Search Results (5,523)

Background/Objectives: Intra-abdominal infections (IAIs) constitute significant clinical challenges that can rapidly progress to life-threatening conditions if not promptly diagnosed and treated. Traditional pathogen identification methodologies, predominantly culture-based, frequently necessitate extended turnaround times (TATs) and exhibit limitations in detecting polymicrobial or anaerobic infections. Methods: We implemented Oxford Nanopore Technology (ONT) sequencing to analyze the microbiota in patients with IAIs at Taichung Veterans General Hospital. The study cohort comprised sixteen patients with IAIs. Following specimen collection, DNA extraction was performed, and then full-length 16S rRNA and ITS region amplification and subsequent ONT sequencing were conducted. Results: Conventional clinical culture-based methodologies detected pathogens in 13 patients. Among the 14 successfully sequenced specimens, ONT sequencing elucidated a diverse spectrum of bacteria and fungi, with read counts ranging from 375 to 19,716. Polymicrobial and anaerobe-enriched communities were predominantly observed in lower gastrointestinal tract infections, specifically colonic or small bowel perforations, whereas upper gastrointestinal perforations, including those of the stomach or duodenum, were frequently dominated by Streptococcus, Granulicatella, or Candida species. The sequencing identified pathogens concordant with culture results, including Escherichia coli, Enterococcus, and Candida albicans. In addition, anaerobic or low-abundance taxa were exclusively identifiable through sequencing methodologies. Conclusions: ONT sequencing facilitated results within up to 24 h and successfully detected pathogens in culture-negative cases. These findings underscore the utility of ONT sequencing as an expeditious and comprehensive diagnostic modality for IAIs. Full article

This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. As such, several key values are extracted from about 900 competition recordings in order to generate a dataset for the identification of patterns in athletes’ technique and efficiency. A CNN-based framework is used to automate the detection of human keypoints and features, enabling a large-scale evaluation of climbing dynamics. The results revealed significant variations in performance for single sections of the wall, particularly in relation to start reaction times (with differences of up to 0.27 s) and increased split times the closer the athletes are to the end of the Speed Climbing wall (from 0.39 s to 0.45 s). In addition, a more detailed examination of the movement sequences was carried out by analyzing the velocity trajectories of hands and feet. The results showed that coordinated and harmonic movements, especially of the lower limbs, correlate strongly with the performance outcome. To ensure an individualized view of the data points, a comparison was made between multiple athletes, revealing insights into the influence of individual biomechanics on the efficiency of movements. The findings provide both trainers and athletes with interesting insights in relation to tailoring training methods by including split time benchmarks and limb coordination. Full article

In recent years, the research trend of HSI classification has focused on the innovative integration of deep learning and Transformer architecture to enhance classification performance through multi-scale feature extraction, attention mechanism optimization, and spectral–spatial collaborative modeling. However, due to the excessive computational complexity and the large number of parameters of the Transformer, there is an expansion bottleneck in long sequence tasks, and the collaborative optimization of the algorithm and hardware is required. To better handle this issue, our paper proposes a method which integrates RWKV linear attention with Transformer through a novel TC-Former framework, combining TimeMixFormer and HyperMixFormer architectures. Specifically, TimeMixFormer has optimized the computational complexity through time decay weights and gating design, significantly improving the processing efficiency of long sequences and reducing the computational complexity. HyperMixFormer employs a gated WKV mechanism and dynamic channel weighting, combined with Mish activation and time-shift operations, to optimize computational overhead while achieving efficient cross-channel interaction, significantly enhancing the discriminative representation of spectral features. The pivotal characteristic of the proposed method lies in its innovative integration of linear attention mechanisms, which enhance HSI classification accuracy while achieving lower computational complexity. Evaluation experiments on three public hyperspectral datasets confirm that this framework outperforms the previous state-of-the-art algorithms in classification accuracy. Full article

