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IntroductionRituximab (RTX) showed good efficacy and safety for patients with myasthenia gravis. However, the percentage of peripheral CD20+ B cell may be absent for years after low dose of RTX treatment. Persistent hypogammaglobulinemia and opportunistic infection may occur in patients under treatment of RTX with thymoma relapse.Case representationWe report a case of refractory myasthenia gravis. After two doses of 100 mg rituximab, the patient developed transient neutropenia. The peripheral blood CD20+ B cell percentage was 0 more than 3 years. Eighteen months later, the patient's symptoms relapsed with thymoma recurred. She had persistent hypogammaglobulinemia and multiple opportunistic infections.ConclusionIn MG patient under B cell depletion therapy had thymoma relapse, Good's syndrome may induce prolonged B cell depletion, hypogammaglobulinemia and opportunistic infections.

The novel concept of subjective cognitive decline (SCD) in Parkinson's disease (PD) refers to subjective cognitive impairment without concurrent objective cognitive deficits. This study aimed to determine the prevalence and affective correlates of SCD in de novo PD patients.A total of 139 de novo PD patients underwent comprehensive neuropsychological evaluation. PD patients with SCD (PD-SCD) did not meet the diagnostic criteria for mild cognitive impairment in PD (PD-MCI) based on the Movement Disorder Society Level II Criteria and were defined by a Domain-5 Score ≥1 on the Non-Motor Symptoms Questionnaire. Affective symptoms were measured using the Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA).In de novo PD cohort, the prevalence of SCD was 28.1%. PD-SCD patients performed significantly better than PD-MCI patients on tests of five cognitive domains. The more commonly affected domains in PD-SCD patients were memory (28.2%) and attention/working memory (25.6%). Multivariable linear regression analysis revealed that PD-SCD was significantly associated with both HAMD (β = 4.518, 95% CI = 0.754-8.281, p = .019) and HAMA scores (β = 4.259, 95% CI = 1.054-7.464, p = .010). Furthermore, binary logistic regression analysis revealed that higher HAMD (OR = 1.128, 95% CI = 1.019-1.249, p = .020) and HAMA scores (OR = 1.176, 95% CI = 1.030-1.343, p = .017) increased the risk of PD-SCD.Our findings suggest that SCD is highly prevalent in de novo PD patients. The presence of PD-SCD is suggestive of an underlying affective disorder.

Although various studies have reported a high prevalence of depression among Parkinson's disease (PD) patients, the pathophysiological mechanism of depression in PD (DPD) is still unclear. The core region of the reward network, the ventral striatum (VS), is critical in the occurrence and development of DPD. This study aimed to explore the altered functional connectivity (FC) of VS subregions in DPD. We recruited 20 DPD patients, 37 non-depressed PD (NDPD) patients, and 41 healthy controls (HC) matched in age, gender, and years of education. The patients' diagnosis with PD was de-novo. We then used resting-state functional magnetic resonance imaging to detect the FC differences of VS subregions among these groups. The FC between the left ventral caudate (vCa_L) and the left middle occipital gyrus (MOG.L) was significantly increased in DPD than in NDPD patients or HC. Compared with HC, NDPD patients exhibited significantly increased FCs between bilateral ventromedial putamen and the left paracentral lobule, the right ventromedial putamen (vmPu_R), and the right precentral gyrus, the vmPu_R, and the left precuneus. Besides, a significant negative correlation was found between the FC values of the vCa_L with the MOG.L and the HAMD-17 scores in the DPD group. The hyperconnectivity between vCa_L and the MOG.L might be viewed as a compensatory mechanism for depression in the early stage of PD. This study provides new insight into the neural mechanism of depression in the early stage of PD and contributes to explore the potential neuroimaging markers for DPD.

