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Within the framework of global climate change, there is a recurring occurrence of floods and waterlogging disasters, which pose significant risks to human lives and overall safety. The phenomenon of urbanization has led to an increased vulnerability of cities to flood disasters. Evacuation serves as a viable strategy for adapting to climate change, managing intense precipitation events, and mitigating the impact of flood disasters. Hence, the meticulous choice of shelters has emerged as a crucial element in enhancing the effectiveness of evacuations. Nevertheless, the process of choosing shelters sometimes neglects to consider the potential for flooding and has not undergone thorough testing. Hence, the objective of this research is to systematically choose shelters within the central metropolitan region, incorporate flood risk considerations into the shelter site model, and assist cities in mitigating the consequences of floods resulting from climate change. To begin with, employ a two-dimensional shallow water model in order to replicate instances of intense precipitation and produce data on flood inundation. Subsequently, the simulation results are utilized to assess the flood risk using the entropy weight approach. This risk is then incorporated into the multi-objective optimization model to determine the optimal shelter placement. Ultimately, the ABM model was employed to create a simulation of crowd evacuation in urban areas, confirming the efficacy of shelter selection and thoroughly examining the influence of various shelters on the efficiency of community emergency evacuation. The study's findings indicate that a total of 26 shelters were chosen within the study area using a multi-objective methodology, ensuring the inclusion of all affected populations. The utilization of multi-objective models in the selection of shelter places has been shown to enhance evacuation efficiency, as confirmed by the ABM model. The simulation findings indicate a decline in the population's death rate from 1.94% to 0.98%, an increase in the evacuation rate from 97.4% to 98.4%, and a reduction in the number of casualties by 133 individuals.

ObjectiveThis study explored the influence of the life course on mental health by identifying key trends, seminal works, and themes in existing research. Additionally, it highlights the major discussions at the intersection of life course and mental health.MethodsDocuments were extracted from the Web of Science Core Collection (WoSCC), to systematically analyze themes on mental health outcomes across the life course. The analysis was based on key bibliometric tools, including VOSviewer 1.6.11, R Studio software, and GraphPad Prism 9 to analyze the evolution and impact of scholarly contributions in this domain.ResultsThe accumulated body of research concerning the life course’s impact on mental health, which began to emerge around 1990 displayed a consistently upward trend. Predominant contributions originate from developed nations and frequently look into the psychosocial determinants of mental health over life course. Life course and mental health studies have been extensively infused with biopsychosocial frameworks that consider the role of genetic makeup, neurodevelopment, cognition, affect, sociocultural dynamics, and interpersonal relationships. Life course theory application in mental health highlight the substantive effects of accumulated adversities, notably social determinants of health, adverse childhood experiences (ACEs), and their implications for subsequent mental health outcomes.ConclusionThe nexus of life course and mental health outcomes demands further scholarly interrogation, particularly within underserved regions, to strengthen protective mechanisms for vulnerable populations.

The dichotomized brain system is a concept that was generalized from the ‘dual syndrome hypothesis’ to explain the heterogeneity of cognitive impairment, in which anterior and posterior brain systems are independent but partially overlap. The dopaminergic system acts on the anterior brain and is responsible for executive function, working memory, and planning. In contrast, the cholinergic system acts on the posterior brain and is responsible for semantic fluency and visuospatial function. Evidence from dopaminergic/cholinergic imaging or functional neuroimaging has shed significant insight relating to the involvement of the cerebellum in the cognitive process of patients with Parkinson’s disease. Previous research has reported evidence that the cerebellum receives both dopaminergic and cholinergic projections. However, whether these two neurotransmitter systems are associated with cognitive function has yet to be fully elucidated. Furthermore, the precise role of the cerebellum in patients with Parkinson’s disease and cognitive impairment remains unclear. Therefore, in this review, we summarize the cerebellar dopaminergic and cholinergic projections and their relationships with cognition, as reported by previous studies, and investigated the role of the cerebellum in patients with Parkinson’s disease and cognitive impairment, as determined by functional neuroimaging. Our findings will help us to understand the role of the cerebellum in the mechanisms underlying cognitive impairment in Parkinson’s disease.

Impacts of economy-society-environment on surface water resource in China are complex and unclear. Revealing these connections is vital to understand responses of surface water quality to anthropogenic activities. This study made an attempt to explore potential indications on surface water quality from economic, social and environmental factors in eight separate regions of China during the period of 2000–2019. Five machine learning models were employed including Greedy Thick Thinning Bayesian Belief Network, Naive Bayes, Augmented Naive Bayes (ANB), Logistic Regression and Random Forest. A total of 8 economic variables, 5 social variables and 8 environmental variables were introduced into the models. Results showed that ANB presented the best performance in estimating the surface water quality class with the highest accuracies of 81%, 75% and 87% for three surface water quality groups (Class I–III, Class IV–V and worse than Class V), respectively. The higher the surface water environmental carrying capacity in a region, the better the estimation performance of ANB on the surface water quality class. Surface water quality with Class I–III was more closely related to economic and social development, while environmental variables largely interpreted the quality of surface water with Class IV–V in most regions. The critical factors filtered by the importance analysis were indicative on surface water quality. This study provided a feasible framework in revealing the economy-society-environment nexus in the context of comprehensive management on regional surface water quality.

Dissolved organic carbon (DOC) is indicative of water quality in drinking source water and has an impact on drinking water treatment. Exploring occurrence of DOC in drinking source water is of great significance for the drinking water safety in Beijing. This study elucidated spatial-temporal DOC dynamics in the Miyun section of Chaobai River in Beijing of China and identified sources of DOC based on DOC fluorescent spectral characteristics. Results showed that the average riverine DOC concentration was 30.60 mg/L. DOC concentrations in the Miyun section of the Chaobai River were 30.40, 30.24, and 31.09 mg/L in spring, summer and autumn, respectively. DOC concentrations at the three segments of the Miyun section of the Chaobai River (the Chaohe River, the Baihe River and the Chaobai River) were 29.93, 30.29 and 32.57 mg/L, respectively. Land use contributed to DOC variations rather than season and river segment. Significant increases in DOC concentration were observed in river water flowing through farmland and urban areas, and DOC concentration presented the highest after flowing through the farmland area. The results of FI, HIX and BIX showed that DOC mainly came from endogenous sources such as aquatic biological activities, and was less affected by terrestrial inputs. Special attentions ought to be paid on prevention and control of endogenous DOC inputs.

Development of rainfed maize (Zea mays L.) is sensitive to fluctuations of environmental conditions, while whether the sensitivity varies across the growth stages is still unclear. Based on a 5-year dataset collected from consecutive observations, this study examined the sensitivities of biomass and yield production of rainfed maize to root zone soil water, air temperature and shortwave radiation at four growth stages in the Sanjiang Plain of Northeastern China under sub-humid cool-temperate climates. The multiple linear regression model was employed to establish functional relations between biomass and yield production and significant explanatory variables. A Monte-Carlo simulation was used to test sensitivities of biomass and yield production to perturbation of a single significant explanatory variable or co-perturbation of multiple significant explanatory variables. Results showed that root zone soil water prevailed over air temperature and shortwave radiation in affecting rainfed maize development for the most time of the growing period. Biomass production was most sensitive to root zone soil water which had positive variance contributions of 70 – 100% at the early and late vegetative stages and a negative variance contribution of −99.4% at the early reproductive stage. Yield production was also sensitive to root zone soil water at the early reproductive stage with a 100% positive variance contribution. Biomass and yield production were most sensitive to air temperature at the late reproductive stage and the positive variance contributions of air temperature were 97.7 – 100%. Shortwave radiation negatively contributed to biomass production by −28.6% at the late vegetative stage. The findings of this study suggest that more attention could be paid to the most sensitive factor at different growth stages of rainfed maize for great biomass accumulation and high grain yield.

In this study, FeS and chitosan (CTS) were compounded to form a new Cr (Ⅵ) treatment material FeS/CTS. FeS/CTS was characterized by SEM, FTIR, XRD, XPS, BET, etc, and the removal performance and mechanism of FeS/CTS for Cr (Ⅵ) were also discussed. The removal of Cr (Ⅵ) by the composite was the result of electrostatic force, redox reaction, complexation and precipitation. When the initial concentration of Cr (Ⅵ) was 50 mg∙L−1, the removal of Cr (Ⅵ) was attributed to 87.14% reduction and 12.86% adsorption. The fitting results of the kinetic and isothermal models proved that chemical adsorption playsed a dominant role in the removal process. At pH 5 and the initial Cr (Ⅵ) concentration of 40 mg∙L−1, the removal rate of Cr (Ⅵ) by the composite could reach 99.90%. All results showed that FeS/CTS composite can be used as a potential Cr (Ⅵ) removal material in water.

Abstract Background Chorea-acanthocytosis (ChAc) is a rare hereditary autosomal recessive neurodegenerative disorder caused by pathogenic variants of the Vacuolar Protein Sorting 13 homolog A (VPS13A) gene. The variant spectrum of VPS13A has not been completely elucidated. This study reports two novel heterozygous VPS13A pathogenic variants in ChAc that expand the variant spectrum of VPS13A. Case presentation We described a case of a 29-year-old man with typical clinical manifestations of ChAc, including chorea, orofacial lingual dyskinesia, vocal tics, elevated serum biochemical indicators, increased acanthocytes in peripheral blood, and caudate nucleus atrophy. Next-generation sequencing revealed two heterozygous variants of VPS13A: a nonsense variant (NM_033305.2: c.8215G > T, p. Glu2739Ter) and a deletion variant in the exons 25–31. Conclusion The identified nonsense variant gives rise to premature translation termination, while the deletion variant is expected to cause a significant in-frame deletion of amino acid residues in the encoded protein. Both variants are considered to be pathogenic and result in loss-of-function proteins. These findings have implications for the genetic counseling of patients with VPS13A variants.

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life. Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale (CPSS) and the Face Arm Speech Test (FAST) are commonly used for stroke screening, accurate administration is dependent on specialized training. In this study, we proposed a novel multimodal deep learning approach, based on the FAST, for assessing suspected stroke patients exhibiting symptoms such as limb weakness, facial paresis, and speech disorders in acute settings. We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements, facial expressions, and speech tests based on the FAST. We compared the constructed deep learning model, which was designed to process multi-modal datasets, with six prior models that achieved good action classification performance, including the I3D, SlowFast, X3D, TPN, TimeSformer, and MViT. We found that the findings of our deep learning model had a higher clinical value compared with the other approaches. Moreover, the multi-modal model outperformed its single-module variants, highlighting the benefit of utilizing multiple types of patient data, such as action videos and speech audio. These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke, thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Abstract One of the important pathological features of Parkinson’s disease (PD) is the pathological aggregation of α-synuclein (α-Syn) in the substantia nigra. Preventing the aggregation of α-Syn has become a potential strategy for treating PD. However, the molecular mechanism of α-Syn aggregation is unclear. In this study, using the dynamic network biomarker (DNB) method, we first identified the critical time point when α-Syn undergoes pathological aggregation based on a SH-SY5Y cell model and found that DNB genes encode transcription factors that regulated target genes that were differentially expressed. Interestingly, we found that these DNB genes and their neighbouring genes were significantly enriched in the cellular senescence pathway and thus proposed that the DNB genes HSF1 and MAPKAPK2 regulate the expression of the neighbouring gene SERPINE1. Notably, in Gene Expression Omnibus (GEO) data obtained from substantia nigra, prefrontal cortex and peripheral blood samples, the expression level of MAPKAPK2 was significantly higher in PD patients than in healthy people, suggesting that MAPKAPK2 has potential as an early diagnostic biomarker of diseases related to pathological aggregation of α-Syn, such as PD. These findings provide new insights into the mechanisms underlying the pathological aggregation of α-Syn.

Highlights Synthesized biochar-based catalyst enhanced adsorption and photocatalytic activity. The catalys simultaneously removed 91% of TC and 79% of Cr(VI) in combined systems. Synergistic photocatalytic effect accelerated TC and Cr(VI) removal in combined systems.

Abstract The pathological accumulation of α‐synuclein (α‐Syn) and the transmission of misfolded α‐Syn underlie α‐synucleinopathies. Increased plasma α‐Syn levels are associated with cognitive impairment in Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies, but it is still unknown whether the cognitive deficits in α‐synucleinopathies have a common vascular pathological origin. Here, it is reported that combined injection of α‐Syn preformed fibrils (PFFs) in the unilateral substantia nigra pars compacta, hippocampus, and cerebral cortex results in impaired spatial learning and memory abilities at 6 months post‐injection and that this cognitive decline is related to cerebral microvascular injury. Moreover, insoluble α‐Syn inclusions are found to form in primary mouse brain microvascular endothelial cells (BMVECs) through lymphocyte‐activation gene 3 (Lag3)‐dependent α‐Syn PFFs endocytosis, causing poly(ADP‐ribose)‐driven cell death and reducing the expression of tight junction proteins in BMVECs. Knockout of Lag3 in vitro prevents α‐Syn PFFs from entering BMVECs, thereby reducing the abovementioned response induced by α‐Syn PFFs. Deletion of endothelial cell‐specific Lag3 in vivo reverses the negative effects of α‐Syn PFFs on cerebral microvessels and cognitive function. In short, this study reveals the effectiveness of targeting Lag3 to block the spread of α‐Syn fibrils to endothelial cells in order to improve cognition.

Summary: α-Synuclein preformed fibrils (α-syn PFF) in the blood can cross the blood–brain barrier and invade the central nervous system. Our previous study proved that α-syn PFF can be taken up by brain microvascular endothelial cells (BMVECs). Here, we found that α-syn PFF spread from BMVECs to pericytes with the highest transmission efficiency. We observed abundant tunneling nanotubes (TNTs) connecting BMVECs and pericytes, and α-syn PFF transmitted through these TNTs. Furthermore, α-syn PFF accumulation in BMVECs did not promote TNT formation, but activated the molecular motor Myo1d. Inhibition of Myo1d prevented α-syn PFF transfer from BMVECs to pericytes and decreased the colocalization of Myo1d and F-actin in BMVECs. In summary, we are the first to demonstrate that α-syn PFF spread from BMVECs to pericytes through a mechanism involving TNTs and myosin. Targeting Myo1d may be a promising approach to prevent α-syn spreading from the blood to the brain.

