Purpose: Fazamorexant is a dual orexin receptor antagonist being developed for the treatment of insomnia. This study aims to determine the dose-exposure-response relationship of single-dose fazamorexant versus zolpidem in young adult and elderly healthy Chinese volunteers. Methods: This was a single-center, randomized, double-blind, double-dummy, placebo- and active-controlled, 4-period crossover study. The dose of fazamorexant was 40mg/80mg in young adults and 20mg/40mg in the elderly, while zolpidem was administered at the clinically recommended dose, 10mg. Pharmacokinetic and pharmacodynamic measurements were scheduled during each period. Safety was assessed throughout the study. Results: There was no significant pharmacokinetic difference between young adults and the elderly with fazamorexant. In general, high-dose fazamorexant caused similar or less impairment versus zolpidem in eye movements, choice-reaction-time performance, body sway, and memory tests in young and elderly cohorts. The effects of low-dose fazamorexant on these psychomotor and cognitive measurements were significantly smaller than those of high-dose fazamorexant and zolpidem in both age cohorts. Despite of similarity in exposure, fazamorexant demonstrated larger effects on the pharmacodynamic measurements in the elderly than in young adults, suggesting an age-related increase of pharmacological sensitivity. Conclusion: Fazamorexant induced less balance, judgement and memory impairment compared to the comprehensive suppression of zolpidem on the neurological system. Such favorable spectrum makes fazamorexant a potentially safer drug for insomnia. No age-related pharmacokinetic difference was identified with fazamorexant. Key words: Fazamorexant, Zolpidem, pharmacokinetics, pharmacodynamics, insomnia.

Two basic patterns describe local plant community assembly and functioning: the species abundance distribution (SAD) and the distribution of species functional traits (TAD). These patterns have been extensively studied for dominant and rare plants, while subordinates, the species of intermediate abundance in a community, have received less research attention although this group is most species rich and important for community functioning. Here, we study the functional role of subordinate species (those covering the intermediate 50% of abundance ranks) using a large data set of Palearctic dry and semi-dry grassland plant communities and data on specific leaf area, seed mass and plant height. Theory predicts that dominant and subordinate plants should be functionally complementary. However, our findings indicate that species rank orders of SADs and TADs tend to be negatively correlated, causing the TAD to have higher evenness than the associated SAD. Subordinate species represented on average less than 15% of total plant abundance and trait space. Functional diversity of subordinates was lower than expected by a null model that assumed an equiprobable random distribution of trait values among plant species. Climate seasonality in combination with elevation, appeared to be the most important drivers of subordinate abundance and functional diversity. We conclude that subordinates differ from dominants in trait composition, leading to partial functional complementarity. We hypothesise that both groups are assembled through different processes and habitat filtering, partly triggered by local climatic conditions.

The complex mass spectra of entire proteins present significant challenges for data analysis in top-down proteomics (TDP). A key step in TDP analysis is spectral deconvolution, which extracts ion masses from spectral signals. Inaccuracies in this step can propagate to downstream analysis, such as proteoform identification and quantification, undermining the accuracy of the entire workflow. Despite its critical role, few robust methods have been introduced to estimate the false-discovery rate (FDR) for spectral deconvolution. We have developed a novel FDR estimation method specifically designed for spectral deconvolution. Our approach introduces decoy masses (DMs), artificially generated masses that mimic the behaviour of false positives (FPs) arising during the deconvolution process, which in turn enables the estimation of the FDR. We have validated our method using both in silico and experimental spectra. In silico datasets, our method achieved an average maximum absolute difference of 0.337 between estimated and true FDR across six datasets with varying resolution and noise levels. When applied to experimental spectra of known proteins, FDRs of approximately 5.64% and 9.75% were observed when 5% and 10% FDR thresholds were applied, respectively. The FDR estimation feature is implemented in the FLASHDeconv algorithm as part of the OpenMS open-source project.

I present a categorical framework for analyzing fixed-point emergence in educational feedback systems, where exam-grade collapse mechanisms prevent the formation of stable learner identities. Using endofunctors and initial algebras from category theory, I model learning processes as generative functors φ that would naturally converge to fixed-point identities under transfinite iteration. However, when educational assessment introduces entropy-reducing collapse functors E, I prove that no nontrivial initial algebra can exist for the composite functor F = E • φ. This mathematical obstruction categorically blocks creativity-driven identity emergence, creating what I term "fixed-point traps" in educational systems. My results demonstrate that exam-driven feedback loops fundamentally prevent the stabilization of learner identities, offering a formal explanation for creativity suppression in assessment-heavy educational environments.