A mutation detection strategy based on mismatch digestion was applied previously in barley seedlings carrying the chloroplast mutator (cpm) genotype through many generations. Sixty-one mutations were detected along with four large indels: a 15 bp insertion in the intergenic region between tRNA^His and rps19 genes, a 620 bp deletion in the psbA gene, a 79 bp deletion in the intergenic region between rpl33 and rps18 genes and a 45 bp deletion in the rps3 gene. The present investigation aims to understand the mechanisms producing the large indels and to better characterize the cpm mutagenic effect. Whole plastome sequencing revealed novel polymorphisms that were identified either in regions not previously examined or in regions that were explored but not detected through celery juice extract (CJE) digestion. The 620 bp deletion in the psbA gene was lethal when homoplastomic, whereas the 45 bp deletion in the rps3 gene did not affect the viability of the seedlings even in homoplastomy. The presence of direct repeats at the borders of large indels suggests that they could have originated by illegitimate recombination because of CPM protein malfunction. A truncated mismatch repair MSH1 protein identified in cpm seedlings suggests that CPM is involved in organellar genome stability maintenance. Full article

Some Melanothamnus species have been documented growing epiphytically on other algae in seaweed aquaculture farms as fouling organisms. Such turf-forming Polysiphonia-looking algae were collected from a small (<1.0 km² area) Agarophyton tenuistipitata (Gracilariaceae, Gracilariales) farm on the east coast of the Bay of Bengal and examined for their taxonomy. DNA was extracted from silica gel-preserved specimens, and plastid-encoded rbcL, nuclear-encoded small subunit SSU, large subunit LSU, and universal plastid amplicon (UPA) were amplified and sequenced. Maximum likelihood (ML) and Bayesian inference were performed for the phylogenetic analysis. Four single-locus species delimitation methods (SDMs), namely, the generalized mixed Yule-coalescent (GMYC) method, a Poisson tree processes (PTP) model, the automatic barcode gap discovery (ABGD), and the assemble species by automatic partitioning (ASAP) method, were performed to segregate the putative species from other taxa in the Polysiphonia sensu lato clades. Our results revealed that rbcL had 1.4% interspecific genetic divergence, whereas LSU, UPA, and SSU had 1.6%, 2.5%, and 5.4% genetic divergence, respectively, from the nearest neighbors. Both comparative genetic and distinct morphological data revealed that the collected Bay of Bengal specimens comprise a species new to science. In addition, the above-mentioned SDMs supported the genetic data and segregated our specimens as Melanothamnus coxsbazarensis sp. nov. as a distinct species. Full article

Chlorogenic acid (CGA)-rich extracts from Eucommia ulmoides Oliver (CAE) are known for their gut health and antioxidant benefits in livestock. This study examines the effects of CAE supplementation during the peri-implantation period on sow reproductive performance and the gut microbiota. Sixty Dongliao black sows were randomized to receive either no supplementation (control) or CAE at 600 or 2000 mg/kg daily from gestation day −5 through day 15. High-dose CAE intake significantly increased total antioxidant capacity (T-AOC), superoxide dismutase (SOD), catalase (CAT), immunoglobulin A (IgA), and immunoglobulin M (IgM) levels in sow serum but decreased malondialdehyde (MDA) levels. Fecal short-chain fatty acids (SCFAs) also increase significantly. These changes correlate with improved reproductive performance, including a larger litter size, higher numbers of live-born piglets, a greater individual birth weight of live-born piglets, a higher total litter birth weight of live-born piglets, and a lower mortality rate. 16S rRNA sequencing of the fecal microbiota revealed that CAE markedly altered microbial diversity and composition, reducing the abundance of potentially harmful bacteria but increasing the abundance of beneficial bacteria. In conclusion, supplementation with CAE during the peri-implantation phase can reduce oxidative stress, alter the gut microbiota composition, and improve sow reproductive performance, thus potentially increasing breeding farm profitability. Full article