Background: Repetitive transcranial magnetic stimulation (rTMS) is a potential treatment option for Parkinson’s disease patients with depression (DPD), but conflicting results in previous studies have questioned its efficacy. Method: To investigate the safety and efficacy of neuronavigated high-frequency rTMS at the left DLPFC in DPD patients, we conducted a randomized, double-blind, sham-controlled study (NCT04707378). Sixty patients were randomly assigned to either a sham or active stimulation group and received rTMS for ten consecutive days. The primary outcome was HAMD, while secondary outcomes included HAMA, MMSE, MoCA and MDS-UPDRS-III. Assessments were performed at baseline, immediately after treatment, 2 weeks, and 4 weeks post-treatment. Results: The GEE analysis showed that the active stimulation group had significant improvements in depression, anxiety, and motor symptoms at various time points. Specifically, there were significant time-by-group interaction effects in depression immediately after treatment (β, −4.34 [95% CI, −6.90 to −1.74; P = 0.001]), at 2 weeks post-treatment (β, −3.66 [95% CI, −6.43 to −0.90; P = 0.010]), and at 4 weeks post-treatment (β, −4.94 [95% CI, −7.60 to −2.29; P

Article Highlights Biomass type and thermochemical conversion temperature significantly affect biochar physicochemical properties. Biochar-based catalysts applied in Fenton-like system exhibited better performance for refractory emerging pollutants. Biochar-matrix, metal nanoparticles of catalysts, as well as intermediates are the main risks.

Neurovascular imaging is essential for investigating neurodegenerative diseases. However, the existing neurovascular imaging technology suffers from a trade-off between a field of view (FOV) and resolution in the whole brain, resulting in an inhomogeneous resolution and lack of information. Here, homogeneous-resolution arched-scanning photoacoustic microscopy (AS-PAM), which has an ultrawide FOV to cover the entire mouse cerebral cortex, was developed. Imaging of the neurovasculature was performed with a homogenous resolution of 6.9 µm from the superior sagittal sinus to the middle cerebral artery and caudal rhinal vein in an FOV of 12 × 12 mm2. Moreover, using AS-PAM, vascular features of the meninges and cortex were quantified in early Alzheimer’s disease (AD) and wild-type (WT) mice. The results demonstrated high sensitivity to the pathological progression of AD on tortuosity and branch index. The high-fidelity imaging capability in large FOV enables AS-PAM to be a promising tool for precise brain neurovascular visualization and quantification.

To explore the contribution of microorganisms to forest ecosystem function, we studied the ectomycorrhizal (ECM) fungal and soil bacterial community of the five typical tree species (Pinus massoniana, PM; Castanopsis carlesii, CC; Castanopsis eyrei, CE; Castanopsis fargesii, CF; and Keteleeria cyclolepis, KC) at the Junzifeng National Nature Reserve. The results indicated that the ECM fungal and soil bacterial diversity of CC and CF was similar, and the diversity rates of CC and CF were higher than those of PM, CE, and KC. Cenococcum geophilum and unclassified_Cortinariaceae II were the most prevalent occurring ECM fungi species in the five typical tree species, followed by unclassified_Cortinariaceae I and Lactarius atrofuscus. In bacteria, the dominant bacterial genera were Acidothermus, Bradyrhizobium, Acidibacter, Candidatus_Solibacter, Candidatus_Koribacter, Roseiarcus, and Bryobacter. EMF fungi and soil bacteria were correlated with edaphic factors, especially the soil pH, TP, and TK, caused by stand development. The results show that the community characteristics of ECM fungi and bacteria in the typical tree species of the Junzifeng National Nature Reserve reflect the critical role of soil microorganisms in stabilizing forest ecosystems.

Pinus massoniana (Massion’s pine), a pioneer tree species, exhibits restoration potential in polluted mining areas. However, the physiological and molecular mechanisms of ectomycorrhizal (ECM) fungi in Massion’s pine adaptability to multiple-toxic-metal stress are still unclear. Hence, Massion’s pine seedlings inoculated with two strains of C. geophilum, which were screened and isolated from a polluted mine area, were cultivated in mine soil for 90 days to investigate the roles of EMF in mediating toxic metal tolerance in host plants. The results showed that compared with the non-inoculation control, C. geophilum (CG1 and CG2) significantly promoted the biomass, root morphology, element absorption, photosynthetic characteristics, antioxidant enzyme activities (CAT, POD, and SOD), and proline content of Massion’s pine seedlings in mine soil. C. geophilum increased the concentrations of Cr, Cd, Pb, and Mn in the roots of Massion’s pine seedlings, with CG1 significantly increasing the concentrations of Pb and Mn by 246% and 162% and CG2 significantly increasing the concentrations of Cr and Pb by 102% and 78%. In contrast, C. geophilum reduced the concentrations of Cr, Cd, Pb, and Mn in the shoots by 14%, 33%, 27%, and 14% on average, respectively. In addition, C. geophilum significantly reduced the transfer factor (TF) of Cr, Cd, Pb, and Mn by 32–58%, 17–26%, 68–75%, and 18–64%, respectively, and the bio-concentration factor (BF) of Cd by 39–71%. Comparative transcriptomic analysis demonstrated that the differently expressed genes (DEGs) were mainly encoding functions involved in “transmembrane transport”, “ion transport”, “oxidation reduction process”, “oxidative phosphorylation”, “carbon metabolism”, “glycolysis/gluconeogenesis”, “photosynthesis”, and “biosynthesis of amino acids.” These results indicate that C. geophilum is able to mitigate toxic metals stress by promoting nutrient uptake, photosynthesis, and plant growth, thereby modulating the antioxidant system to reduce oxidative stress and reducing the transport and enrichment of toxic metals from the root to the shoot of Massion’s pine seedlings.

BackgroundLipid storage myopathy (LSM) is an autosomal recessive inherited lipid and amino metabolic disorder with great clinical heterogeneity. Variations in the electron transfer flavoprotein dehydrogenase (ETFDH) gene cause multiple acyl-CoA dehydrogenase deficiency (MADD), and have a manifestation of LSM. Muscle biopsy helps clarify the diagnosis of LSM, and next-generation sequencing (NGS) can be useful in identifying genomic mutation sites. The diagnosis of MADD contributes to targeted therapy.Case presentationWe report on a teenager who appeared to have muscle weakness and exercise intolerance at the onset. Before the referral to our hospital, he was unsuccessfully treated with glucocorticoid for suspected polymyositis. The next-generation sequencing of the proband and his parents revealed heterozygous variations, c.365G>A (p.G122D) inherited from the father, c.176-194_176-193del, and c.832-316C>T inherited from the mother in the ETFDH gene. The tandem mass spectrometry identified the mutations to be pathogenic. However, his parents and his younger sister who were detected with a mutation of c.365G>A presented no clinical symptoms. This indicates that the combination of the three compound heterozygous mutations in ETFDH is significant. After MADD was diagnosed, a dramatic clinical recovery and biochemical improvement presented as riboflavin was given to the patient across a week, which further confirmed the diagnosis of MADD.ConclusionOur observations extend the spectrum of ETFDH variants in Chinese the population and reinforce the role of NGS in diagnosis of MADD. Early diagnosis and appropriate treatment of LSM lead to great clinical efficacy and avoid some lethal complications.

