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Advancements in noise reduction for wheel speed sensing using enhanced LSTM models
Analysis of thermal wave scattering and temperature distribution in sub-surface, defects of gradient construction materials
A molecular specimen bank for contemporary and future study captures landscape-scale biodiversity baselines before Klamath River dam removal
Nitrogen-doped carbon-based phenolic resin loaded with Pd NPs for hydrodechlorination of 4-Chlorophenol
Evaluating energy efficiency in Turkish electric distribution using network DEA and GA models
Generation of picosecond pulses using soliton compression in a dual cavity laser
Abstract In this work, the development of a novel ultra-short laser system is presented, building upon previous research in passive mode-locking using cross-amplitude modulation (XAM). By combining XAM with cascaded second-order nonlinearity mode-locking (CSM) the system produced a stable bright-dark two-color output with picosecond pulses and a repetition rate of 275 MHz with an average output power of 100 mW for an 808 nm pump power of 4 W. The experimental setup involved two Nd: YVO4 lasers operating at 1064 nm and 1342 nm, where the two cavities were interconnected with a dichroic mirror allowing for a shared section where a periodically poled KTiOPO4 (PPKTP) was introduced. In the separate sections, the independently diode-pumped laser crystals were placed. The enhanced intra-cavity intensity achieved through XAM enabled effective pulse compression via CSM. The results demonstrate the system’s ability to generate near-transform-limited pulses as short as 14 ps, offering potential for applications such as medical imaging and LIDAR.
Enhanced performance of oil-based drilling fluids under HPHT conditions using an organophilic phyllosilicate
MIP-3α-antigen fusion DNA vaccine enhances sex differences in tuberculosis model and alters dendritic cell activity early post vaccination
Abstract Tuberculosis disease (TB) caused by Mycobacterium tuberculosis (Mtb) bacteria remains a major cause of global morbidity and mortality. Efforts to control TB are hampered by the lengthy and cumbersome treatment required to eradicate the Mtb infection. Bacterial persistence during exposure to bactericidal antibiotics is at least partially mediated by the bacterial stringent response enzyme, RelMtb. A therapeutic DNA vaccine targeting RelMtb has been shown to increase the efficacy of antitubercular drugs, and fusing macrophage-inflammatory protein 3α (MIP-3α), which interacts with CCR6 on immature dendritic cells (iDCs), to RelMtb further increases the vaccine’s therapeutic efficacy. A secondary analysis of these prior studies elucidated prominent sex-based differences in vaccine therapeutic efficacy, with female mice showing improved microbial outcomes compared to males as a result of the Rel and MIP-3α-Rel vaccine constructs, with a more pronounced sex-associated difference in the MIP-3α-Rel group. In the current study, we addressed the hypothesis that these sex-related differences are at least in part due to differential DC activation/function soon after vaccination. An EαGFP reporter vaccine model was used to track vaccine antigen presentation in the draining node with flow cytometry panels by an antibody Y-Ae which binds the Eα peptide tag in complex with I-Ab MHC-II molecules. Additionally, a qRT-PCR panel assessing DC-related genes compared sexes receiving the MIP-3α-Rel vaccine. MIP-3α-EαGFP groups had more DCs presenting vaccine antigen infiltrating from the periphery, with more abundant Langerhans cells in males and greater CD8+ CD103+ cross-presenting dermal DCs in females. This model also shows there is greater DC activation, as measured by CD80 and MHC II MFI, by MIP-3α compared to EαGFP alone, especially in female mice. The genetic panel showed females more enriched for chemokines and genes related to cell movement and cross-presentation. Our findings are consistent with the sex- and MIP-3α-related differences seen in the therapeutic model and supports the hypothesis that in both sexes MIP-3α enhances vaccine uptake and cell activation by peripheral iDCs. Additionally, female mice showed greater levels of antigen presentation, especially in DCs able to cross-present antigen, likely explaining why they had the best outcomes. Further studies are required to understand underlying mechanisms and to link APC results directly to T-cell responses.
