Seasonal and Genetic Characteristics of Human Metapneumovirus Circulating - Henan Province, China, 2017-2023.

Introduction: This study examines the seasonal and genetic characteristics of human metapneumovirus (HMPV) in Henan from 2017 to 2023. Methods: Samples from patients with acute respiratory infection (ARI) testing positive for HMPV were subjected to real-time reverse transcription polymerase chain reaction The G gene was amplified and sequenced from these samples for epidemiological and phylogenetic analysis. Results: We enrolled 2,707 ARI patients from October 2017 to March 2023, finding an HMPV positivity rate of 6.17% (167/2,707). Children under five exhibited the highest infection rate at 7.78% (138/1,774). The 2018 and 2019 HMPV outbreaks predominantly occurred in spring (March to May), with peak positivity rates of 31.11% in May 2018 and 19.57% in May 2019. A notable increase occurred in November 2020, when positivity reached a historic high of 42.11%, continuing until January 2021. From February 2021 through March 2023, no significant seasonal peaks were observed, with rates ranging from 0% to 8.70%. Out of 81 G gene sequences analyzed, 46.91% (38/81) were identified as subtype A (A2c: 45.67%, 37/81; A2b: 1.23%, 1/81) and 53.09% (43/81) as subtype B (B1: 9.88%, 8/81; B2: 43.21%, 35/81). Notably, an AAABBA switch pattern was observed in HMPV subtypes. The dominant strains were A2c111nt-dup in subtype A and B2 in subtype B. Conclusions: Six years of surveillance in Henan Province has detailed the seasonal and genetic dynamics of HMPV, contributing valuable insights for the control and prevention of HMPV infections in China. These findings support the development of targeted HMPV vaccines and immunization strategies.

Polymeric microlens array formed on a discontinuous wetting surface using a self-assembly technique.

In this paper, we demonstrate a facile way to prepare polymeric microlens arrays (MLAs) based on a discontinuous wetting surface using a self-assembly technique. A patterned hydrophobic-octadecyltrichlorosilane (OTS) surface was prepared by U V/O 3 irradiation through a shadow mask. The area exposed to U V/O 3 irradiation turned highly hydrophilic, whereas the area protected by the mask remained highly hydrophobic, generating the patterned OTS surface. The surface energy of the OTS/glass surface changed from 23 to 72.8 mN/m after 17 min of U V/O 3 treatment. The scribing of the optical glue-NOA 81 onto the microhole array enabled one to obtain the MLAs due to the generation of the NOA 81 droplet array via the surface tension. After UV light curing, the cured NOA 81 droplet array with uniform dimensions within a large area exhibited excellent MLA characteristics. Moreover, the method developed in this study is simple in operation, low-cost, and requires neither a clean room nor expensive equipment.

Selenium nanoparticles enhance mucosal immunity against Mycobacterium bovis infection.

Selenium nanoparticles (SeNPs) enhance the immune response as adjuvants, increasing the efficacy of viral vaccines, including those for COVID-19. However, the efficiency of mucosal SeNPs in boosting vaccine-induced protective immunity against tuberculosis remains unclear. Therefore, this study aims to investigate whether the combination of SeNPs with the AH antigen (Ag85A-HspX) can boost respiratory mucosal immunity and thereby enhance the protective effects against tuberculosis. We synthesized SeNPs and assessed their impact on the immune response and protection against Mycobacterium bovis (M. bovis) as a mucosal adjuvant in mice, administered intranasally at a dose of 20 µg. SeNPs outperformed polyinosinic-polycytidylic acid (Poly IC) in stimulating the maturation of bone marrow-derived dendritic cells (BMDCs), which enhanced antigen presentation. SeNPs significantly activated and proliferated tissue-resident memory T cells (TRMs) and effector CD4+ T cells in the lungs. The vaccines elicited specific antibody responses in the respiratory tract and stimulated systemic Th1 and Th17 immune responses. Immunization with AH and SeNPs led to higher levels of mucosal secretory IgA in bronchoalveolar lavage fluid (BALF) and secretory IL-17 in splenocytes. Moreover, SeNPs immunized mice showed reduced M. bovis infection loads and inflammatory lesions in the lungs post-challenge. Notably, immunization with AH and SeNPs significantly reduced bacterial load in the lungs, achieving the lowest levels compared to all other tested groups. This study calls for pre-clinical investigation of AHB-SeNPs as an anti-bovine tuberculosis vaccine and for exploring its human vaccine potential, which is anticipated to aid in the development of innovative vaccines or adjuvants.

