High Activity Mutants of Butyrylcholinesterase for Cocaine Hydrolysis

Butyrylcholinesterase (BChE) polypeptide variants of the presently-disclosed subject matter have enhanced catalytic efficiency for (−)-cocaine, as compared to wild-type BChE. Pharmaceutical compositions of the presently-disclosed subject matter include a BChE polypeptide variant having an enhanced catalytic efficiency for (−)-cocaine. A method of the presently-disclosed subject matter for treating a cocaine-induced condition includes administering to an individual an effective amount of a BChE polypeptide variant, as disclosed herein, to lower blood cocaine concentration

Research on the Interaction between Tubeimoside 1 and HepG2 Cells Using the Microscopic Imaging and Fluorescent Spectra Method

The treatment of cancer draws interest from researchers worldwide. Of the different extracts from traditional Chinese medicines, Tubeimoside 1 (TBMS 1) is regarded as an effective treatment for cancer. To determine the mechanism of TBMS 1, the shape/pattern of HepG2 cells based on the microscopic imaging technology was determined to analyze experimental results; then the fluorescent spectra method was designed to investigate whether TBMS 1 affected HepG2 cells. A three-dimensional (3D) fluorescent spectra sweep was performed to determine the characteristic wave peak of HepG2 cells. A 2D fluorescent spectra method was then used to show the florescence change in HepG2 cells following treatment with TBMS 1. Finally, flow cytometry was employed to analyze the cell cycle of HepG2 cells. It was shown that TBMS 1 accelerated the death of HepG2 cells and had a strong dose- and time-dependent growth inhibitory effect on HepG2 cells, especially at the G2/M phase. These results indicate that the fluorescent spectra method is a promising substitute for flow cytometry as it is rapid and cost-effective in HepG2 cells

MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems

Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost, multi-accelerator systems widely exist in data centers, cloud platforms, and SoCs. Thus, a challenging problem arises in multi-accelerator systems: selecting a proper combination of accelerators from available designs and searching for efficient DNN mapping strategies. To this end, we propose MARS, a novel mapping framework that can perform computation-aware accelerator selection, and apply communication-aware sharding strategies to maximize parallelism. Experimental results show that MARS can achieve 32.2% latency reduction on average for typical DNN workloads compared to the baseline, and 59.4% latency reduction on heterogeneous models compared to the corresponding state-of-the-art method.Comment: Accepted by 60th DA

Integration analysis of senescence-related genes to predict prognosis and immunotherapy response in soft-tissue sarcoma: evidence based on machine learning and experiments

Background: Soft tissue sarcoma (STS) is the malignancy that exhibits remarkable histologic diversity. The diagnosis and treatment of STS is currently challenging, resulting in a high lethality. Chronic inflammation has also been identified as a key characteristic of tumors, including sarcomas. Although senescence plays an important role in the progression of various tumors, its molecular profile remains unclear in STS.Methods: We identified the senescence-related genes (SRGs) in database and depicted characteristics of genomic and transcriptomic profiling using cohort within TCGA and GEO database. In order to investigate the expression of SRGs in different cellular subtypes, single-cell RNA sequencing data was applied. The qPCR and our own sequencing data were utilized for further validation. We used unsupervised consensus clustering analysis to establish senescence-related clusters and subtypes. A senescence scoring system was established by using principal component analysis (PCA). The evaluation of clinical and molecular characteristics was conducted among distinct groups.Results: These SRGs showed differences in SCNV, mutation and mRNA expression in STS tissues compared to normal tissues. Across several cancer types, certain shared features of SRGs were identified. Several SRGs closely correlated with immune cell infiltration. Four clusters related to senescence and three subtypes related to senescence, each with unique clinical and biological traits, were established. The senescence scoring system exhibited effectiveness in predicting outcomes, clinical traits, infiltrations of immune cells and immunotherapy responses.Conclusion: Overall, the current study provided a comprehensive review of molecular profiling for SRGs in STS. The SRGs based clustering and scoring model could help guiding the clinical management of STS

Deciphering the role of NETosis-related signatures in the prognosis and immunotherapy of soft-tissue sarcoma using machine learning

