Tremor-associated short tandem repeat intermediate and pathogenic expansions in familial essential tremor.

There is an obvious clinical-pathological overlap between essential tremor and some known tremor-associated short tandem repeat expansion disorders. The aim is to analyse whether these short tandem repeat genes, including ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, ATXN8OS, ATXN10, PPP2R2B, TBP, BEAN1, NOP56, DAB1, ATN1, SADM12 and FMR1, are associated with familial essential tremor patients. Genetic analysis of repeat sizes in tremor-associated short tandem repeat expansions was performed in a large cohort of 515 familial essential tremor probands and 300 controls. The demographic and clinical features among carriers of pathogenic expansions, intermediate repeats and non-carriers were compared. A total of 18 out of 515 (18/515, 3.7%) patients were found to have repeats expansions, including 12 cases (12/515, 2.5%) with intermediate repeat expansions (one ATXN1, eight TBP, two FMR1, one ATN1), and six cases (6/515, 1.2%) with pathogenic expansions (one ATXN1, one ATXN2, one ATXN8OS, one PPP2R2B, one FMR1, one SAMD12). There were no statistically significant differences in intermediate repeats compared to healthy controls. Furthermore, there were no significant differences in demographics and clinical features among individuals with pathogenic expansions, intermediate repeat expansions carriers and non-carriers. Our study indicates that the intermediate repeat expansion in tremor-associated short tandem repeat expansions does not pose an increased risk for essential tremor, and rare pathogenic expansion carriers have been found in the familial essential tremor cohort. The diagnosis of essential tremor based solely on clinical symptoms remains a challenge in distinguishing it from known short tandem repeat expansions diseases with overlapping clinical-pathological features.

An Incremental-Self-Training-Guided Semi-Supervised Broad Learning System.

The broad learning system (BLS) has recently been applied in numerous fields. However, it is mainly a supervised learning system and thus not suitable for specific practical applications with a mixture of labeled and unlabeled data. Despite a manifold regularization-based semi-supervised BLS, its performance still requires improvement, because its assumption is not always applicable. Therefore, this article proposes an incremental-self-training-guided semi-supervised BLS (ISTSS-BLS). Distinctive to traditional self-training, where all unlabeled data are labeled simultaneously, incremental self-training (IST) obtains unlabeled data incrementally from an established sorted list based on the distance between the data and their cluster center. During iterative learning, a small portion of labeled data is first used to train BLS. The system recursively self-updates its structure and meta-parameters using: 1) the double-restricted mechanism and 2) the dynamic neuron-incremental mechanism. The double-restricted mechanism is beneficial to preventing the introduction of incorrect pseudo-labeled samples, and the dynamic neuron-incremental mechanism guides the self-updating of the network structure effectively based on the training accuracy of the labeled data. These strategies guarantee a parsimonious model during the update. Besides, a novel metric, the accuracy-time ratio (A/T), is proposed to evaluate the model's performance comprehensively regarding time and accuracy. In experimental verifications, ISTSS-BLS performs outstandingly on 11 datasets. Specifically, the IST is compared with the traditional one on three scales data, saving up to 52.02% learning time. In addition, ISTSS-BLS is compared with different state-of-the-art alternatives, and all results indicate that it possesses significant advantages in performance.

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.

The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.

A Robust Semi-Supervised Broad Learning System Guided by Ensemble-Based Self-Training.

