Aimed to comprehensively investigate the presence of neural autoantibodies in the cerebrospinal fluid (CSF) and plasma of COVID-19 patients experiencing neurological complications during the Omicron wave in China. Forty consecutive COVID-19 patients with severe neurological complications and 15 disease controls (DC) were enrolled in the study. Neural autoantibodies were detected using both the indirect immunofluorescence assay (IFA) on mouse brain tissue and the Brain-neuronal-antigen microarray. A bioinformatics analysis was conducted to evaluate the similarity between SARS-CoV-2 proteins and the specific antigens identified by the microarray. Our results showed a markedly elevated prevalence of neural autoantibodies in CSF (62.16% vs. 0.0%) and in plasma of COVID-19 patients compared to DC using IFA. Additionally, we identified 12 upregulated intrathecal IgG autoantibodies with differential levels between COVID-19 patients and DC, as well as 51 upregulated IgG autoantibodies in plasma. Anti-mGluR2 antibodies exhibited the highest positivity rate in COVID-19 patients, a finding further confirmed by cell-based assays (CBA). Moreover, we discovered a shared peptide similarity between the SARS-CoV-2 N, S protein and mGluR2. Among two COVID-19 patients with positive anti-mGluR2 antibodies, there were specific and prominent bindings to both the RBD-Fc of S and mGluR2, while only weak binding was observed to N.

Traditional maritime Search and Rescue (SAR) operations face notable limitations, including reliance on manual observation and the high costs associated with deploying search vessels and aircraft, which often contribute to delayed response times. In contrast, the advent of Unmanned Aerial Vehicles (UAVs) equipped with high-resolution sensors has demonstrated considerable potential in enhancing SAR efforts. UAVs provide rapid aerial surveillance over expansive areas, offering a cost-effective, flexible solution with a lower environmental footprint. The recognition of the benefits these technologies can bring to SAR missions has driven researchers to investigate computer vision based object detection methodologies. Ongoing advancements in machine learning and computer vision are focused on creating robust object detection systems that can operate in real-time, even in the dynamic and challenging conditions typical of maritime environments. This paper investigates integrating computer vision techniques into UAV-based maritime SAR operations. It offers a comprehensive review of current research, including types of sensors, datasets, and object detection methods, along with the evaluation metrics used to assess system performance. By analyzing the current state of this field, the study aims to identify key challenges, propose viable solutions, and outline future research directions to further advance UAV-based SAR capabilities.

not-yet-known not-yet-known not-yet-known unknown Opercular Syndrome as a Sequelae of Herpes Simplex Virus Infection: A Case Report Nischal Neupane1, Devansh Upadhyay1*, Gehendra Jung Kunwar1, Prajjwol Luitel1, Sujan Paudel1, Bibek Koirala2, Sunil Babu Khanal3, Pradeep Panthi4, Ragesh Karn4, Rajeev Ojha4 1 Maharajgunj Medical Campus, Tribhuvan University, Institute of Medicine Kathmandu, Nepal. 2 Department of Radio-diagnosis and Interventional radiology, Tribhuvan University, Institute of Medicine Kathmandu, Nepal 3 Department of Internal Medicine, Tribhuvan University, Institute of Medicine Kathmandu, Nepal 4 Department of Neurology, Tribhuvan University, Institute of Medicine Kathmandu, Nepal. *Corresponding Author: Devansh Upadhyay Devansh.upd11@gmail.com Maharajgunj Medical Campus, Tribhuvan University, Institute of Medicine Kathmandu, Nepal. Orcid: 0000-0002-1186-7844