Aim We investigate whether the eye parameter of bumblebees – a visual trait measuring the trade-off between light sensitivity and visual resolution – is associated with: (i) local habitats, (ii) forest cover at the landscape scale (1km radius) and (iii) the shade tolerance of the plants they forage on. Methods The association of bumblebee species with local habitat and forest cover at the landscape scale was analysed using generalised linear mixed models. We combined data from the Norwegian national bumblebee monitoring program with Corine CLC+ land cover, and bumblebee functional traits: eye parameter and inter-tegular distance. These analyses were done at species and community level. To determine if bumblebee light sensitivity correlated with the shade-tolerance of the plant they forage on, we combined bumblebee-plant interactions from a British database with a Swedish plant trait database. Results Our findings showed that bumblebee species with high eye parameters were more common and abundant in forest habitats and areas with greater forest cover, while species with low eye parameters showed the opposite trend. This pattern was reflected at the community level, as indicated by the community-weighted mean of the eye parameter, which increased with forest cover and was higher in forest habitats. Furthermore, bumblebees with higher eye parameters tended to forage on plants with greater shade tolerance. Main conclusions These results suggest that visual adaptations for light sensitivity contribute to shaping bumblebee species distributions across different scales. Our study underscores the importance of pollinator vision in understanding species niches and its value for species distribution modelling. Moreover, by relating pollinator visual abilities to plant niches for the first time, this study provides an important basis for future modelling of plant-pollinator interactions and targeted conservation measures for plants and pollinators in forested landscapes.

Mountain bongo (Tragelaphus eurycerus isaaci) are critically endangered antelopes found only in Kenya’s montane forests. The need to re-establish a viable, healthy and self-sustaining population is urgent. With less than 100 individuals left in the wild and rapidly declining, the impact of endoparasites remains largely unstudied. We opportunistically collected and analyzed the fecal samples from a reintroduced- population over a two-year period using random focal sampling method. Coccidia and Strongylids as the predominant gastrointestinal parasites. Infestation levels of coccidia varied significantly by season (χ2 = 1.50.707, p < 0.01) and age group (χ2 = 2, 97.471p < 0.01. Strongylids infestation exhibited significant variation only among age groups. These results expand information on endoparasite affecting the species. Researchers and health authorities are particularly concerned about endoparasites of zoonotic significance.

Title PageTitle: Unexpected Neurological Sequelae in a Patient on Short-Term Mexiletine TherapyAuthors: Shubhika Jain1, Elina Momin1, Rahul Vyas1, Sahith Reddy2, Vijaywant Brar2

Background: Aspergillus fumigatus sensitized asthma (AFSA) is associated with severe exacerbations and progressive lung damage, however, diagnosis remains challenging in resource-limited settings due to limited access to Aspergillus-specific IgE ( A.f-sIgE) testing. We aimed to develop a clinical prediction model using routinely available biomarkers for AFSA identification. Methods: This retrospective study enrolled 92 adult asthma patients at The First Hospital of Qinhuangdao City (2023–2025). Participants were classified into AFSA and non-AFSA groups. Candidate predictors (demographics and hematological parameters) were analyzed using LASSO regression, with subsequent multivariable logistic regression. Performance was validated via receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results: Among 92 patients (mean age 56.5±12.8 years; 60.9% female), 44.6% (n=41) had AFSA. LASSO selected five predictors: sex, monocyte percentage, monocyte absolute count, lymphocyte percentage, and total IgE. Final model retained male sex (OR=10.688; 95%CI:1.661–152.999) and TIgE (OR=1.006; 95%CI:1.003–1.011). The model achieved excellent discrimination: training cohort (AUC=0.96, sensitivity=0.93, specificity=0.92); validation cohort (AUC=0.88, sensitivity=0.75, specificity=1.00). Sex-specific TIgE cutoffs (527.5 IU/mL [males], 906.1 IU/mL [females]) yielded 79.2% accuracy. Conclusions: The developed prediction model using gender and TIgE provides a practical, accessible tool for AFSA screening, overcoming diagnostic barriers in settings lacking A.f-sIgE testing. Its high accuracy and clinical utility support early identification of high-risk patients, facilitating timely intervention and improved outcomes in asthma management.

