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Image enhancement is crucial in digital image processing to improve visual quality across various applications. Recent advancements in deep learning and computer vision have significantly advanced automatic color correction. While heavyweight solutions excel in quality, they demand substantial computational resources, whereas emerging lightweight models promise efficient operation on mobile devices. This study introduces a lightweight neural network model suitable for mobile devices for image color gamut correction. Our model demonstrates performance comparable with heavyweight models. We propose an approach that integrates unsupervised learning methods with multimodal visual-language priors. To our knowledge, this is the first study to use multimodal architectures as a discriminator for automatic image color correction. Also, we proposed a method for evaluating the quality of Image Enhancement models based on unpaired data using binary questions answering.

Abstract The observation of the gate-controlled supercurrent (GCS) effect in superconducting nanostructures increased the hopes for realizing a superconducting equivalent of semiconductor field-effect transistors. However, recent works attribute this effect to various leakage-based scenarios, giving rise to a debate on its origin. A proper understanding of the microscopic process underlying the GCS effect and the relevant time scales would be beneficial to evaluate the possible applications. In this work, we observed gate-induced two-level fluctuations between the superconducting state and normal state in Al/InAs nanowires (NWs). Noise correlation measurements show a strong correlation with leakage current fluctuations. The time-domain measurements show that these fluctuations have Poissonian statistics. Our detailed analysis of the leakage current measurements reveals that it is consistent with the stress-induced leakage current (SILC), in which inelastic tunneling with phonon generation is the predominant transport mechanism. Our findings shed light on the microscopic origin of the GCS effect and give deeper insight into the switching dynamics of the superconducting NW under the influence of the strong gate voltage.

Despite full recognition of strategic management in the success of small and medium-sized enterprises (SMEs), little attention has been paid to the factors that contribute to the failure of its implementation in the Ecuadorian context. Therefore, the purpose of this study is to investigate the obstacles that hinder the successful implementation of strategic management in SMEs in Ecuador. A quantitative study approach was employed. The initial sample comprised 105 entrepreneurs, who were administered a checklist to evaluate the status of strategic planning implementation within their organizations. Based on their responses, nine entrepreneurs were selected for in-depth analysis, categorized as follows: three entrepreneurs with a high level of strategic management implementation, three with a moderate level of implementation, and three with minimal or no strategic management practices. This identified the primary obstacles to effective strategic management implementation. Finally, a comprehensive survey was applied to all participants to quantify these barriers, which allowed the construction of the final model used for the subsequent analysis. Through structural equation modeling, the study demonstrated that these obstacles negatively and significantly correlated with the level of strategic management implementation, revealing that businesses with minimal or no implementation experienced significantly different sales performance compared to those with higher levels of strategic management practices.

Abstract Objectives Current data suggests that Bacille Calmette-Guerin (BCG) vaccination contributes to nonspecific enhancement of resistance to various infections. Thus, BCG vaccination induces both specific immunity against mycobacteria and non-specific “trained immunity” against various pathogens. To understand the fundamental mechanisms of “trained” immunity, studies of transcriptome changes occurring during BCG vaccination in innate immunity cells, as well as in their precursors, are necessary. Furthermore, this data possesses important significance for practical applications associated with the development of recombinant BCG strains aimed to enhance innate immunity against diverse infectious agents. Data description We performed RNA sequencing of innate immune cells derived from murine bone marrow and spleen three days after subcutaneous BCG vaccination. Using fluorescence-activated cell sorting we obtained three cell populations for each mouse from both control and BCG vaccinated groups: bone marrow monocytes and neutrophils and splenic NK-cells. Then double-indexed cDNA libraries for Illumina sequencing from the collected samples were prepared, the resulting cDNA library mix was subjected to NovaSeq 6000 sequencing. This paper describes the collection of 24 RNA sequencing samples comprising 4 sets of immune cell populations obtained from subcutaneously BCG-vaccinated and control mice

This systematic review provides investigation into the evolution of Supply Chain Management (SCM) practices from Industry 4.0 to Industry 5.0 in South African enterprises, with a specific focus on the integration of Blue Ocean Strategy. Industry 5.0 marks a significant shift towards more personalised, sustainable, and human- centric industrial practices, leveraging advanced technologies to improve operational efficiency and address environmental concerns. The research aims to investigate how SCM practices in South African firms are transforming under the influence of Industry 5.0 paradigms and to assess how the Blue Ocean Strategy can bolster competitiveness, operational efficiency, and sustainable growth. Adopting a comprehensive methodology, this study systematically reviewed peer-reviewed articles from 2019 to 2023. Employing the PRISMA framework for structured literature review, and thematic analysis using Atlas-ti 24 software, the research scrutinised the evolution of SCM practices, focusing on technological adoption, strategic innovation, and sustainability integration. The findings reveal that South African firms are progressively adopting Industry 5.0 technologies, integrating them within SCM to improve flexibility, responsiveness, and sustainability. The application of Blue Ocean Strategy is identified as a catalyst for creating uncontested market spaces and driving innovation, which not only improves the firms’ market position but also aligns with sustainable business practices. However, challenges such as technological integration complexity, high transition costs, and the need for skilled human resources are notable impediments. This research significantly contributes to academic literature by providing a nuanced understanding of the SCM evolution in the context of Industry 5.0 within emerging markets, particularly focusing on South African enterprises. It also offers practical insights for industry practitioners on leveraging Blue Ocean Strategy alongside Industry 5.0 technologies to achieve competitive advantage and sustainable growth. The study underlines the need for policy interventions to support technological adaptation and capacity building in the SCM sector, suggesting avenues for future research on overcoming the barriers to Industry 5.0 adoption.

