Circulating microbial content in myeloid malignancy patients is associated with disease subtypes and patient outcomes

Although recent work has described the microbiome in solid tumors, microbial content in hematological malignancies is not well-characterized. Here we analyze existing deep DNA sequence data from the blood and bone marrow of 1870 patients with myeloid malignancies, along with healthy controls, for bacterial, fungal, and viral content. After strict quality filtering, we find evidence for dysbiosis in disease cases, and distinct microbial signatures among disease subtypes. We also find that microbial content is associated with host gene mutations and with myeloblast cell percentages. In patients with low-risk myelodysplastic syndrome, we provide evidence that Epstein-Barr virus status refines risk stratification into more precise categories than the current standard. Motivated by these observations, we construct machine-learning classifiers that can discriminate among disease subtypes based solely on bacterial content. Our study highlights the association between the circulating microbiome and patient outcome, and its relationship with disease subtype

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies

Cardio-Protection of Salvianolic Acid B through Inhibition of Apoptosis Network

Targeting cellular function as a system rather than on the level of the single target significantly increases therapeutic potency. In the present study, we detect the target pathway of salvianolic acid B (SalB) in vivo. Acute myocardial infarction (AMI) was induced in rats followed by the treatment with 10 mg/kg SalB. Hemodynamic detection and pathological stain, 2-dimensional electrophoresis, MALDI-TOF MS/MS, Western blot, pathway identification, apoptosis assay and transmission electron microscope were used to elucidate the effects and mechanism of SalB on cardioprotection. Higher SalB concentration was found in ischemic area compared to no-ischemic area of heart, correlating with improved heart function and histological structure. Thirty-three proteins regulated by SalB in AMI rats were identified by biochemical analysis and were classified as the components of metabolism and apoptosis networks. SalB protected cardiomyocytes from apoptosis, inhibited poly (ADP-ribose) polymerase-1 pathway, and improved the integrity of mitochondrial and nucleus of heart tissue during AMI. Furthermore, the protective effects of SalB against apoptosis were verified in H9c2 cells. Our results provide evidence that SalB regulates multi-targets involved in the apoptosis pathway during AMI and therefore may be a candidate for novel therapeutics of heart diseases

Population genomics of an icefish reveals mechanisms of glacier-driven adaptive radiation in Antarctic notothenioids

Background Antarctica harbors the bulk of the species diversity of the dominant teleost fish suborder—Notothenioidei. However, the forces that shape their evolution are still under debate. Results We sequenced the genome of an icefish, Chionodraco hamatus, and used population genomics and demographic modelling of sequenced genomes of 52 C. hamatus individuals collected mainly from two East Antarctic regions to investigate the factors driving speciation. Results revealed four icefish populations with clear reproduction separation were established 15 to 50 kya (kilo years ago) during the last glacial maxima (LGM). Selection sweeps in genes involving immune responses, cardiovascular development, and photoperception occurred differentially among the populations and were correlated with population-specific microbial communities and acquisition of distinct morphological features in the icefish taxa. Population and species-specific antifreeze glycoprotein gene expansion and glacial cycle-paced duplication/degeneration of the zona pellucida protein gene families indicated fluctuating thermal environments and periodic influence of glacial cycles on notothenioid divergence. Conclusions We revealed a series of genomic evidence indicating differential adaptation of C. hamatus populations and notothenioid species divergence in the extreme and unique marine environment. We conclude that geographic separation and adaptation to heterogeneous pathogen, oxygen, and light conditions of local habitats, periodically shaped by the glacial cycles, were the key drivers propelling species diversity in Antarctica.info:eu-repo/semantics/publishedVersio

Auditor’s Consideration of Client Cybersecurity Risk – A Machine Learning-Based Analysis

Emerging technologies have changed financial reporting. As a result, client cybersecurity risk could result in material misstatements directly related to the financial statement audit. This study estimates client cybersecurity risk using a machine learning algorithm and investigates how cybersecurity risk explains audit fees. I find that clients with higher cybersecurity risk pay higher audit fees. Moreover, auditors only charge a fee premium following a client data breach if the client has heightened cybersecurity risk. In addition, Big 4 auditors charge a smaller cybersecurity-related fee premium than non-Big 4 auditors, suggesting that Big 4 auditors are more efficient in evaluating and addressing cybersecurity-related financial risks. Finally, the auditor’s office experience of client cybersecurity events does not affect how auditors incorporate client cybersecurity risk into audit fees, indicating the auditor’s preference to keep the auditing process consistent for each client

Equivalent Minimum Hydrogen Consumption of Fuzzy Control-Based Fuel Cells: Exploration of Energy Management Strategies for Ships

Aiming to solve the problems of insufficient dynamic responses, the large loss of energy storage life of a single power cell, and the large fluctuation in DC (direct current) bus voltage in fuel cell vessels, this study takes a certain type of fuel cell ferry as the research object and proposes an improved equivalent minimum hydrogen consumption energy management strategy, based on fuzzy logic control. First, a hybrid power system including a fuel cell, a lithium–iron–phosphate battery, and a supercapacitor is proposed, with the simulation of the power system of the modified mother ship. Second, a power system simulation model and a double-closed-loop PI (proportion integration) control model are established in MATLAB/Simulink to design the equivalent hydrogen consumption model and fuzzy logic control strategy. The simulation results show that, under the premise of meeting the load requirements, the control strategy designed in this paper improves the Li-ion battery’s power, the Li-ion battery’s SOC (state of charge), the bus voltage stability, and the equivalent hydrogen consumption significantly, compared with those before optimization, which improves the stability and economy of the power system and has certain practical engineering value

The Research of Health Assessment of E-business Ecosystem

The e-business ecosystem may be deviate from the equilibrium state in its development process. In this article the authors defined the e-business ecosystem health by combining ecological principles, and developed ecological indicators of e-business ecosystem health. The method of ecosystem health assessment (include biodiversity index method and index of biological integrity method) was introduced in the field of e-business and was used in B2B e-market analysis as a preliminary evaluation example

Semisupervised Complex Network With Spatial Statistics Fusion for PolSAR Image Classification

Deep learning has achieved satisfactory results in polarimetric synthetic aperture radar (PolSAR) image classification, which requires a large number of labeled samples for training. However, in practice, labeling work is time-consuming and laborious. As a result, an insufficient number of labeled samples will lead to a limited ability of the network to recognize different terrains. To alleviate this problem, we take advantage of labeled and unlabeled samples simultaneously to train the deep learning model and, thus, propose a semisupervised complex network with spatial statistics fusion (SCN-SSF) for PolSAR image classification. First, the semisupervised complex network (SCN) continuously updates the pseudolabels of unlabeled samples during the training of complex-valued CNN, and their errors constitute the regularization term of the objective function, which improves the generalization of the network. As a result, SCN can recognize different terrains more accurately, and the classification has a higher belief. Then, a parameter-free spatial statistics module is built to model neighborhood label interactions based on the product of experts (POEs), thus enhancing contextual smoothness and correcting some misclassifications. Finally, based on the Dempster–Shafer theory, the contextual label information of POE and pixel-level information obtained by SCN are integrated to preserve image structure. Overall, with only a small number of labeled samples, SCN-SSF can accurately identify each terrain and obtain smooth classification while preserving edge information. The effectiveness of SCN-SSF is demonstrated by classifying PolSAR images with a small number of labeled samples