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Abstract Background Hypoxanthine, prevalent in animals and plants, is used in the production of food additives, nucleoside antiviral drugs, and disease diagnosis. Current biological fermentation methods synthesize quantities insufficient to meet industrial demands. Therefore, this study aimed to develop a strain capable of industrial-scale production of hypoxanthine. Results De novo synthesis of hypoxanthine was achieved by blocking the hypoxanthine decomposition pathway, thus alleviating transcriptional repression and multiple feedback inhibition, and introducing a purine operon from Bacillus subtilis to construct a chassis strain. The effects of knocking out the IMP(Inosine 5’-monophosphate) branch on the growth status and titer of the strain were then investigated, and the effectiveness of adenosine deaminase and adenine deaminase was verified. Overexpressing these enzymes created a dual pathway for hypoxanthine synthesis, enhancing the metabolic flow of hypoxanthine synthesis and preventing auxotrophic strain formation. Introducing IMP-specific 5’ -nucleotidase addressed the issue of adenylate accumulation. In addition, the metabolic flow of the guanine branch was dynamically regulated by the guaB gene. The supply of glutamine and aspartic acid precursors was enhanced by introducing an exogenous glnA mutant gene, overexpressing aspC, and replacing the weaker promoter to regulate the aspartic acid branching pathway. Ultimately, fermentation in a 5 L bioreactor for 48 h produced 30.6 g/L hypoxanthine, with a maximum real-time productivity of 1.4 g/L/h, the highest value of hypoxanthine production by microbial fermentation reported so far. Conclusions The intracellular purine biosynthesis pathway is extensive and regulated at multiple levels in cells. The IMP branch in the hypoxanthine synthesis pathway has a higher metabolic flux. The current challenge lies in systematically allocating the metabolic flux within the branch pathway to achieve substantial product accumulation. In this study, E. coli was used as the chassis strain to construct a dual pathway for IMP and AMP(Adenosine 5’-monophosphate) synergistic hypoxanthine synthesis and dynamically regulate the guanine branch pathway. Overall, our experimental efforts culminated in a high-yield, plasmid- and defect-free engineered hypoxanthine strain.

Abstract Brain cancer pose significant life-threats by destructively invading normal brain tissues, causing dysneuria, disability and death, and its therapeutics is limited by underdosage and toxicity lying in conventional drug delivery that relied on passive delivery. The application of nanorobots-based drug delivery systems is an emerging field that holds great potential for brain cancer active targeting and controllable treatment. The ability of nanorobots to encapsulate, transport, and supply therapies directly to the lesion site through blood–brain barriers makes it possible to deliver drugs to hard-to-reach areas. In order to improve the efficiency of drug delivery and problems such as precision and sustained release, nanorobots are effectively realized by converting other forms of energy into propulsion and motion, which are considered as high-efficiency methods for drug delivery. In this article, we described recent advances in the treatment of brain cancer with nanorobots mainly from three aspects: firstly, the development history and characteristics of nanorobots are reviewed; secondly, recent research progress of nanorobots in brain cancer is comprehensively investigated, like the driving mode and mechanism of nanorobots are described; thirdly, the potential translation of nanorobotics for brain diseases is discussed and the challenges and opportunities for future research are outlined.

Abstract Background Blood–brain barrier (BBB) dysfunction has been viewed as a potential underlying mechanism of neurodegenerative disorders, possibly involved in the pathogenesis and progression of Alzheimer’s disease (AD). However, a relation between BBB dysfunction and dementia with Lewy bodies (DLB) has yet to be systematically investigated. Given the overlapping clinical features and neuropathology of AD and DLB, we sought to evaluate BBB permeability in the context of DLB and determine its association with plasma amyloid-β (Aβ) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods For this prospective study, we examined healthy controls (n = 24, HC group) and patients diagnosed with AD (n = 29) or DLB (n = 20) between December 2020 and April 2022. Based on DCE-MRI studies, mean rates of contrast agent transfer from intra- to extravascular spaces (K trans) were calculated within regions of interest. Spearman’s correlation and multivariate linear regression were applied to analyze associations between K trans and specific clinical characteristics. Results In members of the DLB (vs HC) group, K trans values of cerebral cortex (p = 0.024), parietal lobe (p = 0.007), and occipital lobe (p = 0.014) were significantly higher; and K trans values of cerebral cortex (p = 0.041) and occipital lobe (p = 0.018) in the DLB group were significantly increased, relative to those of the AD group. All participants also showed increased K trans values of parietal ( $$\upbeta$$ β = 0.391; p = 0.001) and occipital ( $$\upbeta$$ β = 0.357; p = 0.002) lobes that were significantly associated with higher scores of the Clinical Dementia Rating, once adjusted for age and sex. Similarly, increased K trans values of cerebral cortex ( $$\upbeta$$ β = 0.285; p = 0.015), frontal lobe ( $$\upbeta$$ β = 0.237; p = 0.043), and parietal lobe ( $$\upbeta$$ β = 0.265; p = 0.024) were significantly linked to higher plasma Aβ1-42/Aβ1-40 ratios, after above adjustments. Conclusion BBB leakage is a common feature of DLB and possibly is even more severe than in the setting of AD for certain regions of the brain. BBB leakage appears to correlate with plasma Aβ1-42/Aβ1-40 ratio and dementia severity.

Abstract Background Despite evidence showing a connection between inflammation and endometrial cancer (EC) risk, the surveys on genetic correlation and cohort studies investigating the impact on long-term outcomes have yet to be refined. We aimed to address the impact of inflammation factors on the pathogenesis, progression and consequences of EC. Methods For the genetic correlation analyses, a two-sample of Mendelian randomization (MR) study was applied to investigate inflammation-related single-nucleotide polymorphisms involved with endometrial cancer from GWAS databases. The observational retrospective study included consecutive patients diagnosed with EC (stage I to IV) with surgeries between January 2010 and October 2020 at the Cancer Hospital of Shantou University Medical College. Results The 2-sample MR surveys indicated no causal relationship between inflammatory cytokines and endometrial cancer. 780 cases (median age, 55.0 years ) diagnosed with EC were included in the cohort and followed up for an average of 6.8 years. Increased inflammatory parameters at baseline were associated with a higher FIGO stage and invasive EC risk (odds ratios [OR] 1.01 to 4.20). Multivariate-cox regression suggested that multiple inflammatory indicators were significantly associated with overall survival (OS) and progression-free survival (PFS) (P

