Search results for: multi-objective genetic algorithm

Authors: Julia Kapr, Andrea Rossi, Haribaskar Ramachandran, Marius Pollet, Ilka Egger, Selina Dangeleit, Katharina Koch, Jean Krutmann, Ellen Fritsche

Abstract:

It is widely acknowledged that animal models do not always represent human disease. Especially human brain development is difficult to model in animals due to a variety of structural and functional species-specificities. This causes significant discrepancies between predicted and apparent drug efficacies in clinical trials and their subsequent failure. Emerging alternatives based on 3D in vitro approaches, such as human brain spheres or organoids, may in the future reduce and ultimately replace animal models. Here, we present a human induced pluripotent stem cell (hiPSC)-based 3D neural in a vitro disease model for the Cockayne Syndrome B (CSB). CSB is a rare hereditary disease and is accompanied by severe neurologic defects, such as microcephaly, ataxia and intellectual disability, with currently no treatment options. Therefore, the aim of this study is to investigate the molecular and cellular defects found in neural hiPSC-derived CSB models. Understanding the underlying pathology of CSB enables the development of treatment options. The two CSB models used in this study comprise a patient-derived hiPSC line and its isogenic control as well as a CSB-deficient cell line based on a healthy hiPSC line (IMR90-4) background thereby excluding genetic background-related effects. Neurally induced and differentiated brain sphere cultures were characterized via RNA Sequencing, western blot (WB), immunocytochemistry (ICC) and multielectrode arrays (MEAs). CSB-deficiency leads to an altered gene expression of markers for autophagy, focal adhesion and neural network formation. Cell migration was significantly reduced and electrical activity was significantly increased in the disease cell lines. These data hint that the cellular pathologies is possibly underlying CSB. By induction of autophagy, the migration phenotype could be partially rescued, suggesting a crucial role of disturbed autophagy in defective neural migration of the disease lines. Altered autophagy may also lead to inefficient mitophagy. Accordingly, disease cell lines were shown to have a lower mitochondrial base activity and a higher susceptibility to mitochondrial stress induced by rotenone. Since mitochondria play an important role in neurotransmitter cycling, we suggest that defective mitochondria may lead to altered electrical activity in the disease cell lines. Failure to clear the defective mitochondria by mitophagy and thus missing initiation cues for new mitochondrial production could potentiate this problem. With our data, we aim at establishing a disease adverse outcome pathway (AOP), thereby adding to the in-depth understanding of this multi-faced disorder and subsequently contributing to alternative drug development.

Keywords: autophagy, disease modeling, in vitro, pluripotent stem cells

Procedia PDF Downloads 120

Authors: Simone Raimondi De Souza, Glaucia Maria Moraes De Oliveira, Ronir Raggio Luiz, Glorimar Rosa

Abstract:

Introduction: Obesity is among the main risk factors for cardiovascular disease (CVD). Genesis is multifactorial, including genetic, hormonal and environmental factors disorders, among which inadequate feeding pattern, for which nutritional counseling strategies have proven effective. The consumption of beta-glucans (soluble fibers that reportedly promote satiety) present in oat bran can be an effective strategy for preventing and treating obesity. Other benefits have been observed with oat bran consumption, such as reduction of hypercholesterolemia and hyperglycemia, two other risk factors for CVD. Objectives: To analyze the effect of oat bran consumption associated with nutritional counseling in reducing body mass index (BMI), blood cholesterol, glucose profile, waist and neck circumference in obese individuals, and to evaluate the change in eating pattern. Methods: clinical trial, randomized, double-blind, placebo-controlled, lasting 90 days with adults of both genders, with BMI ≥30kg/m2. The study was approved by the Ethics in Research involving human beings in a public institute of cardiology, in Rio de Janeiro, Brazil. Individuals were invited to participate and accepted formally by signing the Terms of Consent. Participants were randomized into oat bran group (gOB) or placebo group (gPCB) and received, respectively: morning prepared consisting of 40g oat bran, 30g of skimmed milk powder and 1g sweetener sucralose; refined flour 40g rice, 30g of milk powder and 1g sweetener sucralose. The Ten Steps to Healthy Eating, of Brazilian Ministry of Health were used to support the nutritional counseling. Variables analyzed: gender; age; BMI, waist circumference (WC) neck circumference (NC); systolic blood pressure (SBP); diastolic blood pressure (DBP); food consumption, total cholesterol (TC), LDL-cholesterol (LDL-c), HDL-cholesterol (HDL-c), non-HDL cholesterol (nHDLc), triglycerides (TG), fasting glucose (FG), fasting insulin (FI) and HOMA-IR. Dietary intake was assessed by 24-hour dietary recall. The Diet Quality Index revised for the Brazilian population (IQD-R) assessed quality of feeding pattern. Statistical analyzes were performed using SPSS version 21, considering statistically significant p-value less than 0.05. Results: A total of 38 participants were included, age = 50 ± 7,6years, 63% women. 19 subjects were placed in gOB and 19 in gPCB. After intervention, statistically significant reductions were observed in the following parameters: in gOB: IQD-R, TC, LDL-c, nHDL-c, FI, SBP, DBP, BMI, WC, NC; in gPCB: IQD-R, LDL-c, SBP, DBP, BMI, WC, NC. No statistically significant differences were observed in the results between groups. Conclusion: Our results reinforce nutritional counseling as important strategy for prevention and treatment of obesity and suggest that inclusion of oat bran in daily diet can bring additional benefits controlling risk factors for CVD. More studies are needed to establish all benefits of oat bran to human health as well as the ideal daily dose for consumption.

Keywords: oat bran, cardiovascular disease, nutritional counseling, obesity

Procedia PDF Downloads 231

Authors: Nisha Sharma, Mili Kaur, Ashutosh Halder, Seema Kaushal, Manoj Kumar, Manish Jain

Abstract:

