Search results for: binary tree

Authors: Tamar Lin, Nufar Buchshtab, Yifat Elharar, Julian Nicenboim, Rotem Edgar, Iddo Weiner, Lior Zelcbuch, Ariel Cohen, Sharon Kredo-Russo, Inbar Gahali-Sass, Naomi Zak, Sailaja Puttagunta, Merav Bassan

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Background: Atopic dermatitis (AD) is a chronic, relapsing inflammatory skin disorder that is characterized by dry skin and flares of eczematous lesions and intense pruritus. Multiple lines of evidence suggest that AD is associated with increased colonization by Staphylococcus aureus, which contributes to disease pathogenesis through the release of virulence factors that affect both keratinocytes and immune cells, leading to disruption of the skin barrier and immune cell dysfunction. The aim of the current study is to develop a bacteriophage-based product that specifically targets S. aureus. Methods: For the discovery of phage, environmental samples were screened on 118 S. aureus strains isolated from skin samples, followed by multiple enrichment steps. Natural phages were isolated, subjected to Next-generation Sequencing (NGS), and analyzed using proprietary bioinformatics tools for undesirable genes (toxins, antibiotic resistance genes, lysogeny potential), taxonomic classification, and purity. Phage host range was determined by an efficiency of plating (EOP) value above 0.1 and the ability of the cocktail to completely lyse liquid bacterial culture under different growth conditions (e.g., temperature, bacterial stage). Results: Sequencing analysis demonstrated that the 118 S. aureus clinical strains were distributed across the phylogenetic tree of all available Refseq S. aureus (~10,750 strains). Screening environmental samples on the S. aureus isolates resulted in the isolation of 50 lytic phages from different genera, including Silviavirus, Kayvirus, Podoviridae, and a novel unidentified phage. NGS sequencing confirmed the absence of toxic elements in the phages’ genomes. The host range of the individual phages, as measured by the efficiency of plating (EOP), ranged between 41% (48/118) to 79% (93/118). Host range studies in liquid culture revealed that a subset of the phages can infect a broad range of S. aureus strains in different metabolic states, including stationary state. Combining the single-phage EOP results of selected phages resulted in a broad host range cocktail which infected 92% (109/118) of the strains. When tested in vitro in a liquid infection assay, clearance was achieved in 87% (103/118) of the strains, with no evidence of phage resistance throughout the study (24 hours). A S. aureus host was identified that can be used for the production of all the phages in the cocktail at high titers suitable for large-scale manufacturing. This host was validated for the absence of contaminating prophages using advanced NGS methods combined with multiple production cycles. The phages are produced under optimized scale-up conditions and are being used for the development of a topical formulation (BX005) that may be administered to subjects with atopic dermatitis. Conclusions: A cocktail of natural phages targeting S. aureus was effective in reducing bacterial burden across multiple assays. Phage products may offer safe and effective steroid-sparing options for atopic dermatitis.

Keywords: atopic dermatitis, bacteriophage cocktail, host range, Staphylococcus aureus

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Gloria M. Ntuba-Jua, Afui M. Mih, Eneke E. T. Bechem

