Indonesia, home to a significant portion of the world’s fish biodiversity, faces challenges in accurately identifying and conserving its freshwater fish species, including Ompok hypophthalmus. This study examines the morphological and molecular identification status of O. hypophthalmus, highlighting inconsistencies in taxonomic practices and the need for integrative approaches. Morphological studies, such as those by Ng (2003), identified three distinct species within the O. hypophthalmus group, yet subsequent research often misclassified O. rhadinurus and O. urbaini as O. hypophthalmus. Molecular analyses, including DNA barcoding of cytochrome oxidase I (COI) and cytochrome b (Cyt b) genes, revealed significant genetic divergence among populations from different Indonesian rivers, suggesting speciation or misidentification in prior studies. Phylogenetic and genetic analyses confirmed the presence of O. hypophthalmus in Arut River, while Indragiri and Tapung Rivers were dominated by O. rhadinurus. These findings emphasize the importance of integrating morphological and genetic tools to resolve taxonomic ambiguities and support effective fish management and conservation strategies. This study provides critical genetic data, advocates for molecular methods in biodiversity assessments, and underscores the ecological significance of accurately identifying native and invasive fish species in Indonesia and World.

The mechanisms underlying high-altitude hypoxia adaptation on the Qinghai-Tibet Plateau have been extensively studied, yet the role of alternative splicing in this process remains poorly understood. Yunnan’s vertical zoning with pig breeds distributed across varying elevations provides an excellent model for investigating hypoxic adaptation. Here, we examined three indigenous Yunnan pig breeds: Diannan small-ear pigs (DSE, 500 m), Baoshan pigs (BS, 1500 m), and Diqing Tibetan pigs (DT, 3200 m). Using PacBio Iso-Seq, we obtained comprehensive full-length transcriptomes from five tissues (heart, kidney, liver, lung, and spleen), identifying 51,774 transcripts, including 34,813 novel ones, and 74,843 Alternative Splicing (AS) events across 10,686 AS genes. Significantly, skipped exons (SE) were the predominant form of alternative splicing (AS) based on the reference dataset, whereas alternative first exons (AF) were the most frequent AS events in novel isoforms. We further identified five actin-binding genes (FHOD3, TNNC1, ACTN2, PDLIM5, and TNNI3) crucial for maintaining cellular structure and function under hypoxia, and five HIF pathway genes (PFKM, CAMK2D, PDHA1, TF, TFRC) with extensive alternative splicing variants, implicating their roles in regulating energy and iron metabolism for hypoxia adaptation. Differential alternative splicing (DAS) analysis identified numerous DAS events between high- and low-altitude pig breeds, including 3,816 events for BS vs. DT and 1,741 for DSE vs. DT. Furthermore, we identified six alternative splicing genes (AGO2, PDK1, ZNF12, FBLN1, CSF1, RRBP1), exhibiting significant differences in transcript usage across breeds, highlighting their regulatory importance in hypoxia adaptation. Our findings improve the genome annotation, deepen the understanding of hypoxia molecular mechanisms in pigs, and provide valuable insight into the genetic basis of high-altitude adaptation in other species.

This study investigates the capability of FAST.Farm, a mid-fidelity wind farm simulation tool employing the dynamic wake meandering approach, to accurately predict loads on wind turbines in a small wind farm. The wind farm consists of three 1:150 scale models of the DTU 10 MW wind turbine tested in a wind tunnel under scenarios including steady-state operation, wake steering, and dynamic wake actuation. The results demonstrate that FAST.Farm, once calibrated with experimental data, effectively predicts the thrust force and yaw moment of wind turbines across diverse wake conditions. Notably, the Curl wake model—designed to replicate the kidney-shaped wake deficit—has better accuracy in capturing yaw moments of downstream turbines under yaw misalignment. However, its tendency to overestimate wake expansion reduces accuracy in non-skewed inflow scenarios compared to the Polar model. The study highlights the necessity of optimizing FAST.Farm dynamic wake meandering parameters to enhance its precision, particularly by accounting for turbine spacing and wake interactions. Furthermore, it is crucial to improve the accuracy of aerodynamic load calculations under skewed inflow conditions. These findings provide a validated framework for advancing wind farm simulation tools and optimizing wind turbine performance in complex operational conditions.

