Infective endocarditis (IE) is a severe cardiovascular disease characterized by the formation of vegetations on heart valves. Our previous research found that streptococcal endocarditis is more common in elderly people and is prone to embolic complications leading to adverse clinical outcomes. von Willebrand Factor (VWF) is a crucial regulatory factor in the formation process and composition of vegetation in bacterial endocarditis, but its mechanism remains to be elucidated. This study investigated the role of VWF in the pathogenesis of Streptococcus mutans-induced endocarditis using two distinct mouse models: damage-induced and inflammation-induced endocarditis. We employed histological analysis, immunofluorescence, scanning electron microscopy, and molecular techniques to elucidate the mechanisms of vegetation formation and the contribution of VWF in each model.Our findings reveal significant differences in the timing and extent of VWF involvement between the two models, with a more rapid and pronounced role in the damage-induced model. VWF knockout mice showed reduced vegetation formation, particularly in the damage-induced model, while VWF supplementation restored vegetation development.These results highlight the critical role of VWF in endocarditis pathogenesis and suggest potential therapeutic strategies targeting VWF for the prevention and treatment of IE. Furthermore, our study provides novel insights into the interplay between VWF and inflammatory responses during endocarditis progression, opening new avenues for understanding the complex pathophysiology of this life-threatening condition.

Clinical considerations in the diagnosis and management of enteric fever and its complications

Background: Acute Lung Injury (ALI) and Acute Respiratory Distress Syndrome (ARDS) are severe pulmonary conditions with high morbidity and mortality rates, often triggered by various insults that lead to inflammation and impaired gas exchange. Investigating the molecular mechanisms behind ALI/ARDS is essential for improving diagnostic and therapeutic strategies. Methods: To explore the molecular mechanisms of ALI/ARDS, we utilized bioinformatics, machine learning, and experimental techniques. Transcriptional data from ALI mouse models and control tissues were analyzed, identifying 197 differentially expressed genes (DEGs) with significant upregulation in immune response pathways. Weighted Gene Co-Expression Network Analysis (WGCNA) and machine learning algorithms (SVM-RFE, LASSO, random forest) were used to identify key genes. Additionally, qRT-PCR was employed to assess the expression differences of four core genes related to inflammation. Single-cell sequencing was used to investigate the distribution of Il1r2 in neutrophils, and immunofluorescence co-localization was applied to validate these findings. Results: Four key genes—Cebpd, Hspa12b, Pim1, and Il1r2—were identified as potential biomarkers and therapeutic targets. Il1r2 was notably enriched in neutrophils, highlighting its role in regulating inflammation. Immune cell infiltration analysis revealed increased levels of monocytes, dendritic cells, and neutrophils in ALI samples. qRT-PCR confirmed the expression differences of the four core genes, while single-cell sequencing and immunofluorescence co-localization further validated the distribution of Il1r2 in neutrophils. Conclusion: This study identifies Cebpd, Hspa12b, Pim1, and Il1r2 as key genes in ALI/ARDS, with Il1r2’s expression in neutrophils being particularly significant for understanding inflammation and developing targeted therapies.

Numerous specimens stored in natural history collections have been involuntarily preserved together with their associated microbiomes. We propose exploiting centuries-old soils occasionally found on the roots of herbarium plants to assess the diversity of ancient soil microbial communities originally associated with these plants. We validated this approach extracting and sequencing DNA from rhizospheric soils and roots of four plant species preserved in herbaria for more than 120 years. Extracted DNA displayed typical features of ancient DNA, with cytosine deamination at the ends of fragments predominantly shorter than 50 bp. When compared to extant microbiomes, herbarium microbial communities clustered with soil communities and were distinct from communities from other environments. Herbarium communities also displayed biodiversity features and assembly rules typical of soil and plant-associated ones. Soil communities were richer than root-associated ones with which they shared most taxa. Regarding community turnover, we detected collection site, soil versus root, and also plant species effects. Eukaryotic taxa that displayed a higher abundance in roots were mostly plant pathogens or obligate symbionts that were not identified among soil-enriched ones. Conservation of these biodiversity features and assembly rules in herbarium-associated microbial communities indicates that herbarium-extracted DNA could reflect the composition of the original plant-associated microbial communities and that preservation in herbaria seemingly did not alter these characteristics. Through the use of this approach, it should be possible to investigate historical soils and herbarium plant roots to explore the diversity and temporal dynamics of soil microbial communities.