Broad learning system (BLS) with semi-supervised learning relieves label dependence and expands application. Despite some efforts and progress, the semi-supervised BLS still needs improvement, especially in handling imbalanced data or concept drift scenarios for self-training-based methods. To this extent, this article proposes a robust semi-supervised BLS guided by ensemble-based self-training (ESTSS-BLS). Distinctive to self-training that assigns the pseudo-label via a single classifier and confidence, the advocated ensemble-based self-training determines the pseudo-label according to the turnout of multiple BLSs. In addition, label purity is proposed to ensure the correctness and credibility of the auxiliary training data, which is a comprehensive evaluation of the voting. During iterative learning, a small portion of labeled data first trains multiple BLSs in parallel. Then, the system recursively updates its data, structure, and meta-parameters using label purity and a data-driven dynamic nodes mechanism that dynamically guides the network's structural adjustments to solve the concept drift problem caused by a large amount of auxiliary training data. The experimental results demonstrate that ESTSS-BLS exhibits exceptional performance compared to existing methods, with the lowest-time consumption and the highest accuracy, precision, recall, F1 score, and AUC. Exhilaratingly, it achieves an accuracy of 87.84% with only 0.1% labeled data on MNIST, and with just 2% labeled data, it matches the performance of supervised learning using all training data on NORB. In addition, ESTSS-BLS also performs stably on medical or biological data, verifying its high adaptability.

Association Analysis of Essential Tremor-Associated Genetic Variants in Sporadic Late-Onset Parkinson's Disease.

Background: Parkinson's disease (PD) and Essential tremor (ET) are the two most common tremor diseases with recognized genetic pathogenesis. The overlapping clinical features suggest they may share genetic predispositions. Our previous study systematically investigated the association between rare coding variants in ET-associated genes and early-onset PD (EOPD), and found the suggestive association between teneurin transmembrane protein 4 (TENM4) and EOPD. In the current research, we explored the potential genetic interplay between ET-associated genetic loci/genes and sporadic late-onset PD (LOPD). Methods: We performed whole-genome sequencing in the 1962 sporadic LOPD cases and 1279 controls from mainland China. We first used logistic regression analysis to test the top 16 SNPs identified by the ET genome-wide association study for the association between ET and LOPD. Then we applied the optimized sequence kernel association testing to explore the rare variant burden of 33 ET-associated genes in this cohort. Results: We did not observe a significant association between the included SNPs with LOPD. We also did not discover a significant burden of rare deleterious variants of ET-associated genes in association with LOPD risk. Conclusion: Our results do not support the role of ET-associated genetic loci and variants in LOPD. Highlights: 1962 cases and 1279 controls were recruited to study the potential genetic interplay between ET-associated genetic loci/variants and sporadic LOPD.No significant association between the ET-associated SNPs and LOPD were observed.No significant burden of rare deleterious variants of ET-associated gene in LOPD risk were found.

Performance on adsorption of toluene by ionic liquid-modified AC in high-humidity exhaust gas.

Volatile organic compounds (VOCs) frequently pose a threat to the biosphere, impacting ecosystems, flora, fauna, and the surrounding environment. Industrial emissions of VOCs often include the presence of water vapor, which, in turn, diminishes the adsorption capacity and efficacy of adsorbents. This occurs due to the competitive adsorption of water vapor, which competes with target pollutants for adsorption sites on the adsorbent material. In this study, hydrophobic activated carbons (BMIMPF6-AC (L), BMIMPF6-AC (g), and BMIMPF6-AC-H) were successfully prepared using 1-butyl-3-methylimidazolium hexafluorophosphate (BMIMPF6) to adsorb toluene under humidity environment. The adsorption performance and mechanism of the resulting ionic liquid-modified activated carbon for toluene in a high-humidity environment were evaluated to explore the potential application of ionic liquids as hydrophobic modifiers. The results indicated that BMIMPF6-AC-H exhibited superior hydrophobicity. The toluene adsorption capacity of BMIMPF6-AC-H was 1.53 times higher than that of original activated carbon, while the adsorption capacity for water vapor was only 37.30% of it at 27 °C and 77% RH. The Y-N model well-fitted the dynamic adsorption experiments. To elucidate the microscopic mechanism of hydrophobic modification, the Independent Gradient Model (IGM) method was employed to characterize the intermolecular interactions between BMIMPF6 and toluene. Overall, this study introduces a new modifier for hydrophobic modification of activated carbon, which could enhance the efficiency of activated carbon in treating industrial VOCs.