The integration of Artificial Intelligence (AI) within supply chain management (SCM) has precipitated a significant transformation in business processes, particularly in emerging economies like South Africa. This systematic literature review explores the extent of AI’s influence on SCM practices across various industries within the South African context, underpinning the potential of AI to improve operational efficiency, strategic decision-making, and innovation. This review thoroughly followed the PRISMA guidelines. The findings reveal that AI significantly contributes to the advancement of SCM by improving accuracy in demand forecasting, optimising resource allocation, and facilitating real-time decision-making. This review synthesizes current research, offering a comprehensive overview of AI’s transformative potential in SCM within South Africa. It also suggests areas for future research, particularly in addressing the challenges of AI implementation and exploring its impact on sustainable SCM practices. The implications for both practitioners and policymakers include prioritising digital infrastructure development, ethical AI integration, and encouraging public-private partnerships to support AI-driven innovations in supply chain networks. Future research should prioritise the development of practical methods that specifically target the demands and obstacles of incorporating AI into supply chains in South Africa, with the aim of promoting fair and sustainable growth.

This study evaluates the disruptions in South African pharmaceutical supply chains, focusing on resilience mechanisms and risk factors essential for maintaining continuity and efficiency amidst various disruptions. It leverages a comprehensive literature review and qualitative insights from interviews with 25 local experts, employing a phenomenological approach to explore the strategic experiences and adaptations to supply chain challenges. Findings highlight the significance of diversified sourcing, strategic adaptability, and local production enhancement in ensuring supply chain robustness and pharmaceutical availability. The research underscores the importance of robust risk management, strategic foresight, and local capability investment, offering practical insights for stakeholders to enhance supply chain resilience. Contributing valuable perspectives to supply chain management discourse, it enriches the understanding of resilience and risk mitigation in the pharmaceutical sector, providing a basis for informed strategic planning and policymaking to safeguard against future disruptions.

Abstract Background Repetitive transcranial magnetic stimulation (rTMS) has recently gained relevance in treating different psychiatric disorders. Limited evidence suggests that the beneficial effects of rTMS on psychopathology could be at least partly mediated through changes in inflammatory response. This systematic review summarizes the literature on whether rTMS can modulate inflammatory markers and thus positively influence the course of psychiatric illnesses. Materials and methods A systematic review of rTMS and inflammatory markers in psychiatric diseases was conducted according to PRISMA guidelines. Information on the association between rTMS treatment response and changes of inflammatory markers was extracted. The quality of the studies was assessed using the National Heart, Lung, and Blood Institute for human studies and the Systematic Review Center for Laboratory Animal Experimentation for animal studies. Results This review includes 17 studies (2 animal and 15 human studies) on the relationship between rTMS treatment response and changes of inflammatory markers. Positive changes in microglial activity and anti-inflammatory effects were associated with behavioral improvement in animal models of depression. However, these findings have not been consistently replicated in human studies focusing on treatment-resistant depression. While several studies reported rTMS-induced alterations in peripheral inflammatory markers, only two could demonstrate their association to clinical treatment response. Notably, most studies showed poor or moderate quality in the bias assessment. Conclusions While certain human studies suggest an association between rTMS-induced anti-inflammatory effects and improvement in psychopathology, heterogeneity, and underpowered analyses constrain the generalizability of these results. The discrepancy between animal and human findings highlights the need for larger, standardized human studies. Trial registration (PROSPERO Registration: CRD42023492732).

Sustainable technologies to replace current fossil solutions are essential to meet future CO2 emission reduction targets. Therefore, this paper compares key performance indicators of a hydrogen- and a methane-flame-fired crucible furnace with computational fluid dynamics simulations at identical firing powers, aiming to fully decarbonize the process. Validated numerical models from the literature were used to compare temperatures, radiation fields, radiation parameters and heat transfer characteristics. As a result, we observed higher combustion temperatures and a 19.0% higher fuel utilization rate in the hydrogen case, indicating more efficient operating modes, which could be related to the increased radiant heat flux and temperature ranges above 1750 K. Furthermore, higher scattering of the heat flux distribution on the crucible surface could be determined indicating more uneven melt bath temperatures. Further research could focus on quantifying the total fuel consumption required for the heating up of the furnace, for which a transient numerical model could be developed.

Abstract Circulating tumor DNA (ctDNA) is the cornerstone of liquid biopsy diagnostics, revealing clinically relevant genomic aberrations from blood of cancer patients. Genomic analysis of single circulating tumor cells (CTCs) could provide additional insights into intra-patient heterogeneity, but it requires whole-genome amplification (WGA) of DNA, which might introduce bias. Here, we describe a novel approach based on mass spectrometry for mutation detection from individual CTCs not requiring WGA and complex bioinformatics pipelines. After establishment of our protocol on tumor cell line-derived single cells, it was validated on CTCs of 33 metastatic melanoma patients and the mutations were compared to those obtained from tumor tissue and ctDNA. Although concordance with tumor tissue was superior for ctDNA over CTC analysis, a larger number of mutations were found within CTCs compared to ctDNA (p = 0.039), including mutations in melanoma driver genes, or those associated with resistance to therapy or metastasis. Thus, our results demonstrate proof-of-principle data that CTC analysis can provide clinically relevant genomic information that is not redundant to tumor tissue or ctDNA analysis.

Abstract The quantum regression theorem states that the correlations of a system at two different times are governed by the same equations of motion as the single-time averages. This provides a powerful framework for the investigation of the intrinsic microscopic behaviour of physical systems by studying their macroscopic response to a controlled external perturbation. Here we experimentally demonstrate that the two-time particle number correlations in a photon Bose-Einstein condensate inside a dye-filled microcavity exhibit the same dynamics as the response of the condensate to a sudden perturbation of the dye molecule bath. This confirms the regression theorem for a quantum gas, and, moreover, demonstrates it in an unconventional form where the perturbation acts on the bath and only the condensate response is monitored. For strong perturbations, we observe nonlinear relaxation dynamics which our microscopic theory relates to the equilibrium fluctuations, thereby extending the regression theorem beyond the regime of linear response.

