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(1) Background: Urinary tract infection (UTI) is a leading cause of emergency department visits and hospital admissions. Despite many studies identifying UTI-related risk factors for bacteremia or sepsis, a significant gap remains in developing predictive models for in-hospital mortality or the necessity for emergent intensive care unit admission in the emergency department. This study aimed to construct interpretable machine learning models capable of identifying patients at high risk for critical outcomes. (2) Methods: This was a retrospective study of adult patients with urinary tract infection (UTI), extracted from the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database. The critical outcome is defined as either in-hospital mortality or transfer to an intensive care unit within 12 h. ED visits were randomly partitioned into a 70%/30% split for training and validation. The extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) algorithms were constructed using variables selected from the stepwise logistic regression model. The XGBoost model was then compared to the traditional model and clinical decision rules (CDRs) on the validation data using the area under the curve (AUC). (3) Results: There were 3622 visits among 3235 unique patients diagnosed with UTI. Of the 2535 patients in the training group, 836 (33%) experienced critical outcomes, and of the 1087 patients in the validation group, 358 (32.9%) did. The AUCs for different machine learning models were as follows: XGBoost, 0.833; RF, 0.814; and SVM, 0.799. The XGBoost model performed better than others. (4) Conclusions: Machine learning models outperformed existing traditional CDRs for predicting critical outcomes of ED patients with UTI. Future research should prospectively evaluate the effectiveness of this approach and integrate it into clinical practice.

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BACKGROUND: Obesity may serve as a protective factor in blunt abdominal trauma (BAT) patients due to a "cushion effect". In this study, we aim to use computed tomography (CT) scans to measure abdominal adiposity and its correlation with injury severity in BAT patients. METHODS: We conducted a retrospective analysis of male BAT patients who had undergone CT scans. Subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) were manually measured and height-normalized for analysis at lumbar levels L2 and L3. Statistical methods were used to compare differences in adiposity between patients with and without severe abdominal injuries. For controls, we also compared adipose tissue in patients with and without severe trauma to the chest, where less fat typically accumulates. RESULTS: We included 361 male participants and conducted a comparative analysis of their demographic and injury characteristics. Patients without severe abdominal injuries had significantly higher SAT and VAT indices at both L2 and L3 (p < 0.05). However, these measures showed no significant differences between patients with and without severe chest trauma. Solid organ injuries, particularly liver injuries, were associated with decreased SAT and VAT. CONCLUSION: Increase abdominal adiposity was linked to lower abdominal injury severity and solid organ injuries, particularly liver injuries. In addition to conventional BMI for evaluating obesity, either subcutaneous or visceral adipose tissue over lumbar levels L2 and L3 can be used to assess the "cushion effect."

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BACKGROUND: Rotational angiography acquires radiographs at multiple projection angles to demonstrate superimposed vasculature. However, this comes at the expense of the inherent risk of increased ionizing radiation. In this paper, building upon a successful deep learning model, we developed a novel technique to super-resolve the radiography at different projection angles to reduce the actual projections needed for a diagnosable radiographic procedure. METHODS: Ten models were trained for different levels of angular super-resolution (ASR), denoted as ASRN, where for every N+2 frames, the first and the last frames were submitted as inputs to super-resolve the intermediate N frames. RESULTS: The results show that large arterial structures were well preserved in all ASR levels. Small arteries were adequately visualized in lower ASR levels but progressively blurred out in higher ASR levels. Noninferiority of image quality was demonstrated in ASR1-4 (99.75% confidence intervals: -0.16-0.03, -0.19-0.04, -0.17-0.01, -0.15-0.05 respectively). CONCLUSIONS: ASR technique is capable of super-resolving rotational angiographic frames at intermediate projection angles.

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Aeromonas hydrophila is the most common opportunistic pathogen that plagues freshwater and euryhaline fishponds. Here, we present the complete genome sequence of A. hydrophila strain LP0103, which was isolated from a bacterial septicemia outbreak among suckermouth catfish (Pterygoplichthys pardalis) at Lotus Pond in Kaohsiung City, Taiwan.

