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Metagenomics, the application of shotgun sequencing, facilitates the reconstruction of the genomes of individual species from natural environments. A major challenge in the genome recovery domain is to agglomerate or ‘bin’ sequences assembled from metagenomic reads into individual groups. Metagenomic binning without consideration of reference sequences enables the comprehensive discovery of new microbial organisms and aids in the microbial genome reconstruction process. Here we present MyCC, an automated binning tool that combines genomic signatures, marker genes and optional contig coverages within one or multiple samples, in order to visualize the metagenomes and to identify the reconstructed genomic fragments. We demonstrate the superior performance of MyCC compared to other binning tools including CONCOCT, GroopM, MaxBin and MetaBAT on both synthetic and real human gut communities with a small sample size (one to 11 samples), as well as on a large metagenome dataset (over 250 samples). Moreover, we demonstrate the visualization of metagenomes in MyCC to aid in the reconstruction of genomes from distinct bins. MyCC is freely available at http://sourceforge.net/projects/sb2nhri/files/MyCC/ .

Sweet potato (Ipomoea batatas) is one of the most important food crops worldwide and its leaves provide a dietary source of nutrients and various bioactive compounds. These constituents of sweet potato leaves (SPL) vary among varieties and play important roles in treating and preventing various diseases. Recently, more attentions in health-promoting benefits have led to several in vitro and in vivo investigations, as well as the identification and quantification of bioactive compounds in SPL. Among them, many new compounds have been reported as the first identified compounds from SPL with their dominant bioactivities. This review summarizes the current knowledge of the bioactive compositions of SPL and their health benefits. Since SPL serve as a potential source of micronutrients and functional compounds, they can be further developed as a sustainable crop for food and medicinal industries.

Since its discovery nearly a century ago, antibiotics has been one of the most effective methods in treating infectious diseases and limiting pathogen spread. However, pathogens often build up antibiotic resistance over time, leading to serious failure of the treatment. Silver nanoparticle (AgNP) is an appealing alternative, but successful treatment of the bacterial infection requires a plentiful supply of AgNP, which can negatively impact human health if people are excessively exposed to the particles. Here, we present a method to overcome this challenge by synthesizing nanodiamond-supported AgNP noncovalently conjugated with albumin molecules to achieve enhanced antibacterial activity and strengthened biocompatibility. Using Escherichia coli as a model bacterium, we found that the albumin-conjugated silver-diamond nanohybrids showed a long-term bactericidal effect after 36 days of the treatment at the AgNP concentration of 250 µg mL −1 . Moreover, the toxicity of the nanohybrids to human cells (including human fibroblasts, lung adenocarcinoma epithelial cells, and breast adenocarcinoma cells) is low even at the particle concentration of 500 µg mL −1 . The method provides a general and practical solution to the concerns of bacterial resistance against AgNP and issues associated with the size, shape, aggregation, and toxicity of AgNP are largely resolved. Finally, we demonstrate that the nanohybrids can be readily incorporated into natural polysaccharides (such as guar gum) to form three-in-one hydrogels, showing promising applications in nanomedicine.

MicroRNAs (miRNAs) are small noncoding RNAs which post-transcriptionally regulate gene expression by directing mRNA cleavage or translational inhibition. miRNAs play multiple roles in the growth, development and stress responses in plants. However, little is known of the wounding-responsive miRNAs and their regulation. Here, we investigated the expression patterns of microR828 (miR828) on wounding in sweet potato (Ipomoea batatas cv Tainung 57). The expression of miR828 was only detected in leaves, and was induced by wounding rather than by ethylene, hydrogen peroxide (H2O2), methyl jasmonate or nitric oxide (NO). Moreover, cyclic guanosine monophosphate (cGMP) was necessary for miR828 accumulation in leaves on wounding. Two miR828 target candidates, named IbMYB and IbTLD, were obtained by cDNA cloning, and their mRNA cleavage caused by miR828 was confirmed by cleavage site mapping, agro-infiltration and transgenics studies. The reduction in IbMYB and IbTLD expression coincided with the induction of miR828, demonstrating thatIbMYB and IbTLD might be miR828 targets. Furthermore, transgenic sweet potato overexpressing miR828 precursor affected lignin and H2O2 contents. These results showed that cGMP could regulate wounding-responsive miR828, which repressed the expression of IbMYB and IbTLD. Subsequently, lignin and H2O2 were accumulated to participate in defense mechanisms.

