Search Results (3,253)

The flexible job shop scheduling problem (FJSP) becomes significantly more complex when real-world factors such as due dates, sequence-dependent setup times, and processing times are considered as multiple criteria. This study presents a hybrid scheduling approach that combines a genetic algorithm (GA) and variable neighborhood search (VNS), where several dispatching rules are used to create the initial population and improve exploration. The multiple objectives are to minimize makespan, total tardiness, and total setup time while improving overall production efficiency. To test the proposed approach, standard FJSP datasets were extended with due dates and setup times for two different environments. Due dates were generated using the Total Work Content (TWK) method. This study also introduces a dynamic scheduling framework that addresses dynamic events such as machine breakdowns and new job arrivals. A rescheduling strategy was developed to maintain optimal solutions in dynamic situations. Experimental results show that the proposed hybrid framework consistently performs better than other methods in static scheduling and maintains high performance under dynamic conditions. The proposed method achieved 6.5% and 2.59% improvement over the baseline GA in two different environments. The results confirm that the proposed strategies effectively address complex, multi-constraint scheduling problems relevant to Industry 4.0 and smart manufacturing environments. Full article

Parasites of the Leishmania genus are globally distributed and cause various clinical presentations in animals and humans, collectively known as leishmaniasis. The genomes of Leishmania and other trypanosomatids exhibit remarkable plasticity, shaped by several distinctive genetic features. Although these features can hinder the application of next-generation DNA sequencing (NGS) technologies, NGS data have been successfully used to characterize the whole-genome diversity of circulating Leishmania strains. The results complement and are broadly aligned with previous findings obtained with more traditional methods, offering greater resolution when working with geographically closer strains. In this review, we summarize advances over the past two decades in characterizing the genome diversity of Leishmania parasites using NGS strategies. We also discuss the application of these strategies to elucidate other aspects relevant to the epidemiology of these parasites, including their population structure and mode of reproduction. The vast majority of the studies to date have focused on species within the L. donovani/infantum complex or the L. (Viannia) subgenus, highlighting the need to incorporate other relevant underrepresented species and regions from both the Old and New World. Full article

Increasingly, discussions that once took place in social environments are transitioning to digital platforms. The role of news media is significant in shaping and enhancing discussions around many topics. This study argues that health-related topics in public discourse, transitioning from offline to online, necessitate rigorous validation. That is why this study proposed the application of deep learning techniques to the boundaries and deviation of accuracies in health-related topics by analyzing health-related tweets from major news outlets such as BBC, CNN, CBC, and Reuters. The study developed LSTM and CNN classifiers to categorize content pertinent to the discourse following the formal deep learning process and employed a sequence of VAEs to verify the learnability and stability of the classifiers. The LSTM demonstrated superior performance compared to CNN, attaining validation accuracies of 98.4% on BBC and CNN, 97.8% on CBC, and 97.3% on Reuters. The optimal configuration of our LSTM achieved a precision of 98.69%, a recall of 98.20%, and an F1-score of 97.90% and recorded the lowest false positive rate, at 1.30%. This provided us with the optimal overall equilibrium for operational oversight. The VAE runs demonstrated that the model exhibited stability and the ability to generalize across different sources, achieving approximately 99.6% for Reuters and around 98.4% for BBC. The findings confirm that deep learning models are capable of reliably tracking the online migration of health discourse driven by news media. This provides a solid foundation for near-real-time monitoring of public engagement and for informing sustainable healthcare recommendation systems. Full article

The mitochondrial DNA of trypanosomatid parasites consists of thousands of catenated minicircles and dozens of maxicircles that form a complex network structure, the kinetoplast (kDNA). Although kDNA replication and segregation during mitotic division are well studied, its inheritance during genetic exchange events remains unclear. In Trypanosoma brucei, hybrids inherit minicircles biparentally but retain maxicircles from a single parent. Although biparental inheritance of minicircles has been described in natural Trypanosoma cruzi hybrids, this process has not been explored in laboratory-generated hybrids of this parasite. In the present study, we analyzed kDNA inheritance in T. cruzi experimental hybrids using a comprehensive minicircle hypervariable region (mHVR) database and genome sequencing data. Our findings revealed biparental inheritance of minicircles, with hybrid lines retaining mHVRs from both parents for over 800 generations. In contrast, maxicircles were exclusively inherited from one parent. Unexpectedly, we observed an increase in kDNA content in hybrids, affecting both minicircles and maxicircles, and exhibiting instability over time. To explain these findings, we propose a Replicative Mixing (REMIX) model, where the hybrid inherits one kinetoplast from each parent and they are replicated allowing minicircle mixing. Instead maxicircle networks remain physically separated, leading to uniparental fixation after segregation in the first cell division of the hybrid. This model challenges previous assumptions regarding kDNA inheritance and provides a new framework for understanding kinetoplast dynamics in hybrid trypanosomes. Full article

