Characteristics of Klebsiella pneumoniae harboring QnrB32, Aac(6’)-Ib-cr, GyrA and CTX-M-22 genes

Quinolone resistance in members of the Enterobacteriaceae family is mostly due to mutations in the quinolone resistance-determining regions of topoisomerase genes. CTX-M-22 is a member of the CTX-M family which can reduce extended-spectrum β-lactamase (ESBL) production and modulate antibiotic resistance, resulting in low ceftazidime minimum inhibitory concentrations (MICs). There are four different genes in Klebsiella pneumoniae (KP4707) including qnrB32 (novel qnr allele gene, HQ704413), aac(6’)-Ib-cr (novel aac(6’)-Ib allele gene, HQ680690), gyrA (novel gyrA allele gene, HQ680691) and CTX-M-22 gene. Five point amino acid mutations Arn(N)27 → Leu(L), Val(V)129 → Ala(A), Iie(I)142 → Met(M), Gly(G)188 → Arg(R), Val(V)212 → Iie(I) were observed in the qnr32 gene when compared to qnrB1. Of all qnrB alleles, a novel variant of the qnrB32 gene, with qnrB31, had the highest amino acid homology. Three point amino acid mutations including Trp(W)105 → Arg(R), Asp(D)182 → Tyr(Y) and Val(V)201 → Asp(D) were observed in aac(6’)-Ib-cr gene, when compared to GenBank number AF479774. New variants of qnr32, aac(6’)-Ib-cr, gyrA and CTX-M-22 or other genotype determinants continuously appear in different genomic sites and also outside the Enterobacteriaceae family

eX-ViT: A Novel eXplainable Vision Transformer for Weakly Supervised Semantic Segmentation

Recently vision transformer models have become prominent models for a range of vision tasks. These models, however, are usually opaque with weak feature interpretability. Moreover, there is no method currently built for an intrinsically interpretable transformer, which is able to explain its reasoning process and provide a faithful explanation. To close these crucial gaps, we propose a novel vision transformer dubbed the eXplainable Vision Transformer (eX-ViT), an intrinsically interpretable transformer model that is able to jointly discover robust interpretable features and perform the prediction. Specifically, eX-ViT is composed of the Explainable Multi-Head Attention (E-MHA) module, the Attribute-guided Explainer (AttE) module and the self-supervised attribute-guided loss. The E-MHA tailors explainable attention weights that are able to learn semantically interpretable representations from local patches in terms of model decisions with noise robustness. Meanwhile, AttE is proposed to encode discriminative attribute features for the target object through diverse attribute discovery, which constitutes faithful evidence for the model's predictions. In addition, a self-supervised attribute-guided loss is developed for our eX-ViT, which aims at learning enhanced representations through the attribute discriminability mechanism and attribute diversity mechanism, to localize diverse and discriminative attributes and generate more robust explanations. As a result, we can uncover faithful and robust interpretations with diverse attributes through the proposed eX-ViT

Synthesis and electrochemical performance of hierarchical Sb2S3 nanorod-bundles for lithium-ion batteries

Uniform hierarchical Sb2S3 nanorod-bundles were synthesised successfully by L-cysteine hydrochloride-assisted solvothermal treatment, and were then characterised by X-ray diffraction, field emission scanning electron microscopy, and high-resolution transmission electron microscopy, respectively. The electrochemical performance of the synthesised Sb2S3 nanorod-bundles was investigated by cyclic voltammetry and galvanostatic charge−discharge technique, respectively. This material was found to exhibit a high initial charge specific capacity of 803 mA h g-1 at a rate of 100 mA g-1, a good cyclability of 614 mA h g-1 at a rate of 100 mA g-1 after 30 cycles, and a good rate capability of 400 mA h g-1 at a rate of 500 mA g-1 when evaluated as an electrode candidate material for lithium-ion batteries

Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values

The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial process control. However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values. In this work, we introduce a novel framework called GST-Pro, which utilizes a graph spatiotemporal process and anomaly scorer to tackle the aforementioned challenges in detecting anomalies on irregularly-sampled multivariate time series. Our approach comprises two main components. First, we propose a graph spatiotemporal process based on neural controlled differential equations. This process enables effective modeling of multivariate time series from both spatial and temporal perspectives, even when the data contains missing values. Second, we present a novel distribution-based anomaly scoring mechanism that alleviates the reliance on complete uniform observations. By analyzing the predictions of the graph spatiotemporal process, our approach allows anomalies to be easily detected. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods, regardless of whether there are missing values present in the data. Our code is available: https://github.com/huankoh/GST-Pro.Comment: Accepted by Information Fusio