Missing genotypes in DNA sequence data are an issue in many evolutionary genomic studies, especially of non-model organisms. It can be addressed using genotype imputation. However, algorithms that do not require additional genotype data as reference for imputation, which is often not available for non-model taxa, and are able to work with large whole-genome data sets are scarce. Therefore, we developed a new algorithm called GenoPop-Impute, which imputes the whole genome in separate batches and employs a random forest algorithm for imputation of correlated data sets. The batch-wise approach utilizes linkage disequilibrium to increase imputation accuracy and allows computational parallelization and thus efficiency. Tests on simulated data demonstrate that linkage disequilibrium between SNPs has a positive effect on imputation accuracy, due to correlation that originated in a shared evolutionary history. In comparison to two alternative algorithms, GenoPop-Impute is more accurate and is the only one computationally applicable to data sets of whole genomes. In addition, we found that GenoPop-Impute also increases the accuracy of commonly estimated population genomic metrics and mitigates biases due to missing data in demographic modeling experiments. We conclude that genotype imputation can be a valuable tool for evolutionary genomic studies of non-model taxa and that GenoPop-Impute is a highly suitable algorithm for this.

Trait-based approaches offer valuable perspectives for vegetation classification, but functional traits struggle to capture resource allocation among competing plants, showing limitations across scales. This study aimed to introduce plant architecture to enhance trait-based vegetation classification. From 2021 to 2023, 32 plots of Coastal Dwarf Forests (CDF) and Coastal Normal Forests (NCDF) along China’s eastern coast were surveyed. Their community characteristics were quantified, and classification and clustering models assessed the advantages of plant architecture in distinguishing these communities. The results indicated plant architecture traits are more critical for distinguishing different community types than leaf-based functional traits. Additionally, plant architecture traits are effective in clustering plant associations within the same community type. This is because plant architecture traits are closely linked to habitat, phylogeny, and community structure, providing a comprehensive description of vegetation, while functional traits reflect only partial habitat information related to soil nutrients. Our findings underscore the importance of plant architecture in optimizing trait-based vegetation classification and suggest that variations in the plasticity of plant architecture traits may support the classification of CDF as a distinct vegetation unit.

The study focused on thread damage, kinetic analysis, and the analysis of the looseness curves of the bolts. Experimental results show that fatigue and abrasive wear are the dominant wear mechanisms observed on the thread contact surface. A quantitative analysis of rotation angle and eccentricity shows that the loosening process of the bolt joint structure can be divided into the following three stages: the rapid decline stage of axial force, the slow decline stage, and the rapid decline stage. The first-stage looseness was found to be minimally affected by the rotation angle, whereas the second-stage looseness exhibited sensitivity to the combined influence of eccentric distance and angle.

not-yet-known not-yet-known not-yet-known unknown In the context of climate change, associated with increasingly frequent water deficit and heat waves, there is an urgent need to maintain the performance of soybean the most extensively grown legume before it declines. The objective of this study was to explore which plant traits improve soybean resilience to heat and/or water stress, with a focus on traits involved in plant architecture and nutrient uptake. For this purpose, two soybean genotypes, already shown to have contrasted root architecture were grown under controlled conditions in the high-throughput phenotyping platform 4PMI where either optimal condition, heat waves, water stress or both heat waves and water stresses were applied during the vegetative stage. Under stress conditions the two genotypes displayed contrasted architectural features traits such as root width, root angle branching or number of roots. By correlating architectural to functional traits, related to water, carbon allocation and nutrient absorption, we were able to explain the stress susceptibility level of the two genotypes. This cross analysis of plant ecophysiology and architectural traits under different stresses provides new information on the adaptation of soybean and can inform future crop design, particularly under changing climatic conditions.

