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Abstract Leeches are famous for their high medical value and wide environmental adaptability. Among them, cave leeches are a very fascinating and rare group, which is an important component in the study of adaptive evolution of leeches. However, no study has yet reported a reference genome for this group. In this study, we assembled a high-quality chromosome-level genome of the cave terrestrial leech Sinospelaeobdella cavatuses, through Illumina and PacBio sequencing, alongside chromosome conformation capture techniques. The resulting genome spans 153.67 Mb across 9 pseudochromosomes(range: 11.33 to 23.53 Mb), with a mounting rate of up to 95.37% and features an N50 length of 17.15 Mb. This genome is composed of 35.16% repetitive elements and contains 21180 predicted protein-coding genes. Decoding the S. cavatuses genome not only promotes future studies on study of its phylogeny, evolution, and behavior, but also provides valuable resources for in-depth investigation on adaptive evolution of leech.

A novel wire-arc additive manufacturing (WAAM) process utilising titanium flux-cored wire (FCW) containing TiB2, Al60V40 and Ti6Al4V powders was proposed for fabricating TiB/Ti6Al4V composites. The FCW exhibited the capability in customising the composition of TiB/Ti6Al4V composites from hypoeutectic to hypereutectic by tailoring the proportion of TiB2 in blended powders. The comprehensive influence of TiB2 content on molten pool flow, compositional homogeneity and porosity of as-printed composites was attributed to the reduction of melt flowability, driven by viscosity escalation. It was unravelled the porosity was the intrinsic deficiency for the FCW-WAAM process, originating from the gases within FCW, while the interlayered TiB clusters were caused by the lagging melt of TiB2 powders. Due to directional solidification, TiB whiskers possessed a large aspect ratio and were partial to upward growth in hypoeutectic composites, which enhanced the load transfer strengthening. However, the porosity degraded the ductility of composites and caused the variation in mechanical performance.

[Objective] DC circuit breaker is a key equipment in metro traction power supply system. To improve the reliability of the traction and power supply system through reasonable maintenance strategy, it is necessary to evaluate the health condition of DC circuit breaker accurately. [Method] Health indexes are used to characterize the overall health condition of the equipment and establish a comprehensive evaluation model of the equipment health condition. On the basis that the changes of the equipment characteristic parameters reflect the equipment health condition, evaluation indexes are reasonably selected and a multi-index evaluation system of equipment operating condition is established. With the method of hierarchy and entropy weight, the calculation method of the weight of each sub-index is improved, making the equipment health condition evaluation model more reasonable. The equipment health index is calculated based on the evaluation model, and the service life model based on historical health data is established to fit the trend of the equipment health index, evaluate the future health condition and predict the remaining service life. The proposed evaluation model is validated by the actual data of a traction substation. [Result & Conclusion] The example analysis results show that the proposed evaluation model of DC circuit breaker is effective and rational to some extent in that it can scientifically judge and predict the healthy condition and remaining service life of the DC circuit breaker.

This paper presents a novel hybrid algorithm that combines the Butterfly Optimization Algorithm (BOA) and Quantum-behavior Particle Swarm Optimization (QPSO) algorithms, leveraging $ gbest $ to establish an algorithm communication channel for cooperation. Initially, the population is split into two equal subgroups optimized by BOA and QPSO respectively, with the latter incorporating the Lévy flight for enhanced performance. Subsequently, a hybrid mechanism comprising a weight hybrid mechanism, a elite strategy, and a diversification mechanism is introduced to blend the two algorithms. Experimental evaluation on 12 benchmark test functions and the Muskin model demonstrates that the synergy between BOA and QPSO significantly enhances algorithm performance. The hybrid mechanism further boosts algorithm performance, positioning the new algorithm as a high-performance method. In the Muskingum model experiment, the algorithm proposed in this article can give the best sum of the square of deviation (SSQ) and is superior in the comparison of other indicators. Overall, through benchmark test function experiments and Muskin model evaluations, it is evident that the algorithm proposed in this paper exhibits strong optimization capabilities and is effective in addressing practical problems.

Copper-based pesticides are extensively used in agriculture, yet their impacts on beneficial insects remain poorly understood. Here, we investigate how cupric chloride exposure affects the gut microbiome of Bombyx mori, a model organism crucial for silk production. Using 16S rRNA sequencing, we analyzed the gut bacterial communities of fifth-instar silkworm larvae exposed to different concentrations of cupric chloride (0, 4, and 8 g/kg) in an artificial diet. The high-dose exposure dramatically altered the microbial diversity and community structure, where the Bacteroidota abundance decreased from 50.43% to 23.50%, while Firmicutes increased from 0.93% to 18.92%. A network analysis revealed complex interactions between the bacterial genera, with Proteobacteria and Firmicutes emerging as key players in the community response to copper stress. The functional prediction indicated significant shifts in metabolic pathways and genetic information processing in the high-dose group. Notably, the low-dose treatment induced minimal changes in both the taxonomic composition and predicted functions, suggesting a threshold effect in the microbiome response to copper exposure. Our findings provide novel insights into how agricultural chemicals influence insect gut microbiota and highlight potential implications for silkworm health and silk production. This work contributes to understanding the ecological impacts of copper-based pesticides and may inform evidence-based policies for their use in sericulture regions.

The transition from traditional mulberry leaf feeding to artificial diet cultivation represents a major advancement in modern sericulture, yet the genetic mechanisms driving this adaptation remain largely unexplored. This study investigates the genomic basis of artificial diet adaptation in the silkworm strain Guican No.5 through whole-genome resequencing and transcriptome analysis. We identified 8,935,179 single-nucleotide polymorphisms (SNPs) across all chromosomes, accounting for 2.01% of the genome, with particularly high densities observed in chromosomes 23, 26, and 28. Our analysis also revealed 879 novel transcripts, many of which are involved in digestion, detoxification, and stress response pathways. Key novel genes, including three carboxylesterases, two cytochrome P450s, one heat shock protein, and one copper/zinc superoxide dismutase, exhibited varying degrees of sequence similarity to known proteins, suggesting modifications to existing genetic frameworks. Notably, one novel P450 gene displayed only 74.07% sequence identity with its closest homolog, indicating the emergence of a new protein sequence. Additionally, several key genes showed high similarity to wild silkworm (Bombyx mandarina) proteins, underscoring their evolutionary origins. These findings provide valuable insights into the molecular mechanisms underpinning artificial diet adaptation in silkworms and offer genomic resources to enhance artificial diet formulations and breeding programs in sericulture.

Ferns represent the second-largest group of vascular plants, yet their genomic resources lag far behind. Here, we present a chromosome-scale genome assembly of Cibotium barometz (L.) J. Sm., a medicinally important fern species. The 3.49 Gb genome, assembled into 66 chromosomes with 99.41% sequence anchorage, revealed an exceptionally high proportion (83.93%) of repetitive elements, dominated by recently expanded LTR retrotransposons. We identified 30,616 protein-coding genes, providing insights into fern-specific gene families. Genomic analyses uncover the evolutionary dynamics of 513 key biosynthetic genes, particularly those involved in terpenoid and flavonoid production. Expression profiling across tissues revealed tissue-specific regulation of these pathways, with notable upregulation of chalcone synthase genes in roots. Our structural analysis of 1-deoxy-d-xylulose-5-phosphate synthase, a key enzyme in terpenoid biosynthesis, demonstrated high conservation across land plants while highlighting fern-specific adaptations. The identification of multiple isoforms for key enzymes points to potential gene-duplication events or the evolution of fern-specific variants. This genome provides a foundation for understanding fern biology, evolution, and the molecular basis of their medicinal properties. It also offers valuable resources for conservation efforts and pharmacological research, paving the way for sustainable utilization of this valuable medicinal plant and advancing our understanding of plant diversity and natural product biosynthesis.

