Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation in Monogenic Diabetes of the Impact of GCK, HNF1A, and HNF4A Variants on Splicing through the Combined Use of In Silico Tools and Minigene Assays

Variants in GCK, HNF1A, and HNF4A genes are the three main causes of monogenic diabetes. Determining the molecular etiology is essential for patients with monogenic diabetes to benefit from the most appropriate treatment. The increasing number of variants of unknown significance (VUS) is a major issue in genetic diagnosis, and assessing the impact of variants on RNA splicing is challenging, particularly for genes expressed in tissues not easily accessible as in monogenic diabetes. The in vitro functional splicing assay based on a minigene construct is an appropriate approach. Here, we performed in silico analysis using SpliceAI and SPiP and prioritized 36 spliceogenic variants in GCK, HNF1A, and HNF4A. Predictions were secondarily compared with Pangolin and AbSplice-DNA bioinformatics tools which include tissue-specific annotations. We assessed the effect of selected variants on RNA splicing using minigene assays. These assays validated splicing defects for 33 out of 36 spliceogenic variants consisting of exon skipping (15%), exonic deletions (18%), intronic retentions (24%), and complex splicing patterns (42%). This provided additional evidence to reclassify 23 out of 31 (74%) VUS including missense, synonymous, and intronic noncanonical splice site variants as likely pathogenic variants. Comparison of in silico analysis with minigene results showed the robustness of bioinformatics tools to prioritize spliceogenic variants, but revealed inconsistencies in the location of cryptic splice sites underlying the importance of confirming predicted splicing alterations with functional splicing assays. Our study underlines the feasibility and the benefits of implementing minigene-splicing assays in the genetic testing of monogenic diabetes after a prior in-depth in silico analysis.
Selection of Salinity-Adapted Endorhizal Fungal Consortia from Two Inoculum Sources and Six Halophyte Plants

Soil salinity is a limiting factor in crop productivity. Inoculating crops with microorganisms adapted to salt stress is an alternative to increasing plant salinity tolerance. Few studies have simultaneously propagated arbuscular mycorrhizal fungi (AMF) and dark septate fungi (DSF) using different sources of native inoculum from halophyte plants and evaluated their effectiveness. In alfalfa plants as trap culture, this study assessed the infectivity of 38 microbial consortia native from rhizosphere soil (19) or roots (19) from six halophyte plants, as well as their effectiveness in mitigating salinity stress. Inoculation with soil resulted in 26–56% colonization by AMF and 12–32% by DSF. Root inoculation produced 10–56% and 8–24% colonization by AMF and DSF, respectively. There was no difference in the number of spores of AMF produced with both inoculum types. The effective consortia were selected based on low Na but high P and K shoot concentrations that are variable and are relevant for plant nutrition and salt stress mitigation. This microbial consortia selection may be a novel and applicable model, which would allow the production of native microbial inoculants adapted to salinity to diminish the harmful effects of salinity stress in glycophyte plants in the context of sustainable agriculture.
Unsupervised Hyperspectral Band Selection via Structure-Conserved and Neighborhood-Grouped Evolutionary Algorithm

Hyperspectral images (HSIs) contain hundreds of bands, which provide a wealth of spectral information and enable better characterization of features. However, the excessive dimensions and redundant information also cause a dimensional disaster for subsequent processing. Band selection (BS) is a widely used dimension reduction technique for HSIs. Traditional methods mainly consider the hyperspectral BS problem at the level of data and maintain the information contained in the data, without considering the spatial structures inside HSIs. To fill the gap, in this work, an unsupervised hyperspectral BS method through structure-conserved and neighborhood-grouped evolutionary algorithm (SNEA) is proposed. Different from other EAs for hyperspectral BS, first, two spatial-structure-related optimization objectives are designed, including the locally spatial structure denoted by the pixel’s spatial consistency with its adjacent neighbors and the globally spatial structure denoted by the affinity graph among pixels. With the designed objectives, the hyperspectral BS is formulated as the problem of conserving spatial structures. Moreover, a neighborhood-grouped pairwise learning strategy is proposed to generate high-quality offsprings. In this novel strategy, a neighborhood-grouping operation is developed to divide the band space into several groups. The population can be initialized efficiently, and the offspring solutions can be generated pairwisely under the guidance of grouping. Compared with nine state-of-the-art comparison algorithms, experimental results on three standard hyperspectral datasets demonstrate that the band subset obtained by our proposed SNEA has a better classification performance than the comparison algorithms.
The Impact of Heat Waves on Health Care Services in Low- or Middle-Income Countries: Protocol for a Systematic Review