Robust Incremental Broad Learning System for Data Streams of Uncertain Scale.

Due to its marvelous performance and remarkable scalability, a broad learning system (BLS) has aroused a wide range of attention. However, its incremental learning suffers from low accuracy and long training time, especially when dealing with unstable data streams, making it difficult to apply in real-world scenarios. To overcome these issues and enrich its relevant research, a robust incremental BLS (RI-BLS) is proposed. In this method, the proposed weight update strategy introduces two memory matrices to store the learned information, thus the computational procedure of ridge regression is decomposed, resulting in precomputed ridge regression. During incremental learning, RI-BLS updates two memory matrices and renews weights via precomputed ridge regression efficiently. In addition, this update strategy is theoretically analyzed in error, time complexity, and space complexity compared with existing incremental BLSs. Different from Greville's method used in the original incremental BLS, its results are closer to the solution of one-shot calculation. Compared with the existing incremental BLSs, the proposed method exhibits more stable time complexity and superior space complexity. The experiments prove that RI-BLS outperforms other incremental BLSs when handling both stable and unstable data streams. Furthermore, experiments demonstrate that the proposed weight update strategy applies to other random neural networks as well.

Comprehensive variant analysis of phospholipase A2 superfamily genes in large Chinese Parkinson' s disease cohorts.

To clarify the genetic role of phospholipase A2 (PLA2) genes in Parkinson's disease (PD), we performed a genetic association study in large Chinese population cohorts using next-generation sequencing. In this study, we analyzed both rare and common variants of 38 phospholipase A2 genes in two large cohorts. We detected 1558 and 1115 rare variants in these two cohorts, respectively. In both cohorts, we observed suggestive associations between specific subgroups and the risk of PD. At the single-gene level, several genes (PLA2G2D, PLA2G12A, PLA2G12B, PLA2G4F, PNPLA1, PNPLA3, PNPLA7, PLA2G7, PLA2G15, PLAAT5, and ABHD12) are suggestively associated with PD. Meanwhile, 364 and 2261 common variants were identified in two cohorts, respectively. Our study has expanded the genetic spectrum of the PLA2 family genes and suggested potential pathogenetic roles of PLA2 superfamily in PD.

Integrated analysis of gut metabolome, microbiome, and brain function reveal the role of gut-brain axis in longevity.

The role of microbiota-gut-brain axis in modulating longevity remains undetermined. Here, we performed a multiomics analysis of gut metagenomics, gut metabolomics, and brain functional near-infrared spectroscopy (fNIRS) in a cohort of 164 participants, including 83 nonagenarians (NAs) and 81 non-nonagenarians (NNAs) matched with their spouses and offspring. We found that 438 metabolites were significantly different between the two groups; among them, neuroactive compounds and anti-inflammatory substances were enriched in NAs. In addition, increased levels of neuroactive metabolites in NAs were significantly associated with NA-enriched species that had three corresponding biosynthetic potentials: Enterocloster asparagiformis, Hungatella hathewayi and Oxalobacter formigenes. Further analysis showed that the altered gut microbes and metabolites were linked to the enhanced brain connectivity in NAs, including the left dorsolateral prefrontal cortex (DLPFC)-left premotor cortex (PMC), left DLPFC-right primary motor area (M1), and right inferior frontal gyrus (IFG)-right M1. Finally, we found that neuroactive metabolites, altered microbe and enhanced brain connectivity contributed to the cognitive preservation in NAs. Our findings provide a comprehensive understanding of the microbiota-gut-brain axis in a long-lived population and insights into the establishment of a microbiome and metabolite homeostasis that can benefit human longevity and cognition by enhancing functional brain connectivity.

The genetic landscape and phenotypic spectrum of GAA-FGF14 ataxia in China: a large cohort study.

