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Abstract Southeast Asia includes several global biodiversity hotspots and bats account for nearly one-third of mammal species currently known in the region. While acoustic methods have become widespread in bat research, basic information is often lacking on the echolocation calls produced by Asian bat species. Since such information can aid a wide variety of research and conservation initiatives, descriptions of the calls emitted by Asian bats are fundamental. The aim of our study was to provide a standardized analysis and description of the Vietnamese bat echolocation calls. We analyzed call recordings of 87 species arranged in eight families. This constitutes 74% of the echolocating bats presently known in Vietnam and includes the first call descriptions for five taxa. Our use of an open-source software and the deposition of recordings in the ChiroVox repository will facilitate comparative studies in Asia and the information we provide represents one of the most comprehensive bioacoustic databases for Asian bats to date.

Abstract Miombo woodlands are integral to livelihoods across southern Africa, biodiversity in the region, and the global carbon cycle, making accurate and precise monitoring of their state and change essential. Here, we assembled a terrestrial and airborne lidar dataset covering 50 kha of intact and degraded miombo woodlands, and generated aboveground biomass estimates with low uncertainty via direct 3D measurements of forest structure. We found 1.71 ± 0.09 TgC was stored in aboveground biomass across this landscape, between 1.5 and 2.2 times more than the 0.79–1.14 TgC estimated by conventional methods. This difference is in part owing to the systematic underestimation of large trees by allometry. If these results were extrapolated across Africa’s miombo woodlands, their carbon stock would potentially require an upward revision of approximately 3.7 PgC, implying we currently underestimate their carbon sequestration and emissions potential, and disincentivise their protection and restoration.

INTRODUCTION: Gen AI, exemplified by tools like ChatGPT, has transformed various sectors, including healthcare education. Despite its rapid adoption across fields, the utilisation of Gen AI in healthcare education in the UK remains inconsistent, with research predominantly focused on academic integrity and examination performance, rather than exploring its potential benefits in educational practices. Notably, there is a paucity of literature within emergency healthcare education and paramedic courses. Objective - to investigate the integration and impact of Generative AI (Gen AI) within emergency healthcare education, focusing on undergraduate and postgraduate pre-registration courses in the UK. MATERIALS AND METHODS: Using the JBI methodology and PRISMA-ScR guidelines, the databases MEDLINE, CINAHL, PubMed, and Cochrane were searched. The selection process involved screening by title and abstract, followed by full text review. Thematic analysis identified prevalent Gen AI applications within healthcare education. Inclusion criteria - the review targeted peer-reviewed studies that discuss the use of Gen AI in healthcare education, without publication date or geographical restrictions. RESULTS: A total of 13 studies were included in the review. These studies demonstrated Gen AI’s application in curriculum development, assessment design, student implementation, academic support, and clinical application. Gen AI aids educators in curriculum design and assessment creation, supports students through digital literacy enhancement, and facilitates academic writing and research practices. Additionally, its role extends to practical skill enhancement in clinical scenarios. CONCLUSIONS: Gen AI offers transformative potential for all healthcare education and applications to the emergency education setting, providing innovative solutions for curriculum design, learning tools, and clinical simulations. While the identified themes are generalisable to healthcare education, they hold particular relevance for individuals seeking to advance the utilisation of Gen AI technologies within the specialised domain of emergency and prehospital care. However, its integration requires careful consideration of legal, ethical, and pedagogical implications.

In an effort to reduce the occurrence of chlorine derived residues such as chlorate and trichloromethane (TCM) in milk and ultimately in dairy products, ‘chlorine-free’ cleaning of milking equipment became compulsory in the Republic of Ireland (ROI) from January 2021. While data exists on TCM levels in bulk tank milk, little is known about the prominence and typical levels (mg/kg) of chlorate residue in bulk tank milk. To address this, 3625 bulk milk samples were collected from six milk processors and were analysed for chlorate and TCM residues across 2020 and 2021, with 2020 representing a period before chlorine-free cleaning was introduced and 2021 being the period after chlorine removal. In 2020, 15% of the samples analysed had detectable levels of chlorate (0.0020–1.6 mg/kg), but this reduced to 8% in 2021 (0.0020–3.9 mg/kg), following the introduction of ‘chlorine-free’ cleaning. Chlorate and TCM residues have not been totally eliminated because sources of residue other than cleaning chemicals exist, i.e., chlorinated water.