River water enters human life in various ways, with many disease outbreaks closely linked to contaminated sources. This study collected water samples from the Perak River in Malaysia, extracted environmental DNA (eDNA), and analyzed biological communities using metabarcoding and sequencing techniques to assess the local environmental health of the river. Through 16S rRNA sequencing, 4045 bacterial OTUs were identified, while 18S rRNA sequencing revealed 3422 eukaryotic OTUs, highlighting the diverse microbial and eukaryotic communities in the Perak River. The results showed certain organisms such as Serratia marcescens and Strombidium with potentially abnormal abundance, based on comparisons with other studies, suggesting possible organic and heavy metal pollution. Additionally, 35 potential pathogens, including bacteria, fungi, and parasites, were detected in the samples, all of which pose potential threats to human and animal health. While most bacterial pathogens are opportunistic, their potential risks should not be overlooked. These findings provide valuable insights into the river’s ecological status and help guide targeted conservation, surveillance and pollution management strategies. Ultimately, this study highlights environmental health issues through biodiversity analysis and identifies pathogens, contributing to the protection of human and animal health and aligning with the principles of the One Health approach. Full article

In this paper, a class of fully probabilistic time series models based on Gaussian Vector Mixtures (VMs), i.e., on linear combinations of multivariate Gaussian distributions, is proposed to model electricity Day Ahead Market (DAM) hourly prices and to generate consistent related DAM prices dynamic scenarios. These models, based on latent variables, intrinsically allow for organizing DAM data in hierarchically organized clusters, and for recreating the delicate balance of price spikes and baseline price dynamics present in the DAM data. The latent variables and the parameters of these models have a simple and clear interpretation in terms of market phenomenology, like market conditions, spikes and night/day seasonality. In the machine learning community, different to current deep learning models, VMs and the other members of the class discussed in the paper could be seen as just ‘oldish’ probabilistic models. In this paper it is shown, on the contrary, that they are still worthy models, excellent at extracting relevant features from data, and directly interpretable as a subset of the regime switching autoregressions still currently largely used in the econometric community. In addition, it is shown how they can include mixtures of mixtures, thus allowing for the unsupervised detection of hierarchical structures in the data. It is also pointed out that, as such, VMs cannot fully accommodate the autocorrelation information intrinsic to DAM data time series, hence extensions of VMs are needed. The paper is thus divided into two parts. In the first part, VMs are estimated and used to model daily vector sequences of 24 prices, thus assessing their scenario generation capability. In this part, it is shown that VMs can very well preserve and encode infra-day dynamic structure like autocorrelation up to 24 lags, but also that they cannot handle inter-day structure. In the second part, these mixtures are dynamically extended to incorporate dynamic features typical of hidden Markov models, thus becoming Vector Hidden Markov Mixtures (VHMMs) of Gaussian distributions, endowed with daily latent dynamics. VHMMs are thus shown to be very much able to model both infra-day and inter-day phenomenology, hence able to include autocorrelation beyond 24 lags. Building on the VM discussion on latent variables and mixtures of mixtures, these models are also shown to possess enough internal structure to exploit and carry forward hierarchical clustering also in their dynamics, their small number of parameters still preserving a simple and clear interpretation in terms of market phenomenology and in terms of standard econometrics. All these properties are thus also available to their regime switching counterparts from econometrics. In practice, these very simple models, bridging machine learning and econometrics, are able to learn latent price regimes from historical data in an unsupervised fashion, enabling the generation of realistic market scenarios while maintaining straightforward econometrics-like explainability. Full article

Background/Objective: Orthodontic mini-implants (MI) create new niches that may alter the oral microbiota and modulate host immune responses. While clinical inflammation is not always evident, microbial and molecular changes may precede visible signs of peri-implant infection. This study investigated microbial shifts and inflammatory responses following MI placement, with a focus on Saccharibacteria, nitrate-reducing bacteria (NRB), and periodontopathogens. Methods: Saliva and peri mini-implant crevicular fluid (PMICF) samples were collected from eight orthodontic patients at baseline (T0), one week (T1), and one month (T2) after mini-implant placement. DNA was extracted from each saliva and PMICF sample and pooled across the eight patients for each time point. The pooled DNA were then subjected to 16S rRNA gene sequencing using the Oxford Nanopore MinION platform. Statistical analysis was performed to determine shifts in bacterial abundance, diversity, and co-occurrence patterns across the different sample types (saliva vs. PMICF) and time points. Results: Alpha diversity decreased in PMICF at T2, while it remained stable in saliva samples. Periodontopathogens (Porphyromonas gingivalis, Treponema denticola, Fusobacterium nucleatum) increased in PMICF at T2, while NRB and Saccharibacteria, along with a representative host bacterium (Schaalia odontolytica), remained relatively stable. Co-occurrence analysis showed antagonistic relationships between Saccahribacteria/NRB and periodontopathogens. IL-6 significantly decreased from T1 to T2, while CRP showed a non-significant downward trend. The expression of nitrate reductase genes narG and napA remained stable across time intervals. Conclusions: Despite no clinical inflammation, MI placement led to localized microbial shift and mild inflammatory responses. NRB and Saccharibacteria’s stability and antagonistic relationship to periodontopathogens may indicate that they could be involved in maintaining microbial homeostasis. These findings highlight possible early biomarkers and ecological strategies to support oral health in MI patients. Full article