BACKGROUND: An intronic GAA repeat expansion in FGF14 was recently identified as a cause of GAA-FGF14 ataxia. We aimed to characterise the frequency and phenotypic profile of GAA-FGF14 ataxia in a large Chinese ataxia cohort. METHODS: A total of 1216 patients that included 399 typical late-onset cerebellar ataxia (LOCA), 290 early-onset cerebellar ataxia (EOCA), and 527 multiple system atrophy with predominant cerebellar ataxia (MSA-c) were enrolled. Long-range and repeat-primed PCR were performed to screen for GAA expansions in FGF14. Targeted long-read and whole-genome sequencing were performed to determine repeat size and sequence configuration. A multi-modal study including clinical assessment, MRI, and neurofilament light chain was conducted for disease assessment. FINDINGS: 17 GAA-FGF14 positive patients with a (GAA)≥250 expansion (12 patients with a GAA-pure expansion, five patients with a (GAA)≥250-[(GAA)n (GCA)m]z expansion) and two possible patients with biallelic (GAA)202/222 alleles were identified. The clinical phenotypes of the 19 positive and possible positive cases covered LOCA phenotype, EOCA phenotype and MSA-c phenotype. Five of six patients with EOCA phenotype were found to have another genetic disorder. The NfL levels of patients with EOCA and MSA-c phenotypes were significantly higher than patients with LOCA phenotype and age-matched controls (p < 0.001). NfL levels of pre-ataxic GAA-FGF14 positive individuals were lower than pre-ataxic SCA3 (p < 0.001) and similar to controls. INTERPRETATION: The frequency of GAA-FGF14 expansion in a large Chinese LOCA cohort was low (1.3%). Biallelic (GAA)202/222 alleles and co-occurrence with other acquired or hereditary diseases may contribute to phenotypic variation and different progression. FUNDING: This study was funded by the National Key R&D Program of China (2021YFA0805200 to H.J.), the National Natural Science Foundation of China (81974176 and 82171254 to H.J.; 82371272 to Z.C.; 82301628 to L.W.; 82301438 to Z.L.; 82201411 to L.H.), the Innovation Research Group Project of Natural Science Foundation of Hunan Province (2020JJ1008 to H.J.), the Key Research and Development Program of Hunan Province (2020SK2064 to H.J.), the Innovative Research and Development Program of Development and Reform Commission of Hunan Province to H.J., the Natural Science Foundation of Hunan Province (2024JJ3050 to H.J.; 2022JJ20094 and 2021JJ40974 to Z.C.; 2022JJ40783 to L.H.; 2022JJ40703 to Z.L.), the Project Program of National Clinical Research Center for Geriatric Disorders (Xiangya Hospital, 2020LNJJ12 to H.J.), the Central South University Research Programme of Advanced Interdisciplinary Study (2023QYJC010 to H.J.) and the Science and Technology Innovation Program of Hunan Province (2022RC1027 to Z.C.). D.P. holds a Fellowship award from the Canadian Institutes of Health Research (CIHR).

Progress on control of harmful algae by sustained-release technology of allelochemical: A review.

The outbreak of harmful algae blooms caused by water eutrophication seriously jeopardizes the aquatic ecological environment and human health. Therefore, algae control technology has attracted widespread attention between environmental scholars. Allelochemical sustained-release technology which releases the active ingredient to the target medium at a certain rate within the effective time, so that the system maintains a certain concentration, thus prolonging its influence on the target organism. Allelochemical sustained-release technology has become the focus of research due to the characteristics of high efficiency, safety, low-cost, environment friendly and no secondary pollution. This paper reviews the characteristics of allelochemical substances and the status quo of plant extraction, explains the detailed classification of allelochemical sustained-release microspheres (ASRMs) and the application of algae inhibition, summarizes the preparation method of ASRMs, elaborates on the mechanism of algae inhibition of sustained-release technology from the perspective of photosynthesis, cellular enzyme activity, algae cell structure, gene expression, and target site action. Focuses on the summary of the factors influencing the effect of algae inhibition of ASRMs, including particle size of sustained-release microspheres, selection of carrier materials, and the growth stage of algae. The future direction and prospect of algae inhibition by allelochemical sustained-release technology were prospected to provide the scientific basis for water ecological restoration.

