An Empirical Bayesian Method for Detecting Differentially Expressed Genes Using EST Data

Detection of differentially expressed genes from expressed sequence tags (ESTs) data has received much attention. An empirical Bayesian method is introduced in which gene expression patterns are estimated and used to define detection statistics. Significantly differentially expressed genes can be declared given detection statistics. Simulation is done to evaluate the performance of proposed method. Two real applications are studied

Experimental Study of Smoke Spread in Titled Urban Traffic Tunnels Fires

AbstractMore and more urban traffic tunnels are built due to the heavy traffic in dense urban areas. Fire safety in tunnels is a concern due to the large traffic flow. Most of the urban tunnels have slope. Knowledge on smoke spread in titled tunnel is still limited. Experimental studies on the smoke spread in a titled tunnel are carried out by the reduced-scale tunnel model tests and will be discussed in this paper. Effects of the titled gradients and longitudinal ventilation speed on the temperature distribution and smoke stratification downstream from the fire along the tunnel will be studied The results show that the smoke spread upwards much faster with the slop gradient increasing, and a thick smoke layer was formed when smoke moved to the upward part of the tunnel and filled it up. The ventilation speed had a great influence on temperature distribution along the tunnel, and the smoke stratification will be distributed when the longitudinal ventilation speed becomes large. Lower ventilation speed should be adopted at the beginning to ensure the smoke downstream of the fire keep stratified to give the tunnel users more time to escape

Accuracy of radiomics in the diagnosis and preoperative high-risk assessment of endometrial cancer: a systematic review and meta-analysis

BackgroundWith the increasing use of radiomics in cancer diagnosis and treatment, it has been applied by some researchers to the preoperative risk assessment of endometrial cancer (EC) patients. However, comprehensive and systematic evidence is needed to assess its clinical value. Therefore, this study aims to investigate the application value of radiomics in the diagnosis and treatment of EC.MethodsPubmed, Cochrane, Embase, and Web of Science databases were retrieved up to March 2023. Preoperative risk assessment of EC included high-grade EC, lymph node metastasis, deep myometrial invasion status, and lymphovascular space invasion status. The quality of the included studies was appraised utilizing the RQS scale.ResultsA total of 33 primary studies were included in our systematic review, with an average RQS score of 7 (range: 5–12). ML models based on radiomics for the diagnosis of malignant lesions predominantly employed logistic regression. In the validation set, the pooled c-index of the ML models based on radiomics and clinical features for the preoperative diagnosis of endometrial malignancy, high-grade tumors, lymph node metastasis, lymphovascular space invasion, and deep myometrial invasion was 0.900 (95%CI: 0.871–0.929), 0.901 (95%CI: 0.877–0.926), 0.906 (95%CI: 0.882–0.929), 0.795 (95%CI: 0.693–0.897), and 0.819 (95%CI: 0.705–0.933), respectively.ConclusionsRadiomics shows excellent accuracy in detecting endometrial malignancies and in identifying preoperative risk. However, the methodological diversity of radiomics results in significant heterogeneity among studies. Therefore, future research should establish guidelines for radiomics studies based on different imaging sources.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=364320 identifier CRD42022364320

Heterologous expression and characterization of a malathion-hydrolyzing carboxylesterase from a thermophilic bacterium, Alicyclobacillus tengchongensis

A carboxylesterase gene from thermophilic bacterium, Alicyclobacillus tengchongensis, was cloned and expressed in Escherichia coli BL21 (DE3). The gene coded for a 513 amino acid protein with a calculated molecular mass of 57.82 kDa. The deduced amino acid sequence had structural features highly conserved among serine hydrolases, including Ser204, Glu325, and His415 as a catalytic triad, as well as type-B carboxylesterase serine active site (FGGDPENITIGGQSAG) and type-B carboxylesterase signature 2 (EDCLYLNIWTP). The purified enzyme exhibited optimum activity with β-naphthyl acetate at 60 °C and pH 7 as well as stability at 25 °C and pH 7. One unit of the enzyme hydrolyzed 5 mg malathion l(−1) by 50 % within 25 min and 89 % within 100 min. The enzyme strongly degraded malathion and has a potential use for the detoxification of malathion residues

