survival of human GBM cells and identify CD97 as a promising therapeutically targetable vulnerability in GBM.

Keywords: adhesion GPCR, CD97, GBM stem cell, glioblastoma

Procedia PDF Downloads 109

Authors: Z. Gharaee

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Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

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Authors: Bahareh Ansari

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Background: The Open Government Data (OGD) movement in the last decade has encouraged many government organizations around the world to make their data publicly available to advance democratic processes. But current open data platforms have not yet reached to their full potential in supporting all interested parties. To make the data useful and understandable for everyone, scholars suggested that opening the data should be supplemented by visualization. However, different visualizations of the same information can dramatically change an individual’s cognitive and emotional experience in working with the data. This study reviews the data visualization literature to create a list of the methods empirically tested to enhance users’ performance and experience in working with a visualization tool. This list can be used in evaluating the OGD visualization practices and informing the future open data initiatives. Methods: Previous reviews of visualization literature categorized the visualization outcomes into four categories including recall/memorability, insight/comprehension, engagement, and enjoyment. To identify the papers, a search for these outcomes was conducted in the abstract of the publications of top-tier visualization venues including IEEE Transactions for Visualization and Computer Graphics, Computer Graphics, and proceedings of the CHI Conference on Human Factors in Computing Systems. The search results are complemented with a search in the references of the identified articles, and a search for 'open data visualization,' and 'visualization evaluation' keywords in the IEEE explore and ACM digital libraries. Articles are included if they provide empirical evidence through conducting controlled user experiments, or provide a review of these empirical studies. The qualitative synthesis of the studies focuses on identification and classifying the methods, and the conditions under which they are examined to positively affect the visualization outcomes. Findings: The keyword search yields 760 studies, of which 30 are included after the title/abstract review. The classification of the included articles shows five distinct methods: interactive design, aesthetic (artistic) style, storytelling, decorative elements that do not provide extra information including text, image, and embellishment on the graphs), and animation. Studies on decorative elements show consistency on the positive effects of these elements on user engagement and recall but are less consistent in their examination of the user performance. This inconsistency could be attributable to the particular data type or specific design method used in each study. The interactive design studies are consistent in their findings of the positive effect on the outcomes. Storytelling studies show some inconsistencies regarding the design effect on user engagement, enjoyment, recall, and performance, which could be indicative of the specific conditions required for the use of this method. Last two methods, aesthetics and animation, have been less frequent in the included articles, and provide consistent positive results on some of the outcomes. Implications for e-government: Review of the visualization best-practice methods show that each of these methods is beneficial under specific conditions. By using these methods in a potentially beneficial condition, OGD practices can promote a wide range of individuals to involve and work with the government data and ultimately engage in government policy-making procedures.

Keywords: best practices, data visualization, literature review, open government data

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Authors: Andreas Schweiger, Matthias Gnigler, Elisabeth Wieder, Michael Grobbauer

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Especially in the alpine region, available areas for new residential development are limited. One possible solution is to exploit the potential of existing settlements. Urban redensification, especially the addition of floors to existing buildings, requires efficient, lightweight constructions with short construction times. This topic is being addressed in the five-year Alpine Building Centre. The focus of this cooperation between Salzburg University of Applied Sciences and RSA GH Studio iSPACE is on transdisciplinary research in the fields of building and energy technology, building envelopes and geoinformation, as well as the transfer of research results to industry. One development objective is a system of wood panel system construction with a high degree of prefabrication to optimize the construction quality, the construction time and the applicability for small and medium-sized enterprises. The system serves as a reliable working basis for mastering the complex building task of redensification. The technical solution is the development of an open system in timber frame and solid wood construction, which is suitable for a maximum two-story addition of residential buildings. The applicability of the system is mainly influenced by the existing building stock. Therefore, timber frame and solid timber construction are combined where necessary to bridge large spans of the existing structure while keeping the dead weight as low as possible. Escape routes are usually constructed in reinforced concrete and are located outside the system boundary. Thus, within the framework of the legal and normative requirements of timber construction, a hybrid construction method for redensification created. Component structure, load-bearing structure and detail constructions are developed in accordance with the relevant requirements. The results are directly applicable in individual cases, with the exception of the required verifications. In order to verify the practical suitability of the developed system, stakeholder workshops are held on the one hand, and the system is applied in the planning of a two-storey extension on the other hand. A company-independent construction standard offers the possibility of cooperation and bundling of capacities in order to be able to handle larger construction volumes in collaboration with several companies. Numerous further developments can take place on the basis of the system, which is under open license. The construction system will support planners and contractors from design to execution. In this context, open means publicly published and freely usable and modifiable for own use as long as the authorship and deviations are mentioned. The companies are provided with a system manual, which contains the system description and an application manual. This manual will facilitate the selection of the correct component cross-sections for the specific construction projects by means of all component and detail specifications. This presentation highlights the initial situation, the motivation, the approach, but especially the technical solution as well as the possibilities for the application. After an explanation of the objectives and working methods, the component and detail specifications are presented as work results and their application.

Keywords: redensification, SME, urban development, wood building system

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Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Dana Zelicha

