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Amid declining fish diversity and human pressures in freshwater ecosystems, robust basin-scale assessments are vital for effective fisheries management. This study collated nearly four decades of fishery yields from the Pearl and Yangtze Rivers to identify conservation priorities in the Pearl River Basin. It introduced a novel cumulative effect indicator based on zeta diversity—a biodiversity pattern metric—integrated with cumulative effects analysis for management decision-making. The research employed a multi-site generalized dissimilarity model to examine the non-linear relationships between fish species composition (ζn) and human pressures, environmental factors, and geospatial variations across elevation gradients. The cumulative effect indicator, reflecting responses to anthropogenic stress when assessing ζ2 (related to β diversity), helped evaluate basins for conservation or restoration needs based on their unique or homogenized biotic communities. The results suggest that ζ diversity in low-elevation sub-basins has a stronger filtering effect on ζ by human pressures than in mid- to high-elevation sub-basins, where community aggregation is more random. The impact varied with diversity aspects (nestedness vs. turnover) and zeta order. A negative correlation between cumulative effects and community uniqueness validated the novel cumulative effect indicator’s effectiveness for guiding restoration in the Pearl River Delta, potential fishing bans, and karst conservation. This approach offers a theoretical basis for prioritizing areas for freshwater fish diversity conservation and fishing restrictions in the Pearl River Basin.

This paper provides a comprehensive review of the existing research on the Dual Active Bridge (DAB) DC-DC converter, focusing on modeling methods, modulation strategies, optimization algorithms, and control methods. A comparative analysis of selected methods along with guidelines to assist engineers and researchers in their study of DAB is also presented. Firstly, a comprehensive review of modulation strategies for DAB is provided, ranging from classical phase-shift modulation to the popular asymmetric duty modulation. The intrinsic relationships among different modulation methods are summarized, and a comparison is made based on the difficulty of control and DAB operating characteristics. Secondly, the various modeling methods for DAB are described, including reduced-order modeling, generalized state-space averaging modeling, and discrete-time modeling methods. A comparison is made based on the suitability for different application scenarios, providing recommendations for the adoption of different modeling methods. Furthermore, a survey of optimization algorithms for modulation methods is presented, including classical algorithms, swarm intelligence optimization, and reinforcement learning algorithms. A number of criteria are proposed for different algorithms, and an analysis of the unresolved challenges and future prospects is provided. Finally, the advanced control methods for DAB are summarized based on control effectiveness and applicability. The article concludes with a summary and an outlook on future research directions is also provided.

Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles’ properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors’ performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM’s mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.

Aba Tibetan and Qiang Autonomous Prefecture (Aba Prefecture), located in Southwest China, has complex geological conditions and frequent seismic activity, facing an increasing landslide risk that threatens the safety of local communities. This study aims to improve the regional geohazard database by identifying slow-moving landslides in the area. We combined Stacking Interferometric Synthetic Aperture Radar (Stacking-InSAR) technology for deformation detection, optical satellite imagery for landslide boundary mapping, and field investigations for validation. A total of 474 slow-moving landslides were identified, covering an area of 149.84 km2, with landslides predominantly concentrated in the river valleys of the southern and southeastern regions. The distribution of these landslides is strongly influenced by bedrock lithology, fault distribution, topographic features, proximity to rivers, and folds. Additionally, 236 previously unknown landslides were detected and incorporated into the local geohazard database. This study provides important scientific support for landslide risk management, infrastructure planning, and mitigation strategies in Aba Prefecture, offering valuable insights for disaster response and prevention efforts.