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The development of organic-inorganic hybrid materials with long-lived room temperature phosphorescence (RTP) has attracted tremendous attention owing to their promising applications in the optoelectronic and anti-counterfeiting fields. In this work, by the selection of lead halide and electron-poor heteroaromatic molecule 1,10-phenanthroline (phen), a coordination polymer [Pb(phen)Cl2] has been synthesized under hydrothermal conditions. This complex shows an alternating arrangement of a long-range order of phen π-conjugated systems and lead halide inorganic chains as revealed by X-ray single-crystal structural analysis. This structural character and special chemical components endow this hybrid material with a rare example of red room temperature phosphorescence. Its electronic structure and electronic transition behavior were further examined by theoretical calculations. Meanwhile, the film of the complex features remarkable angle-dependent polarized emission and photoelectric performance.

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The development of luminescent metal-organic frameworks (MOFs) has attracted extensive attention due to their applications in photoelectric devices, organic light-emitting diodes (OLEDs), anti-counterfeiting, biological imaging and so on. In this work, a novel anthracene based metal-organic framework, [Cd(DCPA)(DMF)]·(H2O) (1) (H2DCPA = 9,10-di(p-carboxyphenyl)anthracene), has been successfully synthesized under solvothermal conditions. The highly ordered arrangement and special spatial conformation of the anthracene chromophore play a significant role in the photophysical properties of 1. The combination of theoretical calculations and experiments shows that the molecular orbitals have good separation for inhibiting the recombination of electrons and holes. Furthermore, the fluorescence emission of 1 can be instantaneously and reversibly tuned between blue and green at different polarizing angles. Temperature-dependent fluorescence measurements indicate a good linear relationship between the maximum emission intensity/wavelength and the temperature for efficient thermochromism and luminescence thermometry. Photoelectric measurements reveal that 1 shows high performance of photocurrent generation under light illumination. Therefore, our research affords a new perspective to extend the application of luminescent MOFs in the fields of polarized emission, thermometry and photoelectronic response.

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Network alignment provides a comprehensive way to discover the similar parts between molecular systems of different species based on topological and biological similarity. With such a strong basis, one can do comparative studies at a systems level in the field of computational biology. In this survey paper, we focus on protein-protein interaction networks and review some representative algorithms for network alignment in the past two decades as well as the state-of-the-art aligners. We also introduce the most popular evaluation measures in the literature to benchmark the performance of these approaches. Finally, we address several future challenges and the possible ways to conquer the existing problems of biological network alignment.

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MOTIVATION: Protein complexes are one of the keys to studying the behavior of a cell system. Many biological functions are carried out by protein complexes. During the past decade, the main strategy used to identify protein complexes from high-throughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-protein interaction (PPI) network. Although experimental PPI data have increased significantly over recent years, most PPI networks still have many false positive interactions and false negative edge loss due to the limitations of high-throughput experiments. In particular, the false negative errors restrict the search space of such conventional protein complex identification approaches. Thus, it has become one of the most challenging tasks in systems biology to automatically identify protein complexes. RESULTS: In this study, we propose a new algorithm, NEOComplex ( NE CC- and O rtholog-based Complex identification by multiple network alignment), which integrates functional orthology information that can be obtained from different types of multiple network alignment (MNA) approaches to expand the search space of protein complex detection. As part of our approach, we also define a new edge clustering coefficient (NECC) to assign weights to interaction edges in PPI networks so that protein complexes can be identified more accurately. The NECC is based on the intuition that there is functional information captured in the common neighbors of the common neighbors as well. Our results show that our algorithm outperforms well-known protein complex identification tools in a balance between precision and recall on three eukaryotic species: human, yeast, and fly. As a result of MNAs of the species, the proposed approach can tolerate edge loss in PPI networks and even discover sparse protein complexes which have traditionally been a challenge to predict. AVAILABILITY AND IMPLEMENTATION: http://acolab.ie.nthu.edu.tw/bionetwork/NEOComplex. CONTACT: bab@csail.mit.edu or csliao@ie.nthu.edu.tw. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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MOTIVATION: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species' evolution. RESULTS: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. AVAILABILITY: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. CONTACT: bab@csail.mit.edu or csliao@ie.nthu.edu.tw. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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BACKGROUND: In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information. RESULTS: We explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis. CONCLUSIONS: We demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.

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Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project.

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