Objective To analyze the correlation of HBV serological characteristics between non-reproducible reactivity (NRR) samples and occult hepatitis B virus infection (OBI) samples for blood screening. Methods A total of 144 samples with negative ELISA (HBV, HCV and HIV test) results and reactive nucleic acid tests(NAT) were collected from January 2021 to January 2023 in Anhui Blood Center, including 92 reactive samples by TMA method (combined ID-NAT) and 52 HBV DNA reactive samples by PCR method (ID-NAT). Supplementary differential testing and ID-NAT by PCR were performed on the reactive samples of the combined ID-NAT, samples that were non-reactive by both differential testing and ID-NAT by PCR were included in the NRR group, and samples that were reactive for HBV DNA detected by either method were included in the OBI group. Supplemented with HBsAg, anti-HBs, HBeAg, anti-HBe and anti-HBc tests, the differences in serological pattern and positive rate between NRR samples and OBI samples were analyzed. Results A total of 53 samples were negative for differential testing and ID-NAT and were included in the NRR group, 91 samples were detected as HBV DNA reactive in either method and were included in the OBI group. HBsAg and HBeAg were not detected by serological testing in either group. The detection rates of anti-HBs, anti-HBc and anti-HBe in the NRR group and the OBI group were 64.15% vs 47.25%, 86.79% vs 94.51%, 35.85% vs 52.75%, respectively. Comparison of serological patterns between the two groups: the most frequent pattern in the NRR group was anti-HBs (+ ) and anti-HBc (+ ) (32.08%), and the most frequent pattern in the OBI group was anti-HBe (+ ) and anti-HBc (+ ) (37.36%). Conclusion There were differences in some of the test results between the NRR samples and the OBI samples in HBV serological testing, and higher anti-HBc positive rate in the NRR samples suggests a higher possibility of HBV infection.

Considering the constraints of inputs and angle of attack (AOA), the trajectory tracking control of a flexible air-breathing hypersonic vehicle (FAHV) is investigated. To solve the problem, the Lyapunov-based model predictive control (LMPC) is presented. Firstly, the virtual controller is designed by nonlinear backstepping control (BSC), and it is integrated into the Lyapunov contraction constraint in LMPC to ensure the stability of the closed-loop system. Secondly, LMPC law is obtained by optimizing the weighted summation performance index of the tracking error and the control. Within the LMPC framework, the input constraints and AOA constraints can be handled simply, and by designing the appropriate parameters of the virtual controller, closed-loop stability is achieved. The effectiveness and superiority of the proposed method are verified by comparing it with the BSC and traditional model predictive control.

This study investigated the impact of sweeteners on the release of heavy metals during the heating and atomization processes in electronic cigarettes. Based on a PG/VG base e-liquid with the addition of 2% and 5% neotame or sucralose, we quantitatively analyzed the impact of sweetener content on the levels of heavy metals such as Ni, Cr, and Fe in the e-liquid and aerosol after heating and atomization. Additionally, the heated e-liquid samples were used to culture SH-SY-5Y and Beas-2B cells, and their cytotoxic effects were assessed using the CCK-8 assay. The results indicated that the e-liquid with 5% sucralose had the highest average levels of heavy metals after heating and atomization, particularly nickel (13.36 ± 2.50 mg/kg in the e-liquid and 12,109 ± 3,229 ng/200 puffs in the aerosol), whereas the e-liquid with neotame had significantly lower average heavy metal content in comparison. Additionally, it was measured that the chloride ion concentration in the e-liquid with 5% sucralose reached 191 mg/kg after heating at 200°C for 1 h, indicating that heating sucralose generated chloride ions, Which might corrode metal parts components leading to heavy metal release. Cytotoxicity tests revealed that the base e-liquid without sweeteners exhibited the highest average cell viability after heating, at 64.80% ± 2.84% in SH-SY-5Y cells and 63.24% ± 0.86% in Beas-2B cells. Conversely, the e-liquid variant with 5% sucralose showed a significant reduction in average cell viability, reducing it to 50.74% ± 0.88% in SH-SY-5Y cells and 53.03% ± 0.76% in Beas-2B cells, highlighting its more pronounced cytotoxic effects compared to other tested e-liquids. In conclusion, sucralose in e-liquids should be limited preferably less than 2%, or replaced with neotame, a safer alternative, to minimize health risks.

Objectives: The game between socio-economic development and ecological development has always been the core issue in coal areas, but the internal mechanism of tradeoff and cooperative dynamic change of ecosystem services in mining areas under long-term mineral resources development is still lacking in in-depth research.Methods: Therefore, taking Shendong mining area as an example, this study used InVEST model to evaluate the changes of four major ecosystem service functions in Shendong mining area from 1990 to 2020, namely, water yield (WY), net primary productivity (NPP), soil conservation (SC) and habitat quality (HQ). Meanwhile, correlation analysis was used to explore the trade-off and synergistic relationship among these services. On this basis, the coupling effect between the four ecosystem services is further discussed by using the constraint line method. Finally, the key drivers of ecosystem service trade-offs/synergies in the region are explored by using geodetectors and the explanations of each influence factor for RMS errors are obtained.Results: The results show that 1) from 1990 to 2020, the water yield and soil retention in the mining area decrease first and then increase, and the net primary productivity and habitat quality increase slowly, mainly in the southeast of the mining area. 2) In terms of constraint relationship, all the four ecosystem services showed hump-like constraint relationship, that is, there was obvious constraint threshold effect. 3) In the Shendong mining area, the synergistic relationship is the dominant relationship between ecosystem services, and the tradeoff effect mainly occurs between water yield and habitat quality. 4) In terms of the driving mechanism of tradeoff/synergy, land use type, temperature, and rainfall are the main factors that cause the spatial differentiation of tradeoff synergy intensity among ecosystem services in Shendong mining area.Conclusions: The results of this study provide a scientific basis for the improvement of ecological environment and sustainable utilization of mineral resources under long-term exploitation.

Abstract Artificial intelligence models play a crucial role in monitoring and maintaining railroad infrastructure by analyzing image data of foreign objects on power transmission lines. However, the availability of publicly accessible datasets for railroad foreign objects is limited, and the rarity of anomalies in railroad image data, combined with restricted data sharing, poses challenges for training effective foreign object detection models. In this paper, the aim is to present a new dataset of foreign objects on railroad transmission lines, and evaluating the overall performance of mainstream detection models in this context. Taking a unique approach and leveraging large-scale models such as ChatGPT (Chat Generative Pre-trained Transformer) and text-to-image generation models, we synthesize a series of foreign object data. The dataset includes 14,615 images with 40,541 annotated objects, covering four common foreign objects on railroad power transmission lines. Through empirical research on this dataset, we validate the performance of various baseline models in foreign object detection, providing valuable insights for the monitoring and maintenance of railroad facilities.