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Summary: To understand the decision process of genomic sequence-to-function models, explainable AI algorithms determine the importance of each nucleotide in a given input sequence to the model’s predictions and enable discovery of cis-regulatory motifs for gene regulation. The most commonly applied method is in silico saturation mutagenesis (ISM) because its per-nucleotide importance scores can be intuitively understood as the computational counterpart to in vivo saturation mutagenesis experiments. While ISM is highly interpretable, it is computationally challenging to perform for many sequences, and becomes prohibitive as the length of the input sequences and size of the model grows. Here, we use the first-order Taylor approximation to approximate ISM values from the model’s gradient, which reduces its computation cost to a single forward pass for an input sequence. We show that the Taylor ISM (TISM) approximation is robust across different model ablations, random initializations, training parameters, and dataset sizes.

Background: Heart disease is one of the leading causes of death in the United States. Increased education and utilization of BLS by first responders have had a significant impact, but certain populations remain high risk, such as African Americans. Raising awareness among at-risk populations may lead to more bystander CPR performed, improving mortality rates. The influence of celebrity deaths and illnesses is an important driver of public awareness. Therefore, the cardiac arrests of both Bronny James and Damar Hamlin may have influenced cardiac arrest awareness. Methods: Google Trends data was pulled for the following search terms from 8/21/2022–8/14/2023: Cardiac arrest (disease), Cardiopulmonary Resuscitation (topic), Basic Life Support (topic), Myocardial Infarction (disease), Defibrillation (topic) and Automatic External Defibrillator (topic). The average relative search volume (RSV) for each search term was taken for a three-week period encompassing the week of and two weeks following the cardiac arrests of Damar Hamlin and Lebron James Jr., respectively. We used one-way ANOVA and independent sample t-tests to compare the average values of Damar Hamlin's and LeBron James Jr.'s incidents with their respective 12-month averages. Results: RSV was significantly higher surrounding Hamlin's cardiac arrest compared to James Jr.'s for Cardiopulmonary Resuscitation and Automatic External Defibrillator. RSV for Basic Life Support was increased in LeBron James Jr.'s time compared to the 12-month average and Damar Hamlin's incident. Compared to the 12-month average, Cardiac arrest, Cardiopulmonary Resuscitation, Defibrillation, and Automatic External Defibrillator during Hamlin's incident. Myocardial infarction RSV was higher during James Jr.'s incident compared to baseline. Over the long term, the search terms showed a significant increase after Damar Hamlin's incident when compared to before.RSV was significantly higher surrounding Hamlin's cardiac arrest compared to James Jr.'s for “Cardiopulmonary Resuscitation” (23.56 vs. 22.0, p

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

Abstract Background It is unknown if serum concentrations of cobalamin, folate, canine pancreatic lipase immunoreactivity (cPLI), and canine trypsin‐like immunoreactivity (cTLI) obtained postprandially are equivalent to measurements obtained after withholding food in dogs with suspected gastrointestinal disease. Hypothesis/Objectives Measurements of serum concentrations of cobalamin, folate, cPLI, and cTLI postprandially will be equivalent to measurements after 12 hours of withholding food in dogs with signs of chronic gastrointestinal disease. Changes observed will not alter clinical interpretation. Animals 51 client‐owned dogs with signs of gastrointestinal disease. Methods Prospective single arm clinical trial. Serum concentrations of cobalamin, folate, cPLI and cTLI 2, 4, and 8 hours postprandially were compared by equivalence testing to values after withholding food for 12 hours (baseline). Results Mean serum cobalamin concentrations 2 hours (498.1 ± 213.1 ng/L; P = 0.024) and 4 hours (501.9 ± 207.4 ng/L; P = 0.008) postprandial were equivalent to baseline (517.3 ± 211.5 ng/L). Mean serum cTLI 2 hours (31.3 ± 14 μg/L; P

Abstract Background Liver uptake in [68Ga]Ga-PSMA-11 PET is used as an internal reference in addition to clinical parameters to select patients for [177Lu]Lu-PSMA-617 radioligand therapy (RLT). Due to increased demand, [68Ga]Ga-PSMA-11 was replaced by [18F]F-PSMA-1007, a more lipophilic tracer with different biodistribution and splenic uptake was suggested as a new internal reference. We compared the intra-patient tracer distribution between [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007. Methods Fifty patients who underwent PET examinations in two centers with both [18F]F-PSMA-1007 and [68Ga]Ga-PSMA-11 within one year were included. Mean standardized uptake values (SUVmean) were obtained for liver, spleen, salivary glands, blood pool, and bone. Primary tumor, local recurrence, lymph node, bone or visceral metastasis were also assessed for intra- and inter-individual comparison. Results Liver SUVmean was significantly higher with [18F]F-PSMA-1007 (11.7 ± 3.9) compared to [68Ga]Ga-PSMA-11 (5.4 ± 1.7, p