Congenital eye malformations like microphthalmia–anophthalmia–coloboma (MAC), anterior segment dysgenesis (ASD), primary congenital glaucoma (PCG) and congenital cataracts (CC) are significant causes of childhood visual impairment. Phenotypic heterogeneity often complicates diagnosis. The goal of this study was to optimize the diagnostic strategy for next-generation sequencing (NGS)-based procedures, thereby aiming to identify genetic causes of congenital eye malformations. Forty patients with congenital eye malformations were included. A primary diagnostic testing (PD) of a limited number of genes was followed by multigene panel (MGP) testing, including 186 eye-related genes, and exome sequencing. Causative variants were identified in 17 patients (43%) and clinically relevant variants of uncertain significance (VUS) in 6 patients (15%). PD had a diagnostic yield (DY) of 15%, MGP of 29% and exome sequencing of 4%, leading to a cumulative DY of 43%. Diagnostic rates were highest in CC (75%), bilateral cases (46%), complex ocular phenotypes (78%), patients with extraocular manifestations (55%) and positive family history (70%). Rare and possible new genotype–phenotype correlations and candidate genes (FAT1, POGZ) could be identified. In total, eight (likely) pathogenic variants in six genes (CYP1B1, ADAMTS18, MAB21L2, NHS, MFRP, CRYBB1) were not yet reported. A stepwise genetic testing approach starting with a broad multigene panel followed by exome sequencing provides higher diagnostic yield than limited phenotype-specific testing. Comprehensive genetic diagnosis is essential for prognosis, treatment and genetic counseling, underscoring the need for routine genetic testing and interdisciplinary collaboration in managing congenital eye malformations. Full article

Earle’s balanced salt solution (EBSS) is a classical autophagy inducer that provides a special culture environment lacking amino acids and serum, causing cell starvation. However, the production of relevant omics data surrounding EBSS-induced autophagy is still in the early stage. The objective of this study was to identify new potential functional proteins in the autophagy process through omics analysis. We selected EBSS-induced autophagy as our research object and uncovered autophagy-regulatory proteins using RNA-seq analysis. Western blotting showed that EBSS increased LC3B-II protein levels in NRK cells, reaching the maximum amount at 2 h of culture. Then, we used next-generation sequencing to obtain quantified RNA-seq data from cells incubated with EBSS and the bowtie–tophat–cufflinks flow path to analyze the transcriptome data. Using significant differences in the FPKM values of genes in the treated group compared with those in the control group to indicate differential expression, 470 candidate genes were selected. Subsequently, GO and KEGG analyses of these genes were performed, revealing that most of these signaling pathways were closely associated with autophagy, and to better understand the potential functions and connections of these genes, protein–protein interaction networks were studied. Considering all the conclusions of the analysis, 27 candidate genes were selected for verification, where the knockdown of Txnrd1 decreased LC3B-II protein levels in NRK cells, consistent with the results of confocal experiments. In conclusion, we uncovered autophagy-regulatory proteins using RNA-seq analysis, with our results indicating that TXNRD1 may play a role in regulating EBSS-induced autophagy via an unknown pathway. We hope that our research can provide useful information for further autophagy omics research. Full article

Type 1 Diabetes Mellitus (T1D) is an autoimmune disease characterized by the destruction of pancreatic β-cells, predominantly manifesting in childhood or adolescence. The lack of clearly interpretable biological markers in the early stages, combined with the insidious onset of the disease, poses significant challenges to early diagnosis and the implementation of preventive strategies. The applicability of classic T1D biomarkers for understanding the mechanisms of the autoimmune process, preclinical diagnostics and treatment efficiency is limited. Despite advances in next-generation sequencing (NGS) technologies, which have enabled large-scale genome-wide association studies (GWASs) and the identification of polygenic risk scores (PRSs) associated with T1D predisposition, as well as progress in bioinformatics approaches for assessing dysregulated gene expression, no universally accepted risk assessment model or definitive predictive biomarker has been established. Until now, the use of new promising biomarkers for T1D diagnostics is limited by insufficient evidence base. However, they have great potential for the development of diagnostic methods on their basis, which has been shown in single or serial large-scale studies. This critical review covers both well-known biomarkers widely used in clinical practice, such as HLA-haplotype, non-HLA SNPs, islet antigen autoantibodies, C-peptide, and the promising ones, such as cytokines, cfDNA, microRNA, T1D-specific immune cells, islet-TCR, and T1D-specific vibrational bands. Additionally, we highlight new approaches that have been gaining popularity and have already demonstrated their potential: GWAS, single-cell transcriptomics, identification of antigen-specific T cells using scRNA-seq, and FTIR spectroscopy. Although some of the biomarkers, in our opinion, are still limited to a research context or are far from being implemented in clinical diagnostics of T1D, they have the greatest potential of being applied in clinical practice. When integrated with the monitoring of the classical autoimmune diabetes markers, they would increase the sensitivity and specificity during diagnostics of early and preclinical stages of the disease. This critical review aims to evaluate the current landscape of classical and emerging biomarkers in autoimmune diabetes, with a focus on those enabling early detection—prior to extensive destruction of pancreatic islets. Another goal of the review is to focus the attention of the scientific community on the gaps in early T1D diagnostics, and to help in the selection of markers, targets, and methods for scientific studies on creating novel diagnostic panels. Full article