Unveiling the role of iPLA2ß in neurodegeneration: From molecular mechanisms to advanced therapies.

Calcium-independent phospholipase A2ß (iPLA2ß), a member of the phospholipase A2 (PLA2s) superfamily, is encoded by the PLA2G6 gene. Mutations in the PLA2G6 gene have been identified as the primary cause of infantile neuroaxonal dystrophy (INAD) and, less commonly, as a contributor to Parkinson's disease (PD). Recent studies have revealed that iPLA2ß deficiency leads to neuroinflammation, iron accumulation, mitochondrial dysfunction, lipid dysregulation, and other pathological changes, forming a complex pathogenic network. These discoveries shed light on potential mechanisms underlying PLA2G6-associated neurodegeneration (PLAN) and offer valuable insights for therapeutic development. This review provides a comprehensive analysis of the fundamental characteristics of iPLA2ß, its association with neurodegeneration, the pathogenic mechanisms involved in PLAN, and potential targets for therapeutic intervention. It offers an overview of the latest advancements in this field, aiming to contribute to ongoing research endeavors and facilitate the development of effective therapies for PLAN.

Genetic analysis of transcription factors in dopaminergic neuronal development in Parkinson's disease.

BACKGROUND: Genetic variants of dopaminergic transcription factor-encoding genes are suggested to be Parkinson's disease (PD) risk factors; however, no comprehensive analyses of these genes in patients with PD have been undertaken. Therefore, we aimed to genetically analyze 16 dopaminergic transcription factor genes in Chinese patients with PD. METHODS: Whole-exome sequencing (WES) was performed using a Chinese cohort comprising 1917 unrelated patients with familial or sporadic early-onset PD and 1652 controls. Additionally, whole-genome sequencing (WGS) was performed using another Chinese cohort comprising 1962 unrelated patients with sporadic late-onset PD and 1279 controls. RESULTS: We detected 308 rare and 208 rare protein-altering variants in the WES and WGS cohorts, respectively. Gene-based association analyses of rare variants suggested that MSX1 is enriched in sporadic late-onset PD. However, the significance did not pass the Bonferroni correction. Meanwhile, 72 and 1730 common variants were found in the WES and WGS cohorts, respectively. Unfortunately, single-variant logistic association analyses did not identify significant associations between common variants and PD. CONCLUSIONS: Variants of 16 typical dopaminergic transcription factors might not be major genetic risk factors for PD in Chinese patients. However, we highlight the complexity of PD and the need for extensive research elucidating its etiology.

Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion.

This article explores deep reinforcement learning (DRL) for the flocking control of unmanned aerial vehicle (UAV) swarms. The flocking control policy is trained using a centralized-learning-decentralized-execution (CTDE) paradigm, where a centralized critic network augmented with additional information about the entire UAV swarm is utilized to improve learning efficiency. Instead of learning inter-UAV collision avoidance capabilities, a repulsion function is encoded as an inner-UAV "instinct." In addition, the UAVs can obtain the states of other UAVs through onboard sensors in communication-denied environments, and the impact of varying visual fields on flocking control is analyzed. Through extensive simulations, it is shown that the proposed policy with the repulsion function and limited visual field has a success rate of 93.8% in training environments, 85.6% in environments with a high number of UAVs, 91.2% in environments with a high number of obstacles, and 82.2% in environments with dynamic obstacles. Furthermore, the results indicate that the proposed learning-based methods are more suitable than traditional methods in cluttered environments.

GGC expansions in NOTCH2NLC contribute to Parkinson disease and dopaminergic neuron degeneration.