Activating Macrophage Continual Efferocytosis via Microenvironment Biomimetic Short Fibers for Reversing Inflammation in Bone Repair.

Efferocytosis-mediated inflammatory reversal plays a crucial role in bone repairing process. However, in refractory bone defects, the macrophage continual efferocytosis may be suppressed due to the disrupted microenvironment homeostasis, particularly the loss of apoptotic signals and overactivation of intracellular oxidative stress. In this study, a polydopamine-coated short fiber matrix containing biomimetic "apoptotic signals" to reconstruct the microenvironment and reactivate macrophage continual efferocytosis for inflammatory reversal and bone defect repair is presented. The "apoptotic signals" (AM/CeO2) are prepared using CeO2 nanoenzymes with apoptotic neutrophil membrane coating for macrophage recognition and oxidative stress regulation. Additionally, a short fiber "biomimetic matrix" is utilized for loading AM/CeO2 signals via abundant adhesion sites involving π-π stacking and hydrogen bonding interactions. Ultimately, the implantable apoptosis-mimetic nanoenzyme/short-fiber matrixes (PFS@AM/CeO2), integrating apoptotic signals and biomimetic matrixes, are constructed to facilitate inflammatory reversal and reestablish the pro-efferocytosis microenvironment. In vitro and in vivo data indicate that the microenvironment biomimetic short fibers can activate macrophage continual efferocytosis, leading to the suppression of overactivated inflammation. The enhanced repair of rat femoral defect further demonstrates the osteogenic potential of the pro-efferocytosis strategy. It is believed that the regulation of macrophage efferocytosis through microenvironment biomimetic materials can provide a new perspective for tissue repair.

Clinical study of 57 cases of endovascular treatment for total subclavian artery occlusion.

OBJECTIVE: To investigate the safety and efficacy of endovascular treatment for totally occlusive lesions of the subclavian artery (SCA). METHODS: A retrospective study was performed on 57 patients treated with angioplasty and stenting, including 42 males and 15 females, with an average age of 61.8 years (range: 49 to 81 years). Efficacy, safety, and complications were evaluated. RESULTS: Procedural success was achieved for 47/57 patients and symptoms were relieved. Rat-tail occlusion is the most common type, and all cases were successfully recanalized. Plain type occlusion is less common with a recanalization rate of 55.6%. Hilly and plain occlusions are the main types of stent implantation failure. Through univariate analysis and trend matching analysis, the type of SCA occlusion and surgical approach had statistical significance on the success rate of surgery. The mean follow-up time was 34.6 ± 16.2 months. The cumulative stent patency rates at 1, 3, and 5 years were 95.5%, 86.4%, and 77.3% in the calcified plaque group and 92.0%, 76.0%, and 68.0% in the non-calcified plaque group, respectively. The 3-year and 5-year patency rates in the calcified plaque group were higher than those in the non-calcified plaque group (p < .05). CONCLUSION: Different occlusion types and surgical approaches can affect the surgical success rate. The combined femoral and brachial approach can improve the rate of recanalization of SCA occlusions. The patency rates at 3 and 5 years in the calcified plaque group were higher than those in the non-calcified plaque group.

Catalytic Intramolecular Ketone Haloacylation Enabled Stereoselective Heterolytic Cleavage of Cyclopropyl Ketones with Enhanced Reactivity and Regioselectivity beyond Electronics.