not-yet-known not-yet-known not-yet-known unknown Background: Fungal aeroallergen (mould) sensitisation is associated with poor control in children, particularly amongst low-income and ethnic minority groups. However, its association with deprivation and ethnicity remains unclear. Objective: To determine whether fungal sensitisation is associated with deprivation and ethnicity in children with difficult-to-treat asthma. Methods: Cross-sectional analysis of difficult-to-treat asthmatic children (5-16 years) referred to a regional severe asthma centre between 2018 and 2024. All eligible patients were prescribed high dose inhaled corticosteroids. Socioeconomic status was assessed using index of multiple deprivation deciles (IMDD) based on patient postcodes. Logistic regression was used to identify predictors of fungal sensitisation. Results: Of 89 difficult-to-treat asthmatics, median age was 10.5 years (IQR 5.5-16.0), 61 (68.5%) were male and 45 (50.6%) were non-Caucasian. 43 (48.3%) were fungal sensitised, with higher non-Caucasian representation in the sensitised group (67.7% vs 39.5%, p<0.05). Logistic regression identified IMDD, but not ethnicity, as a significant predictor of fungal sensitisation (IMDD OR: 1.29, p<0.05; non-Caucasian ethnicity OR: 2.87, p=0.088). Conclusions: Socioeconomic deprivation was the strongest predictor of fungal sensitisation in this cohort while ethnicity displayed a non-significant trend as a possible risk factor . However, children from ethnic groups were disproportionately represented among the fungal sensitised group. Targeted interventions to improve the indoor environment in vulnerable populations may improve asthma control. Further research into the mechanisms driving fungal sensitisation in these groups has the potential to inform preventative and therapeutic interventions.

BACKGROUND: Foreign body aspiration (FBA) is a major pediatric emergency causing severe airway obstruction and potential fatal outcomes. This study examined 20 years of pediatric FBA cases at a pulmonary institute in Rabka, Zdrój, Poland, analyzing demographics, foreign body characteristics, and outcomes. METHODS: We retrospectively analyzed 256 pediatric FBA cases from 2001 to 2021, collecting data on patient demographics, aspiration circumstances, foreign body types and locations, radiographic findings, bronchoscopy outcomes, and complications. Statistical significance was p < 0.05. RESULTS: Children aged 13 months to 3 years represented 64.8% of cases, with males comprising 60.9%. Most incidents (56.6%) occurred during feeding, with nuts being the most common foreign bodies. Radiographic findings showed air trapping (32.4%), atelectasis (18.4%), and no changes in 16.4%. Rigid bronchoscopy was successful with minimal complications, while flexible bronchoscopy alone worked in 9.38% of cases, requiring conversion to rigid in 19.14%. Respiratory cultures performed in 72.3% of cases identified pathogens in 54.7%, mainly Haemophilus influenzae and Staphylococcus aureus. Complications occurred in 12.89%, primarily pneumonia (3.91%), granuloma (2.73%), and bronchiectasis (2.34%). CONCLUSION: Timely bronchoscopy minimizes FBA complications. This study emphasizes preventive measures in feeding practices and the importance of caregiver education to improve outcomes.

Background: Breast cancer, as one of the most common malignant tumors among women worldwide, its heterogeneity, invasiveness and drug resistance pose great challenges to clinical treatment. Lactylation, as an emerging post-translational modification of proteins, has been proven to play an important role in tumor biology, especially in the regulation of the tumor microenvironment (TME) and immune microenvironment. However, the role of lactylation in breast cancer is not yet fully understood. Methods: Download the RNA-seq data and clinical data of breast cancer from TCGA, GEO and Metabric databases. Use Cox regression analysis and LASSO analysis to build a risk prediction model, and use Kaplan - Meier survival analysis and ROC curve to verify the accuracy of the model. In addition, use the consensus clustering method to determine the relevant clusters of breast cancer patients and verify the clinical predictive significance of the lactylation model genes again. We also conduct a detailed exploration of the potential mechanisms by which the lactylation model genes lead to poor prognosis in patients through single-cell and spatial transcriptomics (ST). Results: Six lactylation-related breast cancer prognosis models with prognostic value for patients were constructed, and these six genes were all risk factors for breast cancer patients. Through the consensus clustering method, it was found that these six lactylation-related DEGs (LRDEGs) could also be used to classify breast cancer patients, and the subtypes with poor prognosis had high expression of these genes. Then, through immune analysis, obvious immune heterogeneity was found among patients of different subtypes. In order to explore the potential mechanism of poor prognosis, single-cell and ST analysis revealed that CORO6+Epi might be the lactylation-related subtypes leading to poor prognosis in breast cancer. SCENIC transcription factor analysis identified that E2F3 might drive the formation of the malignant phenotype of CORO6+Epi. Finally, through cell communication analysis, it was found that CORO6+Epi might be regulated by POSTN fibroblasts through the THBS2 - SDC4 axis to promote tumor progression. Conclusion: A new lactylation-related gene prediction model was successfully constructed, which can accurately predict breast cancer patients with poor prognosis. It was also found that this might be related to the transcription factor E2F3 and the THBS2 - SDC4 axis between CORO6+Epi and POSTN fibroblasts, providing new therapeutic targets for the precision treatment of breast cancer.