Aptamer nucleotide analog drug conjugates in the targeting therapy of cancers

Aptamers are short single-strand oligonucleotides that can form secondary and tertiary structures, fitting targets with high affinity and specificity. They are so-called “chemical antibodies” and can target specific biomarkers in both diagnostic and therapeutic applications. Systematic evolution of ligands by exponential enrichment (SELEX) is usually used for the enrichment and selection of aptamers, and the targets could be metal ions, small molecules, nucleotides, proteins, cells, or even tissues or organs. Due to the high specificity and distinctive binding affinity of aptamers, aptamer–drug conjugates (ApDCs) have demonstrated their potential role in drug delivery for cancer-targeting therapies. Compared with antibodies which are produced by a cell-based bioreactor, aptamers are chemically synthesized molecules that can be easily conjugated to drugs and modified; however, the conventional ApDCs conjugate the aptamer with an active drug using a linker which may add more concerns to the stability of the ApDC, the drug-releasing efficiency, and the drug-loading capacity. The function of aptamer in conventional ApDC is just as a targeting moiety which could not fully perform the advantages of aptamers. To address these drawbacks, scientists have started using active nucleotide analogs as the cargoes of ApDCs, such as clofarabine, ara-guanosine, gemcitabine, and floxuridine, to replace all or part of the natural nucleotides in aptamer sequences. In turn, these new types of ApDCs, aptamer nucleotide analog drug conjugates, show the strength for targeting efficacy but avoid the complex drug linker designation and improve the synthetic efficiency. More importantly, these classic nucleotide analog drugs have been used for many years, and aptamer nucleotide analog drug conjugates would not increase any unknown druggability risk but improve the target tumor accumulation. In this review, we mainly summarized aptamer-conjugated nucleotide analog drugs in cancer-targeting therapies

Comment on "Carnot efficiency at divergent power output" (and additional discussion)

In a recent Letter [EPL, 118 (2017) 40003], Polettini and Esposito claimed that it is theoretically possible for a thermodynamic machine to achieve Carnot efficiency at divergent power output through the use of infinitely-fast processes. It appears however that this assertion is misleading as it is not supported by their derivations as demonstrated below. In this Comment, we first show that there is a confusion regarding the notion of optimal efficiency. We then analyze the quantum dot engine described in Ref. [EPL, 118 (2017) 40003] and demonstrate that Carnot efficiency is recovered only for vanishing output power. Moreover, a discussion on the use of infinite thermodynamical forces to reach Carnot efficiency is also presented in the appendix.Comment: Modified version compared to the manuscript submitted to EP

Matriks Jordan Dan Aplikasinya Pada Sistem Linier Waktu Diskrit

Matrix is diagonalizable (similar with matrix diagonal) if and only if the sum of geometric multiplicities of its eigenvalues is n.If we search for an upper triangular form that is nearly diagonal as possible but is still attainable by similarity for every matrix, especially the sum of geometric multiplicities of its eigenvalues is less than n, the result is the Jordan canonical form, which is denoted by , and . In this paper, will be described how to get matrix S(in order to get matrix ) by using generalized eigenvector. In addition, it will also describe the Jordan canonical form and its properties, and some observation and application on discrete time linear system

Monocyte at diagnosis as a prognosis biomarker in tuberculosis patients with anemia

BackgroundAnemia leads to a lower cure rate and poor prognosis in tuberculosis patients. Effective predictors for the prognosis of tuberculosis with anemia (A-TB) are urgently needed. Monocyte has been proven to be a prognostic biomarker of many lung diseases. Whether monocyte that the predominant innate immune cell as early defense against tuberculosis can predict A-TB is not known.MethodsData for A-TB patients with initial treatment in Shanghai Pulmonary Hospital were retrospectively collected and analyzed. Logistics regression analysis was used to study the correlation between peripheral blood cells and treatment outcomes. The receiver operating characteristic (ROC) curve was used to determine the cut-off value. We estimated a 12-month prognosis using Kaplan–Meier techniques. The Cox proportional hazards model was used for the univariate and multivariate analyses to analyze the predictors of poor prognosis of A-TB.ResultsOf 181 patients analyzed, 94 were cured and 87 non-cured. Logistic regression analysis identified monocyte as an independent immune-related risk factor for the prognosis of A-TB (OR: 7.881, 95% CI: 1.675–37.075, P = 0.009). The ROC curve analysis proved that the most discriminative cut-off value of monocyte was 0.535 × 10^9/L. K–M analysis demonstrated that the cumulative cure rates of A-TB were significantly higher in A-TB with monocyte < 0.535 × 10^9/L (69.62%) than that in those with monocyte ≥ 0.535 × 10^9/L (38.24%) (Log-rank, χ2 = 16.530, P < 0.0001). On univariate and multivariable analysis, monocyte was an independent predictor of poor prognosis in A-TB. Similarly, monocyte was also an independent predictor of poor pulmonary cavity closure in A-TB (HR: 3.614, 95% CI: 1.335–9.787, P = 0.011).ConclusionIn A-TB patients, elevated monocyte was associated with poor prognosis and poor cavity pulmonary closure. Monocyte may provide a simple and inexpensive prognostic biomarker in A-TB

Assessing Reproducibility of Inherited Variants Detected With Short-Read Whole Genome Sequencing

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when \u3e 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. Conclusions: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30x. Conclusions: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.Peer reviewe