By integration of oxocarbenium activation and Lewis acid coordination activation via conformational proximity-driven, Pd(II)- or Cu(I)-catalyzed intramolecular ketone haloacylation, regio- and stereoselective heterolytic ring-opening 1,5-haloacylation of cyclopropyl ketones, including those with weak single alkyl donors, has been developed for the synthesis of valuable α-quaternary halo-γ-butenolides. The vicinal carboxylic acid and ketone acceptors are no longer just spectator activators. Further, this reaction delivers a constant regioselectivity regardless of the electronic nature of substituents, even the malonate.

Mixture-of-experts and semantic-guided network for brain tumor segmentation with missing MRI modalities.

Accurate brain tumor segmentation with multi-modal MRI images is crucial, but missing modalities in clinical practice often reduce accuracy. The aim of this study is to propose a mixture-of-experts and semantic-guided network to tackle the issue of missing modalities in brain tumor segmentation. We introduce a transformer-based encoder with novel mixture-of-experts blocks. In each block, four modality experts aim for modality-specific feature learning. Learnable modality embeddings are employed to alleviate the negative effect of missing modalities. We also introduce a decoder guided by semantic information, designed to pay higher attention to various tumor regions. Finally, we conduct extensive comparison experiments with other models as well as ablation experiments to validate the performance of the proposed model on the BraTS2018 dataset. The proposed model can accurately segment brain tumor sub-regions even with missing modalities. It achieves an average Dice score of 0.81 for the whole tumor, 0.66 for the tumor core, and 0.52 for the enhanced tumor across the 15 modality combinations, achieving top or near-top results in most cases, while also exhibiting a lower computational cost. Our mixture-of-experts and sematic-guided network achieves accurate and reliable brain tumor segmentation results with missing modalities, indicating its significant potential for clinical applications. Our source code is already available at https://github.com/MaggieLSY/MESG-Net .

Efficient photocatalytic hydrogen peroxide production over S-scheme In2S3/molten salt modified C3N5 heterojunction.

Graphitic phase carbon nitride (g-C3N5), as a novel n-type metal-free material, is employed as a visible light-receptive catalyst because of its narrow band gap and abundant nitrogen. To overcome the low carrier mobility efficiency of g-C3N5, its modification by K ions was adopted. In addition, In2S3 was selected to couple with modified g-C3N5 to overcome the recombination of photogenerated e-/h+. As a novel photocatalytic material, it was proven to possess a high visible light absorption capacity and a strong H2O2 production ability (up to 3.89 mmol⋅L-1 in 2 h). Moreover, a S-scheme heterojunction structure was successfully constructed between the two materials, which was tested and confirmed to be successful in raising the photogenerated e-/h+ separation efficiency. Ultimately, the primary processes of photocatalytic H2O2 production were summarized by superoxide radical and rotating disc electron measurements. This research provides a fresh perspective for the synthesis of C3N5-based S-scheme heterojunction photocatalysts for producing H2O2.

Impact of climate change on the global circulation of West Nile virus and adaptation responses: a scoping review.