Variants in the glucocerebrosidase (GBA) gene are recognized as a common and important genetic risk factor for Parkinson disease (PD). However, the impact of variant severity on the clinical phenotype of PD in the Chinese population remains unclear. Thus, the present study aimed to determine the frequency of GBA-related PD (GBA-PD) and the relationship of GBA variant severity with clinical characteristics in a large Chinese cohort.Long-range polymerase chain reaction and next generation sequencing were performed for the entire GBA gene. GBA variant severity was classified into five classes: mild, severe, risk, complex, and unknown.Among the total 737 PD patients, 47 GBA variants were detected in 79 (10.72%) patients, and the most common GBA variants were R163Q, L444P, and R120W. Complete demographic and clinical data were obtained for 673 patients, which revealed that 18.50% of early onset PD patients had GBA variants. Compared with patients without GBA variants, GBA-PD patients experienced PD onset an average of 4 years earlier and had more severe motor and nonmotor symptoms. Patients carrying severe and complex variants had a higher burden of nonmotor symptoms, especially depression, and more mood/cognitive and gastrointestinal symptoms than patients carrying mild variants.GBA-PD is highly prevalent in the Chinese population. The severity of GBA variants underlies distinct phenotypic spectrums, with PD patients carrying severe and complex variants seeming to have similar phenotypes. PD patient stratification by GBA variant severity should become a prerequisite for selecting specific treatments.

The pathogenesis of cognitive impairment in Parkinson's disease (PD) patients remains unclear, and there is no ideal diagnostic tool available at present. We assessed integrated clinical features with plasma and multi-modal neuroimaging biomarkers to identify mild cognitive impairment (MCI) in early drug-naive PD patients. 49 early drug-naive PD patients, including 26 with MCI (PD-MCI) and 23 with normal cognition (PD-NC), and 20 controls were recruited. Plasma markers [α-synuclein, beta-amyloid 1-40 (Aβ40), beta-amyloid 1-42 (Aβ42), and phosphorylated Tau 181 (p-Tau181) levels], functional connectivity (FC) of the default mode network, and cortical thickness (CTh) were evaluated to identify PD-MCI. The PD-MCI group had significantly higher plasma p-Tau181 levels and p-Tau181/Aβ42 ratio and lower Aβ42/Aβ40 ratio compared to the PD-NC group. Compared to PD-NC, the PD-MCI group showed increased FC between left posterior cingulate cortex (pCC) and the left parahippocampal gyrus (PHG), and between the right hippocampal formation and the left anterior cingulate and paracingulate gyri, and the right middle temporal gyrus. Additionally, the PD-MCI group had thinner cortex thickness in the right lateral occipital and frontal pole compared to the PD-NC group. The final model combining clinical characteristics and several variables (age, sex, plasma p-Tau181 level, Aβ42/Aβ40 ratio, the right lateral occipital CTh, and the FC value between the left pCC and left PHG) had the highest diagnostic accuracy for PD-MCI (AUC = 0.987, 95% CI 0.903-1.000

From the perspective that the change in population age structure could affect human capital accumulation, this paper introduces a concept of “the potential growth speed in human capital stock” and discusses the future of China’s human capital growth from different aspects. This paper uses the perpetual inventory method and China’s sixth national population census data to predict the maximum potential space for China’s human capital stock growth in the future. Firstly, we use the average years of schooling of the working-age population as an index to measure the human capital stock. Though decomposing the differences in human capital stock, we introduce the concept of potential growth speed in human capital stock. Secondly, by decomposing accumulation rate of human capital stock in China, differences of human capital stock between China and South Korea, and differences of human capital stock between China and Japan, this paper finds that the age structure change will have a negative factor on China’s accumulation of human capital in the next 20 years. To conclude, China will probably accumulate human capital at a much faster rate until 2040, but the human capital growth potential is fully exploited on the condition of that.

Background and Purpose Little is known about non-motor symptoms (NMSs) associated with the postural instability and gait difficulty (PIGD) phenotype, especially in de novo Parkinson’s disease (PD) patients. The aims of this study were to compare NMSs between the tremor dominant (TD) and PIGD phenotypes in de novo PD patients and to determine factors that are associated with the PIGD subtype. Patients and Methods In a cross-sectional study conducted at our single center, 226 de novo PD patients with a median disease duration of 2 years were recruited. Data, including comprehensive demographics, motor subtypes and NMSs were obtained. Motor subtypes were classified as PIGD and non-PIGD (TD and indeterminate) by Jankovic’s method. NMSs were evaluated by the non-motor symptoms questionnaire (NMSQuest). Results We identified 73 (32.3%), 34 (15.0%) and 119 (52.7%) patients with TD, intermediate and PIGD subtypes, respectively. Patients with the PIGD subtype had poorer ADL, motor, depression, anxiety, sleep, and non-motor scores compared with those with the TD subtype. In the NMSQuest, the prevalence of cardiovascular, sleep, mood/cognitive and miscellaneous domains was increased in patients with the PIGD subtype compared with patients with the TD subtype. Multivariable forward stepwise logistic regression revealed that the Hamilton Depression Scale (HAMD) [odds ratio (OR), 1.059; 95% confidence interval (CI), 1.016–1.104, p = 0.007] and pain (OR, 3.175; 95% CI, 1.695–5.947, p < 0.001) exhibit significant discriminative power in differentiating PIGD and non-PIGD groups. Conclusion The PIGD group had more severe cardiovascular symptoms, sleep impairments, mood disturbances and pain. We demonstrated for the first time that pain was associated with the PIGD phenotype. Prompt detection and early treatment of NMSs related to the PIGD phenotype may improve patient outcomes.

