HiTE: a fast and accurate dynamic boundary adjustment approach for full-length transposable element detection and annotation.

Recent advancements in genome assembly have greatly improved the prospects for comprehensive annotation of Transposable Elements (TEs). However, existing methods for TE annotation using genome assemblies suffer from limited accuracy and robustness, requiring extensive manual editing. In addition, the currently available gold-standard TE databases are not comprehensive, even for extensively studied species, highlighting the critical need for an automated TE detection method to supplement existing repositories. In this study, we introduce HiTE, a fast and accurate dynamic boundary adjustment approach designed to detect full-length TEs. The experimental results demonstrate that HiTE outperforms RepeatModeler2, the state-of-the-art tool, across various species. Furthermore, HiTE has identified numerous novel transposons with well-defined structures containing protein-coding domains, some of which are directly inserted within crucial genes, leading to direct alterations in gene expression. A Nextflow version of HiTE is also available, with enhanced parallelism, reproducibility, and portability.

RNA m6A detection using raw current signals and basecalling errors from Nanopore direct RNA sequencing reads.

MOTIVATION: Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads. RESULTS: Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%-9.9% and 5.5%-13.8% higher than other methods, respectively. AVAILABILITY AND IMPLEMENTATION: The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.

Recent research advances in polysaccharide-based hemostatic materials: A review.

Massive bleeding resulting from civil and martial accidents can often lead to shock or even death, highlighting the critical need for the development of rapid and efficient hemostatic materials. While various types of hemostatic materials are currently utilized in clinical practice, they often come with limitations such as poor biocompatibility, toxicity, and biodegradability. Polysaccharides, such as alginate (AG), chitosan (CS), cellulose, starch, hyaluronic acid (HA), and dextran, have exhibit excellent biocompatibility and in vivo biodegradability. Their degradation products are non-toxic to surrounding tissues and can be absorbed by the body. As a result, polysaccharides have been extensively utilized in the development of hemostatic materials and have gained significant attention in the field of in vivo hemostasis. This review offers an overview of the different forms, hemostatic mechanisms, and specific applications of polysaccharides. Additionally, it discusses the future opportunities and challenges associated with polysaccharide-based hemostats.

De novo diploid genome assembly using long noisy reads.

The high sequencing error rate has impeded the application of long noisy reads for diploid genome assembly. Most existing assemblers failed to generate high-quality phased assemblies using long noisy reads. Here, we present PECAT, a Phased Error Correction and Assembly Tool, for reconstructing diploid genomes from long noisy reads. We design a haplotype-aware error correction method that can retain heterozygote alleles while correcting sequencing errors. We combine a corrected read SNP caller and a raw read SNP caller to further improve the identification of inconsistent overlaps in the string graph. We use a grouping method to assign reads to different haplotype groups. PECAT efficiently assembles diploid genomes using Nanopore R9, PacBio CLR or Nanopore R10 reads only. PECAT generates more contiguous haplotype-specific contigs compared to other assemblers. Especially, PECAT achieves nearly haplotype-resolved assembly on B. taurus (Bison×Simmental) using Nanopore R9 reads and phase block NG50 with 59.4/58.0 Mb for HG002 using Nanopore R10 reads.

Vitamin D3 improves glucose metabolism and attenuates inflammation in prediabetic human and mice.

Prediabetes is a crucial stage for prevention and treatment of diabetes, and vitamin D (VD) has been found to be linked to the development of prediabetes and diabetes. Thus, we aimed to identify the effect of VD supplementation on glucose metabolism in prediabetic participants and mice. A 1:1 paired design of randomized, placebo-controlled trial with 1600 IU/day VD3 or placebo was administered to individuals with prediabetes, two-way repeated-measures ANCOVA was used to analyze glycolipid and inflammatory factors. A high-fat diet induced prediabetic KKay mice were utilized to evaluate the effects of VD3 with 16 weeks supplementation. Generalized estimation equation, one way ANOVA were used to analyze continuous monitoring indexes and terminal indexes, respectively. Exercise capacity, skeletal muscle pathological features and relevant proteins were examined. The clinical results showed that VD3 could improve insulin secretion and decrease inflammation. Results of KKay mice exhibited that VD3 not only ameliorate glycolipid metabolism and inflammatory indicators, but also regulated pathological changes of skeletal muscle and exercise capacity. Mechanistically, our results demonstrated that VD3 could inhibit the TLR4/NFκB and activate PI3K/AKT signaling pathway. Collectively, the study indicated that VD3 exerts its beneficial effects by inhibiting TLR4/NFκB to decrease inflammatory response, and activating PI3K/AKT signaling pathway to regulate glucose homeostasis.