Purpose: To investigate microRNAs (miRNA) as an epigenomic etiological factor in idiopathic non-obstructive azoospermia (NOA). In order to achieve the same, an association was seen between occupational exposure to radiation, thermal, and chemical factors and idiopathic cases of non-obstructive azoospermia, and later, testicular differential miRNA expression profiling was done in exposure group NOA cases. Method: It is a prospective study in which 200 apparent idiopathic male factor infertility cases, who have been advised to undergo testicular fine needle aspiration (FNA) evaluation, are recruited. A detailed occupational history was taken to understand the possible type of exposure due to the nature and duration of work. A total of 26 patients were excluded upon XY-FISH and Yq microdeletion tests due to the presence of genetic causes of infertility, 6 hypospermatogeneis (HS), six Sertoli cell-only syndrome (SCOS), and six normospermatogeneis patients testicular FNA samples were used for RNA isolation followed by small RNA sequencing and nCounter miRNA expression analysis. Differential miRNA expression profile of HS and SCOS patients was done. A web-based tool, miRNet, was used to predict the interacting compounds or chemicals using the shortlisted miRNAs with high fold change. The major limitation encountered in this study was the insufficient quantity of testicular FNA sample used for total RNA isolation, which resulted in a low yield and RNA integrity number (RIN) value. Therefore, the number of RNA samples admissible for differential miRNA expression analysis was very small in comparison to the total number of patients recruited. Results: Differential expression analysis revealed 69 down-regulated and 40 up-regulated miRNAs in HS and 66 down-regulated and 33 up-regulated miRNAs in SCOS in comparison to normospermatogenesis controls. The miRNA interaction analysis using the miRNet tool showed that the differential expression profiles of HS and SCOS patients were associated with arsenic trioxide, bisphenol-A, calcium sulphate, lithium, and cadmium. These compounds are reproductive toxins and might be responsible for miRNA-mediated epigenetic deregulation leading to NOA. The association between occupational risk factor exposure and the non-exposure group of NOA patients was not statistically significant, with ꭓ2 (3, N= 178) = 6.70, p= 0.082. The association between individual exposure groups (radiation, thermal, and chemical) and various sub-types of NOA is also not significant, with ꭓ2 (9, N= 178) = 15.06, p= 0.089. Functional analysis of HS and SCOS patients' miRNA profiles revealed some important miR-family members in terms of male fertility. The miR-181 family plays a role in the differentiation of spermatogonia and spermatocytes, as well as the transcriptional regulation of haploid germ cells. The miR-34 family is expressed in spermatocytes and round spermatids and is involved in the regulation of SSCs differentiation. Conclusion: The reproductive toxins might adopt the miRNA-mediated mechanism of disease development in idiopathic cases of NOA. Chemical compound induced; miRNA-mediated epigenetic deregulation can give a future perspective on the etiopathogenesis of the disease.

Keywords: microRNA, non-obstructive azoospermia (NOA), occupational exposure, hypospermatogenesis (HS), Sertoli cell only syndrome (SCOS)

Procedia PDF Downloads 87

Authors: Reeta Devi, Hitender Kumar Tyagi, Dinesh Kumar

Abstract:

In present scenario, cardiovascular problems are growing challenge for researchers and physiologists. As heart disease have no geographic, gender or socioeconomic specific reasons; detecting cardiac irregularities at early stage followed by quick and correct treatment is very important. Electrocardiogram is the finest tool for continuous monitoring of heart activity. Heart rate variability (HRV) is used to measure naturally occurring oscillations between consecutive cardiac cycles. Analysis of this variability is carried out using time domain, frequency domain and non-linear parameters. This paper presents HRV analysis of the online dataset for normal sinus rhythm (taken as healthy subject) and sudden cardiac death (SCD subject) using all three methods computing values for parameters like standard deviation of node to node intervals (SDNN), square root of mean of the sequences of difference between adjacent RR intervals (RMSSD), mean of R to R intervals (mean RR) in time domain, very low-frequency (VLF), low-frequency (LF), high frequency (HF) and ratio of low to high frequency (LF/HF ratio) in frequency domain and Poincare plot for non linear analysis. To differentiate HRV of healthy subject from subject died with SCD, k –nearest neighbor (k-NN) classifier has been used because of its high accuracy. Results show highly reduced values for all stated parameters for SCD subjects as compared to healthy ones. As the dataset used for SCD patients is recording of their ECG signal one hour prior to their death, it is therefore, verified with an accuracy of 95% that proposed algorithm can identify mortality risk of a patient one hour before its death. The identification of a patient’s mortality risk at such an early stage may prevent him/her meeting sudden death if in-time and right treatment is given by the doctor.

Keywords: early stage prediction, heart rate variability, linear and non-linear analysis, sudden cardiac death

Procedia PDF Downloads 341

Authors: Feifei Wu, Pius Babuna, Xiaohua Yang

Abstract:

The suitability evaluation of human settlements over time and space is essential to track potential challenges towards suitable human settlements and provide references for policy-makers. This study established a theoretical framework of human settlements based on the nature, human, economy, society and residence subsystems. Evaluation indicators were determined with the consideration of the coupling effect among subsystems. Based on the extended Fourier amplitude sensitivity test algorithm, the global sensitivity analysis that considered the coupling effect among indicators was used to determine the weights of indicators. The human settlement suitability was evaluated at both subsystems and comprehensive system levels in 30 provinces of China between 2000 and 2016. The findings were as follows: (1) human settlements suitability index (HSSI) values increased significantly in all 30 provinces from 2000 to 2016. Among the five subsystems, the suitability index of the residence subsystem in China exhibited the fastest growinggrowth, fol-lowed by the society and economy subsystems. (2) HSSI in eastern provinces with a developed economy was higher than that in western provinces with an underdeveloped economy. In con-trast, the growing rate of HSSI in eastern provinces was significantly higher than that in western provinces. (3) The inter-provincial difference of in HSSI decreased from 2000 to 2016. For sub-systems, it decreased for the residence system, whereas it increased for the economy system. (4) The suitability of the natural subsystem has become a limiting factor for the improvement of human settlements suitability, especially in economically developed provinces such as Beijing, Shanghai, and Guangdong. The results can be helpful to support decision-making and policy for improving the quality of human settlements in a broad nature, human, economy, society and residence context.

Keywords: human settlements, suitability evaluation, extended fourier amplitude, human settlement suitability

Procedia PDF Downloads 80

Authors: Fuyuan Ma, Yuhan Wang, Junhe Zhang, Ying Wang

Abstract:

Identifying the sources of information dissemination is a pivotal task in the study of collective behaviors in networks, enabling us to discern and intercept the critical pathways through which information propagates from its origins. This allows for the control of the information’s dissemination impact in its early stages. Numerous methods for source detection rely on pre-existing, underlying propagation models as prior knowledge. Current models that eschew prior knowledge attempt to harness label propagation algorithms to model the statistical characteristics of propagation states or employ Graph Neural Networks (GNNs) for deep reverse modeling of the diffusion process. These approaches are either deficient in modeling the propagation patterns of information or are constrained by the over-smoothing problem inherent in GNNs, which limits the stacking of sufficient model depth to excavate global propagation patterns. Consequently, we introduce the ODESI model. Initially, the model employs a label propagation algorithm to delineate the distribution density of infected states within a graph structure and extends the representation of infected states from integers to state vectors, which serve as the initial states of nodes. Subsequently, the model constructs a deep architecture based on GNNs-coupled Ordinary Differential Equations (ODEs) to model the global propagation patterns of continuous propagation processes. Addressing the challenges associated with solving ODEs on graphs, we approximate the analytical solutions to reduce computational costs. Finally, we conduct simulation experiments on two real-world social network datasets, and the results affirm the efficacy of our proposed ODESI model in source identification tasks.