BACKGROUND AND PURPOSE: The role of GGC repeat expansions within NOTCH2NLC in Parkinson's disease (PD) and the substantia nigra (SN) dopaminergic neuron remains unclear. Here, we profile the NOTCH2NLC GGC repeat expansions in a large cohort of patients with PD. We also investigate the role of GGC repeat expansions within NOTCH2NLC in the dopaminergic neurodegeneration of SN. METHODS: A total of 2,522 patients diagnosed with PD and 1,085 health controls were analyzed for the repeat expansions of NOTCH2NLC by repeat-primed PCR and GC-rich PCR assay. Furthermore, the effects of GGC repeat expansions in NOTCH2NLC on dopaminergic neurons were investigated by using recombinant adeno-associated virus (AAV)-mediated overexpression of NOTCH2NLC with 98 GGC repeats in the SN of mice by stereotactic injection. RESULTS: Four PD pedigrees (4/333, 1.2%) and three sporadic PD patients (3/2189, 0.14%) were identified with pathogenic GGC repeat expansions (larger than 60 GGC repeats) in the NOTCH2NLC gene, while eight PD patients and one healthy control were identified with intermediate GGC repeat expansions ranging from 41 to 60 repeats. No significant difference was observed in the distribution of intermediate NOTCH2NLC GGC repeat expansions between PD cases and controls (Fisher's exact test p-value = 0.29). Skin biopsy showed P62-positive intranuclear NOTCH2NLC-polyGlycine (polyG) inclusions in the skin nerve fibers of patient. Expanded GGC repeats in NOTCH2NLC produced widespread intranuclear and perinuclear polyG inclusions, which led to a severe loss of dopaminergic neurons in the SN. Consistently, polyG inclusions were presented in the SN of EIIa-NOTCH2NLC-(GGC)98 transgenic mice and also led to dopaminergic neuron loss in the SN. CONCLUSIONS: Overall, our findings provide strong evidence that GGC repeat expansions within NOTCH2NLC contribute to the pathogenesis of PD and cause degeneration of nigral dopaminergic neurons.

A detection model of cognitive impairment via the integrated gait and eye movement analysis from a large Chinese community cohort.

INTRODUCTION: Whether the integration of eye-tracking, gait, and corresponding dual-task analysis can distinguish cognitive impairment (CI) patients from controls remains unclear. METHODS: One thousand four hundred eighty-one participants, including 724 CI and 757 controls, were enrolled in this study. Eye movement and gait, combined with dual-task patterns, were measured. The LightGBM machine learning models were constructed. RESULTS: A total of 105 gait and eye-tracking features were extracted. Forty-six parameters, including 32 gait and 14 eye-tracking features, showed significant differences between two groups (P < 0.05). Of these, the Gait_3Back-TurnTime and Dual-task cost-TurnTime patterns were significantly correlated with plasma phosphorylated tau 181 (p-tau181) level. A model based on dual-task gait, dual-task smooth pursuit, prosaccade, and anti-saccade achieved the best area under the receiver operating characteristics curve (AUC) of 0.987 for CI detection, while combined with p-tau181, the model discriminated mild cognitive impairment from controls with an AUC of 0.824. DISCUSSION: Combining dual-task gait and dual-task eye-tracking analysis is feasible for the detection of CI. HIGHLIGHTS: This is the first study to report the efficiency of integrated parameters of dual-task gait and eye-tracking for cognitive impairment (CI) detection in a large cohort. We identified 46 gait and eye-tracking features associated with CI, and two were correlated to plasma phosphorylated tau 181. We constructed the model based on dual-task gait, smooth pursuit, prosaccade, and anti-saccade, achieving the best area under the curve of 0.987 for CI detection.

Mutation and clinical analysis of the CLCC1 gene in amyotrophic lateral sclerosis patients from Central South China.