Abstract mRNA lipid nanoparticle (LNP) vaccines would be useful during an influenza virus pandemic since they can be produced rapidly and do not require the generation of egg-adapted vaccine seed stocks. Highly pathogenic avian influenza viruses from H5 clade 2.3.4.4b are circulating at unprecedently high levels in wild and domestic birds and have the potential to adapt to humans. Here, we generate an mRNA lipid nanoparticle (LNP) vaccine encoding the hemagglutinin (HA) glycoprotein from a clade 2.3.4.4b H5 isolate. The H5 mRNA-LNP vaccine elicits strong T cell and antibody responses in female mice, including neutralizing antibodies and broadly-reactive anti-HA stalk antibodies. The H5 mRNA-LNP vaccine elicits antibodies at similar levels compared to whole inactivated vaccines in female mice with and without prior H1N1 exposures. Finally, we find that the H5 mRNA-LNP vaccine is immunogenic in male ferrets and prevents morbidity and mortality of animals following 2.3.4.4b H5N1 challenge. Together, our data demonstrate that a monovalent mRNA-LNP vaccine expressing 2.3.4.4b H5 is immunogenic and protective in pre-clinical animal models.

Abstract Polymerized β-actin may provide a structural basis for chromatin accessibility and actin transport into the nucleus can guide mesenchymal stem cell (MSC) differentiation. Using MSC, we show that using CK666 to inhibit Arp2/3 directed secondary actin branching results in decreased nuclear actin structure, and significantly alters chromatin access measured with ATACseq at 24 h. The ATAC-seq results due to CK666 are distinct from those caused by cytochalasin D (CytoD), which enhances nuclear actin structure. In addition, nuclear visualization shows Arp2/3 inhibition decreases pericentric H3K9me3 marks. CytoD, alternatively, induces redistribution of H3K27me3 marks centrally. Such alterations in chromatin landscape are consistent with differential gene expression associated with distinctive differentiation patterns. Further, knockdown of the non-enzymatic monomeric actin binding protein, Arp4, leads to extensive chromatin unpacking, but only a modest increase in transcription, indicating an active role for actin-Arp4 in transcription. These data indicate that dynamic actin remodeling can regulate chromatin interactions.

In this retrospective cohort study examining 13,763,447 patients with 16 different malignancies, including 1,232,841 patients with five gynecologic malignancies (uterus [n = 690,590], ovary [n = 276,812], cervix [n = 166,779], vulva [n = 81,575], and vagina [n = 17,085]), identified in the Commission-on-Cancer’s National Cancer Database from 2004 to 2020, cervical cancer (25.3 %) had the highest rate of adolescent and young adult (AYA) patients among 27 gender-stratified cancer groups (25.3%). There were 8 groups that the annual rates of AYA patients statistically increased during the study period at a P

Microwaves are electromagnetic waves which move at the speed of light. Samples directly exposed to this phenomenon are heated faster than conventional conduction heating methods. This is exploited in acid assisted digestion where a food sample in a suitable acid matrix can be completely destroyed leaving a clear solution of measurable elements. In pressurised systems the temperature can be rapidly increased far above the conventional boiling point of the solvent and the closed microwave vessel environment is ideal for volatile elements.There is a constant aspiration to achieve lower levels of detection and advances in instrument capabilities with specialised plasma based techniques for the identification and quantification of individual elements at ultra-trace levels have evolved. To realise this; sample preparation, digestion efficiency and sample introduction need to be optimised. Exogenous inputs such as preparation environment, reagent grade, the analyst and apparatus cleaning steps all have the potential to leave an elemental footprint on the sample and therefore contribute to the uncertainty of the analytical result. Knowledge of sample composition and subsequent interaction with microwaves and plasma are an important consideration for complete digestion without which the result is not accurate.