Background: Acute pancreatitis (AP) is an inflammatory disease. The effective components of Chaihu Shugan Powder (CSP), paeoniflorin, hesperidin, and glycyrrhizic acid, have been reported to alleviate the damage caused by AP, but the effect of CSP on AP is still unclear. This study explores the fundamental mechanism here. Methods: The AP models were constructed by applying cerulein in AR42J cells and rats. PGC-1α transfection effectiveness was verified using qPCR and western blot. Through gain- and loss-of-function tests, the cell viability, inflammatory factors, lipid ROS, MDA, GSH, Fe2+, and iron levels were analyzed. Ferroptosis protein markers and PGC-1α/Nrf2/HO-1 pathway-associated markers were examined using western blot. The pathomorphological alterations were quantified using the histopathologic test. Results: CSP partially reversed cerulein-induced cell damage, as reflected by increased cell viability, the level of GSH, and ferroptosis protein markers but decreased the contents of TNF-α, IL-6, IL-β, lipid ROS, MDA, Fe2+, and iron. The further data indicated that CSP activated the PGC-1α/Nrf2/HO-1 pathway, which in turn reduced ferroptosis in cerulein-exposed AR42J cells. Importantly, PGC-1α silencing partially neutralized the cerulein-induced CSP activation on PGC-1α/Nrf2/HO-1 signaling in AR42J cells. In AP rats, CSP alleviated AP-related pathomorphological changes and ferroptosis in rats by activating PGC-1α/Nrf2/HO-1 pathway. Conclusions: Altogether, the mechanism by which CSP alleviates AP injury in rats may be correlated with the activation of PGC-1α/Nrf2/HO-1 pathway.

In recent decades, neuroscience has advanced with increasingly sophisticated strategies for recording and analyzing brain activity, enabling detailed investigations into the roles of functional units, such as individual neurons, brain regions, and their interactions. Recently, new strategies for the investigation of cognitive functions regard the study of higher-order interactions---that is, the interactions involving more than two brain regions or neurons. While methods focusing on individual units and their interactions at various levels offer valuable and often complementary insights, each approach comes with its own set of limitations. In this context, a conceptual map to categorize and locate diverse strategies could be crucial to orient researchers and guide future research directions. To this end, we define the spectrum of orders of interaction, namely a framework that categorizes the interactions among neurons or brain regions based on the number of elements involved in these interactions. We use a simulation of a toy model and a few case studies to demonstrate the utility and the challenges of the exploration of the spectrum. We conclude by proposing future research directions aimed at enhancing our understanding of brain function and cognition through a more nuanced methodological framework.

A coupled medium-fidelity drivetrain model is developed and implemented in OpenFAST for a 10-MW land-based reference turbine. The implementation is verified against a fully coupled multibody wind turbine model, including a detailed drivetrain. The new model can simultaneously and accurately estimate main bearing loads and represent elastic bending of the drivetrain. It has low computational cost, useful for early design phases, sensitivity analyses and complex systems like wind farms (where computational expense must be expended elsewhere). Here, the model is extended to a monopile offshore wind turbine and used to investigate sensitivity of predicted main bearing rolling contact fatigue to different synthetic turbulence models. Large eddy simulations (LES) intentionally targeting stable, neutral, and unstable atmospheric conditions at below-, near- and above-rated wind speeds, were used as a reference. The turbulence models recommended by IEC, the Mann spectral tensor model and the Kaimal spectral model with exponential coherence, were fitted to the LES data. Additionally, a constrained turbulence generator, PyConTurb, based on LES data, was applied in the aero-hydro-servo-elastic simulations. Taking PyConTurb as the baseline, the Kaimal model significantly underestimates fatigue of the downwind main bearing, with between 10 and 40% less damage. The Mann model also underestimates the downwind main bearing fatigue with up to 30%. The upwind main bearing damage is driven by mean loads, and differences between models are less significant, although the trends are similar. Reasons for these discrepancies are investigated and attributed to differences in spatial and temporal variations among the turbulence models.

