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Ticks are obligate blood-feeding arthropods that cause significant economic losses in domestic animal husbandry and threaten public health. However, information about soft ticks (Acari: Argasidae) and tick-borne pathogens in the Xinjiang Uygur Autonomous Region (XUAR) of China is scarce.

In Egypt, camel trypanosomiasis is widespread. From October 2021 to March 2022, we collected 181 blood samples from apparently healthy one-humped camels (Camelus dromedarius) in Cairo and Giza Governates. The objective of this study was to assess infection rates of trypanosomes using blood smear examination and PCR-sequencing assays. Trypanosomes were detected in 8.3% (15/181) of camels by blood smear and in 23.8% (43/181) by PCR targeting the internal transcribed spacer (ITS). Based on blood smear and ITS-PCR results, and the absence of tsetse flies in the study area, we hypothesized that the Trypanosoma species was likely T. evansi. Validation using PCR based on the variant surface glycoprotein (VSG) of T. evansi Rode Trypanozoon antigen type (RoTat) 1.2 (RoTat 1.2 VSG gene) on ITS-PCR-positive samples (n=43) confirmed that 88.4% (38/43) were RoTat 1.2 T. evansi, while 11.6% (5/43) were non-RoTat 1.2 T. evansi. This marks the second report of non- RoTat 1.2 T. evansi in dromedary camels in Egypt. Considering the underestimated zoonotic risk of T. evansi in Egypt, there is a potential threat to humans, underscoring the need for a "One Health" approach to safeguard animal and human health. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) has emerged as a transformative technology in the Architecture, Engineering, and Construction (AEC) industry, with increasing application in civil infrastructure projects. This study comprehensively reviews the research landscape of BIM applications in civil infrastructure through bibliometric analysis. Based on data from the Web of Science database, 646 relevant papers published between 2020 and 2024 were collected, and 416 papers were selected for in-depth analysis after screening. Using bibliometric methods, the analysis reveals the evolution of research trends, identifies key contributors and influential publications, and maps the knowledge structure of the field. Our study shows a significant increase in research output over the past five years, particularly in studies focusing on the integration of BIM with emerging technologies such as Digital Twins, the Internet of Things (IoT), and Machine Learning. The results indicate that the United States, China, and the United Kingdom lead in terms of research output and citation impact. Additionally, based on clustering results and representative keywords, several key research clusters were identified, including BIM in infrastructure lifecycle management, BIM collaboration in large-scale projects, and BIM for sustainable infrastructure design. [ABSTRACT FROM AUTHOR]

In recent years, research on the intention to adopt solar photovoltaic technology has yielded rich results. However, controversy still exists regarding the key antecedents of households' intention to adopt solar photovoltaic technologies. To clarify the critical factors influencing the intention to adopt solar photovoltaic technology and potential moderating variables, this study utilized meta-analysis to perform a quantitative literature analysis on 29 empirical articles. It identified eight key influencing factors and tested the moderating effects of two variables: sample size and research area. The results show that "Attitude" and "Government Incentive" are moderately correlated with the intention to adopt. "Social Influence", "Product Knowledge", "Effort Expectancy", "Perceived Cost-benefit", "Performance Expectancy", and "Perceived Behavioral Control" are weakly correlated with the adoption intention. The study also found that using the sample size and research area as moderating variables can partly reveal differences between various studies. Overall, the findings of this study offer theoretical guidance for subsequent in-depth studies and support for the practical promotion of solar photovoltaic technology. [ABSTRACT FROM AUTHOR]

Background: Hyperglycemia‐induced neuroinflammation significantly contributes to diabetic neuropathic pain (DNP), but the underlying mechanisms remain unclear. Objective: To investigate the role of Sirt3, a mitochondrial deacetylase, in hyperglycemia‐induced neuroinflammation and DNP and to explore potential therapeutic interventions. Method and Results: Here, we found that Sirt3 was downregulated in spinal dorsal horn (SDH) of diabetic mice by RNA‐sequencing, which was further confirmed at the mRNA and protein level. Sirt3 deficiency exacerbated hyperglycemia‐induced neuroinflammation and DNP by enhancing microglial aerobic glycolysis in vivo and in vitro. Overexpression of Sirt3 in microglia alleviated inflammation by reducing aerobic glycolysis. Mechanistically, high‐glucose stimulation activated Akt, which phosphorylates and inactivates FoxO1. The inactivation of FoxO1 diminished the transcription of Sirt3. Besides that, we also found that hyperglycemia induced Sirt3 degradation via the mitophagy‐lysosomal pathway. Blocking Akt activation by GSK69093 or metformin rescued the degradation of Sirt3 protein and transcription inhibition of Sirt3 mRNA, which substantially diminished hyperglycemia‐induced inflammation. Metformin in vivo treatment alleviated neuroinflammation and diabetic neuropathic pain by rescuing hyperglycemia‐induced Sirt3 downregulation. Conclusion: Hyperglycemia induces metabolic reprogramming and inflammatory activation in microglia through the regulation of Sirt3 transcription and degradation. This novel mechanism identifies Sirt3 as a potential drug target for treating DNP. [ABSTRACT FROM AUTHOR]