Haploblocks are regions of the genome that coalesce to an ancestor as a single unit. Differentiated haplotypes in these regions can result from the accumulation of mutational differences in low-recombination chromosomal regions, especially when selective sweeps occur within geographically structured populations. We introduce a method to identify large well-differentiated haploblock regions (LHBRs), based on the variance in standardized heterozygosity (ViSHet) of single nucleotide polymorphism (SNP) genotypes among individuals, calculated across a genomic region (500 SNPs in our case). We apply this method to the greenish warbler (Phylloscopus trochiloides) ring species, using a newly assembled reference genome and genotypes at more than 1 million SNPs among 257 individuals. Most chromosomes carry a single distinctive LHBR, containing 4-6 distinct haplotypes that are associated with geography, enabling detection of hybridization events and transition zones between taxa. LHBRs have exceptionally low within-haplotype nucleotide variation and moderately low between-haplotype nucleotide distance, suggesting their establishment through recurrent selective sweeps at varying geographic scales. Meiotic drive is potentially a powerful mechanism of producing such selective sweeps, and the LHBRs are likely to often represent centromeric regions where recombination is restricted. Links between populations enable introgression of favored haplotypes and we identify one haploblock showing a highly discordant distribution compared to the rest of the genome, being present in two distantly separated geographic regions that are at similar latitudes in both east and west Asia. Our results set the stage for detailed studies of haploblocks, including their genomic location, gene content, and contribution to reproductive isolation.

This work is the demonstration of a two-storage inventory model for deteriorating items, considering bi-level trade credit facility. The customers’ demand is dependent on selling price of the item and credit period offered by the retailer. Further, the items are deteriorating at a constant rate during stock-in situation but the deterioration rates of the items in different warehouse are different. According to the credit period provided by the supplier to retailer, three distinct scenarios may arise among which are discussed in detail. The corresponding optimization problems are solved with the help of three tournament-based hybrid algorithms (tournament differential evolution (TDE), tournament teaching learning based optimization (TTLBO) and tournament RAO-3 algorithms). Further, the feasibility of the proposed model is validated by considering a numerical example. Finally, the impacts of different system parameters on optimal policy are studied with the help of sensitivity experiment and its impacts are shown graphically.

The growing demands on memory and contextual processing within expansive language models present critical challenges, particularly in resource efficiency and scalability across diverse linguistic tasks. Introducing a novel framework, Dynamic Contextual Scope Management (DCSM), this approach significantly redefines adaptive memory reallocation, equipping language models with a refined mechanism to allocate and segment memory based on dynamic context relevance. By realigning memory allocation in real-time to maintain only pertinent information, the DCSM framework achieves substantial improvements in memory usage, response coherence, and overall model adaptability. Experimental results demonstrate the framework's impact, with DCSM-enhanced models demonstrating up to 33.6% reductions in memory consumption and notable enhancements in response accuracy, latency, and computational efficiency. The integration of DCSM within large-scale language models not only addresses longstanding limitations in handling extended contexts but also establishes a scalable pathway for adaptive, high-performance language processing. These findings highlight DCSM's potential for transformative applications in contexts requiring efficient handling of extensive and evolving information, fostering a new standard for adaptive memory systems in the field of advanced language models.

not-yet-known not-yet-known not-yet-known unknown Abstract Background: Peanut allergy (PA) is a significant health concern. Children with a relatively low reaction threshold PA have an increased risk of accidental life-threatening reactions, compared to those with a higher threshold. Diagnostic and treatment protocols for high threshold PA are also safer and easier to implement. Our study aims to identify clinical factors determining the reaction threshold in PA children. Methods: A retrospective study, collecting and analyzing data of challenge-proven PA children under 12 years of age, from our pediatric food allergy center, where all children with suspected PA are offered a diagnostic challenge. The data included demographics, medical history, skin test and oral food challenge results. Results: A total of 202 children under 12 years of age (mean 42 months, 95% CI 38-46) with challenge-proven PA were enrolled in the study. Children with low threshold PA (threshold less than 100mg) were more likely to be older than 4 years (43.6% Vs 21.7%, p < 0.001). In a gender subanalysis we found girls had additional influencing factors such as previous systemic/anaphylactic reaction (p<0.001). In a decision tree analysis, the major modifiable risk factor was a diagnosis at a young age. Conclusion: Since an oral food challenge is the gold standard for the diagnosis of PA, performing a diagnostic challenge at a young age, optimizes our chance to diagnose PA with a relatively high threshold, increasing the safety and outcomes of potential interventions. Therefore, the early referral of young patients for a diagnostic workup is mandated.