Background and objective Zishen Pingchan granule (ZPG), a traditional Chinese herbal recipe for treating Parkinson’s disease (PD), is usually used as an add-on drug with some antiparkinsonian drugs in China. The objectives of this study were to evaluate the efficacy, safety, and tolerability of ZPG combined with pramipexole in the treatment of depression in PD (dPD). Methods A 12-week, multicenter, randomized, double-blind, and placebo-controlled study on ZPG was performed on a total of 200 patients who were treated with pramipexole but still had mild to moderate depressive symptoms. Patients were randomly divided into ZPG (n = 100) or placebo (n = 100). The primary effective result was the mean change from the baseline on the Hamilton Depression Scale 17 items (HAM-D-17) over 12 weeks and the clinical efficacy rate. Secondary endpoints were the mean change from the baseline in the Geriatric Depression Scale (GDS-15), Unified Parkinson's disease rating scale Part III (UPDRS III), Parkinson's quality of life scale (PDQ-8), and Parkinson's disease sleep scale (PDSS-2) over 12 weeks. Results After 12 weeks of treatment, ZPG significantly reduced the mean [95% confidence interval] HAMD score vs. placebo (− 1.43 scores [− 2.50, − 0.36]; p = 0.009). The clinical remission rate and responders of the ZPG group were higher than those of the placebo (46.1% vs. 31.0%; p = 0.041; 34.8% vs. 18.4%; p = 0.014). A significant improvement in the PDSS-2 score was also observed in the ZPG group compared with that in the placebo group (− 3.56 scores [− 5.77, − 1.35]; p = 0.002). A total of 7 patients (7.1%) in the ZPG group had mild adverse events (AEs) vs 9 patients (9%) in the placebo group. No severe AEs were observed in either group. The randomization and controlled clinical study revealed that ZPG was effective, safe, and well-tolerated. Conclusion ZPG combined with pramipexole further reduced the depressive symptoms and improved the sleeping quality of PD patients. Trial registration The protocol was retrospectively registered at the Chinese Clinical Trial Registry, Unique identifier: ChiCTR1800019942, date of registration: December 9, 2018; http://www.chictr.org.cn/showproj.aspx?proj=30432

Background: Acute disseminated encephalomyelitis is an acute onset, rapid progress immune-mediated demyelinating disease of the central nervous system. In 50-80% of the patients, cerebrospinal fluid analysis was abnormal. Some studies suggest that molecular simulation mechanism or direct infection of the central nervous system can cause autoimmune tolerance damage, resulting in perivascular inflammatory response and paraventricular demyelination in acute disseminated encephalomyelitis. Both the two cause the activation of T lymphocytes, leading to the imaging lesions and clinical manifestations.Case presentation: Herein we report an old man who presented acute disseminated encephalomyelitis without prodromal infection or vaccination injection history. We focused on the changes of lymphocyte subsets in cerebrospinal fluid and peripheral blood during the course of the disease to explore the potential immunological mechanisms of the disease. Conclusion: We found that cerebrospinal fluid and peripheral blood lymphocyte counts were significantly higher in patients during the acute phase of the disease, with a predominance of T lymphocytes. While methylprednisolone therapy plays a powerful role in suppressing T cell activation and autoimmune diseases, but the detailed molecular mechanisms still need further investigation.