INTRODUCTION: Recently, chloride channel CLIC-like 1 (CLCC1) was reported to be a novel ALS-related gene. We aimed to screen CLCC1 variants in our ALS cohort and further explore the genotype-phenotype correlation of CLCC1-related ALS. METHODS: We screened rare damaging variants in CLCC1 from our cohorts of 1005 ALS patients and 1224 healthy controls with whole-exome sequencing in Central South China. Fisher's exact test was conducted for association analysis at the entire gene level and single variant level. RESULTS: In total, four heterozygous missense variants in CLCC1 were identified from four unrelated sporadic ALS patients and predicted to be putative pathogenic by in silico tools and protein model prediction, accounting for 0.40% of all patients (4/1005). The four variants were c.A275C (p.Q92P), c.G1139A (p.R380K), c.C1244T (p.T415M), and c.G1328A (p.R443Q), respectively, which had not been reported in ALS patients previously. Three of four variants were located in exon 10. Patients harboring CLCC1 variants seemed to share a group of similar clinical features, including earlier age at onset, rapid progression, spinal onset, and vulnerable cognitive status. Statistically, we did not find CLCC1 to be associated with the risk of ALS at the entire gene level or single variant level. CONCLUSION: Our findings further expanded the genetic and clinical spectrum of CLCC1-related ALS and provided more genetic evidence for anion channel involvement in the pathogenesis of ALS, but further investigations are needed to verify our findings.

Estimating the sequence of biomarker changes in Parkinson's disease.

OBJECTIVE: To estimate the sequence of several common biomarker changes in Parkinson's disease (PD) using a novel data-driven method. METHODS: We included 374 PD patients and 169 healthy controls (HC) from the Parkinson's Progression Markers Initiative (PPMI). Biomarkers, including the left putamen striatal binding ratio (SBR), right putamen SBR, left caudate SBR, right caudate SBR, cerebrospinal fluid (CSF) α-synuclein, and serum neurofilament light chain (NfL), were selected in our study. The discriminative event-based model (DEBM) was utilized to model the sequence of biomarker changes and establish the disease progression timeline. The estimated disease stages for each subject were obtained through cross-validation. The associations between the estimated disease stages and the clinical symptoms of PD were explored using Spearman's correlation. RESULTS: The left putamen is the earliest biomarker to become abnormal among the selected biomarkers, followed by the right putamen, CSF α-synuclein, right caudate, left caudate, and serum NfL. The estimated disease stages are significantly different between PD and HC and yield a high accuracy for distinguishing PD from HC, with an area under the curve (AUC) of 0.98 (95% confidence interval 0.97-0.99), a sensitivity of 0.95, and a specificity of 0.92. Moreover, the estimated disease stages correlate with motor experiences of daily living, motor symptoms, autonomic dysfunction, and anxiety in PD patients. CONCLUSION: We determined the sequence of several common biomarker changes in PD using DEBM, providing data-driven evidence of the disease progression of PD.

Specific serum autoantibodies predict the development and progression of Alzheimer's disease with high accuracy.

Autoimmunity plays a key role in the pathogenesis of Alzheimer's disease (AD). However, whether autoantibodies in peripheral blood can be used as biomarkers for AD has been elusive. Serum samples were obtained from 1,686 participants, including 767 with AD, 146 with mild cognitive impairment (MCI), 255 with other neurodegenerative diseases, and 518 healthy controls. Specific autoantibodies were measured using a custom-made immunoassay. Multivariate support vector machine models were employed to investigate the correlation between serum autoantibody levels and disease states. As a result, seven candidate AD-specific autoantibodies were identified, including MAPT, DNAJC8, KDM4D, SERF1A, CDKN1A, AGER, and ASXL1. A classification model with high accuracy (area under the curve (AUC) = 0.94) was established. Importantly, these autoantibodies could distinguish AD from other neurodegenerative diseases and out-performed amyloid and tau protein concentrations in cerebrospinal fluid in predicting cognitive decline (P < 0.001). This study indicated that AD onset and progression are possibly accompanied by an unappreciated serum autoantibody response. Therefore, future studies could optimize its application as a convenient biomarker for the early detection of AD.

A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD.