Deep learning framework for bovine iris segmentation.

Iris segmentation is an initial step for identifying the biometrics of animals when establishing a traceability system for livestock. In this study, we propose a deep learning framework for pixel-wise segmentation of bovine iris with a minimized use of annotation labels utilizing the BovineAAEyes80 public dataset. The proposed image segmentation framework encompasses data collection, data preparation, data augmentation selection, training of 15 deep neural network (DNN) models with varying encoder backbones and segmentation decoder DNNs, and evaluation of the models using multiple metrics and graphical segmentation results. This framework aims to provide comprehensive and in-depth information on each model's training and testing outcomes to optimize bovine iris segmentation performance. In the experiment, U-Net with a VGG16 backbone was identified as the optimal combination of encoder and decoder models for the dataset, achieving an accuracy and dice coefficient score of 99.50% and 98.35%, respectively. Notably, the selected model accurately segmented even corrupted images without proper annotation data. This study contributes to the advancement of iris segmentation and the establishment of a reliable DNN training framework.

Oligopeptide-based molecular labelling of (bio)degradable polyester biomaterials.

Nowadays, a very important motivation for the development of new functional materials for medical purposes is not only their performance but also whether they are environmentally friendly. In recent years, there has been a growing interest in the possibility of labelling (bio)degradable polymers, in particular those intended for specific applications, especially in the medical sector, and the potential of information storage in such polymers, making it possible, for example, to track the ultimate environmental fate of plastics. This article presents a straightforward green approach that combines both aspects using an oligopeptide, which is an integral part of polymer material, to store binary information in a physical mixture of polymer and oligopeptide. In the proposed procedure the year of production of polymer films made of poly(l-lactide) (PLLA) and a blend of poly(1,4-butylene adipate-co-1,4-butylene terephthalate) and polylactide (PBAT/PLA) were encoded as the sequence of the appropriate amino acids in the oligopeptide (PEP) added to these polymers. The decoding of the recorded information was carried out using mass spectrometry technique as a new method of decoding, which enabled the successful retrieval and reading of the stored information. Furthermore, the properties of labelled (bio)degradable polymer films and stability during biodegradation of PLLA/PEP film under industrial composting conditions have been investigated. The labelled films exhibited good oligopeptide stability, allowing the recorded information to be retrieved from a green polymer/oligopeptide system before and after biodegradation. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide assay) study of the PLLA and PLLA/PBAT using the MRC-5 mammalian fibroblasts was presented for the first time.

The role of experimenter familiarity in children's eyewitness identification.

Child eyewitnesses show a high false identification rate on target-absent (TA) lineups despite good performance on target-present (TP) lineups. One explanation is that children feel a social pressure to choose when presented with a TA lineup. We investigated whether experimenter familiarity would reduce social pressure and improve accuracy on TA lineups. Children (5-7 years, N = 120) watched a short video of a staged theft; 1-2 days later they completed a TP or TA lineup with a familiar or unfamiliar experimenter. Experimenter familiarity had an impact on lineup response in TA lineups only, with more correct 'not there' and fewer 'not sure' responses when the children were familiar with the experimenter. The results provide further evidence to support the social aspect of eyewitness identification decisions in children and provide a possible strategy to improve identification accuracy for those working with children in the criminal justice system.

Gender Identification by Fingerprint Pattern and Salivary Blood Group Antigen Expression: A Forensic Approach.