BackgroundDepression is one of the most prevalent and disturbing non-motor symptoms in Parkinson’s disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics in de novo patients with PD with depression (dPD).MethodsWe performed dFC analysis on the data of resting-state functional MRI from 21 de novo dPD, 34 de novo patients with PD without depression (ndPD), and 43 healthy controls (HCs). Group independent component analysis, a sliding window approach, followed by k-means clustering were conducted to assess functional connectivity states (which represented highly structured connectivity patterns reoccurring over time) and temporal properties for comparison between groups. We further performed dynamic graph-theoretical analysis to examine the variability of topological metrics.ResultsFour distinct functional connectivity states were clustered via dFC analysis. Compared to patients with ndPD and HCs, patients with dPD showed increased fractional time and mean dwell time in state 2, characterized by default mode network (DMN)-dominated and cognitive executive network (CEN)-disconnected patterns. Besides, compared to HCs, patients with dPD and patients with ndPD both showed weaker dynamic connectivity within the sensorimotor network (SMN) in state 4, a regionally densely connected state. We additionally observed that patients with dPD presented less variability in the local efficiency of the network.ConclusionsOur study demonstrated that altered network connection over time, mainly involving the DMN and CEN, with abnormal dynamic graph properties, may contribute to the presence of depression in patients with PD.

BackgroundGenetic factors play an important role in the pathogenesis of early-onset Parkinson’s disease (EOPD). To date, more than 20 pathogenic genes associated with Parkinson’s disease (PD) have been identified. This study aims to explore the mutation spectrum of EOPD and the clinical characteristics of mutation carriers in eastern China.MethodsWe recruited 155 unrelated EOPD patients, including 8 familial and 147 sporadic EOPD (age at onset ≤ 50 years). Overall, 24 known PD-associated genes were detected by whole exome sequencing and multiplex ligation-dependent probe amplification (MLPA) from patient samples. The genetic and clinical characteristics of pathogenic/likely pathogenic (P/LP) loci in this cohort were analyzed.ResultsOverall, 14 (9.03%) patients were detected with P/LP variants distributed in seven genes. The most frequent mutation occurred in PRKN (7/155, 4.52%), followed by LRRK2 (2/155, 1.29%), SNCA, CHCHD2, TMEM230, DNAJC13 and PLA2G6 (1/155, 0.64%, respectively). Exon rearrangement mutations accounted for 57.9% (11/19) of all mutations in PRKN. Four novel variants were detected: c.14T > C (p.M5T) in SNCA, c.297C > A (p.Y99X) in CHCHD2, c.2578C > T (p.R860C) in DNAJC13 and c.4C > T (p.Q2X) in TMEM230. We found the first case of LRRK2 c.6055G > A (p.G2019S) mutation in Chinese population. The median onset age of patients with P/LP mutations in autosomal recessive genes (PRKN and PLA2G6) was about 18.0 years earlier than patients without mutation. The proportion of patients with mutations were 63.64%, 27.03% and 9.68% when patients were stratified according to the age of onset at ≤ 30, ≤ 40 and ≤ 50 years, respectively.ConclusionEarly-onset Parkinson’s disease patients from eastern China present a regional specific mutation spectrum. Analysis of larger patient cohorts is required to support these findings, and mechanistic studies of the four novel missense/non-sense mutations will clarify their role in the pathogenicity of EOPD.

Depression, one of the most frequent non-motor symptoms in Parkinson's disease (PD), was proposed to be related to neural network dysfunction in advanced PD patients. However, the underlying mechanisms in the early stage remain unclear. The study was aimed to explore the alterations of large-scale neural networks inWe performed independent component analysis (ICA) on the data of resting-state functional magnetic resonance imaging from 21We observed an ordered decrease of connectivity among groups within the ventral attention network (VAN) (dPDndPDHCs), mainly located in the left middle temporal cortex. Besides, dPD patients exhibited hypoconnectivity between the auditory network (AUD) and default mode network (DMN) or VAN compared to ndPD patients or healthy controls. Correlation analysis revealed that depression severity was negatively correlated with connectivity value within VAN and positively correlated with the connectivity value of AUD-VAN in dPD patients, respectively. Further analysis showed that the area under the curve (AUC) for dPD prediction was 0.863 when combining the intranetwork connectivity in VAN and internetwork connectivity in AUD-DMN and AUD-VAN.Our results demonstrated that early dPD may be associated with abnormality of attention bias and especially auditory attention processing. Altered neural network connectivity is expected to be a potential neuroimaging biomarker to predict depression in PD.

