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Abstract Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops.

The benefits of undergraduate research to student persistence and success has been established in the literature. Less studied, however, is the long term impact of early exposure to research among students from underserved backgrounds. This qualitative study of undergraduates participating in a unique summer research program uncovers the deeper meaning of the overall experience on the students: from the lab itself, to the mentors, peers, professional development, socials, and the impact of the program staff. Three major themes emerged: (1) Early exposure to research as a foundation for career direction; (2) Relationships with peers and mentors as highly valued; and (3) Development of skills leads to personal and professional growth, and confidence. Additionally, underrepresented students described the value of having minority role models and peers, and the excitement of continuing their research throughout their undergraduate careers. A full compensation package of stipend and housing made a practical difference for several of the participants. This qualitative study offers a deeper understanding of these impacts through the voices of the participants.

PurposeStigma is a fundamental driver of HIV disparities among transgender women (TW). The gender minority stress and resilience (GMSR) measure has not been validated in Spanish-speaking, resource-limited settings. We examined the psychometric properties of a translated and abbreviated GMSR among TW in Tijuana, Mexico.MethodsFrom 2020 to 2021, 152 participants were recruited through social media and venue-based sampling. We collected information on the abbreviated GMSR, psychosocial factors (e.g., depressive symptoms), and sociodemographics. The abbreviated GMSR assessed 7 factors (Discrimination, Rejection, Internalized Transphobia, Negative Expectations, Nondisclosure, Pride, and Community Connectedness). Confirmatory factor analysis, Cronbach's alphas, and McDonald's omegas assessed structural validity. Pearson's partial correlations assessed criterion, convergent, and discriminant validities.ResultsThe 7-factor structure solution had acceptable fit (root mean square error of approximation [95% confidence interval]=0.05 [0.05-0.06]; comparative fit index/Tucker-Lewis index=0.92/0.91); and internal reliability (α=0.62-0.89; ω=0.62-0.89). Depressive (r=0.22-0.43; p

It is becoming increasingly clear that not all students require the same education, and the requirement of personalized education is increasingly in demand. The incorporation of adaptive learning (AL) has increased in recent years. However, research on this subject is still evolving at the university level. In this study, we investigated the impact of integrating an AL tool (CogBooks[R]) in a university course (statistics for decision making) taught in an innovative online modality called FIT (flexible, interactive, and with technology), in which the course is designed in the CANVAS[R] platform and uses Zoom[R] as a means of communication with students. Learning outcomes were compared between the FIT courses with or without AL and between AL strategies in online and face-to-face courses. It was clear that AL improved the students' achievement regardless of the modality. In addition, we conclude that students achieve better in AL courses in the classroom than in distance courses. Satisfaction surveys favor a preference for FIT courses with AL over classroom classes with AL. Our results suggest that AL is a solid strategy for teaching undergraduate courses.

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as 'databases' throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL  https://www.agbiodata.org/databases.

Abstract The timing of flowering in soybean [Glycine max (L.) Merr.], a key legume crop, is influenced by many factors, including daylight length or photoperiodic sensitivity, that affect crop yield, productivity, and geographical adaptation. Despite its importance, a comprehensive understanding of the local linkage landscape and allelic diversity within regions of the genome influencing flowering and contributing to phenotypic variation in subpopulations has been limited. This study addresses these gaps by conducting an in‐depth trait association and linkage analysis coupled with local haplotyping using advanced bioinformatics tools, including crosshap, to characterize genomic variation using a pangenome dataset representing 915 domesticated and wild‐type individuals. The association analysis identified eight significant loci on seven chromosomes. Moving beyond traditional association analysis, local haplotyping of targeted regions on chromosomes 6 and 20 identified distinct haplotype structures, variation patterns, and genomic candidates influencing flowering in subpopulations. These results suggest the action of a network of genomic candidates influencing flowering time and an untapped reservoir of genomic variation for this trait in wild germplasm. Notably, GlymaLee.20G147200 on chromosome 20 was identified as a candidate gene that may cause delayed flowering in soybean, potentially through histone modifications of floral repressor loci as seen in Arabidopsis thaliana (L.) Heynh. These findings support future functional validation of haplotype‐based alleles for marker‐assisted breeding and genomic selection to enhance latitude adaptability of soybean without compromising yield.

