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The strategies of classifying APP , PSEN1 , and PSEN2 variants varied substantially in the previous studies. We aimed to re-evaluate these variants systematically according to the American college of medical genetics and genomics and the association for molecular pathology (ACMG-AMP) guidelines. In our study, APP , PSEN1 , and PSEN2 variants were collected by searching Alzforum and PubMed database with keywords “PSEN1,” “PSEN2,” and “APP.” These variants were re-evaluated based on the ACMG-AMP guidelines. We compared the number of pathogenic/likely pathogenic variants of APP , PSEN1 , and PSEN2 . In total, 66 APP variants, 323 PSEN1 variants, and 63 PSEN2 variants were re-evaluated in our study. 94.91% of previously reported pathogenic variants were re-classified as pathogenic/likely pathogenic variants, while 5.09% of them were variants of uncertain significance (VUS). PSEN1 carried the most prevalent pathogenic/likely pathogenic variants, followed by APP and PSEN2 . Significant statistically difference was identified among these three genes when comparing the number of pathogenic/likely pathogenic variants ( P < 2.2 × 10 –16 ). Most of the previously reported pathogenic variants were re-classified as pathogenic/likely pathogenic variants while the others were re-evaluated as VUS, highlighting the importance of interpreting APP , PSEN1 , and PSEN2 variants with caution according to ACMG-AMP guidelines.

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is performed via blind tests and the test results are consistent.

Functional intestinal imaging holds importance for the diagnosis and evaluation of treatment of gastrointestinal diseases. Currently, preclinical imaging of intestinal motility in animal models is performed either invasively with excised intestines or noninvasively under anesthesia, and cannot reveal intestinal dynamics in the awake condition. Capitalizing on near-infrared optics and a high-absorbing contrast agent, we report the Trans-illumination Intestine Projection (TIP) imaging system for free-moving mice. After a complete system evaluation, we performed in vivo studies, and obtained peristalsis and segmentation motor patterns of free-moving mice. We show the in vivo typical segmentation motor pattern, that was previously shown in ex vivo studies to be controlled by intestinal pacemaker cells. We also show the effects of anesthesia on motor patterns, highlighting the possibility to study the role of the extrinsic nervous system in controlling motor patterns, which requires unanesthetized live animals. Combining with light-field technologies, we further demonstrated 3D imaging of intestine in vivo (3D-TIP). Importantly, the added depth information allows us to extract intestines located away from the abdominal wall, and to quantify intestinal motor patterns along different directions. The TIP system should open up avenues for functional imaging of the GI tract in conscious animals in natural physiological states. Current preclinical imaging of intestine in animal models cannot reveal intestinal dynamics in awake condition. Here the authors report a Transillumination Intestine Projection (TIP) imaging system for free-moving mice, and showed the intestine dynamics in conscious animal in natural physiological states.

With the development of sequencing technique, more than 40 repeat expansion diseases were identified during the past 2 decades. Moreover, the clinical features of these diseases show some commonality, and the nervous system, especially cognitive function was affected in part of these diseases. However, the specific cognitive domains impaired in different diseases were inconsistent. Here we survey the literature on the cognitive consequences of the following disorders presenting cognitive dysfunction, and summarized the pathogenic genes, epidemiology, and different domains affected in these diseases. We found that the cognitive domains affected in neuronal intranuclear inclusion disease were widespread including executive function, memory, information processing speed, attention, visuospatial function and language; C9ORF72-frontotemporal dementia patients showed impairment in executive function, memory, language and visuospatial function. While in Huntington's disease, executive function, memory and information processing speed were affected. As for fragile X-associated tremor/ataxia syndrome, executive function, memory, information processing speed and attention were impaired. Moreover, spinocerebellar ataxias showed abroad damage in almost all the cognitive domains except for the relatively intact language ability. Some other diseases with relatively rare clinical data also indicated cognitive dysfunction, such as myotonic dystrophy type 1, progressive myoclonus epilepsy, Friedreich ataxia, Huntington Disease like 2 and cerebellar ataxia, neuropathy, vestibular areflexia syndrome. We drew a cognitive function landscape of the relating repeat expansion diseases and may provide an aspect for differential diagnosis through cognitive domains.