Nitrous oxide (N₂O), a potent greenhouse gas, is an important environmental concern associated with biological nitrogen removal in wastewater treatment plants. Anaerobic ammonium oxidation (anammox), recognized as an advanced carbon-neutral nitrogen removal technology, requires a continuous supply of nitrite, which also serves as a key precursor for N₂O generation. However, the regulation of the carbon-to-nitrogen (C/N) ratio to minimize N₂O emission in mainstream anammox systems remains insufficiently understood. In this study, we evaluated the long-term nitrogen removal performance and N₂O emission potential of an oxygen-limited anammox biofilm reactor treating synthetic municipal wastewater with a typical C/N range of 4.0–6.0. Experimental results demonstrated that the highest nitrogen removal efficiency (95.3%), achieved through coupled anammox and denitrification, and the lowest N₂O emission factor (0.73%) occurred at a C/N ratio of 5.0. As the C/N ratio increased from 4.0 to 5.0, N₂O emissions decreased progressively, but rose slightly when the ratio was further increased to 6.0. High-throughput sequencing revealed that microbial community composition and the abundance of key functional taxa were significantly influenced by the C/N ratio. At a C/N ratio of 5.0, proliferation of anammox bacteria and the disappearance of Acinetobacter populations appeared to contribute to the significant reduction in N₂O emission. Furthermore, gene annotation analysis indicated higher abundances of anammox-associated genes (hzs, hdh) and N₂O reductase gene (nosZ) at this ratio compared with others. Overall, this study identifies a C/N-dependent strategy for mitigating N₂O emissions in mainstream anammox systems and provides new insights into advancing carbon-neutral wastewater treatment. Full article

Background/Objectives: Despite their ecological significance, DNA barcoding data for African rainforest Orthoptera remain underrepresented globally, limiting progress in species discovery, biodiversity assessment, and conservation. This study aimed to generate molecular data for morphologically identified Serpusia Karsch, 1891 species to evaluate their taxonomic status. Methods: Specimens were collected from multiple sites in Cameroon and analyzed using DNA barcoding with COI-5P and 16S rDNA markers. Species delimitation was performed with Automatic Barcode Gap Discovery, and phylogenetic relationships were inferred using Maximum Likelihood and Bayesian Inference. Additionally, external morphology and the male phallic complex were examined. Results: Molecular analyses delineated 19 MOTUs, five corresponding to Serpusia opacula, seven to Serpusia succursor and the remainder to outgroups. Similarity-based assignments matched these MOTUs to 19 BINs. Phylogenetic reconstruction revealed S. opacula and S. succursor as two genetically distinct clades, with the S. opacula group more closely related to Aresceutica Karsch, 1896 than to the S. succursor group. Accordingly, we established a new genus, Paraserpusia gen. nov., to accommodate S. succursor. Within the S. opacula group, five species are recognized: one previously described (S. opacula) and four new species (S. kennei sp. nov., S. missoupi sp. nov., S. seinoi sp. nov., and S. verhaaghi sp. nov.). The former S. succursor, now Paraserpusia succursor, is divided into six well-supported lineages, five of which are formally described here (P. hoeferi sp. nov., P. husemanni sp. nov., P. kekeunoui sp. nov., P. tamessei sp. nov., and P. tindoi sp. nov.). A haplotype network based on COI-5P sequences corroborates three major clades corresponding to the S. opacula group, the S. succursor group, and Aresceutica. Diagnostic morphological differences between Serpusia and Paraserpusia are consistently supported across characters. Conclusions: This integrative approach reveals substantial hidden diversity within Serpusia and highlights the importance of combining molecular and morphological data to uncover and formally describe previously overlooked taxa. Full article

