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BackgroundMost studies have indicated that peripheral insulin-like growth levels factor-1 (IGF-1) is valuable in diagnosing heart failure, although the results have been inconsistent. To help solve the debate, we performed a meta-analysis to explore the relationship between IGF-1 and heart failure (HF).MethodsWe conducted an extensive search across various databases such as Embase, Cochrane Library, Pubmed, Medline, and Web of Science on May 30, 2023. From the extensive pool of studies, we selected 16 relevant articles, encompassing a total of 1,380 cases and 1,153 controls, to conduct a rigorous meta-analysis.ResultsThe total results indicated that there is an association between lower IGF-1 level and HF. The random-effects model yielded a pooled standardized mean difference (SMD) of −0.598 (95% CI: −1.081 to −0.116, P = 0.015). Further subgroup analysis also showed that IGF-1 levels were associated with HF in the age difference ≥5 years subgroup and body mass index difference >1 subgroup. Additionally, significant association between IGF-1 levels and HF were detected in the “serum” samples and “Europe” subgroups. Importantly, we observed IGF-1 showed significant lower levels in patients with reduced ejection fraction (HFrEF) compared to the controls, not in patients with preserved ejection fraction (HFpEF). The Begg’s and Egger’s tests revealed no indication of publication bias.ConclusionsOur meta-analysis has provided evidence suggesting a substantial correlation between reduced levels of IGF-1 and the occurrence of HF. Further prospective studies are necessary to ascertain the use of IGF-1 as a reliable biomarker for diagnosing HF, especially for HFrEF. But the diagnosis of HFpEF should be cautious.

Alzheimer′s disease(AD) is a widely recognized chronic neurodegenerative condition.Drosophila, with its well-defined genetic structure, straightforward experimental methodologies, and the presence of human disease-related genes or highly similar homologous genes, serves as an advantageous model organism.The utilization of Drosophila′s renowned Galactose-regulated Upstream Promoter Element 4-Upstream Activating Sequence(GAL4-UAS) system has led to the development of various AD models.These models facilitate the exploration of AD′s molecular mechanisms and the advancement of drug discovery efforts.This paper reviews the foundational principles, methodologies, and diverse applications of different AD Drosophila models, offering insights and innovative approaches for AD research utilizing Drosophila.

BackgroundThe relationship between type 2 diabetes mellitus (T2DM) and osteoporosis (OP) has been widely recognized in recent years, but the mechanism of interaction remains unknown. The aim of this study was to investigate the genetic features and signaling pathways that are shared between T2DM and OP.MethodsWe analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). Shared genes were then further analyzed for functional pathway enrichment. We selected the best common biomarkers using the least absolute shrinkage and selection operator (LASSO) algorithm and validated the common biomarkers, followed by RT-PCR, immunofluorescence, Western blotting, and enzyme-linked immunosorbent assay (ELISA) to validate the expression of these hub genes in T2DM and OP mouse models and patients.ResultsWe found 8,506 and 2,030 DEGs in T2DM and OP, respectively. Four modules were identified as significant for T2DM and OP using WGCNA. A total of 19 genes overlapped with the strongest positive and negative modules of T2DM and OP. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed these genes may be involved in pantothenate and CoA biosynthesis and the glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and renin-angiotensin system signaling pathway. The LASSO algorithm calculates the six optimal common biomarkers. RT-PCR results show that LTB, TPBG, and VNN1 were upregulated in T2DM and OP. Immunofluorescence and Western blot show that VNN1 is upregulated in the pancreas and bones of T2DM model mice and osteoporosis model mice. Similarly, the level of VNN1 in the sera of patients with T2DM, OP, and T2DM and OP was higher than that in the healthy group.ConclusionBased on the WGCNA and LASSO algorithms, we identified genes and pathways that were shared between T2DM and OP. Both pantothenate and CoA biosynthesis and the glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and renin–angiotensin systems may be associated with the pathogenesis of T2DM and OP. Moreover, VNN1 may be a potential diagnostic marker for patients with T2DM complicated by OP. This study provides a new perspective for the systematic study of possible mechanisms of combined OP and T2DM.

