Falls had been identified as one of the nursing-sensitive indicators for nursing care in hospitals. With technological progress, health information systems make it possible for health care professionals to manage patient care better. However, there is a dearth of research on health information systems used to manage inpatient falls.

Clinical drug-drug interactions (DDIs) have been a major cause for not only medical error but also adverse drug events (ADEs). The published literature on DDI clinical toxicity continues to grow significantly, and high-performance DDI information retrieval (IR) text mining methods are in high demand. The effectiveness of IR and its machine learning (ML) algorithm depends on the availability of a large amount of training and validation data that have been manually reviewed and annotated. In this study, we investigated how active learning (AL) might improve ML performance in clinical safety DDI IR analysis. We recognized that a direct application of AL would not address several primary challenges in DDI IR from the literature. For instance, the vast majority of abstracts in PubMed will be negative, existing positive and negative labeled samples do not represent the general sample distributions, and potentially biased samples may arise during uncertainty sampling in an AL algorithm. Therefore, we developed several novel sampling and ML schemes to improve AL performance in DDI IR analysis. In particular, random negative sampling was added as a part of AL since it has no expanse in the manual data label. We also used two ML algorithms in an AL process to differentiate random negative samples from manually labeled negative samples, and updated both the training and validation samples during the AL process to avoid or reduce biased sampling. Two supervised ML algorithms, support vector machine (SVM) and logistic regression (LR), were used to investigate the consistency of our proposed AL algorithm. Because the ultimate goal of clinical safety DDI IR is to retrieve all DDI toxicity-relevant abstracts, a recall rate of 0.99 was set in developing the AL methods. When we used our newly proposed AL method with SVM, the precision in differentiating the positive samples from manually labeled negative samples improved from 0.45 in the first round to 0.83 in the second round, and the precision in differentiating the positive samples from random negative samples improved from 0.70 to 0.82 in the first and second rounds, respectively. When our proposed AL method was used with LR, the improvements in precision followed a similar trend. However, the other AL algorithms tested did not show improved precision largely because of biased samples caused by the uncertainty sampling or differences between training and validation data sets.

Large-scale green tides have occurred continuously in the Yellow Sea of China from 2007 to 2018, and the causative species of the Yellow Sea green tide (YSGT) is Ulva prolifera. The thalli form floated thallus mats, and the thalli from different layers of the thallus mat suffer significantly different environmental conditions. In the present study, the environmental conditions of the surface layer (SL), middle layer (ML), and lower layer (LL) of the thallus mat from mid-June (Stage I) to mid-July (Stage II) were simulated. Photosynthetic traits and antioxidant systems were measured. The results showed that (1) photoprotective [non-photochemical quenching (NPQ) and cyclic electron transport (CEF)] and antioxidant systems both play important roles in protecting against abiotic factors in U. prolifera. (2) Cooperation between NPQ and CEF was observed in the ML group; CEF and the antioxidant system in the SL group work synergistically to protect the thalli. Furthermore, an inferred spatiotemporal attribute regarding the YSGT is presented: the significant changes in abiotic factors on the sea surface can easily affect the thalli of SL and ML from mid-June to mid-July, and those of LL can be affected in mid-July. This cooperation combined with the spatiotemporal attributes offers an explanation for the annual occurrence of the YSGT. HIGHLIGHTS     -Adaptive mechanisms of Ulva prolifera against abiotic factors.     -Cooperation between photosynthetic and antioxidant systems.     -Spatiotemporal attributes regarding the Yellow Sea green tide are presented.

Glycyrrhizin (GL) and Glycyrrhetic Acid 3-O-mono-β-D-glucuronide (GAMG) are the typical triterpenoid glycosides found in the root of licorice, a popular medicinal plant that exhibits diverse physiological effects and pharmacological manifestations. However, only few reports are available on the glycosylation enzymes involved in the biosynthesis of these valuable compounds with low conversion yield so far. In mammals, glycosyltransferases are involved in the phase II metabolism and may provide new solutions for us to engineer microbial strains to produce high valued compounds due to the substrate promiscuity of these glycosyltransferases. In this study, we mined the genomic databases of mammals and evaluated 22 candidate genes of O-glycosyltransferases by analyzing their catalytic potential for O-glycosylation of the native substrate, glycyrrhetinic acid (GA) for its glycodiversification. Out of 22 selected glycosyltransferases, only UGT1A1 exhibited high catalytic performance for biosynthesis of the key licorice compounds GL and GAMG. Molecular docking results proposed that the enzymatic activity of UGT1A1 was likely owing to the stable hydrogen bonding interactions and favorite conformations between the amino acid residues around substrate channels (P82~R85) and substrates. Furthermore, the complete biosynthesis pathway of GL was reconstructed in Saccharomyces cerevisiae for the first time, resulting in the production of 5.98 ± 0.47 mg/L and 2.31 ± 0.21 mg/L of GL and GAMG, respectively.