Keywords: source identification, ordinary differential equations, label propagation, complex networks

Procedia PDF Downloads 20

Authors: Sharma Aseem, Dhurat Rachita, Pawar Varsha, Khalse Manisha

Abstract:

Introduction: Androgenetic alopecia (AGA) is a prevalent condition characterized by progressive hair thinning driven by genetic and androgen-related factors. The current FDA-approved treatments include oral finasteride and topical minoxidil, though many patients seek combination therapies to enhance results. This study aims to evaluate the effectiveness of a combination therapy involving Minoxidil, Finasteride, and Capixyl™ based on feedback from dermatologists. Methodology: A survey, validated by experts, was distributed to 29 leading dermatologists across India (in Tier 1 and 2 cities). The survey examined real-world clinical experiences, focusing on patient outcomes and the overall effectiveness of the mentioned formulation. Results: Among the surveyed dermatologists, 41.4% identified women aged 35-40 as the most frequently diagnosed with female pattern hair loss. The combination therapy with Minoxidil, Finasteride, and Capixyl™ was utilized by 34.5% of dermatologists for over 60 patients per month. The majority highlighted the benefits of this combination therapy, which acts via multiple mechanisms, such as vasodilation and dihydrotestosterone (DHT) receptor blockade, resulting in improved hair regrowth. Additionally, patients demonstrated better clinical outcomes, enhanced compliance, and fewer side effects. Demographically, younger patients, particularly those with AGA for less than 10 years, responded more positively to the treatment. Early intervention led to quicker and more significant results. Overall satisfaction among dermatologists was high, with 86.2% expressing positive feedback on the therapy. In terms of treatment outcomes, 51.7% of dermatologists observed visible results within 4-6 months, while 34.5% noticed a significant reduction in hair fall within 8-12 weeks. Improvements in scalp health were reported by 48.3%, and 51.7% saw an increased hair density within 3-4 months. Despite mild side effects such as scalp irritation, dryness, flaking, and occasional issues like folliculitis, headaches, itching, and redness, patient satisfaction remained high. Dermatologists reported that 93.1% of patients experienced faster and better hair regrowth with Capixyl™ compared to Minoxidil alone. Suggestions for improving the formulation included incorporating peptides like Saw Palmetto and enhancing product packaging to better meet patient needs. Discussion: The combination of Minoxidil, Finasteride, and Capixyl™ yielded positive clinical outcomes, especially in improving hair density, scalp health, and overall patient satisfaction. Dermatologists found that Capixyl™ peptides enhanced the therapeutic effect, promoting hair regrowth and improving compliance. While side effects were generally mild, there were suggestions to further improve the formulation by adding additional peptides like Saw Palmetto. Conclusion: The combination of Minoxidil and Finasteride fortified with Capixyl™ presents a promising therapeutic option for managing AGA. Dermatologists reported significant improvements in hair density, scalp health, and patient satisfaction. With its favorable efficacy and manageable side effects, this formulation proves to be a valuable addition to the treatment landscape for AGA.

Keywords: androgenetic alopecia, combination therapy, minoxidil, finasteride, capixyl

Procedia PDF Downloads 13

Authors: Arup Dutta, Badrul Alam, Sayed M. Wazed, Taslima Newaz, Srobonti Dutta

Abstract:

Background: In today's world, with modern science and contemporary technology, a lot of diseases may be quickly identified and ruled out, but children's fever of unknown origin (FUO) still presents diagnostic difficulties in clinical settings. Any fever that reaches 38 °C and lasts for more than seven days without a known cause is now classified as a fever of unknown origin (FUO). Despite tremendous progress in the medical sector, fever of unknown origin, or FOU, persists as a major health issue and a major contributor to morbidity and mortality, particularly in children, and its spectrum is sometimes unpredictable. The etiology is influenced by geographic location, age, socioeconomic level, frequency of antibiotic resistance, and genetic vulnerability. Since there are currently no known diagnostic algorithms, doctors are forced to evaluate each patient one at a time with extreme caution. A persistent fever poses difficulties for both the patient and the doctor. This prospective observational study was carried out in a Bangladeshi tertiary care hospital from June 2018 to May 2019 with the goal of identifying the epidemiological patterns of fever of unknown origin in pediatric patients. Methods: It was a hospital-based prospective observational study carried out on 106 children (between 2 months and 12 years) with prolonged fever of >38.0 °C lasting for more than 7 days without a clear source. Children with additional chronic diseases or known immunodeficiency problems were not allowed. Clinical practices that helped determine the definitive etiology were assessed. Initial testing included a complete blood count, a routine urine examination, PBF, a chest X-ray, CRP measurement, blood cultures, serology, and additional pertinent investigations. The analysis focused mostly on the etiological results. The standard program SPSS 21 was used to analyze all of the study data. Findings: A total of 106 patients identified as having FUO were assessed, with over half (57.5%) being female and the majority (40.6%) falling within the 1 to 3-year age range. The study categorized the etiological outcomes into five groups: infections, malignancies, connective tissue conditions, miscellaneous, and undiagnosed. In the group that was being studied, infections were found to be the main cause in 44.3% of cases. Undiagnosed cases came in at 31.1%, cancers at 10.4%, other causes at 8.5%, and connective tissue disorders at 4.7%. Hepato-splenomegaly was seen in people with enteric fever, malaria, acute lymphoid leukemia, lymphoma, and hepatic abscesses, either by itself or in combination with other conditions. About 53% of people who were not diagnosed also had hepato-splenomegaly at the same time. Conclusion: Infections are the primary cause of PUO (pyrexia of unknown origin) in children, with undiagnosed cases being the second most common cause. An incremental approach is beneficial in the process of diagnosing a condition. Non-invasive examinations are used to diagnose infections and connective tissue disorders, while invasive investigations are used to diagnose cancer and other ailments. According to this study, the prevalence of undiagnosed diseases is still remarkable, so extensive historical analysis and physical examinations are necessary in order to provide a precise diagnosis.