Introduction Fingerprints found at the crime scene are important and valuable evidence, as they are unique to every individual. Determining the blood group from the blood samples obtained at the site of the crime helps in identifying a person. However, where blood stains are not available, saliva obtained at the crime site can be used to identify the victim. Since fingerprint patterns and blood groups are unique to every individual and remain unchanged throughout life, the correlation between dermatoglyphics and blood groups can be of use in victim identification. Objectives The present study is conducted with the objective of finding out if there is any association between the distribution of fingerprint patterns and blood groups and if this association is of use in gender identification. Materials and method Fingerprint patterns were determined in 200 (females: n = 152, males: n = 48) dental undergraduate students in the age range of 18 to 24 years. ABO blood grouping was done on saliva by using the absorption-elution method. To determine the accuracy of ABO blood group determination using saliva, it was correlated with the ABO blood grouping in blood.  Observations and result The most common fingerprint pattern was found to be loops (87, 43.50%), followed by whorls (81, 40.50%) and arches (32, 16.00%). The most common blood group was B (68, 34%), followed by O (46, 23%) and A (42, 21%), and the least common was AB (12, 6%). A higher percentage of secretors in saliva was observed in females (130, 86%) than males (38, 79%). The correlation of gender with blood group and fingerprint pattern showed that in males, the most common blood group was B (20, 42%), and the most common fingerprint pattern was whorls (21, 44%). In females, the most common blood group was B (48, 32%), while the most common fingerprint pattern was loop (68, 45%). Conclusion Present study reports an association between blood group and dermatoglyphics, which may help in gender identification. Saliva can be used as a helpful tool in victim identification in cases where blood stains are not available.

Proteomic Analyses of the Mouse Brain.

Proteomics is a scientific field that aims to identify and characterize all proteins within a biological system, including their posttranslational modifications (PTMs), quantitative changes, and protein-protein interactions. Over the last two decades, proteomic approaches have been widely used in neuroscience research, providing multidimensional insights into the biology and pathology of the brain.Here, we present a basic protocol for profiling protein expression in the mouse brain, which involves total protein extraction, fractionation, digestion, and identification through liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). This method is compatible with many prevalent techniques used for protein quantitation, PTM analysis, and protein-protein interaction mapping.

Elastic parameter identification of three-dimensional soft tissue based on deep neural network.

In the field of virtual surgery and deformation simulation, the identification of elastic parameters of human soft tissues is a critical technology that directly affects the accuracy of deformation simulation. Current research on soft tissue deformation simulation predominantly assumes that the elasticity of tissues is fixed and already known, leading to the difficulty in populating with the elasticity measured or identified from specific tissues of real patients. Existing elasticity modeling efforts struggle to be implemented on irregularly structured soft tissues, failing to adapt to clinical surgical practices. Therefore, this paper proposes a new method for identifying human soft tissue elastic parameters based on the finite element method and the deep neural network, UNet. This method requires only the full-field displacement data of soft tissues under external loads to predict their elastic distribution. The performance and validity of the algorithm are assessed using test data and clinical data from rhinoplasty surgeries. Experiments demonstrate that the method proposed in this paper can achieve an accuracy of over 99% in predicting elastic parameters. Clinical data validation shows that the predicted elastic distribution can reduce the error in finite element deformation simulations by more than 80% at the maximum compared to the error with traditional uniform elastic parameters, effectively enhancing the computational accuracy in virtual surgery simulations and soft tissue deformation modeling.

Shotgun proteomic analyses of Pseudomonas species isolated from fish products.

Numerous Pseudomonas species can infect aquatic animals, such as farmed rainbow trout, sea trout, sea bass, and sea bream, by causing disease or stress reactions. In aquaculture facilities, a number of Pseudomonas species have been isolated and identified as the main pathogens. The present study describes the characterization of 18 Pseudomonas strains, isolated from fish products using shotgun proteomics. The bacterial proteomes obtained were further analyzed to identify the main functional pathway proteins involved. In addition, this study revealed the presence of 1015 non-redundant peptides related to virulence factors. An additional 25 species-specific peptides were identified as putative Pseudomonas spp. biomarkers. The results constitute the largest dataset, described thus far for the rapid identification and characterization of Pseudomonas species present in edible fish; furthermore, these data can provide the basis for further research into the development of new therapies against these harmful pathogens.

Ecological pollution features and health risk exposure to heavy metals via street dust and topsoil from Nkpor and Onitsha in Anambra, Nigeria.