The gut microbiome significantly influences the health and productivity of silkworms (Bombyx mori), the cornerstone of sericulture. With the increasing use of cost-effective artificial diets in sericulture, it is crucial to understand how these diets impact the silkworm gut microbiomes. Here we employed 16S rRNA sequencing to delineate the impact of three distinct dietary regimens on the silkworm gut microbiomes: exclusive mulberry leaf diet (SY), exclusive artificial feed diet (SL), and a sequential transition from artificial feed to mulberry leaves (ZS). Our results unveiled stark differences in microbial diversity across the groups, with the ZS group displaying an intermediary complexity. LefSe and random forest analyses identified Methylobacteriaceae, Microbacterium, and Rhodococcus as significantly enriched in the ZS group, suggesting their potential to facilitate silkworms’ adaptation to dietary transitions. Functional profiling revealed differential pathway regulation, indicating a metabolic reconfiguration in response to dietary modulations. Notably, the enrichment of Lactobacillus and Weissella in both the SL and ZS groups highlights their potential as probiotics in artificial diets. Our findings provide insights into the diet adaptation mechanisms of silkworm gut microbiota, paving the way for harnessing the intestinal bacteria to enhance silkworm health and silk production through targeted microbial interventions in sericulture practices.

As one of the key enzymes that maintain the dynamic balance of metal content in animals and take part in the process of heavy metals detoxification, metallothionein (MT) is also associated with the process of immune response. In this study, the full-length metallothionein cDNA sequence of 324 bp from Mytilus coruscus was obtained (named as McMT) with a 222bp ORF encoding 73 amino acid residues in length and its theoretical pI/MW was 7.75/7.21 kDa. Blastp showed that the deduced amino acid sequence of McMT had significant homology with metallothionein from many animals, plants and bacteria, especially with other known MT of mollusca, for example Mytilus edulis and Mytilus galloprovincialis and they fell into the same evolutionary branch. Real-time PCR was used to examine the transcript pattern of McMT in hemocyte, gill, hepatopancreas, gonad, adductor and mantle. The highest transcript level appeared in hepatopancreas, which showed its multiple physiological roles with relation to the tissue type and the maximum expression in hepatopancreas could contribute to detoxification. Under the stress of heavy metal exposure for 15 days, the time-dependent expression pattern of McMT was observed in each treated group, but the highest expression appeared at different times for various concentrations of Cu2+ and Pb2+. The temporal expression of McMT after bacteria (Vibrio parahemolyticus and Aeromonas hydrophila) challenge firstly rose and then went down, and the expression peak appeared at 4h after V.parahemolyticus infection with 40-fold and 100-fold under A.hydrophila stress at 24h. The results suggested that McMT played a significant role in antioxidation and the expression of McMT can be used as a biomarker for monitoring the situation of marine pollution.

Satyrinae is a 3000-species butterfly subfamily of Nymphalidae. The higher-level classification of this family is still controversial. In this research, we sequenced the complete mitogenomes of three satyrid butterfly species, Hipparchia autonoe, Paroeneis palaearctica, and Oeneis buddha, and studied the phylogeny of Satyrinae with all known complete mitogenomes. The results showed that the lengths of the three satyrid butterfly mitogenomes are 15,435 bp (H. Autonoe), 15,942 bp (P. palaearctica), and 15,259 bp (O. buddha). Gene content and arrangement of newly sequenced mitogenomes are highly conserved and are typical of Lepidoptera. These three mitogenomes were found to have a typical set of 37 genes and an A + T-rich region. The tRNA genes in these three mitogenomes showed a typical clover leaf structure, but the stem of tRNASer (AGN) was lacking dihydroacridine. In these three species, the lengths of the A + T-rich regions were different, which led to differences in mitochondrial genome sizes. The characterizations of the three mitogenomes enrich our knowledge on the Lepidopteran mitogenome and provide us genetic information to reconstruct the phylogenetic tree. Finally, the phylogenetic results confirmed the position of the genus Davidina in the subfamily Satyrini, had a closer phylogenetic relationship with Oeneis, and the phylogenetic analysis supported the formation of Oeneis buddha as an independent taxon in Oeneis.

Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID., Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.)., Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events., Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions., Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438., Competing Interests: Declaration of interests Danielle Mowery serves as an unpaid member of the Epic Cosmos Governing Council. Romain Pirracchio received funding from the FDA CERSI grant U01FD005978 and the PCORI grant P0562155 and received a consulting honorarium from Phillips. Martin van der Laan received funding from the NIAID grant 5R01AI074345. Johanna Loomba received contract funding from the NIH RECOVER program. Emily Pfaff received funding from the NIH and PCORI. The views expressed in this manuscript are solely those of the authors and do not necessarily represent those of the National Institutes of Health, the U.S. Department of Health and Human Services or the U.S. government. Qi Long was supported by grants from the NIH., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Despite recommendations by the United States Preventive Services Task Force and the Society for Vascular Surgery, adoption of screening for abdominal aortic aneurysms (AAAs) remains low. One challenge is the low prevalence of AAAs in the unscreened population, and therefore a low detection rate for AAA screenings. We sought to use machine learning to identify factors associated with the presence of AAAs and create a model to identify individuals at highest risk for AAAs, with the aim of increasing the detection rate of AAA screenings., Methods: A machine-learning model was trained using longitudinal medical records containing lab results, medications, and other data from our institutional database. A retrospective cohort study was performed identifying current or past smoking in patients aged 65 to 75 years and stratifying the patients by sex and smoking status as well as determining which patients had a confirmed diagnosis of AAA. The model was then adjusted to maximize fairness between sexes without significantly reducing precision and validated using six-fold cross validation., Results: Validation of the algorithm on the single-center institutional data utilized 18,660 selected patients over 2 years and identified 314 AAAs. There were 41 factors identified in the medical record included in the machine-learning algorithm, with several factors never having been previously identified to be associated with AAAs. With an estimated 100 screening ultrasounds completed monthly, detection of AAAs is increased with a lift of 200% using the algorithm as compared with screening based on guidelines. The increased detection of AAAs in the model-selected individuals is statistically significant across all cutoff points., Conclusions: By utilizing a machine-learning model, we created a novel algorithm to detect patients who are at high risk for AAAs. By selecting individuals at greatest risk for targeted screening, this algorithm resulted in a 200% lift in the detection of AAAs when compared with standard screening guidelines. Using machine learning, we also identified several new factors associated with the presence of AAAs. This automated process has been integrated into our current workflows to improve screening rates and yield of high-risk individuals for AAAs., Competing Interests: Disclosures A.L., T.S., and E.C. are employed by Medial EarlySign., (Copyright © 2023 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Research rarely explores LGBTQ+ youth bullying in the context of culture-specific outcomes (e.g., LGBTQ+ identity development) and what can mitigate the impact of peer stressors. This study used a concurrent mixed methods design to explore how experiences of peer victimization predicted LGBTQ+ youth's identity development (i.e., stigma sensitivity, concealment motivation, and difficult process) and whether social support and outness served as protective, moderating factors. The mixed methods approach provides a culture-specific context via qualitative inquiry to inform whether the quantitative findings align with how youth qualitatively discuss their experience of peer victimization, negative outcomes, and social support. Our sample consisted of 349 LGBTQ+ youth 14-17 years old who completed a survey (quantitative sample) and a subset of 39 LGBTQ+ youth who completed a semi-structured interview (qualitative sample). Our quantitative findings indicated that greater overall peer victimization was positively related to LGBIS-revised subscales of stigma sensitivity, concealment motivation, and difficult process, where both outness and social support moderated such relations. Qualitatively, victimized youth also reported stigma sensitivity and concealment motivation while also endorsing how being out and having a support system played a role in their experience of being victimized. These qualitative findings align with our quantitative findings that classmate support mitigated the effects of peer victimization on the difficulty of coming out. Implications for practitioners and researchers are provided.