The purpose of this study is to investigate the relationship between the greenhouse effect and the overuse of electricity and energy under a sustainable environment. The goal is to investigate the airflow that is induced by ceiling fans, by measuring human body temperature. In the simulation model, the thermal plume phenomenon is observed in the indoor environment. By changing the ceiling fan parameters, the influence of the airflow is investigated by practical measurement of human body temperature. The indoor convective heat transfer is enhanced by installing a ceiling fan, which affects the whole body thermal sensation (WBTS). Different scenarios are reviewed by adjusting the fan speed in the simulation model, so that the distribution of human body temperature can be determined. By modeling the blade plane of the ceiling fan, the airflow characteristics can be determined by making the simulation model rotate in order to assess the thermal comfort characteristics. As the ceiling fan generates circulation within the domain, the thermal comfort is significantly enhanced. By keeping a reasonable thermal comfort level, a higher room temperature or a higher heat load is allowed so that a sustainable environment can be maintained without affecting the indoor thermal comfort or the efficiency of energy usage.

Time-series transcriptomes of a biological process obtained under different conditions are useful for identifying the regulators of the process and their regulatory networks. However, such data are 3D (gene expression, time, and condition), and there is currently no method that can deal with their full complexity. Here, we developed a method that avoids time-point alignment and normalization between conditions. We applied it to analyze time-series transcriptomes of developing maize leaves under light–dark cycles and under total darkness and obtained eight time-ordered gene coexpression networks (TO-GCNs), which can be used to predict upstream regulators of any genes in the GCNs. One of the eight TO-GCNs is light-independent and likely includes all genes involved in the development of Kranz anatomy, which is a structure crucial for the high efficiency of photosynthesis in C₄ plants. Using this TO-GCN, we predicted and experimentally validated a regulatory cascade upstream of SHORTROOT1, a key Kranz anatomy regulator. Moreover, we applied the method to compare transcriptomes from maize and rice leaf segments and identified regulators of maize C₄ enzyme genes and RUBISCO SMALL SUBUNIT2. Our study provides not only a powerful method but also novel insights into the regulatory networks underlying Kranz anatomy development and C₄ photosynthesis.

Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems. While bacterial cultures remain vital, their time-consuming nature and susceptibility to false negatives limit their effectiveness. Even promising existing machine learning approaches are restricted by reliance on complex clinical factors that could delay results, underscoring the need for faster, simpler, and more reliable diagnostic strategies. This study introduces innovative machine learning models using complete blood count with differential (CBC+DIFF) data-a routine, minimally invasive test that assesses immune response through blood cell measurements, critical for sepsis identification. The primary objective was to implement this model within an artificial intelligence-clinical decision support system (AI-CDSS) to enhance early sepsis detection and management in critical care settings. This retrospective study at Tri-Service General Hospital (September to December 2023) analyzed 746 ICU patients with suspected pneumonia-induced sepsis (supported by radiographic evidence and a SOFA score increase of ≥2 points), alongside 746 stable outpatients as controls. Sepsis infection sources were confirmed through positive sputum, blood cultures, or FilmArray results. The dataset incorporated both basic hematological factors and advanced neutrophil characteristics (side scatter light intensity, cytoplasmic complexity, and neutrophil-to-lymphocyte ratio), with data from September to November used for training and data from December used for validation. Machine learning models, including light gradient boosting machine (LGBM), random forest classifier, and gradient boosting classifier, were developed using CBC+DIFF data and were assessed using metrics such as area under the curve, sensitivity, and specificity. The best-performing model was integrated into the AI-CDSS, with its implementation supported through workshops and training sessions. Pathogen identification in ICU patients found 243 FilmArray-positive, 411 culture-positive, and 92 undetected cases, yielding a final dataset of 654 (43.8%) sepsis cases out of 1492 total cases. The machine learning models demonstrated high predictive accuracy, with LGBM achieving the highest area under the curve (0.90), followed by the random forest classifier (0.89) and gradient boosting classifier (0.88). The best-performing LGBM model was selected and integrated as the core of our AI-CDSS, which was built on a web interface to facilitate rapid sepsis risk assessment using CBC+DIFF data. This study demonstrates that by providing streamlined predictions using CBC+DIFF data without requiring extensive clinical parameters, the AI-CDSS can be seamlessly integrated into clinical workflows, enhancing rapid, accurate identification of sepsis and improving patient care and treatment timeliness.