The paper constructs an algebraic-geometric framework around the "phenomenological velocity" expression v = N/D that arose in previous informal work. We introduce (i) the energy-number field E, (ii) a non-commutative velocity-string algebra V, (iii) a curvature scalar K PV defined from a "PV-Hessian", and (iv) a curved A ∞ category Fuk v (M) obtained from an ordinary Fukaya category by multiplication with v. Basic structural results are proved; several phenomena are left as conjectures to stimulate further study.

In the digital era, data security and confidentiality are of paramount importance, particularly in image-based communications. This study explores the integration of Artificial Intelligence (AI) with edge computing to enhance data hiding techniques in image processing. Traditional methods of steganography often face challenges related to detection resistance, data capacity, and computational efficiency. By leveraging the localized processing capabilities of edge computing and the adaptive learning features of AI, this research proposes a hybrid model that ensures real-time, secure, and intelligent data embedding. The model employs deep learning algorithms for identifying optimal embedding regions within images based on texture complexity and perceptual invisibility, thereby maximizing data payload while maintaining visual integrity. Additionally, edge devices are used to process and embed data at the source, significantly reducing latency and exposure to cyber threats. Experimental results demonstrate improved robustness against stainless attacks, enhanced embedding efficiency, and greater adaptability to dynamic image conditions. This approach not only bolsters data security but also aligns with the growing demand for decentralized and privacy-preserving computing in Internet of Things (IoT) and multimedia applications.

The ecological crisis is a serious concern nationally and internationally which is trying its best to overcome environmental problems through education. For this reason, a pattern is needed to accelerate the improvement of environmentally friendly culture and character. This article aims to present the results of a study on environmental education culture conducted by the Makassar State Forestry Vocational School. This study uses a type of qualitative research with a realist ethnographic approach which explains cultural phenomena objectively from a third-person perspective with data analysis taking place before in the field. Based on the research that has been conducted, the researcher found an interesting educational pattern, namely that every learning activity is not only provided with academic materials but is invited to be involved to feel firsthand the importance of protecting the environment. Through these patterns form an integrated pathway, the first pathway builds students' self-pride that they belong to and are part of the pro-environment community. The second path of learning about the natural environment material itself with the aim of improving the cognitive and information dimensions of students and learning that uses the environment as a learning resource, as a source of material for real activities in learning. The third path is community service that makes the environment a model that emphasizes the development of information about concern for the surrounding environment which is not only about acquiring knowledge and skills but also the development of involvement in increasing values and environmentally friendly behaviors in the community

To achieve high-quality development in metropolitan area. It was essential to explore the mutual influences and constraints between ecological environment protection and social and economic development, the degree of coordination and the evolutionary characteristics of the coupling among these three elements. Therefore, we proposed a new assessment framework based on various scenarios, which integrates the Ecosystem Services and Trade-offs Comprehensive Assessment (InVEST) model, Patch-generating Land Use Simulation (PLUS) model, Grey GM(1,1) model, coupled coordination model, and geographic detector. We used the Invest model and the coupling coordination degree model to calculate how well the ecological, social, and economic systems work together in the study region from 2010 to 2020. Combined with PLUS and grey GM (1,1) models, the coupling coordination degree of the complex system in 2035 was predicted. At the same time, a geographic tool was used to assess how much the influencing factors interact with each other. The results showed that: (1) From 2010 to 2035, the areas with high value on the Comprehensive index for Ecosystem Services (CES) in the study region were all located in the southwestern and northern parts. The CES in the study region kept going down between 2010 and 2020. (2) From 2010 to 2020, the coupling coordination degree of the composite system in the study region continued to grow, but the overall value was still low. There was a trend of the low-value area getting closer to the high-value area, and the similarity increased. By 2035, the coupling coordination degrees under all scenarios will have improved, among which the ecological protection scenario will increase the most. (3) The geodetector analysis showed that social factors and economic factors have the greatest influence on the coupling and coordination degree, and the factor with the greatest impact was the proportion of secondary and tertiary industries