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The objective of this paper is to present innovative mindfulness tools specifically designed by OWBA—The Well Being Agency for organisations and corporate wellness programmes. The OWBA Mindfulness Tools (OWBA-MT) consist of practical mindfulness exercises to educate and train employees and business leaders to think, feel, and act more mindfully. Among these cutting-edge interventions are Mindful Meetings, Mindful Decision Making and Unitasking activities, intended to cultivate mindful communication and compassion in the workplace and transform organisational culture. In addition to targeting CEO’s and leaders within large corporations, OWBA-MT is also directed at the needs of specific populations such as entrepreneurs’ resilience and women empowerment. The goals of the OWBA-MT are threefold: to inform, inspire and implement. The first goal is to inform participants about the relationship between workplace stress, distractibility and miscommunication in the framework of mindfulness. The second goal is for the audience to be inspired to share those practices with other members of their organisation. The final objective is to equip participants with the tools to foster a compassionate, mindful and well-balanced work environment. To assess these tools, a 6-week case study was conducted as part of an employee wellness programme for a large international corporation. The OWBA-MT were introduced in a workshop forum once-a-week, with participants practicing these tools both in the office and at home. The workshops occurred 1 day a week (2 hours each), with themes and exercises varying weekly. To reinforce practice at home, participants received reflection forms and guided meditations online. Materials were sent via-email at the same time each day to ensure consistency and participation. To evaluate the effectiveness of the mindfulness intervention, improvements in four categories were measured: listening skills, mindfulness levels, prioritising skills and happiness levels. These factors were assessed using online self-reported questionnaires administered at the start of the intervention, and then again 4-weeks following completion. The measures included the Mindfulness Attention Awareness Scale (MAAS), Listening Skills Inventory (LSI), Time Management Behaviour Scale (TMBS) and a modified version of the Oxford Happiness Questionnaire (OHQ). All four parameters showed significant improvements from the start of the programme to the 4-week follow-up. Participant testimonials exhibited high levels of satisfaction and the overall results indicate that the OWBA-MT intervention substantially impacted the corporation in a positive way. The implications of these results suggest that OWBA-MT can improve employees’ capacities to listen and work well with others, to manage time effectively, and to experience enhanced satisfaction both at work and in life. Although corporate mindfulness programmes have proven to be effective, the challenge remains the low engagement levels at home in between training sessions and to implement the tools beyond the scope of the intervention. OWBA-MT has offered an innovative approach to enforce engagement levels at home by sending daily online materials outside the workshop forum with a personalised response. The limitations also noteworthy to consider for future research include the afterglow effect and lack of generalisability, as this study was conducted on a small and fairly homogenous sample.

Keywords: corporate mindfulness, listening skills, mindful leadership, mindfulness tools, organisational well being

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Authors: Marcelo Pereira Introini

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The global value chains (GVC) approach contributes to a better understanding of the international production organization amid globalization’s second unbundling from the 1970s on. Mainly due to the tools that help to understand the importance of critical competences, technological capabilities, and functions performed by each player, GVC research flourished in recent years, rooted in discussing the possibilities of integration and repositioning along regional and global value chains. Regarding this context, part of the literature endorsed a more optimistic view that engaging in fragmented production networks could represent learning opportunities for developing countries’ firms, since the relationship with transnational corporations could allow them build skills and competences. Increasing recognition that GVCs are based on asymmetric power relations provided another sight about benefits, costs, and development possibilities though. Once leading companies tend to restrict the replication of their technologies and capabilities by their suppliers, alternative strategies beyond the functional specialization, seen as a way to integrate value chains, began to be broadly highlighted. This paper organizes a coherent narrative about the shortcomings of the GVC analytical framework, while recognizing its multidimensional contributions and recent developments. We adopt two different and complementary perspectives to explore the idea of integration in the international production. On one hand, we emphasize obstacles beyond production components, analyzing the role played by intangible assets and intellectual property regimes. On the other hand, we consider the importance of domestic production and innovation systems for technological development. In order to provide a deeper understanding of the restrictions on technological learning of developing countries’ firms, we firstly build from the notion of intellectual monopoly to analyze how flagship companies can prevent subordinated firms from improving their positions in fragmented production networks. Based on intellectual property protection regimes we discuss the increasing asymmetries between these players and the decreasing access of part of them to strategic intangible assets. Second, we debate the role of productive-technological ecosystems and of interactive and systemic technological development processes, as concepts of the Innovation Systems approach. Supporting the idea that not only endogenous advantages are important for international competition of developing countries’ firms, but also that the building of these advantages itself can be a source of technological learning, we focus on local efforts as a crucial element, which is not replaceable for technology imported from abroad. Finally, the paper contributes to the discussion about technological development as a two-dimensional dynamic. If GVC analysis tends to underline a company-based perspective, stressing the learning opportunities associated to GVC integration, historical involvement of national States brings up the debate about technology as a central aspect of interstate disputes. In this sense, technology is seen as part of military modernization before being also used in civil contexts, what presupposes its role for national security and productive autonomy strategies. From this outlook, it is important to consider it as an asset that, incorporated in sophisticated machinery, can be the target of state policies besides the protection provided by intellectual property regimes, such as in export controls and inward-investment restrictions.

Keywords: global value chains, innovation systems, intellectual monopoly, technological development

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Authors: Jessica J. Gillies

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Awareness of the tragedy that is human trafficking has increased exponentially over the past two decades. The four pillars widely recognised as global solutions to the problem are prevention, prosecution, protection, and partnership between government and non-government organisations. While ‘sex-trafficking’ initially received major attention, this focus has shifted to other industries that conceal broader experiences of exploitation. However, within the regions of focus for this study, namely Australia and Thailand, trafficking for the purpose of sexual exploitation remains the commonly uncovered narrative of criminal justice investigations. In these regions anti-trafficking action is characterised by government-led prevention and prosecution efforts; whereas protection and reintegration practices have received criticism. Typically, non-government organisations straddle the critical chasm between policy and practice; therefore, they are perfectly positioned to contribute valuable experiential knowledge toward understanding how both sectors can support survivors in the post-trafficking experience. The aim of this research is to inform improved partnerships throughout government and non-government post-trafficking services by illuminating gaps in protection and reintegration initiatives. This research will explore government and non-government responses to human trafficking in Thailand and Australia, in order to understand how meaning is constructed in this context and how the construction of meaning effects survivors in the post-trafficking experience. A qualitative, three-stage methodology was adopted for this study. The initial stage of enquiry consisted of a discursive analysis, in order to deconstruct the broader discourses surrounding human trafficking. The data included empirical papers, grey literature such as publicly available government and non-government reports, and anti-trafficking policy documents. The second and third stages of enquiry will attempt to further explore the findings of the discourse analysis and will focus more specifically on protection and reintegration in Australia and Thailand. Stages two and three will incorporate process observations in government and non-government survivor support services, and semi-structured interviews with employees and volunteers within these settings. Two key findings emerged from the discursive analysis. The first exposed conflicting feminist arguments embedded throughout anti-trafficking discourse. Informed by conflicting feminist discourses on sex-work, a discursive relationship has been constructed between sex-industry policy and anti-trafficking policy. In response to this finding, data emerging from the process observations and semi-structured interviews will be interpreted using a feminist theoretical framework. The second finding progresses from the construction in the first. The discursive construction of sex-trafficking appears to have had influence over perceptions of the legitimacy of survivors, and therefore the support they receive in the post-trafficking experience. For example; women who willingly migrate for employment in the sex-industry, and on arrival are faced with exploitative conditions, are not perceived to be deserving of the same support as a woman who is not coerced, but rather physically forced, into such circumstances, yet both meet the criteria for a victim of human trafficking. The forthcoming study is intended to contribute toward building knowledge and understanding around the implications of the construction of legitimacy; and contextualise this in reference to government led protection and reintegration support services for survivors in the post-trafficking experience.