This study aimed to develop and validate a bone marrow edema model using a magnetic resonance imaging-based radiomics nomogram for the diagnosis of osteoarthritis. Clinical and magnetic resonance imaging (MRI) data of 302 patients with and without osteoarthritis were retrospectively collected from April 2022 to October 2023 at Longhua Hospital affiliated with the Shanghai University of Traditional Chinese Medicine. The participants were randomly divided into two groups (a training group, n = 211 and a testing group, n = 91). We used logistic regression to analyze clinical characteristics and established a clinical model. Radiomics signatures were developed by extracting radiomic features from the bone marrow edema area using MRI. A nomogram was developed based on the rad-score and clinical characteristics. The diagnostic performance of the three models was compared using the receiver operating characteristic curve and Delong’s test. The accuracy and clinical application value of the nomogram were evaluated using calibration curve and decision curve analysis. Clinical characteristics such as age, radiographic grading, Western Ontario and McMaster Universities Arthritis Index score, and radiological features were significantly correlated with the diagnosis of osteoarthritis. The Rad score was constructed from 11 radiological features. A clinical model was developed to diagnose osteoarthritis (training group: area under the curve [AUC], 0.819; testing group: AUC, 0.815). Radiomics models were used to effectively diagnose osteoarthritis (training group,: AUC, 0.901; testing group: AUC, 0.841). The nomogram model composed of Rad score and clinical characteristics had better diagnostic performance than a simple clinical model (training group: AUC, 0.906; testing group: AUC, 0.845; p < 0.01). Based on DCA, the nomogram model can provide better diagnostic performance in most cases. In conclusion, the MRI-bone marrow edema-based radiomics-clinical nomogram model showed good performance in diagnosing early osteoarthritis.

Abstract Background Blood tests would be much easier to implement in the clinical diagnosis of Alzheimer’s disease (AD) as minimally invasive measurements. Multiple inspection technologies promoted AD-associated blood biomarkers’ exploration. However, there was a lack of further screening and validation for these explored blood-based biomarkers. We selected four potential biomarkers to explore their plasma levels in AD and amnestic mild cognitive impairment (aMCI) and developed a composite panel for AD and aMCI screening. Method The plasma concentrations of soluble low-density lipoprotein receptor-associated protein 1 (sLRP1), Gelsolin (GSN), Kallikrein 4 (KLK4) and Caspase 3 were measured in the discovery and validation cohort. The receiver operating characteristic (ROC) curve was generated to assess the classification panel with the area under the curve (AUC). Results A total of 233 participants (26 CN, 27 aMCI, and 26 AD in the discovery cohort, and 51 CN, 50 aMCI, and 53 AD in the validation cohort) with complete data were included in the study. The plasma concentrations of sLRP1 and Caspase 3 were significantly decreased in AD and aMCI when compared with those in the CN group. Compared with the CN group, the concentrations of KLK4 and GSN were increased in AD, but not in MCI. Interestingly, one of four proteins, sLRP1 in plasma level was higher in Apolipoprotein E (APOE) ε4 non-carriers than that in APOE ε4 carriers, especially among CN and MCI. No significant difference was found between females and males in the plasma levels of four proteins. The composite panel is based on four blood biomarkers accurately classifying AD from CN (AUC = 0.903–0.928), and MCI from CN (AUC = 0.846–0.865). Moreover, dynamic changes in the plasma levels of four proteins exhibited a significant correlation with cognitive assessment. Conclusions Altogether, these findings indicate that the plasma levels of sLRP1, KLK4, GSN and Caspase 3 changed with the progression of AD. And their combination could be used to develop a panel for classifying AD and aMCI with high accuracy, which would provide an alternative approach for developing a blood-based test for AD and aMCI screening.

Abstract Background Neuropsychology and imaging changes have been reported in the preclinical stage of familial Alzheimer’s disease (FAD). This study investigated the effects of APOEε4 and known pathogenic gene mutation on different cognitive domains and circuit imaging markers in preclinical FAD. Methods One hundred thirty-nine asymptomatic subjects in FAD families, including 26 APOEε4 carriers, 17 APP and 20 PS1 mutation carriers, and 76 control subjects, went through a series of neuropsychological tests and MRI scanning. Test scores and imaging measures including volumes, diffusion indices, and functional connectivity (FC) of frontostriatal and hippocampus to posterior cingulate cortex pathways were compared between groups and analyzed for correlation. Results Compared with controls, the APOEε4 group showed increased hippocampal volume and decreased FC of fronto-caudate pathway. The APP group showed increased recall scores in auditory verbal learning test, decreased fiber number, and increased radial diffusivity and FC of frontostriatal pathway. All three genetic groups showed decreased fractional anisotropy of hippocampus to posterior cingulate cortex pathway. These neuropsychological and imaging measures were able to discriminate genetic groups from controls, with areas under the curve from 0.733 to 0.837. Circuit imaging measures are differentially associated with scores in various cognitive scales in control and genetic groups. Conclusions There are neuropsychological and imaging changes in the preclinical stage of FAD, some of which are shared by APOEε4 and known pathogenic gene mutation, while some are unique to different genetic groups. These findings are helpful for the early identification of Alzheimer’s disease and for developing generalized and individualized prevention and intervention strategies.