Plants have evolved a complex, multilayered immune system that integrates molecular recognition, signaling pathways, epigenetic regulation, and small RNA-mediated control. Recent studies have shown that DNA-level regulatory mechanisms, such as RNA-directed DNA methylation (RdDM), histone modifications, and chromatin remodeling, are critical for modulating immune gene expression, allowing for rapid and accurate pathogen-defense responses. The epigenetic landscape not only maintains immunological homeostasis but also promotes stress-responsive transcription via stable chromatin modifications. These changes contribute to immunological priming, a process in which earlier exposure to pathogens or abiotic stress causes a heightened state of preparedness for future encounters. Small RNAs, including siRNAs, miRNAs, and phasiRNAs, are essential for gene silencing before and after transcription, fine-tuning immune responses, and inhibiting negative regulators. These RNA molecules interact closely with chromatin features, influencing histone acetylation/methylation (e.g., H3K4me3, H3K27me3) and guiding DNA methylation patterns. Epigenetically encoded immune memory can be stable across multiple generations, resulting in the transgenerational inheritance of stress resilience. Such memory effects have been observed in rice, tomato, maize, and Arabidopsis. This review summarizes new findings on short RNA biology, chromatin-level immunological control, and epigenetic memory in plant defense. Emerging technologies, such as ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing), ChIP-seq (Chromatin Immunoprecipitation followed by Sequencing), bisulfite sequencing, and CRISPR/dCas9-based epigenome editing, are helping researchers comprehend these pathways. These developments hold an opportunity for establishing epigenetic breeding strategies that target the production of non-GMO, stress-resistant crops for sustainable agriculture. Full article

Periprosthetic joint infection (PJI) remains a major complication in orthopedic surgery, with accurate and timely diagnosis being essential for optimal patient management. Traditional culture-based diagnostics are often limited by suboptimal sensitivity, especially in biofilm-associated and low-virulence infections. In recent years, non-culture-based methodologies have gained prominence. Molecular techniques, such as polymerase chain reaction (PCR) and next-generation sequencing (NGS), offer enhanced detection of microbial DNA, even in culture-negative cases, and enable precise pathogen identification. In parallel, extensive research has focused on biomarkers, including systemic (e.g., C-reactive protein, fibrinogen, D-dimer), synovial (e.g., alpha-defensin, calprotectin, interleukins), and pathogen-derived markers (e.g., D-lactate), the latter reflecting metabolic products secreted by microorganisms during infection. The development of multiplex platforms now allows for the simultaneous measurement of multiple synovial biomarkers, improving diagnostic accuracy and turnaround time. Furthermore, the integration of artificial intelligence (AI) and machine learning algorithms into diagnostic workflows has opened new avenues for combining clinical, molecular, and biochemical data. These models can generate probability scores for PJI diagnosis with high accuracy, supporting clinical decision-making. While these technologies are still being validated for routine use, their convergence marks a significant step toward precision diagnostics in PJI, potentially improving early detection, reducing diagnostic uncertainty, and guiding targeted therapy. Full article

Background/Objectives: Mosaicism in preimplantation embryos has important implications for embryo selection and reproductive outcomes. This study investigates the age-related frequency of mosaicism, analyzes its subtypes, and evaluates its clinical significance. Methods: A total of 36,506 blastocysts were analyzed using next-generation sequencing-based preimplantation genetic testing for aneuploidy between January 2021 and December 2023. The overall frequencies of euploid, aneuploid, mosaic, and no-call embryos were 20%, 56%, 23%, and 1%, respectively. In this study, we propose a new classification. Embryos were classified into two categories: Mosaic-A, referring to embryos identified as mosaic in standard genetic testing reports, and Mosaic-B, which includes both Mosaic-A and aneuploid embryos containing mosaicism. Results: The proportion of Mosaic-A embryos significantly decreased with maternal age (31% in women <35 years, 30% at 35–37 years, 23% at 38–40 years, 16% at 41–42 years, and 10% in women >42 years). By contrast, Mosaic-B embryos, which include Mosaic-A and aneuploid embryos with mosaicism, increased with age (46%, 49%, 53%, 56%, and 62% across the same age groups). Notably, as maternal age advanced, low-level complex mosaicism decreased, whereas high-level complex mosaicism significantly increased (p < 0.001, chi-square test for trend). Other mosaicism subtypes followed similar trends. These findings suggest that the increase in high-level complex mosaicism resulted from a higher incidence of post-zygotic mitotic errors occurring earlier in development and affecting a larger proportion of cells in older women. Conclusions: This study underscores the significance of incorporating a broader classification of mosaicism, including Mosaic-A and B, to enhance understanding of the biological behavior of mosaic embryos according to age and highlights the clinical importance of cryopreserving high-level or complex mosaic embryos for transfer in women of advanced age. Full article