Despite the ever-increasing output of next-generation sequencing data along with developing assemblers, dozens to hundreds of gaps still exist in de novo microbial assemblies due to uneven coverage and large genomic repeats. Third-generation single-molecule, real-time (SMRT) sequencing technology avoids amplification artifacts and generates kilobase-long reads with the potential to complete microbial genome assembly. However, due to the low accuracy (~85%) of third-generation sequences, a considerable amount of long reads (>50X) are required for self-correction and for subsequent de novo assembly. Recently-developed hybrid approaches, using next-generation sequencing data and as few as 5X long reads, have been proposed to improve the completeness of microbial assembly. In this study we have evaluated the contemporary hybrid approaches and demonstrated that assembling corrected long reads (by runCA) produced the best assembly compared to long-read scaffolding (e.g., AHA, Cerulean and SSPACE-LongRead) and gap-filling (SPAdes). For generating corrected long reads, we further examined long-read correction tools, such as ECTools, LSC, LoRDEC, PBcR pipeline and proovread. We have demonstrated that three microbial genomes including Escherichia coli K12 MG1655, Meiothermus ruber DSM1279 and Pdeobacter heparinus DSM2366 were successfully hybrid assembled by runCA into near-perfect assemblies using ECTools-corrected long reads. In addition, we developed a tool, Patch, which implements corrected long reads and pre-assembled contigs as inputs, to enhance microbial genome assemblies. With the additional 20X long reads, short reads of S. cerevisiae W303 were hybrid assembled into 115 contigs using the verified strategy, ECTools + runCA. Patch was subsequently applied to upgrade the assembly to a 35-contig draft genome. Our evaluation of the hybrid approaches shows that assembling the ECTools-corrected long reads via runCA generates near complete microbial genomes, suggesting that genome assembly could benefit from re-analyzing the available hybrid datasets that were not assembled in an optimal fashion.

Determining the genomic sequences of microorganisms is the basis and prerequisite for understanding their biology and functional characterization. While the advent of low-cost, extremely high-throughput second-generation sequencing technologies and the parallel development of assembly algorithms have generated rapid and cost-effective genome assemblies, such assemblies are often unfinished, fragmented draft genomes as a result of short read lengths and long repeats present in multiple copies. Third-generation, PacBio sequencing technologies circumvented this problem by greatly increasing read length. Hybrid approaches including ALLPATHS-LG, PacBio corrected reads pipeline, SPAdes and SSPACE-LongRead and non-hybrid approaches—hierarchical genome-assembly process (HGAP) and PacBio corrected reads pipeline via self-correction—have therefore been proposed to utilize the PacBio long reads that can span many thousands of bases to facilitate the assembly of complete microbial genomes. However, standardized procedures that aim at evaluating and comparing these approaches are currently insufficient. To address the issue, we herein provide a comprehensive comparison by collecting datasets for the comparative assessment on the above-mentioned five assemblers. In addition to offering explicit and beneficial recommendations to practitioners, this study aims to aid in the design of a paradigm positioned to complete bacterial genome assembly.

Deer genera around the globe are threatened by anthropogenic interference. The translocation of alien species and their subsequent genetic introgression into indigenous deer populations is particularly harmful to the species of greatest conservation concern. Products derived from deer, including venison and antler velvet, are also at risk of fraudulent labeling. The current molecular markers used to genetically identify deer species were developed from genome sequences and have limited applicability for cross-species amplification. The absence of efficacious diagnostic techniques for identifying deer species has hampered conservation and wildlife crime investigation efforts. Expressed sequence tag-simple sequence repeat (EST-SSR) markers are reliable tools for individual and species identification, especially in terms of cross-species genotyping. We conducted transcriptome sequencing of sambar ( Rusa unicolor ) antler velvet and acquired 11,190 EST-SSRs from 65,074 newly assembled unigenes. We identified a total of 55 unambiguous amplicons in sambar (n = 45), which were selected as markers to evaluate cross-species genotyping in sika deer ( Cervus nippon , n = 30) and red deer ( Cervus elaphus , n = 46), resulting in cross-species amplification rates of 94.5% and 89.1%, respectively. Based on polymorphic information content (>0.25) and genotyping fidelity, we selected 16 of these EST-SSRs for species identification. This marker set revealed significant genetic differentiation based on the fixation index and genetic distance values. Principal coordinate analysis and STRUCTURE analysis revealed distinct clusters of species and clearly identified red-sika hybrids. These markers showed applicability across different genera and proved suitable for identification and phylogenetic analyses across deer species.