The manuscript presents the investigation results on the pollution and risk of metal mines, and it is considered an important report on environmental pollution near mines in Nigeria, with archival value. The research involved soil sampling and heavy metal analysis for about 12 months in three metal mines. Based on these results, the paper provides information on pollution levels and hazards using well-known methods like pollution and ecological risk indexes. The increasing population in urban communities attracted by various industrial, economic and social activities causes contamination of atmospheric environment that can affect human health. We investigated heavy metal distributions, correlation coefficient among elements, ecological indices and probable health risk assessment in street dust and topsoil from Nkpor and Onitsha urban suburb, Nigeria. The mean concentration of heavy metals in car dust from Onitsha and Nkpor suburb follows thus: Fe > Mn > Cu > As > Pb > Ni > Cr. The decreasing trend of heavy metal in rooftop dust from both area: Fe > Mn > Cu > Pb > As > Ni > Cr whereas metal contents in topsoil were: Fe > Mn > Cu > Pb > Ni > Cr > As for both areas. The degree of pollution indices was characterized by contamination factor (CF), geo-accumulation factor (I-geo), pollution load index (PLI), Nemerow (PN), ecological and potential ecological risk index (ER and PERI) which indicated low pollution in the urban street environment. The results of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) showed that the estimated heavy metals displayed sources from atmospheric deposition, natural origin and anthropogenic sources. Risk assessment revealed that ingestion of dust and soil was the significant route for heavy metals exposure to the populace followed by inhalation, then dermal contact. Considering all factors, non-cancer risk was more prominent in children than adults and no significant health hazard could be attributed to both aged groups as of the period of study except for As and Ni that needs constant monitoring to avoid exceeding organ damaging threshold limit of 1 × 10-4.

Revolutionizing aquatic eco-environmental monitoring: Utilizing the RPA-Cas-FQ detection platform for zooplankton.

The integration of recombinase polymerase amplification (RPA) with CRISPR/Cas technology has revolutionized molecular diagnostics and pathogen detection due to its unparalleled sensitivity and trans-cleavage ability. However, its potential in the ecological and environmental monitoring scenarios for aquatic ecosystems remains largely unexplored, particularly in accurate qualitative/quantitative detection, and its actual performance in handling complex real environmental samples. Using zooplankton as a model, we have successfully optimized the RPA-CRISPR/Cas12a fluorescence detection platform (RPA-Cas-FQ), providing several crucial "technical tips". Our findings indicate the sensitivity of CRISPR/Cas12a alone is 5 × 109 copies/reaction, which can be dramatically increased to 5 copies/reaction when combined with RPA. The optimized RPA-Cas-FQ enables reliable qualitative and semi-quantitative detection within 50 min, and exhibits a good linear relationship between fluorescence intensity and DNA concentration (R2 = 0.956-0.974***). Additionally, we developed a rapid and straightforward identification procedure for single zooplankton by incorporating heat-lysis and DNA-barcode techniques. We evaluated the platform's effectiveness using real environmental DNA (eDNA) samples from the Three Gorges Reservoir, confirming its practicality. The eDNA-RPA-Cas-FQ demonstrated strong consistency (Kappa = 0.43***) with eDNA-Metabarcoding in detecting species presence/absence in the reservoir. Furthermore, the two semi-quantitative eDNA quantification technologies showed a strong positive correlation (R2 = 0.58-0.87***). This platform also has the potential to monitor environmental pollutants by selecting appropriate indicator species. The novel insights and methodologies presented in this study represent a significant advancement in meeting the complex needs of aquatic ecosystem protection and monitoring.

Social dimensions as resources in promoting academic well-being: the case study of the University of Foggia.

Recently, scholars have focused more on changes in higher education, leading to significant insights into the working lives of academics and certain related processes, such as stress or well-being. The interest in academia is also justified by the role of universities as institutions that promote health and well-being, serving as a bridge between society, the world of work, and the local community. This study aims to identify social factors that can enhance the well-being of academic workers (lecturers and technical-administrative staff), highlighting how processes linked to social identity, based on the dynamics of identification with a territory or an organization, can serve as resources that promote well-being. Researchers conducted the survey on 198 workers at the University of Foggia (South Italy). Correlation and reliability assessments were first performed between the variables. Finally, a SEM study was completed. The goodness of fit of the model seems to be sufficient. The social aspects examined in the study, namely, organization identification, territorial well-being, and place attachment, were positively and significantly correlated with general well-being. Findings of the study demonstrated that for teaching and technical-administrative staff, among the key components for enhancing well-being in the academic setting was the social dimension of relationships, understood both inside and outside the university. Therefore, acting the belonging process to an area, implementing and strengthening relations with the social actors involved, as well as on the sense of belonging and identification with an organization, can have precise impact in enhancing well-being.