In situ hydrodeoxygenation of heavy bio-oil using a Ce/Fe-based oxygen carrier in methanol-zero valent aluminum media.

Resource recovery from solid organic wastes, such as degradable plastics, and upgrading raw bio-oil are important ways for reducing carbon and pollution emissions. Hydrodeoxygenation (HDO) is a common thermochemical treatment to upgrade crude bio-oil. In this study, in order to realize in situ HDO during the hydropyrolysis of heavy bio-oil and degradable plastics, a reduced Fe/Ce oxygen carrier (OC) was used to catalytically remove oxygen from organics under the methanol-zero valent aluminum (ZV Al) media, where the hydrogen was produced during pyrolysis instead of a direct hydrogen supply. The results showed that the reduced OC captured the oxygen from the pyrolysis products of heavy bio-oil and degradable plastic, representing the multi-selectivity of reduced OC to phenols, ketones, etc. The ZV Al system promoted the production and utilization of hydrogen, as evidenced by the increased hydrogen content in gas phase and hydrocarbon content in liquid phase. The hydrocarbon component distribution in the liquid phase increased clearly when hydropyrolysis with degradable plastics addtion, but the excess degradable plastics addition caused increasing of the liquid product viscosity, and decreasing of the liquid products yield for the higher ash content in degradable plastic, and a higher ZV Al amount was required to maintain the hydropyrolysis. Molecular dynamics simulations verified the synergistic effect of degradable plastics and bio-oil by the pyrolysis behavior in different systems and temperatures, and the pyrolysis pathways were proposed. This non-autocatalytic system realized the resource recovery and heavy bio-oil upgrading using an Fe/Ce OC.

The UCLA Cosmochemistry Database.

The UCLA Cosmochemistry Database was initiated as part of a data-rescue and -storage project aimed at archiving a variety of cosmochemical data acquired at University of California, Los Angeles (UCLA). The data collection includes elemental compositions of extraterrestrial materials analyzed by UCLA cosmochemists over the last five decades. The analytical techniques include atomic absorption spectrometry (AAS) and neutron activation analysis (NAA) at UCLA. The data collection is stored on the Astromaterials Data System (Astromat). We provide both interactive tables and downloadable datasheets for users to access all data. The UCLA Cosmochemistry Database archives cosmochemical data that are essential tools for increasing our understanding of the nature and origin of extraterrestrial materials. Future studies can reference the data collection in the examination, analysis, and classification of newly acquired extraterrestrial samples.

The association of sickle cell disorder with adverse outcomes in COVID-19 patients: A meta-analysis.