Abstract Myasthenia gravis (MG) is a rare, autoimmune, antibody-mediated, neuromuscular disease. This study analyzed digital conversations about MG to explore unprovoked perspectives. Advanced search, data extraction, and artificial intelligence-powered algorithms were used to harvest, mine, and structure public domain digital conversations about MG from US Internet Protocol addresses (August 2021 to August 2022). Thematic analyses examined topics, mindsets, and sentiments/key drivers via natural language processing and text analytics. Findings were described by sex/gender and treatment experience with steroids or intravenous immunoglobulin (IVIg). The 13,234 conversations were extracted from message boards (51%), social media networks (22%), topical sites (21%), and blogs (6%). Sex/gender was confirmed as female in 5703 and male in 2781 conversations, and treatment experience was with steroids in 3255 and IVIg in 2106 conversations. Topics focused on diagnosis (29%), living with MG (28%), symptoms (24%), and treatment (19%). Within 3176 conversations about symptoms, eye problems (21%), facial muscle problems (18%), and fatigue (18%) were most commonly described. Negative sentiments about MG were expressed in 59% of conversations, with only 2% considered positive. Negative conversations were dominated by themes of impact on life (29%), misdiagnosis problems (27%), treatment issues (24%), and symptom severity (20%). Impact on life was a key driver of negativity in conversations by both men (27%) and women (34%), and treatment issues was a dominant theme in conversations by steroid-treated (29%) and IVIg-treated (31%) patients. Of 1382 conversations discussing treatment barriers, 36% focused on side effects, 33% on lack of efficacy, 21% on misdiagnosis, and 10% on cost/insurance. Side effects formed the main barrier in conversations by both steroid-treated and IVIg-treated patients. Capturing the patient voice via digital conversations reveals a high degree of concern related to burden of disease, misdiagnosis, and common MG treatments among those with MG, pointing to a need for treatment options that can improve quality of life.

Abstract Pediatric Crohn’s disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD.

Abstract This parallel‐arm, phase I study investigated the potential cytochrome P450 (CYP)3A induction effect of NBI‐1065845 (TAK‐653), an investigational α‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid receptor potentiator in phase II development for major depressive disorder. The midazolam treatment arm received the sensitive CYP3A substrate midazolam on Day 1, followed by NBI‐1065845 alone on Days 5–13; on Day 14, NBI‐1065845 was administered with midazolam, then NBI‐1065845 alone on Day 15. The oral contraceptive treatment arm received ethinyl estradiol–levonorgestrel on Day 1, then NBI‐1065845 alone on Days 5–13; on Day 14, NBI‐1065845 was administered with ethinyl estradiol–levonorgestrel, then NBI‐1065845 alone on Days 15–17. Blood samples were collected for pharmacokinetic analyses. The midazolam treatment arm comprised 14 men and 4 women, of whom 16 completed the study. Sixteen of the 17 healthy women completed the oral contraceptive treatment arm. After multiple daily doses of NBI‐1065845, the geometric mean ratios (GMRs) (90% confidence interval) for maximum observed concentration were: midazolam, 0.94 (0.79–1.13); ethinyl estradiol, 1.00 (0.87–1.15); and levonorgestrel, 0.99 (0.87–1.13). For area under the plasma concentration–time curve (AUC) from time 0 to infinity, the GMRs were as follows: midazolam, 0.88 (0.78–0.98); and ethinyl estradiol, 1.01 (0.88–1.15). For levonorgestrel, the GMR for AUC from time 0 to the last quantifiable concentration was 0.87 (0.78–0.96). These findings indicate that NBI‐1065845 is not a CYP3A inducer and support its administration with CYP3A substrates. NBI‐1065845 was generally well tolerated, with no new safety signals observed after coadministration of midazolam, ethinyl estradiol, or levonorgestrel.

Abstract Background Genomic heterozygosity has been shown to confer a health advantage in humans and play a protective role in complex diseases. Given osteoarthritis (OA) is a highly polygenic disease, we set out to determine if an association exists between OA and genomic heterozygosity. Results End-stage knee and hip OA patients and healthy controls were recruited from the Newfoundland and Labrador (NL) population. The Arthritis Research UK Osteoarthritis Genetics (arcOGEN) consortium database was utilized as a replication cohort. DNA was extracted from blood samples and genotyped. Individual rates of observed heterozygosity (HetRate) and heterozygosity excess (HetExcess) relative to the expected were mathematically derived, and standardized to a z-score. Logistic regression modeling was used to examine the association between OA and HetRate or HetExcess. A total of 559 knee and hip OA patients (mean age 66.5 years, body mass index (BMI) 33.7 kg/m2, and 55% females) and 118 healthy controls (mean age 56.4 years, BMI 29.5 kg/m2, and 59% female) were included in the NL cohort analysis. We found that OA had an inverse relationship with HetRate and HetExcess with odds ratios of 0.64 (95% CI: 0.45–0.91) and 0.65 (95% CI: 0.45–0.93) per standard deviation (SD), respectively. The arcOGEN data included 2,019 end-stage knee and hip OA patients and 2,029 healthy controls, validating our findings with HetRate and HetExcess odds ratios of 0.60 (95% CI: 0.56–0.64) and 0.44 (95% CI: 0.40–0.47) per SD, respectively. Conclusions Our results are the first to clearly show evidence, from two separate cohorts, that reduced genomic heterozygosity confers a risk for the future development of OA.