Keywords: children, diagnostic challenges, fever of unknown origin, pediatric fever, undiagnosed diseases

Procedia PDF Downloads 27

Authors: Sarra Haouala, Issam Doghri

Abstract:

In this work, a multiscale computational strategy is proposed for the analysis of structures, which are described at a refined level both in space and in time. The proposal is applied to two-phase viscoelastic-viscoplastic (VE-VP) reinforced thermoplastics subjected to large numbers of cycles. The main aim is to predict the effective long time response while reducing the computational cost considerably. The proposed computational framework is a combination of the mean-field space homogenization based on the generalized incrementally affine formulation for VE-VP composites, and the asymptotic time homogenization approach for coupled isotropic VE-VP homogeneous solids under large numbers of cycles. The time homogenization method is based on the definition of micro and macro-chronological time scales, and on asymptotic expansions of the unknown variables. First, the original anisotropic VE-VP initial-boundary value problem of the composite material is decomposed into coupled micro-chronological (fast time scale) and macro-chronological (slow time-scale) problems. The former is purely VE, and solved once for each macro time step, whereas the latter problem is nonlinear and solved iteratively using fully implicit time integration. Second, mean-field space homogenization is used for both micro and macro-chronological problems to determine the micro and macro-chronological effective behavior of the composite material. The response of the matrix material is VE-VP with J2 flow theory assuming small strains. The formulation exploits the return-mapping algorithm for the J2 model, with its two steps: viscoelastic predictor and plastic corrections. The proposal is implemented for an extended Mori-Tanaka scheme, and verified against finite element simulations of representative volume elements, for a number of polymer composite materials subjected to large numbers of cycles.

Keywords: asymptotic expansions, cyclic loadings, inclusion-reinforced thermoplastics, mean-field homogenization, time homogenization

Procedia PDF Downloads 368

Authors: El Harraj Abdeslam, Raissouni Naoufal

Abstract:

The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

Procedia PDF Downloads 256

Authors: Mahmoud Huleihel, George Abu-Aqil, Manal Suleiman, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman

Abstract:

Urinary tract infections (UTIs) are considered to be the most common bacterial infections worldwide, which are caused mainly by Escherichia (E.) coli (about 80%). Klebsiella pneumoniae (about 10%) and Pseudomonas aeruginosa (about 6%). Although antibiotics are considered as the most effective treatment for bacterial infectious diseases, unfortunately, most of the bacteria already have developed resistance to the majority of the commonly available antibiotics. Therefore, it is crucial to identify the infecting bacteria and to determine its susceptibility to antibiotics for prescribing effective treatment. Classical methods are time consuming, require ~48 hours for determining bacterial susceptibility. Thus, it is highly urgent to develop a new method that can significantly reduce the time required for determining both infecting bacterium at the species level and diagnose its susceptibility to antibiotics. Fourier-Transform Infrared (FTIR) spectroscopy is well known as a sensitive and rapid method, which can detect minor molecular changes in bacterial genome associated with the development of resistance to antibiotics. The main goal of this study is to examine the potential of FTIR spectroscopy, in tandem with machine learning algorithms, to identify the infected bacteria at the species level and to determine E. coli susceptibility to different antibiotics directly from patients' urine in about 30minutes. For this goal, 1600 different E. coli isolates were isolated for different patients' urine sample, measured by FTIR, and analyzed using different machine learning algorithm like Random Forest, XGBoost, and CNN. We achieved 98% success in isolate level identification and 89% accuracy in susceptibility determination.

Keywords: urinary tract infections (UTIs), E. coli, Klebsiella pneumonia, Pseudomonas aeruginosa, bacterial, susceptibility to antibiotics, infrared microscopy, machine learning

Procedia PDF Downloads 170

Authors: Emiel Caron

Abstract:

Patent databases contain patent related data, organized in a relational data model, and are used to produce various patent statistics. These databases store raw data about scientific references cited by patents. For example, Patstat holds references to tens of millions of scientific journal publications and conference proceedings. These references might be used to connect patent databases with bibliographic databases, e.g. to study to the relation between science, technology, and innovation in various domains. Problematic in such studies is the low data quality of the references, i.e. they are often ambiguous, unstructured, and incomplete. Moreover, a complete bibliographic reference is stored in only one attribute. Therefore, a computerized cleaning and disambiguation method for large patent databases is developed in this work. The method uses rule-based scoring and clustering. The rules are based on bibliographic metadata, retrieved from the raw data by regular expressions, and are transparent and adaptable. The rules in combination with string similarity measures are used to detect pairs of records that are potential duplicates. Due to the scoring, different rules can be combined, to join scientific references, i.e. the rules reinforce each other. The scores are based on expert knowledge and initial method evaluation. After the scoring, pairs of scientific references that are above a certain threshold, are clustered by means of single-linkage clustering algorithm to form connected components. The method is designed to disambiguate all the scientific references in the Patstat database. The performance evaluation of the clustering method, on a large golden set with highly cited papers, shows on average a 99% precision and a 95% recall. The method is therefore accurate but careful, i.e. it weighs precision over recall. Consequently, separate clusters of high precision are sometimes formed, when there is not enough evidence for connecting scientific references, e.g. in the case of missing year and journal information for a reference. The clusters produced by the method can be used to directly link the Patstat database with bibliographic databases as the Web of Science or Scopus.

Keywords: clustering, data cleaning, data disambiguation, data mining, patent analysis, scientometrics

Procedia PDF Downloads 194

Authors: Daniele Calvi, Loris Franchi, Sabrina Corpino

Abstract:

Thanks to the latest Mars mission, the planetary exploration has made enormous strides over the past ten years increasing the interest of the scientific community and beyond. These missions aim to fulfill many complex operations which are of paramount importance to mission success. Among these, a special mention goes to the Entry, Descent and Landing (EDL) functions which require a dedicated system to overcome all the obstacles of these critical phases. The general objective of the system is to safely bring the spacecraft from orbital conditions to rest on the planet surface, following the designed mission profile. For this reason, this work aims to develop a simulation tool integrating the re-entry trajectory algorithm in order to support the EDL design during the preliminary phase of the mission. This tool was used on a reference unmanned mission, whose objective is finding bio-evidence and bio-hazards on Martian (sub)surface in order to support the future manned mission. Regarding the concept of operations (CONOPS) of the mission, it concerns the use of Space Penetrator Systems (SPS) that will descend on Mars surface following a ballistic fall and will penetrate the ground after the impact with the surface (around 50 and 300 cm of depth). Each SPS shall contain all the instrumentation required to sample and make the required analyses. Respecting the low-cost and low-mass requirements, as result of the tool, an Entry Descent and Impact (EDI) system based on inflatable structure has been designed. Hence, a solution could be the one chosen by Finnish Meteorological Institute in the Mars Met-Net mission, using an inflatable Thermal Protection System (TPS) called Inflatable Braking Unit (IBU) and an additional inflatable decelerator. Consequently, there are three configurations during the EDI: at altitude of 125 km the IBU is inflated at speed 5.5 km/s; at altitude of 16 km the IBU is jettisoned and an Additional Inflatable Braking Unit (AIBU) is inflated; Lastly at about 13 km, the SPS is ejected from AIBU and it impacts on the Martian surface. Since all parameters are evaluated, it is possible to confirm that the chosen EDI system and strategy verify the requirements of the mission.