Background: The rapid advancement from Industry 4.0 to Industry 5.0 is reshaping global operational landscapes, introducing a new era where technology, strategy, and sustainability intersect. This evolution is particularly significant in South Africa, where supply chains are a critical component of the economic framework and are increasingly influenced by technological innovations, demanding a comprehensive understanding of their implications. Purpose of the Study: This study aims to systematically review the existing literature to explore the evolving paradigms in operations management as businesses transition from Industry 4.0 to Industry 5.0, focusing on the South African supply chain context. The primary objective is to ascertain how this transition impacts operational practices, decision-making processes, and overall supply chain efficiency. Design/Methodology/Approach: Adopting a systematic literature review methodology, the study analyses 13 peer-reviewed articles, employing thematic analysis to identify, synthesise, and interpret the significant themes emerging from the literature. The PRISMA framework guided the selection and review process, ensuring a rigorous and replicable research approach. Results/Findings: The findings reveal that the transition from Industry 4.0 to Industry 5.0 in South African supply chains is driving a significant shift toward more integrated, intelligent, and sustainable operations. Key themes identified include the pivotal role of advanced technologies like AI and IoT, the increasing importance of data-driven decision-making, and the integration of sustainability and innovation in operational frameworks. Recommendations: Future research should focus on empirical studies to validate the theoretical insights and explore the practical implications of Industry 5.0 technologies. For industry practitioners, embracing a strategic approach to technology adoption, fostering a culture of innovation, and prioritizing sustainability are recommended to leverage the opportunities presented by this transition. Managerial Implication: Managers and industry leaders must navigate the complexities of this new industrial era by developing competencies in emerging technologies, adopting resilient and adaptive operational strategies, and championing sustainability. Policymakers should facilitate this transition through supportive regulations, fostering innovation ecosystems, and ensuring the workforce is equipped with the requisite skills for a future-oriented industry.

Objective: Lipoprotein (a) [Lp(a)] is a causal, genetically-inherited risk amplifier for atherosclerotic cardiovascular disease (ASCVD). Practice guidelines increasingly recommend broad Lp(a) screening among various populations to optimize preventive care. Corresponding changes in testing rates and population-level detection of elevated Lp(a) in recent years has not been well described. Methods: Using Veterans Affairs electronic health record data, we performed a retrospective cohort study evaluating temporal trends in Lp(a) testing and detection of elevated Lp(a) levels (defined as greater than 50 mg/dL) from January 1, 2014 to December 31, 2023 among United States Veterans without prior Lp(a) testing. Testing rates were stratified based on demographic and clinical factors to investigate possible drivers for and disparities in testing: age, sex, race and ethnicity, history of ASCVD, and neighborhood social vulnerability. Results: Lp(a) testing increased nationally from 1 test per 10,000 eligible Veterans (558 tests) in 2014 to 9 tests per 10,000 (4,440 tests) in 2023, while the proportion of elevated Lp(a) levels remained stable. Factors associated with higher likelihood of Lp(a) testing over time were a history of ASCVD, Asian race, and residing in neighborhoods with less social vulnerability. Conclusion: Despite a 9-fold increase in Lp(a) testing among US Veterans over the last decade, the overall testing rate remains extremely low. The steady proportion of Veterans with elevated Lp(a) over time supports the clinical utility of testing expansion. Efforts to increase testing, especially among Veterans living in neighborhoods with high social vulnerability, will be important to reduce emerging disparities as novel therapeutics to target Lp(a) become available.

Abstract Metabolic dysfunction‐associated steatotic liver disease (MASLD) is hallmarked by hepatic steatosis, cell injury, inflammation, and fibrosis. This study elaborates on a multicellular biochip‐based liver sinusoid model to mimic MASLD pathomechanisms and investigate the therapeutic effects of drug candidates lanifibranor and resmetirom. Mouse liver primary hepatocytes, hepatic stellate cells, Kupffer cells, and endothelial cells are seeded in a dual‐chamber biocompatible liver‐on‐a‐chip (LoC). The LoC is then perfused with circulating immune cells (CICs). Acetaminophen (APAP) and free fatty acids (FFAs) treatment recapitulate acute drug‐induced liver injury and MASLD, respectively. As a benchmark for the LoC, multiplex immunofluorescence on livers from APAP‐injected and dietary MASLD‐induced mice reveals characteristic changes on parenchymal and immune cell populations. APAP exposure induces cell death in the LoC, and increased inflammatory cytokine levels in the circulating perfusate. Under FFA stimulation, lipid accumulation, cellular damage, inflammatory secretome, and fibrogenesis are increased in the LoC, reflecting MASLD. Both injury conditions potentiate CIC migration from the perfusate to the LoC cellular layers. Lanifibranor prevents the onset of inflammation, while resmetirom decreases lipid accumulation in hepatocytes and increases the generation of FFA metabolites in the LoC. This study demonstrates the LoC potential for functional and molecular evaluation of liver disease drug candidates.

Summary: Background: Psoriasis (Pso) is a chronic inflammatory skin disease that poses both physical and psychological challenges. Dysbiosis of the skin microbiome has been implicated in Pso, yet a comprehensive multi-omics analysis of host-microbe interactions is still lacking. To bridge this gap, we conducted an exploratory study by adopting the integrated approach that combines whole metagenomic shotgun sequencing with skin transcriptomics. Methods: This was a cross-sectional study, adult patients with plaque-type Psoriasis (Pso) and healthy volunteers were included. Skin microbiota samples and biopsies were collected from both lesional and non-lesional skin areas on the lower back. Weighted Gene Correlation Network Analysis (WGCNA) was employed for co-expression network analysis, and cell deconvolution was conducted to estimate cell fractions. Taxonomic and functional features of the microbiome were identified using whole metagenomic shotgun sequencing. Association between host genes and microbes was analyzed using Spearman correlation. Findings: Host anti-viral responses and interferon-related networks were identified and correlated with the severity of psoriasis. The skin microbiome showed a greater prevalence of Corynebacterium simulans in the PASI severe-moderate groups, which correlated with interferon-induced host genes. Two distinct psoriatic clusters with varying disease severities were identified. Variations in the expression of cell apoptosis-associated antimicrobial peptides (AMPs) and microbial aerobic respiration I pathway may partly account for these differences in disease severity. Interpretation: Our multi-omics analysis revealed for the first time anti-viral responses and the presence of C. simulans associated with psoriasis severity. It also identified two psoriatic subtypes with distinct AMP and metabolic pathway expression. Our study provides new insights into understanding the host-microbe interaction in psoriasis and lays the groundwork for developing subtype-specific strategies for managing this chronic skin disease. Funding: The research has received funding from the FP7 (MAARS–Grant 261366) and the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 821511 (BIOMAP). The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This publication reflects only the author's view and the JU is not responsible for any use that may be made of the information it contains. GAM was supported by a scholarship provided by CAPES-PRINT, financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES (Brazilian Government Agency). The authors thank all patients who participated in our study.