Photothermal heating of nanoparticles has applications in nanomedicine, photocatalysis, photoelectrochemistry, and data storage, but accurate measurements of temperature at the nanoparticle surface are lacking. Here we demonstrate progress toward a super-resolution DNA nanothermometry technique capable of reporting the surface temperature on single plasmonic nanoparticles. Gold nanoparticles are functionalized with double-stranded DNA, and the extent of DNA denaturation under heating conditions serves as a reporter of temperature. Fluorescently labeled DNA oligomers are used to probe the denatured DNA through transient binding interactions. By counting the number of fluorescent binding events as a function of temperature, we reconstruct DNA melting curves that reproduce trends seen for solution-phase DNA. In addition, we demonstrate our ability to control the temperature of denaturation by changing the Na + concentration and the base pair length of the double-stranded DNA on the nanoparticle surface. This degree of control allows us to select narrow temperature windows to probe, providing quantitative measurements of temperature at nanoscale surfaces.

Background: An outbreak of coronavirus disease 2019 (Covid-19) occurred on the U.S.S. Theodore Roosevelt , a nuclear-powered aircraft carrier with a crew of 4779 personnel., Methods: We obtained clinical and demographic data for all crew members, including results of testing by real-time reverse-transcriptase polymerase chain reaction (rRT-PCR). All crew members were followed up for a minimum of 10 weeks, regardless of test results or the absence of symptoms., Results: The crew was predominantly young (mean age, 27 years) and was in general good health, meeting U.S. Navy standards for sea duty. Over the course of the outbreak, 1271 crew members (26.6% of the crew) tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by rRT-PCR testing, and more than 1000 infections were identified within 5 weeks after the first laboratory-confirmed infection. An additional 60 crew members had suspected Covid-19 (i.e., illness that met Council of State and Territorial Epidemiologists clinical criteria for Covid-19 without a positive test result). Among the crew members with laboratory-confirmed infection, 76.9% (978 of 1271) had no symptoms at the time that they tested positive and 55.0% had symptoms develop at any time during the clinical course. Among the 1331 crew members with suspected or confirmed Covid-19, 23 (1.7%) were hospitalized, 4 (0.3%) received intensive care, and 1 died. Crew members who worked in confined spaces appeared more likely to become infected., Conclusions: SARS-CoV-2 spread quickly among the crew of the U.S.S. Theodore Roosevelt . Transmission was facilitated by close-quarters conditions and by asymptomatic and presymptomatic infected crew members. Nearly half of those who tested positive for the virus never had symptoms., (Copyright © 2020 Massachusetts Medical Society.)

Birth rates in the United States have recently fallen. Birth rates per 1000 females aged 25-29 fell from 118 in 2007 to 105 in 2015. One factor may involve the vaccination against the human papillomavirus (HPV). Shortly after the vaccine was licensed, several reports of recipients experiencing primary ovarian failure emerged. This study analyzed information gathered in National Health and Nutrition Examination Survey, which represented 8 million 25-to-29-year-old women residing in the United States between 2007 and 2014. Approximately 60% of women who did not receive the HPV vaccine had been pregnant at least once, whereas only 35% of women who were exposed to the vaccine had conceived. For married women, 75% who did not receive the shot were found to conceive, while only 50% who received the vaccine had ever been pregnant. Using logistic regression to analyze the data, the probability of having been pregnant was estimated for females who received an HPV vaccine compared with females who did not receive the shot. Results suggest that females who received the HPV shot were less likely to have ever been pregnant than women in the same age group who did not receive the shot. If 100% of females in this study had received the HPV vaccine, data suggest the number of women having ever conceived would have fallen by 2 million. Further study into the influence of HPV vaccine on fertility is thus warranted.