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Few evidence-based trainings exist on how to equip healthcare providers, particularly nurses, with the skills to engage in cost of care conversations with patients/caregivers to mitigate the impact of cancer-related financial toxicity. This study evaluated a pilot training developed in collaboration with Triage Cancer® to prepare oncology nurses to identify and assist patients/caregivers facing financial and/or legal barriers to care. Ten pediatric oncology nurses completed the training and pre/post-surveys on behaviors related to financial and legal need screening, frequency and comfort level of answering questions, knowledge, and behavior changes, along with training evaluation questions. At baseline, six nurses reported never screening for financial needs and nine for legal needs. Following the training, seven nurses stated they were likely to screen for financial/legal needs. At six months post-training, nurses had referred 85 patients/caregivers to financial/legal navigation services. Comfort levels in answering financial/legal questions increased by 6.5 points and knowledge scores increased by 1.7 points post-training. Most nurses recommended this training to other healthcare providers who work with patients with cancer and their caregivers. This study highlights the importance of providing oncology nurses with resources to engage in cost of care conversations and oncology financial legal navigation programs to mitigate the impact of cancer-related financial toxicity.

Behavioral health services access for justice- and child welfare-involved youth is limited despite significant need. Structural interventions to address limited access are nascent. Technology can advance access, but few interventions focus on system-impacted youth and their mental health needs and challenges. This article describes the development, process, and initial outcomes of the Youth Justice and Family Well-Being Technology Collaborative (JTC) that was formed to leverage technology within and across public health and justice-related systems to promote increased behavioral health services access. Cross-system considerations are identified for public health, court, and other key stakeholders to successfully integrate technology into practice to expand access to these critical services.

In comparison with traditional lectures, active learning methods have the potential to foster learner agency and consequently raise academic performance. However, these methods are often applied with little knowledge about their effectiveness. In this study, we introduced two concept maps in an undergraduate cell biology course, to investigate their effect on student performance, but also mental health, in a quasi-experimental setup. By implementing group tasks into the work, we aimed to reduce feelings of loneliness among the participants, a common phenomenon among students in the study country. We evaluated the effects of the concept maps via exam grades and in an anonymous student survey. Here, we found no increase in student performance after introduction of the active learning task. Students also did not report improvements in their feelings of loneliness. We conclude that this active learning method did not detectably improve student performance or mental health, a result which does not conform to the popular perception of positive effects from active learning. The survey revealed that students do not have a preference for either active learning or traditional lectures, however, the students experienced that the concept map helped them to describe, explain and hypothesize about the topics. Overall, this shows that the concept map method is a valid alternative to traditional lectures, since students perform equally between both learning approaches.

Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. The main objective of this study was to assess if the inclusion of historical weather data, maize growth captured through imagery, and important phenotypic traits would improve the predictive power of an established multimodal deep learning model. Evaluation of the model performance when training from scratch showed its ability to accurately predict ~89% of hybrids with high-yield potential and demonstrated enhanced explanatory power compared with previously published models. Shapley Additive explanations (SHAP) analysis indicated the top influential features include plant density, hybrid placement in the field, date to anthesis, parental line, temperature, humidity, and solar radiation. Including weather historical data was important for model performance, significantly enhancing the predictive and explanatory power of the model. For future research, the use of the model can move beyond maize yield prediction by fine-tuning the model on other crop data, serving as a potential decision-making tool for crop breeders to determine high-performing individuals from diverse crop types.

A global international initiative, such as the Earth BioGenome Project (EBP), requires both agreement and coordination on standards to ensure that the collective effort generates rapid progress toward its goals. To this end, the EBP initiated five technical standards committees comprising volunteer members from the global genomics scientific community: Sample Collection and Processing, Sequencing and Assembly, Annotation, Analysis, and IT and Informatics. The current versions of the resulting standards documents are available on the EBP website, with the recognition that opportunities, technologies, and challenges may improve or change in the future, requiring flexibility for the EBP to meet its goals. Here, we describe some highlights from the proposed standards, and areas where additional challenges will need to be met.