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Prunus africana (Hook. F.) Kalkman, commonly known as Pygeum or African cherry belongs to the Rosaceae family. It is a medium to large, evergreen tree with a spreading crown of 10 to 20 m. It is used by the traditional medical practitioners for the treatment of over 45ailments in Cameroon and sub-Sahara Africa. In modern medicine, it is used in the treatment of benign prostrate hyperplasia (BPH), prostate gland hypertrophy (enlarged prostate glands). This is possible because of its ability to produce some secondary metabolites which are believed to have bioactivity against these ailments. The ready international market for the sale of Prunus bark, uncontrolled exploitation, illegal harvesting using inappropriate techniques and poor timing of harvesting have contributed enormously to making the plant endangered. It is known to harbor a large number of endophytic fungi with the potential to produce similar secondary metabolites as the parent plant. Alternative sourcing of medicinal principles through endophytic fungi requires succinct knowledge of the endophytic fungi. This will serve as a conservation measure for Prunus africana by reducing dependence on Prunus bark for such metabolites. This work thus sought to compare the production of some major secondary metabolites produced by P. africana and some of its associated endophytic fungi. The leaves and stem bark of the plant from different provenances were soaked in methanol for 72 hrs to yield the methanolic crude extract. The phytochemical screening of the methanolic crude extracts using different standard procedures revealed the presence of tannins, flavonoids, terpenoids, saponins, phenolics and steroids. Pure cultures of some predominantly isolated endophyte species from the difference Prunus provenances such as Curvularia sp, and Morphospecies P001 were also grown in Potato Dextrose Broth (PDB) for 21 days and later extracted with Methylene dichloride (MDC) solvent after 24hrs to produce crude culture extracts. Qualitative assessment of crude culture extracts showed the presence of tannins, terpenoids, phenolics and steroids particularly β-Sitosterol, (a major bioactive metabolite) as did the plant tissues. Qualitative analysis by thin layer chromatography (TLC) was done to confirm and compare the production of β-Sitosterol (as marker compounds) in the crude extracts of the plant and endophyte. Samples were loaded on TLC silica gel aluminium barked plate (Kieselgel 60 F254, 0.2 mm, Merck) using acetone/hexane, (3.0:7.0) solvent system. They were visualized under an ultra violet lamp (UV254 and UV360). TLC revealed that leaves had a higher concentration of β-sitosterol in terms of band intensity than stem barks from the different provenances. The intensity of β-sitosterol bands in the culture extracts of endophytes was comparable to the plant extracts except for Curvularia sp (very minute) whose band was very faint. The ability of these fungi to make β-sitosterol was confirmed by TLC analysis with the compound having chromatographic properties (retention factor) similar to those of β-sitosterol standard. The ability of these major endophytes to produce secondary metabolites similar to the host has therefore been demonstrated. There is, therefore, the potential of developing the in vitro production system of Prunus secondary metabolites thereby enhancing its conservation.

Keywords: Caneroon, endophytic fungi, Prunus africana, secondary metabolite

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Authors: Deepanwita Deka, Dhruva Kumar Jha

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Endophytes are the microbes that colonize living, internal tissues of plants without causing any immediate, overt negative effects. Endophytes are rich source of therapeutic substances like antimicrobial, anticancerous, herbicidal, insecticidal, immunomodulatory compounds. Brucea mollis, commonly known as Quinine in Assam, belonging to the family Simaroubaceae, is a shrub or small tree, recorded as endangered species in North East India by CAMP survey in 2003. It is traditionally being used as antimalarial and antimicrobial agent and has antiplasmodial, cytotoxic, anticancer, diuretic, cardiovascular effect etc. Being endangered and medicinal; this plant may host certain noble endophytes which need to be studied in depth. The aim of the present study was isolation and identification of potent endophytic fungi from Brucea mollis, an endangered medicinal plant, to protect it from extinction due to over use for medicinal purposes. Aseptically collected leaves, barks and roots samples of healthy plants were washed and cut into a total of 648 segments of about 2 cm long and 0.5 cm broad with sterile knife, comprising 216 segments each from leaves, barks and roots. These segments were surface sterilized using ethanol, mercuric chloride (HgCl2) and aqueous solution of sodium hypochlorite (NaClO). Different media viz., Czapeck-Dox-Agar (CDA, Himedia), Potato-Dextrose-Agar (PDA, Himedia), Malt Extract Agar (MEA, Himedia), Sabourad Dextrose Agar (SDA, Himedia), V8 juice agar, nutrient agar and water agar media and media amended with plant extracts were used separately for the isolation of the endophytic fungi. A total of 11 fungal species were recovered from leaf, bark and root tissues of B. mollis. The isolates were screened for antimicrobial, antioxidant and enzymatic activities using certain protocols. Cochliobolus geniculatus was identified as the most dominant species. The mycelia sterilia (creamy white) showing highest inhibitory activity against Candida albicans (MTCC 183) was induced to sporulate using modified PDA media. The isolate was identified as Geosmithia pallida. The internal transcribed spacer of rDNA was sequenced for confirmation of the taxonomic identity of the sterile mycelia (creamy white). The internal transcribed spacer r-DNA sequence was submitted to the NCBI (KU693285) for the first time from India. G. pallida and Penicillium showed highest antioxidant activity among all the isolates. The antioxidant activity of G. pallida and Penicillium didn’t show statistically significant difference (P˃0.05). G. pallida, Cochliobolus geniculatus and P. purpurogenum respectively showed highest cellulase, amylase and protease activities. Thus, endopytic fungal isolates may be used as potential natural resource of pharmaceutical importance. The endophytic fungi, Geosmithia pallida, may be used for synthesis of pharmaceutically important natural products and consequently can replace plants hitherto used for the same purpose. This study suggests that endophytes should be investigated more aggressively to better understand the endophyte biology of B. mollis.