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Case report

Thermal tolerance is often used to explain range limits, abundance, and population dynamics; however, recent theory suggests thermal tolerance itself can be fundamentally altered by food limitation. We tested this hypothesis by quantifying how temperature and food concentrations interact synergistically to shape growth, development, and survival throughout larval development of purple urchins (Strongylocentrotus purpuratus). Developmental rates and time to metamorphic competency were driven largely by temperature but were not affected by the range of food concentrations. However, food limitation had a substantial impact on survival response to temperature. With ample food, larvae exhibited modest survival across temperatures currently experienced by larvae in nature. Reductions in food lowered optimal survival temperatures and shifted the thermal window towards cooler temperatures. These results are consistent with the “metabolic meltdown” hypothesis - shifting optima to cooler temperatures- and illustrate how present day warming with lower productivity may lead to substantial, unexpected declines in larval recruitment.

Background: Influenza presents a significant global health challenge, with seasonal epidemics causing 3 to 5 million cases of severe illness and 290,000 to 650,000 respiratory deaths annually. In Bhutan, the highest rates of influenza-associated hospitalizations were observed among children under 5 years of age emphasizing the need for robust surveillance and preparedness. Objective: This study aims to assess influenza severity in Bhutan using the World Health Organization’s (WHO) Pandemic Influenza Severity Assessment (PISA) framework. By integrating syndromic and influenza-specific data, we establish national-level baseline and threshold values for influenza activity. Methods: The WHO Average Curve Method was employed to establish seasonal and intensity thresholds, categorizing influenza severity based on historical data from 2016-2019 and 2023. Results: Analysis of influenza activity revealed near-continuous activity with two annual peaks. Thresholds for epidemic, moderate, high, and extraordinary levels of transmissibility and morbidity were determined. The 2019 season exhibited the highest transmissibility and morbidity, with significant variability in intensity across different seasons. Conclusion: The study demonstrates the effectiveness of the PISA framework in assessing influenza severity in Bhutan. The established thresholds provide a valuable tool for public health decision-making, enhancing the country’s preparedness for both seasonal and pandemic influenza. These findings underscore the importance of maintaining and adapting surveillance systems to monitor influenza activity year-round.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spreads rapidly and can lead to a high mortality rate, particularly in severe cases. The emergence of SARS-CoV-2 variants with enhanced immune evasion capabilities and the persistent symptoms of Long Coronavirus Disease (COVID), including intermittent dyspnea, fatigue, and brain fog, have garnered global attention. The pathogenesis of COVID-19 is attributed to both direct viral damage and an excessive secondary inflammatory response within the host. The latter, in particular, is a crucial factor in disrupting immune homeostasis. Recent research suggests a strong association between PANoptosis, a complex cell death process involving multiple pathways, and the occurrence of cytokine storm syndrome following SARS-CoV-2 infection. PANoptosis has been implicated in the development of various infectious diseases. This review explores the potential mechanisms underlying SARS-CoV-2-induced inflammatory cell death, specifically PANoptosis, and the subsequent cytokine storm. By understanding these processes, we can identify potential therapeutic targets for related diseases.

Effective home-based rehabilitation depends on accurate assessment. This study evaluates the accuracy of LiDAR technology for home measurements and has significant implications for the future of home-based rehabilitation. Three researchers from different professional backgrounds—a healthcare professional, a public health researcher, and an engineer with expertise in the LiDAR system—scanned the interior of a typical UK house using LiDAR-equipped devices (iPhone-13-ProMax, iPad-Pro 11-inch, and Leica_BLK360-G1). Room dimensions were also measured using tape as a reference standard with an accuracy of ±0.1 cm. The environmental light level in each room was measured with a LUX-light meter app (Google-Pixel3a). The reliability of the measurements from the LiDAR devices was assessed against the tape measure, which served as the reference standard, using the intraclass correlation coefficient (ICC) for absolute agreement. The environmental light ranged from 54 to 1051 LUX. All three devices demonstrated high reliability in measuring room dimensions: iPad Pro (ICC =0.989 to 1.000), iPhone (ICC =0.967 to 0.999), and Leica (ICC =0.998 to 1.000). However, the Leica device showed limitations under low-light conditions. These findings support using LiDAR technology by healthcare professionals to accurately assess patients’ home environments, facilitate sustainable rehabilitation services, and potentially reduce the need for home visits.