Synchronous fluorescence detection of nitrite in meat products based on dual-emitting dye@MOF and its portable hydrogel test kit.

peer reviewedA novel ratiometric fluorescent nanoprobe (Rh6G@UIO-66-NH2) was fabricated for efficient nitrite (NO2-) detection in the present study. When NO2- was introduced, it interacted with the amino groups on the surface of Rh6G@UIO-66-NH2, forming diazonium salts that led to the quenching of blue fluorescence. With this strategy, a good linear relationship between NO2- concentration and the fluorescent intensity ratio of the nanoprobe in the range of 1-100 μM was established, with a detection limit of 0.021 μM. This dual-readout nanosensor was applied to analyze the concentration of NO2- in real meat samples, achieving satisfactory recovery rates of 94.72-104.52%, highlighting the practical potential of this method. Furthermore, a portable Gel/Rh6G@UIO-66-NH2 hydrogel test kit was constructed for on-spot dual-mode detection of NO2-. This kit allows for convenient colorimetric analysis and fluorometric detection when used in conjunction with a smartphone. All the photos taken with the portable kit was converted into digital information using ImageJ software. It provides colorimetric and fluorescent visual detection of NO2- over a range of 0.1-1.5 mM, achieving a direct quantitative tool for NO2- identification. This methodology presents a promising strategy for NO2- detection and expands the application prospects for on-spot monitoring of food safety assessment.Beijing Science and Technology Planning ProjectNational Key Research and Development Program of Chin

Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)

This paper describes the three-dimensional variational (3D-Var) data assimilation (DA) system for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS). Its core element is a multivariate background error covariance implemented through multiple linear variable changes, including a wind variable change from stream function and velocity potential to zonal- and meridional-wind components, a vertical linear regression representing wind–mass balance, and multiplication by a diagonal matrix of error standard deviations. The univariate spatial correlations for the “unbalanced” variables utilize the Background error on Unstructured Mesh Package (BUMP), which is one of the generic components in the JEDI framework. The variable changes and univariate correlations are modeled directly on the native MPAS unstructured mesh. BUMP provides utilities to diagnose parameters of the covariance model, such as correlation lengths, from an ensemble of forecast differences, though some manual adjustment of the parameters is necessary because of mismatches between the univariate correlation function assumed by BUMP and the correlation structure in the sample of forecast differences. The resulting multivariate covariances, as revealed by single-observation tests, are qualitatively similar to those found in previous global 3D-Var systems. Month-long cycling DA experiments using a global quasi-uniform 60 km mesh demonstrate that 3D-Var, as expected, performs somewhat worse than a pure ensemble-based covariance, while a hybrid covariance, which combines that used in 3D-Var with the ensemble covariance, significantly outperforms both 3D-Var and the pure ensemble covariance. Due to its simple workflow and minimal computational requirements, the JEDI-MPAS 3D-Var system can be useful for the research community.</p

Detection of Pine Nut Allergen in Three Kinds of Food by Ultra-high Performance Liquid Chromatography-Tandem Mass Spectrometry

An ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was established for the detection of pine nut allergen Pin k 2 in food matrices. Pine nuts were ground, degreased, and enzymatically extracted and the hydrolysate was separated and analyzed by using an Easy-nLC 1000-QExecutive high-resolution mass spectrometer, and the mass spectral data obtained were processed using Protein Pilot TM software and the Uniprot protein database. The specificity was verified by Basic Local Alignment Search Tool (BLAST), and three pine nut-specific peptides were selected finally. The developed method exhibited a good linear relationship in the concentration range of 0.001–50 mg/mL, and the limit of quantification was 1 mg/kg. The average recoveries for blank biscuit, chocolate and beverage were 88.50%–107.57%, with a relative standard deviation (RSD) not exceeding 6.08%, and the matrix effect was 89.77%–96.13%. This method has the advantages of high sensitivity and good specificity, and can be applied to the detection of pine nut allergens in food samples such as biscuits, chocolate, and beverages, which provides technical support for the authentication of food labels and the detection of latent allergens in foods