Coal mining has led to escalating ecological and environmental issues in significant coal and grain production areas, posing a severe danger to food security. This study examines the disturbance patterns of soil factors and microbial communities in coal and grain production areas, and attempts to understand the impact of subsidence and water accumulation stress on soil characteristics and microbial communities in coal mining subsidence areas with high subsidence levels. Five specific regions of Zhao Gu Yi Mine, situated in Henan Province and under the ownership of Jiaozuo Coal Group, were chosen. Aside from the control group (CK), the study blocks situated in the coal mining subsidence zones consisted of perennial subsidence ponding (PSP), seasonal subsidence ponding (SSP), the neutral zone (NZ), and the horizontal deformation zone (HDZ). The soil nutrient indices and the stoichiometric properties of soil C, N, and P were assessed on the surface of each block. The organization of the soil microbial community was identified using high-throughput sequencing. The findings indicate that: 1. Substantial disparities exist in soil properties and microbial community structure between the subsidence and non-subsidence zones. The levels of soil organic mater (SOM), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), and available phosphorus (AP) all decrease to different extents in the subsidence area. Additionally, the coal mining subsidence waterlogged area exhibits higher levels compared to the coal mining subsidence non-waterlogged area. Conversely, the soil water content (SWC), C/N ratio, C/P ratio, and N/P ratio all increase to varying degrees. 2. Regarding the composition of the community, the presence of Proteobacteria is considerably greater in the non-water-logged area of coal mining subsidence (NZ, HDZ) compared to the water-logged area and control group (p < 0.05). The prevalence of Firmicutes in the subsidence water area was substantially greater compared to both the subsidence non-waterlogged area and the control group (p < 0.05). The prevalence of Gemmatimonadota is markedly greater in the waterlogged area of mining subsidence compared to the non-waterlogged area and CK (p < 0.05). The Ascomycota population reached its highest value in the neutral zone (NZ), which was significantly greater than the values observed in the seasonal subsidence ponding (SSP) and perennial subsidence ponding (PSP) regions (p < 0.05). On the other hand, the Rozellomycota population had its highest value in the SSP region, which was significantly greater than the values observed in the other regions (p < 0.05). 3. The abundance and variety of soil bacteria and fungi, as well as their important populations, are associated with different levels of soil characteristics. The primary elements that influence the alteration of microbial communities are soil nutrients and soil water content. The presence of coal mine subsidence and water accumulation has a notable impact on the properties of the soil in the surrounding area. This study offers a scientific foundation for reclaiming land affected by subsidence caused by coal mining in regions where coal and grain production are the dominant industries.

The Henan Yellow River Basin is an ecological support belt for the entire basin. It holds a significant position in high-quality development and ecological conservation within the Yellow River Basin. However, due to improper development activities, such as urban expansion and deforestation of farmland, certain areas of the region have encountered a series of ecological issues, posing significant challenges to ecosystem services. The scientific foundation for the sustainable development of the ecological environment in the Henan Yellow River Basin is established by research on the evolution characteristics and driving factors of ecosystem service functions. This study focuses on the Henan Yellow River Basin, by introducing remote sensing data and biomass data, assessing the spatiotemporal variations in ecosystem service by the InVEST model—including carbon stock, water yield, and soil conservation—from 2000 to 2020. It analyzes the ecosystem service functions of different land use types. It employs the Geodetector to identify the dominant driving factors behind the changes in these functions based on the improved InVEST model evaluated results. The findings reveal that from 2000 to 2020, total carbon stock increased by 1.86%, carbon stock per unit area rose by 1.81%, and the spatial distribution remained largely stable. The high-value regions were clustered in the west and part of the north, primarily consisting of forest land. Carbon stock capacity in other regions, mainly farmland and construction land, was poor, with forest land having the strongest carbon sequestration capacity, followed by grassland. Total water yield decreased by 20.08%, and water yield per unit area decreased by 20.03%, with a spatial distribution closely following the trend of precipitation distribution. The high-value regions were clustered in the south, primarily in forest land and farmland. The total amount of soil conservation decreased by 19.96%, and soil conservation per unit area decreased by 19.93%, with spatial distribution patterns similar to those of carbon stock and water yield. The high-value regions were concentrated in the southwestern and northern forested regions, while soil conservation capacity in areas primarily consisting of farmland and construction land was weaker. The divergence of carbon stock was most influenced by population density, water yield by precipitation, and soil conservation by slope. In conclusion, during the study period, while carbon storage increased, the significant decline in water yield and soil conservation highlighted critical issues in the ecosystem service functions of the region. These findings indicate the need for targeted conservation measures and sustainable development strategies to address the decline in ecosystem services and mitigate adverse environmental impacts, ensuring the long-term health of the region’s ecosystems. This study offers an in-depth understanding of the differentiation of ecosystem service functions and their driving factors, enabling precise assessment of regional ecosystem services, and providing a theoretical foundation for formulating effective regional ecological conservation policies.

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q > 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

Anxiety disorder is a universal disease related to neuro-inflammation. Solanesol has shown positive effects because of its anti-inflammatory, anti-tumor, and anti-ulcer properties. This study focused on determining whether solanesol could ameliorate anxiety-like behaviors in a mouse model of neuro-inflammation and identify its working targets. Complete Freund’s adjuvant (CFA)-induced mice that were intra-peritoneally administered with solanesol (50 mg/kg) for 1 week showed a statistically significant reduction in anxiety-like behaviors, as measured by open field and elevated plus-maze tests. Western blot analysis revealed that CFA-induced upregulation of the levels of pro-inflammatory cytokines interleukin (IL)-1β and tumor necrosis factor α (TNF-α), which played crucial roles in regulating anxiety, returned to normal in the anterior cingulate cortex (ACC) after solanesol treatment. The level of T cell-restricted intracellular antigen-1 (TIA1), a key component of stress granules, also decreased in the ACC. Moreover, immunofluorescence results indicated that solanesol suppressed CFA-induced microglial and astrocytic activation in the ACC. CFA was injected in the hind paws of TIA1Nestin conditional knockout (cKO) mice to confirm whether TIA1 is a potential modulatory molecule that influences pro-inflammatory cytokines and anxiety-like behaviors. Anxiety-like behaviors could not be observed in cKO mice after CFA injection with IL-1β and TNF-α levels not remarkedly increasing. Our findings suggest that solanesol inhibits neuro-inflammation by decreasing the TIA1 level to reduce IL-1β and TNF-α expression, meanwhile inhibiting microglial and astrocytic activation in the ACC and ultimately ameliorating anxiety-like behaviors in mice.