ObjectivesWe employed quantitative susceptibility mapping (QSM) to assess iron deposition in parkinsonian disorders and explored whether combining QSM values and neurofilament light (NfL) chain levels can improve the accuracy of distinguishing Parkinson’s disease (PD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP).Materials and methodsForty-seven patients with PD, 28 patients with MSA, 18 patients with PSP, and 28 healthy controls (HC) were enrolled, and QSM data were reconstructed. Susceptibility values in the bilateral globus pallidus (GP), putamen (PUT), caudate nucleus (CN), red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN) were obtained. Plasma NfL levels of 47 PD, 18 MSA, and 14 PSP patients and 22 HC were measured by ultrasensitive Simoa technology.ResultsThe highest diagnostic accuracy distinguishing MSA from PD patients was observed with increased susceptibility values in CN (AUC: 0.740). The susceptibility values in RN yielded the highest diagnostic performance for distinguishing PSP from PD patients (AUC: 0.829). Plasma NfL levels were significantly higher in the MSA and PSP groups than in PD and HC groups. Combining the susceptibility values in the RN and plasma NfL levels improved the diagnostic performance for PSP vs. PD (AUC: 0.904), whereas plasma NfL levels had higher diagnostic accuracy for MSA vs. PD (AUC: 0.877).ConclusionThe exploratory study indicates different patterns of iron accumulation in deep gray matter nuclei in Parkinsonian disorders. Combining QSM values with NfL levels may be a promising biomarker for distinguishing PSP from PD, whereas plasma NfL may be a reliable biomarker for differentiating MSA from PD. QSM and NfL measures appeared to have low accuracy for separating PD from controls.

BackgroundsBile acid (BA) plays a crucial role in various neurodegenerative diseases, including Parkinson's disease (PD). However, no clinical evidence supports BA's potential role in patients with PD with mild cognitive impairment (PD-MCI).ObjectivesThis study aimed at investigating the differential BA profile between patients with PD-MCI and those with normal cognitive function (PD-NC).MethodsUltra-high performance liquid chromatography-MS/MS was applied for BA quantitation. After between-group differences of the BA profile were addressed, orthogonal projections to latent structures—discriminant analysis (OPLS-DA) and the area under the receiver-operating-characteristic curve (AUC-ROC) were implemented for further verification.ResultsLower levels of chenodeoxycholic acid (CDCA), cholic acid (CA), and ursodeoxycholic acid (UDCA) were significantly associated with PD-MCI (p < 0.01 for both; VIP ≈ 2.67, 1.66, and 1.26, respectively). AUC-ROC were 78.1, 74.2, and 74.5% for CDCA, CA, and UDCA, respectively.ConclusionCA, CDCA, and UDCA might be distinct BA signatures for patients with PD-MCI.

ObjectiveTo investigate white matter microstructural alterations in Parkinson's disease (PD) patients with depression using the whole-brain diffusion tensor imaging (DTI) method and to explore the DTI–based machine learning model in identifying depressed PD (dPD).MethodsThe DTI data were collected from 37 patients with dPD and 35 patients with non-depressed PD (ndPD), and 25 healthy control (HC) subjects were collected as the reference. An atlas-based analysis method was used to compare fractional anisotropy (FA) and mean diffusivity (MD) among the three groups. A support vector machine (SVM) was trained to examine the probability of discriminating between dPD and ndPD.ResultsAs compared with ndPD, dPD group exhibited significantly decreased FA in the bilateral corticospinal tract, right cingulum (cingulate gyrus), left cingulum hippocampus, bilateral inferior longitudinal fasciculus, and bilateral superior longitudinal fasciculus, and increased MD in the right cingulum (cingulate gyrus) and left superior longitudinal fasciculus-temporal part. For discriminating between dPD and ndPD, the SVM model with DTI features exhibited an accuracy of 0.70 in the training set [area under the receiver operating characteristic curve (ROC) was 0.78] and an accuracy of 0.73 in the test set (area under the ROC was 0.71).ConclusionDepression in PD is associated with white matter microstructural alterations. The SVM machine learning model based on DTI parameters could be valuable for the individualized diagnosis of dPD.

Continuous monitoring of wetland dynamics and regional-scale assessment of wetland degradation are important to understand wetland ecosystem processes and to formulate restoration measures. The lake wetlands in Bashang Plateau play vital roles in providing essential resources and maintaining biodiversity in North China, while they were not fully investigated in previous studies. In addition, previous studies on wetland degradation assessment mostly relied on wetland area change every five or ten years. Continuous dynamics of wetlands was rarely considered. In this study, we developed a new wetland degradation index (WDI) to assess lake wetland degradation in Bashang Plateau based on time series Landsat imagery from 1985 to 2020 on Google Earth Engine platform. WDI integrates three indicators, namely lake shrinkage index (LSI), vegetation degradation index (VDI) and soil degradation index (SDI), which represents hydrodynamics of wetland inundation, vegetation, and soil conditions of wetlands, respectively. Different from previous research that utilized remotely sensed image to extract wetland every five or ten years, the LSI considers time-series continuous dynamics of lake water inundations and the transitions between water and non-water on a pixel-by-pixel basis. Our results showed that small lake wetlands with area less than 8 ha are dominant in Bashang Plateau. Both the number and the area of lake wetlands showed large interannual variations. The number (area) of lake wetlands reached 1715 (31,419 ha) in 1995–1996 and were only 493 (7,717 ha) in 2009–2010. The total area of the lake wetlands in 2019–2020 was only around 1/3 of that in 1995–1996. The LSI, VDI, SDI, as well as WDI demonstrate large spatial variations among the lake wetlands and within the lake wetlands. Wetlands in the western and central subregions experienced more severe degradation than those in the eastern subregion, and the edge of the wetlands normally showed more severe degradation. The resulted degradation grades are consistent with our field investigations. Among the 11 counties in Bashang Plateau, Zhangbei (ZB) county in the western subregion has the largest area with severe and very severe degradation grades. Degradation of lake wetlands was mainly associated with groundwater level declination which was induced by anthropogenic activities and economic development. The proposed WDI in this study can be widely applicable for regional-scale wetland degradation assessment using remote sensing techniques and thus further help decision making in lake wetland conservation and restoration.

Abstract Paroxysmal dyskinesias are a group of neurological diseases characterized by intermittent episodes of involuntary movements with different causes. Paroxysmal kinesigenic dyskinesia (PKD) is the most common type of paroxysmal dyskinesia and can be divided into primary and secondary types based on the etiology. Clinically, PKD is characterized by recurrent and transient attacks of involuntary movements precipitated by a sudden voluntary action. The major cause of primary PKD is genetic abnormalities, and the inheritance pattern of PKD is mainly autosomal-dominant with incomplete penetrance. The proline-rich transmembrane protein 2 (PRRT2) was the first identified causative gene of PKD, accounting for the majority of PKD cases worldwide. An increasing number of studies has revealed the clinical and genetic characteristics, as well as the underlying mechanisms of PKD. By seeking the views of domestic experts, we propose an expert consensus regarding the diagnosis and treatment of PKD to help establish standardized clinical evaluation and therapies for PKD. In this consensus, we review the clinical manifestations, etiology, clinical diagnostic criteria and therapeutic recommendations for PKD, and results of genetic analyses in PKD patients performed in domestic hospitals.