Abstract Background Neuronal- and astrocyte-derived exosomes have been identified as an optimal source for screening biomarkers for Alzheimer’s disease (AD). However, few studies focus on the bulk exosome population isolated from plasma of AD. This study investigated whether proteins in bulk exosomes can aid in the diagnosis of AD. Methods The plasma exosomes were collected by ultracentrifuge. Protein samples were extracted from exosomes. Cerebrospinal fluid levels of amyloid β (Aβ)42 and phosphorylated tau (P-tau)181 were measured for diagnostic purposes. A pilot study (controls, 20; AD, 20) followed by a second dataset (controls, 56; AD, 58) was used to establish a diagnostic model of AD. Mass spectrometry-based proteomics was performed to profile the plasma exosomal proteome. Parallel reaction monitoring was used to further confirm the differentially expressed proteins. Results In total, 328 proteins in plasma exosomes were quantified. Among them, 31 proteins were altered in AD patients, and 12 were validated. The receiver operating characteristic curve analysis revealed a combination of six proteins (upregulated: Ig-like domain-containing protein (A0A0G2JRQ6), complement C1q subcomponent subunit C (C1QC), complement component C9 (CO9), platelet glycoprotein Ib beta chain (GP1BB), Ras suppressor protein 1 (RSU1); downregulated: disintegrin and metalloproteinase domain 10 (ADA10)) has the capacity to differentiate AD patients from healthy controls with high accuracy. Linear correlation analysis showed that the combination was significantly correlated with cognitive performance. Conclusions The combination of plasma exosomal proteins A0A0G2JRQ6, C1QC, CO9, GP1BB, RSU1, and ADA10 acts as a novel candidate biomarker to differentiate AD patients from healthy individuals.

The impacts of Arctic sea ice on climate in middle and high latitudes have been extensively studied. However, its effects on climate in low latitudes, particularly on summer monsoon rainfall in the South China Sea (SCS), have received limited attention. Thus, this study investigates the connection between the Arctic sea ice concentration (SIC) anomaly and the early summer monsoon rainfall (ESMR) in the SCS and its underlying physical mechanism. The results reveal a significant positive correlation between the Barents Sea (BS) SIC in May and the ESMR in the SCS. When there is more (less) SIC in the Barents Sea (BS) during May, this results in a positive (negative) anomaly of the local turbulent heat flux, which lasts until June. This, in turn, excites an upward (downward) air motion anomaly in the vicinity of the BS, causing a corresponding downward (upward) motion anomaly over the Black Sea. Consequently, this triggers a wave train similar to the Eurasian (SEU) teleconnection, propagating eastward towards East Asia. The SEU further leads to an (a) upward (downward) motion anomaly and weakens (strengthens) the western Pacific subtropical high (WPSH) over the SCS, which is accompanied by a southwest adequate (scarce) water vapor anomaly transporting from the Indian Ocean, resulting in more (less) precipitation in the SCS. Furthermore, the response of ESMR in the SCS to the SIC in the BS is further verified by using the Community Atmosphere Model version 5.3 (CAM5.3). This study introduces novel precursor factors that influence the South China Sea summer monsoon (SCSSM), presenting a new insight for climate prediction in this region, which holds significant implications.

Angiotensin-converting enzyme 2 (ACE2) is a member of the renin-angiotensin system (RAS), which was once considered a linear cascade. ACE2 mainly functions to convert AngiotensinⅡ (AngⅡ) to Angiotensin1-7 (A1-7). The biologically active product A1-7 then binds to the Mas receptor to form the ACE2/A1-7/Mas axis. In contrast to classic RAS, which plays a decisive role in regulation, the ACE2/A1-7/Mas axis effectively counteracts vasoconstriction, the inflammatory response, oxidative stress, and cell proliferation, and is thus a negative regulator of the RAS. ACE2 also functions as a chaperone to regulate intestinal amino acid uptake. It is widely expressed in the lungs, cardiovascular system, gastrointestinal tract, kidney, pancreas and adipose tissue. Previous studies have confirmed that ACE2 has a vital role in homeostasis. ACE2 also has a variety of other biological activities and plays a critical role in Type 2 diabetes (T2DM) and its complications, especially diabetic nephropathy, obesity, dyslipidemia and other diseases. In this review, we summarize the latest research on the regulation of glucose and lipid metabolism by ACE2 in different organs. Our focus was particularly on T2DM, with the aim of providing new clinical ideas for the use of ACE2 as an effective target in the prevention and treatment of metabolic diseases.