Abstract Background The relationship of cumulative remnant-cholesterol (Cum-RC) concentration with the risk of cardiovascular disease (CVD) in patients with hypertension remains unclear. Methods We studied data for 28,698 individuals for whom three consecutive total cholesterol, high-density lipoprotein-cholesterol (HDL-C), and triglyceride concentrations were available, and who did not have CVD (14,349 with hypertension and 14,349 without), that was collected between 2006 and 2010. Participants with hypertension were placed into four groups based on Cum-RC quartile: a Q1 group (

Abstract Background The effect of stress on Graves’ disease (GD) is controversial. Our purpose was to quantify the impacts of stress on patients with Graves’ disease. Methods Systematic searches of PubMed, MEDLINE, Embase, Web of Science, Scopus, Cochrane Library and PsycInfo were conducted from inception to 1 January 2023. Studies comparing the incidence of stressful life events (SLEs) that occurred before diagnosis and during drug therapy in cases diagnosed with GD and controls were included in the final analysis. Results Nine case-control studies and four cohort studies enrolling 2892 participants (1685 [58%] patients) were included. Meta-analysis revealed a high and significant effect-size index in a random effect model (d = 1.81, P = 0.01), indicating that stress is an important factor in the onset of GD. The relationship between SLEs and GD was stronger in studies with higher proportions of female patients (β = 0.22, P

Ceramsites are extensively employed as substrates for adsorbents in studies focused on phosphorus adsorption, leaving ceramsites crafted from diatomite less explored. Diatomite-based ceramsite, with its distinct porous architecture and surface silanol functionalities, is adept at supporting a variety of metal oxides, presenting a distinct advantage over other ceramsite variants. In light of this, the present study embarked on producing diatomite-based ceramsite using diatomite as the foundational material, subsequently enhancing it through the incorporation of Fe, thus yielding an Fe-modified diatomite-based ceramsite. Through adsorption testing, the modified ceramsite demonstrated a significantly improved adsorption capacity of 4.06 mg P/g, marking a substantial enhancement from the initial capacity of 0.9 mg P/g. The process of phosphorus adsorption exhibited a strong alignment with the Langmuir isotherm model and the pseudo-second-order kinetic model. In-depth analyses employing XRD, FTIR, zeta potential, and XPS techniques have revealed that the principal mechanisms driving the adsorption process are centered on interactions involving electrostatic forces, the formation of chemical precipitates, and the exchange of ligands. This investigation not only opens new avenues for the application of diatomite-based ceramsite but also lays down a theoretical foundation for its modification, thereby enriching the spectrum of its utility.

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought.

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model’s capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE.

Elucidating the response mechanism of vegetation change trends is of great value for environmental resource management, especially in coal mining areas where climate fluctuations and human activities are intense. Taking the Shendong mining area as an example, based on the Google Earth Engine cloud platform, this study used the kernel Normalized Vegetation Index (kNDVI) to study the spatiotemporal change characteristics of vegetation cover during 1994–2022. Then, it carried out an attribution analysis through the partial derivative analysis method to explore the driving mechanism behind vegetation greening. The results showed that (1) the growth rate of vegetation cover change from 1994 to 2022 was 0.0052/a. The area with an upward trend of kNDVI accounted for 94.11% of the total area of the study area. The greening effect was obvious, and the kNDVI change would continue to rise. (2) Under the scenario of regional climate warming and humidifying, kNDVI responds slightly differently to different climatic factors, and kNDVI is positively correlated with temperature and precipitation in 85.20% of the mining area. The average contribution of precipitation, temperature, and human activities to kNDVI change in the Shendong mining area were 0.00094/a, 0.00066/a, and 0.0036/a, respectively. The relative contribution rates of human activities and climate change were 69.23% and 30.77%, respectively. Thus, human activities are the main driving factor for the changing of vegetation cover in this mining area, and climate change is the secondary driving factor. (3) The dynamic change in land use presents an increase in forest area under the ecological restoration project. The results of this study can provide a scientific basis for the future ecological construction of the Shendong mining area and help in the realization of regional green sustainable development goals.

Underground coal mining in coal-grain overlapped areas leads to land subsidence and deformation above the goaf, damaging cultivated land. Understanding the influencing process of coal mining on cultivated land and crops is important for carrying out timely land reclamation and stabilizing crop yield. Research has been carried out by using crop growth parameters to evaluate the damaging degree of cultivated land when the mining subsidence is stable, but few studies focus on the influence of land damage on crop growth when the subsidence is unstable during coal mining. Therefore, this study tracked the three growth stages of winter wheat by using UAV multispectral imagery to explore the dynamic influence of underground mining on winter wheat aboveground biomass (AGB). Firstly, a winter-wheat-AGB estimation model (R2 = 0.89, RMSE = 2.18 t/ha) was developed by using vegetation indexes (VIs), textures, and terrain data extracted from UAV imagery. Secondly, based on the winter-wheat-AGB estimation model, the winter wheat AGB was successfully estimated and mapped at different growth stages. The AGB of winter wheat in the coal mining-affected area was approximately 5.59 t/ha at the reviving stage, 8.2 t/ha at the jointing stage, and 15.6 t/ha at the flowering stage. Finally, combined with the progress of coal mining, the dynamic changing process of crops during underground mining can be inferred by analyzing the spatiotemporal variation in winter wheat AGB. Results showed that, in the dip direction, winter wheat AGB at the flowering stage was the highest at the compression zone, followed by the inner stretch zone, outer stretch zone, and neutral zone. The distance from the waterlogged area and the existence of cracks were found to be the important moderating variables affecting the crop growth status in the mining subsidence area. In the strike direction, there were significant differences in the wheat AGB-affected area as the mining proceeded. Even areas where AGB had previously significantly increased gradually transitioned to significant decreases with the end of mining. The research explores the dynamic changes in winter wheat AGB and land damage status during coal mining. It provides a rapid and non-destructive land-damage-monitoring method to protect cultivated land in mining areas.

BackgroundThe contribution of gut microbiota to the pathogenesis of polycystic ovary syndrome (PCOS) is controversial. The causal relationship to this question is worth an in-depth comprehensive of known single nucleotide polymorphisms associated with gut microbiota.MethodsWe conducted bidirectional Mendelian randomization (MR) utilizing instrumental variables associated with gut microbiota (N = 18,340) from MiBioGen GWAS to assess their impact on PCOS risk in the FinnGen GWAS (27,943 PCOS cases and 162,936 controls). Two-sample MR using inverse variance weighting (IVW) was undertaken, followed by the weighted median, weighted mode, and MR-Egger regression. In a subsample, we replicated our findings using the meta-analysis PCOS consortium (10,074 cases and 103,164 controls) from European ancestry.ResultsIVWMR results suggested that six gut microbiota were causally associated with PCOS features. After adjusting BMI, SHBG, fasting insulin, testosterone, and alcohol intake frequency, the effect sizes were significantly reduced. Reverse MR analysis revealed that the effects of PCOS features on 13 gut microbiota no longer remained significant after sensitivity analysis and Bonferroni corrections. MR replication analysis was consistent and the results suggest that gut microbiota was likely not an independent cause of PCOS.ConclusionOur findings did not support the causal relationships between the gut microbiota and PCOS features at the genetic level. More comprehensive genome-wide association studies of the gut microbiota and PCOS are warranted to confirm their genetic relationship.DeclarationThis study contains 3533 words, 0 tables, and six figures in the text as well as night supplementary files and 0 supplementary figures in the Supplementary material.