Positive school climate and strong school identification as protective factors of adolescent mental health and learning engagement: A longitudinal investigation before and during COVID-19.

The COVID-19 pandemic resulted in significant disruptions for children and youth around the world, especially given school closures and shifts in teaching modes (on-line and hybrid). However, the impact of these disruptions remains unclear given data limitations such as a reliance on cross-sectional and/or short-interval surveys as well as a lack of broad indicators of key outcomes of interest. The current research employs a quasi-experimental design by using an Australian four-year longitudinal survey with student responses from Grade 7 to 10 (aged 12-15 years old) (N = 8,735 from 20 schools) in one education jurisdiction. Responses are available pre-pandemic (2018 and 2019) and during the pandemic (2020 and 2021). Importantly the survey included measures of well-being, mental health and learning engagement as well as potential known school-environment factors that could buffer against adversity: school climate and school identification. The findings were generally in line with key hypotheses; 1) during COVID-19 students' learning engagement and well-being significantly declined and 2) students with more positive school climate or stronger school identification pre-COVID-19 fared better through the disruption of the pandemic. However, these same students suffered from a steeper decline in well-being and engagement which may be explained through the impact of losing meaningful social or group connections. This decline was evident after controlling for gender, academic grade (as a proxy of age), parental education, and socioeconomic status. It is concluded that investing in the social environment of schools is important in crisis preparedness and can facilitate better crisis response among youth.

Simulation and calibration of radiation monitoring of nuclear power plant containment sump waste liquid.

Waste liquid stored in the containment sumps of nuclear power plants may contain a variety of radionuclides. Real-time monitoring of containment sump waste liquid can ensure that accidents, such as leakage of cooling water, can be avoided. This paper presents the design of a radioactive monitoring system for waste liquid in a containment sump. The detector and the lead-shield in the measurement unit are optimized through Monte Carlo simulations. Experimental verification showed that the background count rate of the measurement chamber in the system was 418.3 cps, and the detection limit of the detection system was 3.01 Bq/L. Distinct gamma-ray characteristic peaks were also observable, demonstrating the system's ability to identify radioactive nuclides in the waste.

Identification and health risk evaluation of soil contaminated by polycyclic aromatic hydrocarbons at shale gas extraction sites based on positive matrix factorization.

The impact of shale gas extraction on surrounding environmental media remains unclear. In this study, the current state of contamination by polycyclic aromatic hydrocarbons (PAHs), which are high-frequency contaminants of shale gas, was investigated in the soil surrounding emerging shale gas development sites. The source analysis of PAHs was conducted in the soils of shale gas extraction sites using positive matrix factorization (PMF). The health risk assessment (HRA) was calculated for ingestion, dermal contact, and inhalation exposures, and the priority sources of PAHs in the soil were jointly identified by PMF and HRA to refine the contribution level of different individual PAHs to the carcinogenic risk. The results showed that both Sichuan and Chongqing mining site soils were contaminated to different degrees. Shale gas extraction has an impact on the surrounding soil, and the highest contributing source of PAHs in the mining site soil of Sichuan was anthropogenic activity, accounting for 31.6%, whereas that in the mining site soil of Chongqing was biomass combustion and mixed automobile combustion, accounting for 35.9%. At the two mining sites in Sichuan and Chongqing, none of the three exposure pathways (ingestion, dermal contact, and inhalation) posed a carcinogenic risk to children, whereas the dermal exposure pathway posed a carcinogenic risk to adults. Health risk assessments based on specific source assignments indicate that when managing soil pollution, the control of fossil fuel combustion and vehicular emissions should be prioritized.

Murine skeletal muscle satellite cells isolation and preliminary study on regulation in immune microenvironment during nurse cells formation of Trichinella spiralis infection.