The introduction of non-native fish species into southern China has been widespread, with tilapia being a major contributor. Commonly known as tilapia, Coptodon zillii, Oreochromis niloticus, and Sarotherodon galilaeus, have become the most common non-native fish in southern Chinese waters, even becoming the dominant species in some waters. In order to elucidate the seasonal and spatial distributions and environmental drivers of three tilapia species in the inland waters of Guangxi, systematic sampling was performed at 34 sampling sites in the major water systems through the four seasons in 2023. A total of 6093 specimens were collected, of which C. zillii dominated the catch (59.74%). In addition, the occurrence frequency of C. zillii was 92.65%, O. niloticus was 80.88%, and S. galilaeus was 45.59%. Seasonally, all species exhibited pronounced seasonality, peaking in summer and declining to winter minima. Similarly, principal coordinate analysis (PCoA) indicated summer dispersion to a greater extent, while winter dispersion was the least of the four seasons. Spatially, C. zillii dominated the northern study area, and there was no distribution of S. galilaeus, while the proportion of S. galilaeus and O. niloticus increased as latitude moved south. The Mantel test showed three tilapia species correlated with latitude, temperature seasonality, and the min temperature of the coldest month. In addition, the results of the Generalized Additive Model (GAM) showed that the number of three tilapia species was also affected by water environmental factors: S. galilaeus was affected by turbidity, O. niloticus was affected by pH and CODMN, and C. zillii was affected by TN and water temperature. These findings highlight the synergistic effects of environmental gradients and spatial factors in shaping tilapia distribution, providing critical insights for freshwater ecosystem management under climate change.

Troglonectes daqikongensis is an obligate cave-dwelling fish with a narrowly restricted distribution in southwestern China and pronounced troglomorphic traits. Despite its ecological and evolutionary significance, genomic resources for this species remain scarce. Here, we report a chromosome-level reference genome of Troglonectes daqikongensis , assembled using PacBio HiFi long reads, Hi-C data, and MGI T7 short reads. The final assembly spans 615.17 Mb across 47 scaffolds, with a contig N50 of 20.92 Mb and a scaffold N50 of 23.57 Mb. In total, 97.96% of the assembly was anchored to 25 pseudo-chromosomes. Repetitive elements comprise 276.51 Mb, accounting for 44.95% of the genome. A total of 24,652 protein-coding genes were predicted using a combination of de novo prediction, homology-based annotation, and transcriptome evidence, with 99.89% functionally annotated. BUSCO assessments indicated high completeness for both the genome assembly (96.6%) and gene annotation (95.5%). This high-quality genome provides a foundational resource for evolutionary and ecological research, including studies on the genetic basis of cave adaptation and the conservation of the microendemic species Troglonectes daqikongensis .

Polyvinylpyrrolidone (PVP) is used in a wide variety of applications because of its unique chemical and physical features, including its biocompatibility and low toxicity. In this study, hollow PVP/silver nanoparticle (PVP/Ag NP) composite fibers were synthesized. Stable, spherical Ag NPs, with an average size of 14.4 nm, were produced through a facile sonochemical reduction method. A small amount of starch as a potent reducing and stabilizing agent was used during the reduction of Ag ions to Ag NPs. The fabricated Ag NPs were then added to a 10 wt% PVP-dichloromethane (DCM) solution, which was utilized as an electrospinning feed solution under a dense carbon dioxide (CO2) environment at 313 K and 5 MPa and an applied voltage of 15 kV. The dense CO2 enabled rapid extraction of DCM from the PVP-Ag NPs-DCM solution, which was then dissolved into PVP/Ag NPs, resulting in a hollow structure. Scanning electron microscopy, Fourier-transform infrared (FT-iR) spectroscopy, X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analyses, and thermogravimetric analysis (TGA), were used to characterize the electrospinning products.