Keywords: Australia, government, human trafficking, non-government, reintegration, Thailand

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Authors: Andrew Laming, John Hattie, Mark Wilson

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Nine Queensland Independent high schools provided deidentified student-matched ATAR and NAPLAN data for all 1217 ATAR graduates since 2020 who also sat NAPLAN at the school. Graduating cohorts from the nine schools contained a mean 100 ATAR graduates with previous NAPLAN data from their school. Excluded were vocational students (mean=27) and any ATAR graduates without NAPLAN data (mean=20). Based on Index of Community Socio-Educational Access (ICSEA) prediction, all schools had larger that predicted proportions of their students graduating with ATARs. There were an additional 173 students not releasing their ATARs to their school (14%), requiring this data to be inferred by schools. Gain was established by first converting each student’s strongest NAPLAN domain to a statewide percentile, then subtracting this result from final ATAR. The resulting ‘percentile shift’ was corrected for plausible ATAR participation at each NAPLAN level. Strongest NAPLAN domain had the highest correlation with ATAR (R2=0.58). RESULTS School mean NAPLAN scores fitted ICSEA closely (R2=0.97). Schools achieved a mean cohort gain of two ATAR rankings, but only 66% of students gained. This ranged from 46% of top-NAPLAN decile students gaining, rising to 75% achieving gains outside the top decile. The 54% of top-decile students whose ATAR fell short of prediction lost a mean 4.0 percentiles (or 6.2 percentiles prior to correction for regression to the mean). 71% of students in smaller schools gained, compared to 63% in larger schools. NAPLAN variability in each of the 13 ICSEA1100 cohorts was 17%, with both intra-school and inter-school variation of these values extremely low (0.3% to 1.8%). Mean ATAR change between years in each school was just 1.1 ATAR ranks. This suggests consecutive school cohorts and ICSEA-similar schools share very similar distributions and outcomes over time. Quantile analysis of the NAPLAN/ATAR revealed heteroscedasticity, but splines offered little additional benefit over simple linear regression. The NAPLAN/ATAR R2 was 0.33. DISCUSSION Standardised data like NAPLAN and ATAR offer educators a simple no-cost progression metric to analyse performance in conjunction with their internal test results. Change is expressed in percentiles, or ATAR shift per student, which is layperson intuitive. Findings may also reduce ATAR/vocational stream mismatch, reveal proportions of cohorts meeting or falling short of expectation and demonstrate by how much. Finally, ‘crashed’ ATARs well below expectation are revealed, which schools can reasonably work to minimise. The percentile shift method is neither value-add nor a growth percentile. In the absence of exit NAPLAN testing, this metric is unable to discriminate academic gain from legitimate ATAR-maximizing strategies. But by controlling for ICSEA, ATAR proportion variation and student mobility, it uncovers progression to ATAR metrics which are not currently publicly available. However achieved, ATAR maximisation is a sought-after private good. So long as standardised nationwide data is available, this analysis offers useful analytics for educators and reasonable predictivity when counselling subsequent cohorts about their ATAR prospects.  

Keywords: NAPLAN, ATAR, analytics, measurement, gain, performance, data, percentile, value-added, high school, numeracy, reading comprehension, variability, regression to the mean

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Authors: Bianca Peterson, Tomasz J. Sańko, Carlos C. Bezuidenhout, Johnnie Van Den Berg

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Busseola fusca (Fuller) (Lepidoptera: Noctuidae), the maize stem borer, is a major pest in sub-Saharan Africa. It causes economic damage to maize and sorghum crops and has evolved non-recessive resistance to genetically modified (GM) maize expressing the Cry1Ab insecticidal toxin. Since B. fusca is a non-model organism, very little genomic information is publicly available, and is limited to some cytochrome c oxidase I, cytochrome b, and microsatellite data. The biology of B. fusca is well-described, but still poorly understood. This, in combination with its larval-specific behavior, may pose problems for limiting the spread of current resistant B. fusca populations or preventing resistance evolution in other susceptible populations. As part of on-going research into resistance evolution, B. fusca larvae were collected from Bt and non-Bt maize in South Africa, followed by RNA isolation (15 specimens) and sequencing on the Illumina HiSeq 2500 platform. Quality of reads was assessed with FastQC, after which Trimmomatic was used to trim adapters and remove low quality, short reads. Trinity was used for the de novo assembly, whereas TransRate was used for assembly quality assessment. Transcript identification employed BLAST (BLASTn, BLASTp, and tBLASTx comparisons), for which two libraries (nucleotide and protein) were created from 3.27 million lepidopteran sequences. Several transcripts that have previously been implicated in Cry toxin resistance was identified for B. fusca. These included aminopeptidase N, cadherin, alkaline phosphatase, ATP-binding cassette transporter proteins, and mitogen-activated protein kinase. MEGA7 was used to align these transcripts to reference sequences from Lepidoptera to detect mutations that might potentially be contributing to Cry toxin resistance in this pest. RSEM and Bioconductor were used to perform differential gene expression analysis on groups of B. fusca larvae challenged and unchallenged with the Cry1Ab toxin. Pairwise expression comparisons of transcripts that were at least 16-fold expressed at a false-discovery corrected statistical significance (p) ≤ 0.001 were extracted and visualized in a hierarchically clustered heatmap using R. A total of 329,194 transcripts with an N50 of 1,019 bp were generated from the over 167.5 million high-quality paired-end reads. Furthermore, 110 transcripts were over 10 kbp long, of which the largest one was 29,395 bp. BLAST comparisons resulted in identification of 157,099 (47.72%) transcripts, among which only 3,718 (2.37%) were identified as Cry toxin receptors from lepidopteran insects. According to transcript expression profiles, transcripts were grouped into three subclusters according to the similarity of their expression patterns. Several immune-related transcripts (pathogen recognition receptors, antimicrobial peptides, and inhibitors) were up-regulated in the larvae feeding on Bt maize, indicating an enhanced immune status in response to toxin exposure. Above all, extremely up-regulated arylphorin genes suggest that enhanced epithelial healing is one of the resistance mechanisms employed by B. fusca larvae against the Cry1Ab toxin. This study is the first to provide a resource base and some insights into a potential mechanism of Cry1Ab toxin resistance in B. fusca. Transcriptomic data generated in this study allows identification of genes that can be targeted by biotechnological improvements of GM crops.