Context aware hierarchical attention for abstractive dialogue summarization
ICKAN: A deep musical instrument classification model incorporating Kolmogorov-Arnold network
Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning
Abstract This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Additionally, this current study compared the performance of CNN models with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) and Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) ultrasound-based malignancy risk stratification systems. A total of 327 eligible patients with FTC and FTA who underwent preoperative thyroid ultrasound examination were retrospectively enrolled between August 2017, and August 2024. Patients were randomly assigned to a training cohort (n = 263) and a test cohort (n = 64) in an 8:2 ratio using stratified sampling. Five CNN models, including VGG16, ResNet101, MobileNetV2, ResNet152, and ResNet50, pre-trained with ImageNet, were developed and tested to distinguish FTC from FTA. The CNN models exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) ranging from 0.64 to 0.77. The ResNet152 model demonstrated the highest AUC (0.77; 95% CI, 0.67–0.87) for distinguishing between FTC and FTA. Decision curve and calibration curve analyses demonstrated the models’ favorable clinical value and calibration. Furthermore, when comparing the performance of the developed models with that of the C-TIRADS and ACR-TIRADS systems, the models developed in this study demonstrated superior performance. This can potentially guide appropriate management of FTC in patients with follicular neoplasms.
Circulating MiRNAs as diagnostic biomarkers of lupus nephritis in patients with systemic lupus erythematosus: a systematic review and meta-analysis
Genetic characteristics of novel extreme alkaline-inducible promoter located in five prime upstream region of peptidyl-prolyl cis/trans isomerase from Vibrio anguillarum
Abstract This study presents the identification and characterization of the promoter region of Vibrio anguillarum NB10, which enhances the expression of FK506-binding protein (FKBP)-type peptidyl-prolyl cis/trans isomerase (PPIase; FklB), capable of binding to the immunosuppressant FK506 under extremely alkaline conditions. Our proteomic analysis of V. anguillarum NB10 revealed that FklB (VaFklB) expression is significantly upregulated under extreme alkaline stress (pH 10). When the putative core promoter regions were coupled with a β-galactosidase reporter gene and introduced into Escherichia coli, we observed β-galactosidase activities of 61.47 ± 2.91 and 95.83 ± 6.76 Miller units (MU) at pH 9 and 10, respectively, after 4 h of stress exposure. These values represent 1.97- and 2.88-fold increases compared to normal conditions (25 °C, pH 7: 31.27 ± 1.15 MU). This alkaline-inducible promoter system has potential for biotechnological applications, including the development of pH-responsive gene expression systems, biomanufacturing processes requiring alkaline environments, and targeted activation of silent biosynthetic gene clusters for novel bioactive compound discovery. Our findings provide a valuable molecular tool for synthetic biology and metabolic engineering, enabling precise genetic control under specific environmental conditions that may revolutionize industrial biotechnology.
TERT PfeRNA regulates telomere length during cellular senescence of normal human bronchial epithelial cells
Effect of oral sodium bicarbonate supplementation on urine TGF-𝜷 in normal serum bicarbonate CKD, a randomized controlled trial
Metagenomic insights into resistance trends related to microbial VB12 synthesis in eutrophic urban lakes
A study on classification based concurrent API calls and optimal model combination for tool augmented LLMs for AI agent
Abstract AI Agents have evolved to not only recommend content but also facilitate information retrieval and task processing. Developing AI Agents using general-purpose LLM models necessitates integration with external tools, leading to tool-augmented LLM studies. Despite the availability of multiple tools for the same purpose, existing research has not fully leveraged this diversity. This study categorizes external tools by type and proposes a method to simultaneously call tools of the same type. This allows for the utilization of diverse external tools in LLM inference, thereby achieving a higher accuracy compared to when only a single tool for one task is used. Experimental results show an accuracy improvement of 4.4–9.3% over existing studies. Furthermore, when utilizing tool-augmented LLM, a multi-step reasoning approach that divides the process into stages such as planning and tool invocation is widely employed. With the rapid advancement of LLMs, enhanced models continue to emerge. Considering the trade-offs between performance and cost in models, it is crucial to find an optimal combination of models in each stage of tool augmented LLM. In this study, we propose a novel method for efficiently utilizing both enhanced LLM models and existing models, which reduces response errors by up to 9%.