Keywords: EDL, Mars, mission, SPS, TPS

Procedia PDF Downloads 169

Authors: Samir Chawdhury, Guido Morgenthal

Abstract:

The paper presents a novel extension of Vortex Particle Methods (VPM) where the study aims to reproduce a template simulation of complex flow field that is generated from impulsively started flow past an upstream bluff body at certain Reynolds number Re-Vibration of a structural system under upstream wake flow is often considered its governing design criteria. Therefore, the attention is given in this study especially for the reproduction of wake flow simulation. The basic methodology for the implementation of the flow reproduction requires the downstream velocity sampling from the template flow simulation; therefore, at particular distances from the upstream section the instantaneous velocity components are sampled using a series of square sampling-cells arranged vertically where each of the cell contains four velocity sampling points at its corner. Since the grid free Lagrangian VPM algorithm discretises vorticity on particle elements, the method requires transformation of the velocity components into vortex circulation, and finally the simulation of the reproduction of the template flow field by seeding these vortex circulations or particles into a free stream flow. It is noteworthy that the vortex particles have to be released into the free stream exactly at same rate of velocity sampling. Studies have been done, specifically, in terms of different sampling rates and velocity sampling positions to find their effects on flow reproduction quality. The quality assessments are mainly done, using a downstream flow monitoring profile, by comparing the characteristic wind flow profiles using several statistical turbulence measures. Additionally, the comparisons are performed using velocity time histories, snapshots of the flow fields, and the vibration of a downstream bluff section by performing wake buffeting analyses of the section under the original and reproduced wake flows. Convergence study is performed for the validation of the method. The study also describes the possibilities how to achieve flow reproductions with less computational effort.

Keywords: vortex particle method, wake flow, flow reproduction, wake buffeting analysis

Procedia PDF Downloads 311

Authors: Fathi Kallel, Abdulelah Alabd Uljabbar, Abdulrahman Aldukhail, Abdulaziz Alomran

Abstract:

The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface.

Keywords: MRI, brain tumor, CAD, feature extraction, DWT, PCA, classification, SVM

Procedia PDF Downloads 249

Authors: Christopher B. Obida, George A. Blackburn, James D. Whyatt, Kirk T. Semple

Abstract:

In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study, a method was developed for delineating river networks using Sentinel 1 imagery. Unsupervised classification was applied to multi-temporal Sentinel 1 data to discriminate water bodies from other land covers then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines, which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available water body products from United States Geological Survey, European Space Agency and OpenStreetMap and a river network derived from a Shuttle Rader Topography Mission Digital Elevation Model. From both raster-based and vector-based accuracy assessments, it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The geometric river network that was constructed permitted a flow routing analysis which is important for a variety of environmental management and planning applications. The extracted network will potentially be applied for modelling dispersion of hydrocarbon pollutants in Ogoniland, a part of the Niger Delta. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries where such data are deficient.

Keywords: Sentinel 1, image processing, river delineation, large scale mapping, data comparison, geometric network

Procedia PDF Downloads 139

Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

Abstract:

In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

Procedia PDF Downloads 53

Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

Abstract:

A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

Procedia PDF Downloads 157

Authors: Mariam Khaled, Noha Farag, Ghada Mohamed Abdel Salam, Khaled Abu-Aisha, Mohamed El-Azizi

Abstract:

Gastric and colorectal cancers are among the most frequent causes of cancer-associated mortalities in Africa. They have been considered as a global public health concern, as nearly one million new cases are reported per year. IL-1β is a pro-inflammatory cytokine-produced by activated macrophages and monocytes- and a member of the IL-1 family. The inactive IL-1β precursor is cleaved and activated by caspase-1 enzyme, which itself is activated by the assembly of intracellular structures defined as NLRP3 (Nod Like receptor P3) inflammasomes. Activated IL-1β stimulates the Interleukin-1 receptor type-1 (IL-1R1), which is responsible for the initiation of a signal transduction pathway leading to cell proliferation. It has been proven that the IL-1β gene is a highly polymorphic gene in which single nucleotide polymorphisms (SNPs) may affect its expression. It has been previously reported that SNPs including base transitions between C and T at positions, -511 (C-T; dbSNP: rs16944) and -31 (C-T; dbSNP: rs1143627), from the transcriptional start site, contribute to the pathogenesis of gastric and colorectal cancers by affecting IL-1β levels. Altered production of IL-1β due to such polymorphisms is suspected to stimulate an amplified inflammatory response and promote Epithelial Mesenchymal Transition leading to malignancy. Allele frequency distribution of the IL-1β-31 and -511 SNPs, in different populations, and their correlation to the incidence of gastric and colorectal cancers, has been intriguing to researchers worldwide. The current study aims to investigate allele distributions of the IL-1β SNPs among gastric and colorectal cancers Egyptian patients. In order to achieve to that, 89 Biopsy and surgical specimens from the antrum and corpus mucosa of chronic gastritis subjects and gastric and colorectal carcinoma patients was collected for DNA extraction followed by restriction fragment length polymorphism polymerase chain reaction (RFLP-PCR). The amplified PCR products of IL-1β-31C > T and IL-1β-511T > C were digested by incubation with the restriction endonuclease enzymes ALu1 and Ava1. Statistical analysis was carried out to determine the allele frequency distribution in the three studied groups. Also, the effect of the IL-1β -31 and -511 SNPs on nuclear factor binding was analyzed using Fluorescence Electrophoretic Mobility Shift Assay (EMSA), preceded by nuclear factor extraction from gastric and colorectal tissue samples and LPS stimulated monocytes. The results of this study showed that a significantly higher percentage of Egyptian gastric cancer patients have a homozygous CC genotype at the IL-1β-31 position and a heterozygous TC genotype at the IL-1β-511 position. Moreover, a significantly higher percentage of the colorectal cancer patients have a homozygous CC genotype at the IL-1β-31 and -511 positions as compared to the control group. In addition, the EMSA results showed that IL-1β-31C/T and IL-1β-511T/C SNPs do not affect nuclear factor binding. Results of this study suggest that the IL-1β-31 C/T and IL-1β-511 T/C may be correlated to the incidence of gastric cancer in Egyptian patients; however, similar findings couldn’t be proven in the colorectal cancer patients group for the IL-1β-511 T/C SNP. This is the first study to investigate IL-1β -31 and -511 SNPs in the Egyptian population.