Background Heat waves significantly impact ecosystems and human health, especially that of vulnerable populations, and are associated with increased morbidity and mortality. Besides being directly related to climate-sensitive health outcomes, heat waves have indirectly increased the burden on our health care systems. Although the existing literature examines the impact of heat waves and morbidity, past research has mostly been conducted in high-income countries (HICs), and studies on the impact of heat waves on morbidity in low- or middle-income countries (LMICs) are still scarce. Objective This paper presents the protocol for a systematic review that aims to provide evidence of the impact of heat waves on health care services in LMICs. Methods We will identify peer-reviewed studies from 3 online databases, including the Web of Science, PubMed, and SCOPUS, published from January 2002 to April 2023, using the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. Quality assessment will be conducted using the Navigation Guide checklist. Key search terms include heatwaves, extreme heat, hospitalization, outpatient visit, burden, health services, and morbidity. Results This systematic review will provide insight into the impact of heat waves on health care services in LMICs, especially on emergency department visits, ambulance call-outs, hospital admissions, outpatient department visits, in-hospital mortality, and health care operational costs. Conclusions The results of this review are anticipated to help policymakers and key stakeholders obtain a better understanding of the impact of heat waves on health care services and prioritize investments to mitigate the effects of heat waves in LMICs. This entails creating a comprehensive heat wave plan and ensuring that adequate infrastructure, capacity, and human resources are allocated in the health care sector. These measures will undoubtedly contribute to the development of resilience in health care systems and hence protect the health and well-being of individuals and communities. Trial Registration PROSPERO CRD42022365471; https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=365471 International Registered Report Identifier (IRRID) DERR1-10.2196/44702
Seasonal Effect of Grass Nutritional Value on Enteric Methane Emission in Islands Pasture Systems

Simple Summary This study investigates the impact of seasonality on enteric methane emissions in islands with pasture systems, focusing on the 2006 Intergovernmental Panel on Climate Change (IPCC) Tier 2 methodology, refined in 2019. Feed samples for Azorean bovine were collected throughout the year, and their nutritional value and digestibility were determined. Significant differences were found between winter and summer pastures, with autumn presenting better nutritional quality. The total volume of enteric methane produced in the Azores was 20,341 t of methane (CH4), with peak emissions reaching 5837 t CH4 during the summer. Breeding bulls, beef cows, and heifers produce the highest amount of methane per animal, while pregnant dairy cows had the highest CH4 emissions per year, due to the high number of dairy cows in the archipelago. The study suggests that pastures are better managed during the autumn, resulting in lower emissions of enteric methane into the atmosphere. Abstract Quantifying entericCH4 from grazing systems is a challenge for all regions of the world, especially when cattle feed mostly on pasture throughout the year, as pasture quality varies with the seasons. In this study, we examine the influence of seasonality on enteric methane emissions in the Azores, considering the most recent IPCC updates, to minimise errors in estimating enteric methane emissions in this region. For this purpose, samples of corn and grass silage, different types of concentrate, and pasture were collected throughout the year, and their nutritional value and digestibility were determined according to standard conventional methods. The estimation of methane production was conducted using the 2006 IPCC Tier 2 methodology, refined in 2019. The results revealed significant differences (p < 0.05) between the chemical composition of winter and summer pastures. However, it was in the autumn that these pastures presented the best nutritional quality. We estimated that the total volume of enteric methane produced in the Azores was 20,341 t CH4, with peak enteric methane emissions (5837 t CH4) reached during the summer. Breeding bulls, beef cows, and heifers are the categories that produce the highest amount of methane per animal. However, if we consider the total number of animals existing in the region, pregnant dairy cows are the category of cattle with the highest emissions of CH4. Thus, considering the current system of cattle production in the region, we can infer that the pastures are better managed during the autumn, which translates into lower emissions of enteric methane into the atmosphere during this season.
Digital photography use by marine mammal scientists

Digital photography has widely replaced film in recent years, yet there has been relatively little research into digital photography as a socio-technical phenomenon. This project examines the computerization of scientific photography among marine mammal researchers. Scientists studying marine mammals use photoidentification to identify individual animals (whales, dolphins, etc.) in their research, and have recently widely switched to digital photography.
Assessing the implementation of a nursing home-based physical and mental training: Utilizing the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework: A Qualitative Descriptive Study