This article presents a novel method for cultivating plants in isolated environments, which significantly reduces water consumption in agriculture. This solution has the potential to increase agricultural production and food supply. Wheat, a staple food for humans, is particularly important for addressing global food shortages and hunger. The article introduces a new method for growing wheat using minimal water in isolated conditions, emphasizing the importance of disinfection and sterilization for successful grain growth.

Nisin is an antimicrobial peptide widely used as a biopreservative to extend shelf life and control foodborne pathogens, particularly Gram-positive bacteria such as Listeria monocytogenes. However, its efficacy against Gram-negative bacteria is limited due to its inability to penetrate their outer membrane and reach its target, lipid II, in the inner membrane. Agents that alter the outer membrane’s permeability can overcome this limitation, enabling nisin to access the periplasm and expand its antimicrobial spectrum. In this study, we aimed to extend nisin’s spectrum of action towards Gram-negative bacteria by fusing it with salmochelin, a siderophore produced by Salmonella.Siderophores capture extracellular iron and enter cells through specific receptors. The nisin-salmochelin conjugate was synthesized through an external chemical reaction, yielding a compound with activity against Gram-positive and Gram-negative bacteria. Among the Gram-negative bacteria, notable strains include S. enterica Newport, S. entericaTyphimurium, E. coli O157, E. coli EC3 (uropathogenic isolate), and Providencia rettgeri. This novel conjugate exhibits broad-spectrum activity against foodborne pathogens, significantly enhancing nisin’s potential as a food biopreservative and expanding its clinical applications.

In recent years, the capabilities of artificial intelligence in generating contextually rich and coherent language have dramatically expanded, yet challenges persist in achieving high levels of adaptability across diverse linguistic tasks. Introducing the Dynamic Neural Alignment Mechanism (DNAM), a novel approach designed to dynamically adjust alignment within neural networks, offers a significant enhancement to contextual coherence through adaptive real-time modifications that respond directly to varying input demands. DNAM leverages adaptive alignment matrices and context-sensitive gating functions, facilitating a continuous feedback loop within the model, which contributes to a more responsive handling of linguistic subtleties across tasks such as text generation, translation, and domain-specific applications. Experimental results illustrate DNAM's effectiveness in outperforming baseline models in contextual integrity metrics, exhibiting improvements across coherence, robustness to noise, and adaptability to new domains, which collectively demonstrate its capacity for complex contextual understanding. Furthermore, the DNAM-integrated architecture achieved notable convergence rate advancements, highlighting the potential for efficient training dynamics without compromising output quality. By addressing inherent challenges such as computational efficiency and reliance on quality data, DNAM represents a foundational shift toward more sophisticated and adaptable neural architectures, promising new directions for research in scalable language model alignment.

The current technological transformations, driven by Artificial Intelligence (AI), have impacted various fields, including journalism. This study investigates how AI is being applied in journalistic practice by analyzing scientific texts about its positive and negative effects. The methodology adopted combines quantitative and qualitative approaches, with the analysis of 20 documents collected from the Google Scholar and CAPES Periodicals Portal databases. The results reveal that the use of AI in journalism can accelerate content production and data interpretation but also raises concerns such as misinformation and the potential decline of the journalism profession. Negative impacts observed include the low quality of complex texts and the lack of information verification. On the other hand, rapid production and human quality in fact-checking highlight the importance of the journalist's role as a mediator of information. The study suggests the need for ethical regulations and transparency in AI use, while emphasizing that, although the technology offers opportunities, the essence of journalism — the pursuit of truth — remains irreplaceable. Therefore, further research is needed to fully understand the implications of AI in journalism, aiming to maximize its benefits and mitigate the associated risks.

A document by Kamal Acharya. Click on the document to view its contents.