not-yet-known not-yet-known not-yet-known unknown Constraint-based reconstruction and analysis (COBRA) is a powerful systems biology approach for computational bioengineering. Synechococcus elongatus PCC 11801 and PCC 11802 are fast-growing, stress-tolerant cyanobacteria that are promising platforms for photosynthetic biomanufacturing. Here, we present constraint-based models (CBMs) iLV1052 and iLV1087 of PCC 11801 and PCC 11802, respectively, to facilitate and streamline strain engineering efforts. Following draft reconstruction using a template model, the models underwent extensive manual curation to reduce redundancy, and verification using BiGG, KEGG and BRENDA databases. We added 281 and 69 new reactions for PCC11801 and PCC11802, respectively, associated with stress tolerance, growth stability, antioxidant defense, energy regulation, and sulfur acquisition. The models were refined through iterative debugging and validation using flux balance analysis, flux variability analysis, and single gene/reaction deletion analysis. Gene essentiality predictions gave 69% accuracy for PCC 11801 and 83% for PCC 11802. The flux maps captured key features of cyanobacterial metabolism, including an incomplete TCA cycle. The final PCC11802 CBM contained 1130 reactions, 1052 genes, and 930 metabolites, while the PCC 11802 CBM included 1199 reactions, 1087 genes, and 951 metabolites. Using the Optknock framework, phosphoenolpyruvate carboxylase (PEPC) was identified to be a metabolic hotspot for bioengineering of valuable products like ethanol, butanol, succinic acid and butanediol.

In the spirit of Einstein's 1915 variational formulation and the Standard Model's reliance on symmetry group closures, we propose a background-independent theory wherein all physical observables emerge from the evolution of a single real-valued scalar field Φ(x, t), defined over a compact functional domain with no metric, manifold, quantization, or imposed symmetry. The dynamics follow an action A[Φ] incorporating kinetic flow, spectral rigidity, and nonlinear entropy.

A document by Arsh Jha. Click on the document to view its contents.

This pioneering research represents a historic advancement in drug discovery by being the first to predict and report the quantum tunneling probability for TDZD-8, a selective GSK3β inhibitor, using sophisticated machine learning techniques. By integrating Convolutional Neural Networks (CNNs) with quantum mechanical principles, the study achieves highly accurate simulations of tunneling probabilities, revealing results within an optimal range crucial for therapeutic efficacy. This novel approach enhances the understanding of drug-target interactions and establishes a new paradigm in drug design, with significant implications for Alzheimer’s disease treatment. The integration of advanced predictive modeling with quantum mechanics marks a transformative step in the development of targeted therapies, offering a deeper insight into the molecular mechanisms underlying drug action.

Alpine fluvial networks experience rapid climate change with extensive consequences on hydrology, including an increase in the prevalence and severity of flow intermittence and its constraint on aquatic assemblages. While the ecological effects of intermittency are relatively well documented in some biogeographic regions (e.g., Mediterranean), they are essentially unknown for alpine streams. We characterized flow intermittence and associated local and regional responses of macroinvertebrate assemblages across 75 headwater streams in 4 glacierized Alpine catchments over one year. Flow intermittence had a temporal effect on assemblages through localized drying, rather than a spatial effect related to stream network fragmentation. Specifically, high drying frequency reduced α and β diversity through homogeneous selection of resilient taxa and shifted community composition towards higher resilience. However, the structuring effects of flow intermittence on taxonomic and functional β diversity were mild. Local environmental conditions consistently had a stronger influence on assemblage composition than flow intermittence. Benthic macroinvertebrates in alpine stream networks of glacierized catchments are relatively well adapted to moderate flow intermittence, resulting from the environmental filtering of relatively harsh environmental conditions of alpine landscapes. As flow intermittence becomes more intense and prevalent with environmental change in alpine landscapes, biological resilience thresholds could be exceeded with more severe consequences on stream biota.