The aim is to elucidate the relationship between sickle cell disorder and severe COVID-19. We systematically searched the required articles in three electronic databases, extracting and pooling effect sizes (ES) and 95% confidence interval (CI) from each eligible study to evaluate the effect of combined sickle cell disorder on adverse consequences in patients with COVID-19. This meta-analysis included 21 studies. Sickle cell disease (SCD) was a risk factor for mortality (pooled ES = 1.70, 95% CI: 1.00-2.92, p = 0.001), hospitalization (pooled ES = 6.21, 95% CI: 3.60-10.70, p = 0.000) and intensive care unit (ICU) admission (pooled ES = 2.29, 95% CI: 1.61-3.24, p = 0.099) in COVID-19 patients. Patients with SCD had an increased risk of respiratory failure/mechanical ventilation, but a statistical association was not found (pooled ES = 1.21, 95%CI: 0.74-1.98, p = 0.036). There was significant heterogeneity between SCD and death, hospitalization, and respiratory failure/mechanical ventilation. The results of meta-regression of SCD and hospitalization suggested that the tested variables including Area (p = 0.642), study design (p = 0.739), sample size (p = 0.397), proportion of males (p = 0.708), effect type (p = 0.723), whether confounding factors are adjusted (p = 0.606) might not be the source of heterogeneity. In addition, sickle cell trait (SCT) was significantly associated with the mortality (pooled ES = 1.54, 95% CI: 1.28-1.85, p = 0.771) and hospitalization (pooled ES = 1.20, 95% CI: 1.07-1.35，p = 0.519) in patients with COVID-19. But any increased risk of ICU admission/severe (pooled ES = 1.24, 95% CI: 0.95-1.62, p = 0.520) and mechanical ventilation (OR = 1.00, 95%CI:0.59-1.69) in COVID-19 patients with SCT was not observed. Sensitivity analysis demonstrated that the results were robust. The results of the funnel plot and Egger's test did not support the existence of publication bias. Current meta-analysis indicated that sickle cell disorder has a meaningful impact on COVID-19 progression to severe cases and associated deaths. However, further investigations and research to validate the current findings is indispensable.

Tough and On-Demand Detachable Wet Tissue Adhesive Hydrogel Made from Catechol Derivatives with a Long Aliphatic Side Chain.

Wet adhesion is critical in cases of wound closure, but it is usually deterred by the hydration layer on tissues. Inspired by dopamine-mediated underwater adhesion in mussel foot proteins, wet tissue adhesives containing catechol with 2-3 carbons side chains are reported mostly. To make wet adhesion of this type of adhesives much tougher, catechol derivatives with a long aliphatic side chain (≈10 atoms length) are synthesized. Then, a series of strong wet tissue adhesive hydrogels are prepared through photoinduced copolymerization of acrylic acid with synthetic monomers. The adhesive hydrogel has a high cohesion strength, that is, tensile strength and strain, and toughness of ≈1800 kPa, ≈540%, and ≈4100 kJ m-3 , respectively. Its interfacial toughness on wet and underwater porcine skin is respectively ≈1300 and ≈1100 J m-2 , and its adhesion strength to wet porcine skin is ≈153 kPa. These values are much higher than those of dopamine-based adhesives in the same conditions, demonstrating that the long aliphatic side chain on catechol can greatly improve the wet tissue-adhesion. Additionally, the tough interfacial adhesion can be broken on demand with 5 wt.% aqueous urea solution. This adhesive hydrogel is highly promising in safe wound closure.

Global disease burden attributed to unsafe sex in 204 countries and territories from 1990 to 2019: results from the Global Burden of Disease Study 2019.

Unsafe sex has become a public safety problem that endangers society, and research on deaths and disability-adjusted life years (DALYs) related to unsafe sex is valuable for global policy-making. We aimed to estimate the deaths and DALYs attributable to unsafe sex by country, gender, age group, and sociodemographic status from 1990 to 2019. We extracted data on disease burden from the Global Disease Burden 2019 (GBD 2019) database for unsafe sex, including deaths, DALYs and age-standardized rates (ASRs). Comparative analyses were performed on data about deaths, DALYs and the responding ASRs attributable to unsafe sex in different countries and regions using the Social Demographic Index (SDI). The global age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) attributable to unsafe sex were 11.98 (95% uncertainty intervals (UI): 10.97-13.52) per 100,000 people and 570.78 (95% UI: 510.24-658.10) per 100,000 people, respectively. Both the ASMRs and ASDRs were the highest in southern sub-Saharan Africa and lowest in Australasia and decreased with increasing SDI levels. About unsafe-sex-related disease, HIV/AIDS has the highest ASMR [8.48 (95% UI: 7.62-9.95)/100,000 people] and ASDR [447.44 (95% UI: 394.82-533.10)/100,000 people], followed by Cervical cancer [ASMR: 3.40 (95% UI: 2.90-3.81)/100,000 people and ASDR: 107.2 (95% UI: 90.52-119.43)/100,000 people] and sexually transmitted infections excluding HIV [ASMR: 0.10 (95% UI: 0.08-0.11)/100,000 people and ASDR: 16.14 (95% UI: 10.51-25.83)/100,000 people]. The death and DALY burden caused by these three diseases were more serious in the over 75 years old age group. The 40-44 age group for men and the 35-39 age group for women had the highest population of unsafe sex-related deaths and DALYs, respectively. In addition, the burden of unsafe sex in women was more serious than those in men. Unsafe sex is an important risk factor for global disease burden and a leading cause of substantial health loss. We found that the risk of ASMRs and ASDRs attributable to unsafe sex had negative correlation with SDI levels. These results demonstrate that the need for revised policies that focus on efforts to reduce overall unsafe sex worldwide.

Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework.

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the seriousness of clinical outcomes of adverse reactions to drugs. GCAP has two tasks: one is to predict whether adverse reactions to drugs cause serious clinical outcomes, and the other is to infer the corresponding classes of serious clinical outcomes. Experimental results demonstrate that our method is a powerful and robust framework with high extendibility. GCAP can serve as a useful tool to successfully address the challenge of predicting the seriousness of clinical outcomes stemming from adverse reactions to drugs.

DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing.

Long single-molecular sequencing technologies, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, are advantageous in detecting DNA 5-methylcytosine in CpGs (5mCpGs), especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence polymerase-chain-reaction treated and M.SssI-methyltransferase treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long (≥10 Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 Area Under the Curve on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10× reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5-methylcytosines.

Mussel Foot Protein Inspired Tape-Type Adhesive with Water-Responsive, High Conformal, Tough, and On-Demand Detachable Adhesion to Wet Tissue.

Wet adhesion is highly demanded in noninvasive wound closure, tissue repair, and biomedical devices, but it is still a big challenge for developing biosafe and tough wet bioadhesives due to low or even nonadhesion in the wet state for conventional adhesives. Inspired by the wet-adhesion-contributing factors of mussel foot proteins, a water-responsive dry robust tissue adhesive PAGU tape is made with thickness of <0.5 mm through fast UV-initiated copolymerization of acrylic acid (AA), gelatin (Gel), and hexadecenyl-1,2-catechol (UH). The tape shows strong cohesive mechanical properties and strong interfacial adhesion bonds. Upon application onto wet tissue, the adhesive tape can conform to the tissue, quickly dry tissue surface through absorbing surface/interfacial water and then allows formation of interfacial bonding with a high interfacial toughness of ≈818 J m-2 . Furthermore, it can be readily detached by treating with aq. urea solution. A highly efficient avenue is provided here for producing conformable, tough, and easy detachable wet bioadhesive tapes.

PF-D-Trimer, a protective SARS-CoV-2 subunit vaccine: immunogenicity and application.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has had and continues to have a significant impact on global public health. One of the characteristics of SARS-CoV-2 is a surface homotrimeric spike protein, which is primarily responsible for the host immune response upon infection. Here we present the preclinical studies of a broadly protective SARS-CoV-2 subunit vaccine developed from our trimer domain platform using the Delta spike protein, from antigen design through purification, vaccine evaluation and manufacturability. The pre-fusion trimerized Delta spike protein, PF-D-Trimer, was highly expressed in Chinese hamster ovary (CHO) cells, purified by a rapid one-step anti-Trimer Domain monoclonal antibody immunoaffinity process and prepared as a vaccine formulation with an adjuvant. Immunogenicity studies have shown that this vaccine candidate induces robust immune responses in mouse, rat and Syrian hamster models. It also protects K18-hACE2 transgenic mice in a homologous viral challenge. Neutralizing antibodies induced by this vaccine show cross-reactivity against the ancestral WA1, Delta and several Omicrons, including BA.5.2. The formulated PF-D Trimer is stable for up to six months without refrigeration. The Trimer Domain platform was proven to be a key technology in the rapid production of PF-D-Trimer vaccine and may be crucial to accelerate the development and accessibility of updated versions of SARS-CoV-2 vaccines.

