Comparative genomic analyses of Cutibacterium granulosum provide insights into genomic diversity

Cutibacterium granulosum, a commensal bacterium found on human skin, formerly known as Propionibacterium granulosum, rarely causes infections and is generally considered non-pathogenic. Recent research has revealed the transferability of the multidrug-resistant plasmid pTZC1 between C. granulosum and Cutibacterium acnes, the latter being an opportunistic pathogen in surgical site infections. However, there is a noticeable lack of research on the genome of C. granulosum, and the genetic landscape of this species remains largely uncharted. We investigated the genomic features and evolutionary structure of C. granulosum by analyzing a total of 30 Metagenome-Assembled Genomes (MAGs) and isolate genomes retrieved from public databases, as well as those generated in this study. A pan-genome of 6,077 genes was identified for C. granulosum. Remarkably, the ‘cloud genes’ constituted 62.38% of the pan-genome. Genes associated with mobilome: prophages, transposons [X], defense mechanisms [V] and replication, recombination and repair [L] were enriched in the cloud genome. Phylogenomic analysis revealed two distinct mono-clades, highlighting the genomic diversity of C. granulosum. The genomic diversity was further confirmed by the distribution of Average Nucleotide Identity (ANI) values. The functional profiles analysis of C. granulosum unveiled a wide range of potential Antibiotic Resistance Genes (ARGs) and virulence factors, suggesting its potential tolerance to various environmental challenges. Subtype I-E of the CRISPR-Cas system was the most abundant in these genomes, a feature also detected in C. acnes genomes. Given the widespread distribution of C. granulosum strains within skin microbiome, our findings make a substantial contribution to our broader understanding of the genetic diversity, which may open new avenues for investigating the mechanisms and treatment of conditions such as acne vulgaris

Subclinical cardiac abnormalities in children with biliary atresia correlate with outcomes after liver transplantation

ObjectiveThere are subclinical cardiac abnormalities (SCA) in children with biliary atresia (BA). However, data on the consequences of these cardiac changes after liver transplantation (LT) remain controversial in the pediatric field. We aimed to determine the relationship between outcomes and the subclinical cardiac abnormalities in pediatric patients with BA based on two-dimensional echocardiography (2DE) parameters.MethodsA total of 205 children with BA were enrolled in this study. The relationship between 2DE parameters and outcomes, including death and serious adverse events (SAE) after LT, was analyzed by regression analysis. Using receiver operator characteristic (ROC) curves to determine the optimal cut-off values of 2DE parameters for outcomes. Differences in the AUCs were compared using DeLong's test. The Kaplan -Meier method with log-rank testing was used to evaluate survival outcomes between groups.ResultsLeft ventricular mass index (LVMI) and relative wall thickness (RWT) were found to be independently associated with SAE (OR: 1.112, 95% CI: 1.061 − 1.165, P < 0.001 and OR: 1.193, 95% CI: 1.078 − 1.320, P = 0.001, respectively). The cutoff value of LVMI for predicting the SAE was 68 g/m2.7 (AUC = 0.833, 95% CI 0.727-0.940, P < 0.001), and the cutoff value of RWT for predicting the SAE was 0.41 (AUC = 0.732, 95% CI 0.641-0.823, P < 0.001). The presence of subclinical cardiac abnormalities (LVMI > 68 g/m2.7, and/or RWT > 0.41) was associated with lower patient survival (1-year, 90.5% vs 100.0%; 3-year, 89.7% vs 100.0, log-rank P = 0.001). and higher incidence of SAE events.ConclusionsSubclinical cardiac abnormalities were correlated with post-LT mortality and morbidity in children with BA. LVMI can predict the occurrence of death and serious adverse events after liver transplantation

Directional Selection from Host Plants Is a Major Force Driving Host Specificity in Magnaporthe Species