Precise global solar radiation (GSR) data are indispensable to the design, planning, operation, and management of solar radiation utilization equipment. Some examples prove that the uncertainty of the prediction of solar radiation provides more value than deterministic ones in the management of power systems. This study appraises the potential of random forest (RF), V-support vector regression (V-SVR), and a resilient backpropagation artificial neural network (Rprop-ANN) for daily global solar radiation (DGSR) point prediction from average relative humidity (RHU), daily average temperature (AT), and daily sunshine duration (SD). To acquire more accurate predictions of DGSR and examine the influence of historical DGSR on the performance of point prediction models, two different model inputs are considered: (1) three meteorological variables and (2) the lags of DGSR and three meteorological variables. Then, two interval prediction methods are developed by introducing the KDE to out-of-bag (OOB), introducing kernel density estimation (KDE) to split conformal (SC) based on the three machine learning models. The two methods for interval prediction are denoted as OOB-KDE and SC-KDE. The mean absolute error (MAE), mean relative error (MRE), and Kendall rank correlation (Kendall) are used to assess the point prediction models. The performance of interval prediction methods is evaluated by the prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and coverage width criteria (CWC). The following conclusions are drawn from this study. First, the V-SVR model performs best with the lowest mean absolute error (MAE) of 0.016 and mean relative error (MRE) of 0.001. Second, the lags of DGSR improve the prediction accuracy by about 30%. Third, the OOB-KDE and SC-KDE methods improved the quality of the prediction interval (PI). OOB-KDE improved CWC by 81%, and SC-KDE improved CWC by 99.99%. Fourth, the best interval prediction result is obtained using the SC-KDE method using the V-SVR model. The average difference between its PICP and prediction interval nominal coverage (PINC) is only 3% of the PINC, and its PINAW is less than 0.007.

Abstract Parkinson’s disease (PD) is the second most common neurodegenerative disease, and mild cognitive impairment (MCI) is a well-established risk factor for the development of dementia in PD. A growing body of evidence suggests that low expression of glucocerebrosidase (GBA) promotes the transmission of α-synuclein (α-Syn) interpolymers and the progression of PD. However, how GBA mutations affect the pathogenesis of PD via abnormal aggregation of α-Syn is unclear, and no clinically valid PD-MCI genetic markers have been identified. Here, we first located a GBA eQTL, rs12411216, by analysing DHS, eQTL SNP, and transcription factor binding site data using the UCSC database. Subsequently, we found that rs12411216 was significantly associated with PD-MCI (P

Parkinson’s disease (PD) is the second most neurodegenerative disease in the world. T cell infiltration in the central nervous system (CNS) has provided insights that the peripheral immune cells participate in the pathogenesis of PD. However, the association between the peripheral immune system and CNS remains to be elucidated. In this study, we analyzed incorporative substantia nigra (SN) expression data and blood expression data using the CIBERSORT to obtain the 22 immune cell fractions and then explored the molecular function to identify the potential key immune cell types and genes of PD. We observed that the proportions of naïve CD4 T cells, gamma delta T cells, resting natural killer (NK) cells, neutrophils in the blood, and regulatory T cells (Tregs) in the SN were significantly different between patients with PD and healthy controls (HCs). We identified p53-induced death domain protein 1 (PIDD1) as the hub gene of a PD-related module. The enrichment score of the neuron-specific gene set was significantly different between PD and HC, and genes in the neuron-related module were enriched in the biological process about mitochondria and synapses. These results suggested that the fractions of naïve CD4 T cells, gamma delta T cells, resting NK cells, and neutrophils may be used as a combined diagnostic marker in the blood, and Tregs in SN may be a potential therapeutic design target for PD.

Background: Abnormal spontaneous neural activity is often found in patients with Parkinson’s disease with mild cognitive impairment (PD-MCI). However, the frequency dependence of neuronal interaction activities, especially the fractional amplitude of low-frequency fluctuation (fALFF) and degree centrality (DC), in PD-MCI is still unclear. Thus, this study aimed to explore the frequency dependence of PD-MCI based on fALFF and DC maps.Methods: Twenty-four patients with PD-MCI, 42 PD patients with normal cognition (PD-NC), and 33 healthy controls (HCs) were enrolled. Neuropsychological assessments and resting-state functional MRI (rs-fMRI) were performed. The fALFF and DC values in the conventional, slow4 and slow5 frequency bands were compared among the groups.Results: In the conventional frequency band, the DC value in the left precentral area was decreased in PD-MCI patients, while that in the right fusiform area was increased in PD-NC patients compared with HCs. Regarding fALFFs, both the PD-MCI and PD-NC patients had decreased values in the right precentral area compared with those of the HCs. The fALFFs did not differ between PD-MCI and PD-NC patients. The fALFF results in the slow4 subfrequency band were consistent with those in the conventional frequency band. In the slow5 band, the DC value in the left middle temporal lobe was higher in PD-MCI patients than in PD-NC patients and was positively correlated with the performance of the PD-MCI patients on the Montreal Cognitive Assessment (MoCA). Additionally, both PD-MCI and PD-NC patients showed lower fALFF values in the bilateral putamen than the HCs, and the fALFF in the bilateral putamen was negatively correlated with the Hoehn and Yahr stages of PD-MCI. The fALFF in the left putamen was negatively correlated with the scores of PD-MCI patients on the Movement Disorder Society-Unified Parkinson Disease Rating Scale Part III (MDS-UPRDS-III).Conclusion: Our results suggested that abnormal neuronal activities, such as fALFF and DC, are dependent on frequency in PD-MCI. Some subfrequency bands could distinguish PD-MCI from PD. Our findings may be helpful for further revealing the frequency-dependent resting functional disruption in PD-MCI.

This study aimed to investigate the influence of serum folate, vitamin B12 (VitB12) levels, and inflammation-based scores on the motor performance status in Parkinson's disease (PD). We retrospectively collected data from 148 consecutive patients with idiopathic PD first admitted to our hospital. We measured whole blood count, albumin, lactate dehydrogenase, C-reactive protein, folate, and VitB12 levels and calculated the inflammation-based scores. The following scales were applied to assess the motor performance status: activity of daily living scale (ADL, the Barthel Index), the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III), and Hoehn–Yahr (H–Y) classification. The geometric mean of folate and VitB12 levels were 11.87 (ng/ml) and 330.52 (pmol/L), respectively. Folate deficiency (serum level < 4.0 ng/ml) and VitB12 deficiency (serum level < 133 pg/ml) were present in 0.7 and 5.4% of the patients, respectively. The mean prognostic nutritional index (PNI) and systemic immune-inflammation index (SII) were 47.78 ± 4.42 and 470.81 ± 254.05, respectively. The multivariate analyses showed that serum VitB12 level (P = 0.002) and SII (P = 0.005) were significant factors for ADL score; serum folate (P = 0.027) and VitB12 (P = 0.037) levels for UPDRS-III score; and serum folate (P = 0.066) and VitB12 (P = 0.017) levels for H–Y classification. The elevated folate level did correlate with greater decline in UPDRS-III score (P = 0.023) and H–Y classification (P = 0.003), whereas there was an obvious increase in ADL score (P = 0.048). SII was negatively associated (P < 0.001) with the ADL score. The three-dimensional drawing, combined with the effect of folate and VitB12 levels, showed that the lowest level of folate was associated with the lowest ADL score and the highest UPDRS-III score and H–Y classification. This study indicates that serum folate, VitB12 levels, and SII are significant factors influencing the motor performance status in patients with PD. SII is negatively associated with ADL. Elevated serum folate level correlates with mild motor impairment in patients with PD.

