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This article considers the parameter estimation problems for the controlled autoregressive systems interfered by moving average noises. A recursive extended gradient algorithm with penalty term is proposed by using the penalty criterion function. By introducing three fictitious output variables, the original system can be decomposed into three subsystems based on the hierarchical principle. The hierarchical recursive extended gradient algorithm with penalty term is then proposed to achieve the parameter estimation. Finally, the experimental results demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

During the first weeks of life, microbial colonization of the gut impacts human immune system maturation and other developmental processes. In premature infants, aberrant colonization has been implicated in the onset of necrotizing enterocolitis (NEC), a life-threatening intestinal disease. To study the premature infant gut colonization process, genome-resolved metagenomics was conducted on 343 fecal samples collected during the first 3 months of life from 35 premature infants housed in a neonatal intensive care unit, 14 of whom developed NEC, and metaproteomic measurements were made on 87 samples. Microbial community composition and proteomic profiles remained relatively stable on the time scale of a week, but the proteome was more variable. Although genetically similar organisms colonized many infants, most infants were colonized by distinct strains with metabolic profiles that could be distinguished using metaproteomics. Microbiome composition correlated with infant, antibiotics administration, and NEC diagnosis. Communities were found to cluster into seven primary types, and community type switched within infants, sometimes multiple times. Interestingly, some communities sampled from the same infant at subsequent time points clustered with those of other infants. In some cases, switches preceded onset of NEC; however, no species or community type could account for NEC across the majority of infants. In addition to a correlation of protein abundances with organism replication rates, we found that organism proteomes correlated with overall community composition. Thus, this genome-resolved proteomics study demonstrated that the contributions of individual organisms to microbiome development depend on microbial community context.IMPORTANCE Humans are colonized by microbes at birth, a process that is important to health and development. However, much remains to be known about the fine-scale microbial dynamics that occur during the colonization period. We conducted a genome-resolved study of microbial community composition, replication rates, and proteomes during the first 3 months of life of both healthy and sick premature infants. Infants were found to be colonized by similar microbes, but each underwent a distinct colonization trajectory. Interestingly, related microbes colonizing different infants were found to have distinct proteomes, indicating that microbiome function is not only driven by which organisms are present, but also largely depends on microbial responses to the unique set of physiological conditions in the infant gut.

BackgroundEstablishment of the human gut microbiota begins at birth. This early-life microbiota development can impact host physiology during infancy and even across an entire life span. However, the functional stability and population structure of the gut microbiota during initial colonization remain poorly understood. Metaproteomics is an emerging technology for the large-scale characterization of metabolic functions in complex microbial communities (gut microbiota).ResultsWe applied a metagenome-informed metaproteomic approach to study the temporal and inter-individual differences of metabolic functions during microbial colonization of preterm human infants' gut. By analyzing 30 individual fecal samples, we identified up to 12,568 protein groups for each of four infants, including both human and microbial proteins. With genome-resolved matched metagenomics, proteins were confidently identified at the species/strain level. The maximum percentage of the proteome detected for the abundant organisms was ~45%. A time-dependent increase in the relative abundance of microbial versus human proteins suggested increasing microbial colonization during the first few weeks of early life. We observed remarkable variations and temporal shifts in the relative protein abundances of each organism in these preterm gut communities. Given the dissimilarity of the communities, only 81 microbial EggNOG orthologous groups and 57 human proteins were observed across all samples. These conserved microbial proteins were involved in carbohydrate, energy, amino acid and nucleotide metabolism while conserved human proteins were related to immune response and mucosal maturation. We identified seven proteome clusters for the communities and showed infant gut proteome profiles were unstable across time and not individual-specific. Applying a gut-specific metabolic module (GMM) analysis, we found that gut communities varied primarily in the contribution of nutrient (carbohydrates, lipids, and amino acids) utilization and short-chain fatty acid production.ConclusionsOverall, this study reports species-specific proteome profiles and metabolic functions of human gut microbiota during early colonization. In particular, our work contributes to reveal microbiota-associated shifts and variations in the metabolism of three major nutrient sources and short-chain fatty acid during colonization of preterm infant gut.