Pentastomiasis is a rare zoonotic disease caused by pentastomids. Despite their worm-like appearance, they are commonly placed into a separate sub-class of the subphylum Crustacea, phylum Arthropoda. However, until now, the systematic classification of the pentastomids and the diagnosis of pentastomiasis are immature, and genetic information about pentastomid nylum is almost nonexistent. The objective of this study was to obtain information on pentastomid nymph genes and identify the gene homologues related to host-parasite interactions or stage-specific antigens.

To investigate patterns of continuously monitored physical activity in older patients rehabilitating after hip fracture surgery and the association with patient characteristics.

Human coronavirus (HCoV) OC43 is the most prevalent HCoV in respiratory tract infections. Its molecular epidemiological characterization, particularly the genotyping, was poorly addressed.

Recombination plays an important role in the maintenance of genetic diversity in many types of organisms, especially diploid eukaryotes. Recombination can be studied and used to map diseases. However, recombination adds a great deal of complexity to the genetic information. This renders estimation of evolutionary parameters more difficult. After the coalescent process was formulated, models capable of describing recombination using graphs, such as ancestral recombination graphs (ARG) were also developed. There are two typical models based on which to simulate ARG: back-in-time model such as ms and spatial model including Wiuf&Hein's, SMC, SMC', and MaCS.

In many organisms, interactions among genes lead to multiple functional states, and changes to interactions can lead to transitions into new states. These transitions can be related to bifurcations (or critical points) in dynamical systems theory. Characterizing these collective transitions is a major challenge for systems biology. Here, we develop a statistical method for identifying bistability near a continuous transition directly from high-dimensional gene expression data. We apply the method to data from honey bees, where a known developmental transition occurs between bees performing tasks in the nest and leaving the nest to forage. Our method, which makes use of the expected shape of the distribution of gene expression levels near a transition, successfully identifies the emergence of bistability and links it to genes that are known to be involved in the behavioral transition. This proof of concept demonstrates that going beyond correlative analysis to infer the shape of gene expression distributions might be used more generally to identify collective transitions from gene expression data.

Building up physical activity is a highly important aspect in an older patient's rehabilitation process after hip fracture surgery. The patterns of physical activity during rehabilitation are associated with the duration of rehabilitation stay. Predicting physical activity patterns early in the rehabilitation phase can provide patients and health care professionals an early indication of the duration of rehabilitation stay as well as insight into the degree of patients' recovery for timely adaptive interventions.

The comparison of samples, or beta diversity, is one of the essential problems in ecological studies. Next generation sequencing (NGS) technologies make it possible to obtain large amounts of metagenomic and metatranscriptomic short read sequences across many microbial communities. De novo assembly of the short reads can be especially challenging because the number of genomes and their sequences are generally unknown and the coverage of each genome can be very low, where the traditional alignment-based sequence comparison methods cannot be used. Alignment-free approaches based on k-tuple frequencies, on the other hand, have yielded promising results for the comparison of metagenomic samples. However, it is not known if these approaches can be used for the comparison of metatranscriptome datasets and which dissimilarity measures perform the best.

Gene regulatory networks (GRNs) control development via cell type-specific gene expression and interactions between transcription factors (TFs) and regulatory promoter regions. Plant organ boundaries separate lateral organs from the apical meristem and harbor axillary meristems (AMs). AMs, as stem cell niches, make the shoot a ramifying system. Although AMs have important functions in plant development, our knowledge of organ boundary and AM formation remains rudimentary. Here, we generated a cellular-resolution genomewide gene expression map for low-abundance Arabidopsis thaliana organ boundary cells and constructed a genomewide protein-DNA interaction map focusing on genes affecting boundary and AM formation. The resulting GRN uncovers transcriptional signatures, predicts cellular functions, and identifies promoter hub regions that are bound by many TFs. Importantly, further experimental studies determined the regulatory effects of many TFs on their targets, identifying regulators and regulatory relationships in AM initiation. This systems biology approach thus enhances our understanding of a key developmental process.