Keywords: Antimicrobial activity, antioxidant activity, Brucea mollis, endophytic fungi, enzyme activity, Geosmithia pallida

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Authors: Chuyuan Wang, Nayan Khare, Lily Villa, Patricia Solis, Elizabeth A. Wentz

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Maricopa County, Arizona, has a semi-arid hot desert climate that is one of the hottest regions in the United States. The exacerbated urban heat island (UHI) effect caused by rapid urbanization has made the urban area even hotter than the rural surroundings. The Phoenix metropolitan area experiences extremely high temperatures in the summer from June to September that can reach the daily highest of 120 °F (48.9 °C). Morbidity and mortality due to the environmental heat is, therefore, a significant public health issue in Maricopa County, especially because it is largely preventable. Public records from the Maricopa County Department of Public Health (MCDPH) revealed that between 2012 and 2016, there were 10,825 incidents of heat-related morbidity incidents, 267 outdoor environmental heat deaths, and 173 indoor heat-related deaths. A lot of research has examined heat-related death and its contributing factors around the world, but little has been done regarding heat-related morbidity issues, especially for regions that are naturally hot in the summer. The objective of this study is to examine the demographic, socio-economic, housing, and environmental factors that contribute to heat-related morbidity in Maricopa County. We obtained heat-related morbidity data between 2012 and 2016 at census tract level from MCDPH. Demographic, socio-economic, and housing variables were derived using 2012-2016 American Community Survey 5-year estimate from the U.S. Census. Remotely sensed Landsat 7 ETM+ and Landsat 8 OLI satellite images and Level-1 products were acquired for all the summer months (June to September) from 2012 and 2016. The National Land Cover Database (NLCD) 2016 percent tree canopy and percent developed imperviousness data were obtained from the U.S. Geological Survey (USGS). We used ordinary least squares (OLS) regression analysis to examine the empirical relationship between all the independent variables and heat-related morbidity rate. Results showed that higher morbidity rates are found in census tracts with higher values in population aged 65 and older, population under poverty, disability, no vehicle ownership, white non-Hispanic, population with less than high school degree, land surface temperature, and surface reflectance, but lower values in normalized difference vegetation index (NDVI) and housing occupancy. The regression model can be used to explain up to 59.4% of total variation of heat-related morbidity in Maricopa County. The multiscale geographically weighted regression (MGWR) technique was then used to examine the spatially varying relationships between heat-related morbidity rate and all the significant independent variables. The R-squared value of the MGWR model increased to 0.691, that shows a significant improvement in goodness-of-fit than the global OLS model, which means that spatial heterogeneity of some independent variables is another important factor that influences the relationship with heat-related morbidity in Maricopa County. Among these variables, population aged 65 and older, the Hispanic population, disability, vehicle ownership, and housing occupancy have much stronger local effects than other variables.

Keywords: census, empirical modeling, heat-related morbidity, spatial analysis

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Authors: Shagufta Jabeen, Faez J. Firdaus Abdullah, Zunita Zakaria, Nurulfiza M. Isa, Yung C. Tan, Wai Y. Yee, Abdul R. Omar