Keywords: epithelial healing, Lepidoptera, resistance, transcriptome

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Authors: Maddalena Arigoni, Maria Luisa Ratto, Raffaele A. Calogero, Luca Alessandri

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The presence of tumor heterogeneity, where distinct cancer cells exhibit diverse morphological and phenotypic profiles, including gene expression, metabolism, and proliferation, poses challenges for molecular prognostic markers and patient classification for targeted therapies. Understanding the causes and progression of cancer requires research efforts aimed at characterizing heterogeneity, which can be facilitated by evolving single-cell sequencing technologies. However, analyzing single-cell data necessitates computational methods that often lack objective validation. Therefore, the establishment of benchmarking datasets is necessary to provide a controlled environment for validating bioinformatics tools in the field of single-cell oncology. Benchmarking bioinformatics tools for single-cell experiments can be costly due to the high expense involved. Therefore, datasets used for benchmarking are typically sourced from publicly available experiments, which often lack a comprehensive cell annotation. This limitation can affect the accuracy and effectiveness of such experiments as benchmarking tools. To address this issue, we introduce omics benchmark experiments designed to evaluate bioinformatics tools to depict the heterogeneity in single-cell tumor experiments. We conducted single-cell RNA sequencing on six lung cancer tumor cell lines that display resistant clones upon treatment of EGFR mutated tumors and are characterized by driver genes, namely ROS1, ALK, HER2, MET, KRAS, and BRAF. These driver genes are associated with downstream networks controlled by EGFR mutations, such as JAK-STAT, PI3K-AKT-mTOR, and MEK-ERK. The experiment also featured an EGFR-mutated cell line. Using 10XGenomics platform with cellplex technology, we analyzed the seven cell lines together with a pseudo-immunological microenvironment consisting of PBMC cells labeled with the Biolegend TotalSeq™-B Human Universal Cocktail (CITEseq). This technology allowed for independent labeling of each cell line and single-cell analysis of the pooled seven cell lines and the pseudo-microenvironment. The data generated from the aforementioned experiments are available as part of an online tool, which allows users to define cell heterogeneity and generates count tables as an output. The tool provides the cell line derivation for each cell and cell annotations for the pseudo-microenvironment based on CITEseq data by an experienced immunologist. Additionally, we created a range of pseudo-tumor tissues using different ratios of the aforementioned cells embedded in matrigel. These tissues were analyzed using 10XGenomics (FFPE samples) and Curio Bioscience (fresh frozen samples) platforms for spatial transcriptomics, further expanding the scope of our benchmark experiments. The benchmark experiments we conducted provide a unique opportunity to evaluate the performance of bioinformatics tools for detecting and characterizing tumor heterogeneity at the single-cell level. Overall, our experiments provide a controlled and standardized environment for assessing the accuracy and robustness of bioinformatics tools for studying tumor heterogeneity at the single-cell level, which can ultimately lead to more precise and effective cancer diagnosis and treatment.

Keywords: single cell omics, benchmark, spatial transcriptomics, CITEseq

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Authors: Laurie Dempsey

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Relationships are considered to be beneficial for emotional wellbeing, happiness and physical health. However, they are also complicated: individuals engage in a multitude of complex and volatile relationships during their lifetime, where the change to or ending of these dynamics can be deeply disruptive. As the internet is further integrated into everyday life and relationships are increasingly mediated, Media Studies’ and Sociology’s research interests intersect and converge. This study longitudinally explores how relationship change over time corresponds with the developing UK technological landscape between 2005-2018. Since the early 2000s, the use of computer-mediated communication (CMC) in the UK has dramatically reshaped interaction. Its use has compelled individuals to renegotiate how they consider their relationships: some argue it has allowed for vast networks to be accumulated and strengthened; others contend that it has eradicated the core values and norms associated with communication, damaging relationships. This research collaborated with UK media regulator Ofcom, utilising the longitudinal dataset from their Adult Media Lives study to explore how relationships and CMC use developed over time. This is a unique qualitative dataset covering 2005-2018, where the same 18 participants partook in annual in-home filmed depth interviews. The interviews’ raw video footage was examined year-on-year to consider how the same people changed their reported behaviour and outlooks towards their relationships, and how this coincided with CMC featuring more prominently in their everyday lives. Each interview was transcribed, thematically analysed and coded using NVivo 11 software. This study allowed for a comprehensive exploration into these individuals’ changing relationships over time, as participants grew older, experienced marriages or divorces, conceived and raised children, or lost loved ones. It found that as technology developed between 2005-2018, everyday CMC use was increasingly normalised and incorporated into relationship maintenance. It played a crucial role in altering relationship dynamics, even factoring in the breakdown of several ties. Three key relationships were identified as being shaped by CMC use: parent-child; extended family; and friendships. Over the years there were substantial instances of relationship conflict: for parents renegotiating their dynamic with their child as they tried to both restrict and encourage their child’s technology use; for estranged family members ‘forced’ together in the online sphere; and for friendships compelled to publicly display their relationship on social media, for fear of social exclusion. However, it was also evident that CMC acted as a crucial lifeline for these participants, providing opportunities to strengthen and maintain their bonds via previously unachievable means, both over time and distance. A longitudinal study of this length and nature utilising the same participants does not currently exist, thus provides crucial insight into how and why relationship dynamics alter over time. This unique and topical piece of research draws together Sociology and Media Studies, illustrating how the UK’s changing technological landscape can reshape one of the most basic human compulsions. This collaboration with Ofcom allows for insight that can be utilised in both academia and policymaking alike, making this research relevant and impactful across a range of academic fields and industries.

