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"Includes all data and plotting codes from the article 'Enhanced photosynthetic and hydraulic traits of Myriophyllum aquaticum by artificial height adjustment'."

Mutation of the small G protein member RAB18 can lead to Warburg Micro Syndrome, characterized clinically by visual impairment and hind limb weakness. However, the cellular and molecular functions of RAB18 in mice are not fully understood. We obtained C57BL/6J Rab18-/+ mice by using CRISPR/Cas9 technology. The Rab18-/+ mice were mated to produce 3 Rab18+/+ and 3 Rab18-/- mice, which were 1 male and 2 female. Our Rab18-/- mice exhibit hind limb weakness and exhibit grip/curling when tail suspended. Through metabolomics analysis, we found that Rab18 knockout affects lipid, vitamin, and amino acid metabolism while also impacting the autophagy signaling pathway. Lipid analysis of mouse liver revealed that Rab18 knockout impaired fatty acid release. Interestingly, Rab18 knockout promoted the expression of lipogenic genes and proteins but did not affect the expression of lipolytic genes and proteins. Since lipophagy, involved in lipid droplet breakdown, plays a key role, we found that Rab18 knockout inhibited the expression of liver autophagy-related genes and proteins. In summary, our results suggest that Rab18 plays a role in autophagy in mice, likely contributing to mechanisms of lipid accumulation.

This database organizes the impact of variable i on variable j based on the question: “What is the impact of variable i on variable j?” Information was collected from a variety of sources, including patents, research articles, literature reviews, books, and other relevant materials related to the trucking industry. For each comparison, an impact level was assigned based on the relevance and quantity of available data. In total, 156 unique comparisons were made, each supported by pertinent literature, with detailed explanations provided in each row.

In the context of the project zirkulierBAR (Intermunicipal acceptance for sustainable value added from separately collected sanitary streams – from linear sanitation to circular nutrient recycling), a total of 11 composts were subjected to analysis in accordance with the specifications set forth in DIN SPEC 91421:2020-12. The objective was to ascertain the quality of recycling products derived from dry toilets for utilisation in horticulture. The analysis encompassed an assessment of the nutrient content, hygiene parameters and pollutants present, including heavy metals, polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), perfluorinated tensides (PFTs) and pharmaceutical residues. In order to identify potential discrepancies between laboratories, the composts were subjected to parallel analysis by two different project participants with regard to standard parameters, nutrient content, trace elements and heavy metals. The analysis of hygiene parameters and pollutants was conducted by external laboratories. It should be noted that the data set does not include information regarding the composting process. The goal of the data set is to expand the database on composts containing human faeces, thereby providing a basis for classifying the recycling route and promoting acceptance of circular use in order to preserve nutrients.

Bovine respiratory disease (BRD) remains a leading cause of economic losses, hampered animal welfare and antimicrobial use. The use of acute phase proteins (APPs), such as serum amyloid A (SAA) and haptoglobin (Hp), has been explored for the detection of BRD, as defined by clinical signs. However, whether these APPs are also associated with lung consolidation, as determined by thoracic ultrasonography, and have the potential to differentiate causative pathogens is unknown. Therefore, the primary objective of this study was to explore the association between lung consolidation and SAA and Hp. The second objective was to determine the ability of both SAA and Hp to differentiate pathogen groups (Mycoplasmopsis bovis, viruses and Pasteurellaceae). A cross-sectional study including 170 calves from 25 different herds with a history of Mycoplasmopsis bovis (endemic) or herds (including M. bovis positive herds) that met the inclusion criteria of an outbreak of BRD (i.e., the presence of one or more clinical signs associated with BRD, affecting multiple animals (5 animals; 15% ill animals in the same airspace on a farm within a 48-h period) (epidemic) was conducted between 2020 and 2022 in Belgium. Clinical examination, quick thoracic ultrasonography, blood sampling and non-endoscopic bronchoalveolar lavage were performed on each calf. Linear mixed effect models with a random intercept for herd were fitted, to determine the association between lung consolidation or clinical BRD (and serum SAA and Hp concentrations. Furthermore, multivariable models (linear mixed effect models) were used to evaluate the mutually adjusted associations between pathogen groups and APP concentrations. Test characteristics of APP concentrations to predict the presence of lung consolidation or pathogen groups were assessed, and potential SAA and Hp cutoffs, determined by Youden index, were evaluated. Lung consolidation (≥1 cm) was positively association with higher Hp concentrations in epidemic conditions [regression coefficient (β): 5.3 (95% confidence interval (CI): 1.4–20.3)], while more severe lung consolidation (≥3 cm) was associated with higher Hp concentrations in endemic conditions [β: 3.6 (95% CI: 1.2–11.0)]. No positive association was found between lung consolidation and SAA. Yet, a positive association between M. bovis and SAA was observed [β: 2.3 (95% CI: 1.3–4.3)]. At last, no association was found between M. Bovis and Hp. For Hp, the best cutoff to predict a lung consolidation (≥1 cm) was 2.33 µg/mL (Se: 81.8% and Sp: 66.7%). Moreover, to detect M. bovis among calves with a lung consolidation (≥1 cm), a SAA cutoff of 174.84 µg/mL was determined (Se: 77.8% and Sp: 87.5%). Despite the observed relationships between lung consolidation, pathogen groups, and APPs, the test characteristics in this study suggest that Hp and SAA are currently limited in their (practical) use for the detection of lung consolidation or M. bovis.