BACKGROUND: West Nile virus (WNV), the most widely distributed flavivirus causing encephalitis globally, is a vector-borne pathogen of global importance. The changing climate is poised to reshape the landscape of various infectious diseases, particularly vector-borne ones like WNV. Understanding the anticipated geographical and range shifts in disease transmission due to climate change, alongside effective adaptation strategies, is critical for mitigating future public health impacts. This scoping review aims to consolidate evidence on the impact of climate change on WNV and to identify a spectrum of applicable adaptation strategies. MAIN BODY: We systematically analyzed research articles from PubMed, Web of Science, Scopus, and EBSCOhost. Our criteria included English-language research articles published between 2007 and 2023, focusing on the impacts of climate change on WNV and related adaptation strategies. We extracted data concerning study objectives, populations, geographical focus, and specific findings. Literature was categorized into two primary themes: 1) climate-WNV associations, and 2) climate change impacts on WNV transmission, providing a clear understanding. Out of 2168 articles reviewed, 120 met our criteria. Most evidence originated from North America (59.2%) and Europe (28.3%), with a primary focus on human cases (31.7%). Studies on climate-WNV correlations (n = 83) highlighted temperature (67.5%) as a pivotal climate factor. In the analysis of climate change impacts on WNV (n = 37), most evidence suggested that climate change may affect the transmission and distribution of WNV, with the extent of the impact depending on local and regional conditions. Although few studies directly addressed the implementation of adaptation strategies for climate-induced disease transmission, the proposed strategies (n = 49) fell into six categories: 1) surveillance and monitoring (38.8%), 2) predictive modeling (18.4%), 3) cross-disciplinary collaboration (16.3%), 4) environmental management (12.2%), 5) public education (8.2%), and 6) health system readiness (6.1%). Additionally, we developed an accessible online platform to summarize the evidence on climate change impacts on WNV transmission ( https://2xzl2o-neaop.shinyapps.io/WNVScopingReview/ ). CONCLUSIONS: This review reveals that climate change may affect the transmission and distribution of WNV, but the literature reflects only a small share of the global WNV dynamics. There is an urgent need for adaptive responses to anticipate and respond to the climate-driven spread of WNV. Nevertheless, studies focusing on these adaptation responses are sparse compared to those examining the impacts of climate change. Further research on the impacts of climate change and adaptation strategies for vector-borne diseases, along with more comprehensive evidence synthesis, is needed to inform effective policy responses tailored to local contexts.

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test.

BACKGROUND: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. METHODOLOGY: In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. RESULTS: The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. CONCLUSIONS: In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff.

A comprehensive survey on deep active learning in medical image analysis.

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis. An accompanying paper list and code for the comparative analysis is available in https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysis.

Circ_0003945: an emerging biomarker and therapeutic target for human diseases.

Due to the rapid development of RNA sequencing techniques, a circular non-coding RNA (ncRNA) known as circular RNAs (circRNAs) has gradually come into focus. As a distinguished member of the circRNA family, circ_0003945 has garnered attention for its aberrant expression and biochemical functions in human diseases. Subsequent studies have revealed that circ_0003945 could regulate tumor cells proliferation, migration, invasion, apoptosis, autophagy, angiogenesis, drug resistance, and radio resistance through the molecular mechanism of competitive endogenous RNA (ceRNA) during tumorigenesis. The expression of circ_0003945 is frequently associated with some clinical parameters and implies a poorer prognosis in the majority of cancers. In non-malignant conditions, circ_0003945 also holds considerable importance in diseases pathogenesis. This review aims to recapitulate molecular mechanism of circ_0003945 and elucidates its potential as a diagnostic and therapeutic target in neoplasms and other diseases.

The Glucose-Succinate Pathway: A Crucial Anaerobic Metabolic Pathway in the Scallop Chlamys farreri Experiencing Heat Stress.

Recently, the increase in marine temperatures has become an important global marine environmental issue. The ability of energy supply in marine animals plays a crucial role in avoiding the stress of elevated temperatures. The investigation into anaerobic metabolism, an essential mechanism for regulating energy provision under heat stress, is limited in mollusks. In this study, key enzymes of four anaerobic metabolic pathways were identified in the genome of scallop Chlamys farreri, respectively including five opine dehydrogenases (CfOpDHs), two aspartate aminotransferases (CfASTs) divided into cytoplasmic (CfAST1) and mitochondrial subtype (CfAST2), and two phosphoenolpyruvate carboxykinases (CfPEPCKs) divided into a primitive type (CfPEPCK2) and a cytoplasmic subtype (CfPEPCK1). It was surprising that lactate dehydrogenase (LDH), a key enzyme in the anaerobic metabolism of the glucose-lactate pathway in vertebrates, was absent in the genome of scallops. Phylogenetic analysis verified that CfOpDHs clustered according to the phylogenetic relationships of the organisms rather than substrate specificity. Furthermore, CfOpDHs, CfASTs, and CfPEPCKs displayed distinct expression patterns throughout the developmental process and showed a prominent expression in muscle, foot, kidney, male gonad, and ganglia tissues. Notably, CfASTs displayed the highest level of expression among these genes during the developmental process and in adult tissues. Under heat stress, the expression of CfASTs exhibited a general downregulation trend in the six tissues examined. The expression of CfOpDHs also displayed a downregulation trend in most tissues, except CfOpDH1/3 in striated muscle showing significant up-regulation at some time points. Remarkably, CfPEPCK1 was significantly upregulated in all six tested tissues at almost all time points. Therefore, we speculated that the glucose-succinate pathway, catalyzed by CfPEPCK1, serves as the primary anaerobic metabolic pathway in mollusks experiencing heat stress, with CfOpDH3 catalyzing the glucose-opine pathway in striated muscle as supplementary. Additionally, the high and stable expression level of CfASTs is crucial for the maintenance of the essential functions of aspartate aminotransferase (AST). This study provides a comprehensive and systematic analysis of the key enzymes involved in anaerobic metabolism pathways, which holds significant importance in understanding the mechanism of energy supply in mollusks.