Keywords: computer mediated communication, longitudinal research, personal relationships, qualitative data

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Authors: Walid A. Alkeridy, Arwa Aljasser, Khalid Mohammed Alayed, Saad Alsaad, Amani S. Alqahtani, Claire Ann Lim, Sultan H. Alamri, Doaa Zainhom Mekkawy, Mohammed Al-Sofiani

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Introduction: The history of publicly funded Home Health Care (HHC) service in Saudi Arabia dates back to 1991. The first HC program was launched to provide palliative home care services for patients with terminal cancer. Thereafter, more programs launched across Saudi Arabia most remarkably was launching the national program for HHC by the Ministry Of Health (MOH) in 2008. The national HHC MOH program is mainly providing long-term care home care services for over 40,000 Saudi citizens. The scope of the HHC service program provided by the Saudi MOH is quite diverse, ranging from basic nursing care to specialized care programs, e.g., home peritoneal dialysis, home ventilation, home infusion therapy, etc. Objectives: The primary aim of our study is to report the prevalence of chronic conditions among Saudi people receiving long-term HHC services. Secondary aims include identifying the predictors of mortality among individuals receiving long-term HHC services and studying the association between frailty and poor health outcomes among HHC users. Methods: We conducted a retrospective and cross-sectional data collection from participants receiving HHC services at King Saud University Medical City, Riyadh, Saudi Arabia. Data were collected from electronic health records (EHR), patient charts, and interviewing caregivers from the year 2019 to 2022. We assessed functional performance by Katz's activity of daily living and the Bristol Activity of Daily Living Scale (BADLS). A trained health care provider assessed frailty using the Clinical Frailty Scale (CFS). Mortality was assessed by reviewing the death certificates if patients were hospitalized through discharge status ascertainment from EHR. Results: The mean age for deceased individuals in HHC was 78.3 years. Over twenty percent of individuals receiving HHC services were readmitted to the hospital. The following variables were statistically significant between deceased and alive individuals receiving HHC services; clinical frailty scale, the total number of comorbid conditions, and functional performance based on the KATZ activity of daily living scale and the BADLS. We found that the strongest predictors for mortality were pressure ulcers which had an odds ratio of 3.75 and p-value of < 0.0001, and the clinical frailty scale, which had an odds ratio of 1.69 and p-value of 0.002, using multivariate regression analysis. In conclusion, our study found that pressure ulcers and frailty are the strongest predictors of mortality for individuals receiving home health care services. Moreover, we found a high rate of annual readmission for individuals enrolled in HHC, which requires further analysis to understand the possible contributing factors for the increased rate of hospital readmission and develop strategies to address them. Future studies should focus on designing quality improvement projects aimed at improving the quality of life for individuals receiving HHC services, especially those who have pressure ulcers at the end of life.

Keywords: homecare, Saudi, prevalence, chronic

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Authors: Joel Niklaus, Matthias Sturmer

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The publication of judicial proceedings is a cornerstone of many democracies. It enables the court system to be made accountable by ensuring that justice is made in accordance with the laws. Equally important is privacy, as a fundamental human right (Article 12 in the Declaration of Human Rights). Therefore, it is important that the parties (especially minors, victims, or witnesses) involved in these court decisions be anonymized securely. Today, the anonymization of court decisions in Switzerland is performed either manually or semi-automatically using primitive software. While much research has been conducted on anonymization for tabular data, the literature on anonymization for unstructured text documents is thin and virtually non-existent for court decisions. In 2019, it has been shown that manual anonymization is not secure enough. In 21 of 25 attempted Swiss federal court decisions related to pharmaceutical companies, pharmaceuticals, and legal parties involved could be manually re-identified. This was achieved by linking the decisions with external databases using regular expressions. An automated re-identification system serves as an automated test for the safety of existing anonymizations and thus promotes the right to privacy. Manual anonymization is very expensive (recurring annual costs of over CHF 20M in Switzerland alone, according to an estimation). Consequently, many Swiss courts only publish a fraction of their decisions. An automated anonymization system reduces these costs substantially, further leading to more capacity for publishing court decisions much more comprehensively. For the re-identification system, topic modeling with latent dirichlet allocation is used to cluster an amount of over 500K Swiss court decisions into meaningful related categories. A comprehensive knowledge base with publicly available data (such as social media, newspapers, government documents, geographical information systems, business registers, online address books, obituary portal, web archive, etc.) is constructed to serve as an information hub for re-identifications. For the actual re-identification, a general-purpose language model is fine-tuned on the respective part of the knowledge base for each category of court decisions separately. The input to the model is the court decision to be re-identified, and the output is a probability distribution over named entities constituting possible re-identifications. For the anonymization system, named entity recognition (NER) is used to recognize the tokens that need to be anonymized. Since the focus lies on Swiss court decisions in German, a corpus for Swiss legal texts will be built for training the NER model. The recognized named entities are replaced by the category determined by the NER model and an identifier to preserve context. This work is part of an ongoing research project conducted by an interdisciplinary research consortium. Both a legal analysis and the implementation of the proposed system design ESRA will be performed within the next three years. This study introduces the system design of ESRA, an end-to-end system for re-identification and anonymization of Swiss court decisions. Firstly, the re-identification system tests the safety of existing anonymizations and thus promotes privacy. Secondly, the anonymization system substantially reduces the costs of manual anonymization of court decisions and thus introduces a more comprehensive publication practice.

Keywords: artificial intelligence, courts, legal tech, named entity recognition, natural language processing, ·privacy, topic modeling

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Authors: Sonja Giese, Tess N. Peacock

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South African based Innovation Edge is experimenting with technology to drive positive behavior change, enable data-driven decision making, and scale quality early years services. This paper uses five case studies to illustrate how technology can be used in resource-constrained environments to first, encourage parenting practices that build early language development (using a stage-based mobile messaging pilot, ChildConnect), secondly, to improve the quality of ECD programs (using a mobile application, CareUp), thirdly, how to affordably scale services for the early detection of visual and hearing impairments (using a mobile tool, HearX), fourthly, how to build a transparent and accountable system for the registration and funding of ECD (using a blockchain enabled platform, Amply), and finally enable rapid data collection and feedback to facilitate quality enhancement of programs at scale (the Early Learning Outcomes Measure). ChildConnect and CareUp were both developed using a design based iterative research approach. The usage and uptake of ChildConnect and CareUp was evaluated with qualitative and quantitative methods. Actual child outcomes were not measured in the initial pilots. Although parents who used and engaged on either platform felt more supported and informed, parent engagement and usage remains a challenge. This is contrast to ECD practitioners whose usage and knowledge with CareUp showed both sustained engagement and knowledge improvement. HearX is an easy-to-use tool to identify hearing loss and visual impairment. The tool was tested with 10000 children in an informal settlement. The feasibility of cost-effectively decentralising screening services was demonstrated. Practical and financial barriers remain with respect to parental consent and for successful referrals. Amply uses mobile and blockchain technology to increase impact and accountability of public services. In the pilot project, Amply is being used to replace an existing paper-based system to register children for a government-funded pre-school subsidy in South Africa. Early Learning Outcomes Measure defines what it means for a child to be developmentally ‘on track’ at aged 50-69 months. ELOM administration is enabled via a tablet which allows for easy and accurate data collection, transfer, analysis, and feedback. ELOM is being used extensively to drive quality enhancement of ECD programs across multiple modalities. The nature of ECD services in South Africa is that they are in large part provided by disconnected private individuals or Non-Governmental Organizations (in contrast to basic education which is publicly provided by the government). It is a disparate sector which means that scaling successful interventions is that much harder. All five interventions show the potential of technology to support and enhance a range of ECD services, but pathways to scale are still being tested.

