Causal relationship between gut microbiota and immune thrombocytopenia: a Mendelian randomization study of two samples

BackgroundSome observational studies have shown that immune thrombocytopenia (ITP) is highly associated with the alteration-composition of gut microbiota. However, the causality of gut microbiota on ITP has not yet been determined.MethodsBased on accessible summary statistics of the genome-wide union, the latent connection between ITP and gut microbiota was estimated using bi-directional Mendelian randomization (MR) and multivariable MR (MVMR) analyses. Inverse variance weighted (IVW), weighted median analyses, and MR-Egger regression methods were performed to examine the causal correlation between ITP and the gut microbiota. Several sensitivity analyses verified the MR results. The strength of causal relationships was evaluated using the MR-Steiger test. MVMR analysis was undertaken to test the independent causal effect. MR analyses of reverse direction were made to exclude the potential of reverse correlations. Finally, GO enrichment analyses were carried out to explore the biological functions.ResultsAfter FDR adjustment, two microbial taxa were identified to be causally associated with ITP (PFDR < 0.10), namely Alcaligenaceae (PFDR = 7.31 × 10–2) and Methanobacteriaceae (PFDR = 7.31 × 10–2). In addition, eight microbial taxa were considered as potentially causal features under the nominal significance (P < 0.05): Actinobacteria, Lachnospiraceae, Methanobacteria, Bacillales, Methanobacteriales, Coprococcus2, Gordonibacter, and Veillonella. According to the reverse-direction MR study findings, the gut microbiota was not significantly affected by ITP. There was no discernible horizontal pleiotropy or instrument heterogeneity. Finally, GO enrichment analyses showed how the identified microbial taxa participate in ITP through their underlying biological mechanisms.ConclusionSeveral microbial taxa were discovered to be causally linked to ITP in this MR investigation. The findings improve our understanding of the gut microbiome in the risk of ITP

Single well control area splitting method based on reservoir sphysical properties and gas well productivity differences

The determination of the control area of a single well is the prerequisite for the evaluation of the reserves of a single well. The current calculation methods of the control area of a single well are mainly divided into: experience formula, area balancing method, and the physical model, in order to solve the different limitations of the existing single-well control area splitting method and the problem of large error in use, this paper puts forward a kind of based on gas reservoir physical property and the growth of single well productivity difference algorithm for single well control area is split, according to the results of the split combining static reservoir parameters, using volumetric method for single well and the calculation of reserves of gas reservoir evaluation, further clarify the original and the remaining gas distribution of gas reservoir, for the subsequent reasonable development of the gas reservoir and enhance oil recovery. In this paper, block S of Sulige gas field is taken as an example, and the geological reserve of block is calculated as 354.75×108m3, compared with the basic proven reserves of Block S, 364.84×108m3, the error is 2.61% and the reliability is stron

Distributed random load balancing

Low latency is highly desirable for cloud services spanning thousands of servers. With the rapid development of cloud market, the size of server farms grows fast. Hence, stringent timing requirements are needed for task scheduling in a large-scale server farm. Conventionally, the Join-the-Shortest-Queue (JSQ) algorithm, which directs an arriving task to the least loaded server, is adopted in scheduling. Despite its excellent delay performance, JSQ is throughput-limited, and thus doesn't scale well with the number of servers. There are two distributed algorithms proposed as "approximations" of the idealized JSQ. The first one is the Power-of-d-choices (Pod) algorithm, which selects d servers at random and routes a task to the least loaded server of the d servers. Despite its scalability, Pod suffers from long tail response times. The second one is the distributed Join-the-Idle-Queue (JIQ), which take advantages idle servers for task scheduling. In this thesis, we are interested in exploring Pod and JIQ further. First, a hybrid scheduling strategy called Pod-Helper is proposed. It consists of a Pod scheduler and a throughput-limited helper. Hybrid scheduling takes the best of both worlds, enjoying scalability and low tail response times. In particular, hybrid scheduling has bounded maximum queue size in the large-system regime, which is in sharp contrast to the Pod scheduling whose maximum queue size is unbounded. Second, we conduct an in-depth analysis for distributed Join-the-Idle-Queue (JIQ), a promising new approximation of an idealized task-scheduling algorithm. In particular, we derive semi-closed form expressions for the delay performance of distributed JIQ. Third, we propose a new variant of distributed JIQ that offers clear advantages over alternative algorithms for large systems.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat

Semi-Supervised Deep Kernel Active Learning for Material Removal Rate Prediction in Chemical Mechanical Planarization