A broadly applicable protein-polymer adjuvant system for antiviral vaccines.

Although protein subunit vaccines generally have acceptable safety profiles with precise antigenic content, limited immunogenicity can lead to unsatisfactory humoral and cellular immunity and the need for vaccine adjuvants and delivery system. Herein, we assess a vaccine adjuvant system comprising Quillaja Saponaria-21(QS-21) and cobalt porphyrin polymeric micelles that enabling the display of His-tagged antigen on its surface. The nanoscale micelles promote antigen uptake and dendritic cell activation to induce robust cytotoxic T lymphocyte response and germinal center formation. Using the recombinant protein antigens from influenza A and rabies virus, the micelle adjuvant system elicited robust antiviral responses and protected mice from lethal challenge. In addition, this system could be combined with other antigens to induce high titers of neutralizing antibodies in models of three highly pathogenic viral pathogens: Ebola virus, Marburg virus, and Nipah virus. Collectively, our results demonstrate this polymeric micelle adjuvant system can be used as a potent nanoplatform for developing antiviral vaccine countermeasures that promote humoral and cellular immunity.

Development and characterization of a novel specific monoclonal antibody against mink enteritis virus and its antigen epitope analysis.

This study prepared a novel monoclonal antibody (MAb) against mink enteritis parvovirus (MEV) and identified its antigen epitope. The antibody subclass is identified as IgG1, the titers of the MAb is up to 1:1 × 106 and keeps stably after low-temperature storage for 9 months or 11 passages of the MAb cells. The MAb can specifically recognize MEV in the cells in IFA, but not Aleutian disease virus (ADV) or canine distemper virus (CDV). Its antigen epitope was identified as a polypeptide containing 5 key amino acids (378YAFGR382) and the homology in 20 MEV strains, 4 canine parvovirus strains, and 4 feline panleukopenia virus strains was 100%. This study supplies a biological material for developing new methods to detect MEV.

Grappling with the trade-offs of carbon emission trading and green certificate: Achieving carbon neutrality in China.

Although our knowledge of national carbon emission trading system and green certificate trading system are powerful incentive instruments that can deliver on increasingly ambitious climate targets in China, there remains an uncertainty of systems' structural reforms. This study builds on and extends a well-established dynamic computable general equilibrium (CGE) model to incorporate carbon trading system and green certificate trading system into the modeling framework, simulating a diverse of system development pathways further allows an exploration of the many possible policy effect. Then, using total factor productivity as a comprehensive indicator to asses policy effectiveness, the evolutionary trend of comprehensive effects under different paths are separately evaluated to discover the reforms' optimal range. Our work offers main results: First, these instruments provide a price signal. The introduction of a carbon allowance auction drive up carbon prices, while the implementation of a green certificate punishment and the expansion of the trading scope promote an increase in green certificate prices. Second, all policy scenarios that help reduce carbon emission intensity and optimize the power supply structure. However, in achieving the net-zero goal, the green certificate policy incurs more economic costs than the carbon trading policy. Indeed, the combination of multiple policy tools alleviates the decline of social welfare levels. Third, synergism design among policy tools: the focus should be on carbon trading policy from 2021 to 2030, green certificate trading policy from 2030 to 2050, and strengthened policy from 2050 to 2060. Reform measures within policies may need to be introduced in a timely manner. This study offers specific insights and tailored policy proposals to support policymakers in balancing environmental goals with economic and social needs in light of the aforementioned findings.