Keywords: assessment, behavior change, communication, data, disabilities, mobile, scale, technology, quality

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Authors: Melina Kopke, Jelle Van Dijk

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Persons with cognitive disabilities, such as Autism Spectrum Disorder (ASD) are often dependent on some form of professional support. Recent transformations in Dutch healthcare have spurred institutions to apply new, empowering methods and tools to enable their clients to cope (more) independently in daily life. Assistive Technologies (ATs) seem promising as empowering tools. While ATs can, functionally speaking, help people to perform certain activities without human assistance, we hold that, from a design-theoretical perspective, such technologies often fail to empower in a deeper sense. Most technologies serve either to prescribe or to monitor users’ actions, which in some sense objectifies them, rather than strengthening their agency. This paper proposes that theories of embodied interaction could help formulating a design vision in which interactive assistive devices augment, rather than replace, human agency and thereby add to a persons’ empowerment in daily life settings. It aims to close the gap between empowerment theory and the opportunities provided by assistive technologies, by showing how embodiment and empowerment theory can be applied in practice in the design of new, interactive assistive devices. Taking a Research-through-Design approach, we conducted a case study of designing to support independently living people with ASD with structuring daily activities. In three iterations we interlaced design action, active involvement and prototype evaluations with future end-users and healthcare professionals, and theoretical reflection. Our co-design sessions revealed the issue of handling daily activities being multidimensional. Not having the ability to self-manage one’s daily life has immense consequences on one’s self-image, and also has major effects on the relationship with professional caregivers. Over the course of the project relevant theoretical principles of both embodiment and empowerment theory together with user-insights, informed our design decisions. This resulted in a system of wireless light units that users can program as a reminder for tasks, but also to record and reflect on their actions. The iterative process helped to gradually refine and reframe our growing understanding of what it concretely means for a technology to empower a person in daily life. Drawing on the case study insights we propose a set of concrete design principles that together form what we call the embodied empowerment design framework. The framework includes four main principles: Enabling ‘reflection-in-action’; making information ‘publicly available’ in order to enable co-reflection and social coupling; enabling the implementation of shared reflections into an ‘endurable-external feedback loop’ embedded in the persons familiar ’lifeworld’; and nudging situated actions with self-created action-affordances. In essence, the framework aims for the self-development of a suitable routine, or ‘situated practice’, by building on a growing shared insight of what works for the person. The framework, we propose, may serve as a starting point for AT designers to create truly empowering interactive products. In a set of follow-up projects involving the participation of persons with ASD, Intellectual Disabilities, Dementia and Acquired Brain Injury, the framework will be applied, evaluated and further refined.

Keywords: assistive technology, design, embodiment, empowerment

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Authors: Charalabos Antonatos, Mariza Panoutsopoulou, Georgios K. Georgakilas, Evangelos Evangelou, Yiannis Vasilopoulos

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The extended tissue damage and severe clinical outcomes of autoimmune diseases, accompanied by the high annual costs to the overall health care system, highlight the need for an efficient therapy. Increasing knowledge over the pathophysiology of specific chronic inflammatory diseases, namely Psoriasis (PsO), Inflammatory Bowel Diseases (IBD) consisting of Crohn’s disease (CD) and Ulcerative colitis (UC), and Rheumatoid Arthritis (RA), has provided insights into the underlying mechanisms that lead to the maintenance of the inflammation, such as Tumor Necrosis Factor alpha (TNF-α). Hence, the anti-TNFα biological agents pose as an ideal therapeutic approach. Despite the efficacy of anti-TNFα agents, several clinical trials have shown that 20-40% of patients do not respond to treatment. Nowadays, high-throughput technologies have been recruited in order to elucidate the complex interactions in multifactorial phenotypes, with the most ubiquitous ones referring to transcriptome quantification analyses. In this context, a random effects meta-analysis of available gene expression cDNA microarray datasets was performed between responders and non-responders to anti-TNFα therapy in patients with IBD, PsO, and RA. Publicly available datasets were systematically searched from inception to 10th of November 2020 and selected for further analysis if they assessed the response to anti-TNFα therapy with clinical score indexes from inflamed biopsies. Specifically, 4 IBD (79 responders/72 non-responders), 3 PsO (40 responders/11 non-responders) and 2 RA (16 responders/6 non-responders) datasetswere selected. After the separate pre-processing of each dataset, 4 separate meta-analyses were conducted; three disease-specific and a single combined meta-analysis on the disease-specific results. The MetaVolcano R package (v.1.8.0) was utilized for a random-effects meta-analysis through theRestricted Maximum Likelihood (RELM) method. The top 1% of the most consistently perturbed genes in the included datasets was highlighted through the TopConfects approach while maintaining a 5% False Discovery Rate (FDR). Genes were considered as Differentialy Expressed (DEGs) as those with P ≤ 0.05, |log2(FC)| ≥ log2(1.25) and perturbed in at least 75% of the included datasets. Over-representation analysis was performed using Gene Ontology and Reactome Pathways for both up- and down-regulated genes in all 4 performed meta-analyses. Protein-Protein interaction networks were also incorporated in the subsequentanalyses with STRING v11.5 and Cytoscape v3.9. Disease-specific meta-analyses detected multiple distinct pro-inflammatory and immune-related down-regulated genes for each disease, such asNFKBIA, IL36, and IRAK1, respectively. Pathway analyses revealed unique and shared pathways between each disease, such as Neutrophil Degranulation and Signaling by Interleukins. The combined meta-analysis unveiled 436 DEGs, 86 out of which were up- and 350 down-regulated, confirming the aforementioned shared pathways and genes, as well as uncovering genes that participate in anti-inflammatory pathways, namely IL-10 signaling. The identification of key biological pathways and regulatory elements is imperative for the accurate prediction of the patient’s response to biological drugs. Meta-analysis of such gene expression data could aid the challenging approach to unravel the complex interactions implicated in the response to anti-TNFα therapy in patients with PsO, IBD, and RA, as well as distinguish gene clusters and pathways that are altered through this heterogeneous phenotype.