Objective The International Parkinson and Movement Disorder Society (MDS) has published research criteria for prodromal Parkinson's disease (pPD), which includes cognitive impairment as a prodromal marker. However, the clinical features of mild cognitive impairment (MCI) in pPD remain unknown. Our study aimed to evaluate the frequency and clinical features of mild cognitive impairment of pPD in the elderly in China. Methods The cross‐sectional community‐based study recruited 2688 participants aged ≥50 years. Subjects were diagnosed with pPD according to the MDS criteria. Overall, 39 pPD and 22 healthy controls underwent comprehensive clinical and neuropsychological assessment. MCI was also diagnosed by the MDS criteria. Next, we investigated the relationship between clinical factors and cognition. Results Among the 2,663 dementia‐free and Parkinson disease (PD)‐free participants, 55 met the criteria for pPD (2.1%) and 23 pPD met the criteria for MCI. Memory, attention/working memory, and executive function were the most frequent impaired domains, and amnestic MCI multidomain phenotype was the most frequent MCI subtype (69.57%) in pPD. Additionally, correlation analysis revealed that the global cognitive performance was negatively related to UPDRS‐III score (r = −0.456, p = 0.004). Conclusion MCI, specifically impairment in memory, attention/working memory, and executive domain, is present at the prodromal stage of PD. In addition, cognitive performance is correlated with motor symptoms in pPD. Our results reflect that cognitive profile, combined with motor symptoms, can help clinicians to identify individuals with pPD early, as those would be the optimal candidates for neuroprotective therapy., Mild cognitive impairment, specifically impairment in memory, attention/working memory, and executive, is present in prodromal PD in an elderly population of China. In addition, cognitive performance is correlated with motor symptoms in prodromal PD. The results reflect that cognitive profile, combined with motor symptoms, can help clinicians to identify individuals with prodromal PD early, as those would be the optimal candidates for neuroprotective therapy.

Depression is one of the most prevalent and disturbing non-motor symptoms in Parkinson's disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics inWe performed dFC analysis on the data of resting-state functional MRI from 21Four distinct functional connectivity states were clusteredOur study demonstrated that altered network connection over time, mainly involving the DMN and CEN, with abnormal dynamic graph properties, may contribute to the presence of depression in patients with PD.

Convolutional neural networks have been successfully applied to detect salient objects in an image. However, how to better use convolutional features for co-saliency detection, which is an emerging branch of saliency detection, is not fully explored. This paper proposes a convolutional neural network based co-saliency detection model, which consists of two key parts including the integration of multi-layer convolutional features extracted from a group of images and the inter-image saliency propagation. Firstly, the input image and its four co-images belonging to the same image category are passed through the VGG16 model, to obtain the multi-layer convolutional features of these images. Secondly, multi-scale synthesized feature maps, which contain both internal features and correlative features, are generated by integrating the multi-layer convolutional features. Thirdly, via the integration of low-level boundary features and high-level semantic features, the multi-scale synthesized feature maps are enhanced and fused together to generate the initial co-saliency map. Finally, an inter-image saliency propagation method is utilized to refine the initial co-saliency map, yielding the final co-saliency map with the improved quality. Experimental results on two public datasets demonstrate that the proposed model achieves the best performance compared to the state-of-the-art co-saliency detection models.

Circulating microRNAs (miRNAs) have been proposed to be accessible biomarkers for Parkinson’s disease (PD). However, there is a lack of known miRNAs that can serve as biomarkers for prodromal PD (pPD). We previously identified that miR-31 and miR-214 were dysregulated in PD. The aim of this study was to explore the roles of miR-31 and miR-214 in pPD. We recruited 25 pPD patients, 20 patients with de novo PD (dnPD), 24 advanced PD (aPD) patients and 21 controls. Next, we investigated the expression of miR-31 and miR-214. Compared to controls, miR-214 was found to be significantly upregulated in pPD patients while miR-31 was significantly upregulated in aPD patients. In addition, the expression of miR-214 was lower in aPD patients compared to both dnPD or pPD patients, while the expression of miR-31 was higher in aPD patients compared to dnPD patients. In order to predict pPD via miRNA expression, the receiver operating characteristic curve was constructed and the area under curve (AUC) was calculated. For pPD prediction by miR-214, the AUC was 0.756. The optimal cut-off value of miR-214 was 0.1962, and the sensitivity and specificity were 72.0% and 76.2%, respectively. On the other hand, the AUC for aPD detection by miR-31 was 0.744. The optimal cut-off value for miR-31 was 0.0148, with a sensitivity of 87.5% and a specificity of 71.4%. In conclusion, miR-214 can distinguish pPD patients from controls and may be used as a potential biomarker for pPD diagnosis.