Seeking continuous development, a modern community must also be able to adapt to future possible challenges using constrained or limited resources. As a revolutionary communication paradigm, the Internet of Things (IoT) empowers the cutting-edge and emerging applications which enable manifold new intelligent services towards a smart community. The sophisticated ecosystem of a digital community is made feasible by the IoT infrastructure, which also provides community control with access to a wealth of actual data. In addition, IoT platforms empower the ubiquitous computing ability, providing more potentials to the actuators in perception layer in the IoT architecture. With more and more population in the urban areas, sustainability issues have become a key factor to consider in the development of a digital community. We give a modern survey in this study on the most recent developments in IoT for sustainable digital communities. After carefully examining the most recent literature, we specifically highlight the various smart digital community application scenarios, such as smart buildings, energy management, green transportation, trash management, etc. We also look into a number of major issues facing the use of IoT technology in digital communities. Furthermore, we discuss potential future applications and future research areas for IoT, the critical component of sustainable digital communities.

Electric power fittings are the link between conductors and transmission towers, and it is essential to improve their ability to withstand damage. To obtain the actual load of electric power fittings and study the stress of each electric power fitting under the actual load, a transmission line model of catenary-shaped and an integral model of tension insulator string are established in this paper. Then, conductors with a uniform thickness of ice coating and ice shedding process are simulated by changing density method. The results show that the maximum dynamic tension of the conductor of the isolated span transmission line is greater than the static tension of conductor icing only when the length of the ice-shedding section is small. Under any ice-shedding condition, the maximum dynamic tension of the conductor of the multi-span transmission line will be close to or greater than the static tension after conductor icing. When the form conductor tension is loaded on the tension insulator string, it can be found that the most dangerous fitting in the tension insulator string is found, and the stress analysis is carried out on the key connecting fittings, which provides a reference for the design and optimization of electric power fitting.

PurposeThis study aimed to use meta-analysis to determine the impact of resistance and balance training on athletic ability and quality of life for patients with osteoporotic vertebral fracture (OVF).MethodsThis study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) criteria for systematic reviews and meta-analyzes. The PubMed, Web of science, Cochrane, Embase, and CNKI databases were searched for randomized controlled trials (RCTs) up to September 2022. The search strategy was related to the intervention measures, population, and results, and was structured around the search terms: “Exercise,” “Osteoporotic vertebral fracture,” and “activities of function.” Two reviewers strictly implemented the inclusion and exclusion criteria. Subgroup analyzes of age and training duration were performed for the main outcomes.ResultsWe included 12 RCTs (n = 1,289) of resistance and balance training in patients with OVF. Compared with controls, the intervention group showed improvements on the Quality of Life Questionnaire issued by the European Foundation for Osteoporosis, visual analog pain scale, Timed Up and Go, falls efficacy scale international (FES-I), kyphosis, and functional reach. On subgroup analysis, the effect was more significant when training continued >10 weeks.ConclusionResistance and balance exercise training improved function and balance, and reduced fall risk in patients with OVF. We recommend resistance and balance training for at least 10 weeks. Future multicenter, large sample trials are needed for more reliable conclusions.

Background: The associations of legacy per- and polyfluoroalkyl substances (PFAS) with lipid metabolism are controversial, and there is little information about the impact of emerging PFAS (6:2 Cl-PFESA) on lipid metabolism in China. Objectives: We aimed to explore the associations of legacy and emerging PFAS with lipid profiles and dyslipidemia in Chinese adults. Methods: We included 10,855 Chinese participants aged 18 years and above in the China National Human Biomonitoring. The associations of 8 PFAS with 5 lipid profiles and 4 dyslipidemia were investigated using weighted multiple linear regression or weighted logistic regression, and the dose–response associations were investigated using restricted cubic spline model. Results: Among the 8 PFAS, the concentration of PFOS was the highest, with a geometric mean of 5.15 ng/mL, followed by PFOA and 6:2 Cl-PFESA, which were 4.26 and 1.63 ng/mL, respectively. Legacy (PFOA, PFOS, PFUnDA) or emerging (6:2 Cl-PFESA) PFAS were associated with lipid profiles (TC, LDL-C, HDL-C, non HDL-C) and dyslipidemia (high LDL-C, high TC, low HDL-C), and their effects on TC were most obvious. TC concentration increased by 0.595 mmol/L in the highest quartile (Q4) of PFOS when compared with the lowest quartile (Q1), (95 % CI:0.396, 0.794). Restricted cubic spline models showed that PFAS are nonlinearly associated with TC, non HDL-C, LDL-C and HDL-C, and that the lipid concentrations tend to be stable when PFOS and PFOA were > 20 ng/mL well as when the 6:2 Cl-PFESA level was > 10 ng/mL. The positive associations between PFAS mixtures and lipid profiles were also significant. Conclusions: Single and mixed exposure to PFAS were positively associated with lipid profiles, and China's unique legacy PFAS substitutes (6:2 Cl-PFESA) contributed less to lipid profiles than legacy PFAS. In the future, cohort studies will be needed to confirm our findings.