Abstract Background Macrophage migration inhibitory factor (MIF) is a pro-inflammatory cytokine. Chronic inflammation induced by amyloid β proteins (Aβ) is one prominent neuropathological feature in Alzheimer’s disease (AD) brain. Methods Elisa, Western blot, and immunohistochemical staining analysis were performed to examine the level of MIF protein in CSF and brain tissues. MTT and LDH assays were used to examine the neurotoxicity, and the Morris Water Maze test was performed to examine the cognitive function in the MIF+/−/APP23 transgenic mice. Results MIF expression was upregulated in the brain of AD patients and AD model mice. Elevated MIF concentration was detected in the cerebrospinal fluid of AD patients but not in that of the patients suffering from mild cognitive impairment and vascular dementia. Reduced MIF expression impaired learning and memory in the AD model mice. MIF expression largely associates with Aβ deposits and microglia. The binding assay revealed a direct association between MIF and Aβ oligomers. Neurons instead of glial cells were responsible for the secretion of MIF upon stimulation by Aβ oligomers. In addition, overexpression of MIF significantly protected neuronal cells from Aβ-induced cytotoxicity. Conclusion Our study suggests that neuronal secretion of MIF may serve as a defense mechanism to compensate for declined cognitive function in AD, and increased MIF level could be a potential AD biomarker.

Abstract Background Dopamine agonists (DAs) are efficacious for the treatment of motor and nonmotor symptoms in patients with Parkinson’s disease (PD). The treatment of PD with DAs is often complicated by adverse drug reactions (ADRs) of dopaminergic and non-dopaminergic origins. The DA piribedil is widely used in Asian, European, and Latin American countries; therefore, its ADRs are pertinent to clinicians. Here we present a rare case of hypotension and bradycardia that is significantly related to the dosage of piribedil. Case presentation A middle-aged male, diagnosed with PD, received dopamine replacement with piribedil. When taking 50 mg piribedil daily dose, the patient didn’t feel any discomfort. Two hours after taking 100 mg piribedil he presented with serious concomitant hypotension and bradycardia with a blood pressure (BP) reading of 85/48 mmHg and a heart rate (HR) of 45 beats/min when sitting. After taking 75 mg piribedil, the patient showed the same symptoms with BP reading at 70/45 mmHg and HR of 47 beats/min in the same position. Upon replacing treatment with pramipexole 0.125 mg, 0.25 mg, and 0.375 mg three times a day, no further cardiovascular effects persisted. Conclusions No studies have previously reported the simultaneous observation of position-unrelated hypotension and bradycardia after taking small doses of piribedil. More studies are needed to explore the effects of DAs on BP and HR, especially piribedil. Piribedil is efficacious for the treatment of PD, but it is important to weigh the potential risk of hypotension and bradycardia against the clinical benefits of this drug.

Abstract Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986–2005) and future periods (2016–2035 and 2046–2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5‐SSP3 scenario produces the highest global population exposure for 2046–2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6‐SSP1 scenario produces the highest global GDP exposure for 2046–2065, with a 5.56‐fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $‐days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64–77% and 78–91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.

Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease worldwide and involves deficiencies in alpha-synuclein (α-Syn) degradation. Effective therapeutic strategies for PD are urgently needed. L-asparaginase (L-ASNase) has been developed for therapeutic applications in many fields because it catalyzes the hydrolysis of asparagine and glutamine in cancer cells, which may also activate autophagy and induce the degradation of accumulated α-Syn. However, the efficacy and related mechanism of L-ASNase in PD remain poorly understood.Methods: We determined the correlation between L-ASNase and autophagic degradation of α-Syn in a cell model of PD. Mitochondrial function and apoptosis were examined in the presence or absence of L-ASNase. Then, we applied GC-MS/MS targeted amino acid metabolomics analysis to validate the amino acid regulation induced by L-ASNase treatment. Glutamine was added to verify whether the neuroprotective effect was induced by deprivation of glutamine. α-Syn-related autophagy and mitochondrial fusion/fission dynamics were detected to explore the mechanism of L-ASNase-based therapy in PD.Results: L-ASNase activated the autophagic degradation of α-Syn in a cell model of PD without cytotoxicity at specific concentrations/times. Under these conditions, L-ASNase showed substantial neuroprotective effects, including improvements in mitochondrial function and decreased apoptosis. Through GC-MS/MS targeted analysis, glutamine metabolism was identified as the target of L-ASNase in PD treatment, and the neuroprotective effect of L-ASNase was reduced after glutamine supplementation.Conclusions: Our study demonstrated for the first time that L-ASNase had a neuroprotective effect on a cell model of PD through a moderate deprivation of glutamine, which induced autophagic activation and mitochondrial fusion. Therefore, we demonstrated that L-ASNase could be a promising and effective drug for PD treatment.

Abstract Soil moisture is a key factor affecting crop growth and crop yield. Information on soil moisture is critical for crop growth monitoring and yield estimation. This study evaluated the OPtical TRApezoid Model (OPTRAM) on soil moisture estimates in the Sanjiang Plain, China, using Moderate Resolution Imaging Spectroradiometer (MODIS) data and in situ soil moisture data acquired from May to September in 2016 and 2017. This model was empirically calculated by parameterizing the relationship between the normalized difference vegetation index (NDVI) and shortwave infrared transformed reflectance (STR). In addition, the precipitation data were collected and used for verification of the results. According to the scatterplots of STR and NDVI, OPTRAM can estimate soil moisture from July to September, while it's inapplicable in May and June. Evaluation results indicated that OPTRAM‐based soil moisture estimates provide overall RMSE from 0.05 to 0.13 cm3/cm3, bias from −0.11 to 0.06 cm3/cm3, and R2 from 0.10 to 0.50, respectively, for all investigated sites. The performance of OPTRAM compared with the precipitation data showed good agreement. In conclusion, OPTRAM could be used to estimate soil moisture with reasonable accuracy in most areas from July to September in Sanjiang Plain.

Background and Aim: Gut bacteria play an important role in the pathogenesis of Parkinson's disease (PD). However, the alteration of fecal microbiota in PD with cognitive impairment remains unexplored. This study aimed to explore whether the gut microbiota of patients with PD having mild cognitive impairment (PD-MCI) were different from those with PD having normal cognition (PD-NC) and from healthy controls (HC). Also, the study probed the association between altered gut microbiota and cognitive ability in patients with PD.Methods: The fecal bacteria composition and short-chain fatty acids of 13 patients with PD-MCI, 14 patients with PD-NC, and 13 healthy spouses were analyzed using 16S ribosomal RNA sequencing and gas chromatography–mass spectrometry.Results: Compared with HC, the fecal microbial diversities increased in patients with PD-MCI and PD-NC. After adjusting the influence of age, sex, body mass index, education, and constipation using the statistical method, the relative abundances of two families (Rikenellaceae and Ruminococcaceae) and four genera (Alistipes, Barnesiella, Butyricimonas, and Odoribacter) were found to be higher in the feces of the PD-MCI group compared with the other two groups. Moreover, the abundance of genus Blautia and Ruminococcus decreased obviously in the PD-MCI group compared with the PD-NC group. Further, the abundance of genera Butyricimonas, Barnesiella, Alistipes, Odoribacter, and Ruminococcus negatively correlated with cognition ability.Conclusion: Compared with HC and patients with PD-NC, the gut microbiota of patients with PD-MCI was significantly altered, particularly manifesting in enriched genera from Porphyromonadaceae family and decreased the abundance of genera Blautia and Ruminococcus.