The relationship between bilingualism and cognitive control remains a topic of ongoing debate, partly due to the reliance on solely behavioral measures of cognitive control (Bialystok, 2017), which may not always capture subtle individual differences (Draheim et al., 2019). Prior research has utilized electrophysiological measures to investigate the links between bilingualism and cognitive control; however, the results of these studies are mixed (Antoniou, 2023). Mixed evidence may stem from traditional approaches treating bilingualism as a categorical variable (i.e., bilinguals vs. monolinguals). However, a more nuanced, continuous measure of bilingualism is now advocated by many researchers (e.g., Backer & Bortfeld, 2021; DeLuca et al., 2020; Luk & Bialystok, 2013). To address this gap, we examined whether the degree of bilingualism was associated with an established event-related potential (ERP) index of cognitive control—the P3b. We used the Language Social Background Questionnaire to derive an aggregated bilingualism composite factor score, a proxy for a participant’s overall degree of bilingual language experience (Anderson et al., 2018). A discriminability index ( d′) was used to measure behavioral performance. We recorded ERP data from 70 adults during a visual oddball task to elicit the P3b. We examined whether composite scores corresponded with the P3b effect, indicative of cognitive control, while taking into consideration childhood family socioeconomic status (SES), specifically parent education, as it has been linked to cognitive control in adulthood (Isbell et al., 2024). We found a positive association between composite scores and the P3b effect, indicating that more bilingual language experience was associated with greater attention allocation and working memory updating, independent of childhood family SES. However, we found no links between composite scores and behavior. These findings underscore the importance of characterizing bilingualism along a continuum and acknowledging the variability in neural processing strategies among adults with diverse bilingual experience.

For decades managers have sought to mitigate the effects of fragmentation on wildlife. In aquatic ecosystems, fragmentation strongly affects headwater streams due to the architecture of riverine networks and the abundance of road crossing culverts. Standardized road crossing assessments offer an alternative to historical methods in facilitating the identification and prioritization of barriers for restoration. However, the ecological relevance of these assessments are seldom empirically investigated, and most assessments assume homogenous environmental and biotic conditions observed during snapshot surveys. Our goal was to assess both the efficacy and assumptions of the widely adopted Southeastern Resource Aquatic Partnership’s (SARP AOP) Road Crossing Assessment for predicting fish passage. We used model selection of generalized linear mixed models to compare SARP AOP scores to observed movement calculated through mark-recapture. We also compared the SARP AOP score with a modified version of the score that included alterations to better reflect local environmental conditions. Although limited in scope, our results suggest an overall lack of support for the efficacy of the SARP AOP score in predicting fish passage and only marginal improvements under the modified score. This study is an important first step in our ability to modify standardized score calculations to increase efficacy without additional surveys. Regardless of the scoring framework efficacy, standardized road crossing surveys remain highly useful in collecting information related to potentially harmful structures (i.e., failing infrastructure). Future studies should further explore how to improve the efficacy of these assessments, which represent a promising tool to facilitate efficient and effective restoration.

The purpose of this study is to investigate public health care managers’ views on knowledge management (KM), and how managers’ individual and organisational factors are related to their views. Data was gathered through a survey of strategic, middle and first-line managers (n=406) in the public health care sector in Finland. The data were analysed using SPSS (version 28.0). Factor analysis was performed to formulate sum variables. The Kruskal-Wallis test and the Mann Whitney U test was used to determine the relationship between managers’ background and sum variables. Organisational factors, such as management position in the organisation, the nature of the work and the organisation they work had more influence on managers’ views on KM than their individual characteristics. Health care managers expressed the most positive views about knowledge use in management. By contrast the creation of common managerial knowledge was considered the weakest aspect of KM.

Vocalization is an important feature in anuran identification that could vary among individuals and populations. We present an investigation of multiple-level variation on advertisement calls of Microhyla fissipes from nine populations across Hainan Island and further test the differences between the two geographical groups that were divided by morphological features in a previous study. We found that dominant frequency is the most static call parameter at the individual-level. Four of six call parameters show significant differences between groups and could be useful for identification among groups. The southwest (SW) group from Hainan Island represents the highest dominant frequency among all reported advertisement calls in the literature implying a need for re-evaluation of its taxonomy position.

Withania somnifera (Ashwagandha), a vital medicinal plant, faces significant losses due to fungal diseases such as root rot, wilt, and leaf spot caused by Fusarium annulatum and Alternaria alstroemeriae. To manage these pathogens, metabolites of Cordyceps militaris were extracted following methods from Vinale et al. (2006) and others, with modifications. These metabolites were tested for antifungal efficacy using the poison food technique. Results showed the minimum inhibitory concentrations (MIC) against F. annulatum and A. alstroemeriae were 15 mg/mL and 20 mg/mL, respectively, with cidal effects observed at 20 mg/mL and 30 mg/mL. In-silico investigations revealed that Cordycepin, a metabolite, exhibited strong binding affinity to the fungal chitin synthetase protein. These findings suggest that C. militaris metabolites could serve as a potential alternative to synthetic fungicides, pending further research.