Objectives: Alpinia japonica (A. japonica) is traditionally used for digestive disorders, but its hypolipidemic mechanisms remain unclear. This study investigated the lipid-lowering effects of its fruit (SJGS), rhizome (SJGJ), and leaf (SJY) extracts, exploring their bioactive constituents and organ-specific mechanisms. Methods: Sprague Dawley rats (n = 8/group) fed a high-fat diet received SJGS, SJGJ, or SJY (200 mg/kg/day) for 4 weeks. Serum lipids (TC, TG), liver enzymes (AST, ALT), and intestinal barrier markers (DAO) were measured. Gut microbiota (16S rDNA sequencing), hepatic histopathology, and ileal tight junction proteins were analyzed. Transcriptomics and qPCR assessed ileal gene expression. LC-MS identified chemical constituents, while network pharmacology predicted compound-target interactions. Results: All extracts significantly reduced serum TC (↓ 27–33%), TG (↓ 29–38%), AST/ALT (↓ 22–30%), and DAO (↓ 35–42%) versus controls (p < 0.05). They improved hepatic steatosis, enhanced intestinal barrier function, and modulated gut microbiota (↑ α-diversity, ↓ Firmicutes/Bacteroidetes ratio). Transcriptomics revealed PPAR signaling as the core pathway: SJGS/SJGJ downregulated fatty acid oxidation genes (ACSL1, ACOX1, ACADM), while SJY upregulated APOA1 (2.3-fold). LC-MS identified 33–48 compounds/part, with seven shared constituents. Network analysis prioritized three flavonoids (pinocembrin, luteolin, galangin) targeting TNF, AKT1, and PPAR pathways. Conclusions: The findings suggest A. japonica extracts ameliorate hyperlipidemia through distinct mechanisms—SJGS/SJGJ may inhibit fatty acid oxidation, while SJY potentially enhances APOA1-mediated clearance. Shared flavonoids likely contribute to these effects via PPAR signaling, supporting its traditional use. This study provides a scientific basis for the sustainable utilization of A. japonica resources. Full article

Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional network to capture structural dependencies and symmetric interactions. This symmetry is justified by the inherent bilateral nature of human facial anatomy, where AU relationships are based on co-occurrence and facial anatomy analysis (as per the FACS), which are typically undirected and symmetric. Human faces are symmetric, and such relationships align with the design of classic spectral GCNs for undirected graphs, assuming that adjacency matrices are symmetric to model non-directional co-occurrences effectively. Predicted AU activations and timestamps are interpolated into continuous motion curves using B-spline functions and mapped to skeletal controls within a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 77.93% and an accuracy of 84.80% (k-fold cross-validation, k = 5), outperforming baselines in temporal segmentation. Subjective evaluations confirm that the rendered digital human exhibits improvements in perceptual clarity, naturalness, and realism. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions through curve extraction from AU values and timestamps. Full article