Abstract Background Bystander administration of naloxone is a critical strategy to mitigate opioid overdose mortality. To ensure bystanders’ willingness to carry and administer naloxone in response to a suspected overdose, it is critical to select products for community distribution with the highest likelihood of being utilized. This study examines bystanders’ preference for and willingness to administer three naloxone products approved by the FDA for bystander use and identify product features driving preference. Methods The population was a convenience sample of individuals who attended the Kentucky State Fair, August 18–28, 2022, in Louisville, Kentucky. Participants (n = 503) watched a standardized overdose education and naloxone training video, rated their willingness to administer each of three products (i.e., higher-dose nasal spray, lower-dose nasal spray, intramuscular injection), selected a product to take home, and rated factors affecting choice. Results After training, 44.4% chose the higher-dose nasal spray, 30.1% chose the intramuscular injection, and 25.5% chose the lower-dose nasal spray. Factors most influencing choice on a 10-point Likert scale were ease of use (9 [7–10]), naloxone dose (8 [5–10]), and product familiarity (5 [5–9]). Conclusions Bystanders expressed high willingness to administer all studied formulations of naloxone products. Product choice preference varied as a function of product features. As the number and variety of available products continue to increase, continuous evaluation of formulation acceptability, in addition to including individuals with lived experience who are receiving and administering overdose reversal agents, is critical to support adoption and save lives.

Abstract Graphene is emerging in numerous wearable and biomedical applications. Understanding the impact of graphene‐based nanocomposites over bacterial activity is critical for design appropriate technical solutions. In this work, antibacterial activity of monolayer and few‐layer graphene is tested against Gram‐negative and Gram‐positive model bacteria. Such graphene‐based nanomaterials present different effects on bacteria viability, depending on concentration and shape. The presence of dopant agents can either increase or revert graphene antibacterial effect. Among other bactericidal mechanisms, we also demonstrate that graphene surface polarity is connected to antibacterial properties and can be easily modified by surface charge transfer doping. This electrophilicity tuning offers a novel mechanism for the molecular basis of graphene's antibacterial activity.

BackgroundHalyomorpha halys (Stål), the brown marmorated stink bug, is a highly invasive insect species due in part to its exceptionally high levels of polyphagy. This species is also a nuisance due to overwintering in human-made structures. It has caused significant agricultural losses in recent years along the Atlantic seaboard of North America and in continental Europe. Genomic resources will assist with determining the molecular basis for this species' feeding and habitat traits, defining potential targets for pest management strategies.ResultsAnalysis of the 1.15-Gb draft genome assembly has identified a wide variety of genetic elements underpinning the biological characteristics of this formidable pest species, encompassing the roles of sensory functions, digestion, immunity, detoxification and development, all of which likely support H. halys' capacity for invasiveness. Many of the genes identified herein have potential for biomolecular pesticide applications.ConclusionsAvailability of the H. halys genome sequence will be useful for the development of environmentally friendly biomolecular pesticides to be applied in concert with more traditional, synthetic chemical-based controls.

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BackgroundThe western flower thrips, Frankliniella occidentalis (Pergande), is a globally invasive pest and plant virus vector on a wide array of food, fiber, and ornamental crops. The underlying genetic mechanisms of the processes governing thrips pest and vector biology, feeding behaviors, ecology, and insecticide resistance are largely unknown. To address this gap, we present the F. occidentalis draft genome assembly and official gene set.ResultsWe report on the first genome sequence for any member of the insect order Thysanoptera. Benchmarking Universal Single-Copy Ortholog (BUSCO) assessments of the genome assembly (size = 415.8 Mb, scaffold N50 = 948.9 kb) revealed a relatively complete and well-annotated assembly in comparison to other insect genomes. The genome is unusually GC-rich (50%) compared to other insect genomes to date. The official gene set (OGS v1.0) contains 16,859 genes, of which ~ 10% were manually verified and corrected by our consortium. We focused on manual annotation, phylogenetic, and expression evidence analyses for gene sets centered on primary themes in the life histories and activities of plant-colonizing insects. Highlights include the following: (1) divergent clades and large expansions in genes associated with environmental sensing (chemosensory receptors) and detoxification (CYP4, CYP6, and CCE enzymes) of substances encountered in agricultural environments; (2) a comprehensive set of salivary gland genes supported by enriched expression; (3) apparent absence of members of the IMD innate immune defense pathway; and (4) developmental- and sex-specific expression analyses of genes associated with progression from larvae to adulthood through neometaboly, a distinct form of maturation differing from either incomplete or complete metamorphosis in the Insecta.ConclusionsAnalysis of the F. occidentalis genome offers insights into the polyphagous behavior of this insect pest that finds, colonizes, and survives on a widely diverse array of plants. The genomic resources presented here enable a more complete analysis of insect evolution and biology, providing a missing taxon for contemporary insect genomics-based analyses. Our study also offers a genomic benchmark for molecular and evolutionary investigations of other Thysanoptera species.