Keywords: colorectal cancer, Egyptian patients, gastric cancer, interleukin-1β, single nucleotide polymorphisms

Procedia PDF Downloads 140

Authors: Srinaath Anbu Durai, Wang Zhaoxia

Abstract:

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

Procedia PDF Downloads 116

Authors: Shaikah Alsubayae, Gianluigi Casse, Carlos Chavez, Jon Taylor, Alan Taylor, Mohammad Alsulimane

Abstract:

Ensuring accurate radiotherapy with carbon therapy requires precise monitoring of radiation dose distribution within the patient's body. This monitoring is essential for targeted tumor treatment, minimizing harm to healthy tissues, and improving treatment effectiveness while lowering side effects. In our investigation, we employed a methodological approach to monitor secondary proton doses in carbon therapy using Monte Carlo simulations. Initially, Geant4 simulations were utilized to extract the initial positions of secondary particles formed during interactions between carbon ions and water. These particles included protons, gamma rays, alpha particles, neutrons, and tritons. Subsequently, we studied the relationship between the carbon ion beam and these secondary particles. Interaction Vertex Imaging (IVI) is valuable for monitoring dose distribution in carbon therapy. It provides details about the positions and amounts of secondary particles, particularly protons. The IVI method depends on charged particles produced during ion fragmentation to gather information about the range by reconstructing particle trajectories back to their point of origin, referred to as the vertex. In our simulations regarding carbon ion therapy, we observed a strong correlation between some secondary particles and the range of carbon ions. However, challenges arose due to the target's unique elongated geometry, which hindered the straightforward transmission of forward-generated protons. Consequently, the limited protons that emerged mostly originated from points close to the target entrance. The trajectories of fragments (protons) were approximated as straight lines, and a beam back-projection algorithm, using recorded interaction positions in Si detectors, was developed to reconstruct vertices. The analysis revealed a correlation between the reconstructed and actual positions.

Keywords: radiotherapy, carbon therapy, monitoring of radiation dose, interaction vertex imaging

Procedia PDF Downloads 84

Authors: Sachin Mulmi Shrestha, Xin Fang, Hui Ye, Lihua Ren, Qinghua Ji, Ruihua Shi

Abstract:

Background: Esophageal squamous cell carcinoma (ESCC) is a tumor arising from esophageal epithelial cells and is one of the major disease subtype in Asian countries, including China. Esophageal cancer is the 7th highest incidence based on the 2020 data of GLOBOCAN. The pathogenesis of cancer is still not well understood as many molecular and genetic basis of esophageal carcinogenesis has yet to be clearly elucidated. Circular RNAs are RNA molecules that are formed by back-splicing covalently joined 3′- and 5′-endsrather than canonical splicing, and recent data suggest circular RNAs could sponge miRNAs and are enriched with functional miRNA binding sites. Hence, we studied the mechanism of circular RNA, its biological function, and the relationship between microRNA in the carcinogenesis of ESCC. Methods: 4 pairs of normal and esophageal cancer tissues were collected in Zhongda hospital, affiliated to Southeast University, and high-throughput RNA sequencing was done. The result revealed that circ_0032746 was upregulated, and thus we selected circ_0032746 for further study. The backsplice junction of circRNA was validated by sanger sequence, and stability was determined by RNASE R assay. The binding site of circRNA and microRNA was predicted by circinteractome,mirandaand RNAhybrid database. Furthermore, circRNA was silenced by siRNA and then by lentivirus. The regulatory axis of circ0032746/miR4270 was validated by shRNA, mimic, and inhibitor transfection. Then, in vitro experiments were performed to assess the role of circ0032746 on proliferation (CCK-8 assay and colon formation assay), migration and invasion (Transewell assay), and apoptosis of ESCC. Results: The upregulated circ0032746 was validated in 9 pairs of tissues and 5 types of cell lines by qPCR, which showed high expression and was statistically significant (P<0.005) ). Upregulated circ0032746 was silenced by shRNA, which showed significant knockdown in KYSE 30 and TE-1 cell lines expression compared to control. Nuclear and cytoplasmic mRNA fraction experiment displayed the cytoplasmic location of circ0032746. The sponging of miR4270 was validated by co-transfection of sh-circ0032746 and mimic or inhibitor. Transfection with mimic showed the decreased expression of circ_0032746, whereas inhibitor inhibited the result. In vitro experiments showed that silencing of circ_0032746 inhibited the proliferation, migration, and invasion compared to the negative control group. The apoptosis was seen higher in a knockdown group than in the control group. Furthermore, 11 common mircoRNA target mRNAs were predicted by Targetscan, MirTarbase, and miRanda database, which may further play role in the pathogenesis. Conclusion: Our results showed that novel circ_0032746 is upregulated in ESCC and plays role in itsoncogenicity. Silencing of circ_0032746 inhibits the proliferation and migration of ESCC whereas increases the apoptosis of cancer cells. Hence, circ0032746 acts as an oncogene in ESCC by sponging with miR4270 and could be a potential biomarker in the diagnosis of ESCC in the future.

Keywords: circRNA, esophageal squamous cell carcinoma, microRNA, upregulated

Procedia PDF Downloads 113

Authors: Naum Simanovsky, Vladimir Goldman, Michael Zaidman

Abstract:

Background: The optimal algorithm for the management of symptomatic tarsal coalition is still under discussion in pediatric literature. It's debatable what surgical steps are essential to achieve the best outcome. Method: The investigators retrospectively reviewed the records of twelve patients with symptomatic tarsal coalition that were treated operatively between 2017 and 2019. Only painful flat feet were operated. Two patients were excluded from the study due to lack of sufficient follow-up. Ten of eleven feet were treated with the combination of calcaneal lengthening osteotomy (CLO) and resection of coalition (RC). Only one foot was operated with CLO alone. In half of our patients, Achilles lengthening was performed. For two children, medial plication was added. Short leg cast was applied to all children for 6-8 weeks, and soft shoe insoles for medial arch support were prescribed after. Demographic, clinical, and radiographic records were reviewed. The outcome was evaluated using American Orthopedic Foot and Ankle Society (AOFAS) Ankle Hindfoot Score. Results: There were seven boys and three girls. The mean age at the time of surgery was 13.9 (range 12 to 17) years, and the mean follow-up was 18 (range 8 to 34) months. The early complications included one superficial wound infection and dehiscence. Late complication includes two children with residual forefoot supination. None of our patients required additional operations during the follow-up period. All feet achieved complete deformity correction or dramatic improvement. In the last follow-up, seven feet were painless, and four children had some mild pain after intensive activities. All feet achieved excellent and good scoring on AOFAS. Conclusions: Many patients with talocalcaneal coalition also have rigid or stiff, painful, flat feet. For these patients, the resection of coalition with concomitant CLO can be safely recommended.