Aims: To explore the relationship between non-high-density to high-density lipoprotein cholesterol ratio (NHHR) and nephrolithiasis incidence among United State adult men and women. Methods: Data was obtained from the 2007-2018 National Health and Nutrition Examination Survey (NHANES) which examined a non-pregnant cohort, ≥20 years of age, with adequate NHHR index and nephrolithiasis information. We employed subgroup assessment and weighted uni- and multivariate logistic regression models to determine the distinct link between NHHR and nephrolithiasis risk. Results: In all, 30969 subjects were recruited for analysis, with 9.97% nephrolithiasis and 2.92±0.02 mean NHHR value. Nephrolithiasis incidence increased with higher quartiles of NHHR. After fully adjusting the covariates, participants with the largest NHHR quartiles exhibited a 34% (OR: 1.34, 95%CI: 1.04–1.72, P = 0.02) raised nephrolithiasis risk, relative to participants with least NHHR quartiles. Based on the two-piece-wise regression, the breakpoint was NHHR = 3.53, and a positive association was more evident when NHHR < 3.53 (OR = 1.22, 95%CI: 1.08–1.37, P = 0.002). When NHHR ≥ 3.53, the correlation disappeared (P = 0.14). Using subgroup and interaction analysis, we reveled a strong association between NHHR and nephrolithiasis. This association was consistent across different population environments, except for a significant interaction effect of smoking status on NHHR.

Incidence of mucormycosis was increased during COVID pandemic mainly during 2 nd wave. Study was done to find Clinical, Epidemiological profile of COVID associated Mucormycosis patients in a Tertiary care hospital in COVID 2 nd wave . To describe clinical profile , causes, risk factors symptoms, signs & microbiological features of COVID associated Mucormycosis. Biopsy samples collected from patients admitted from May 2021 to August 2021and collected their history of COVID symptoms, treatment taken and their biopsy samples were sent to laboratory and were analyzed using Potassium hydroxide mount and culture on Sabaraud’s Dextrose Agar , Dichloran Rose Bengal agar plates and Lactophenol cotton blue results were noted .Out of 101 patients,75 males, 26 females.71 patients were on steroid treatment for COVID. 45 patients were denovo of diabetes mellitus ,56 were chronic diabetic. 24 patients had vision loss, 64 had periorbital edema, 56 had facial pain , 15 had loss of eye movements ,14 had hard palate necrosis, each patient having one or more signs. . On KOH mount , we got 55 broad aseptate hyphae, 8 narrow aseptate hyphae,3 yeasts. In 7 samples, no fungal elements were seen.28 were KOH positive & culture negative. On Culture , Rhizopus species was isolated in 43 , Aspergillus species were isolated in 8, Candida species was isolated 3 , Demeticious fungi in 1& “ no growth” in 40 . . High clinical suspicion, prompt diagnosis, and early initiation of medical & surgical intervention are required are essential for successful outcome.

Trophic interactions are crucial for maintaining ecological balance. In cities, avian predation has been shown to be particularly relevant due to its effects on community structure, pest control, and nutrient cycling. As humanity relies upon ecosystem services for survival and 70% of the human population is projected to live in cities by 2050, understanding the effects of urbanization on avian predation is imperative. This study investigates the effects of urban microclimates, impacted by impervious surface and green/blue infrastructure, and human-induced disturbances, on avian predation in urban areas. Plasticine caterpillars were placed in Quercus robur trees to assess the avian predation rate. The analyses evaluated the impact of artificial lighting at night, human population density, noise pollution, vegetation, and water bodies on predation rates during two months in the city of Amsterdam. Results indicated a substantial increase in predation during the second month, likely caused by an increase in naïve fledglings or elevated ambient temperatures. Noise pollution consistently emerged as the most reliable predictor of predation, consistently leading to a reduction in predation rates, possibly due to avoidance behaviour. Other predictors exhibited substantial temporal and spatial variability. Artificial lighting at night increased predation in the first month, suggesting that insectivorous birds predate near illuminated areas. The diminished effect of artificial lighting in the second month may be attributed to the increased daylength. During the second month, all predictors negatively affected predation, supporting the increasing disturbance hypothesis. These findings underscore the complex relationship between urban factors and avian predation, highlighting the need for mitigation efforts in urban planning.