Keywords: anti-TNFα, autoimmune, meta-analysis, microarrays

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Authors: R. Verwiebe, L. Seewann, M. Wolf

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This presentation addresses marginalization and discrimination as experienced by young academics with a migrant background in the Austrian labor market. Focusing on second generation academics of Central Eastern European and Turkish descent we explore two major issues. First, we ask whether their career-entry and everyday professional life entails origin-specific barriers. As educational residents, they show competences which, when lacking, tend to be drawn upon to explain discrimination: excellent linguistic skills, accredited high-level training, and networks. Second, we concentrate on how this group reacts to discrimination and overcomes experiences of marginalization. To answer these questions, we utilize recent sociological and social psychological theories that focus on the diversity of individual experiences. This distinguishes us from a long tradition of research that has dealt with the motives that inform discrimination, but has less often considered the effects on those concerned. Similarly, applied coping strategies have less often been investigated, though they may provide unique insights into current problematic issues. Building upon present literature, we follow recent discrimination research incorporating the concepts of ‘multiple discrimination’, ‘subtle discrimination’, and ‘visual social markers’. 21 problem-centered interviews are the empirical foundation underlying this study. The interviewees completed their entire educational career in Austria, graduated in different universities and disciplines and are working in their first post-graduate jobs (career entry phase). In our analysis, we combined thematic charting with a coding method. The results emanating from our empirical material indicated a variety of discrimination experiences ranging from barely perceptible disadvantages to directly articulated and overt marginalization. The spectrum of experiences covered stereotypical suppositions at job interviews, the disavowal of competencies, symbolic or social exclusion by new colleges, restricted professional participation (e.g. customer contact) and non-recruitment due to religious or ethnical markers (e.g. headscarves). In these experiences the role of the academics education level, networks, or competences seemed to be minimal, as negative prejudice on the basis of visible ‘social markers’ operated ‘ex-ante’. The coping strategies identified in overcoming such barriers are: an increased emphasis on effort, avoidance of potentially marginalizing situations, direct resistance (mostly in the form of verbal opposition) and dismissal of negative experiences by ignoring or ironizing the situation. In some cases, the academics drew into their specific competences, such as an intellectual approach of studying specialist literature, focus on their intercultural competences or planning to migrate back to their parent’s country of origin. Our analysis further suggests a distinction between reactive (i.e. to act on and respond to experienced discrimination) and preventative strategies (applied to obviate discrimination) of coping. In light of our results, we would like to stress that the tension between educational and professional success experienced by academics with a migrant background – and the barriers and marginalization they continue to face – are essential issues to be introduced to socio-political discourse. It seems imperative to publicly accentuate the growing social, political and economic significance of this group, their educational aspirations, as well as their experiences of achievement and difficulties.

Keywords: coping strategies, discrimination, labor market, second generation university graduates

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Authors: Bailey Houghtaling, Sarah Misyak

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Food access; the availability, affordability, convenience, and desirability of food and beverage products within communities, is influential on consumers’ purchasing and consumption decisions. These variables may contribute to lower dietary quality scores and a higher obesity prevalence documented among rural and disadvantaged populations in the United States (U.S.). Current research assessing linkages between food access and obesity outcomes has primarily focused on distance to a traditional grocery/supermarket store as a measure of optimality. However, low-income consumers especially, including U.S. Department of Agriculture’s Supplemental Nutrition Assistance Program (SNAP) participants, seem to utilize non-traditional food store formats with greater frequency for household dietary needs. Non-traditional formats have been associated with less nutritious food and beverage options and consumer purchases that are high in saturated fats, added sugars, and sodium. Authors’ formative research indicated differences by U.S. region and rurality in the distribution of traditional and non-traditional SNAP-authorized food store formats. Therefore, using Virginia as a case study, the purpose of this research was to determine if a relationship between store format, rurality, and obesity exists. This research applied SNAP-authorized food store data (food access points for SNAP as well as non-SNAP consumers) and obesity prevalence data by Virginia county using publicly available databases: (1) SNAP Retailer Locator, and; (2) U.S. County Health Rankings. The alpha level was set a priori at 0.05. All Virginia SNAP-authorized stores (n=6,461) were coded by format – grocery, drug, mass merchandiser, club, convenience, dollar, supercenter, specialty, farmers market, independent grocer, and non-food store. Simple linear regression was applied primarily to assess the relationship between store format and obesity. Thereafter, multiple variables were added to the regression to account for potential moderating relationships (e.g., county income, rurality). Convenience, dollar, non-food or restaurant, mass merchandiser, farmers market, and independent grocer formats were significantly, positively related to obesity prevalence. Upon controlling for urban-rural status and income, results indicated the following formats to be significantly related to county obesity prevalence with a small, positive effect: convenience (p=0.010), accounting for 0.3% of the variance in obesity prevalence; dollar (p=0.005; 0.5% of the variance), and; non-food (p=0.030; 1.3% of the variance) formats. These results align with current literature on consumer behavior at non-traditional formats. For example, consumers’ food and beverage purchases at convenience and dollar stores are documented to be high in saturated fats, added sugars, and sodium. Further, non-food stores (i.e., quick-serve restaurants) often contribute to a large portion of U.S. consumers’ dietary intake and thus poor dietary quality scores. Current food access research investigates grocery/supermarket access and obesity outcomes. These results suggest more research is needed that focuses on non-traditional food store formats. Nutrition interventions within convenience, dollar, and non-food stores, for example, that aim to enhance not only healthy food access but the affordability, convenience, and desirability of nutritious food and beverage options may impact obesity rates in Virginia. More research is warranted utilizing the presented investigative framework in other U.S. and global regions to explore the role and the potential of non-traditional food store formats to prevent and reduce obesity.