As an intracellular parasitic nematode, Trichinella spiralis (T. spiralis) can induce the formation of nurse cells (NC) in host muscles and keep it to survive within the NC for an extended period. The formation of NC is similar to muscle cell injury and repair which lead to the arrest of satellite cells in the G2/M phase and build a suitable parasitic environment for the muscle larvae of T. spiralis. However, the molecular mechanisms involved in skeletal muscle repair through skeletal muscle satellite cells (SMSC) and the host immune response during T. spiralis infection have not been fully elucidated. In this study, histopathological examination revealed that the severity of damage increased as the infection progressed in the soleus muscle. SMSCs were isolated from BALB/c mice infected with T. spiralis at 4, 21 and 35 days post-infection (dpi). The immunological characteristics of these cells were analyzed by real-time PCR and flow cytometry (FCM). FCM analysis revealed a notable increase in the expression of B7 homolog 1 (B7-H1) in SMSCs following T. spiralis infection, while conversely, the expression of inducible costimulatory ligand (ICOSL) significantly decreased. Furthermore, real-time PCR results showed that toll like receptor 3 (TLR3) expression in SMSCs of the infected mice was upregulated at 21 dpi. The expression levels of three subtypes (PPARα, PPARß and PPARγ) of peroxisome proliferator-activated receptors (PPARs) also increased in the cells. This study highlights the immunological regulation significance of SMSCs host during T. spiralis infection and suggests that SMSCs actively participant in the local immune response to T. spiralis by regulating the interaction between the parasite and the host.

Genome-Wide Identification and Expression Pattern Analysis of BAHD Acyltransferase Family in Taxus mairei.

BAHD acyltransferases are involved in catalyzing and regulating the secondary metabolism in plants. Despite this, the members of BAHD family and their functions have not been reported in the Taxus species. In this study, a total of 123 TwBAHD acyltransferases from Taxus wallichiana var. mairei genome were identified and divided into six clades based on phylogenetic analysis, of which Clade VI contained a Taxus-specific branch of 52 members potentially involved in taxol biosynthesis. Most TwBAHDs from the same clade shared similar conserved motifs and gene structures. Besides the typical conserved motifs within the BAHD family, the YPLAGR motif was also conserved in multiple clades of T. mairei. Moreover, only one pair of tandem duplicate genes was found on chromosome 1, with a Ka/Ks ratio < 1, indicating that the function of duplicate genes did not differentiate significantly. RNA-seq analysis revealed different expression patterns of TwBAHDs in MeJA induction and tissue-specific expression experiments. Several TwBAHD genes in the Taxus-specific branch were highly expressed in different tissues of T. mairei, suggesting an important role in the taxol pathway. This study provides comprehensive information for the TwBAHD gene family and sets up a basis for its potential functions.

Genome-Wide Identification and Analysis of the EIN3/EIL Transcription Factor Gene Family in Doubled Haploid (DH) Poplar.

Ethylene (ET) is an important phytohormone that regulates plant growth, development and stress responses. The ethylene-insensitive3/ethylene-insensitive3-like (EIN3/EIL) transcription factor family, as a key regulator of the ET signal transduction pathway, plays an important role in regulating the expression of ET-responsive genes. Although studies of EIN3/EIL family members have been completed in many species, their role in doubled haploid (DH) poplar derived from another culture of diploid Populus simonii × P. nigra (donor tree, DT) remains ambiguous. In this study, a total of seven EIN3/EIL gene family members in the DH poplar genome were identified. Basic physical and chemical property analyses of these genes were performed, and these proteins were predicted to be localized to the nucleus. According to the phylogenetic relationship, EIN3/EIL genes were divided into two groups, and the genes in the same group had a similar gene structure and conserved motifs. The expression patterns of EIN3/EIL genes in the apical buds of different DH poplar plants were analyzed based on transcriptome data. At the same time, the expression patterns of PsnEIL1, PsnEIN3, PsnEIL4 and PsnEIL5 genes in different tissues of different DH plants were detected via RT-qPCR, including the apical buds, young leaves, functional leaves, xylem, cambium and roots. The findings presented above indicate notable variations in the expression levels of PsnEIL genes across various tissues of distinct DH plants. Finally, the PsnEIL1 gene was overexpressed in DT, and the transgenic plants showed a dwarf phenotype, indicating that the PsnEIL1 gene was involved in regulating the growth and development of poplar. In this study, the EIN3/EIL gene family of DH poplar was analyzed and functionally characterized, which provides a theoretical basis for the future exploration of the EIN3/EIL gene function.