The rapid growth in artificial intelligence has spurred the development of increasingly sophisticated models capable of complex language understanding and generation. However, despite remarkable advances in embedding techniques and context modeling, existing methodologies often struggle to capture the fluid, context-dependent relationships within language that are essential for accurate comprehension and coherent generation. The Semantic Interdependency Embedding Dynamics (SIED) framework presents a novel approach to embedding, designed to address these limitations through dynamic adaptation to contextual shifts, thus allowing models to better represent and respond to evolving semantic relationships. Empirical results indicate that SIED-enhanced models outperform traditional LLMs across multiple dimensions, including semantic interdependency, contextual accuracy, and syntactic coherence, showing the framework's potential for improving the adaptability and expressiveness of language models. By enabling real-time adjustments within embeddings, the SIED framework advances the state of LLMs, facilitating applications where complex contextual interpretation is critical and pushing the boundaries of current natural language understanding capabilities.

This paper presents a blended learning framework designed to cultivate computational thinking skills within the context of the "Introduction to Computer Science and Programming" course. By combining online and offline instructional approaches, the proposed model establishes a cohesive educational paradigm that seamlessly integrates computational thinking principles throughout the curriculum. Additionally, an evaluative model is introduced to assess proficiency levels in computational thinking. Through a meticulous analysis of empirical teaching data, the study confirms that the blend of instructional modalities is both a feasible and effective strategy for enhancing computational thinking competencies.

This article presents a circular array using four-elements dipole with beam-switching capabilities for 5G communications. The beam can switch in four directions by controlling the switch states implemented in the arms of each dipole. The different antenna modes lead to different radiation behaviors. The first is a directional behavior containing 4 radiation modes depending on the dipole fed into the network. The second type of radiation has a double beam pattern according to the two fed dipoles. According to the simulated results, a corresponding overall bandwidth of 932.3 MHz (S11 < - 10 dB) can be obtained with a peak gain of 3.15 dBi and an overall efficiency of 80%. The proposed antenna is fabricated and measured to verify the simulation results. This low-cost, efficient, and easy-to-design antenna array can be very useful for 5G beam switching applications.

A SARS-CoV-2, N15 strain was successfully isolated from the nasal swab of a pediatric patient after five blind passages in Vero cells. The biological properties of this isolate were investigated extensively. Cytopathic effects (CPE) were observed within 48 hours post- infection, characterized by aggregates of dead cells. The N15 isolate demonstrated the ability to replicate across various human-origin cell lines, albeit with different growth kinetics. Genetic analyses revealed that the strain belongs to the 19B lineage of SARS-CoV-2, showing 99.9% similarity to the original Wuhan 1 strain. Importantly, the N15 strain did not exhibit genetic signatures of in-vitro adaptation and possessed rare mutations N709S in the spike protein and T11M in the E protein. Drug testing indicated that Remdesivir, Molnupiravir, and Nirmatrelvir effectively inhibited this isolate at varying concentrations. The N15 isolate may serve as valuable material for comparative studies with currently circulating SARS-CoV-2 variants.

Medications selected to treat problems of pregnancy in the mare must be simultaneously safe for the mare, safe for the developing fetus, and able to penetrate the placental barrier. With consideration of these factors, the practitioner must determine when and for how long to administer therapy. Monitoring equine pregnancy can be challenging, owing to the size of the equine abdomen (precluding visualization of the entire fetoplacental unit), individual variation in appearance of the reproductive tract, and (typically) minimal clinical signs of pregnancy compromise until late in a disease process. This paper is an extension of an article originally prepared for the 2023 Proceedings of the American Association of Equine Practitioners, produced with permission.