We experimentally determined the decrease of residual compressive strength of the pultruded glass-fiber laminate after tension-compression cyclic loading. The adapted Arcan rig was used for tension-compression fatigue tests. The cyclic load was applied with the critical stress ratio R=−0.87. The residual compressive strength was determined after applying the predefined number of loading cycles with the stress amplitudes of 242 MPa and 173 MPa. The results indicated that the residual compressive strength was reduced about 20% at 77% of fatigue life under the stress amplitude of 242 MPa and at 83% of fatigue life under the stress amplitude of 173 MPa. The microstructural analysis showed that the crack growth path and failure mode depend on the stress amplitude.

BackgroundNeuroinflammatory processes in depression are associated with treatment resistance to conventional antidepressants. Ketamine is an effective new therapeutic option for treatment-resistant depression (TRD). Its well-established immunomodulatory properties are hypothesized to mediate its antidepressant effect. In this context, higher levels of inflammation may predict a better treatment response. However, conclusive evidence for this hypothesis is lacking. We thus investigated whether standard peripheral inflammatory cell markers and C-reactive protein (CRP) levels could predict symptom improvement during intravenous ketamine therapy in TRD patients.Methods27 participants with TRD were treated with six weight-adjusted intravenous ketamine infusions (0.5 mg/kg bodyweight) over three weeks. Baseline assessments included CRP, absolute monocyte count (AMC), and absolute neutrophil count (ANC). Depression severity was measured using the Montgomery-Åsberg Depression Rating Scale (MADRS) at baseline (D1), after the first (D3) and before the last ketamine infusion (D18). Raters were blinded for the baseline laboratory assessments.Results13 participants responded to ketamine treatment, and 8 participants partially responded. Baseline AMC showed a strong negative correlation with MADRS change at D3 (r=-0.57, p=0.002) and at D18 (r =-0.48, p=0.010), indicating that a high baseline AMC was associated with greater symptom improvement. A generalized linear model confirmed the association of baseline AMC with symptom improvement during ketamine treatment when additionally accounting for age, sex, and body mass index. Specifically, baseline AMC demonstrated predictive value to discriminate responders and partial responders from non-responders, but lacked discriminative ability between partial responders and responders. Baseline ANC correlated with the MADRS changes at D3 (r=-0.39, p=0.046), while CRP values did not correlate at all.ConclusionsOur prospective single-arm open-label observational study demonstrated that baseline AMC reliably predicted symptom improvement during intravenous ketamine treatment in TRD patients. AMC could therefore serve as a simple and easily accessible marker for symptom improvement during ketamine therapy in daily clinical practice. Future studies with larger sample sizes and a more detailed longitudinal assessment of AMC subtypes are needed to better understand the specific relationship between monocytes and the neuromodulatory effects of ketamine.

Background and Purpose: With an increased need for standardized methodology in accurate diagnosis of Traumatic Brain Injury (TBI), Diffusion Tensor Imaging (DTI) has provided promising diagnostic results as an adjunct modality yet remains underutilized. The purpose of this study was to validate the use of DTI with Statistical Parametric Mapping (SPM) for Traumatic Brain Injury (TBI) supporting its use as a diagnostic tool. Materials and Methods: This study was retrospective and compared controls to patients clinically diagnosed with TBI. Forty-two controls (mean age = 34.1; range, 19 - 58; 28 Males and 13 Females) were screened (n = 41) for cognitive impairment and neurological abnormality. Two cohorts, each of eighteen patients (first cohort: mean age, 41.8; range, 23 - 70; 9 Males and 9 Females; second cohort: mean age, 45.7; range, 23 - 68; 9 Males and 9 Females) clinically diagnosed with TBI (n = 36) were pooled. DTI image acquisition was obtained using a 3 Tesla MRI scanner. DTI images were analyzed through voxel-based t-tests using SPM comparing each individual to the normative control group to generate z-maps for each individual control and each individual patient with a TBI. Test statistics were ranged for p-values (0.001-0.05) and cluster extent values (0, 30, 60, 65, 70, 75). Area Underneath A Receiver Operating Characteristic Curve (AUCROC) was used to validate diagnostic capability. AUCROC analysis was conducted on all sets of p-value and extent threshold values. Significance of results was determined by examining the AUCROC values. Results and Conclusions: A maximal AUCROC of 1.000 was obtained across the p-value range and cluster extent thresholding values specified across the two cohorts. The high AUCROC supports validation of the methodology presented and the use of diffusion tensor imaging and SPM as an adjunct diagnostic tool for TBI.

This research focuses on characterizing the orientation of the research that shows the contributions in the field of administrative sciences in Latin America. To do this, publications on Administrative Sciences by researchers in the region reported in Scopus were analyzed. After an initial review of all these publications, the sample was reduced to 87%, identifying the functions or areas of knowledge towards which they were oriented, their evolution over time, the sectors and countries where they were published, the collaboration networks between countries based on the development of the research and the main journals where they have been published. The results show that the administrative sciences are present in the epistemological development of Latin America, its conception and evolution correspond to the theoretical and methodological principles that established it as a science; They also reflect the existence of a vast network of collaboration in research in this field between countries in the region and beyond. At the same time, they show the need to strengthen the ties of collaboration between the most prolific researchers to strengthen the existence of an autochthonous school that responds to the particularities of the socioeconomic environment where they develop.