Due to excellent performance in antitumor, antioxidation, antihypertension, antiatherosclerotic and antidepressant activities, crocetin, naturally exists in Crocus sativus L., has great potential applications in medical and food fields. Microbial production of crocetin has received increasing concern in recent years. However, only a patent from EVOVA Inc. and a report from Lou et al. have illustrated the feasibility of microbial biosynthesis of crocetin, but there was no specific titer data reported so far. Saccharomyces cerevisiae is generally regarded as food safety and productive host, and manipulation of key enzymes is critical to balance metabolic flux, consequently improve output. Therefore, to promote crocetin production in S. cerevisiae, all the key enzymes, such as CrtZ, CCD and ALD should be engineered combinatorially.

Maintaining redox balance is critical for the production of heterologous secondary metabolites, whereas on various occasions the native cofactor balance does not match the needs in engineered microorganisms. In this study, 7-dehydrocholesterol (7-DHC, a crucial precursor of vitamin D3) biosynthesis pathway was constructed in Saccharomyces cerevisiae BY4742 with endogenous ergosterol synthesis pathway blocked by knocking out the erg5 gene (encoding C-22 desaturase). The deletion of erg5 led to redox imbalance with higher ratio of cytosolic free NADH/NAD+ and more glycerol and ethanol accumulation. To alleviate the redox imbalance, a water-forming NADH oxidase (NOX) and an alternative oxidase (AOX1) were employed in our system based on cofactor regeneration strategy. Consequently, the production of 7-dehydrocholesterol was increased by 74.4% in shake flask culture. In the meanwhile, the ratio of free NADH/NAD+ and the concentration of glycerol and ethanol were reduced by 78.0%, 50.7% and 7.9% respectively. In a 5-L bioreactor, the optimal production of 7-DHC reached 44.49(±9.63) mg/L. This study provides a reference to increase the production of some desired compounds that are restricted by redox imbalance.

Rapid and highly efficient mating-type switching of Saccharomyces cerevisiae enables a wide variety of genetic manipulations, such as the construction of strains, for instance, isogenic haploid pairs of both mating-types, diploids and polyploids. We used the CRISPR/Cas9 system to generate a double-strand break at the MAT locus and, in a single cotransformation, both haploid and diploid cells were switched to the specified mating-type at ∼80% efficiency. The mating-type of strains carrying either rod or ring chromosome III were switched, including those lacking HMLα and HMRa cryptic mating loci. Furthermore, we transplanted the synthetic yeast chromosome V to build a haploid polysynthetic chromosome strain by using this method together with an endoreduplication intercross strategy. The CRISPR/Cas9 mating-type switching method will be useful in building the complete synthetic yeast (Sc2.0) genome. Importantly, it is a generally useful method to build polyploids of a defined genotype and generally expedites strain construction, for example, in the construction of fully a/a/α/α isogenic tetraploids.

Senescent astrocytes accumulate with aging and contribute to brain dysfunction and diseases such as Alzheimer's disease (AD), however, the mechanisms underlying the senescence of astrocytes during aging remain unclear. In the present study, we found that Yes-associated Protein (YAP) was downregulated and inactivated in hippocampal astrocytes of aging mice and AD model mice, as well as in D-galactose and paraquat-induced senescent astrocytes, in a Hippo pathway-dependent manner. Conditional knockout of YAP in astrocytes significantly promoted premature senescence of astrocytes, including reduction of cell proliferation, hypertrophic morphology, increase in senescence-associated β-galactosidase activity, and upregulation of several senescence-associated genes such as p16, p53 and NF-κB, and downregulation of Lamin B1. Further exploration of the underlying mechanism revealed that the expression of cyclin-dependent kinase 6 (CDK6) was decreased in YAP knockout astrocytes in vivo and in vitro, and ectopic overexpression of CDK6 partially rescued YAP knockout-induced senescence of astrocytes. Finally, activation of YAP signaling by XMU-MP-1 (an inhibitor of Hippo kinase MST1/2) partially rescued the senescence of astrocytes and improved the cognitive function of AD model mice and aging mice. Taken together, our studies identified unrecognized functions of YAP-CDK6 pathway in preventing astrocytic senescence in vitro and in vivo, which may provide further insights and new targets for delaying brain aging and aging-related neurodegenerative diseases such as AD.