Association Between rs2278426 Polymorphism of the ANGPTL8 Gene and Polycystic Ovary Syndrome.

Purpose: To study the relationship between the single nucleotide polymorphism (SNP) rs2278426 in the angiopoietin-like protein 8 gene (ANGPTL8) and polycystic ovary syndrome (PCOS). Patients and methods: A total of 122 patients with PCOS and 108 controls were recruited for comparison of glucose, lipid, insulin, sex hormone, and ANGPTL8 levels. Polymerase chain reaction (PCR) and gene sequencing were performed for comparison of the frequency of the CC, CT, and TT rs2278426 genotypes and the rs2278426 allele distributions between the PCOS and control groups and between the obese and non-obese subgroups of the PCOS and control groups. Results: The frequency of the T allele was significantly higher in the PCOS group than that in the controls (P = 0.037). In the dominant genetic model, the proportion of the CT+TT genotype in the PCOS group was significantly higher than that in the controls (P = 0.047). Subgroup analysis demonstrated that the T allele proportion was significantly higher in obese PCOS group than obese control group (P = 0.027). PCOS with the CT+TT genotype had significantly higher body mass index (BMI; P = 0.001), triglyceride (TG; P = 0.005), homeostasis model assessment of insulin resistance (HOMA-IR; P = 0.035), testosterone (P = 0.041), and ANGPTL8 (P = 0.037) levels and significantly lower high-density lipoprotein (HDL) levels (P = 0.025) than PCOS with the CC genotype. Obese PCOS group with the CT+TT genotype had significantly higher TG (P = 0.015), luteinizing hormone (LH; P = 0.030), fasting insulin (FINS; P = 0.039), HOMA-IR (P = 0.018), and ANGPTL8 (P = 0.049) levels than obese PCOS group with the CC genotype. Conclusion: Polymorphisms of rs2278426 may induce glycolipid metabolic disorders by affecting ANGPTL8 levels and functions in Han Chinese females with obesity from the Shandong region, increasing the risk of PCOS in this population.

Multi-Time-Scale Optimal Scheduling Strategy for Marine Renewable Energy Based on Deep Reinforcement Learning Algorithm.

Surrounded by the Shandong Peninsula, the Bohai Sea and Yellow Sea possess vast marine energy resources. An analysis of actual meteorological data from these regions indicates significant seasonality and intra-day uncertainty in wind and photovoltaic power generation. The challenge of scheduling to leverage the complementary characteristics of various renewable energy sources for maintaining grid stability is substantial. In response, we have integrated wave energy with offshore photovoltaic and wind power generation and propose a day-ahead and intra-day multi-time-scale rolling optimization scheduling strategy for the complementary dispatch of these three energy sources. Using real meteorological data from this maritime area, we employed a CNN-LSTM neural network to predict the power generation and load demand of the area on both day-ahead 24 h and intra-day 1 h time scales, with the DDPG algorithm applied for refined electricity management through rolling optimization scheduling of the forecast data. Simulation results demonstrate that the proposed strategy effectively meets load demands through complementary scheduling of wave power, wind power, and photovoltaic power generation based on the climatic characteristics of the Bohai and Yellow Sea regions, reducing the negative impacts of the seasonality and intra-day uncertainty of these three energy sources on the grid. Additionally, compared to the day-ahead scheduling strategy alone, the day-ahead and intra-day rolling optimization scheduling strategy achieved a reduction in system costs by 16.1% and 22% for a typical winter day and a typical summer day, respectively.

White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.

Background: Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods: This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. Results: In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions: The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.