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Pasteurella multocida (PM) is an important veterinary opportunistic pathogen particularly associated with septicemic pasteurellosis, pneumonic pasteurellosis and hemorrhagic septicemia in cattle and buffaloes. P. multocida serotype A has been reported to cause fatal pneumonia and septicemia. Pasteurella multocida subspecies multocida of serotype A Malaysian isolate PMTB2.1 was first isolated from buffaloes died of septicemia. In this study, the genome of P. multocida strain PMTB2.1 was sequenced using third-generation sequencing technology, PacBio RS2 system and analyzed bioinformatically via de novo analysis followed by in-depth analysis based on comparative genomics. Bioinformatics analysis based on de novo assembly of PacBio raw reads generated 3 contigs followed by gap filling of aligned contigs with PCR sequencing, generated a single contiguous circular chromosome with a genomic size of 2,315,138 bp and a GC content of approximately 40.32% (Accession number CP007205). The PMTB2.1 genome comprised of 2,176 protein-coding sequences, 6 rRNA operons and 56 tRNA and 4 ncRNAs sequences. The comparative genome sequence analysis of PMTB2.1 with nine complete genomes which include Actinobacillus pleuropneumoniae, Haemophilus parasuis, Escherichia coli and five P. multocida complete genome sequences including, PM70, PM36950, PMHN06, PM3480, PMHB01 and PMTB2.1 was carried out based on OrthoMCL analysis and Venn diagram. The analysis showed that 282 CDs (13%) are unique to PMTB2.1and 1,125 CDs with orthologs in all. This reflects overall close relationship of these bacteria and supports the classification in the Gamma subdivision of the Proteobacteria. In addition, genomic distance analysis among all nine genomes indicated that PMTB2.1 is closely related with other five Pasteurella species with genomic distance less than 0.13. Synteny analysis shows subtle differences in genetic structures among different P.multocida indicating the dynamics of frequent gene transfer events among different P. multocida strains. However, PM3480 and PM70 exhibited exceptionally large structural variation since they were swine and chicken isolates. Furthermore, genomic structure of PMTB2.1 is more resembling that of PM36950 with a genomic size difference of approximately 34,380 kb (smaller than PM36950) and strain-specific Integrative and Conjugative Elements (ICE) which was found only in PM36950 is absent in PMTB2.1. Meanwhile, two intact prophages sequences of approximately 62 kb were found to be present only in PMTB2.1. One of phage is similar to transposable phage SfMu. The phylogenomic tree was constructed and rooted with E. coli, A. pleuropneumoniae and H. parasuis based on OrthoMCL analysis. The genomes of P. multocida strain PMTB2.1 were clustered with bovine isolates of P. multocida strain PM36950 and PMHB01 and were separated from avian isolate PM70 and swine isolates PM3480 and PMHN06 and are distant from Actinobacillus and Haemophilus. Previous studies based on Single Nucleotide Polymorphism (SNPs) and Multilocus Sequence Typing (MLST) unable to show a clear phylogenetic relatedness between Pasteurella multocida and the different host. In conclusion, this study has provided insight on the genomic structure of PMTB2.1 in terms of potential genes that can function as virulence factors for future study in elucidating the mechanisms behind the ability of the bacteria in causing diseases in susceptible animals.

Keywords: comparative genomics, DNA sequencing, phage, phylogenomics

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Authors: Thembelihle Portia Lubisi, Malusi Ntandoyenkosi Mkhize, Jonas Kalebe Johakimu

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Fertilizers play an important role in maintaining the productivity and quality of plants. Inorganic fertilizers (containing nitrogen, phosphorus, and potassium) are largely used in South Africa as they are considered inexpensive and highly productive. When applied, a portion of the excess fertilizer will be retained in the soil, a portion enters water streams due to surface runoff or the irrigation system adopted. Excess nutrient from the fertilizers entering the water stream eventually results harmful algal blooms (HABs) in freshwater systems, which not only disrupt wildlife but can also produce toxins harmful to humans. Use of agro-chemicals such as pesticides and herbicides has been associated with increased antimicrobial resistance (AMR) in humans as the plants are consumed by humans. This resistance of bacterial poses a threat as it prevents the Health sector from being able to treat infectious disease. Archaeological studies have found that pyrolysis liquids were already used in the time of the Neanderthal as a biocide and plant protection product. Pyrolysis is thermal degradation process of plant biomass or organic material under anaerobic conditions leading to production of char, bio-oils and syn gases. Bio-oil constituents can be categorized as water soluble (wood vinegar) and water insoluble fractions (tar and light oils). Wood vinegar (pyro-ligneous acid) is said to contain contains highly oxygenated compounds including acids, alcohols, aldehydes, ketones, phenols, esters, furans, and other multifunctional compounds with various molecular weights and compositions depending on the biomass material derived from and pyrolysis operating conditions. Various researchers have found the wood vinegar to be efficient in the eradication of termites, effective in plant protection and plant growth, has antibacterial characteristics and was found effective in inhibiting the micro-organisms such as candida yeast, E-coli, etc. This study investigated characterisation of South African forestry product processing waste with intention of evaluating the potential of using the respective biomass waste as feedstock for boil oil production via pyrolysis process. Ability to use biomass waste materials in production of wood-vinegar has advantages that it does not only allows for reduction of environmental pollution and landfill requirement, but it also does not negatively affect food security. The biomass wastes investigated were from the popular tree types in KZN, which are, pine saw dust (PSD), pine bark (PB), eucalyptus saw dust (ESD) and eucalyptus bark (EB). Furthermore, the research investigates the possibility of mixing the different wastes with an aim to lessen the cost of raw material separation prior to feeding into pyrolysis process and mixing also increases the amount of biomass material available for beneficiation. A 50/50 mixture of PSD and ESD (EPSD) and mixture containing pine saw dust; eucalyptus saw dust, pine bark and eucalyptus bark (EPSDB). Characterisation of the biomass waste will look at analysis such as proximate (volatiles, ash, fixed carbon), ultimate (carbon, hydrogen, nitrogen, oxygen, sulphur), high heating value, structural (cellulose, hemicellulose and lignin) and thermogravimetric analysis.