Keywords: Tarsal coalition, calcaneal lengthening osteotomy., flat foot, coalition resection

Procedia PDF Downloads 65

Authors: Meiling Yu, Nadia Rouatbi, Khuloud T. Al-Jamal

Abstract:

Background: Treatment of neurological disorders with modern medical and surgical approaches remains difficult. Gene therapy, allowing the delivery of genetic materials that encodes potential therapeutic molecules, represents an attractive option. The treatment of brain diseases with gene therapy requires the gene-editing tool to be delivered efficiently to the central nervous system. In this study, we explored the efficiency of different delivery routes, namely intravenous (i.v.), intra-cranial (i.c.), and intra-nasal (i.n.), to deliver stable nucleic acid-lipid particles (SNALPs) containing gene-editing tools namely Cas9 mRNA and sgRNA encoding for GFP as a reporter protein. We hypothesise that SNALPs can reach the brain and perform gene-editing to different extents depending on the administration route. Intranasal administration (i.n.) offers an attractive and non-invasive way to access the brain circumventing the blood–brain barrier. Successful delivery of gene-editing tools to the brain offers a great opportunity for therapeutic target validation and nucleic acids therapeutics delivery to improve treatment options for a range of neurodegenerative diseases. In this study, we utilised Rosa26-Cas9 knock-in mice, expressing GFP, to study brain distribution and gene-editing efficiency of SNALPs after i.v.; i.c. and i.n. routes of administration. Methods: Single guide RNA (sgRNA) against GFP has been designed and validated by in vitro nuclease assay. SNALPs were formulated and characterised using dynamic light scattering. The encapsulation efficiency of nucleic acids (NA) was measured by RiboGreen™ assay. SNALPs were incubated in serum to assess their ability to protect NA from degradation. Rosa26-Cas9 knock-in mice were i.v., i.n., or i.c. administered with SNALPs to test in vivo gene-editing (GFP knockout) efficiency. SNALPs were given as three doses of 0.64 mg/kg sgGFP following i.v. and i.n. or a single dose of 0.25 mg/kg sgGFP following i.c.. knockout efficiency was assessed after seven days using Sanger Sequencing and Inference of CRISPR Edits (ICE) analysis. In vivo, the biodistribution of DiR labelled SNALPs (SNALPs-DiR) was assessed at 24h post-administration using IVIS Lumina Series III. Results: Serum-stable SNALPs produced were 130-140 nm in diameter with ~90% nucleic acid loading efficiency. SNALPs could reach and stay in the brain for up to 24h following i.v.; i.n. and i.c. administration. Decreasing GFP expression (around 50% after i.v. and i.c. and 20% following i.n.) was confirmed by optical imaging. Despite the small number of mice used, ICE analysis confirmed GFP knockout in mice brains. Additional studies are currently taking place to increase mice numbers. Conclusion: Results confirmed efficient gene knockout achieved by SNALPs in Rosa26-Cas9 knock-in mice expressing GFP following different routes of administrations in the following order i.v.= i.c.> i.n. Each of the administration routes has its pros and cons. The next stages of the project involve assessing gene-editing efficiency in wild-type mice and replacing GFP as a model target with therapeutic target genes implicated in Motor Neuron Disease pathology.

Keywords: CRISPR, nanoparticles, brain diseases, administration routes

Procedia PDF Downloads 101

Authors: Matthias Steffan, Franz Haas

Abstract:

The production process grinding is one of the latest steps in a value-added manufacturing chain. Within this step, workpiece geometry and surface roughness are determined. Up to this process stage, considerable costs and energy have already been spent on components. According to the current state of the art, therefore, large safety reserves are calculated in order to guarantee a process capability. Especially for non-circular grinding, this fact leads to considerable losses of process efficiency. With present technology, various non-circular geometries on a workpiece must be grinded subsequently in an oscillating process where X- and Q-axis of the machine are coupled. With the approach of RPM-Synchronous Noncircular Grinding, such workpieces can be machined in an ordinary plung grinding process. Therefore, the workpieces and the grinding wheels revolutionary rate are in a fixed ratio. A non-circular grinding wheel is used to transfer its geometry onto the workpiece. The authors use a worldwide unique machine tool that was especially designed for this technology. Highest revolution rates on the workpiece spindle (up to 4500 rpm) are mandatory for the success of this grinding process. This grinding approach is performed in a two-step process. For roughing, a highly porous vitrified bonded grinding wheel with medium grain size is used. It ensures high specific material removal rates for efficiently producing the non-circular geometry on the workpiece. This process step is adapted by a force control algorithm, which uses acquired data from a three-component force sensor located in the dead centre of the tailstock. For finishing, a grinding wheel with a fine grain size is used. Roughing and finishing are performed consecutively among the same clamping of the workpiece with two locally separated grinding spindles. The approach of RPM-Synchronous Noncircular Grinding shows great efficiency enhancement in non-circular grinding. For the first time, three-dimensional non-circular shapes can be grinded that opens up various fields of application. Especially automotive industries show big interest in the emerging trend in finishing machining.

Keywords: efficiency enhancement, finishing machining, non-circular grinding, rpm-synchronous grinding

Procedia PDF Downloads 283

Authors: Gianpaolo Ghiani, Emanuela Guerriero, Emanuele Manni, Alessandro Romano

Abstract:

In this article, we deal with a variant of the classical course timetabling problem that has a practical application in many areas of education. In particular, in this paper we are interested in high schools remedial courses. The purpose of such courses is to provide under-prepared students with the skills necessary to succeed in their studies. In particular, a student might be under prepared in an entire course, or only in a part of it. The limited availability of funds, as well as the limited amount of time and teachers at disposal, often requires schools to choose which courses and/or which teaching units to activate. Thus, schools need to model the training offer and the related timetabling, with the goal of ensuring the highest possible teaching quality, by meeting the above-mentioned financial, time and resources constraints. Moreover, there are some prerequisites between the teaching units that must be satisfied. We first present a Mixed-Integer Programming (MIP) model to solve this problem to optimality. However, the presence of many peculiar constraints contributes inevitably in increasing the complexity of the mathematical model. Thus, solving it through a general purpose solver may be performed for small instances only, while solving real-life-sized instances of such model requires specific techniques or heuristic approaches. For this purpose, we also propose a heuristic approach, in which we make use of a fast constructive procedure to obtain a feasible solution. To assess our exact and heuristic approaches we perform extensive computational results on both real-life instances (obtained from a high school in Lecce, Italy) and randomly generated instances. Our tests show that the MIP model is never solved to optimality, with an average optimality gap of 57%. On the other hand, the heuristic algorithm is much faster (in about the 50% of the considered instances it converges in approximately half of the time limit) and in many cases allows achieving an improvement on the objective function value obtained by the MIP model. Such an improvement ranges between 18% and 66%.