Purpose: This study aims to evaluate the implementation of a Spirit program, which consists of physical and mental training for older adults who can perform independent activities as nursing home residents. Methods: In July 2022, the researchers interviewed seven older adults who had attended the Spirit program and two caregivers at a nursing home in Surakarta, Indonesia. The researchers applied the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework during interviews. We measured participant engagement with the Spirit program, effectiveness to assess perceived benefits, adoption to gauge nursing homes’ willingness to adopt the Spirit program, implementation to evaluate consistent execution, and maintenance to assess future program sustainability. Results: Older adults who could carry out independent activities participated in the Spirit program. The Spirit program adds new activities that they can perform, which have various benefits, including increased happiness, stamina, and sleep quality. The participants expressed receiving instructions regarding the Spirit program from caregivers without any pressure to participate, with instructors guiding the exercises and regular schedule management. Older adults move well; however, some experience obstacles while performing balance exercises. None of the participants experienced injuries while participating in the program. The continuity of a program can be supported by the availability of tools that are easily accessible to older adults. Conclusion: The program improved the quality of life of older adults who can move independently as nursing home residents.
Numerical Simulations of Temperature Stability Limits in High-Voltage Direct Current Cable Insulations

The high-voltage direct current cable insulations consist of polymeric or mass-impregnated paper materials. Their electric conductivities depend on the electric field and the temperature. Losses inside the conductor and in the insulation increase the temperature, resulting in increased losses and in a possible thermal runaway process. In this paper, coupled electro-thermal field simulations including nonlinear field-dependent conductivities are carried out. With a varying applied voltage and a current in the conductor, the condition for a thermal breakdown is computed. To compute the losses inside a 1-D insulation model, the finite-difference method is used. The simulation results conform well to experimental measurements. An approximation of the condition for a thermal breakdown is developed and show equal results in comparison to the coupled electro-thermal field simulations.
Crown stratification ratio models for Tectona grandis L. f in Oluwa Forest reserve, Nigeria

This research investigated crown ratio models for Tectona grandis plantation in Oluwa Forest reserve (Ondo State, Nigeria) using variables of slenderness coefficient and merchantable height. With three non-linear regression functions - logistic, Chapman-Richard and exponential we showed that basal area, tree stem volume and mean tree height per hectare were of high significance. In the same vein, there were fewer tree species in the class of higher diameter and height than there were in lower class. There were also more trees in the co-dominant and intermediate classes than in the dominant and suppressed layers. The lack of emergence in the plantation reflected the past disturbance of the ecosystem. Most of the tree growth variables were significantly different in different canopy layers in the study area. Based on the evaluation models, the three functions investigated for tree crown ratio modeling gaveconstant and reliable results in all canopy layers considering their indices. Especially, Chapman-Richard and exponential functions gave consistent trends and good fits for crown ratio models. It is recommended to put strict measures in place to avert any level of illegalities that may likely disrupt the delicate equilibrium of the ecosystem. It is also recommended that complexity revealed in this study is sustained in the region, and encouraged in other parts of Nigeria.
Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy

Analyzing gene expression profiles (GEP) through artificial intelligence provides meaningful insight into cancer disease. This study introduces DeepSHAP Autoencoder Filter for Genes Selection (DSAF-GS), a novel deep learning and explainable artificial intelligence-based approach for feature selection in genomics-scale data. DSAF-GS exploits the autoencoder’s reconstruction capabilities without changing the original feature space, enhancing the interpretation of the results. Explainable artificial intelligence is then used to select the informative genes for chronic lymphocytic leukemia prognosis of 217 cases from a GEP database comprising roughly 20,000 genes. The model for prognosis prediction achieved an accuracy of 86.4%, a sensitivity of 85.0%, and a specificity of 87.5%. According to the proposed approach, predictions were strongly influenced by CEACAM19 and PIGP, moderately influenced by MKL1 and GNE, and poorly influenced by other genes. The 10 most influential genes were selected for further analysis. Among them, FADD, FIBP, FIBP, GNE, IGF1R, MKL1, PIGP, and SLC39A6 were identified in the Reactome pathway database as involved in signal transduction, transcription, protein metabolism, immune system, cell cycle, and apoptosis. Moreover, according to the network model of the 3D protein-protein interaction (PPI) explored using the NetworkAnalyst tool, FADD, FIBP, IGF1R, QTRT1, GNE, SLC39A6, and MKL1 appear coupled into a complex network. Finally, all 10 selected genes showed a predictive power on time to first treatment (TTFT) in univariate analyses on a basic prognostic model including IGHV mutational status, del(11q) and del(17p), NOTCH1 mutations, β2-microglobulin, Rai stage, and B-lymphocytosis known to predict TTFT in CLL. However, only IGF1R [hazard ratio (HR) 1.41, 95% CI 1.08-1.84, P=0.013), COL28A1 (HR 0.32, 95% CI 0.10-0.97, P=0.045), and QTRT1 (HR 7.73, 95% CI 2.48-24.04, P<0.001) genes were significantly associated with TTFT in multivariable analyses when combined with the prognostic factors of the basic model, ultimately increasing the Harrell’s c-index and the explained variation to 78.6% (versus 76.5% of the basic prognostic model) and 52.6% (versus 42.2% of the basic prognostic model), respectively. Also, the goodness of model fit was enhanced (χ2 = 20.1, P=0.002), indicating its improved performance above the basic prognostic model. In conclusion, DSAF-GS identified a group of significant genes for CLL prognosis, suggesting future directions for bio-molecular research.
Stress and Perceived Partnership Quality of Parents to Children With Autism Spectrum Disorder: A Random Intercept Cross-Lagged Panel Approach