Purpose The aim of the study is to investigate the interactive influence of depression on left-behind (LB) and non-left-behind (NLB) children from the perspective of peer effects. The roles of teachers, parents, and friends are also explored. Methods Data on 1817 children, 1817 parents, and 55 teachers were obtained from a field survey in December 2021. All students in the sample were randomly assigned to classrooms. A peer effect model and OLS methods were used to estimate the peer influence of depression. Robustness tests were conducted by randomly removing schools from the sample. Results Depression was contagious among different groups of rural children, and the peer effect of the NLB children’s depression played a dominant role. Both LB and NLB children were more affected by their NLB classmates’ depression. LB children were not significantly affected by depression in other LB children. This conclusion remains robust after robustness testing. In addition, heterogeneity analysis showed that outgoing and cheerful teachers, effective parent–child communication and high-quality friendship all alleviated peer influence on depression. Conclusions LB children have more severe depression than NLB children, but LB children are more affected by depression in their NLB peers. Policymakers should train teachers to engage in positive communication with students to improve mental health in children. In addition, this article recommends that children move and live with their parents when family conditions permit.

Background Ethnic variations and detection methods may lead to differences in diagnostic biomarkers of dementia, and few comparative studies have evaluated the six plasma biomarkers of Alzheimer’s disease (AD) and other neurodegenerative dementias in the Chinese population. Methods A cross-sectional cohort of 668 participants were enrolled, including 245 amnesic mild cognitive impairment (aMCI) or AD patients with Aβ positive pathology, 67 with frontotemporal dementia (FTD), 100 with progressive supranuclear palsy (PSP), 72 with dementia with Lewy bodies (DLB) and 184 healthy controls. Additionally, a longitudinal subset of 19 aMCI and 30 AD patients was followed for an average period of 1 year. Plasma biomarkers, including p-tau181, p-tau217, p-tau231, NfL, GFAP, and α-synuclein, were simultaneously measured using a novel single molecular array method. Aβ42 and p-tau181 levels in CSF, amyloid PET and structural MRI were measured. Results Plasma p-tau217 and p-tau231 were most effective in diagnosing aMCI/AD (AUC = 0.95 and 0.93, respectively), while p-tau217, p-tau231 and p-tau181 presented the best differential diagnosis for AD from PSP, FTD and DLB respectively (AUC = 0.84, 0.81 and 0.83). α-synuclein was presented as the best biomarker for PSP variant and behavior variant FTD subtypes (AUC = 0.81 and 0.74, respectively). Among them, p-tau217, p-tau231, GFAP and a-synuclein were negatively correlated with CSF Aβ42/40, while p-tau217 and GFAP were positively correlated with CSF p-tau181. Besides, p-tau181, p-tau217, and GFAP were associated with temporal lobe volume, while p-tau231 and GFAP were associated with frontal lobe volume. Longitudinal analysis showed the higher p-tau181 could predict the cognitive decline progression. Conclusions This study validate the practicality of blood biomarkers in the Chinese Han population using a novel single molecule immune detection method. Through the clinical performance study for several biomarkers, we found the plasma p-tau217 was the most effective biomarker in AD diagnosis, and p-tau showed high accuracy for differential diagnosis of AD from other dementia, GFAP is associated with multiple aspects of AD pathology, and frontal and temporal lobe volume, and p-tau181 can reflect the dynamic cognitive decline of AD.