Objective: This study aims to identify the optimal treatment strategy and conduct a prognostic analysis for patients with locally advanced Upper Tract Urothelial Carcinoma (UTUC). Methods and materials: The study included 3,829 patients diagnosed with pT3-4N0/+M0 UTUC from 2004 to 2015, with data obtained from the SEER database. Patients were randomly assigned to a training group (70%) and a validation group (30%) for nomogram development. For nomogram development, variables that demonstrated statistical significance in univariate analysis (P < 0.05) were selected for inclusion in the multivariate model. The nomogram’s predictive precision and ability to differentiate were evaluated through the C-index, AUC and calibration curves. The model’s clinical validity was confirmed through the use of decision curve analysis (DCA). Results:The 3-year OS and CSS rates were significantly higher in patients who received surgery followed by adjuvant chemotherapy (S+C) compared to those treated with surgery alone (S). Within the pN+ subgroup, the combination of surgery with both adjuvant chemotherapy and radiotherapy (S+R+C) group and S+C group yielded superior results over the S group, with the S+R+C group regimen showing the most favorable outcomes. Multivariate COX regression analysis identified age, primary tumor location, T and N stages, treatment modality, tumor size, and lymph node count as significant predictors of OS and CSS. These factors were integrated into precisely developed nomograms for predicting OS and CSS, with concordance indices of 0.651 and 0.667 in both sets. Conclusion: For patients with UTUC at stage pT3-4M0, adjuvant chemotherapy following surgical treatment has markedly extended patient longevity. Furthermore, for those with pT3-4N+M0 stage UTUC, the addition of radiotherapy to the surgical and chemotherapy regimen has proven to notably enhance survival rates. Our predictive nomogram reliably forecasts OS and CSS rates for patients with locally advanced UTUC.

The integration of big data into nephrology research has opened new avenues for analyzing and understanding complex biological datasets, driving advancements in personalized management of cardiovascular and kidney diseases. This paper explores the multifaceted challenges and opportunities presented by big data in nephrology, emphasizing the importance of data standardization, sophisticated storage solutions, and advanced analytical methods. We discuss the role of data science workflows, including data collection, preprocessing, integration, and analysis, in facilitating comprehensive insights into disease mechanisms and patient outcomes. Furthermore, we highlight the potential of predictive and prescriptive analytics, as well as the application of large language models (LLMs), in improving clinical decision-making and enhancing the accuracy of disease predictions. The use of high-performance computing (HPC) is also examined, showcasing its critical role in processing large-scale datasets and accelerating machine learning algorithms. Through this exploration, we aim to provide a comprehensive overview of the current state and future directions of big data analytics in nephrology, with a focus on enhancing patient care and advancing medical research.

