Interplay of tRNA-Derived Fragments and T Cell Activation in Breast Cancer Patient Survival

Effector CD8+ T cell activation and its cytotoxic function are positively correlated with improved survival in breast cancer. tRNA-derived fragments (tRFs) have recently been found to be involved in gene regulation in cancer progression. However, it is unclear how interactions between expression of tRFs and T cell activation affect breast cancer patient survival. We used Kaplan–Meier survival and multivariate Cox regression models to evaluate the effect of interactions between expression of tRFs and T cell activation on survival in 1081 breast cancer patients. Spearman correlation analysis and weighted gene co-expression network analysis were conducted to identify genes and pathways that were associated with tRFs. tRFdb-5024a, 5P_tRNA-Leu-CAA-4-1, and ts-49 were positively associated with overall survival, while ts-34 and ts-58 were negatively associated with overall survival. Significant interactions were detected between T cell activation and ts-34 and ts-49. In the T cell exhaustion group, patients with a low level of ts-34 or a high level of ts-49 showed improved survival. In contrast, there was no significant difference in the activation group. Breast cancer related pathways were identified for the five tRFs. In conclusion, the identified five tRFs associated with overall survival may serve as therapeutic targets and improve immunotherapy in breast cancer

High-throughput functional analysis of autism genes in zebrafish identifies convergence in dopaminergic and neuroimmune pathways

Advancing from gene discovery in autism spectrum disorders (ASDs) to the identification of biologically relevant mechanisms remains a central challenge. Here, we perform parallel in vivo functional analysis of 10 ASD genes at the behavioral, structural, and circuit levels in zebrafish mutants, revealing both unique and overlapping effects of gene loss of function. Whole-brain mapping identifies the forebrain and cerebellum as the most significant contributors to brain size differences, while regions involved in sensory-motor control, particularly dopaminergic regions, are associated with altered baseline brain activity. Finally, we show a global increase in microglia resulting from ASD gene loss of function in select mutants, implicating neuroimmune dysfunction as a key pathway relevant to ASD biology

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19.

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100

Surface Settlement Damage Model of Pile-Anchor Supporting Structure in Deep Excavation

In damage mechanics, the deep excavation of soil is a process of damage development, and soil failure can be considered a process of continuously transforming undisturbed soil to damaged soil. Therefore, this study considered the occurrence of soil damage during the pit excavation, established a soil damage model, damage evolution equation, and soil damage constitutive relationship, and then deduced a calculate model of the pile displacement under the consideration of soil damage. Based on the principle of the stratum loss method, the surface settlement around a deep excavated pit was assumed as a skewed distribution curve, and the surface settlement of the pile-anchor supporting pit was solved. Based on this established method, finite element analysis software was used to calculate the surface subsidence for a field case, and the numerical results were compared with monitoring data in the field. The results revealed that, to a certain extent, soil damage affected the distribution of surface settlement in excavated pits. With the development of soil damage, the mechanical properties of soil gradually decreased, which led to increased surface settlement and changes in the direction of the excavation pit. Because soil damage is an important factor causing surface settlement, it is meaningful to consider soil damage when calculating the surface settlement in the deep excavation of pits

Characteristics of Rotary Sprinkler Water Distribution under Dynamic Water Pressure

In order to explore water distribution in sprinkler irrigation systems under dynamic water pressure, an irrigation test was conducted in a sprinkler irrigation system equipped with the Hunter MP2000 full circular rotator ray sprinkler under “trapezoidal” waveform pressure to explore the influencing factors such as basic water pressure, dynamic pressure period, and the sprinkler combination spacing. The result shows that the basic pressure, sprinkler combination spacing, and their interaction significantly affected the coefficient of variation, average intensity, and coefficient of uniformity. The normalized comprehensive evaluation indexes were selected as the measurement standards. The optimal factor combination was found to be the basic pressure of 250 kPa, the dynamic pressure period of 200 s, and the sprinkler combination spacing of 6 m, and the corresponding evaluation index values were 0.08, 10.54 mm/h, and 93.20%, respectively. The uniformity coefficient was increased by 6.54% compared with the constant pressure of the same flow rate. Compared with the constant pressure of the same flow rate, the sprinkler area and the average intensity increased by 39.67% and decreased by 8.62% when the basic pressure was 250 kPa and the dynamic pressure period was 200 s. The average uniformity coefficient increased by 11.76% at the combined spacing of 6.0, 6.5, 7.0, 7.5, and 8.0 m. The results provide a theoretical basis for sprinkler irrigation decisions under dynamic pressure

Improved glomerular filtration rate estimation by an artificial neural network.

BACKGROUND: Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance. METHODS: A total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation. RESULTS: In the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m(2) vs. a range from 71.3 to 101.7 ml/min/1.73 m(2), allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network. CONCLUSIONS: A new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse populations

Data_Sheet_3_Alteration of the gut microbiota profile in children with autism spectrum disorder in China.PDF

BackgroundAutism spectrum disorder (ASD) is associated with alterations in the gut microbiome. However, there are few studies on gut microbiota of children with ASD in China, and there is a lack of consensus on the changes of bacterial species.PurposeAutism spectrum disorder (ASD) is associated with alterations in the gut microbiome. However, there are few studies on gut microbiota of children with ASD in China, and there is a lack of consensus on the changes of bacterial species.MethodsWe used 16S rRNA sequencing to analyze ASD children (2 to 12 years), HC (2 to 12 years).ResultsOur findings showed that the α-diversity, composition, and relative abundance of gut microbiota in the ASD group were significantly different from those in the HC groups. Compared with the HC group, the α-diversity in the ASD group was significantly decreased. At the genus level, the relative abundance of g_Faecalibacterium, g_Blautia, g_Eubacterium_eligens_group, g_Parasutterella, g_Lachnospiraceae_NK4A136_group and g_Veillonella in ASD group was significantly increased than that in HC groups, while the relative abundance of g_Prevotella 9 and g_Agathobacter was significantly decreased than that in HC groups. In addition, KEGG pathway analysis showed that the microbial functional abnormalities in ASD patients were mainly concentrated in metabolic pathways related to fatty acid, amino acid metabolism and aromatic compound metabolism, and were partially involved in neurotransmitter metabolism.ConclusionThis study revealed the characteristics of gut microbiota of Chinese children with ASD and provided further evidence of gut microbial dysbiosis in ASD.</p