Hemiparkinsonism-hemiatrophy syndrome (HPHA) and hemidystonia-hemiatrophy syndrome (HDHA) are rare movement disorders composed of hemidystonia or hemiparkinsonism that present with unilateral limb, face, trunk, or cerebral hemiatrophy and mostly occur following head trauma or postanoxic events. However, relatively little is known about the pathogenesis of these conditions. In our case series, we present three HPHA patients and one HDHA patient who underwent detailed neuropsychological, radiological, motor, and non-motor functional assessments with a mean follow-up of 2 years. We followed two patients who showed differences in their progression for more than 2 years: one barely progressed with no treatment, and the other exhibited levodopa-induce dyskinesia (LID) and definitive progression while receiving multiple adjunctive therapies. In addition, we performed positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) and 18F-dihydroxyphenylalanine (DOPA) in one HPHA patient who showed bilaterally symmetrical uptake of FDG with no significant increase or decrease in the cerebral hemispheres, including the striatum, but exhibited a significant reduction in the uptake of 18F-DOPA in the contralateral posterior striatum. In this study, we followed HPHA patients who showed different disease courses to explore the clinical characteristics and pathogenesis of HPHA and HDHA and illustrate the clinical heterogeneity of these diseases.

The Xinjiang province of China is vulnerable to drought, but the occurrence of drought varies substantially among different sub-areas. This study investigated drought characteristics in Xinjiang province and its sub-area using the Mann–Kendall trend test, cluster analysis and Morlet wavelet analysis. The results show that drought in Xinjiang is generally becoming less severe, and there is a non-uniform spatial variation of drought, which is especially pronounced for stations in northern Xinjiang. There is a unique spatiotemporal distribution trend of drought in Xinjiang, and the inter-decadal variation of drought shows a gradual shift from the east to the west and then back to the east again over the past 55 years. Northern Xinjiang is becoming wetter at a faster rate compared with that of southern Xinjiang, and it also has a higher occurrence of change point sites (70%). The historical drought situation in Xinjiang is better characterized by three clusters. Cluster 1 is the driest, cluster 2 has a clear alleviating tendency of drought, while cluster 3 shows late occurrence of change point. A broader view of the accumulated variation of drought is formulated in this study, which may help to identify potential droughts to support drought disaster management and mitigation.

The study of the origin of asymmetries in mirror β decay is extremely important to understand the fundamental nuclear force and the nuclear structure. The experiment was performed at the National Laboratory of Heavy Ion Research Facility in Lanzhou (HIRFL) to measure the β-delayed γ rays of 26P by silicon array and Clover-type high-purity Germanium (HPGe) detectors. Combining with results from the β decay of 26P and its mirror nucleus 26Na, the mirror asymmetry parameter δ ( ≡ft+/ft−− 1) was determined to be 46(13)% for the transition feeding the first excited state in the daughter nucleus. Our independent results support the conclusion that the large mirror asymmetry is close to the proton halo structure in 26P.

In forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For this newly emerging task, satellite imagery such as WorldView-2 panchromatic images (WPIs) is used as a potential solution for co-prediction of tree-level multifarious SSVs, with static terrestrial laser scanning (TLS) assumed as a ‘bridge’. The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters, and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models (termed as Model1s and Model2s). In the case of Picea abies, Pinus sylvestris, Populus tremul and Quercus robur in a boreal forest, tests showed that Model1s and Model2s for different tree species can be derived (e.g. the maximum R2 = 0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle.

Objectives: This study was designed to explore changes in cortical thickness in patients with early Parkinson’s disease (PD) at different Hoehn and Yahr (H-Y) stages and to demonstrate the association of abnormally altered brain regions with part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III).Materials and Methods: Sixty early PD patients and 29 age- and gender-matched healthy controls (HCs) were enrolled in this study. All PD patients underwent comprehensive clinical and neuropsychological evaluations and 3.0 T magnetic resonance scanning. Patients with H-Y stage ≤1.5 were included in the mild group, and all other patients were included in the moderate group. FreeSurfer software was used to calculate cortical thickness. We assessed the relationship between UPDRS-III and regional changes in cortical thinning, including the bilateral fusiform and the temporal lobe.Results: The average cortical thickness of the temporal pole, fusiform gyrus, insula of the left hemisphere and fusiform gyrus, isthmus cingulate cortex, inferior temporal gyrus, middle temporal cortex and posterior cingulate cortex of the right hemisphere exhibited significant decreasing trends in HCs group and PD groups (i.e., the mild group and moderate group). After controlling for the effects of age, gender, and disease duration, the UPDRS-III scores in patients with early PD were correlated with the cortical thickness of the left and right fusiform gyrus and the left temporal pole (p < 0.05).Conclusion: The average cortical thickness of specific brain regions reduced with increasing disease severity in early PD patients at different H-Y stages, and the UPDRS-III scores of early PD patients were correlated with cortical thickness of the bilateral fusiform gyrus and the left temporal pole.

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE patterns over the HRB. In this study, we estimated the daily LE across the HRB using the gradient boosting regression tree (GBRT) from global land surface satellite NDVI data, reanalysis data and eddy covariance data. Compared with the random forests (RF) and extra tree regressor (ETR) methods, the GBRT obtains the best results, with R2 = 0.86 and root mean square error (RMSE = 18.1 W/m2. Then, we applied the GBRT algorithm to map the average annual terrestrial LE of the HRB from 2016 to 2018 with a spatial resolution of 0.05°. When compared with the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) LE products, the difference between the terrestrial LE estimated by the GBRT algorithm and the GLASS and MODIS products was less than 20 W/m2 in most areas; thus, the GBRT algorithm was reliable and reasonable for estimating the long-term LE estimation over the HRB.

Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007–2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R2 increased by 0.04–0.26, and RMSE decreased by 2–13.3 W/m2) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R2 increased by 0.04–0.14, and RMSE decreased by 3–8.4 W/m2) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE.

Ocean latent heat flux (LHF) is an essential variable for air–sea interactions, which establishes the link between energy balance, water and carbon cycle. The low-latitude ocean is the main heat source of the global ocean and has a great influence on global climate change and energy transmission. Thus, an accuracy estimation of high-resolution ocean LHF over low-latitude area is vital to the understanding of energy and water cycle, and it remains a challenge. To reduce the uncertainties of individual LHF products over low-latitude areas, four machine learning (ML) methods (Artificial Neutral Network (ANN), Random forest (RF), Bayesian Ridge regression and Random Sample Consensus (RANSAC) regression) were applied to estimate low-latitude monthly ocean LHF by using two satellite products (JOFURO-3 and GSSTF-3) and two reanalysis products (MERRA-2 and ERA-I). We validated the estimated ocean LHF using 115 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA and RAMA). The validation results demonstrate that the performance of LHF estimations derived from the ML methods (including ANN, RF, BR and RANSAC) were significantly better than individual LHF products, indicated by R2 increasing by 3.7–46.4%. Among them, the LHF estimation using the ANN method increased the R2 of the four-individual ocean LHF products (ranging from 0.56 to 0.79) to 0.88 and decreased the RMSE (ranging from 19.1 to 37.5) to 11 W m−2. Compared to three other ML methods (RF, BR and RANSAC), ANN method exhibited the best performance according to the validation results. The results of relative uncertainty analysis using the triangle cornered hat (TCH) method show that the ensemble LHF product using ML methods has lower relative uncertainty than individual LHF product in most area. The ANN was employed to implement the mapping of annual average ocean LHF over low-latitude at a spatial resolution of 0.25° during 2003–2007. The ocean LHF fusion products estimated from ANN methods were 10–30 W m−2 lower than those of the four original ocean products (MERRA-2, JOFURO-3, ERA-I and GSSTF-3) and were more similar to observations.