Diagnosis of retinal damage using Resnet rescaling and support vector machine (Resnet-RS-SVM): a case study from an Indian hospital.

PURPOSE: This study aims to address the challenge of identifying retinal damage in medical applications through a computer-aided diagnosis (CAD) approach. Data was collected from four prominent eye hospitals in India for analysis and model development. METHODS: Data was collected from Silchar Medical College and Hospital (SMCH), Aravind Eye Hospital (Tamil Nadu), LV Prasad Eye Hospital (Hyderabad), and Medanta (Gurugram). A modified version of the ResNet-101 architecture, named ResNet-RS, was utilized for retinal damage identification. In this modified architecture, the last layer's softmax function was replaced with a support vector machine (SVM). The resulting model, termed ResNet-RS-SVM, was trained and evaluated on each hospital's dataset individually and collectively. RESULTS: The proposed ResNet-RS-SVM model achieved high accuracies across the datasets from the different hospitals: 99.17% for Aravind, 98.53% for LV Prasad, 98.33% for Medanta, and 100% for SMCH. When considering all hospitals collectively, the model attained an accuracy of 97.19%. CONCLUSION: The findings demonstrate the effectiveness of the ResNet-RS-SVM model in accurately identifying retinal damage in diverse datasets collected from multiple eye hospitals in India. This approach presents a promising advancement in computer-aided diagnosis for improving the detection and management of retinal diseases.

The use of machine learning for the determination of a type/model of firearms by the characteristics on cartridge cases.

Cartridge cases are commonly collected at crime scenes involving firearms. One of the stages in forensic examination is the determination of the type and model of firearms based on the class characteristics of these cartridge cases. A firearm examiner evaluates the class characteristics on the basis of their knowledge and experience, and by referring to collections of cartridge cases representing class characteristics of different firearms, special databases and reference books. However, this process is highly subjective. The novelty of this research is in developing objective methods of firearms determination by applying a machine learning approach. In this study, several Convolutional Neural Networks from Keras programming package were trained to determine the type/model of a firearm based on the class characteristics observed on cartridge cases from seven different categories of firearms. The prediction accuracies received by this method range from 71 to 81 percent for models based on different Convolutional Neural Networks, while using an ensemble of the machine learning models increased the accuracy to 88 %. The research demonstrates the efficacy of machine learning in enhancing accuracy and reducing subjectivity in firearm identification, highlighting its significant potential in forensic science applications.

Developmental validation of an mRNA kit: A 5-dye multiplex assay designed for body-fluid identification.

Identifying the sources of biosamples found at crime scenes is crucial for forensic investigations. Among the markers used for body fluid identification (BFI), mRNA has emerged as a well-studied marker because of its high specificity and remarkable stability. Despite this potential, commercially available mRNA kits specifically designed for BFI are lacking. Therefore, we developed an mRNA kit that includes 21 specific mRNA markers of body fluids, along with three housekeeping genes for BFI, to identify four forensic-relevant fluids (blood, semen, saliva, and vaginal fluids). In this study, we tested 451 single-body-fluid samples, validated the universality of the mRNA kit, and obtained a gene expression profile. We performed the validation studies in triplicates and determined the sensitivity, specificity, stability, precision, and repeatability of the mRNA kit. The sensitivity of the kit was found to be 0.1 ng. Our validation process involved the examination of 59 RNA mixtures, 60 body fluids mixtures, and 20 casework samples, which further established the reliability of the kit. Furthermore, we constructed five classifiers that can handle single-body fluids and mixtures using this kit. The classifiers output possibility values and identify the specific body fluids of interest. Our results showed the reliability and suitability of the BFI kit, and the Random Forest classifier performed the best, with 94% precision. In conclusion, we developed an mRNA kit for BFI which can be a promising tool for forensic practice.