A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.

SRM targeted proteomics in search for biomarkers of HCV ‐induced progression of fibrosis to cirrhosis in HALT ‐ C patients

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92042/1/pmic7038.pd

A wellness study of 108 individuals using personal, dense, dynamic data clouds.

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states

Mouse Organ-Specific Proteins and Functions

Organ-specific proteins (OSPs) possess great medical potential both in clinics and in biomedical research. Applications of them—such as alanine transaminase, aspartate transaminase, and troponins—in clinics have raised certain concerns of their organ specificity. The dynamics and diversity of protein expression in heterogeneous human populations are well known, yet their effects on OSPs are less addressed. Here, we used mice as a model and implemented a breadth study to examine the panorgan proteome for potential variations in organ specificity in different genetic backgrounds. Using reasonable resources, we generated panorgan proteomes of four in-bred mouse strains. The results revealed a large diversity that was more profound among OSPs than among proteomes overall. We defined a robustness score to quantify such variation and derived three sets of OSPs with different stringencies. In the meantime, we found that the enriched biological functions of OSPs are also organ-specific and are sensitive and useful to assess the quality of OSPs. We hope our breadth study can open doors to explore the molecular diversity and dynamics of organ specificity at the protein level.&nbsp

Molecular epidemiological characteristics of Mycobacterium leprae in highly endemic areas of China during the COVID-19 epidemic

ObjectivesThe present study analyzed the impact of the COVID-19 pandemic on the prevalence and incidence of new leprosy cases, as well as the diversity, distribution, and temporal transmission of Mycobacterium leprae strains at the county level in leprae-endemic provinces in Southwest China.MethodsA total of 219 new leprosy cases during two periods, 2018–2019 and 2020–2021, were compared. We genetically characterized 83 clinical isolates of M. leprae in Guizhou using variable number tandem repeats (VNTRs) and single nucleotide polymorphisms (SNPs). The obtained genetic profiles and cluster consequences of M. leprae were compared between the two periods.ResultsThere was an 18.97% decrease in the number of counties and districts reporting cases. Considering the initial months (January–March) of virus emergence, the number of new cases in 2021 increased by 167% compared to 2020. The number of patients with a delay of >12 months before COVID-19 (63.56%) was significantly higher than that during COVID-19 (48.51%). Eighty-one clinical isolates (97.60%) were positive for all 17 VNTR types, whereas two (2.40%) clinical isolates were positive for 16 VNTR types. The (GTA)9, (TA)18, (TTC)21 and (TA)10 loci showed higher polymorphism than the other loci. The VNTR profile of these clinical isolates generated five clusters, among which the counties where the patients were located were adjacent or relatively close to each other. SNP typing revealed that all clinical isolates possessed the single SNP3K.ConclusionCOVID-19 may have a negative/imbalanced impact on the prevention and control measures of leprosy, which could be a considerable fact for official health departments. Isolates formed clusters among counties in Guizhou, indicating that the transmission chain remained during the epidemic and was less influenced by COVID-19 preventative policies

Experimental study on flame combustion characteristics of large-bore marine diesel engine based on endoscopic technology

Large-bore marine diesel engines have the characteristics of poor ignition performance and insufficient combustion in the cylinder. This work revealed the combustion and emission performance of large-bore marine diesel engines based on endoscopic visualization technology. The flame temperature and soot distribution were analyzed in radial and axial directions. Results show that the large-bore diesel engine has a poor combustion effect because of the large fuel injection quantity and insufficient fuel-air mixing effect in the cylinder. The temperature in the cylinder rises twice in the late stage of combustion. The average temperature rises by 3.8%, caused by the secondary ignition of part of the unburned diesel. In addition, the large-bore engine produces a large amount of soot due to an insufficient mixing effect. It can be observed from the radial flame visualization images that the propagation speed of the flame is slow. The time required for the flame to propagate to the wall at 50% load is reduced by 31% compared with 25% load. The downward movement of the piston causes the flame tumble flow in the cylinder, resulting in an apparent asymmetric structure of the flame development

Optimal Control for Hydraulic Cylinder Tracking Displacement of Wave Energy Experimental Platform

The wave energy converter captures the wave power by buoy’s heaving motion, transfers it by hydraulic system, and converts it into the electric power by generator. The hydraulic conversion system plays an important role that can realize the effective regulation of the output power. In order to develop the working characteristics of the hydraulic transmission system, a new wave energy experimental platform was devised. The platform adopts the matching design mode of the driving oil cylinder and the driven oil cylinder. The active hydraulic cylinder and the clump weight can simulate the movement of the oscillating float under certain sea conditions, and the driven oil cylinder realizes the conversion and the output of wave energy. In order to improve the operation accuracy of the active hydraulic cylinder, the control strategy of the active hydraulic cylinder was studied. An adaptive sliding mode control strategy based on the back-stepping method was proposed to overcome the influence of the parameter uncertainty in state equation. The adaptive law was designed by Lyapunov criterion to ensure the stability and the convergence of the closed-loop system. The proposed control strategy was verified and compared with proportional integral derivative control strategy through the concrete experiment, which shows the rapidity and the stability of it. The hydraulic transmission system of wave energy converter was developed; at the same time, the characteristics of hydraulic regulation under different working conditions were summarized through experiments. The results of the research could be the guidance for the power control design

Methods of Conserving and Managing Cultural Heritage in Classical Chinese Royal Gardens Based on 3D Digitalization

In this study, we aimed to implement information obtained and refined from garden elements in heritage conservation, monitoring, and management to precisely construct an information model of classical Chinese gardens, including information on the garden entity, garden space, and garden attributes, etc., and to improve the management efficiency of classical Chinese royal gardens. Three-dimensional laser scanning technology and point cloud information were used to accurately collect and process digital information from classical Chinese royal gardens. After classifying and processing the point cloud data, correlations therein could be further assessed and used to greatly improve the accuracy and management efficiency of spatial information. To provide a more convenient solution for the subsequent conservation and management of landscape heritage, a method for establishing a three-dimensional digital information database and a full life-cycle application management platform for classical Chinese royal gardens is proposed in this research. This method has broad applications for the digital conservation and management of cultural heritage

Effects of Processing Methods and Conditioning Temperatures on the Cassava Starch Digestibility and Growth Performance of Broilers

As an important food crop, cassava is rich in nutrients and high in starch content and is widely used in the production of industrial raw materials. However, the utilization value of cassava is limited due to the reduction of planting area and the existence of anti-nutritional factors. Therefore, we evaluated in vitro cassava starch digestibility and in vivo growth performance of broilers in a 3 × 3 factorial arrangement of treatments using three processing methods (mechanical crushing (MC), steam conditioning (SC), and puffing conditioning (PU)) and three conditioning temperatures (60, 75, and 90 °C) to screen for the optimal processing method and conditioning temperature to improve the utilization of cassava. In the in vitro cassava starch digestion study, the digestibility and digestion rate (p p p p p p p < 0.05) for broilers fed SC diets than for those fed MC diets. These results indicate that cassava starch promoted starch digestion rate by reducing amylose content and amylose/amylose under PU combined with a conditioning temperature of 60 °C, ileum digestibility of starch in broilers fed SC diets was higher than MC diets regardless of conditioning temperature, and SC diets increased AME and decreased F/G to promote growth performance of broilers