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.

Iron is an essential nutrient for the growth of most bacteria. To obtain iron, bacteria have developed specific iron-transport systems located on the membrane surface to uptake iron and iron complexes such as ferrichrome. Interference with the iron-acquisition systems should be therefore an efficient strategy to suppress bacterial growth and infection. Based on the chemical similarity of iron and ruthenium, we used a Ru(II) complex R-825 to compete with ferrichrome for the ferrichrome-transport pathway in Streptococcus pneumoniae. R-825 inhibited the bacterial growth of S. pneumoniae and stimulated the expression of PiuA, the iron-binding protein in the ferrichrome-uptake system on the cell surface. R-825 treatment decreased the cellular content of iron, accompanying with the increase of Ru(II) level in the bacterium. When the piuA gene (SPD_0915) was deleted in the bacterium, the mutant strain became resistant to R-825 treatment, with decreased content of Ru(II). Addition of ferrichrome can rescue the bacterial growth that was suppressed by R-825. Fluorescence spectral quenching showed that R-825 can bind with PiuA in a similar pattern to the ferrichrome-PiuA interaction in vitro. These observations demonstrated that Ru(II) complex R-825 can compete with ferrichrome for the ferrichrome-transport system to enter S. pneumoniae, reduce the cellular iron supply, and thus suppress the bacterial growth. This finding suggests a novel antimicrobial approach by interfering with iron-uptake pathways, which is different from the mechanisms used by current antibiotics.

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.

Accurate prediction of survival of cancer patients is still a key open problem in clinical research. Recently, many large-scale gene expression clusterings have identified sets of genes reportedly predictive of prognosis; however, those gene sets shared few genes in common and were poorly validated using independent data. We have developed a systems biology-based approach by using either combined gene sets and the protein interaction network (Method A) or the protein network alone (Method B) to identify common prognostic genes based on microarray gene expression data of glioblastoma multiforme and compared with differential gene expression clustering (Method C). Validations of prediction performance show that the 23-prognostic gene classifier identified by Method A outperforms other gene classifiers identified by Methods B and C or previously reported for gliomas on 17 of 20 independent sample cohorts across five tumor types. We also find that among the 23 genes are 21 related to cellular proliferation and two related to response to stress/immune response. We further find that the increased expression of the 21 genes and the decreased expression of the other two genes are associated with poorer survival, which is supportive with the notion that cellular proliferation and immune response contribute to a significant portion of predictive power of prognostic classifiers. Our results demonstrate that the systems biology-based approach enables to identify common survival-associated genes.

This study aimed to develop and validate the psychometric properties of Information Needs Questionnaire for Differentiated Thyroid Cancer (INQ-DTC) in DTC patients with radioactive iodine (RAI) therapy.

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.

Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) provides new opportunities to dissect epigenomic heterogeneity and elucidate transcriptional regulatory mechanisms. However, computational modeling of scATAC-seq data is challenging due to its high dimension, extreme sparsity, complex dependencies and high sensitivity to confounding factors from various sources.

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.

In this study, a phase method for analyzing functional near-infrared spectroscopy (fNIRS) signals was developed, which can extract the phase information of fNIRS data by using Hilbert transform. More importantly, the phase analysis method can be further performed to generate the brain phase activation and to construct the brain networks. Meanwhile, the study of translation between Chinese and English has been exciting and interesting from both the language and neuroscience standpoints due to their drastically different linguistic features. In particular, inspecting the brain phase activation and functional connectivity based on the phase data and phase analysis method will enable us to better understand the neural mechanism associated with Chinese/English translation. Our phase analysis results showed that the left prefrontal cortex, including the dorsolateral prefrontal cortex (DLPFC) and frontopolar area, was involved in the translation process of the language pair. In addition, we also discovered that the most significant brain phase activation difference between translating into non-native (English) vs. native (Chinese) language was identified in the Broca's area. As a result, the proposed phase analysis approach can provide us an additional tool to reveal the complex cognitive mechanism associated with Chinese/English sight translation.

Copper, a strictly regulated trace element, is essential for many physiological processes including angiogenesis. Dysregulated angiogenesis has been associated with increased copper in tumors, and thus copper chelators have been used to inhibit tumor angiogenesis. However, it remains unclear whether copper has any effect on epithelial-mesenchymal transition (EMT). Using CoCl2-induced EMT of human breast carcinoma MCF-7 cells, we found that TEPA, a copper chelator, inhibited EMT-like cell morphology and cytoskeleton arrangement triggered by CoCl2; decreased the expression of vimentin and fibronectin, markers typical of EMT; inhibited HIF-1 activation and HIF1-α accumulation in nuclear; and down-regulated the expression of hypoxia-associated transcription factors, Snail and Twist1. Moreover, knockdown copper transport protein, Ctr1, also inhibited CoCl2-induced EMT and reversed the mesenchymal phenotype. In EMT6 xenograft mouse models, TEPA administration inhibited the tumor growth and increased mice survival. Immunohistochemical analysis of the xenograft further demonstrated that TEPA administration significantly inhibited tumor angiogenesis, down-regulated hypoxia-induced transcription factors, Snail and Twist1, leading to decreased transactivation of EMT-associated marker genes, vimentin and fibronectin. These results indicate that TEPA inhibits CoCl2-induced EMT most likely via HIF1-α-Snail/Twist signaling pathway, and copper depletion may be exploited as a therapeutic for breast cancer.

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.

Multimodal single-cell sequencing technologies provide unprecedented information on cellular heterogeneity from multiple layers of genomic readouts. However, joint analysis of two modalities without properly handling the noise often leads to overfitting of one modality by the other and worse clustering results than vanilla single-modality analysis. How to efficiently utilize the extra information from single cell multi-omics to delineate cell states and identify meaningful signal remains as a significant computational challenge. In this work, we propose a deep learning framework, named SAILERX, for efficient, robust, and flexible analysis of multi-modal single-cell data. SAILERX consists of a variational autoencoder with invariant representation learning to correct technical noises from sequencing process, and a multimodal data alignment mechanism to integrate information from different modalities. Instead of performing hard alignment by projecting both modalities to a shared latent space, SAILERX encourages the local structures of two modalities measured by pairwise similarities to be similar. This strategy is more robust against overfitting of noises, which facilitates various downstream analysis such as clustering, imputation, and marker gene detection. Furthermore, the invariant representation learning part enables SAILERX to perform integrative analysis on both multi- and single-modal datasets, making it an applicable and scalable tool for more general scenarios.

To extract vertex-wise features of the hippocampus and amygdala in Parkinson's disease (PD) with mild cognitive impairment (MCI) and normal cognition (NC) and further evaluate their discriminatory efficacy.

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.