The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.

The temporal and spatial characteristics of meteorological drought have been investigated to provide a framework of methodologies for the analysis of drought in the Beijing-Tianjin-Hebei metropolitan area (BTHMA) in China. Using the Reconnaissance Drought Index (RDI) as an indicator of drought severity, the characteristics of droughts have been examined. The Beijing-Tianjin-Hebei metropolitan area was divided into 253 grid-cells of 27 × 27km and monthly precipitation data for the period of 1960–2010 from 33 meteorological stations were used for global interpolation of precipitation using spatial co-ordinate data. Drought severity was assessed from the estimated gridded RDI values at multiple time scales. Firstly, the temporal and spatial characteristics of droughts were analyzed, and then drought severity-areal extent-frequency (SAF) annual curves were developed. The analysis indicated that the frequency of moderate and severe droughts was about 9.10% in the BTHMA. Using the SAF curves, the return period of selected severe drought events was assessed. The identification of the temporal and spatial characteristics of droughts in the BTHMA will be useful for the development of a drought preparedness plan in the region.

Potential evapotranspiration (PET) is an important indicator of atmospheric evaporation demand and has been widely used to characterize hydrological change. However, sparse observations of pan evaporation (EP) prohibit the accurate characterization of the spatial and temporal patterns of PET over large spatial scales. In this study, we have estimated PET of China using the Penman-Monteith (PM) method driven by gridded reanalysis datasets to analyze the spatial and decadal variations of PET in China during 1982–2010. The results show that the estimated PET has decreased on average by 3.3 mm per year (p < 0.05) over China during 1982–1993, while PET began to increase since 1994 by 3.4 mm per year (p < 0.05). The spatial pattern of the linear trend in PET of China illustrates that a widely significant increasing trend in PET appears during 1982–2010 in Northwest China, Central China, Northeast China and South China while there are no obvious variations of PET in other regions. Our findings illustrate that incident solar radiation (Rs) is the largest contributor to the variation of PET in China, followed by vapor pressure deficit (VPD), air temperature (Tair) and wind speed (WS). However, WS is the primary factor controlling inter-annual variation of PET over Northwest China.

Objective. The aim of this study was to investigate the cognitive function characteristics of Parkinson’s disease (PD) with sleep disorders. Methods. Consecutive patients with PD (n=96), patients with primary sleep disorders (n=76), and healthy control subjects (n=66) were assessed. The patients with PD were classified into sleep disorder (PD-SD) and non-sleep disorder (PD-NSD) groups. Results. Among 96 patients with PD, 69 were diagnosed with a sleep disorder. There were 38 sleep disorder cases, 31 RBD cases, and 27 NSD cases. On the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and MoCA subtests, patients in the PD-SD, primary sleep disorder, and PD-NSD groups exhibited lower scores than those in the control group. Moreover, the PD-SD patients exhibited more significant cognitive impairment than was observed in the primary sleep disorder patients. In the PD-SD subgroup, the attention scores on the MoCA and on MoCA subtests were lower in the PD with RBD group than in the PD with insomnia group. Conclusion. PD with sleep disorders may exacerbate cognitive dysfunction in patients. PD associated with different types of sleep disorders differentially affects cognitive functions, and patients with PD with RBD exhibited poorer cognitive function than was seen in patients with PD with insomnia.

Satellite-based vegetation indices (VIs) and Apparent Thermal Inertia (ATI) derived from temperature change provide valuable information for estimating evapotranspiration (LE) and detecting the onset and severity of drought. The modified satellite-based Priestley-Taylor (MS-PT) algorithm that we developed earlier, coupling both VI and ATI, is validated based on observed data from 40 flux towers distributed across the world on all continents. The validation results illustrate that the daily LE can be estimated with the Root Mean Square Error (RMSE) varying from 10.7 W/m2 to 87.6 W/m2, and with the square of correlation coefficient (R2) from 0.41 to 0.89 (p < 0.01). Compared with the Priestley-Taylor-based LE (PT-JPL) algorithm, the MS-PT algorithm improves the LE estimates at most flux tower sites. Importantly, the MS-PT algorithm is also satisfactory in reproducing the inter-annual variability at flux tower sites with at least five years of data. The R2 between measured and predicted annual LE anomalies is 0.42 (p = 0.02). The MS-PT algorithm is then applied to detect the variations of long-term terrestrial LE over Three-North Shelter Forest Region of China and to monitor global land surface drought. The MS-PT algorithm described here demonstrates the ability to map regional terrestrial LE and identify global soil moisture stress, without requiring precipitation information.

Despite improvement in motor symptoms, the effect of dopaminergic medications on cognition in patients with Parkinson's disease (PD) is less clear. The purpose of this study was to reveal levodopa-induced acute changes in the functional connectivity of the striatum in patients with PD compared with matched untreated patients and healthy volunteers.Twenty-two patients with PD underwent functional magnetic resonance imaging both ON and OFF dopamine-replacement therapy on two consecutive days. Twenty-eight normal aging volunteers also did them without taking in levodopa. Three caudate seeds and two putamen seeds were selected to calculate functional connectivity intensity.Motor symptoms measured by UPDRS were significantly worse in PD OFF than PD ON. Decreased functional connectivity in PD OFF compared to controls was detected in the following seed regions: dorsal caudate, ventral putamen and dorsal putamen. Increases in connectivity in PD ON compared to controls were found in the primary and supplementary motor areas and the associative prefrontal and parietal regions, while decreases in anterior cingulate, ventromedial prefrontal cortex, and parahippocampal gyrus. For the ventral striatal seeds, decreased connectivity in PD ON compared to PD OFF was found in the ventromedial prefrontal and orbitofrontal regions, dorsolateral prefrontal regions. For the dorsal striatal seeds, increased connectivity in PD ON compared to PD OFF was observed in the primary and secondary motor areas.Our results suggest that levodopa significantly changes the motor and cognitive networks of the cortico-striatal pathways. This knowledge will lead clinicians to survey a broader range of symptoms in determining optimal therapy.

Terrestrial latent heat flux (LE) is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS) data remains a major challenge. In this study, we estimated the daily LE for different plant functional types (PFTs) across North America using three machine learning algorithms: artificial neural network (ANN); support vector machines (SVM); and, multivariate adaptive regression spline (MARS) driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period 2000–2007, all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002–2004 using a spatial resolution of 0.05°, which proved to be useful for estimating the long-term LE over North America.