Wood structural cracking datasets obtained by computer vision techniques can help researchers and managers understand the status of ancient wooden structures. The complex imaging processing will be involved. Herein, the current mainstream technique for computer recognition is Deep Neural Networks (DNNs), which typically requires robust training datasets. However, lacking large, publicly available datasets for ancient wooden structures hampers its applications in this area. In this paper, the Ancient Wooden Building Cracks (AWBC) dataset was developed, consisting of 10,528 images collected from ancient buildings of various ages, types (house, temple, bridges), and configurations (beams, columns, handrails, doorframes, etc.), annotated with four common types of cracks. To fulfill the requirements of both target recognition and instance segmentation tasks in visual inspection, different annotation files were extracted using various annotation programs. The typical DNN detector was introduced and adapted for the established dataset. The results show that the AP can reach 73% for the more obvious cracks, and the dataset can be applied to most of the current mainstream deep learning training programs and be a tool for detecting cracks on ancient wood structures. A large-scale open image dataset for crack detection on wooden structures of ancient buildings associated with the deep learning framework has been provided for the AEC industry.

The porous characteristics of porous materials result in a distinct fatigue crack extension mechanism under cyclic stress compared to monolithic materials. The study employs 18Ni300 mold steel to fabricate porous CT specimens with varying pore diameters, depths, and spacings using a selective laser melting technique with 18Ni300 powders. Pore morphology was a single variable, and fatigue crack extension tests were conducted under different loads and stress ratios. The Paris formula was applied to fit test data, and crack extension paths were simulated and predicted using the extended finite element method. The study reveals that larger pore diameters and depths increase crack extension rates and reduce life, while smaller spacings lead to lower rates and longer life. Pores also affect the material’s da/ dN–∆ K curve, with higher depth causing larger sudden change amplitude. Pores also influence crack extension trajectory, resulting in a more linear crack propagation.

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Background: The role of tumor genomics in influencing treatment outcomes remains a critical area of investigation in personalized medicine. This study is the first to evaluate the relationship between tumor mutational burden (TMB) and driver mutations (DM) in pediatric brain tumors, examining their association with treatment modalities and patient outcomes. Methods: We conducted a retrospective study of 160 pediatric patients (78 males, 82 females) with a median age of 9 years (range: 2 months to 26 years) who were diagnosed with primary CNS tumors across four academic institutions between 2008 and 2023. The analysis included TMB (high TMB ≥3 mutations/megabase, low TMB <3), DM, treatment modality, clinical outcomes, and matching status, defined as the alignment of tumor genomic markers with corresponding pharmacologic targets. Results: Low-grade glioma (42.5%) and high-grade glioma (22.5%) were the most common tumors, followed by medulloblastoma (10.6%), ependymoma (5%), ganglioglioma (4.4%), atypical teratoid rhabdoid tumor (2.5%), and other rare tumor types. Among patients receiving targeted or immunotherapy (N=30), most had matched therapy (N=19) and were found to have no significant survival advantage. High TMB tumors had better survival with standard therapy (p=0.026). Targeted and immunotherapy were used as second or later lines of treatment, with a non-significant trend suggesting better survival in recurrent or progressive disease. Conclusions: This study found no statistical significance linking matched therapy with molecular markers to improved overall survival. Most patients received targeted or immunotherapy as second-line or later treatment. High TMB tumors had better outcomes with standard therapy. The increased resistance and aggressiveness of recurrent tumors complicate the ability to evaluate targeted therapies as initial treatment options.

We report a case with multiple lineage switches, a rare event that mostly occurred in infants, in a ten-year-old girl with KMT2A-AFF1 positive ALL. Although she received standard chemotherapy, immunotherapy and hematopoietic stem cell transplantation (HSCT), the leukemic clone continuously switched on AML or ALL, maintaining immunoglobulin/T-cell receptor (IG/TR) rearrangements and showing a high grade of therapy-escaping. Since other genes are involved in this event, considering the selective pressure imposed by the applied treatment, it is mandatory to characterize the cell-of-origin that is capable to proliferate towards both lineages in the attempt to design a targeted therapy.