Parents to children with autism spectrum disorder (ASD) experience increased levels of stress compared to other parents. However, it has been difficult to clarify how this parental stress affects the quality of parent partnership, and how stress and partnership quality predict each other longitudinally. Stress and perceived partnership quality of 160 parents to children with ASD who started therapy in an autism therapy center were surveyed at three measurement points. To find out about within-person effects, a random intercept cross-lagged panel model was used. The results showed that a decrease in stress level over the course of therapy led to a decrease in partnership quality. The theory of the parenting alliance can be used to explain these results: coping with demands in parenthood during stressful times seems to tie parents together and strengthen their relationship. Implications for therapeutic practice, such as the use of marriage and family counselors to support the parents’ partnership during their child's autism therapy are presented.
Host Membranes as Drivers of Virus Evolution

The molecular mechanisms controlling the adaptation of viruses to host cells are generally poorly documented. An essential issue to resolve is whether host membranes, and especially lipid rafts, which are usually considered passive gateways for many enveloped viruses, also encode informational guidelines that could determine virus evolution. Due to their enrichment in gangliosides which confer an electronegative surface potential, lipid rafts impose a first control level favoring the selection of viruses with enhanced cationic areas, as illustrated by SARS-CoV-2 variants. Ganglioside clusters attract viral particles in a dynamic electrostatic funnel, the more cationic viruses of a viral population winning the race. However, electrostatic forces account for only a small part of the energy of raft-virus interaction, which depends mainly on the ability of viruses to form a network of hydrogen bonds with raft gangliosides. This fine tuning of virus-ganglioside interactions, which is essential to stabilize the virus on the host membrane, generates a second level of selection pressure driven by a typical induced-fit mechanism. Gangliosides play an active role in this process, wrapping around the virus spikes through a dynamic quicksand-like mechanism. Viruses are thus in an endless race for access to lipid rafts, and they are bound to evolve perpetually, combining speed (electrostatic potential) and precision (fine tuning of amino acids) under the selective pressure of the immune system. Deciphering the host membrane guidelines controlling virus evolution mechanisms may open new avenues for the design of innovative antivirals.
Salivary Oxidative Stress Biomarkers in Thai Adolescents and Young Adults with Type 1 Diabetes Mellitus: A Cross-Sectional Study

Aims and Objectives: The primary objectives of this study were to compare salivary oxidative stress (OS) biomarker levels in patients with type 1 diabetes mellitus (T1DM) and without T1DM (non-T1DM) and evaluate the relationships between diabetes, periodontal status, and OS biomarker levels. Materials and Methods: Twenty patients with T1DM and 20 age-matched patients without T1DM were enrolled. All participants were 15–23 years of age and had permanent dentition. Unstimulated whole saliva was collected in a sterile test tube before examination of clinical periodontal parameters, including bleeding on probing (BOP). Salivary levels of OS biomarkers—malondialdehyde, protein carbonyl, total oxidant status (TOS), and total antioxidant capacity—were determined using oxidative and antioxidative assays followed by spectrophotometric measurement at 375–532 nm. The relationships between diabetes, periodontal status, and OS biomarkers were analyzed using multiple linear regression. Results: TOS was significantly lower in the T1DM group compared with the non-T1DM group (5.06 ± 0.39 vs. 6.44 ± 0.51 µmol H2O2 Eq/l, P = 0.035). After adjusting for confounding factors (age, gender, BMI, clinical periodontal parameters, BOP, or diabetes status accordingly), the multiple linear regression showed that T1DM was significantly associated with a reduction of TOS level (P = 0.008). The BOP > 30% group showed a significant correlation with increased TOS levels compared with the BOP ≤ 30% group (P = 0.002). No relationship was found between OS biomarkers and HbA1c levels. Conclusion: Salivary TOS levels were related to both diabetes status and the extent of gingival inflammation. Further studies to elucidate the role of OS in relation of periodontal disease and T1DM are required.