A technique has been proposed for incorporating a heterocyclic component into a glutarimide framework employing a Rh2(esp)2-catalyzed N–H insertion with the involvement of N-Boc-α-diazo glutarimide. The new diazo reagent is more stable, soluble and convenient to prepare than the previously suggested one. The approach permits the application of diverse heterocycles, including both aromatic and saturated NH-substrates. This yields structures that are appealing for generating cereblon ubiquitin-ligase ligands and for potential use in crafting PROTAC molecules.

This study aims to identify the most polluted areas and sites using the magnetic signal of ornamental plant leaves as an indicator of environmental pollution. Systematic sampling was conducted with 98 sampling sites described according to urban land use, such as road hierarchy and road surface, soil group, collected plant species, and municipality. The magnetic parameters analyzed were low- and high-frequency magnetic susceptibility and the isothermal remanent magnetization acquisition curves in order to calculate the magnetic enhancement factor. For the analysis of variance, a Kruskal–Wallis test was performed to compare urban land uses. Subsequently, the magnetic enhancement factor in dust and surface soil was used to prepare maps of environmental pollution for each urban area. Analyses of the different magnetic parameters of the dust deposited on leaves show that low-coercivity ferrimagnetic minerals dominated the magnetic signal, probably magnetite of anthropic origin, and were closely linked to vehicular traffic and, to a lesser extent, industrial activities.

Ongoing outbreaks and often rapid spread of infections caused by coronaviruses, influenza, Nipah, Dengue, Marburg, monkeypox, and other viruses are a concern for health authorities in most countries. Therefore, the search for and study of new antiviral compounds are in great demand today. Since almost all viruses with pandemic potential have immunotoxic properties of various origins, particular attention is paid to the search and development of immunomodulatory drugs. We have synthesised a new compound related to indole-3-carboxylic acid derivatives (hereinafter referred to as the XXV) that has antiviral and interferon-inducing activity. The purpose of this work is to study the effect of the XXV on the stimulation of the expression of toll-like receptor genes, interferons, and immunoregulatory cytokines in a macrophage-like cell model. In this study, real-time PCR methods were used to obtain data on the transcriptional activity of genes in macrophage-like cells. Stimulation of the genes of toll-like receptors TLR2, TLR3, TLR4, TLR7, TLR8, and TLR9 was detected. A high-fold increase in stimulation (from 6.5 to 16,000) of the expression of the TLR3 and TLR4 genes was detected after 4 h of exposure to the XXV. Increased activity of interferon (IFNA1, IFNA2, IFNB1, IFNK, and IFNλ1) genes with simultaneous stimulation of the expression of interferon receptor (IFNAR1 and IFNAR2) genes and signalling molecule (JAK1 and ISG15) genes was detected. Increased fold stimulation of the expression of the cytokine genes IL6, TNFA, IL12A, and IL12B was also observed. Thus, it is shown that the XXV is an activator of TLR genes of innate immunity, which trigger signalling mechanisms of pathogen “recognition” and lead to stimulation of the expression of genes of proinflammatory cytokines and interferons.

This study aimed to evaluate the diagnostic accuracy of an Optical Pulp Scanning (OPS) device. A total of 421 teeth from 107 emergency patients were investigated. Pulp vitality was assessed using conventional diagnostic methods (ConvDia), cold pulp testing (CPT), and a final diagnosis (FinDia) based on the pulp chamber contents (PCCs) of teeth requiring endodontic treatment (EmeTe). Following ConvDia, OPS screening was performed, endodontic access was prepared, and the FinDia was established. The specificity, sensitivity, as well as positive and negative predictive values of ConvDia, CPT, and FinDia were calculated against the OPS results. The null hypothesis stated that specificity and sensitivity would be equal to or lower than 90% and 80%, respectively. OPS sensitivity was 66.2% and 72.2% (EmeTe) for ConvDia, 66.2% and 69.8% (EmeTe) for CPT, and 76.9% for FinDia. Specificity was 26.4% and 25.5% (EmeTe) for ConvDia, 26.9% and 21.7% (EmeTe) for CPT, and 31.5% for FinDia. These results suggest that, at its current stage of development, the OPS device does not provide an acceptable level of diagnostic accuracy, particularly in vital teeth.

This paper presents the results of analytical studies of the combustion process of semi-coke, cedar sawdust, and their mixtures using the TGA method at three different heating rates with the determination of the main characteristics of heating: the presence of synergetic interaction between the components of the mixture affecting the maximum rate of combustion and kinetic parameters. Calculations of activation energy and pre-exponential multiplier of the Arrhenius equation by the Friedman and Ozawa–Flynn–Wall priori methods for initial combustibles and their mixtures have been carried out. Semi-coke was obtained by thermal treatment of brown coal at 700–900 °C to remove volatile substances, which makes it more environmentally friendly than the original coal. Semi-coke has a higher heat of combustion than biomass, and biomass has a higher reactivity than semi-coke. The combustion process of biomass occurs in a lower temperature range, and adding biomass to semi-coke shifts the combustion process to a lower temperature range than such for biomass. Adding at least 50% of biomass to semi-coke increases the combustion index by at least 1.1 times. Regardless of the heating rate of mixtures, synergetic interaction between the mixture’s components increases the maximum combustion rate of coke residue by 20%.