The Xinjiang Uygur Autonomous Region (Xinjiang) borders eight countries and has a complex geographic environment. There are almost 45.696 million herded sheep in Xinjiang, which occupies 13.80% of China's sheep farming industry. However, there is a scarcity of reports investigating the role of sheep or ticks in Xinjiang in transmitting tick-borne diseases (TBDs). A total of 894 ticks (298 tick pools) were collected from sheep in southern Xinjiang. Out of the 298 tick pools investigated in this study, Rhipicephalus turanicus (Rh. turanicus) and Hyalomma anatolicum (H. anatolicum) were identified through morphological and molecular sequencing. In the southern part of Xinjiang, 142 (47.65%), 86 (28.86%), and 60 (20.13%) tick pools were positive for Rickettsia spp., Theileria spp., and Anaplasma spp., respectively. Interestingly, the infection rate of Rickettsia spp. (73%, 35.10%, and 28.56–41.64%) was higher in Rh. turanicus pools than in H. anatolicum pools (4%, 4.44%, and 0.10–8.79%) in this study. Fifty-one tick pools were found to harbor two pathogens, while nineteen tick pools were detected to have the three pathogens. Our findings indicate the presence of Rickettsia spp., Theileria spp., and Anaplasma spp. potentially transmitted by H. anatolicum and Rh. turanicus in sheep in southern Xinjiang, China. [ABSTRACT FROM AUTHOR]

Sparse representation has been applied to realize an accurate description of the sound field from limited set of measurements. The prerequisite of this application is that sound field can be sparsely represented in a specific basis. However, most of the sparse bases are constructed by using the physical models of sound field, and are only effective for a specific category of sound sources. In this paper, the data-driven dictionary learning approach is exploited to obtain a sparse basis of sound field. Meanwhile, to reduce the difficulty and workload of the collection of data sample, the equivalent source method is utilized to collect data samples by means of simulations and thus the data samples can be generated by taking advantage of sound field properties. The performance of the sound field recovery with sparse sampling based on learned dictionary is examined and is compared with those based on other sparse bases. The result indicates that numerical data-driven model is more flexible and is not limited to a specific category of sound sources. The advantage of the proposal is presented by the results of numerical simulation and experiment. [ABSTRACT FROM AUTHOR]

Sparse regularization has been successfully applied to equivalent source method (ESM) in order to improve the acoustic imaging resolution. However, the application is not always feasible, especially at low frequencies. To overcome the problem, this paper proposes a high-resolution acoustic imaging method. In this method, reweighted l 1 minimization is introduced to ESM to deal with the ill-posed inverse problems. Then the obtained equivalent source strengths are used to locate the sound sources. Compared to the sparse regularization-based ESM, the proposed method can provide a low side lobe and higher spatial resolution of acoustic imaging. Meanwhile, by arranging equivalent sources in three-dimensional space, the proposed method can also realize the acoustic imaging in three-dimensional sound field with high resolution. The results of the simulation and experiment demonstrate the validations. [ABSTRACT FROM AUTHOR]

Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification. This article utilizes an encoding decoding structure to simultaneously extract deep and shallow features of images. A REGCS convolution module was constructed using the idea of group convolution to extract spectral and spatial features of images. We compared the Salinas Valley dataset and MUUFL dataset with various classification algorithms. The experimental results show that compared with other classification models, the RESSU model has achieved stable and excellent results in hyperspectral image classification experiments. Among them, in the classification experiment of the Salinas Valley dataset, the accuracy of single class classification reached over 92%. In the effectiveness analysis experiment, we calculated different model parameter quantities to verify the performance of our method, and ultimately achieved good results. [ABSTRACT FROM AUTHOR]

The recent discovery of infinite-layer nickel oxide superconductors has highlighted the importance of first-principles simulations. We predict an infinite-layer bismuth oxide superconductor BaBiO2, which is isostructural to NdNiO2. In this work, electronic structure, lattice dynamics, and electron–phonon interaction are studied, with special attention paid to the influence of spin–orbit coupling (SOC) on the above-mentioned quantities. Our calculations show that the structure will be dynamically stable under pressure and induce superconductivity, whether SOC is considered or not. In addition, SOC will significantly enhance the electron–phonon coupling (EPC), resulting in an increase in EPC constant λ from 0.43 to 0.73. We further find that the Fermi surface nesting is partially responsible for its superconductivity. A strong SOC changes the Fermi surface and enhances the nesting, and the EPC becomes stronger. Our results propose a bismuth-based superconductor, demonstrating the importance of SOC for its superconductivity and providing clues for further experimental synthesis. [ABSTRACT FROM AUTHOR]

The pro‐inflammatory phenotype of microglia usually induces neuroinflammatory reactions in neuropathic pain. Glycometabolism shift to glycolysis can promote the pro‐inflammatory phenotype transition of microglia. The omics data analysis suggest a critical role for Lyn dysregulation in neuropathic pain. The present study aimed at exploring the mechanism of Lyn‐mediated glycolysis enhancement of microglia in neuropathic pain. Neuropathic pain model was established by chronic constriction injury (CCI), then pain thresholds and Lyn expression were measured. Lyn inhibitor Bafetinib and siRNA‐lyn knockdown were administrated intrathecally to evaluate the effects of Lyn on pain thresholds, glycolysis and interferon regulatory factor 5 (IRF5) nuclear translocation of microglia in vivo and in vitro. ChIP was carried out to observe the binding of transcription factors SP1, PU.1 to glycolytic gene promoters by IRF5 knockdown. Finally, the relationship between glycolysis and pro‐inflammatory phenotype transition of microglia was evaluated. CCI led to the upregulation of Lyn expression and glycolysis enhancement in microglia of spinal dorsal horn. Bafetinib or siRNA‐lyn knockdown intrathecally alleviated pain hyperalgesia, suppressed glycolysis enhancement and inhibited nuclear translocation of IRF5 in CCI mice. Also, IRF5 promoted the binding of transcription factors SP1, PU.1 to glycolytic gene promoters, and then the enhanced glycolysis facilitated the proliferation and pro‐inflammatory phenotype transition of microglia and contributed to neuropathic pain. Lyn‐mediated glycolysis enhancement of microglia contributes to neuropathic pain through facilitating IRF5 nuclear translocation in spinal dorsal horn. [ABSTRACT FROM AUTHOR]