The insula, consisting of functionally diverse subdivisions, plays a significant role in Parkinson’s disease (PD)-related cognitive disorders. However, the functional connectivity (FC) patterns of insular subdivisions in PD remain unclear. Our aim is to investigate the changes in FC patterns of insular subdivisions and their relationships with cognitive domains. Three groups of participants were recruited in this study, including PD patients with mild cognitive impairment (PD-MCI, n = 25), PD patients with normal cognition (PD-NC, n = 13), and healthy controls (HCs, n = 17). Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in insular subdivisions of the three groups. Moreover, all participants underwent a neuropsychological battery to assess cognition so that the relationship between altered FC and cognitive performance could be elucidated. Compared with the PD-NC group, the PD-MCI group exhibited increased FC between the left dorsal anterior insular (dAI) and the right superior parietal gyrus (SPG), and altered FC was negatively correlated with memory and executive function. Compared with the HC group, the PD-MCI group showed significantly increased FC between the right dAI and the right median cingulate and paracingulate gyri (DCG), and altered FC was positively related to attention/working memory, visuospatial function, and language. Our findings highlighted the different abnormal FC patterns of insular subdivisions in PD patients with different cognitive abilities. Furthermore, dysfunction of the dAI may partly contribute to the decline in executive function and memory in early drug-naïve PD patients.

Objective: The aims of this study were to compare the characteristics of three motor subtype classifications in patients with de novo Parkinson's disease (PD) and to find the most suitable motor subtype classification for identifying non-motor symptoms (NMSs).Methods: According to previous studies, a total of 256 patients with de novo PD were classified using the tremor-dominant/mixed/akinetic-rigid (TD/mixed/AR), TD/indeterminate/postural instability and gait disturbance (PIGD), and predominantly TD/predominantly PIGD (p-TD/p-PIGD) classification systems.Results: Among the TD/mixed/AR subgroups, the patients with the AR subtype obtained more severe motor scores than the patients with the TD subtype. Among the TD/indeterminate/PIGD subgroups and between the p-TD and p-PIGD subgroups, the patients with the PIGD/p-PIGD subtype obtained more severe scores related to activities of daily living (ADL), motor and non-motor symptoms, including depression, anxiety, and sleep impairment, than the patients with the TD/p-TD subtype. Furthermore, symptoms in the cardiovascular, gastrointestinal, and miscellaneous domains of the Non-motor Questionnaire (NMSQuest) were more prevalent in the patients with the PIGD/p-PIGD subtypes than the patients with the TD/p-TD subtypes.Conclusions: The PIGD/p-PIGD subtypes had more severe ADL, motor and non-motor symptoms than the TD/p-TD subtypes. We disclosed for the first time that the TD/indeterminate/PIGD classification seems to be the most suitable classification among the three motor subtype classifications for identifying NMSs in PD.

Objective Patients with Parkinson’s disease (PD) are commonly classified into subtypes based on motor symptoms. The aims of the present study were to determine the consistency between PD motor subtypes, to assess the stability of PD motor subtypes over time, and to explore the variables influencing PD motor subtype stability. Methods This study was part of a longitudinal study of de novo PD patients at a single center. Based on three different motor subtype classification systems proposed by Jankovic, Schiess, and Kang, patients were respectively categorized as tremor-dominant/indeterminate/postural instability and gait difficulty (TD/indeterminate/PIGD), TDS/mixedS/akinetic-rigidS (ARS), or TDK/mixedK/ARK at baseline evaluation and then re-assessed 1 month later. Demographic and clinical characteristics were recorded at each evaluation. The consistency between subtypes at baseline evaluation was assessed using Cohen’s kappa coefficient (κ). Additional variables were compared between PD subtype groups using the two-sample t-test, Mann–Whitney U-test or Chi-squared test. Results Of 283 newly diagnosed, untreated PD patients, 79 were followed up at 1 month. There was fair agreement between the Jankovic, Schiess, and Kang classification systems (κS = 0.383 ± 0.044, κK = 0.360 ± 0.042, κSK = 0.368 ± 0.038). Among the three classification systems, the Schiess classification was the most stable and the Jankovic classification was the most unstable. The non-motor symptoms questionnaire (NMSQuest) scores differed significantly between PD patients with stable and unstable subtypes based on the Jankovic classification (p = 0.008), and patients with a consistent subtype had more severe NMSQuest scores than patients with an inconsistent subtype. Conclusion Fair consistency was observed between the Jankovic, Schiess, and Kang classification systems. For the first time, non-motor symptoms (NMSs) scores were found to influence the stability of the TD/indeterminate/PIGD classification. Our findings support combining NMSs with motor symptoms to increase the effectiveness of PD subtypes.