The material removal rate (MRR) is an important variable but difficult to measure in the chemical–mechanical planarization (CMP) process. Most data-based virtual metrology (VM) methods ignore the large number of unlabeled samples, resulting in a waste of information. In this paper, the semi-supervised deep kernel active learning (SSDKAL) model is proposed. Clustering-based phase partition and phase-matching algorithms are used for the initial feature extraction, and a deep network is used to replace the kernel of Gaussian process regression so as to extract hidden deep features. Semi-supervised regression and active learning sample selection strategies are applied to make full use of information on the unlabeled samples. The experimental results of the CMP process dataset validate the effectiveness of the proposed method. Compared with supervised regression and co-training-based semi-supervised regression algorithms, the proposed model has a lower mean square error with different labeled sample proportions. Compared with other frameworks proposed in the literature, such as physics-based VM models, Gaussian-process-based regression models, and stacking models, the proposed method achieves better prediction results without using all the labeled samples

Study on the Microflora Structure in a <i>Litopenaeus vannamei</i>–<i>Sinonovacula constricta</i> Tandem-Culture Model Based on High-Throughput Sequencing under Different Culture Densities

In this study, we evaluated the intestinal contents of Pacific whiteleg shrimp (Litopenaeus vannamei), the visceral mass of razor clams (Sinonovacula constricta) and the water columns and the substrate sediments in different culture-density groups in a L. vannamei–S. constricta tandem-culture model by high-throughput sequencing of the 16S rRNA gene. The results show that the culture density affected the bacterial floral structure of the water columns, substrate sediment and razor-clam gut masses without making significant differences in the bacterial flora structure of the shrimp gut; the Shannon diversity indexes of the bacterial communities in the substrate sediment, shrimp gut and razor-clam gut masses were not significantly different among the density groups, and the Shannon diversity index of the bacterial communities in the water column was higher in the group with higher culture densities; at the phylum level, the dominant bacteria common to the shrimp guts, razor-clam visceral mass, water columns and substrate sediment were Proteobacteria and Bacteroidetes; Chloroflexi was the dominant bacterium specific to the substrate sediment; and Firmicutes was the dominant bacterium specific to the shrimp gut and razor-clam gut mass. We used national standards (GB 17378.4-2007, China) to evaluate the content of water-quality factors through the environmental factors and the genus-level correlation analysis of bacterial flora that follow: the dominant bacterium in the water column, uncultured_bacterium_f_Rhodobacteraceae, was negatively correlated with PO43−-P; the dominant bacteria in the substrate sediments, uncultured_bacterium_f_Anaerolineaceae and Woeseia, were significantly and negatively correlated with DO; and the dominant bacteria Lactococcus spp. in the razor-clam gut mass and the shrimp intestines were positively correlated with DO. These results show that culture density directly affects water-quality factors, which in turn affect the culture environment and the composition structure of the bacterial flora in a cultured organism

Sulfoximines-Assisted Rh(III)-Catalyzed C–H Activation and Intramolecular Annulation for the Synthesis of Fused Isochromeno-1,2-Benzothiazines Scaffolds under Room Temperature

A mild and facile Cp*Rh(III)-catalyzed C&ndash;H activation and intramolecular cascade annulation protocol has been proposed for the furnishing of highly fused isochromeno-1,2-benzothiazines scaffolds using S-phenylsulfoximides and 4-diazoisochroman-3-imine as substrates under room temperature. This method features diverse substituents and functional groups tolerance and relatively mild reaction conditions with moderate to excellent yields. Additionally, retentive configuration of sulfoximides in the conversion has been verified

Functional Research on Three Presumed Asparagine Synthetase Family Members in Poplar

Asparagine synthetase (AS), a key enzyme in plant nitrogen metabolism, plays an important role in plant nitrogen assimilation and distribution. Asparagine (Asn), the product of asparagine synthetase, is one of the main compounds responsible for organic nitrogen transport and storage in plants. In this study, we performed complementation experiments using an Asn-deficient Escherichia coli strain to demonstrate that three putative asparagine synthetase family members in poplar (Populus simonii &#215; P. nigra) function in Asn synthesis. Quantitative real-time PCR revealed that the three members had high expression levels in different tissues of poplar and were regulated by exogenous nitrogen. PnAS1 and PnAS2 were also affected by diurnal rhythm. Long-term dark treatment resulted in a significant increase in PnAS1 and PnAS3 expression levels. Under long-term light conditions, however, PnAS2 expression decreased significantly in the intermediate region of leaves. Exogenous application of ammonium nitrogen, glutamine, and a glutamine synthetase inhibitor revealed that PnAS3 was more sensitive to exogenous glutamine, while PnAS1 and PnAS2 were more susceptible to exogenous ammonium nitrogen. Our results suggest that the various members of the PnAS gene family have distinct roles in different tissues and are regulated in different ways