Kaposi’s sarcoma (KS) is an AIDS-defining cancer and a significant global health challenge caused by Kaposi’s sarcoma-associated herpesvirus (KSHV). NGS-based approaches have profiled KS lesions in a minimal number of studies compared with other neoplastic diseases. Here we present a compiled and harmonized dataset of 131 KS and non-tumor cutaneous samples in the context of their predicted pathway activities, immune infiltrate, KSHV and HIV gene expression profiles, and their associated clinical data representing patient populations from Argentina, United States (USA), and Sub-Saharan Africa cohorts. RNA-seq data from 9 Argentinian KS lesions were generated and integrated with previously published datasets derived from the USA and sub-Saharan African cohorts from Tanzania, Zambia, and Uganda. Unsupervised analysis of 131 KS-related samples allowed us to identify four KS clusters based on their host and KSHV gene expression profiles, immune infiltrate, and the activity of specific signaling pathways. The compiled RNA-seq profile is shared with the research community through the UCSC Xena browser for further visualization, download, and analysis ([https://kaposi.xenahubs.net/](https://kaposi.xenahubs.net/)). These resources will allow biologists without bioinformatics knowledge to explore and correlate the host and viral transcriptome in a curated dataset of different KS RNA-seq-based cohorts, which can lead to novel biological insights and biomarker discovery.

Combining visual and tactile cues has proven effective for object recognition, grasping, and manipulation tasks. However, integrating these modalities is challenging as tactile and visual data convey distinct information and differ structurally. Researchers have addressed this problem by proposing approaches that either do not consider mechanical properties conveyed by tactile sensors or cannot be deployed directly for diverse tasks. In this paper, we propose a cross-task visuo-tactile representation that encodes both the geometrical and mechanical properties of objects in a point cloud (PC) data structure. By physically exploring different areas of a given item, we collect tactile information to estimate the local compliance of the surface, encoding it as the color information of the PC in the probed areas. This color information is extended to the entire object assuming that neighboring points share the same mechanical properties. We apply the proposed PC to 6 real-world objects showing that it can be effectively used to encode their shape along with their information on the local compliance. Further, we show that the augmented PC can be used for different tasks by exploiting this in three robotic tasks-a visuo-tactile object classification, a path following and a reaching in clutter.Corresponding author(s) Email:   giammarco.caroleo@eng.ox.ac.uk

The environment of fish habitat has great effect on alter the composition of fish gut microbiome. However, It is still unknown how assembly process of the fish gut microbiota varies through changing habitat environment, especially for invasive tilapia, a species with strong adaptability to environmental changes. Here, we investigated gut microbiome of redbelly tilapia from three different habitat environment utilizing high-throughput 16S rRNA gene sequencing. Our findings showed that the gut microbiome of wild (high and low fragmented habitat) and pond-cultured redbelly tilapia were significantly different in terms of composition and diversity (alpha and beta). Further, stochastic processes acted as the primary regulator of the gut microbial community assembly of redbelly tilapia. When the habitat were altered from pond-cultured to wild environment, the contribution of undominated processes in the gut microbial community assembly declined, while the contribution of dispersal limitation increased. Co-occurrence network analysis indicated that habitat variation enhanced the gut microbial network complexity of invasive tilapia during the migration from pond to wild habitats. In addition, our findings indicated that the gut microbiome of wild tilapia possessed unique traits, including relatively high alpha diversity and a large relative proportion of genes coding for putative cellulolytic enzymes involved in the digestion of tilapia’s preferred food resource (hydrophytes), especially in the fragmented habitat with highly developed cascade dams. Our results also identified core biomarkers (Clostridium_sensu_stricto_1 and Bacillus) of gut bacterial community in wild redbelly tilapia, which provided beneficial knowledge of monitoring and managing invasive tilapia populations.