Keywords: characterisation, biomass waste, saw dust, wood waste

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Authors: Heather E. Ewart, Keith Jensen, Rebecca N. Cliffe

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Fragmentation and changes in the structural composition of tropical forests – as a result of intensifying anthropogenic disturbance – are increasing pressures on local biodiversity. Species with low dispersal abilities have some of the highest extinction risks in response to environmental change, as even small-scale environmental variation can substantially impact their space use and energetic balance. Understanding the implications of forest disturbance is therefore essential, ultimately allowing for more effective and targeted conservation initiatives. Here, the impact of different levels of forest disturbance on the space use, energetics, movement and behavior of 18 brown-throated sloths (Bradypus variegatus) were assessed in the South Caribbean of Costa Rica. A multi-scale framework was used to measure forest disturbance, including large-scale (landscape-level classifications) and fine-scale (within and surrounding individual home ranges) forest composition. Three landscape-level classifications were identified: primary forests (undisturbed), secondary forests (some disturbance, regenerating) and urban forests (high levels of disturbance and fragmentation). Finer-scale forest composition was determined using measurements of habitat structure and quality within and surrounding individual home ranges for each sloth (home range estimates were calculated using autocorrelated kernel density estimation [AKDE]). Measurements of forest quality included tree connectivity, density, diameter and height, species richness, and percentage of canopy cover. To determine space use, energetics, movement and behavior, six sloths in urban forests, seven sloths in secondary forests and five sloths in primary forests were tracked using a combination of Very High Frequency (VHF) radio transmitters and Global Positioning System (GPS) technology over an average period of 120 days. All sloths were also fitted with micro data-loggers (containing tri-axial accelerometers and pressure loggers) for an average of 30 days to allow for behavior-specific movement analyses (data analysis ongoing for data-loggers and primary forest sloths). Data-loggers included determination of activity budgets, circadian rhythms of activity and energy expenditure (using the vector of the dynamic body acceleration [VeDBA] as a proxy). Analyses to date indicate that home range size significantly increased with the level of forest disturbance. Female sloths inhabiting secondary forests averaged 0.67-hectare home ranges, while female sloths inhabiting urban forests averaged 1.93-hectare home ranges (estimates are represented by median values to account for the individual variation in home range size in sloths). Likewise, home range estimates for male sloths were 2.35 hectares in secondary forests and 4.83 in urban forests. Sloths in urban forests also used nearly double (median = 22.5) the number of trees as sloths in the secondary forest (median = 12). These preliminary data indicate that forest disturbance likely heightens the energetic requirements of sloths, a species already critically limited by low dispersal ability and rates of energy acquisition. Energetic and behavioral analyses from the data-loggers will be considered in the context of fine-scale forest composition measurements (i.e., habitat quality and structure) and are expected to reflect the observed home range and movement constraints. The implications of these results are far-reaching, presenting an opportunity to define a critical index of habitat connectivity for low dispersal species such as sloths.

Keywords: biodiversity conservation, forest disturbance, movement ecology, sloths