Human exposure to infectious aerosols results in the transmission of diseases such as influenza, tuberculosis, and COVID-19. Most dental procedures generate a significant number of aerosolized particles, increasing transmission risk in dental settings. Since the generation of aerosols in dentistry is unavoidable, many clinics have started using intervention strategies such as area-filtration units and extraoral evacuation equipment, especially under the relatively recent constraints of the pandemic. However, the effectiveness of these devices in dental operatories has not been studied. Therefore, the ability of dental personnel to efficiently position and operate such instruments is also limited. To address these challenges, we utilized a real-time sensor network for assessment of aerosol dynamics during dental restoration and cleaning producers with and without intervention. The strategies tested during the procedures were (i) local area High-Efficiency Particle Air (HEPA) filters and (ii) Extra-Oral Suction Device (EOSD). The study was conducted at the University of Washington School of Dentistry using a network of 13 fixed sensors positioned within the operatory and one wearable sensor worn by the dental operator. The sensor network provides time and space-resolved particulate matter (PM) data. Three-dimensional (3D) visualization informed aerosol persistence in the operatory. It was found that area filters did not improve the overall aerosol concentration in dental offices in a significant way. A decrease in PM concentration by an average of 16% was observed when EOSD equipment was used during the procedures. The combination of real-time sensors and 3D visualization can provide dental personnel and facility managers with actionable feedback to effectively assess aerosol transmission in medical settings and develop evidence-based intervention strategies.

The kidney transplant recipients (KTRs) were diagnosed with Chronic Kidney Disease after transplantation (CKD-T). CKD-T can be affected by the microbial composition and metabolites. The present study integrates the analysis of gut microbiome and metabolites to further identify the characteristics of CKD-T. We collected 100 fecal samples of KTRs and divided them into two groups according to the stage progression of CKD-T. Among them, 55 samples were analyzed by Hiseq sequencing, and 100 samples were used for non-targeted metabolomics analysis. The gut microbiome and metabolomics of KTRs were comprehensively characterized. As well as significant differences in gut microbiome diversity between the CKD G1-2T group and CKD G3T group. Eight flora including Akkermansia were found to be enriched in CKD G3T group. As compared with CKD G1-2T group, the relative abundance of some amino acid metabolism, glycerophospholipid metabolism, amino acid biosynthesis, carbohydrate metabolism and purine metabolism in CKD G3T group were differential expressed significantly. In addition, fecal metabolome analysis indicated that CKD G3T group had a unique metabolite distribution characteristic. Two differentially expressed metabolites, N-acetylornithine and 5-deoxy-5'-(Methylthio) Adenosine, were highly correlated with serum creatinine, eGFR and cystatin C. The enrichment of gut microbial function in CKD-T is correlated with the expression of gut metabolites. Gut microbiome and metabolites in the progression of CKD-T display some unique distribution and expression characteristics. The composition of the gut microbiome and their metabolites appears to be different between patients with CKD G3T and those with CKD G1-2T.

Fish invasions can damage the ecological environment of invaded areas, causing negative effects such as monotony of ecological types in invaded waters and endangerment of native species. It is important to monitor their presence and spread in invaded areas. This study aims to update the available data on non-native fish species in the inland waters of Guangxi and to determine their seasonal and spatial distribution in this region. Taking the inland waters of Guangxi as the study area, 34 sampling sites were set up in the major river systems, and systematic sampling was conducted in four seasons in 2023. The data showed that a total of 7690 non-native fish were collected from 23 species, belonging to 7 orders, 13 families, and 20 genera. Of the non-native fish species, 19 species were introduced for aquaculture purposes, and three species were introduced for ornamental purposes. The most non-native fish species were found in summer with 21 species, which were followed by 20 species in winter, 18 species in spring, and only 15 species in autumn. However, the distribution composition was similar in each season, with Coptodon zillii being dominant in each season, which was followed by Oreochromis niloticus. The Hongshuihe River had the highest number of non-native fish species with 16 species, followed by the Xunjiang River with 14 species, the Qianjiang River with 13 species, and the Npanjiang River had the lowest number of non-native fish species with 7 species. In addition, the Xunjiang River and the Qianjiang River showed significant separation in the PCoA results, and the overall test showed significant differences in non-native fish composition among the river system. The main reasons for the differences in their spatial distribution are geographical location and temperature. O. niloticus and C. zillii were found in all river systems and have become the main invasive non-native fish species in the inland waters of Guangxi.