Understanding population demography of threatened species and how they vary in relation to natural and anthropogenic stressors is essential for effective conservation. We used a long-term photographic capture-recapture dataset (1993 – 2020) of Indo-Pacific bottlenose dolphins (Tursiops aduncus) in the highly urbanised Adelaide Dolphin Sanctuary (ADS), South Australia, to estimate key demographic parameters and their variability over time. These parameters were analysed in relation to environmental variables used as indicators of local and large-scale climatic events. Our findings indicate that apparent survival was high (0.98-0.99) and did not vary seasonally. Estimates of abundance were not directly related to environmental variables but were linked to seasonal temporary emigration. Abundance peaked in summer with an average of 85.37 dolphins (SD = 30.23), and was lowest in winter, with 68.57 (SD = 24.70) individuals. Site fidelity at the population level was low, but lagged identification rates revealed a resident population of approximately 28 individuals. Trend analysis suggests high levels of dolphin abundance and persistence of the population over decades despite significant urbanisation, but numbers have declined in recent years. Further research is needed to understand the cumulative impacts leading to this population decline and to assess its future viability under different management scenarios. Conservation strategies aimed at increasing reproductive rates and promoting connectivity to adjacent waters are likely to be more effective in reversing population declines.

Cantrell syndrome is rare in prenatal diagnosis. The diagnosis of Cantrell syndrome can be made antenatally through the use of ultrasonography, although this is challenging in cases where the defects are minor.We have reported a case diagnosis of imcomplete Cantrell syndrome through sections of the four-chamber view and the left ventricular outflow tract view.

Infective endocarditis (IE) continues to have high rates of adverse outcomes, despite recent advances in diagnosis and management. Although the use of computer tomography and nuclear imaging appears to be increasing, echocardiography, widely available in most centers, is the recommended initial modality of choice to diagnose and consequently guide the management of IE in a timely-dependent fashion. Echocardiographic imaging should be performed as soon as the IE diagnosis is suspected. Several factors may delay diagnosis, for example echocardiography findings may be negative early in the disease course. Thus, repeated echocardiography is recommended in patients with negative initial echocardiography if high suspicion for IE persists, in patients at high risk. However systematic echocardiographic screening should not be utilized as a common tool for fever, but only in the presence of a reasonable clinical suspicion of IE. It may increase the risk of false positive rates of patients requiring IE therapy or may exacerbate diagnostic uncertainty about subtle findings. Considering the complexity of the disease, the echocardiographic proper use should be increasingly time-efficient and focused on the correct identification of IE lesions and associated complications. The path to identify patients who need surgery passes through an echocardiographic skill ensuring the identification of the cardiac anatomical structures and their involvement on the destructive infective extension. We pointed out the role of echocardiography focused on the correct identification of IE distinctive lesions and the associated complications, as part of a diagnostic strategy, within an integrated multimodality imaging, managed by an “endocarditis team”.

A new species Meconopsis pathakii is described and illustrated from Dihang Dibang Biosphere Reserve, Arunachal Pradesh, India. This new species belongs to Aculeatae Fedde. Morphologically it shows resemblance to Meconopsis bijiangensis H. Ohba, Tosh. Yoshida, H. Sun and M. sinuata Prain by its habit, indumentum, sinuate leaf margin, and petals, but differs in brilliant maroon flowers. It also shows closely allied to Meconopsis tibetica Grey-Wilson. in habit and brilliant maroon petal color but differs in stigmas capitate, purplish red, obscurely 4-lobed; inflorescence terminal and axillary raceme, 4–5-flowered. This species is narrowly confined, in a small population comprising about 25 matured individuals in two locations. The threat status of this newly found species is provisionally assessed here as “Critically Endangered CRB2ab(III); D)”.