As the genome is organized into a three-dimensional structure in intracellular space, epigenomic information also has a complex spatial arrangement. However, most epigenetic studies describe locations of methylation marks, chromatin accessibility regions, and histone modifications in the horizontal dimension. Proper spatial epigenomic information has rarely been obtained. In this study, we designed spatial chromatin accessibility sequencing (SCA-seq) to resolve the genome conformation by capturing the epigenetic information in single-molecular resolution while simultaneously resolving the genome conformation. Using SCA-seq, we are able to examine the spatial interaction of chromatin accessibility (e.g. enhancer–promoter contacts), CpG island methylation, and spatial insulating functions of the CCCTC-binding factor. We demonstrate that SCA-seq paves the way to explore the mechanism of epigenetic interactions and extends our knowledge in 3D packaging of DNA in the nucleus.

To address the challenge of precise foreign object detection on railway tracks, this study proposes RailSegVITNet, an efficient deep learning-based model that aims to accurately locate tracks and provide necessary information. RailSegVITNet integrates lightweight bottleneck blocks, separable self-attention, and feature aggregation to balance real-time performance and accuracy. It follows an encoder–decoder framework, with bottleneck blocks used for feature extraction and separable self-attention integrated at different stages to enhance feature extraction capability. The decoder utilizes feature aggregation to effectively merge multi-resolution features and improve segmentation performance. On the proposed Railway-seg dataset, the RailSegVITNet demonstrates competitive performance, achieving an MIoU (Mean Intersection over Union ) of 91.43% while utilizing only 2.01 G FLOPs (Floating Point Operations) and 1.4 M parameters. Furthermore, compared to popular models such as Topformer, Segformer, Segmenter, and Deeplab on the publicly available Railsem19 dataset, RailSegVITNet maintains its lightweight architecture while achieving comparable or higher segmentation performance. This showcases its potential practical value for efficient identification of foreign object intrusion.

In this paper, a near-ultraviolet LED structure was fabricated on a sapphire substrate by using metal–organic chemical vapor deposition with an undoped AlGaN insertion layer introduced between multiple quantum wells and electron blocking layers, and the effect of layer thickness on light-electric performance was investigated. Results of epitaxial structure characterization show that the introduction of an undoped AlGaN insertion layer almost does not affect the crystal quality and surface morphology of the LED epitaxial structure. Electroluminescence testing results indicate that as the insertion layer thickness increases, the device performance initially increases and then decreases. Furthermore, simulation analysis suggests that increasing the insertion layer thickness reduces the injection efficiency of carriers, lowers the radiative recombination rate and light emission performance of the active region's carriers. Additionally, the AlGaN insertion layer can effectively suppress the diffusion of Mg impurities, reduce the non-radiative recombination rate in the multiple quantum well active region, and thus improve the LED's light emission performance. Overall, under the competing effects of these two mechanisms, the optimal light emission performance for the near-ultraviolet LED is achieved when the insertion layer thickness is 1 nm.

Alterations in vegetation cover serve as a significant indicator of land ecology. The Shendong Coal Mining Area, being the largest coal base globally, holds significant importance for national energy security. Moreover, it has gained recognition for its environmentally conscious approach to coal mining, characterized by the simultaneous implementation of mining activities and effective governance measures. In order to assess the ongoing vegetation recovery and the temporal changes in vegetation within the Shendong Coal Mining Area, we initially utilized Landsat TM/ETM+/OLI remote sensing data. Using the Google Earth Engine (GEE), we developed a novel kernel-normalized vegetation index (kNDVI) and subsequently generated a comprehensive kNDVI dataset spanning the years 2000 to 2020. In addition, the Sen (Theil-Sen median) trend analysis method and MK (Mann-Kendall) test were utilized to examine the temporal trends over a span of 21 years. Furthermore, the Hurst exponent model was employed to forecast the persistent changing patterns of kNDVI. The utilization of the intensity analysis model was ultimately employed to unveil the magnitude of vegetation dynamics. The findings indicated a notable positive trend in the overall kNDVI of vegetation within the study area. In relation to the analysis of changing trends, the vegetation in the region underwent a slight improvement from 2000 to 2010, followed by a significant improvement from 2010 to 2020. During this transition period, a total of 289.07 km2, which represents 32.36% of the overall transition area, experienced a shift in vegetation. The predictive findings from the Hurst model indicate that while the majority of areas within the mining region will exhibit an upward trend in vegetation growth, there will be certain areas that will demonstrate a decline. These declining areas account for 39.08% of the total transition area. Furthermore, the intensity analysis results reveal notable disparities in the characteristics of vegetation growth and evolution between the periods of 2000-2010 and 2010-2020. Throughout the entirety of the transformation process, the transition from slight improvement to significant improvement prevails in terms of both relative intensity and absolute intensity, surpassing alternative transformation processes. Various trend transitions display diverse intensity characteristics that adhere to the overarching principles governing shifts in vegetation growth. Furthermore, the utilization of the intensity analysis framework and intensity spectrum employed in this study demonstrates their efficacy in elucidating the temporal dynamics of vegetation changes. Furthermore, this study plays a pivotal role in the surveillance and assessment of the efficacy of ecological restoration in mining regions. It carries substantial implications for comparable land ecological restoration efforts in mining and reclamation, thereby furnishing a theoretical foundation.

There are many problems in the arc welding repair of aluminum alloy parts, such as excessive pores, coarse grains, and low hardness. Introducing external energy excitation is an effective solution. In this paper, 7075 aluminum alloy parts are repaired using the arc welding - laser shock forging technique. The effects of laser-wire spacing, laser pulse energy and frequency, welding current and speed, and V-groove depth on the surface morphology, section geometric characteristics, and porosity of the repaired layer are studied. Additionally, the hardness and microstructure of the repaired layer formed by arc welding - laser shock forging and arc welding are compared. The results show that a fine surface morphology of the repaired layer can be achieved by selecting reasonable laser-wire spacing, welding current, welding speed, and V-groove depth. It is also advised to select the maximum laser pulse energy and frequency possible, in order to increase the aspect ratio, reduce the residual coefficient, minimize porosity, and decrease the maximum pore diameter of the repaired layer. When compared to arc welding alone, arc welding - laser shock forging has the advantage of reducing grain size and improving the hardness of the repaired layer.

To address the low-light image (LLI) problem in train driving scenarios, this paper proposes a progressive and lightweight network called BrightsightNet for LLI enhancement. First, to overcome the problem of insufficient local exposure level, two structurally identical light curve parameter estimation sub-networks are used for light enhancement in turn. Second, for real-time inference, an efficient feature extraction operator is proposed that combines depth-separable convolution and attention mechanism. Third, the overall network uses encoder–decoder architecture. For the encoder, the output features of the three layers are fused through skip connections to form an information-rich feature map. For the decoder, a hierarchical decoding approach is used to predict the light curve parameters through the three convolution layers sequentially. Experimental results show that BrightsightNet achieves a user study score (USR) of 4.43 on the proposed dataset, outperforming Zero-DCE++, SCI, RetinexDIP, and RUAS by 0.51, 0.86, 0.64, and 1.39, respectively. Moreover, BrightsightNet has parameters of only 2.6K and a single inference time of 0.052 s, which is an innovative and practical solution for low-light image enhancement in train driving scenarios, contributing to safer and more reliable train operations.