Deep neural networks (DNNs) inevitably have defects like traditional software. When the defective DNN model is applied to safety-critical fields, it may lead to serious accidents. Therefore, how to effectively detect defective DNN models has become an urgent problem to be solved. Maintaining a high-quality test data set is an important guarantee for achieving the above goals for DNN models testing. In order to further improve the accuracy and diversity of test data that can detect model defects, thereby improving the efficiency of detecting DNN defects, a test data selection method based on self-attention mechanism and K-means clustering (SAKCL) is proposed in this paper. Experiments were conducted on the combination of five deep learning data sets and models. The results show that SAKCL is significantly better than the existing methods, whether it is the ratio of test cases that can detect model defects in the selected samples or the diversity of model defect types that test cases can detect.

IntroductionAmeloblastic fibroma (AF) is a rare, benign odontogenic tumor, accounting for approximately 1.5% to 4.5% of all odontogenic tumors. It was first described by Krause in 1891, but it was later recognized as a distinct entity by Thoma and Goldman in 1946.1 It is more commonly seen in children and adolescents, especially within the first two decades of life. There is a slight predilection for males,2 and the posterior mandible is the most frequent site of involvement. It is often associated with impacted or unerupted teeth.3Clinically, AF typically presents as a slow-growing, painless swelling, which may remain undetected until incidental discovery during routine radiographic examination. Radiographically, it often appears as a well-defined, unilocular or multilocular radiolucent lesion often with sclerotic radiopaque borders.1 Although considered benign, AF has the potential for recurrence and, in rare instances, malignant transformation into ameloblastic fibrosarcoma, emphasizing the importance of early diagnosis and appropriate management.Histopathologically, ameloblastic fibroma consists of both epithelial and connective tissue components. The epithelial component shows proliferating islands, cords, and strands of odontogenic epithelium with a peripheral layer of cuboidal or columnar cells and a central area resembling the stellate reticulum. The connective tissue component, resembling dental papilla, contains spindle and angular cells within a myxomatous stroma of delicate collagen. 2,3 Treatment options typically involve enucleation and curettage, surgical excision, partial resection and reconstruction. This case report discusses a rare presentation of ameloblastic fibroma in an 8-year-old female child, who presented with a painless swelling in the right posterior region of the mandible, which was histologically characterized by dentinoid formation.

A conventional target detection technique for FMCW millimeter-wave radar applies a two-dimensional (2D) cell-averaging constant false alarm rate (CA-CFAR) detector to all range-Doppler cells in order to suppress noise and clutter. However, this 2D CA-CFAR method has significant drawbacks, particularly its high computational cost due to the large number of additions required, resulting in a time complexity of O(n^{4}). To decrease the computational complexity while ensuring the detection accuracy, a novelty CA-CFAR technique based on the prefix sum algorithm with the complexity O(n^{2}) is proposed in this article. Simulations prove the feasibility of the proposed method. Compared to both conventional and state-of-the-art optimized CA-CFAR techniques, the proposed method reduces the number of addition operations by 95%, lowers CFAR loss by approximately 0.5 dB, and improves the figure of merit (FoM) by about 20% at a fixed false alarm rate of 10^{-6}. This advanced technique offers significant computational efficiency for radar applications.

Satellite observations of SST and chlorophyll are commonly used to validate Earth system models. However, these observations are impacted by sampling bias due to sea ice, cloud cover, and solar zenith angle, which prevent satellite detection. To bridge the gap between models and observations, we have developed a satellite simulator for SST and chlorophyll within the Community Earth System Model (CESM2), which generates synthetic MODIS observations at model run-time. The modeled observations allow us to remove the impact of sampling bias in order to update estimates of model bias. We present results from a hindcast simulation and analyze years 2003 to 2016, comparing the model to real-world MODIS observations over the same time period. Although satellite sampling bias in SST and chlorophyll is generally small compared to model bias, modeled MODIS observations of chlorophyll and SST reduce apparent ocean model bias on a global scale. However, the relative importance of these two biases varies regionally and on different spatial and temporal scales.

Carbon price serves as a critical factor in decision-making and risk management for macro- and micro-participants in the trading market of carbon emissions. The accurate predicting trading prices of carbon emissions can significantly benefit carbon emissions management of government, risk control and financial market investment activities of enterprises. This research investigated factors influencing carbon prices, established a mode decomposition convolutional bidirectional long short-term memory attention and random forest model (COA-MCBiLSTMA&RF) integrating deep learning network with the intrinsic complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the coati optimization algorithm (COA), and proposed a multi-mode differentiation prediction strategy. The modeling framework proposed in this article has advantages of addressing data noises and limited feature extraction capabilities, improving the prediction accuracy compared with traditional carbon price prediction models.