Genomic Landscape of TCR Alpha-Beta and TCR Gamma-Delta T-Large Granular Lymphocyte Leukemia

Large granular lymphocyte (LGL) leukemia comprises a group of rare lymphoproliferative disorders whose molecular landscape is incompletely defined. We leveraged paired whole exome and transcriptome sequencing in the largest LGL leukemia cohort to date, which included 105 patients (93 TCRab T-LGL and 12 TCRγδ T-LGL). 76 mutations were observed in three or more patients in the cohort, and out of those, STAT3, KMT2D, PIK3R1, TTN, EYS, and SULF1 mutations were shared between both subtypes. We identified ARHGAP25, ABCC9, PCDHA11, SULF1, SLC6A15, DDX59, DNMT3A, FAS, KDM6A, KMT2D, PIK3R1, STAT3, STAT5B, TET2, and TNFAIP3 as recurrently mutated putative drivers using an unbiased driver analysis approach leveraging our whole exome cohort. Hotspot mutations in STAT3, PIK3R1, and FAS were detected, whereas truncating mutations in epigenetic modifying enzymes such as KMT2D and TET2 were observed. Moreover, STAT3 mutations co-occurred with mutations in chromatin and epigenetic modifying genes, especially KMT2D and SETD1B (p < 0.01, p < 0.05 respectively). STAT3 was mutated in 50.5% of the patients. Most common Y640F STAT3 mutation was associated with lower ANC values, and N647I mutation was associated with lower hemoglobin values. Somatic activating mutations (Q160P, D170Y, L287F) in the STAT3 coiled-coil domain were characterized. STAT3 mutant patients exhibited increased mutational burden and enrichment of a mutational signature associated with increased spontaneous deamination of 5-methylcytosine. Finally, gene expression analysis revealed enrichment of interferon gamma signaling and decreased PI3K-Akt signaling for STAT3 mutant patients. These findings highlight the clinical and molecular heterogeneity of this rare disorder.

Presumed Urinary Tract Infection in Patients Admitted with COVID-19: Are We Treating Too Much?

Despite the low rates of bacterial co-/superinfections in COVID-19 patients, antimicrobial drug use has been liberal since the start of the COVID-19 pandemic. Due to the low specificity of markers of bacterial co-/superinfection in the COVID-19 setting, overdiagnosis and antimicrobial overprescription have become widespread. A quantitative and qualitative evaluation of urinary tract infection (UTI) diagnoses and antimicrobial drug prescriptions for UTI diagnoses was performed in patients admitted to the COVID-19 ward of a university hospital between 17 March and 2 November 2020. A team of infectious disease specialists performed an appropriateness evaluation for every diagnosis of UTI and every antimicrobial drug prescription covering a UTI. A driver analysis was performed to identify factors increasing the odds of UTI (over)diagnosis. A total of 622 patients were included. UTI was present in 13% of included admissions, and in 12%, antimicrobials were initiated for a UTI diagnosis (0.71 daily defined doses (DDDs)/admission; 22% were scored as ‘appropriate’). An evaluation of UTI diagnoses by ID specialists revealed that of the 79 UTI diagnoses, 61% were classified as probable overdiagnosis related to the COVID-19 hospitalization. The following factors were associated with UTI overdiagnosis: physicians who are unfamiliar working in an internal medicine ward, urinary incontinence, mechanical ventilation and female sex. Antimicrobial stewardship teams should focus on diagnostic stewardship of UTIs, as UTI overdiagnosis seems to be highly prevalent in admitted COVID-19 patients.

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MODAL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works.

Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis

Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.

Contrasting drivers and trends of ocean acidification in the subarctic Atlantic

The processes of warming, anthropogenic CO2 (Canth) accumulation, decreasing pHT (increasing [H+]T; concentration in total scale) and calcium carbonate saturation in the subarctic zone of the North Atlantic are unequivocal in the time-series measurements of the Iceland (IS-TS, 1985–2003) and Irminger Sea (IRM-TS, 1983–2013) stations. Both stations show high rates of Canth accumulation with different rates of warming, salinification and stratification linked to regional circulation and dynamics. At the IS-TS, advected and stratified waters of Arctic origin drive a strong increase in [H+]T, in the surface layer, which is nearly halved in the deep layer (44.7 ± 3.6 and 25.5 ± 1.0 pmol kg−1 yr−1, respectively). In contrast, the weak stratification at the IRM-TS allows warming, salinification and Canth uptake to reach the deep layer. The acidification trends are even stronger in the deep layer than in the surface layer (44.2 ± 1.0 pmol kg−1 yr−1 and 32.6 ± 3.4 pmol kg−1 yr−1 of [H+]T, respectively). The driver analysis detects that warming contributes up to 50% to the increase in [H+]T at the IRM-TS but has a small positive effect on calcium carbonate saturation. The Canth increase is the main driver of the observed acidification, but it is partially dampened by the northward advection of water with a relatively low natural CO2 content.

Theoretical interpretation of drivers’ gaze strategy influenced by optical flow

Driver analysis, particularly revealing where drivers gaze, is a key factor in understanding drivers’ perception. Several studies have examined drivers’ gaze behavior and the two main hypotheses that have been developed are Tangent Point (TP) and Future Path Point (FP). TP is a point on the inner side of the lane, where the driver’s gaze direction becomes tangential with the lane edge. FP is an arbitrary single point on the ideal future path for an individual driver on the road. The location of this single point is dependent on the individual driver. While these gaze points have been verified and discussed by various psychological experiments, it is unclear why drivers gaze at these points. Therefore, in this study, we used optical flow theory to understand drivers’ gaze strategy. Optical flow theory is a method to quantify the extent to which drivers can perceive the future path of the vehicle. The results of numerical simulations demonstrated that optical flow theory can potentially estimate drivers’ gaze behavior. We also conducted an experiment in which the observed driver gaze behavior was compared to calculated gaze strategy based on optical flow theory. The experimental results demonstrate that drivers’ gaze can be estimated with an accuracy of 70.8% and 65.1% on circular and straight paths, respectively. Thus, these results suggest that optical flow theory can be a determining factor in drivers’ gaze strategy.