Background The relationship of cumulative non high-density lipoprotein-cholesterol (Cum-non-HDL-C) concentration with the risk of cardiovascular disease (CVD) in individuals with hypertension remains unclear. Methods In total 27 234 participants for whom three consecutive total cholesterol and HDL-C concentrations were available, and who did not have CVD, comprising 13 617 with hypertension and 13 617 without from 2006 to 2010. Participants were placed into four groups according to Cum-non-HDL-C. Cox proportional hazards models were used to evaluate the relationship between Cum-non-HDL-C and the risk of CVD. Results Over a median 11 years, 1,298 participants with hypertension developed CVD. After adjustment for multiple potential confounding factors, compared with participants with hypertension and Cum-non-HDL-C

Abstract Epigenetic modifications of histones are associated with development and pathogenesis of disease. Existing approaches cannot provide insights into long-range interactions and represent the average chromatin state. Here we describe BIND&MODIFY, a method using long-read sequencing for profiling histone modifications and transcription factors on individual DNA fibers. We use recombinant fused protein A-M.EcoGII to tether methyltransferase M.EcoGII to protein binding sites to label neighboring regions by methylation. Aggregated BIND&MODIFY signal matches bulk ChIP-seq and CUT&TAG. BIND&MODIFY can simultaneously measure histone modification status, transcription factor binding, and CpG 5mC methylation at single-molecule resolution and also quantifies correlation between local and distal elements.

Abstract In the industry, there is still a substantial amount of old in‐service equipment without a fault warning function. To provide an effective fault monitoring scheme for the in‐service motor drive system inside the equipment, this paper proposes a fault diagnosis method based on the DC‐link current at the power side, which can complete the fault monitoring task only outside the equipment without disassembly and assembly of the equipment and the inside installation of sensors. The effect of open‐circuit faults on DC‐link current is thoroughly examined and classified into four classes. To locate the faulty switching component of voltage‐source‐inverter (VSI), one electric period of DC‐link current is split into six regions according to the current vector position. And the relationship between current vector regions, fault signatures, and fault locations is summarized. Additionally, a low‐pass filter is used to smooth the DC‐link current and reduce the bandwidth limit of current sensors. Open circuit faults can be detected and located by identifying the fault signature in the DC‐link current and rotor position. Finally, the effectiveness and robustness of the proposed method are verified by experiment results.

Diseases of tomato leaves can seriously damage crop yield and financial rewards. The timely and accurate detection of tomato diseases is a major challenge in agriculture. Hence, the early and accurate diagnosis of tomato diseases is crucial. The emergence of deep learning has dramatically helped in plant disease detection. However, the accuracy of deep learning models largely depends on the quantity and quality of training data. To solve the inter-class imbalance problem and improve the generalization ability of the classification model, this paper proposes a cycle-consistent generative-adversarial-network-based Transformer model to generate diseased tomato leaf images for data augmentation. In addition, this paper uses a Transformer model and densely connected CNN architecture to extract multilevel local features. The Transformer module is utilized to capture global dependencies and contextual information accurately to expand the sensory field of the model. Experiments show that the proposed model achieved 99.45% accuracy on the PlantVillage dataset. The 2018 Artificial Intelligence Challenger dataset and the private dataset attained accuracies of 98.30% and 95.4%, and the proposed classification model achieved a higher accuracy and smaller model size compared to previous deep learning models. The classification model is generalizable and robust and can provide a stable theoretical framework for crop disease prevention and control.

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model’s learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model’s ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities.

Abstract Background There is a growing interest in the role of immune and inflammatory responses in mental disorders (MDs). Mean platelet volume (MPV) is an extensively utilized hemogram parameter that reflects systemic inflammation and immune function. Our research sought to determine whether a connection exists between MPV and various types of MDs. Methods We searched PubMed, EMBASE, PsychINFO, and Web of Science for eligible studies from inception to 15 February 2023, supplemented by manual searching the references from relevant articles. We applied standardized mean difference (SMD) and its 95% confidence interval (CI) to estimate the differences in MPV values in patients with MDs compared to controls. Results We analyzed data from 24 surveys with 4843 participants (2450 patients with MDs and 2393 healthy controls). Two‐step meta‐analyses were conducted to estimate the SMD in MPV value between individuals with and without MDs. Higher MPV values were substantially linked to MDs (i.e., depression, anxiety, bipolar disorder, and schizophrenia). Moderator and stratified analyses revealed that the aggregate effects were more robust in specific populations, such as younger patients and those who had not taken antipsychotic medication within the previous month. Conclusions Our findings corroborate the role of inflammatory response in the pathogenesis of MDs and the pharmacological treatment of these conditions. Regarding the considerable heterogeneity among studies, the level of evidence was very low to moderate.

BackgroundExcessive weight gain and obesity are widely accepted as risk factors for diabetes mellitus, and the age at which obesity onsets may be related to the development of cardiovascular diseases and certain cancers. Here, we aimed to investigate associations between the onset-age of overweight/obesity and risk of developing diabetes mellitus in China.Methods42,144 people with the normal weight range and without diabetes at baseline, were enrolled from the Kailuan cohort which began on the 1st June 2006. All participants were followed-up, biennially, until 31st December 2017. During follow-up, 11,220 participants had become overweight/obese. For each case, one normal-weight control was matched according to age ( ± 1 year) and sex. Our final analysis included 10,858 case-control pairs. An age-scaled Cox model was implemented to estimate hazard ratios (HR) with corresponding 95% confidence intervals (CI) for diabetes mellitus incidence across age-groups.ResultsAt a median follow-up of 5.46 years, 1,403 cases of diabetes mellitus were identified. After multivariate adjustments, age-scaled Cox modelling suggested that risk gradually attenuated with every 10 year increase in age of onset of overweight/obesity. Diabetes mellitus adjusted HRs (aHRs) for new-onset overweight/obesity at

This study aims to establish a highly sensitive and specific method for single cell screening and identification of KRAS+/epithelial cell adhesion molecules (EpCAMs)/vimentin+/pancreatic cancer circulating tumor cells (CTCs) and to explore the clinical implications in the diagnosis of pancreatic cancer. A single cell separation and identification system for pancreatic cancer CTCs was constructed based on the preparation of KRAS immunolipid magnetic spheres. The function of the system was evaluated, the physical parameters of the immunoliposomes (IMLs) were characterized, and the isolation effect of the IMLs on CTCs was studied and compared with EpCAM and vimentin phenotypic CTCs. CTCs in the peripheral blood of 50 patients with pancreatic cancer confirmed by preoperative CT diagnosis and postoperative pathology were isolated and identified. The isolated CTCs were stained with CK immune antibody, observed, and identified, the number of CTCs was counted, and the relationship between the CTC count and the clinical malignant grade markers and grade indicators was examined. The consistency of the KRAS mutation rate between CTCs and tissues was analyzed and compared. It was found that the circulating tumor cell sorting system with a high expression of KRAS-ML could effectively enrich the circulating tumor cells with a positive expression of KRAS in pancreatic cancer. The pancreatic cancer CTC sorting and single-cell gene detection system can be used as a new detection technology of peripheral blood CTCs in patients with pancreatic cancer. As a minimally invasive liquid biopsy method, this technology has significant clinical practice implications for pancreatic cancer detection and effectiveness assessment.