6-thioguanine (6-TG) is an antimetabolite drug of purine structure, approved by the FDA for the treatment of acute myeloid lesukemia, and it is of interest in treating other diseases. The interaction of drugs with matrices is of interest to achieving a delayed, sustained, and local release. The interaction of 6-TG with an aluminum metal–organic framework (Al-MOF) DUT-4 is studied using a novel experimental approach, namely, mechano-chemistry by liquid-assisted grinding (LAG). The bonding of 6-TG to the DUT-4 matrix in the composite (6-TG)(DUT-4) was studied using ATR-FTIR spectroscopy and XRD. This interaction involves amino groups and C and N atoms of the heterocyclic ring of 6-TG, as well as the carboxylate COO− and (Al)O-H groups of the matrix, indicating the formation of the complex. Next, an in vitro delayed release of 6-TG was studied from composite powder versus pure 6-TG in phosphate buffered saline (PBS) at 37 °C. Herein, an automated drug dissolution apparatus with an autosampler was utilized, and the molar concentration of the released 6-TG was determined using an HPLC–UV analysis. Pure 6-TG shows a quick (

Summary:. A 51-year-old woman with diabetes and depression was referred to the anesthesia pain clinic with a 15-year history of complex regional pain syndrome type I of the right forearm and hand in the radial sensory nerve distribution. There was no recognized antecedent trauma and she had failed both medical treatment and radial sensory nerve decompression surgery. An ultrasound-guided local anesthetic block of the radial nerve in the upper arm resulted in partial and temporary improvement in pain. She subsequently underwent trial of a peripheral nerve stimulator (PNS) followed by permanent PNS implant placement over the radial nerve proper proximal to the elbow. Within the first month of use, she endorsed substantially improved pain and strength in her right hand. These improvements were sustained for more than 1 year. Applications of PNS technology for treatment of extremity complex regional pain syndrome and neuropathic extremity pain in general are reviewed.

Electric Load Forecasting (ELF) is the central instrument for planning and controlling demand response programs, electricity trading, and consumption optimization. Due to the increasing automation of these processes, meaningful and transparent forecasts become more and more important. Still, at the same time, the complexity of the used machine learning models and architectures increases.Because there is an increasing interest in interpretable and explainable load forecasting methods, this work conducts a literature review to present already applied approaches regarding explainability and interpretability for load forecasts using Machine Learning. Based on extensive literature research covering eight publication portals, recurring modeling approaches, trends, and modeling techniques are identified and clustered by properties to achieve more interpretable and explainable load forecasts.The results on interpretability show an increase in the use of probabilistic models, methods for time series decomposition and the use of fuzzy logic in addition to classically interpretable models. Dominant explainable approaches are Feature Importance and Attention mechanisms. The discussion shows that a lot of knowledge from the related field of time series forecasting still needs to be adapted to the problems in ELF. Compared to other applications of explainable and interpretable methods such as clustering, there are currently relatively few research results, but with an increasing trend.

La econofísica emplea modelos basados en agentes para describir las regularidades en las distribuciones de ingreso encontradas empíricamente. En este trabajo se estudia el efecto que tiene incluir una entidad financiera en la distribución de dinero mediante modelos cinéticos de distribución. Para esta tarea, se considera un sistema cerrado compuesto por agentes económicos que intercambian dinero aleatoriamente junto con una entidad financiera que establece una dinámica de préstamos y depósitos. Los resultados indican que son necesarias condiciones para estabilizar el sistema si se considera deuda y la distribución de probabilidad diverge con una tasa de intermediación diferente de cero.

A re-analysis of the morphological phylogeny of the Nopinae is made, based on an update in the description of Aamunops Galán-Sánchez & Álvarez-Padilla, 2022 and the addition of the recently described genera Nopsma Sánchez-Ruiz, Brescovit & Bonaldo, 2020 and Roddemberryus Sánchez-Ruiz & Bonaldo, 2023. Two new species, Aamunops hoof sp. nov. (male) and Aamunops yiselae sp. nov. (male and female), are also described, which allows a better understanding of the genus morphology and resulted in an emended diagnosis. The description of Aamunops has been updated to include several characteristics of the ultrastructural morphology, legs, chelicerae, palps and female genitalia. The inclusion of these new characters of Aamunops along with those of Nopsma and Roddemberryus in the previous data matrix resulted in a new, completely different hypothesis of the relationships of the nopine genera: Nopsma is part of a group formed by Cubanops and Nyetnops, while Aamunops and Roddemberryus are grouped with representatives of Tarsonops. The four-eyed Nopsides ceralbonus Chamberlin, 1924 was recovered as the most basal species of Nopinae. The relationships among genera of Nopinae and the phylogenetic position of three species, whose taxonomic position is doubtful (Cubanops luquillo Sánchez-Ruiz, Brescovit & Alayón, 2015, Orthonops confuso Galán-Sánchez & Álvarez-Padilla, 2022 and Tarsonops irataylori Bond & Taylor, 2013), is also discussed.

Salinity is among the key climate characteristics of the World Ocean. During the last 15 years, sea surface salinity (SSS) is measured using satellite passive microwave sensors. Standard retrieving SSS algorithms from remote sensing data were developed and verified for the most typical temperature and salinity values of the World Ocean. However, they have far lower accuracy for the Arctic Ocean, especially its shelf areas, which are influenced by large river runoff and have low typical temperature and salinity values. In this study, an improved algorithm has been developed to retrieve SSS in the Arctic Ocean during ice-free season, based on Soil Moisture Active Passive (SMAP) mission data, and using machine learning approaches. Extensive database of in situ salinity measurements in the Russian Arctic seas collected during multiple field surveys is applied to train and validate the machine learning models. The error in SSS retrieval of the developed algorithm compared to the standard algorithm reduced from 3.15 to 2.15 psu, and the correlation with in situ data increased from 0.82 to 0.90. The obtained daily SSS fields are important to improve accurate assessment of spatial and temporal variability of large river plumes in the Arctic Ocean.