Visual saliency refers to the human's ability to quickly focus on important parts of their visual field, which is a crucial aspect of image processing, particularly in fields like medical imaging and robotics. Understanding and simulating this mechanism is crucial for solving complex visual problems. In this paper, we propose a salient object detection method based on boundary enhancement, which is applicable to both 2D and 3D sensors data. To address the problem of large-scale variation of salient objects, our method introduces a multi-level feature aggregation module that enhances the expressive ability of fixed-resolution features by utilizing adjacent features to complement each other. Additionally, we propose a multi-scale information extraction module to capture local contextual information at different scales for back-propagated level-by-level features, which allows for better measurement of the composition of the feature map after back-fusion. To tackle the low confidence issue of boundary pixels, we also introduce a boundary extraction module to extract the boundary information of salient regions. This information is then fused with salient target information to further refine the saliency prediction results. During the training process, our method uses a mixed loss function to constrain the model training from two levels: pixels and images. The experimental results demonstrate that our salient target detection method based on boundary enhancement shows good detection effects on targets of different scales, multi-targets, linear targets, and targets in complex scenes. We compare our method with the best method in four conventional datasets and achieve an average improvement of 6.2% on the mean absolute error (MAE) indicators. Overall, our approach shows promise for improving the accuracy and efficiency of salient object detection in a variety of settings, including those involving 2D/3D semantic analysis and reconstruction/inpainting of image/video/point cloud data.

21-deoxycortisol (21-DF) is the key intermediate to manufacture pharmaceutical glucocorticoids. Recently, a Japan patent has realized 21-DF production via biotransformation of 17-hydroxyprogesterone (17-OHP) by purified steroid 11β-hydroxylase CYP11B1. Due to the less costs on enzyme isolation, purification and stabilization as well as cofactors supply, whole-cell should be preferentially employed as the biocatalyst over purified enzymes. No reports as so far have demonstrated a whole-cell system to produce 21-DF. Therefore, this study aimed to establish a whole-cell biocatalyst to achieve 21-DF transformation with high catalytic activity and product specificity.

Nanotechnology is employed across a wide range of antibacterial applications in clinical settings, food, pharmaceutical and textile industries, water treatment and consumer goods. Depending on type and concentration, engineered nanomaterials (ENMs) can also benefit bacteria in myriad contexts including within the human body, in biotechnology, environmental bioremediation, wastewater treatment, and agriculture. However, to realize the full potential of nanotechnology across broad applications, it is necessary to understand conditions and mechanisms of detrimental or beneficial effects of ENMs to bacteria. To study ENM effects, bacterial population growth or viability are commonly assessed. However, such endpoints alone may be insufficiently sensitive to fully probe ENM effects on bacterial physiology. To reveal more thoroughly how bacteria respond to ENMs, molecular-level omics methods such as transcriptomics, proteomics, and metabolomics are required. Because omics methods are increasingly utilized, a body of literature exists from which to synthesize state-of-the-art knowledge. Here we review relevant literature regarding ENM impacts on bacterial cellular pathways obtained by transcriptomic, proteomic, and metabolomic analyses across three growth and viability effect levels: inhibitory, sub-inhibitory or stimulatory. As indicated by our analysis, a wider range of pathways are affected in bacteria at sub-inhibitory vs. inhibitory ENM effect levels, underscoring the importance of ENM exposure concentration in elucidating ENM mechanisms of action and interpreting omics results. In addition, challenges and future research directions of applying omics approaches in studying bacterial-ENM interactions are discussed.

Influenza virus, which spreads around the world in seasonal epidemics and leads to large numbers of deaths every year, has several ribonucleoproteins in the central core of the viral particle. These viral ribonucleoproteins can specifically bind the conserved 3' and 5' caps of the viral RNAs with responsibility for replication and transcription of the viral RNA in the nucleus of infected cells. A fundamental question of most importance is that how the cap-binding proteins in the influenza virus discriminates between capped RNAs and non-capped ones. To get an answer, we performed molecular dynamics simulations and free energy calculations on the influenza A virus PB2 subunit, an important component of the RNP complexes, with a cap analog m7GTP. Our calculations showed that some key residues in the active site, such as Arg355, His357, Glu361 as well as Gln406, could offer significant hydrogen bonding and hydrophobic interactions with the guanine ring of the cap analog m7GTP to form an aromatic sandwich mechanism for the cap recognition and positioning in the active site. Subsequently, we applied this idea to a virtual screening procedure and identified 5 potential candidates that might be inhibitors against the PB2 subunit. Interestingly, 2 candidates Cpd1 and Cpd2 have been already reported to have inhibitory activities to the influenza virus cap-binding proteins. Further calculation also showed that they had comparatively higher binding affinities to the PB2 subunit than that of m7GTP. We believed that our findings could give an atomic insight into the deeper understanding of the cap recognition and binding mechanism, providing useful information for searching or designing novel drugs against influenza viruses.