Whether structural alterations of intraretinal layers are indicators for the early diagnosis of Parkinson’s disease (PD) remains unclear. We assessed the retinal layer thickness in different stages of PD and explored whether it can be an early diagnostic indicator for PD. In total, 397 [131, 146, and 120 with Hoehn-Yahr I (H-Y I), H-Y II, and H-Y III stages, respectively] patients with PD and 427 healthy controls (HCs) were enrolled. The peripapillary retinal nerve fiber layer (pRNFL), total macular retinal thickness (MRT), and macular volume (TMV) were measured by high-definition optical coherence tomography, and the macular intraretinal thickness was analyzed by the Iowa Reference Algorithms. As a result, the PD group had a significantly lower average, temporal quadrant pRNFL, MRT, and TMV than the HCs group (all p  < 0.001). Moreover, the ganglion cell layer (GCL), inner plexiform layer (IPL), and outer nuclear layer were thinner in patients with PD with H-Y I, and significantly decreased as the H-Y stage increased. In addition, we observed that GCL and IPL thicknesses were both correlated with Movement Disorder Society-Unified Parkinson’s Disease Rating Scale III (MDS-UPDRS III) scores and non-motor symptoms assessment scores. Furthermore, macular IPL thickness in the superior inner (SI) quadrant (IPL-SI) had the best diagnostic performance in patients with PD with H-Y I versus HCs, with a sensitivity and specificity of 75.06% and 81.67%, respectively. In conclusion, we confirmed the retinal structure was significantly altered in patients with PD in different clinical stages, and that GCL and IPL changes occurred during early PD disease and were correlated with MDS-UPDRS III scores and non-motor symptoms assessment scores. Furthermore, macular IPL-SI thickness might be performed as an early diagnostic indicator for PD.

Alzheimer's disease (AD) is a progressive neurodegenerative disease associated with aging, environmental and genetic fac-tors. APP is a known pathogenic gene for familial AD (FAD), accounting for about 1% of FAD. Currently, more than 70 APP mutations have been reported, but the genotype-phenotype correlation remains unclear. In this study, we collected clinical data from patients carrying APP mutations defined as pathogenic/likely pathogenic according to the ACMG guide-lines. Then we reanalyzed the clinical charac-teristics and identified genotype-phenotype correlations in APP mutations. Our results indicated that the clinical phenotypes of APP mutations are generally consistent with typical AD despite some specific and heterogeneous features. We also found some differences in V717I mutation compared to other APP muta-tions, and individuals with extrapyramidal symptoms (EPS) or ataxia were more likely to have spastic par-aplegia.

Among diverse intelligent assistive systems developed for amyotrophic lateral sclerosis (ALS) patients, headwear eye tracking based ones trigger broad interests due to their merits such as noninvasive, cost effective, and high operation freedom. However, with headwear eye trackers, patients are easy to feel tired during human–machine interactivities (HMIs), and the operation accuracy is not satisfied compared with its counterparts. To address these two issues, herein, a visual feedback technique is developed which allows users to recognize machine's vision by positioning a laser spot to the user watched object, according to the location information interpreted from user's eye movement. Through the visual feedback technique, users not only obtain real‐time feedback, but also can fine‐tune the laser spot to the desired location before performing further operations. Experimental results demonstrate that the presented work can successfully reduce user's fatigue and boost operation accuracy by 25.1% and 27.6%, respectively, therefore, advancing the field of intelligent assistive technologies. Amyotrophic lateral sclerosis (ALS) patients cannot control their muscles to interact with the surrounding world. To address this issue, herein, an eye movement tracking and visual feedback‐enabled assistive intelligent system is presented, which allows ALS patients to perform human–machine interactivities with high operation accuracy and low mental and physical fatigue.

Previous epidemiological studies have reported controversial results on the relationship between smoking and Alzheimer's disease (AD). Therefore, we sought to assess the association using Mendelian randomization (MR) analysis. We used single nucleotide polymorphisms (SNPs) associated with smoking quantity (cigarettes per day, CPD) from genome-wide association studies (GWAS) of Japanese population as instrumental variables, then we performed two-sample MR analysis to investigate the association between smoking and AD in a Chinese cohort (1,000 AD cases and 500 controls) and a Japanese cohort (3,962 AD cases and 4,074 controls), respectively. Genetically higher smoking quantity showed no statistical causal association with AD risk (the inverse variance weighted (IVW) estimate in the Chinese cohort: odds ratio (OR) = 0.510, 95% confidence interval (CI) = 0.149-1.744,  = 0.284; IVW estimate in the Japanese cohort: OR = 1.170, 95% confidence interval CI = 0.790-1.734,  = 0.434). This MR study, for the first time in Chinese and Japanese populations, found no significant association between smoking and AD.