The Coronavirus disease 2019 (COVID-19) pandemic had a profound global impact, yet children exhibited distinct clinical and epidemiological patterns compared to adults. Pediatric cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were generally characterized by milder disease, lower hospitalization rates, and few long-term sequelae. However, a subset of children developed severe complications such as multisystem inflammatory syndrome in children (MIS-C), highlighting the heterogeneity in disease presentation. Differences in immune system maturity and comorbidities likely contribute to the age-dependent manifestation of SARS-CoV-2 and other respiratory viruses. Persistent SARS-CoV-2 infection, particularly in immunocompromised individuals, has been implicated in the emergence of new viral variants with immune escape characteristics due to ongoing viral replication in the presence of selective pressure. While SARS-CoV-2 evolution in persistently infected adults has been well-documented, it is less clear how the virus evolves during persistent infection in the pediatric population. To address this question, we performed viral whole genome sequencing of longitudinal specimens collected from immunocompetent and immunocompromised pediatric COVID-19 patients. Similarly to what has been observed in adult cohorts, mutations associated with enhanced viral fitness and immune escape arose intra-host over time. Intra-host diversity accumulated at similar rates in immunocompetent and immunocompromised children, though more mutations overall were observed in the immunocompromised cohort due to the longer infection time courses. Overall, we identified similar viral evolutionary trends over the course of infection despite clinical differences in pediatric COVID-19 manifestation and severity. This similarity suggests that persistent infection in children may be an additional, but not unique, source of ongoing viral diversification. Full article

With the rise of 6G, the exponential growth of data traffic, the proliferation of emerging applications, and the ubiquity of smart devices, the demand for spectral resources is unprecedented. Terahertz communication (100 GHz–3 THz) plays a key role in alleviating spectrum scarcity through ultra-broadband transmission. In this study, terahertz optical carrier-based systems are employed, where fiber-optic components are used to generate the optical signals, and the signal is transmitted via direct detection in the receiver side, without relying on fiber-optic transmission. In these systems, deep learning-based equalization effectively compensates for nonlinear distortions, while probability shaping (PS) enhances system capacity under modulation constraints. However, the probability distribution of signals processed by PS varies with amplitude, making it challenging to extract useful information from the minority class, which in turn limits the effectiveness of nonlinear equalization. Furthermore, in IM-DD systems, optical multipath interference (MPI) noise introduces signal-dependent amplitude jitter after direct detection, degrading system performance. To address these challenges, we propose a lightweight neural network equalizer assisted by the Synthetic Minority Oversampling Technique (SMOTE) and a clustering method. Applying SMOTE prior to the equalizer mitigates training difficulties arising from class imbalance, while the low-complexity clustering algorithm after the equalizer identifies edge jitter levels for decision-making. This joint approach compensates for both nonlinear distortion and jitter-related decision errors. Based on this algorithm, we conducted a 3.75 Gbaud W-band PAM4 wireless transmission experiment over 300 m at Fudan University’s Handan campus, achieving a bit error rate of 1.32 × 10⁻³, which corresponds to a 70.7% improvement over conventional schemes. Compared to traditional equalizers, the proposed new equalizer reduces algorithm complexity by 70.6% and training sequence length by 33%, while achieving the same performance. These advantages highlight its significant potential for future optical carrier-based wireless communication systems. Full article

In the present paper, we introduce and investigate the concepts of Wijsman $f_{\rho }$-statistical convergence of order $\alpha$ and Wijsman strong $f_{\rho }$-convergence of order $\alpha$. These notions are defined as natural generalizations of classical statistical convergence and Wijsman convergence, incorporating the tools of modulus functions and natural density through the function $f$. We provide a detailed analysis of their structural properties, including inclusion relations, basic characterizations, and illustrative examples. Furthermore, we establish the inclusion relations between Wijsman $f_{\rho }$-statistical convergence and Wijsman strong $f_{\rho }$-convergence of order α, showing conditions under which one implies the other. These notions are different in general, while coinciding under certain restrictions on the function f, the parameter $\alpha$, and the sequence $\rho$. The results obtained not only extend some well-known findings in the literature but also open up new directions for further study in the theory of statistical convergence and its applications to analysis and approximation theory. Full article