The tremendous diversity of Hymenoptera is commonly attributed to the evolution of parasitoidism in the last common ancestor of parasitoid sawflies (Orussidae) and wasp-waisted Hymenoptera (Apocrita). However, Apocrita and Orussidae differ dramatically in their species richness, indicating that the diversification of Apocrita was promoted by additional traits. These traits have remained elusive due to a paucity of sawfly genome sequences, in particular those of parasitoid sawflies. Here, we present comparative analyses of draft genomes of the primarily phytophagous sawfly Athalia rosae and the parasitoid sawfly Orussus abietinus. Our analyses revealed that the ancestral hymenopteran genome exhibited traits that were previously considered unique to eusocial Apocrita (e.g., low transposable element content and activity) and a wider gene repertoire than previously thought (e.g., genes for CO2 detection). Moreover, we discovered that Apocrita evolved a significantly larger array of odorant receptors than sawflies, which could be relevant to the remarkable diversification of Apocrita by enabling efficient detection and reliable identification of hosts.

Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (Zea mays, Arabidopsis thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Glycine max and Oryza sativa) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences with the highest accuracy of 83.4% for Z. mays and the lowest accuracy of 57.9% for B. rapa, revealing that genome assembly quality might affect the accuracy of lncRNA identification. Furthermore, we demonstrated the potential of using NLP models for cross-species prediction with an average of 63.1% accuracy using target species not previously seen by the model. As more species are incorporated into the training datasets, we expect the accuracy to increase, becoming a more reliable tool for uncovering novel lncRNAs. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important to lncRNA prediction and that these motifs frequently flanked the lncRNA sequence.

The environment gametes perform in just before fertilization is increasingly recognized to affect offspring fitness, yet the contributions of male and female gametes and their adaptive significance remain largely unexplored. Here, we investigated gametic thermal plasticity and its effects on hatching success and embryo performance in Atlantic salmon (Salmo salar). Eggs and sperm were incubated overnight at 2°C or 8°C, temperatures within the optimal thermal range of this species. Crosses between warm- and cold-incubated gametes were compared using a full-factorial design, with half of each clutch reared in cold temperatures and the other in warm temperatures. This allowed disentangling single-sex interaction effects when pre-fertilization temperature of gametes mismatched embryonic conditions. Pre-fertilization temperature influenced hatch timing and synchrony, and matching sperm and embryo temperatures resulted in earlier hatching. Warm incubation benefited eggs but harmed sperm, reducing the hatching success and, overall, gametic thermal plasticity did not enhance offspring fitness, indicating vulnerability to thermal changes. We highlight the sensitivity of male gametes to higher temperatures, and that gamete acclimation may not effectively buffer against deleterious effects of thermal fluctuations. From an applied angle, we propose the differential storage of male and female gametes as a tool to enhance sustainability within the hatcheries.

Weeds pose a significant threat to agricultural production, leading to substantial yield losses and increased herbicide usage, with severe economic and environmental implications. This paper uses deep learning to explore a novel approach via targeted segmentation mapping of crop plants rather than weeds, focusing on canola (Brassica napus) as the target crop. Multiple deep learning architectures (ResNet-18, ResNet-34, and VGG-16) were trained for the pixel-wise segmentation of canola plants in the presence of other plant species, assuming all non-canola plants are weeds. Three distinct datasets (T1_miling, T2_miling, and YC) containing 3799 images of canola plants in varying field conditions alongside other plant species were collected with handheld devices at 1.5 m. The top performing model, ResNet-34, achieved an average precision of 0.84, a recall of 0.87, a Jaccard index (IoU) of 0.77, and a Macro F1 score of 0.85, with some variations between datasets. This approach offers increased feature variety for model learning, making it applicable to the identification of a wide range of weed species growing among canola plants, without the need for separate weed datasets. Furthermore, it highlights the importance of accounting for the growth stage and positioning of plants in field conditions when developing weed detection models. The study contributes to the growing field of precision agriculture and offers a promising alternative strategy for weed detection in diverse field environments, with implications for the development of innovative weed control techniques.