Effectiveness of mRNA vaccine against Omicron-related infections in the real world: A systematic review and meta-analysis.

OBJECTIVE: We aimed to systematically evaluate the effectiveness of the currently available mRNA vaccines and boosters for the Omicron variant. METHODS: We searched for literature published on PubMed, Embase, Web of Science and preprint servers (medRxiv and bioRxiv) from January 1, 2020 to June 20, 2022. The pooled effect estimate was calculated by the random-effects model. RESULTS: We selected 34 eligible studies in the meta-analysis from 4336 records. For the 2-dose vaccinated group, the mRNA vaccine effectiveness (VE) was 34.74%, 36%, and 63.80% against any Omicron infection, symptomatic infection and severe infection, respectively. For the 3-dose vaccinated group, the mRNA VE was 59.80%, 57.47%, and 87.22% against any infection, symptomatic infection and severe infection. For the 3-dose vaccinated group, the relative mRNA VE was 34.74%, 37.36%, and 63.80% against any infection, symptomatic infection and severe infection. Six months after the 2-dose vaccination, VE with any infection, symptomatic infection, and severe infection decreased to 33.4%, 16.79%, and 60.43%. Three months after the 3-dose vaccination, VE for any infection and severe infection decreased to 55.39% and 73.39%. CONCLUSIONS: Two-dose mRNA vaccines failed to provide sufficient protection against any Omicron infection and symptomatic infection, while 3-dose mRNA vaccines continued to provide effective protection after 3 months.

RNPredATC: A Deep Residual Learning-Based Model With Applications to the Prediction of Drug-ATC Code Association.

The Anatomical Therapeutic Chemical (ATC) classification system, designated by the World Health Organization Collaborating Center (WHOCC), has been widely used in drug screening, repositioning, and similarity research. The ATC classification system assigns different codes to drugs according to the organ or system on which they act and/or their therapeutic and chemical characteristics. Correctly identifying the potential ATC codes for drugs can accelerate drug development and reduce the cost of experiments. Several classifiers have been proposed in this regard. However, they lack of ability to learn basic features from sparsely known drug-ATC code associations. Therefore, there is an urgent need for novel computational methods to precisely predict potential drug-ATC code associations in multiple levels of the ATC classification system based on known associations between drugs and ATC codes. In this paper, we provide a novel end-to-end model, so-called RNPredATC, to predict potential drug-ATC code associations in five ATC classification levels. RNPredATC can extract dense feature vectors from sparsely known drug-ATC code associations and reduce the impact from the degradation problem by a novel deep residual learning. We extensively compare our method with some state-of-the-art methods, including NetPredATC, SPACE, and some multi-label-based methods. Our experimental results show that RNPredATC achieves better performances in five-fold and ten-fold cross validations. Furthermore, the visualization analysis of hidden layers and case studies of predicted associations at the fifth ATC classification level confirm that RNPredATC can effectively identify the potential ATC codes of drugs.

Inaccurate-Supervised Learning With Generative Adversarial Nets.

Inaccurate-supervised learning (ISL) is a weakly supervised learning framework for imprecise annotation, which is derived from some specific popular learning frameworks, mainly including partial label learning (PLL), partial multilabel learning (PML), and multiview PML (MVPML). While PLL, PML, and MVPML are each solved as independent models through different methods and no general framework can currently be applied to these frameworks, most existing methods for solving them were designed based on traditional machine-learning techniques, such as logistic regression, KNN, SVM, decision tree. Prior to this study, there was no single general framework that used adversarial networks to solve ISL problems. To narrow this gap, this study proposed an adversarial network structure to solve ISL problems, called ISL with generative adversarial nets (ISL-GANs). In ISL-GAN, fake samples, which are quite similar to real samples, gradually promote the Discriminator to disambiguate the noise labels of real samples. We also provide theoretical analyses for ISL-GAN in effectively handling ISL data. In this article, we propose a general framework to solve PLL, PML, and MVPML, while in the published conference version, we adopt the specific framework, which is a special case of the general one, to solve the PLL problem. Finally, the effectiveness is demonstrated through extensive experiments on various imprecise annotation learning tasks, including PLL, PML, and MVPML.