This study evaluates the effects of two Trichoderma reesei exogenous fibrolytic enzyme (EFE) preparations on the taxonomic profile, diversity, relative abundance, and population shifts of three ruminal bacteria fractions of lactating cows: free-floating (LIQ), weakly (AS), and tightly (SOL) feed-adhered. Three lactating cows were fed three EFE treatments in a 3 × 3 Latin square design: one control (CON) without enzymes, a cellulase/xylanase mix (MIX), and a high-xylanase treatment (XYL). Rumen contents were collected, and bacteria were extracted from the three ruminal content fractions for next-generation sequencing analysis. Alpha diversity was higher in XYL compared to CON. However, no EFE effect was observed on beta diversity. The relative abundance (RA) of the family Prevotellaceae increased, while that of Ruminococcaceae and Rikenellaceae decreased in XYL compared to MIX and CON. The bacterial community structure (beta diversity) of LIQ was differentiated from that of SOL and AS (p = 0.03), but no effects of fraction were observed on alpha diversity. Lachnospiraceae RA was greater in SOL, followed by AS, and lower in LIQ (p < 0.001), while Spirochaetaceae RA was greater in SOL and AS compared to LIQ (p = 0.003). The effects of EFE supplementation on rumen bacterial RA were independent of the ruminal content fraction. Full article

Background: The gut microbiome affects human health, and patients with cancer are no exception. In those patients, intensive chemotherapy impairs gut barrier integrity, causing dysbiosis, bacterial translocation, and higher infection risk. Objectives: This prospective study, conducted at Children’s Cancer Hospital in Egypt, profiles the microbiome of 29 pediatric patients with AML, and examines how induction chemotherapy and antibiotics affect their microbiome. Methods: Gut microbiome changes were evaluated before treatment (T1), then 7 (T2) and 21–28 days (T3) from induction start. Microbial DNA, extracted from rectal swabs or stool samples, was subjected to 16S rRNA amplicon sequencing, followed by bioinformatics and statistical analyses. Results: Treatment significantly decreased the richness and Shannon diversity of the gut microbiome and caused dysbiosis that was only partially restored at T3. Whereas Firmicutes remained the most abundant phylum throughout, Actinobacteria significantly decreased in abundance after treatment. Proteobacteria had their lowest abundance at T3, while Verrucomicrobacteria were relatively abundant at T1 but undetectable by T3. The abundance of Enterococcus and Klebsiella was associated with stool culture results, and the Proteobacteria-to-Firmicutes ratio was associated with treatment. Conclusions: Gut microbial diversity declined in patients during induction chemotherapy, with a strong association of microbial composition with stool culture results but not with bacteremia. Full article

Rolling bearings are one of the core components of industrial equipment. Owing to the rapid development of deep learning methods, a multitude of data-driven remaining useful life (RUL) estimation approaches have been proposed recently. However, several challenges persist in existing methods: the limited accuracy of traditional data-driven models, instability in sequence prediction, and poor adaptability to diverse operational environments. To address these issues, we propose a novel prognostics approach integrating three key components: time-intrinsic mode functions-derived feature representation (TIR) sequences, a one-dimensional temporal feature convolution–reallocation network (TFCR) with a flexible configuration scheme, and a physics-based Kalman filtering method. The approach first converts denoised signals into TIR-sequences using windowed empirical mode decomposition (EMD). The TFCR network then extracts hidden high-dimensional features from these sequences and maps them to the initial RUL. Finally, physics-based Kalman filtering is applied to enhance prediction stability and enforce physical constraints, producing refined RUL estimates. The experimental results based on the XJTU-SY dataset show the superiority of the proposed approach and further prove the feasibility of this method in bearing RUL estimation. Full article

With the rapid proliferation of unmanned aerial vehicles (UAVs), reliable identification based on radio frequency (RF) signals has become increasingly important for both civilian and security applications. This paper proposes a spatiotemporal feature extraction and classification framework based on bispectral analysis. Specifically, bispectral estimation is used to convert one-dimensional RF signals into two-dimensional bispectrum feature maps that capture higher-order spectral characteristics and nonlinear dependencies. Based on these characteristics, a two-stage network was constructed for spatiotemporal feature extraction and classification. The first stage utilizes a ResNet18 network to extract spatial structural features from individual bispectrum maps. The second stage employs an LSTM network to learn temporal dependencies across the sequence of bispectrum maps, capturing the continuity and evolution of signal characteristics over time. The experimental results on a public dataset of UAV RF signals show that this method improves recognition accuracy by 6.78% to 13.89% compared to other existing methods across five categories of UAVs. Full article