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Marking estimation is crucial in the area of discrete event systems. This paper proposes algorithms for addressing the problem of minimum initial markings (MuIMs) estimation in a known Petri net (PN) structure. The existence of unobservable transitions makes this problem challenging since the number of transition sequences consistent with an observed label sequence can potentially be infinite. It is assumed that only minimal explanations can fire before the firing of each observable transition (OT). We present the definition of minimal explanation places, which allows potential minimal explanations to fire before the firing of each OT to obtain more minimal initial markings. Since the number of initial markings may be infinite, we aim to obtain the initial markings that not only enable at least one transition sequence consistent with both the observed label sequence and the PN structure, but also exhibit the minimum total number of tokens (i.e., the minimum total number of tokens when summed over all places). After developing the algorithms, we also propose two heuristic methods to reduce the computational cost, resulting in a subset or an approximation of the final MuIMs. An illustrative example is provided to indicate how the proposed algorithms and heuristics can be utilized to reveal the minimum number of resources required which are indispensable at the initial state for the completion of a specified task sequence.

The human gut microbiome has primarily been studied through the use of fecal samples, a practice that has generated vital knowledge on the composition and functional capacities of gastrointestinal microbial communities. However, this reliance on fecal materials limits the investigation of microbial dynamics in other locations along the gastrointestinal tract (in situ), and the infrequent availability of fecal samples prevents analysis at finer temporal scales (e.g., hours). In our study, we utilized colonic transendoscopic enteral tubing, a technology originally developed for fecal microbiota transplantation, to sample the ileocecal microbiome twice daily; metagenomic and metatranscriptomic analyses were then conducted on these samples. A total of 43 ileocecal and 28 urine and fecal samples were collected from five healthy volunteers. The ileocecal and fecal microbiomes, as profiled in the five volunteers, were found to be similar in metagenomic profiling, yet their active genes (metatranscriptome) were found to be highly distinct. Both microbiomes were perturbed after laxative exposure; over time, they exhibited reduced dissimilarity to their pre-treatment state, thereby demonstrating resilience as an innate property of the gut microbiome, although they did not fully recover within our observation time window. Sampling of the ileocecal microbiome during the day and at night revealed the existence of diurnal rhythms in a series of bacterial species and functional pathways, particularly those related to short-chain fatty acid production, such as Propionibacterium acnesand coenzyme A biosynthesis II.Autocorrelation analysis and fluctuations decomposition further indicated the significant periodicity of the diurnal oscillations. Metabolomic profiling in the fecal and urine samples mirrored the perturbance and recovery in the gut microbiome, indicating the crucial contribution of the gut microbiome to many key metabolites involved in host health. This study provides novel insights into the human gut microbiome and its inner resilience and diurnal rhythms, as well as the potential consequences of these to the host.

Recent technological advancements and developments have led to a dramatic increase in the amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regression methods. Numerous traditional multivariate approaches such as principal component analysis have been used broadly in various research areas, including investment analysis, image identification, and population genetic structure analysis. However, these common approaches have the limitations of ignoring the correlations between responses and a low variable selection efficiency. Therefore, in this article, we introduce the reduced rank regression method and its extensions, sparse reduced rank regression and subspace assisted regression with row sparsity, which hold potential to meet the above demands and thus improve the interpretability of regression models. We conducted a simulation study to evaluate their performance and compared them with several other variable selection methods. For different application scenarios, we also provide selection suggestions based on predictive ability and variable selection accuracy. Finally, to demonstrate the practical value of these methods in the field of microbiome research, we applied our chosen method to real population-level microbiome data, the results of which validated our method. Our method extensions provide valuable guidelines for future omics research, especially with respect to multivariate regression, and could pave the way for novel discoveries in microbiome and related research fields.