Predicting trends of community participation after hospital discharge for younger adults after stroke.

BACKGROUND: Community participation is an indicator of recovery for younger adults after stroke who generally have a greater need to return to society than do older adults. However, little is known about the trends of participation and their determinants in this population. OBJECTIVE: To explore the trends of community participation by younger (<65 years) adults with stroke in Taiwan after their hospital discharge and to identify predictors of these trends. METHODS: This longitudinal, multicenter, prospective cohort study enrolled 570 relatively young adults (aged 20-65 years) with stroke. Participants were assessed at hospital discharge and at 3-, 6-, and 12-month follow-up. The primary outcome measure was the Participation Measure-3 Domains, 4 Dimensions (PM-3D4D). Sociodemographic and stroke-related variables were derived by using standardized instruments and questionnaires. Mixed models were used to derive the trends of each participation domain and to identify predictors. RESULTS: PM-3D4D score changes showed a positive trend from discharge to 1-year follow-up (particularly in social and community subscales); however, the improvement was mild and mainly occurred in the first 3 months. Sociodemographic factors, such as income and education levels, and stroke-related variables, such as stroke severity, comorbidity, and physical function, significantly predicted changes in PM-3D4D scores over time. Physical function demonstrated the strongest prediction ability for all participation domains and dimensions. CONCLUSION: Multiple sociodemographic and stroke-related variables, particularly physical function, predicted improvement in community participation by younger adults after stroke. These findings may help clinicians identify younger adults at risk of unfavorable long-term participation outcomes after stroke.

NanoSNP: a progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data.

MOTIVATION: Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem. RESULTS: We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002-HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×. AVAILABILITY AND IMPLEMENTATION: https://github.com/huangnengCSU/NanoSNP.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vitamin D deficiency in dengue fever patients' coinfected with H. pylori in Pakistan. A case-control study.

Introduction: Dengue fever is a vector-borne disease with an estimate of 390 million persons getting the infection each year with a significant public health impact. It has been reported DENV patients with vitamin D deficiency led to severe form of dengue infection; while H. pylori coinfection alters vitamin D receptors leading to vitamin D deficiency. We hypothesize that DENV patient's having low vitamin D along with H. pylori coinfection could have worsen dengue severity as well as vitamin D deficiency. In this case-control study, we compared (I) the vitamin D deficiency in dengue fever cases with or without H. pylori coinfection, and (II) negative dengue fever as a control with or without H. pylori coinfection. We have also assessed the correlation between vitamin D levels and its effect on warning signs of the dengue fever. Further, we have investigated whether coinfection with H. pylori has any effect on warning signs in the dengue fever patients and the vitamin D deficiency in all serotypes of the dengue virus infected patients. Methods: In this case control study the association of the vitamin D levels with age, gender and H. pylori coinfection in dengue fever hospitalized patients was assessed using chi-square and multivariate logistic regression analysis. Results: Four hundred dengue fever patients with H. pylori coinfection were compared with 400 dengue negative controls with H. pylori coinfection. The mean age was 29.96 ± 10.5 and 29.88 ± 10.7 years among cases and controls, respectively. Most dengue fever patients with H. pylori coinfection were deficient in vitamin D compared with negative dengue controls with H. pylori coinfection. In multivariate logistic regression, the dengue cases with H. pylori coinfection were.056 times (95% CI: 0.024, 0.128, P = 0.000) more likely to have vitamin D "deficiency', while compared with the cases who did not have H. pylori coinfection. Conclusion: The present study proposes that vitamin D deficiency in dengue fever patients coinfected with H. pylori is much higher than the dengue fever negative controls coinfected with H. pylori. As hypothesized the DENV patient with H. pylori coinfection has vitamin D deficiency as well as increased dengue severity.