Keywords: food access, food store format, non-traditional food stores, obesity prevalence

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Authors: Bailey Houghtaling, Elena Serrano, Vivica Kraak, Samantha Harden, George Davis, Sarah Misyak

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United States (U.S.) Department of Agriculture Supplemental Nutrition Assistance Program (SNAP) participants are low-income Americans receiving federal dollars for supplemental food and beverage purchases. Participants use a variety of (traditional/non-traditional) SNAP-authorized stores for household dietary purchases - also representing food access points for all Americans. Importantly consumers' food and beverage purchases from non-traditional store formats tend to be higher in saturated fats, added sugars, and sodium when compared to purchases from traditional (e.g., grocery/supermarket) formats. Overconsumption of energy-dense and low-nutrient food and beverage products contribute to high obesity rates and adverse health outcomes that differ in severity among urban/rural U.S. locations and high/low-income populations. Little is known about the SNAP-authorized food store format landscape nationally, regionally, or by urban-rural status, as traditional formats are currently used as the gold standard in food access research. This research utilized publicly available U.S. databases to fill this large literature gap and to provide insight into modes of food access for vulnerable U.S. populations: (1) SNAP Retailer Locator which provides a list of all authorized food stores in the U.S., and; (2) Rural-Urban Continuum Codes (RUCC) that categorize U.S. counties as urban (RUCC 1-3) or rural (RUCC 4-9). Frequencies were determined for the highest occurring food store formats nationally and within two regionally diverse U.S. states – Virginia in the east and California in the west. Store format codes were assigned (e.g., grocery, drug, convenience, mass merchandiser, supercenter, dollar, club, or other). RUCC was applied to investigate state-level differences in urbanity-rurality regarding prevalent food store formats and Chi Square test of independence was used to determine if food store format distributions significantly (p < 0.05) differed by region or rurality. The resulting research sample that represented highly prevalent SNAP-authorized food stores nationally included 41.25% of all SNAP stores in the U.S. (N=257,839), comprised primarily of convenience formats (31.94%) followed by dollar (25.58%), drug (19.24%), traditional (10.87%), supercenter (6.85%), mass merchandiser (1.62%), non-food store or restaurant (1.81%), and club formats (1.09%). Results also indicated that the distribution of prevalent SNAP-authorized formats significantly differed by state. California had a lower proportion of traditional (9.96%) and a higher proportion of drug (28.92%) formats than Virginia- 11.55% and 19.97%, respectively (p < 0.001). Virginia also had a higher proportion of dollar formats (26.11%) when compared to California (10.64%) (p < 0.001). Significant differences were also observed for rurality variables (p < 0.001). Prominently, rural Virginia had a significantly higher proportion of dollar formats (41.71%) when compared to urban Virginia (21.78%) and rural California (21.21%). Non-traditional SNAP-authorized formats are highly prevalent and significantly differ in distribution by U.S. region and rurality. The largest proportional difference was observed for dollar formats where the least nutritious consumer purchases are documented in the literature. Researchers/practitioners should investigate non-traditional food stores at the local level using these research findings and similar applied methodologies to determine how access to various store formats impact obesity prevalence. For example, dollar stores may be prime targets for interventions to enhance nutritious consumer purchases in rural Virginia while targeting drug formats in California may be more appropriate.

Keywords: food access, food store format, nutrition interventions, SNAP consumers

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Authors: Wiseman Emmanuel Nkhomah, Chiyembekezo Kachimanga, Moses Banda Aron, Julia Higgins, Manuel Mulwafu, Kondwani Mpinga, Mwayi Chunga, Grace Momba, Enock Ndarama, Dickson Sumphi, Atupere Phiri, Fabien Munyaneza

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Background: Data is key to supporting health service delivery as stakeholders, including NGOs rely on it for effective service delivery, decision-making, and system strengthening. Several studies generated debate on data quality from national health management information systems (HMIS) in sub-Saharan Africa. This limits the utilization of data in resource-limited settings, which already struggle to meet standards set by the World Health Organization (WHO). We aimed to evaluate data quality improvement of Neno district HMIS over a 4-year period (2018 – 2021) following quarterly data reviews introduced in January 2020 by the district health management team and Partners In Health. Methods: Exploratory Mixed Research was used to examine report rates, followed by in-depth interviews using Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs). We used the WHO module desk review to assess the quality of HMIS data in the Neno district captured from 2018 to 2021. The metrics assessed included the completeness and timeliness of 34 reports. Completeness was measured as a percentage of non-missing reports. Timeliness was measured as the span between data inputs and expected outputs meeting needs. We computed T-Test and recorded P-values, summaries, and percentage changes using R and Excel 2016. We analyzed demographics for key informant interviews in Power BI. We developed themes from 7 FGDs and 11 KIIs using Dedoose software, from which we picked perceptions of healthcare workers, interventions implemented, and improvement suggestions. The study was reviewed and approved by Malawi National Health Science Research Committee (IRB: 22/02/2866). Results: Overall, the average reporting completeness rate was 83.4% (before) and 98.1% (after), while timeliness was 68.1% and 76.4 respectively. Completeness of reports increased over time: 2018, 78.8%; 2019, 88%; 2020, 96.3% and 2021, 99.9% (p< 0.004). The trend for timeliness has been declining except in 2021, where it improved: 2018, 68.4%; 2019, 68.3%; 2020, 67.1% and 2021, 81% (p< 0.279). Comparing 2021 reporting rates to the mean of three preceding years, both completeness increased from 88% to 99% (in 2021), while timeliness increased from 68% to 81%. Sixty-five percent of reports have maintained meeting a national standard of 90%+ in completeness while only 24% in timeliness. Thirty-two percent of reports met the national standard. Only 9% improved on both completeness and timeliness, and these are; cervical cancer, nutrition care support and treatment, and youth-friendly health services reports. 50% of reports did not improve to standard in timeliness, and only one did not in completeness. On the other hand, factors associated with improvement included improved communications and reminders using internal communication, data quality assessments, checks, and reviews. Decentralizing data entry at the facility level was suggested to improve timeliness. Conclusion: Findings suggest that data quality in HMIS for the district has improved following collaborative efforts. We recommend maintaining such initiatives to identify remaining quality gaps and that results be shared publicly to support increased use of data. These results can inform Ministry of Health and its partners on some interventions and advise initiatives for improving its quality.

Keywords: data quality, data utilization, HMIS, collaboration, completeness, timeliness, decision-making

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Authors: Philipp Sommer, Amgad Agoub

Abstract:

The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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