Abstract To elucidate the pathogenesis of vein of Galen malformations (VOGMs), the most common and most severe of congenital brain arteriovenous malformations, we performed an integrated analysis of 310 VOGM proband-family exomes and 336,326 human cerebrovasculature single-cell transcriptomes. We found the Ras suppressor p120 RasGAP (RASA1) harbored a genome-wide significant burden of loss-of-function de novo variants (2042.5-fold, p = 4.79 x 10−7). Rare, damaging transmitted variants were enriched in Ephrin receptor-B4 (EPHB4) (17.5-fold, p = 1.22 x 10−5), which cooperates with p120 RasGAP to regulate vascular development. Additional probands had damaging variants in ACVRL1, NOTCH1, ITGB1, and PTPN11. ACVRL1 variants were also identified in a multi-generational VOGM pedigree. Integrative genomic analysis defined developing endothelial cells as a likely spatio-temporal locus of VOGM pathophysiology. Mice expressing a VOGM-specific EPHB4 kinase-domain missense variant (Phe867Leu) exhibited disrupted developmental angiogenesis and impaired hierarchical development of arterial-capillary-venous networks, but only in the presence of a “second-hit” allele. These results illuminate human arterio-venous development and VOGM pathobiology and have implications for patients and their families.

(1) Background: Osteoarthritis (OA) is a heterogeneous disorder, and subgroup classification of OA remains elusive. The aim of our study was to identify endotypes of hip OA and investigate the altered pathways in the different endotypes. (2) Methods: Metabolomic profiling and genome-wide genotyping were performed on fasting blood. Transcriptomic profiling was performed on RNA extracted from cartilage samples. Machine learning methods were used to identify endotypes of hip OA. Pathway analysis was used to identify the altered pathways between hip endotypes and controls. GWAS was performed on each of the identified metabolites. Transcriptomic data was used to examine the expression levels of identified genes in cartilage. (3) Results: 180 hip OA patients and 120 OA-free controls were classified into three clusters based on metabolomic data. The combination of arginine, ornithine, and the average value of 7 lysophosphatidylcholines had an area under the curve (AUC) of 0.97 (95% CI: 0.96–0.99) to discriminate hip OA from controls, and the combination of γ-aminobutyric acid, spermine, aconitic acid, and succinic acid had an AUC of 0.96 (95% CI: 0.94–0.99) to distinguish two hip OA endotypes. GWAS identified 236 SNPs to be associated with identified metabolites at GWAS significance level. Pro-inflammatory cytokine levels were significantly different between two endotypes (all p < 0.05). (4) Conclusions: Hip OA could be classified into two distinct molecular endotypes. The primary differences between the two endotypes involve changes in pro-inflammatory factors and energy metabolism.

Molecular ions that are generated by chemical reactions with trapped atomic ions can serve as an accessible testbed for developing molecular quantum technologies. On the other hand, they are also a hindrance to scaling up quantum computers based on atomic ions, as unavoidable reactions with background gases destroy the information carriers. Here, we investigate the single- and two-photon dissociation processes of single CaOH+ molecular ions co-trapped in Ca+ ion crystals using a femtosecond laser system. We report the photodissociation cross section spectra of CaOH+ for single-photon processes at λ = 245–275 nm and for two-photon processes at λ = 500–540 nm. Measurements are interpreted with quantum-chemical calculations, which predict the photodissociation threshold for CaOH+ → Ca+ + OH at 265 nm. This result can serve as a basis for dissociation-based spectroscopy for studying the internal structure of CaOH+. The result also gives a prescription for recycling Ca+ ions in large-scale trapped Ca+ quantum experiments from undesired CaOH+ ions formed in the presence of background water vapor. [ABSTRACT FROM AUTHOR]