One major threat to global food security that requires immediate attention, is the increasing incidence of host shift and host expansion in growing number of pathogenic fungi and emergence of new pathogens. The threat is more alarming because, yield quality and quantity improvement efforts are encouraging the cultivation of uniform plants with low genetic diversity that are increasingly susceptible to emerging pathogens. However, the influence of host genome differentiation on pathogen genome differentiation and its contribution to emergence and adaptability is still obscure. Here, we compared genome sequence of 6 isolates of Magnaporthe species obtained from three different host plants. We demonstrated the evolutionary relationship between Magnaporthe species and the influence of host differentiation on pathogens. Phylogenetic analysis showed that evolution of pathogen directly corresponds with host divergence, suggesting that host-pathogen interaction has led to co-evolution. Furthermore, we identified an asymmetric selection pressure on Magnaporthe species. Oryza sativa-infecting isolates showed higher directional selection from host and subsequently tends to lower the genetic diversity in its genome. We concluded that, frequent gene loss or gain, new transposon acquisition and sequence divergence are host adaptability mechanisms for Magnaporthe species, and this coevolution processes is greatly driven by directional selection from host plants

Scenario Analysis–Based Decision and Coordination in Supply Chain Management with Production and Transportation Scheduling

The production and rail transportation coordinating problem aims to determine a pre-operational timetable for a set of orders. The orders need to be manufactured on a dedicated production line and be delivered to their destination by the pre-planned trains considering traveling routes and time. The connecting trains are an important and even unique chance for orders to reach their final destinations, and earliness and tardiness penalties in the connection and delivery process are closely related to the symmetry and harmony between production procedure decision and train-allocated choice. A scenario analysis method is adopted to reveal the relationships between production collection connecting time (PCCT) and production collection waiting time (PCWT) in the production process. In the delivery process, the relationships between production delivery connecting time (PDCT) and production delivery waiting time (PDWT) are mainly considered. An integrated scheduling coordination model is established to maximize the production connecting time (PCT) and production delivery time (PDT). The GA genetic algorithm is employed to solve this problem. The numerical results show that the coordinated schedule in our method can significantly reduce the number of missed connections when compared with considering delivery timeliness only through a delivery time window. Additionally, it is revealed that the quantitative performances of the delivery timeliness improved by the connecting quality (PCT) are much better than those from the view of the delivery time window (PDT)

Multisensor Based Neutral Function Identification of Solenoid Valve

Condition monitoring of hydraulic systems has been using automatic control in industrial system. In this paper, a sensor network based intelligent control is proposed for efficient solenoid valve identification. The detection system learns to detect the change of output pressure of multipoints that represent a more complicated task. Linear correlation analysis is introduced for feature extraction, which allows for a significant reduction in the dimension of original data without compromising the change detection performance. Implemented as an agent identifying the valve types under measurement, the support vector machine classifier achieves a significant high accuracy in identification and an increase in deployment efficiency. Experimental results prove that the system is feasible for application designs and could be implemented on technological platforms

The GH-IGF-1 Axis in Circadian Rhythm

Organisms have developed common behavioral and physiological adaptations to the influence of the day/night cycle. The CLOCK system forms an internal circadian rhythm in the suprachiasmatic nucleus (SCN) during light/dark input. The SCN may synchronize the growth hormone (GH) secretion rhythm with the dimming cycle through somatostatin neurons, and the change of the clock system may be related to the pulsatile release of GH. The GH—insulin-like growth factor 1 (IGF-1) axis and clock system may interact further on the metabolism through regulatory pathways in peripheral organs. We have summarized the current clinical and animal evidence on the interaction of clock systems with the GH—IGF-1 axis and discussed their effects on metabolism.</p

Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer

Background: The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. Objectives: To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. Materials and Methods: This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0–3, 3–6, 6–9, 9–12, 12–15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere–shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts. Results: Among the five partitions, the model of 9–12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77–0.94). The AUC was 0.94 (0.85–0.98) for the feature fusion model and 0.91 (0.82–0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88–0.99) for the feature fusion method and 0.94 (0.85–0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81–0.97) and 0.89 (0.79–0.93) in two validation sets, respectively. Conclusions: This integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making