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Michael Josef Schwerer

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Background: The identification of unknown victims from mass disasters, violent crimes, or terrorist attacks is frequently facilitated through information from missing persons lists, portrait photos, old or recent pictures showing unique characteristics of a person such as scars or tattoos, or simply reference samples from blood relatives for DNA analysis. In contrast, the identification or at least the characterization of an unknown perpetrator from criminal or terrorist actions remains challenging, particularly in the absence of material or data for comparison, such as fingerprints, which had been previously stored in criminal records. In scenarios that result in high levels of destruction of the perpetrator’s corpse, for instance, blast or fire events, the chance for a positive identification using standard techniques is further impaired. Objectives: This study shows the forensic genetic procedures in the Legal Medicine Service of the German Air Force for the identification of unknown individuals, including such cases in which reference samples are not available. Scenarios requiring such efforts predominantly involve aircraft crash investigations, which are routinely carried out by the German Air Force Centre of Aerospace Medicine as one of the Institution’s essential missions. Further, casework by military police or military intelligence is supported based on administrative cooperation. In the talk, data from study projects, as well as examples from real casework, will be demonstrated and discussed with the audience. Methods: Forensic genetic identification in our laboratories involves the analysis of Short Tandem Repeats and Single Nucleotide Polymorphisms in nuclear DNA along with mitochondrial DNA haplotyping. Extended DNA analysis involves phenotypic markers for skin, hair, and eye color together with the investigation of a person’s biogeographic ancestry. Assessment of the biological age of an individual employs CpG-island methylation analysis using bisulfite-converted DNA. Forensic Investigative Genealogy assessment allows the detection of an unknown person’s blood relatives in reference databases. Technically, end-point-PCR, real-time PCR, capillary electrophoresis, pyrosequencing as well as next generation sequencing using flow-cell-based and chip-based systems are used. Results and Discussion: Optimization of DNA extraction from various sources, including difficult matrixes like formalin-fixed, paraffin-embedded tissues, degraded specimens from decomposed bodies or from decedents exposed to blast or fire events, provides soil for successful PCR amplification and subsequent genetic profiling. For cases with extremely low yields of extracted DNA, whole genome preamplification protocols are successfully used, particularly regarding genetic phenotyping. Improved primer design for CpG-methylation analysis, together with validated sampling strategies for the analyzed substrates from, e.g., lymphocyte-rich organs, allows successful biological age estimation even in bodies with highly degraded tissue material. Conclusions: Successful identification of unknown individuals or at least their phenotypic characterization using pigmentation markers together with age-informative methylation profiles, possibly supplemented by family tree search employing Forensic Investigative Genealogy, can be provided in specialized laboratories. However, standard laboratory procedures must be adapted to work with difficult and highly degraded sample materials.

Keywords: identification, forensic genetics, phenotypic markers, CPG methylation, biological age estimation, forensic investigative genealogy

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Authors: Tim Crocker-Buque, Sandra Mounier-Jack, Natasha Howard

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Background: Due to both the increasing scale and speed of urbanisation, urban areas in low and middle-income countries (LMICs) host increasingly large populations of under-immunized children, with the additional associated risks of rapid disease transmission in high-density living environments. Multiple interdependent factors are associated with these coverage disparities in urban areas and most evidence comes from relatively few countries, e.g., predominantly India, Kenya, Nigeria, and some from Pakistan, Iran, and Brazil. This study aimed to identify, describe, and assess the main tools used to measure or improve coverage of immunisation services in poor urban areas. Methods: Authors used a qualitative review design, including academic and non-academic literature, to identify tools used to improve coverage of public health interventions in urban areas. Authors selected and extracted sources that provided good examples of specific tools, or categories of tools, used in a context relevant to urban immunization. Diagnostic (e.g., for data collection, analysis, and insight generation) and programme tools (e.g., for investigating or improving ongoing programmes) and interventions (e.g., multi-component or stand-alone with evidence) were selected for inclusion to provide a range of type and availability of relevant tools. These were then prioritised using a decision-analysis framework and a tool selection guide for programme managers developed. Results: Authors reviewed tools used in urban immunisation contexts and tools designed for (i) non-immunization and/or non-health interventions in urban areas, and (ii) immunisation in rural contexts that had relevance for urban areas (e.g., Reaching every District/Child/ Zone). Many approaches combined several tools and methods, which authors categorised as diagnostic, programme, and intervention. The most common diagnostic tools were cross-sectional surveys, key informant interviews, focus group discussions, secondary analysis of routine data, and geographical mapping of outcomes, resources, and services. Programme tools involved multiple stages of data collection, analysis, insight generation, and intervention planning and included guidance documents from WHO (World Health Organisation), UNICEF (United Nations Children's Fund), USAID (United States Agency for International Development), and governments, and articles reporting on diagnostics, interventions, and/or evaluations to improve urban immunisation. Interventions involved service improvement, education, reminder/recall, incentives, outreach, mass-media, or were multi-component. The main gaps in existing tools were an assessment of macro/policy-level factors, exploration of effective immunization communication channels, and measuring in/out-migration. The proposed framework uses a problem tree approach to suggest tools to address five common challenges (i.e. identifying populations, understanding communities, issues with service access and use, improving services, improving coverage) based on context and available data. Conclusion: This study identified many tools relevant to evaluating urban LMIC immunisation programmes, including significant crossover between tools. This was encouraging in terms of supporting the identification of common areas, but problematic as data volumes, instructions, and activities could overwhelm managers and tools are not always suitably applied to suitable contexts. Further research is needed on how best to combine tools and methods to suit local contexts. Authors’ initial framework can be tested and developed further.