Abstract Spondylodiscitis is the commonest spine infection, and pyogenic spondylodiscitis is the most common subtype. Whilst antibiotic therapy is the mainstay of treatment, some advocate that early surgery can improve mortality, relapse rates, and length of stay. Given that the condition carries a high mortality rate of up to 20%, the most effective treatment must be identified. We aimed to compare the mortality, relapse rate, and length of hospital stay of conservative versus early surgical treatment of pyogenic spondylodiscitis. All major databases were searched for original studies, which were evaluated using a qualitative synthesis, meta-analyses, influence, and regression analyses. The meta-analysis, with an overall pooled sample size of 10,954 patients from 21 studies, found that the pooled mortality among the early surgery patient subgroup was 8% versus 13% for patients treated conservatively. The mean proportion of relapse/failure among the early surgery subgroup was 15% versus 21% for the conservative treatment subgroup. Further, it concluded that early surgical treatment, when compared to conservative management, is associated with a 40% and 39% risk reduction in relapse/failure rate and mortality rate, respectively, and a 7.75 days per patient reduction in length of hospital stay (p

Abstract There is a strong medical need to develop suitable biomarkers to improve the diagnosis and treatment of depression, particularly in predicting response to certain therapeutic approaches such as electroconvulsive therapy (ECT). MicroRNAs are small non-coding RNAs that have the ability to influence the transcriptome as well as proteostasis at the systems level. Here, we investigate the role of circulating microRNAs in depression and response prediction towards ECT. Of the 64 patients with treatment-resistant major depression (MDD) who received ECT treatment, 62.5% showed a response, defined as a reduction of ≥50% in the MADRS total score from baseline. We performed smallRNA sequencing in blood samples that were taken before the first ECT, after the first and the last ECT. The microRNAome was compared between responders and non-responders. Co-expression network analysis identified three significant microRNA modules with reverse correlation between ECT- responders and non-responders, that were amongst other biological processes linked to inflammation. A candidate microRNA, namely miR-223-3p was down-regulated in ECT responders when compared to non-responders at baseline. In line with data suggesting a role of miR-223-3p in inflammatory processes we observed higher expression levels of proinflammatory factors Il-6, Il-1b, Nlrp3 and Tnf-α in ECT responders at baseline when compared to non-responders. ROC analysis of confirmed the diagnostic power of miR-223-3p demarcating ECT-responders from non-responder subjects (AUC = 0.76, p = 0.0031). Our data suggest that miR-223-3p expression and related cytokine levels could serve as predictors of response to ECT in individuals with treatment-resistant depressive disorders.

Abstract The Arctic is warming at a rate four times the global average, while also being exposed to other global environmental changes, resulting in widespread vegetation and ecosystem change. Integrating functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational gradients with and without nutrient input from nesting seabirds, we collected data on vegetation composition and structure, plant functional traits, ecosystem fluxes, multispectral remote sensing, and microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular plant taxa, for 9 of which these are the first published trait data. By integrating these comprehensive data, we bridge knowledge gaps and expand trait data coverage, including on intraspecific trait variation. These data can offer insights into ecosystem functioning and provide baselines to assess climate and environmental change impacts. Such knowledge is crucial for effective conservation and management in these vulnerable regions.

Sustainable waste management strategies are urgently needed due to an increasing global population and increased waste production. In this context, biorefineries have recently emerged as a promising approach to valorize waste streams and supply a broad range of products. This study presents the process model and life cycle assessment (LCA) of a biorefinery concept using a novel biochemical method, a so-called “dark photosynthesis” conversion. This process is coupled to a photo-fermentation using microalgae. Overall, the biorefinery concept can produce hydrogen, lutein, β-carotene, and proteins for animal feed. Apple pomace from apple juice production is used as feedstock for the primary conversion step. A process model was created with the process simulation software Aspen Plus® using experimental and literature data. Results from this model were then used in an LCA. The environmental impacts of the proposed biorefinery concept are relatively high, showing the need for process optimization in several areas. Energy system integration, stream recycling, and higher hydrogen yields are recognized as especially important for improving the environmental performance of this concept. Despite these findings, the model shows the feasibility of implementing the biochemical conversion technologies in a biorefinery concept for effectively utilizing residue streams.

Learner control allows for greater autonomy and is supposed to benefit learning motivation, but it might be more advantageous for students with specific learner characteristics. The current study looks into the relationships between self-regulated learning, cognitive load, and performance within learner-controlled environments. The research was conducted in an asynchronous online setting, allowing for learner control. Cognitive load and self-regulated learning were measured using self-report questionnaires. Performance was assessed through case solutions. The participants were 97 graduate law students studying the civil code. Analysis based on structural equation modeling showed that both prior knowledge and self-regulated learning skills significantly contribute to the increase in germane cognitive load and are positively correlated with performance. The implications of these findings underscore the critical role of prior knowledge and self-regulated learning skills in shaping the cognitive processes involved in learning, ultimately impacting academic achievement. These results emphasize the need for careful consideration of learner-control options in asynchronous online environments.

This study reviews existing simulation models and describes a selected model for analysing combustion dynamics in hydrogen and natural gas mixtures, specifically within non-ferrous melting furnaces. The primary objectives are to compare the combustion characteristics of these two energy carriers and assess the impact of hydrogen integration on furnace operation and efficiency. Using computational fluid dynamics (CFD) simulations, incorporating actual furnace geometries and a detailed combustion and NOx emission prediction model, this research aims to accurately quantify the effects of hydrogen blending. Experimental tests on furnaces using only natural gas confirmed the validity of these simulations. By providing precise predictions for temperature distribution and NOx emissions, this approach reduces the need for extensive laboratory testing, facilitates broader exploration of design modifications, accelerates the design process, and ultimately lowers product development costs.