Background: Soft-tissue sarcomas (STSs) are a rare type of cancer, accounting for about 1% of all adult cancers. Treatments for STSs can be difficult to implement because of their diverse histological and molecular features, which lead to variations in tumor behavior and response to therapy. Despite the growing importance of NETosis in cancer diagnosis and treatment, researches on its role in STSs remain limited compared to other cancer types.Methods: The study thoroughly investigated NETosis-related genes (NRGs) in STSs using large cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine Recursive Feature Elimination (SVM-RFE) were employed for screening NRGs. Utilizing single-cell RNA-seq (scRNA-seq) dataset, we elucidated the expression profiles of NRGs within distinct cellular subpopulations. Several NRGs were validated by quantitative PCR (qPCR) and our proprietary sequencing data. To ascertain the impact of NRGs on the sarcoma phenotype, we conducted a series of in vitro experimental investigations. Employing unsupervised consensus clustering analysis, we established the NETosis clusters and respective NETosis subtypes. By analyzing DEGs between NETosis clusters, an NETosis scoring system was developed.Results: By comparing the outcomes obtained from LASSO regression analysis and SVM-RFE, 17 common NRGs were identified. The expression levels of the majority of NRGs exhibited notable dissimilarities between STS and normal tissues. The correlation with immune cell infiltration were demonstrated by the network comprising 17 NRGs. Patients within various NETosis clusters and subtypes exhibited different clinical and biological features. The prognostic and immune cell infiltration predictive capabilities of the scoring system were deemed efficient. Furthermore, the scoring system demonstrated potential for predicting immunotherapy response.Conclusion: The current study presents a systematic analysis of NETosis-related gene patterns in STS. The results of our study highlight the critical role NRGs play in tumor biology and the potential for personalized therapeutic approaches through the application of the NETosis score model in STS patients

DESIGN AND EXPERIMENT OF A COMBINED TYPE SPATIALLY LAYERED PROPORTIONAL FERTILIZATION DEVICE

ABSTRACT To solve the problems of low efficiency, poor fertilizer application, and low fertilizer utilization rate of existing wheat layered fertilization devices (LFD). This paper proposed a layered fertilization mode combining shallow fertilizer application based on rotary tillage and middle/deep fertilizer application of layered fertilizer shovel (LFS), and designed a combined spatial layered fertilization device with a fixed proportion. A discrete element simulation model consisting of soil, LFS and fertilizer was established. Taking the variation coefficients of fertilization amount as the evaluation indicator and changing the sidewall deflection angle, rear inclination angle, upper channel length of LFS, a three-factor three-level quadratic rotational orthogonal experiment was carried out. The optimal parameter combination was obtained: the deflection angle was 9.5°, the inclination angle was 57°, and the channel length was 280 mm. The corresponding variation coefficients of fertilizer application depth were 4.55%, 8.44%, and 6.93% while working stably under the optimal combination, which is consistent with the simulation results. The results showed that LFD can apply fertilizer underground in three layers: 0~10, 15, and 20 cm, with the proportion of shallow, middle, and deep fertilizers being 30%, 35%, and 35%, which can meet the agronomic requirements for winter wheat growth

Comparison of Diagnostic Performance of Three-Dimensional Positron Emission Mammography versus Whole Body Positron Emission Tomography in Breast Cancer

Objective. To compare the diagnostic performance of three-dimensional (3D) positron emission mammography (PEM) versus whole body positron emission tomography (WBPET) for breast cancer. Methods. A total of 410 women with normal breast or benign or highly suspicious malignant tumors were randomized at 1 : 1 ratio to undergo 3D-PEM followed by WBPET or WBPET followed by 3D-PEM. Lumpectomy or mastectomy was performed on eligible participants after the scanning. Results. The sensitivity and specificity of 3D-PEM were 92.8% and 54.5%, respectively. WBPET showed a sensitivity of 95.7% and specificity of 56.8%. After exclusion of the patients with lesions beyond the detecting range of the 3D-PEM instrument, 3D-PEM showed higher sensitivity than WBPET (97.0% versus 95.5%, P = 0.913), particularly for small lesions (<1 cm) (72.0% versus 60.0%, P = 0.685). Conclusions. The 3D-PEM appears more sensitive to small lesions than WBPET but may fail to detect lesions that are beyond the detecting range. This study was approved by the Ethics Committee (E2012052) at the Tianjin Medical University Cancer Institute and Hospital (Tianjin, China). The instrument positron emission mammography (PEMi) was approved by China State Food and Drug Administration under the registration number 20153331166