Keywords: heuristic, MIP model, remedial course, school, timetabling

Procedia PDF Downloads 605

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 127

Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

Abstract:

New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

Procedia PDF Downloads 410

Authors: Faheema Khan

Abstract:

To investigate the ability of sensitive and tolerant genotype of Glycine max to adapt to a saline environment in a field, we examined the growth performance, water relation and activities of antioxidant enzymes in relation to photosynthetic rate, chlorophyll a fluorescence, photosynthetic pigment concentration, protein and proline in plants exposed to salt stress. Ten soybean genotypes (Pusa-20, Pusa-40, Pusa-37, Pusa-16, Pusa-24, Pusa-22, BRAGG, PK-416, PK-1042, and DS-9712) were selected and grown hydroponically. After 3 days of proper germination, the seedlings were transferred to Hoagland’s solution (Hoagland and Arnon 1950). The growth chamber was maintained at a photosynthetic photon flux density of 430 μmol m−2 s−1, 14 h of light, 10 h of dark and a relative humidity of 60%. The nutrient solution was bubbled with sterile air and changed on alternate days. Ten-day-old seedlings were given seven levels of salt in the form of NaCl viz., T1 = 0 mM NaCl, T2=25 mM NaCl, T3=50 mM NaCl, T4=75 mM NaCl, T5=100 mM NaCl, T6=125 mM NaCl, T7=150 mM NaCl. The investigation showed that genotype Pusa-24, PK-416 and Pusa-20 appeared to be the most salt-sensitive. genotypes as inferred from their significantly reduced length, fresh weight and dry weight in response to the NaCl exposure. Pusa-37 appeared to be the most tolerant genotype since no significant effect of NaCl treatment on growth was found. We observed a greater decline in the photosynthetic variables like photosynthetic rate, chlorophyll fluorescence and chlorophyll content, in salt-sensitive (Pusa-24) genotype than in salt-tolerant Pusa-37 under high salinity. Numerous primers were verified on ten soybean genotypes obtained from Operon technologies among which 30 RAPD primers shown high polymorphism and genetic variation. The Jaccard’s similarity coefficient values for each pairwise comparison between cultivars were calculated and similarity coefficient matrix was constructed. The closer varieties in the cluster behaved similar in their response to salinity tolerance. Intra-clustering within the two clusters precisely grouped the 10 genotypes in sub-cluster as expected from their physiological findings.Salt tolerant genotype Pusa-37, was further analysed by 2-Dimensional gel electrophoresis to analyse the differential expression of proteins at high salt stress. In the Present study, 173 protein spots were identified. Of these, 40 proteins responsive to salinity were either up- or down-regulated in Pusa-37. Proteomic analysis in salt-tolerant genotype (Pusa-37) led to the detection of proteins involved in a variety of biological processes, such as protein synthesis (12 %), redox regulation (19 %), primary and secondary metabolism (25 %), or disease- and defence-related processes (32 %). In conclusion, the soybean plants in our study responded to salt stress by changing their protein expression pattern. The photosynthetic, biochemical and molecular study showed that there is variability in salt tolerance behaviour in soybean genotypes. Pusa-24 is the salt-sensitive and Pusa-37 is the salt-tolerant genotype. Moreover this study gives new insights into the salt-stress response in soybean and demonstrates the power of genomic and proteomic approach in plant biology studies which finally could help us in identifying the possible regulatory switches (gene/s) controlling the salt tolerant genotype of the crop plants and their possible role in defence mechanism.

Keywords: glycine max, salt stress, RAPD, genomic and proteomic variability

Procedia PDF Downloads 423

Authors: Massimiliano Biagini, Marco Spinsanti, Gabriella De Angelis, Sara Tomei, Ilaria Ferlenghi, Maria Scarselli, Alessia Biolchi, Alessandro Muzzi, Brunella Brunelli, Silvana Savino, Marzia M. Giuliani, Isabel Delany, Paolo Costantino, Rino Rappuoli, Vega Masignani, Nathalie Norais

Abstract:

The gram-negative bacterium Neisseria meningitidis serogroup B (MenB) is an exclusively human pathogen representing the major cause of meningitides and severe sepsis in infants and children but also in young adults. This pathogen is usually present in the 30% of healthy population that act as a reservoir, spreading it through saliva and respiratory fluids during coughing, sneezing, kissing. Among surface-exposed protein components of this diplococcus, factor H binding protein is a lipoprotein proved to be a protective antigen used as a component of the recently licensed Bexsero vaccine. fHbp is a highly variable meningococcal protein: to reflect its remarkable sequence variability, it has been classified in three variants (or two subfamilies), and with poor cross-protection among the different variants. Furthermore, the level of fHbp expression varies significantly among strains, and this has also been considered an important factor for predicting MenB strain susceptibility to anti-fHbp antisera. Different methods have been used to assess fHbp expression on meningococcal strains, however, all these methods use anti-fHbp antibodies, and for this reason, the results are affected by the different affinity that antibodies can have to different antigenic variants. To overcome the limitations of an antibody-based quantification, we developed a quantitative Mass Spectrometry (MS) approach. Selected Reaction Monitoring (SRM) recently emerged as a powerful MS tool for detecting and quantifying proteins in complex mixtures. SRM is based on the targeted detection of ProteoTypicPeptides (PTPs), which are unique signatures of a protein that can be easily detected and quantified by MS. This approach, proven to be highly sensitive, quantitatively accurate and highly reproducible, was used to quantify the absolute amount of fHbp antigen in total extracts derived from 105 clinical isolates, evenly distributed among the three main variant groups and selected to be representative of the fHbp circulating subvariants around the world. We extended the study at the genetic level investigating the correlation between the differential level of expression and polymorphisms present within the genes and their promoter sequences. The implications of fHbp expression on the susceptibility of the strain to killing by anti-fHbp antisera are also presented. To date this is the first comprehensive fHbp expression profiling in a large panel of Neisseria meningitidis clinical isolates driven by an antibody-independent MS-based methodology, opening the door to new applications in vaccine coverage prediction and reinforcing the molecular understanding of released vaccines.

Keywords: quantitative mass spectrometry, Neisseria meningitidis, vaccines, bexsero, molecular epidemiology

Procedia PDF Downloads 312