Plant potassium content (PKC) is a crucial indicator of crop potassium nutrient status and is vital in making informed fertilization decisions in the field. This study aims to enhance the accuracy of PKC estimation during key potato growth stages by using vegetation indices (VIs) and spatial structure features derived from UAV-based multispectral sensors. Specifically, the fraction of vegetation coverage (FVC), gray-level co-occurrence matrix texture, and multispectral VIs were extracted from multispectral images acquired at the potato tuber formation, tuber growth, and starch accumulation stages. Linear regression and stepwise multiple linear regression analyses were conducted to investigate how VIs, both individually and in combination with spatial structure features, affect potato PKC estimation. The findings lead to the following conclusions: (1) Estimating potato PKC using multispectral VIs is feasible but necessitates further enhancements in accuracy. (2) Augmenting VIs with either the FVC or texture features makes potato PKC estimation more accurate than when using single VIs. (3) Finally, integrating VIs with both the FVC and texture features improves the accuracy of potato PKC estimation, resulting in notable R2 values of 0.63, 0.84, and 0.80 for the three fertility periods, respectively, with corresponding root mean square errors of 0.44%, 0.29%, and 0.25%. Overall, these results highlight the potential of integrating canopy spectral information and spatial-structure information obtained from multispectral sensors mounted on unmanned aerial vehicles for monitoring crop growth and assessing potassium nutrient status. These findings thus have significant implications for agricultural management.

Heavy renewable penetrations and high-voltage cross-regional transmission systems reduce the inertia and critical frequency stability of power systems after disturbances. Therefore, the power system operators should ensure the frequency nadirs after possible disturbances are within the set restriction, e. g., 0.20 Hz. Traditional methods utilize linearized and simplified control models to quantify the frequency nadirs and achieve frequency-constrained unit commitments (FCUCs). However, the simplified models are hard to depict the frequency responses of practical units after disturbances. Also, they usually neglect the regulations from battery storage. This paper achieves FCUCs with linear rules extracted from massive simulation results. We simulate the frequency responses on typical thermal-hydro-storage systems under diverse unit online conditions. Then, we extract the rules of frequency nadirs after disturbances merely with linear support vector machine to evaluate the frequency stability of power systems. The algorithm holds a high accuracy in a wide range of frequency restrictions. Finally, we apply the rules to three typical cases to show the influences of frequency constraints on unit commitments.

Highlights There are significant sex differences in Chinese population with cognitive impairment in Lewy body disease. Women were more common in dementia with Lewy bodies and mild cognitive impairment with Lewy bodies cases, had more frequent and severe neuropsychiatric symptoms, and poorer cognition than men. Men predominant in Parkinson’s disease with mild cognitive impairment and Parkinson’s disease with dementia cases, and performed more frequent RBD and parkinsonism than women. Dementia with Lewy bodies vs. Parkinson’s disease with dementia, and mild cognitive impairment with Lewy bodies vs. Parkinson’s disease with mild cognitive impairment are distinct disease forms and should not be confused.

Abstract Introduction Geriatric symptoms are common in dementia cases, while few studies have focused on these symptoms in Lewy body dementia (LBD). The purpose of this study is to investigate the distributions of Apolipoprotein E (APOE) ε4 and geriatric symptoms, and explore their associaitons in Dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD). Methods A retrospective study with 185 mild-moderate probable DLB (n = 93) and PDD (n = 92) patients was assigned. Demographic and clinical characteristics, neuropsychological assessments, and APOE genotypes were recorded. Description, correlation and logistic regression models were used to analyze the presence of geriatric symptom complaints and their associations with APOE ε4. Results DLB patients displayed more frequency of fluctuating cognition, visual hallucination, rapid eye movement sleep behavior disorder, delusion, depression, anxiety, apathy, and loss of appetite, whereas the PDD cases had constipation, fear of falling, and insomnia more frequently. The APOE ε4 allele was more common in DLB than PDD (29.9% vs. 7.0%, p

Abstract Background GJB2 mutations are among the most important causes of deafness, and their prevalence varies greatly among different countries and ethnic groups. This study aimed to determine the pathogenic mutation spectrum of GJB2 in patients with nonsyndromic hearing loss (NSHL) in Western Guangdong and to explore the pathogenic characteristics of the c.109G>A locus. Methods In total, 97 NSHL patients and 212 normal controls (NC) were included in this study. Genetic sequencing analyses were performed on GJB2. Results In the NSHL group, the main pathogenic mutations in GJB2 were as follows: c.109G>A, c.235delC, and c.299_300delAT with allele frequencies of 9.28%, 4.12%, and 2.06%, respectively. c.109G>A was the most frequently detected pathogenic mutation in this region. In the NC group, the allele frequency of c.109G>A among 30–50 years old subjects was markedly lower than that among 0–30 years old subjects (5.31% vs. 11.11%, p A was the most common GJB2 mutation with unique characteristics, such as clinical phenotypic heterogeneity and delayed onset. Therefore, the c.109G>A mutation should be considered as an essential marker for routine genetic assessment of deafness, which can also be beneficial for preventing deafness.

The Leaf Area Index (LAI) is a crucial indicator of crop photosynthetic potential, which is of great significance in farmland monitoring and precision management. This study aimed to predict potato plant LAI for potato plant growth monitoring, integrating spectral, textural, and morphological data through UAV images and machine learning. A new texture index named VITs was established by fusing multi-channel information. Vegetation growth features (Vis and plant height Hdsm) and texture features (TIs and VITs) were obtained from drone digital images. Various feature combinations (VIs, VIs + TIs, VIs + VITs, VIs + VITs + Hdsm) in three growth stages were adopted to monitor potato plant LAI using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), random forest (RF), and eXtreme gradient boosting (XGBoost), so as to find the best feature combinations and machine learning method. The performance of the newly built VITs was tested. Compared with traditional TIs, the estimation accuracy was obviously improved for all the growth stages and methods, especially in the tuber-growth stage using the RF method with 13.6% of R2 increase. The performance of Hdsm was verified by including it either as one input feature or not. Results showed that Hdsm could raise LAI estimation accuracy in every growth stage, whichever method is used. The most significant improvement appeared in the tuber-formation stage using SVR, with an 11.3% increase of R2. Considering both the feature combinations and the monitoring methods, the combination of VIs + VITs + Hdsm achieved the best results for all the growth stages and simulation methods. The best fitting of LAI in tuber-formation, tuber-growth, and starch-accumulation stages had an R2 of 0.92, 0.83, and 0.93, respectively, using the XGBoost method. This study showed that the combination of different features enhanced the simulation of LAI for multiple growth stages of potato plants by improving the monitoring accuracy. The method presented in this study can provide important references for potato plant growth monitoring.

Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine classification of different winter wheat cultivars. Firstly, we set 90% heading overlap and 85% side overlap as the optimal flight parameters, which can meet the requirements of following hyperspectral imagery mosaicking and spectral stitching of different winter wheat cultivars areas. Secondly, the mosaicking algorithm of UAV hyperspectral imagery was developed, and the correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, which induced this study to extract the resultful spectral curves of six different winter wheat cultivars. Finally, the hyperspectral imagery dimension reduction experiments were compared with principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA); the winter wheat cultivars classification experiments were compared with support vector machines (SVM), maximum likelihood estimate (MLE), and U-net neural network ENVINet5 model. Different dimension reduction methods and classification methods were compared to get the best combination for classification of different winter wheat cultivars. The results show that the mosaicked hyperspectral imagery effectively retains the original spectral feature information, and type 4 and type 6 winter wheat cultivars have the best classification results with the classification accuracy above 84%. Meanwhile, there is a 30% improvement in classification accuracy after dimension reduction, the MNF dimension reduction combined with ENVINet5 classification result is the best, its overall accuracy and Kappa coefficients are 83% and 0.81, respectively. The results indicate that the UAV-based hyperspectral remote sensing system can potentially be used for classifying different cultivars of winter wheat, and it provides a reference for the classification of crops with weak intra-class differences.

Measurement(s) Solar-Induced Fluorescence Technology Type(s) machine learning algorithms

Abstract Background Since there is still controversy about the comparison of the efficacy and safety of RH and RFA in the treatment of recurrent liver cancer, we conducted a meta-analysis to compare the efficacy and safety, in order to provide evidence-based evidence for future research and clinical treatment. Methods We searched PubMed, Embase, and Cochrane Library from the establishment of the database to Feb 2021. We included studies that reported liver cancer patients underwent repeated hepatectomy (RH) or radiofrequency ablation (RFA), and we excluded duplicate publications, research without full text, incomplete information, or inability to conduct data extraction, animal experiments, reviews, and systematic reviews. The STATA 15.1 was used to analyze the data. Results The pooled results show that the 3-year and 5-year overall survival (OS) rate of the repeated hepatectomy group was significantly higher than the radiofrequency ablation group (odds ratio (OR) = 1.95, 95% confidence interval (CI):1.47–2.60, P ≤ 0.001; OR = 1.65, 95% CI: 1.12–2.43, P = 0.012). Similarly, the pooled results show that the 3-year and 5-year disease-free survival (DFS) rate of the repeated hepatectomy group was significantly higher than the radiofrequency ablation group (OR = 1.73, 95% CI: 1.30–2.31, P ≤ 0.001; OR = 1.84, 95% CI: 1.38–2.49, P ≤ 0.001). However, there is no significant difference in the 1-year OS and DFS rate of repeated hepatectomy group and radiofrequency ablation group. Additionally, the pooled results show that the postoperative Clavien-Dindo (CD) grade II or higher complication rate of the repeated hepatectomy group was significantly higher than the radiofrequency ablation group (OR = 2.80, 95% CI: 1.37–5.75, P = 0.005). Conclusion Based on the pooled results of 8 existing retrospective studies, RH has a higher OS rate and DFS rate in the treatment of recurrent liver cancer, while the postoperative complication rate of RFA is lower. When survival is the primary goal, RH should be the first choice for recurrent liver cancer.

BackgroundEmerging research indicates that depression among parents of children/adolescents with type 1 diabetes mellitus (T1DM) has increased significantly. However, the prevalence rates reported by different studies vary substantially.MethodsSeven databases were systematically searched (Pubmed, Embase, MEDLINE, Scopus, Web of Science, Cochrane Library, PsycInfo) from the inception to 15th October 2022. We pooled prevalence rates from each study with a random-effect model. We conducted a stratified meta-analysis to identify the potential sources of heterogeneity among studies. The GRADE (Grading of Recommendations, Assessment, Development and Evaluations) approach was utilized to evaluate the quality of evidence.ResultsTwenty-two studies were included, with a total of 4639 parents living with type 1 diabetic children. Overall, the pooled prevalence rate of depression or depressive symptoms was 22.4% (95%CI 17.2% to 28.7%; I2 = 96.8%). The prevalence was higher among mothers (31.5%) than fathers (16.3%) as well as parents of children (aged < 12 years) with T1DM (32.3%) than those with adolescents (aged ≥ 12 years) (16.0%).ConclusionOur research suggests that more than 1 in 5 parents of type 1 diabetic children/adolescents worldwide suffer from depression or depressive symptom. Depression screening and interventions are required for parents of children with T1DM.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier (CRD42022368702).

Mn-based catalysts have attracted significant attention in the field of catalytic research, particularly in NOx catalytic reductions and CO catalytic oxidation, owing to their good catalytic activity at low temperatures. In this review, we summarize the recent progress of Mn-based catalysts for the removal of NOx and CO. The effects of crystallinity, valence states, morphology, and active component dispersion on the catalytic performance of Mn-based catalysts are thoroughly reviewed. This review delves into the reaction mechanisms of Mn-based catalysts for NOx reduction, CO oxidation, and the simultaneous removal of NOx and CO. Finally, according to the catalytic performance of Mn-based catalysts and the challenges faced, a possible perspective and direction for Mn-based catalysts for abating NOx and CO is proposed. And we expect that this review can serve as a reference for the catalytic treatment of NOx and CO in future studies and applications.

In urban metro systems, train delays make planned timetables uncoordinated, especially during the end-of-service period. Therefore, this study focuses on the timetable rescheduling problem to improve network accessibility. A multi-objective programming model is first proposed to optimize the headway between the last and penultimate train and station dwell time. Minimization of the passenger transfer waiting time for all origin-destination (OD) pairs is meaningful to improve passenger transport efficiency, and a penalty time is used instead of the transfer waiting time of inaccessible passengers, which is important to decrease inaccessible passenger volume. On the other hand, minimization of the total last-train dwell time for all lines is beneficial for decreasing the operating costs for metro operation companies. Then, the adaptive non-dominated sorting genetic algorithm-II (ANSGA-II) is combined with a label setting method to obtain the Pareto optimal solutions. Finally, the Nanjing metro network with randomly-designed delay scenarios is adopted to verify the effectiveness of the proposed rescheduling model and solution algorithm. The results show that this model can yield rescheduling schemes to address the different sudden delay events, resulting in a significant decrease in passenger transfer waiting time and last-train dwell time, thereby improving the network accessibility.