Microbiome research is a quickly developing field in biomedical research, and we have witnessed its potential in understanding the physiology, metabolism and immunology, its critical role in understanding the health and disease of the host, and its vast capacity in disease prediction, intervention and treatment. However, many of the fundamental questions still need to be addressed, including the shaping forces of microbial diversity between individuals and across time. Microbiome research falls into the classical nature vs. nurture scenario, such that host genetics shape part of the microbiome, while environmental influences change the original course of microbiome development. In this review, we focus on the nature, i.e., the genetic part of the equation, and summarize the recent efforts in understanding which parts of the genome, especially the human and mouse genome, play important roles in determining the composition and functions of microbial communities, primarily in the gut but also on the skin. We aim to present an overview of different approaches in studying the intricate relationships between host genetic variations and microbes, its underlying philosophy and methodology, and we aim to highlight a few key discoveries along this exploration, as well as current pitfalls. More evidence and results will surely appear in upcoming studies, and the accumulating knowledge will lead to a deeper understanding of what we could finally term a “hologenome”, that is, the organized, closely interacting genome of the host and the microbiome.

The equation of local exergy loss rate in forced convective heat transfer has been derived in present work. The obtained exergy loss equation involves exergy loss due to heat transfer and pressure drop as well as exergy generation on account of viscous heat dissipation. The dimensionless form of local exergy loss rate and entropy generation different from the previous can also be obtained. Moreover, the calculation results of two convective heat transfer problems about local exergy loss and local entropy generation proposed by Bejan are given. The effect of temperature difference, Prandtl number and Eckert number on exergy loss and entropy generation has been analysed and the comparison between exergy loss and entropy generation under various conditions has been made. Results shows that for Poiseuille flow the local exergy loss greatly differs from the entropy generation due to the presence of pressure drop. However, viscous dissipation takes an important role in exergy loss for Couette flow on account of no pressure drop.

This study was designed to depict prevalence and antimicrobial resistance characteristics of Proteus mirabilis(P. mirabilis) strains in 4 chicken farms and to probe the transfer mechanism of resistance genes. A total of 187 P. mirabilisisolates were isolated from 4 chicken farms. The susceptibility testing of these isolates to 14 antimicrobials showed that the multidrug resistance (MDR) rate was as high as 100%. The β-lactamase resistance genes blaOXA-1, blaCTX-M-1G, blaCTX-M-9Gand colistin resistance gene mcr-1were highly carried in the P. mirabilisisolates. An MDR strain W47 was selected for whole genome sequencing (WGS) and conjugation experiment. The results showed that W47 carried 23 resistance genes and 64 virulence genes, and an SXT/R391 integrated conjugative elements (ICEs) named ICEPmiChn5 carrying 17 genes was identified in chromosome. ICEPmiChn5 was able to be excised from the chromosome of W47 forming a circular intermediate, but repeated conjugation experiments were unsuccessful. Among 187 P. mirabilisisolates, 144 (77.01%, 144/187) isolates carried ICEPmiChn5-like ICEs, suggesting that ICEs may be the major vector for the transmission of resistance genes among MDR chicken P. mirabilisstrains in this study. The findings were conducive to insight into the resistance mechanism of chicken P. mirabilisstrains and provide a theoretical basis for the use of antibiotics for the treatment of MDR P. mirabilisinfections in veterinary clinic.

In modern datacenters, the most common method to solve the network latency problem is to minimize flow completion time during the transmission process. Following the soft real-time nature, the optimization of transport latency is relaxed to meet a flow's deadline in deadline-sensitive services. However, none of existing deadline-sensitive protocols consider deadline as a constraint condition of transmission. They can only simplify the objective of meeting a flow's deadline as a deadline-aware mechanism by assigning a higher priority for tight-deadline constrained flows to finish the transmission as soon as possible, which results in an unsatisfactory effect in the condition of high fan-in degree. It drives us to take a step back and rethink whether minimizing flow completion time is the optimal way in meeting flow's deadline. In this paper, we focus on the design of a soft real-time transport protocol with deadline constraint in datacenters and present a flow-based deadline scheduling scheme for datacenter networks (FBDS). FBDS makes the unilateral deadline-aware flow transmission with priority transform into a compound centralized single-machine deadlinebased flow scheduling decision. In addition, FBDS blocks the flow sets and postpones some flows with extra time until their deadlines to make room for the new arriving flows in order to improve the deadline meeting rate. Our simulation results on flow completion time and deadline meeting rate reveal the potential of FBDS in terms of a considerable deadline-sensitive transport protocol for deadline-sensitive interactive services.