Soil erosion (SE) threatens food security and the environment, and predictive models are essential for guiding prevention efforts. This study couples the PLUS-InVEST model with the CMIP6 climate dataset to examine land use and SE changes in the Xijiang River Basin (XRB) from 2000 to 2020 and predict scenarios for 2030. Using Geodetector, the influence of six driving factors on SE was analyzed. Key findings include:(1) Human activities in the XRB have intensified, with development growing 2.09 times faster in recent years. Rapid urban expansion is challenging the ecological environment, increasing regional risks. (2) Land use predictions for 2030 across scenarios show favorable accuracy (Kappa coefficient: 0.798), with the ecological conservation scenario (ECS) best aligning with sustainable development goals. (3) Ecological protection and land planning can mitigate SE in the XRB. Between 2000 and 2020, actual soil erosion (ASE) was generally lower than potential erosion (PSE). By 2030, under most scenarios except ECS, PSE remains high, with ASE intensity increasing from east to west. (4) Land use change (LUCC), precipitation, and slope are key factors influencing SE, with interactions primarily showing nonlinear enhancement.These findings suggest that rational land planning and comprehensive soil and water conservation are essential for ecological restoration in the XRB, providing valuable scientific support for environmental protection efforts.

Author names and Affiliations: Joshana Shrestha1, Mohan Bhusal2, Smriti Piya1, Rabin Baniya1, Sanjay Dhungana3.Resident Doctor, Department of Dermatology, Nepal Medical College and Teaching Hospital, Kathmandu, Nepal.Lecturer, Department of Dermatology, Nepal Medical College and Teaching Hospital, Kathmandu, Nepal.MBBS Student, Nepal Medical College and Teaching Hospital, Kathmandu, Nepal.Corresponding author:Joshana Shrestha,Nepal Medical College and Teaching Hospital, Kathmandu, NepalEmail: joshana.shrestha1996@gmail.comOrcid ID: https://orcid.org/0009-0000-5570-189XOther Author Details:Mohan Bhusal, Email id: bhusalmohan1@gmail.com , Orcid id: https://orcid.org/0000-0003-3122-2346Smriti Piya, Email id: simplysmritee@gmail.com, Orcid id: https://orcid.org/0009-0002-8408-7547Rabin Baniya, Email id: baniyarabin1357@gmail.com, Orcid id: https://orcid.org/0009-0009-7761-6552Sanjay Dhungana, Email id: sanjaydhungana2000@gmail.com, Orcid id: https://orcid.org/0009-0000-9776-0361Total number of pages: 18Total number of photographs: 7Total number of tables: 1

The Uncommon ECG Signature of Hypokalemia: Left Main Coronary Equivalent

Soil aggregates, as fundamental units of soil structure, play a crucial role in promoting the storage of soil carbon and nitrogen, thereby supporting soil fertility and overall health, especially in fragile karst regions. However, the response of soil aggregate composition and associated carbon and nitrogen pools to different ecological construction measures in such areas remains unclear, particularly in relation to contour reverse-slope terrace practices. In this study, we evaluated the differences in the stability of soil aggregates (macroaggregates >2 mm, medium aggregates 2–0.25 mm, and microaggregates <0.25 mm) in sloping cropland and four ecological construction measures (contour reverse-slope terrace, fallow farmland reforestation, natural vegetation restoration, and meridian forest) in the Chishui River Basin of the karst region in Yunnan Province, China, as well as the relationship between carbon and nitrogen pools within the aggregates and the carbon and nitrogen sequestration potential of different treatments. Compared to sloping cropland, the contour reverse-slope terrace treatment, with its unique “preferential entrainment” function, significantly increased the content of medium and microaggregates by an average of 85.67%. Among the ecological construction measures, plantation forests exhibited the highest aggregate stability, with an average increase of 7.59%–157.14%. The carbon and nitrogen contents across all aggregate sizes were highest in microaggregates, which contributed most to the soil carbon and nitrogen pools in the study area. Natural vegetation restoration resulted in the highest carbon and nitrogen levels across all aggregate types. Moreover, the plantation forest and contour reverse-slope terrace treatments showed a balanced contribution of carbon and nitrogen within all aggregate sizes. Overall, these findings suggest that planted forests are the most effective in enhancing soil and water conservation in fragile karst areas. The contour reverse-slope terrace approach shows potential for stabilizing soil structure due to its unique “preferential entrainment” function over delayed time scales.