Keywords: health equity, immunisation, low and middle-income countries, poverty, urban health

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Authors: C. M. Kalleshwaraswamy, G. S. Sathisha, A. S. Vidyashree, H. B. Pavithra

Abstract:

Isoptera are an interesting group of social insects with different castes and division of labour. They are primarily wood-feeders, but also feed on a variety of other organic substrates, such as living trees, leaf litter, soil, lichens and animal faeces. The number of species and their biomass are especially large in tropics. In natural ecosystems, they perform a beneficial role in nutrient cycles by accelerating decomposition. The magnitude and dimension of ecological role played by termites is a function of their diversity, population density, and biomass. Termite assemblage composition has a strong response to habitat disturbance and may be indicative of quantitative changes in the decomposition process. Many previous studies in Western Ghat region of India suggest increased anthropogenic activities that adversely affect the soil macrofauna and diversity. Shivamogga district provides a good opportunity to study the effect of topography, cropping pattern, human disturbance on the termite fauna, thereby acquiring accurate baseline information for conservation decision making. The district has 3 distinct agro-ecological areas such as maidan area, semi-malnad and Western Ghat region. Thus, the district provides a unique opportunity to study the effect of varied climate and anthropogenic disturbance on the termite diversity. The standard protocol of belt transects method developed by Eggleton et al. (1997) was used for sampling termites. Sampling was done at monthly interval from September-2014 to August-2015 in Western Ghats, semi-malnad and maidan habitats. The transect was 100m long and 2m wide and divided into 20 contiguous sections, each 5 x 2m in each habitat. Within each section, all the probable microhabitats of termites were searched, which include dead logs, fallen tree, branch, sticks, leaf litter, vegetation etc.,. All the castes collected were labelled, preserved in 80% alcohol, counted and identified to species level. The number of encounters of a species in the transect was used as an indicator of relative abundance of species. The species diversity, species richness, density were compared in three different habitats such as Western Ghats, semi-malnad and maidan region. The study indicated differences in the species composition in the three different habitats. A total of 15 species were recorded which belonging to four sub family and five genera in three habitats. Eleven species viz., Odontotermes obesus, O. feae, O. anamallensis, O. bellahunisensis, O. adampurensis, O. boveni, Microcerotermes fletcheri, M. pakistanicus, Nasutitermes anamalaiensis, N. indicola, N. krishna were recorded in Western Ghat region. Similarly, 11 species viz., Odontotermes obesus, O. feae, O. anamallensis, O. bellahunisensis, O. hornii, O. bhagwathi, Microtermes obesi, Microcerotermes fletcheri, M. pakistanicus, Nasutitermes indicola and Pericapritermes sp. were recorded in semi-malnad habitat. However, only four species viz., O. obesus, O. feae, Microtemes obesi and Pericapritermes sp. species were recorded in maidan area. Shannon’s wiener diversity index (H) showed that Western Ghats had more species dominance (1.56) followed by semi- malnad (1.36) and lowest in maidan (0.89) habitats. Highest value of simpson’s index (D) was observed in Western Ghats habitat (0.70) with more diverse species followed by semi-malnad (0.58) and lowest in maidan (0.53). Similarly, evenness was highest (0.65) in Western Ghats followed by maidan (0.64) and least in semi-malnad habitat (0.54). Menhinick’s index (Dmn) value was ranging from 0.03 to 0.06 in different habitats in the study area. Highest index was observed in Western Ghats (0.06) followed by semi-malnad (0.05) and lowest in maidan (0.03). The study conclusively demonstrated that Western Ghat had highest species diversity compared to semi-malnad and maidan habitat indicating these two habitats are continuously subjected to anthropogenic disturbances. Efforts are needed to conserve the uncommon species which otherwise may become extinct due to human activities.

Keywords: anthropogenic disturbance, isoptera, termite species diversity, Western ghats

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