not-yet-known not-yet-known not-yet-known unknown Abstract Few studies have investigated blood glucose levels and complication management in elderly patients with type 2 diabetes (T2D) at community hospitals in China. The objective of this study was to investigate the factors influencing blood glucose control in elderly patients with T2D and assess the adherence of doctors in community hospitals to the latest diabetes guidelines regarding the use of glucose-lowering medications. This study involved 1150 elderly patients (age≥65 years) with diabetes to assess blood glucose control, complications management, and adherence of medication according to the guidelines of American Diabetes Association. To evaluate blood glucose control, different glycated haemoglobin targets were assigned according to patient characteristics and health status (including comorbidities and cognitive status). Univariate and multivariate logistic regression analyses were used to investigate the factors affecting glucose control. Among the 1150 participants, 351 (30.52%) had poor glucose control. Frailty (odds ratio [OR]:0.393; 95% confidence interval [CI]:0.195-0.789; P=0.009), male sex (OR:1.472; 95% CI:1.131-1.915, P=0.004), and insulin treatment (OR:4.364; 95% CI:3.151-6.042; P<0.001) were significantly associated with poor blood glucose control in patients treated with glucose-lowering medications. The proportion of patients without frailty with poor control was higher than those with frailty (31.28% vs. 17.46%, respectively). In conclusion, blood glucose control in elderly Chinese patients with T2D is poor and influenced by frailty, sex, and insulin treatment. Hence, it is crucial to enhance the implementation of sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide-1 receptor agonists in Chinese community hospitals and strengthen the differentiation of updated guidelines within these healthcare settings.

The effect of oven wall chamber insulation on the Nusselt number and heat loss reduction in a specially fabricated direct-fired natural convection oven was studied. Fiber glass and aluminum-reinforced fiber were used as wall chamber insulation materials. Three sets of experiments, namely uninsulated and insulation with each of the two insulation materials were performed and a data acquisition system was used to continuously monitor temperature and environmental changes from the oven’s four sides, ambient, and inside using a heat temperature sensor (REX-C700-FK07-M-AN) and thermocouple (OM-HL-EH-TC). The temperature profiles, the Nusselt number, the Rayleigh number and the heat transfer coefficient were used to assess the heat transfer characteristics of the oven to ascertain the heat loss reduction potential of the insulation materials. Within the range of the Raleigh numbers studied, the average Nusselt number is highest for the uninsulated and higher for the insulated with aluminum-reinforced fiber and least for the insulated with fiberglass. The average percentage reduction in heat loss as a result of insulation was 3.8% and 16.7% for the fiber reinforced aluminum and fiberglass, respectively. Based on the findings, which are consistent with earlier related research, installing thermal insulation materials in oven walls could reduce heat loss, appreciably. The least-square correlation of the Nusselt numbers and the Rayleigh numbers resulted in high values of adjusted R-square values, which could be used for the optimization of natural convective heat transfer for uninsulated and insulated ovens.

Company earnings calls are crucial events that provide transparency into a company’s financial health and prospects. Large language models (LLMs) offer a promising approach to automatically generate the first draft of earnings call scripts from financial data and past examples. We evaluate two methods: 1) Few-shot prompt engineering with a state-of-the-art model, and 2) Fine-tuning a language model on earnings call transcript data. Our results indicate both approaches can produce coherent scripts covering key metrics, updates, and guidance. However, there are trade-offs in comprehensiveness, hallucinations, writing style, ease of use, and cost. We discuss the pros and cons of each method to guide practitioners on effectively leveraging large language models for this financial communication task.

This study explored mental workload recognition methods for carrier-based aircraft pilots utilizing multimodal physiological signal fusion and portable devices. A simulation carrier-based aircraft flight experiment was designed, and subjective mental workload scores and Electroencephalogram (EEG) and Photoplethysmogram (PPG) signals from six pilot cadets were collected using NASA Task Load Index (NASA-TLX) and portable devices. The subjective scores of the pilots in three flight phases were used to label the data into three mental workload levels. Features from the physiological signals were extracted, and the interrelations between mental workload and physiological indicators were evaluated. Machine learning and deep learning algorithms were used to classify the pilots’ mental workload. The performances of the single-modal method and multimodal fusion methods were investigated. The results showed that the multimodal fusion methods outperformed the single-modal methods, achieving higher accuracy, precision, recall, and F1 score. Among all the classifiers, the random forest classifier with feature-level fusion obtained the best results, with an accuracy of 97.69%, precision of 98.08%, recall of 96.98%, and F1 score of 97.44%. The findings of this study demonstrate the effectiveness and feasibility of the proposed method, offering insights into mental workload management and the enhancement of flight safety for carrier-based aircraft pilots.