Co-saliency detection, as a developing research branch of saliency detection, devotes to identify the common salient objects in a group of related images. The major challenge of co-saliency detection is how to effectively represent features considering both intra-image and inter-image information. In this paper, we propose a co-saliency detection model using collaborative feature extraction and high-to-low feature integration. We first feed the target image and its co-images into the Individual Feature Extraction Module (IFEM) to produce multi-level individual features. Then, to capture the collaborative inter-image information, the Collaborative Feature Extraction Module (CFEM) is applied to all highest-level individual features, generating the collaborative feature. Finally, we build a High-to-low Feature Integration Module (HFIM), which integrates the collaborative feature and multi-level individual features of the target image, to enrich the collaborative feature with individual intra-image information. Extensive experiments on two public datasets demonstrate that the proposed model achieves the state-of-the-art performance.

In recent years, air pollution has received serious concerns from researchers, media, and the public sectors, but air pollution from agricultural production activities has not received enough attention. This paper focuses on agricultural air pollution in central China, which is aggravated by the ongoing rural labor migration trend. With a set of panel data released from Hubei and Hunan provinces in China, we adopt the mediating effect model to explore the relationship between rural labor migration and air pollution caused by agricultural activity in China. First, we use the inventory analysis method and principal component analysis method to calculate the comprehensive index of the air pollution of agriculture in 152 counties and districts from Hubei and Hunan provinces, and we empirically test the impact of labor migration on air pollution with a mediating effect model as well as carry out regional heterogeneity analysis on the pollution effect of these two provinces mentioned above. The analysis above indicates that the increase of labor migration has intensified the comprehensive index of air pollution caused by agricultural activity by changing the supply of labor force in the agricultural sector, the budget line of rural residents, and the scale of agricultural production and crop planting structure, but there is a difference in the indirect total effect between the two provinces mentioned above according to our regional heterogeneity analysis. This study is a necessary extension to studies on alleviating and controlling air pollution in China.

This paper proposes a saliency integration approach via the use of similar images to elevate saliency detection performance. Given the input image, a group of similar images are first retrieved, and meanwhile, the corresponding multiple saliency maps of the input image are generated by using existing saliency models. Then, the saliency fusion map is generated by using an adaptive fusion method to integrate such saliency maps, for which the fusion weights are measured by the corresponding similarity between each similar image and the input image. Next, an inter-image graph, for each pair of input image and similar image, is constructed to propagate the confident saliency values from the similar image to the input image, yielding the saliency propagation map. Finally, the saliency fusion map and the saliency propagation map are integrated to obtain the final saliency map. Experimental results on two public datasets demonstrate that the proposed approach achieves the better saliency detection performance compared to the existing saliency models and other saliency integration approaches.

This paper proposes an effective two-stage saliency fusion method to generate the fusion map, which is used as a prior for object segmentation. Given multiple saliency maps generated by different saliency models, the first stage is to produce two fusion maps based on average and min-max statistics, respectively. The second stage is to perform the Fourier transform (FT) on the two fusion maps, and to combine the amplitude spectrum of average fusion map and the phase spectrum of min-max fusion map, so as to reform the spectrum, and the final fusion map is obtained by using the inverse FT on the reformed spectrum. Last, object segmentation is performed under graph cut by using the final fusion map as a prior. Extensive experiments on three public datasets demonstrate that the proposed method facilitates to achieve the better object segmentation performance compared to using individual saliency map and other fusion methods.

Showers with large zenith angles are observed in emulsion chambers exposed at Mt.Kanbala.The intensity of high energy muons is given and the multicore showers with large zenith angles are found.It is indicated that a new phenomenon may exist in the high energy nuclear interactions of cosmic rays.