BackgroundThe Hemiptera (aphids, cicadas, and true bugs) are a key insect order, with high diversity for feeding ecology and excellent experimental tractability for molecular genetics. Building upon recent sequencing of hemipteran pests such as phloem-feeding aphids and blood-feeding bed bugs, we present the genome sequence and comparative analyses centered on the milkweed bug Oncopeltus fasciatus, a seed feeder of the family Lygaeidae.ResultsThe 926-Mb Oncopeltus genome is well represented by the current assembly and official gene set. We use our genomic and RNA-seq data not only to characterize the protein-coding gene repertoire and perform isoform-specific RNAi, but also to elucidate patterns of molecular evolution and physiology. We find ongoing, lineage-specific expansion and diversification of repressive C2H2 zinc finger proteins. The discovery of intron gain and turnover specific to the Hemiptera also prompted the evaluation of lineage and genome size as predictors of gene structure evolution. Furthermore, we identify enzymatic gains and losses that correlate with feeding biology, particularly for reductions associated with derived, fluid nutrition feeding.ConclusionsWith the milkweed bug, we now have a critical mass of sequenced species for a hemimetabolous insect order and close outgroup to the Holometabola, substantially improving the diversity of insect genomics. We thereby define commonalities among the Hemiptera and delve into how hemipteran genomes reflect distinct feeding ecologies. Given Oncopeltus's strength as an experimental model, these new sequence resources bolster the foundation for molecular research and highlight technical considerations for the analysis of medium-sized invertebrate genomes.

Molecular biomarkers correspond to all endogenous or exogenous biomolecules, whose presence or alteration in metabolic pathways indicate the response of a biological system to a disturbance caused by internal or external factors. The search for biomarkers has allowed deepening the knowledge of complex pathophysiological mechanisms of diseases, interactions with potential drugs, and the improvement of diagnostic and therapeutic tests, establishing itself as a central point in personalized medicine. In addition, the development of the omics sciences has fostered an increasing exploration in this field of research. This review presents essential and relevant concepts in biomarker research, explores the various definitions and classifications of biomarkers according to their purpose, the critical aspects to be considered in experimental design, the primary employed study models (in vivo, in vitro, and in silico) and the factors that have hindered their ultimate implementation. Finally, important, and current clinical and pharmacological research areas in which biomarker research play a central role are presented.

Abstract This study reports the optoelectronic characterization of few‐layer ReSe2field effect transistors at different pressures. The output curves reveal dominant n‐type behavior and a low Schottky barrier at the metal contacts. The transfer curves show a significant hysteresis that can be exploited in memory devices with an order ofmagnitude memory window and good cycling. The devices are dramatically affected by air pressure; their conductance and mobility increase with the lowering pressure that desorbs electronegative air molecules from the surface of the material. The photoresponse under white super‐continuum laser illumination reveals that the device exhibits positive photoconductivity (PPC) at ambient and low (≈1 mbar) pressure and negative photoconductivity (NPC) in a higher vacuum (≈10−4 mbar). The transition from PPC to NPC can be explained by considering that the photoresponse is affected by molecule desorption, which yields PPC at higher pressure, and defect trapping of photogenerated carriers, which can dominate at lower pressures. The transient behavior of the device exposed to laser pulses shows a faster response and a higher photodetection efficiency at ambient pressure, with the highest signal‐to‐noise ratio at the valley of the transfer curve between p‐ and n‐type conduction.

Introduction: Treatment with lorlatinib for patients with advanced ALK- and ROS1-rearranged NSCLC (ALK+ and ROS1+ NSCLC) is associated with a unique set of adverse events (AEs) often requiring dose reduction. However, the impact of dose reductions on outcomes remains unclear and is mainly limited to analyses from prospective studies of lorlatinib in the first-line setting. Methods: We reviewed the course of 144 patients with advanced ALK- or ROS1-rearranged NSCLC treated with lorlatinib in the second-line or later setting to assess the frequency of dose reductions resulting from treatment-related AEs (TRAEs) and the association between dose reductions and progression-free survival (PFS) and overall survival (OS). Results: A total of 58 patients (40%) had TRAE-related dose reductions, most (59%) owing to neurocognitive AEs or neuropathy. Among all patients, the median PFS was 8.1 months (95% confidence interval [CI]: 6.4–11.8); the median OS was 20.7 months (95% CI: 16.3–30.5). Among patients who were started on lorlatinib 100 mg/d (n = 122), a Cox regression model with the occurrence of a dose reduction as a time-dependent covariate indicated no association between dose reduction and PFS (hazard ratio = 0.86, 95% CI: 0.54–1.39) or OS (hazard ratio = 0.78, 95% CI: 0.47–1.30). Conclusions: Lorlatinib dose reductions were not associated with inferior clinical outcomes in this multicenter analysis. Prompt identification of lorlatinib TRAEs and implementation of dose reductions may help maximize tolerability without compromising outcomes.