In the incremental least squares twin support vector regression, to solve the problem that the constituted kernel matrix cannot approximate the original kernel matrix well, this paper proposes an incremental reduced least squares twin support vector regression (IRLSTSVR) algorithm. Firstly, in order to reduce the correlation of column vectors in the kernel matrix, the proposed algorithm utilizes a reduced method to determine the correlation between column vectors and then screen support vectors from samples to constitute the column vectors of the kernel matrix. Therefore, the constituted kernel matrix can better approximate the original counterpart, which ensures the sparsity of the solution. Secondly, the inverse matrix is incrementally updated by the block matrix inverse lemma, which further shortens the training time of the proposed algorithm. Finally, the feasibility and efficacy of the proposed algorithm are verified on the benchmark datasets. Experimental results show that the IRLSTSVR algorithm can obtain sparse solution and its generalization performance is closer to offline algorithm compared with state-of-the-art algorithms.

The establishment of early life microbiota in the human infant gut is highly variable and plays a crucial role in host nutrient availability/uptake and maturation of immunity. Although high-performance mass spectrometry (MS)-based metaproteomics is a powerful method for the functional characterization of complex microbial communities, the acquisition of comprehensive metaproteomic information in human fecal samples is inhibited by the presence of abundant human proteins. To alleviate this restriction, we have designed a novel metaproteomic strategy based on double filtering (DF) the raw samples, a method that fractionates microbial from human cells to enhance microbial protein identification and characterization in complex fecal samples from healthy premature infants. This method dramatically improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low-abundance proteins and a greater than 2-fold improvement in microbial protein identification and quantification. This enhancement of proteome measurement depth enabled a more extensive microbiome comparison between infants by not only increasing the confidence of identified microbial functional categories but also revealing previously undetected categories.

Background Methods Results Conclusion Taxanes are the basic components of breast cancer chemotherapy. Nanoparticle albumin‐bound paclitaxel (nab‐paclitaxel) shows improved antitumor effects because of more targeted delivery. However, the effects of nab‐paclitaxel have not been systematically studied in patients with metastatic breast cancer (MBC) pretreated with taxanes. Considering the limited treatment options for MBC, this study retrospectively evaluated the clinical efficacy and adverse effects of nab‐paclitaxel in patients with taxane‐pretreated MBC.Patients who had previously received taxanes and subsequently received nab‐paclitaxel chemotherapy for MBC at Jiangsu Cancer Hospital between October 2014 and April 2022 were included for analysis. The primary end point was progression‐free survival (PFS), and the secondary end points were the objective response rate (ORR), disease control rate (DCR), clinical benefit rate (CBR), and side effects.A total of 236 female patients with MBC were included. The median PFS was 7.20 months (95% confidence interval [CI], 6.63–7.80 months), and the ORR, DCR, and CBR were 29.55% (95% CI, 23.50%–35.60%), 83.64% (95% CI, 78.70%–88.60%), and 56.36% (95% CI, 49.80%–63.00%), respectively. Following nab‐paclitaxel treatment, the median PFS of patients who were sensitive to taxanes during previous treatments was significantly longer than that of patients who were resistant to taxanes (7.57 months vs. 4.43 months, p < .001). The most common adverse events were sensory neuropathy (89.83%), neutropenia (48.73%), leukopenia (46.61%), and anemia (35.59%).Nab‐paclitaxel demonstrated clinical activity in taxane‐pretreated patients with MBC. This beneficial effect was observed both in patients who were sensitive and resistant to taxanes during previous treatments. These results suggest nab‐paclitaxel as the preferred chemotherapy regimen in patients with MBC, regardless of their sensitivity to taxanes during previous treatments. [ABSTRACT FROM AUTHOR]