Introduction: School psychologists work to support the academic, behavioral, and social-emotional well-being of students in schools. Their roles have become increasingly diverse in recent years due to the implementation of Multi-tiered System of Supports (MTSS) frameworks in schools throughout the United States. Statement of the Problem: Despite a national shortage of school psychologists, psychology students are often unaware of the profession or may have an outdated understanding of the role of school psychologists. Psychology students may also be unfamiliar with the MTSS framework, which promotes research-based, data-driven, equitable, and culturally responsive practices through a systemic, ecological approach to service delivery in schools. Literature Review: We review the main components of a typical MTSS prevention and intervention framework and describe the various roles of school psychologists and other key stakeholders within this model. Teaching Implications: This review and supporting resources can be used to introduce the field of school psychology to students. Additionally, it can be used to generate discussions regarding the provision of evidence-based and equitable academic and psychological services to students in the school through an ecological model. Conclusion: This article will help students gain an understanding of school psychologists' roles within a current MTSS educational service delivery model.

Ocean currents play an important role in regulating the Earth's climate by redistributing ocean heat around the planet. One important climate phenomenon that includes several large ocean currents is the Atlantic Meridional Overturning Circulation, which is the process of warm surface currents in the North Atlantic Ocean moving to the Arctic to ultimately form cold currents near the seafloor. This process relieves the tropics of excess ocean heat, which helps reduce the intensity of Atlantic hurricanes. Scientists are concerned that the Atlantic Meridional Overturning Circulation may slow, or even collapse, because of anthropogenic climate change. In this activity, students will use selected portions of a four-year dataset of observations of the Atlantic Meridional Overturning Circulation to learn about the importance of long-term consistent sampling and data biases. The students will plot their data, calculate the slope between the first and last points, listen to sonified versions of the data, and discuss trends in the data. This activity will also give students the opportunity to build a mooring with craft supplies representing the anchor, cable, scientific instruments, and flotation. There are modifications for the visually impaired via the graphing format, data sonification, and with the tactile nature of the mooring construction.

Early colleges are a new model of schooling in which the high school and college experiences are merged, shortening the total amount of time a student spends in school. This study uses a lottery-based experimental design to examine the impact of the model on longer term outcomes, including attainment of a postsecondary credential and academic performance in 4-year institutions. Results show that a significantly higher proportion of early college students were attaining postsecondary credentials. The results also show that early college students were completing their degrees more rapidly but that their performance in 4-year institutions was still comparable with the control students. [For the corresponding grantee submission, see ED604350.]

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

IntroductionAnimals use camouflage (background matching, disruptive coloration, etc.) for protection, confusing predators and making detection difficult. Camouflage Object Detection (COD) tackles this challenge by identifying objects seamlessly blended into their surroundings. Existing COD techniques struggle with hidden objects due to noisy inferences inherent in natural environments. To address this, we propose the Discriminative Context-aware Network (DiCANet) for improved COD performance.MethodsDiCANet addresses camouflage challenges through a two-stage approach. First, an adaptive restoration block intelligently learns feature weights, prioritizing informative channels and pixels. This enhances convolutional neural networks’ ability to represent diverse data and handle complex camouflage. Second, a cascaded detection module with an enlarged receptive field refines the object prediction map, achieving clear boundaries without post-processing.ResultsWithout post-processing, DiCANet achieves state-of-the-art performance on challenging COD datasets (CAMO, CHAMELEON, COD10K) by generating accurate saliency maps with rich contextual details and precise boundaries.DiscussionDiCANet tackles the challenge of identifying camouflaged objects in noisy environments with its two-stage restoration and cascaded detection approach. This innovative architecture surpasses existing methods in COD tasks, as proven by benchmark dataset experiments.

Academic progress monitoring is essential when implementing Individualized Education Programs for students with learning disabilities and more generally in a multi-tiered system of supports framework. Although students' progress is monitored frequently in schools today, these students often are not actively involved in this practice. Research has shown that actively involving students in progress monitoring through performance feedback and goal setting can lead to improved academic and motivational outcomes. This column describes a progress-monitoring performance feedback and goal-setting routine that supplements typical academic progress-monitoring practices. The ways in which the routine aligns with evidence-based practices to improve student outcomes in reading, writing, mathematics, and academic motivation are discussed. Step-by-step guidance, examples, and resources are provided to support practitioner implementation. Resources for evaluating outcomes are described.