not-yet-known not-yet-known not-yet-known unknown The hydrazine oxidation-assisted hydrogen generation system significantly expands the applicability of hydrogen production technology. However, the complex intermediate transformations involved in hydrazine oxidation reaction (HzOR) and hydrogen evolution reaction (HER) desperately in need of developing dual functional catalysts. Manipulating the d-band center of metal catalysts has been identified as one of the most effective approaches to enhance catalytic activity. Herein, Ir nanoparticles (NPs) anchored in B, N-codoped porous carbon (Ir@BNC) was developed and demonstrates excellent performances for both HER and HzOR in alkaline medium, achieving 10 mA cm-2 at -25 mV and 18 mV, respectively. The overall hydrazine splitting (OHzS) electrolyzer reaches 200 mA cm−2 with a cell voltage of just 0.68 V. The direct liquid N2H4/H2O2 fuel cell (DHHPFC) assembly with Ir@BNC can achieve a maximum power density of 199.2 mW cm-2 at room temperature. Furthermore, an H2 production system using an OHzS device powered by DHHPFC realizes hydrogen production at a stable rate (53.08 mol h-1 m-2). In-situ Raman tests and theoretical calculations unravel the metal-support interaction between Ir NPs and B, N-codoped porous carbon optimize the electronic structure and regulate the d-band center of Ir, thus promoting water dissociation and H2 desorption during the reaction process.

We report on the 8th CAPRI Evaluation period, capturing the assessment of CAPRI Rounds 47 to 55 (excluding the CASP and COVID-related Rounds), which have witnessed the transition to AI-driven prediction tools such as AlphaFold and related alternatives. The prediction Rounds in this evaluation are characterized by a high level of difficulty due to various factors including the nature of the targets, the intricacy of the interfaces to be predicted and conformational changes. A total of 11 targets encompassing 21 interfaces, mostly in the difficult prediction category, were evaluated. While a retrospective analysis reveals a strong performance of AlphaFold on those targets, human predictors still outperform AI on difficult targets, particularly those involving antibodies and nucleic acids. Almost 25 years after its birth, CAPRI remains a vibrant and collaborative initiative with active participation from approximately 50 predictor and scorer groups, and 10 servers. Continued contributions from experimentalists providing targets to such blind experiments, and further advances in AI, sampling strategies and improvement in scoring methods will be key to overcoming remaining structural prediction challenges in complex biomolecular systems.

not-yet-known not-yet-known not-yet-known unknown Owing to its excellent eco-friendliness and facile water elution properties, aluminum-based lithium adsorbents have attracted a surge of interest for selectively extracting Li+ from Salt Lake brines, which account for more than 60% of the global lithium resources. However, structural collapse, facile deactivation during desorption process, and ultra-low actual adsorption capacity limit its further large-scale application, particularly in low-grade sulfate type brines. Herein, taking its advantage, disadvantage and structure features into consideration, the collapse of the aluminum-based lithium adsorbents was obviously suppressed by in-situ intercalation of VO3- ions in to [LiAl2(OH)6]+ layers. Evidently, the initial adsorption capacity and α_Mg^Liof as-configured adsorbents powder are 14.96 mg g-1 and 192.42 in real sulfate type West Taijinar Salt Lake brines following NaCl salts removal with 800 mg L-1 Li⁺ and 9.56 g L-1 SO42-, which are even comparable to those of Mn-based and Ti-based adsorbents. Furthermore, the initial and retained adsorption capacities of this novel adsorbents granulate in brines after 100 adsorption/desorption cycles are 26.68 mg g-1 and 10.36 mg g-1, respectively, which are almost 10 times higher than those of industrially utilized products. Based on the experimental and DFT calculations, the intercalation control process and mechanism were initially elucidated. This work significantly overcomes the major utilization challenges of aluminum-based lithium adsorbents, thereby enabling the high-efficiency and stable extraction of Li+ from low-grade brines, including sulfate type brines.

The emergence of Variants of Concern (VOCs) of SARS-COV2 with increased virulence and transmissibility has been linked to multiple mutations in the RBD region, altering their antigenic properties. In this study, we used a specialized ProtGPT2 model trained on the SARS-COV2 spike protein to forecast probable mutations on the spike protein that have not yet emerged. Upon prediction, we systematically studied the stability of single-site and multisite mutations using unbiased molecular dynamics simulations. Binding free energies were used to study the physicochemical significance of the mutations and their affinity to human ACE2 receptor. Our results show that there are specific hot-spots that mutate in the spike protein that enhance binding affinity through electrostatic and improved non-bonded interactions and highlight the role of specific energetic contributions in viral adaptation and infectivity. Our analysis revealed that the reduction of a disulphide bridge within sites 480- 488 lowered the binding free energy and increased the flexibility of the loop region, enhancing its interface interaction with ACE, leading to a more virulent variant than Omicron.