This document includes the list of symbols and abbreviations used in bilevel optimization modeling of the flexibility aggregation.

This dataset presents the experimental campaign for a batch of eleven prismatic CALB L148N58A lithium-ion B-grade battery cells with a nominal capacity of 58 Ah. The experimental campaign, conducted at the Energy Laboratory for Interdisciplinary Storage Applications (ELISA) at the University of Trieste, Italy, employs non-destructive tests to assess the performance of each cell within the batch. The cell-level testing procedures include fixed Constant Current Constant Voltage (CCCV) charging and Constant Current (CC) discharging at low current rates, Hybrid Pulse Power Characterization (HPPC) tests at various C-rates (i.e., 1C and C/3), Electrochemical Impedance Spectroscopy (EIS) at different State of Charge (SOC) levels, and three distinct driving cycles (WLTP, UDDS and US06). All the experiments were conducted at three different ambient temperatures (10°C, 25°C, and 40°C), resulting in a comprehensive dataset for assessing the performance metrics of the battery cells. This dataset provides valuable insights into post-manufacturing cell-to-cell variations in performance metrics such as capacity and impedance within a batch of fresh cells. Additionally, it serves as a crucial resource for developing battery models, including physics-based, empirical, and data-driven approaches. Moreover, it may contribute to validate model-based and data-driven estimation and control strategies within battery management systems, enhancing the reliability and efficiency of energy storage solutions.

This code is part of the paper "Expert K-Means Reconstruction Method: a novel image processing approach for mesostructure reconstruction" and is designed to implement mesostructural reconstruction, taking granite as an example. The code is based on the U-Net model, with the main entry point located in several corresponding top-level scripts. Relevant annotations were completed using LabelMe.

This study aimed to determine the effectiveness of MWM and MET on pain, range of motion, balance and fear of falling (FoF) in elderly with KOA over the time.

Distracted driving-related accidents are a critical global issue, especially as road traffic increases in densely populated areas. To address the challenge of driver distraction, we introduce a novel dataset that supports the development of real-time monitoring and detection systems by capturing authentic driver behaviors. Collected in Ashulia, Dhaka, Bangladesh, in October 2024, this dataset includes images captured under real-world driving conditions within both private vehicles and public buses. The photos were taken using personal mobile phones, ensuring a realistic and diverse set of visual data. This dataset spans a wide range of driving behaviors, including safe driving, turning, texting, talking on the phone, and other potentially risky behaviors, such as drowsy driving. By depicting these behaviors in everyday driving scenarios, the dataset serves as a valuable resource for training and evaluating models designed to detect unsafe driving practices in real-time.The dataset includes high-resolution photos taken inside public buses and personal cars in Ashulia, Dhaka, Bangladesh, under actual driving circumstances. The photographs, which were taken using the cameras on personal cell phones, offer a genuine and varied collection of visual information under normal driving circumstances. The following five behavioral classes comprise the dataset: I. Safe Driving: Images showing a driver who seems to be paying attention to the road, both hands on the wheel, and concentrated or 1 hand on the steering wheel and other on the gear stick. This is the perfect example of driving without distractions. II. Turning: Photographs that show drivers changing direction during turns by moving their heads or full bodies. This behavior is crucial for figuring out how focused the driver is on everyday tasks like rotating the steering wheel. III. Texting Phone: Pictures of drivers using their phones, whether it is to type messages or to interact with the screen. Since texting and driving is one of the main causes of distracted driving, this training is very important for identifying it. IV. Talking Phones: When drivers talk on their phones or hold them up to their ears while driving a vehicle. This category aids in identifying actions connected to phone talks, which are another frequent source of interruptions. V. Others: Contains any actions that go against safe driving practices, like drinking water or anything while driving, sleeping while driving, or talking with someone behind while driving. Relevant photos are included in each session, and they differ in terms of vehicle type and illumination to represent the variety of driving situations found in the real world. Because the images are unprocessed and unannotated, there is freedom in how machine learning applications pre-process them.