Abstract Background The most common biomarkers of Alzheimer’s disease (AD) are amyloid β (Aβ) and tau, detected in cerebrospinal fluid (CSF) or with positron emission tomography imaging. However, these procedures are invasive and expensive, which hamper their availability to the general population. Here, we report a panel of microRNAs (miRNAs) in serum that can predict P-tau/Aβ42 in CSF and readily differentiate AD from other dementias, including vascular dementia (VaD), Parkinson disease dementia (PDD), behavioral variant frontotemporal dementia (bvFTD), and dementia with Lewy body (DLB). Methods RNA samples were extracted from the participant’s blood. P-tau/Aβ42 of CSF was examined for diagnostic purposes. A pilot study (controls, 21; AD, 23), followed by second (controls, 216; AD, 190) and third groups (controls, 153; AD, 151), is used to establish and verify a predictive model of P-tau/Aβ42 in CSF. The test is then applied to a fourth group of patients with different dementias (controls, 139; AD,155; amnestic mild cognitive impairment [aMCI], 55; VaD, 51; PDD, 53; bvFTD, 53; DLB, 52) to assess its diagnostic capacity. Results In the pilot study, 29 upregulated and 31 downregulated miRNAs in the AD group were found. In Dataset 2, these miRNAs were then included as independent variables in the linear regression model. A seven-microRNA panel (miR-139-3p, miR-143-3p, miR-146a-5p, miR-485-5p, miR-10a-5P, miR-26b-5p, and miR-451a-5p) accurately predicted values of P-tau/Aβ42 of CSF. In Datasets 3 and 4, by applying the predicted P-tau/Aβ42, the predictive model successfully differentiates AD from controls and VaD, PDD, bvFTD, and DLB. Conclusions This study suggests that the panel of microRNAs is a promising substitute for traditional measurement of P-tau/Aβ42 in CSF as an effective biomarker of AD.

Visual recognition technologies based on deep learning have been gradually playing an important role in various resource recovery fields. However, in the field of metal resource recycling, there is still a lack of intelligent and accurate recognition of metallic products, which seriously hinders the operation of the metal resource recycling industry chain. In this article, a convolutional neural network with dual attention mechanism and multi-branch residual blocks is proposed to realize the recognition of metallic products with a high accuracy. First, a channel-spatial dual attention mechanism is introduced to enhance the model sensitivity on key features. The model can focus on key features even when extracting features of metallic products with too much confusing information. Second, a deep convolutional network with multi-branch residual blocks as the backbone while embedding a dual-attention mechanism module is designed to satisfy deeper and more effective feature extraction for metallic products with complex characteristic features. To evaluate the proposed model, a waste electrical and electronic equipment (WEEE) dataset containing 9266 images in 18 categories and a waste household metal appliance (WHMA) dataset containing 11,757 images in 23 categories are built. The experimental results show that the accuracy reaches 94.31% and 95.88% in WEEE and WHMA, respectively, achieving high accuracy and high quality recycling.

Abstract Background Accelerated long-term forgetting has been identified in preclinical Alzheimer’s disease (AD) and is attributed to a selective impairment of memory consolidation in which the hippocampus plays a key role. As blood may contain multiple senescence-related factors that involved in neurogenesis and synaptic plasticity in the hippocampus, we tested whether there is an association between blood-borne factors and accelerated long-term forgetting in asymptomatic individuals from families with autosomal dominant AD (ADAD). Methods We analyzed data of 39 asymptomatic participants (n = 18 ADAD mutation carriers, n = 21 non-carriers) from the Chinese Familial Alzheimer’s Disease Network (CFAN) study. Long-term forgetting rates were calculated based on recall or recognition of two materials (word list and complex figure) at three delays comprising immediate, 30 min, and 7 days. Peripheral blood concentrations of candidate pro-aging factors (CC chemokine ligand 11 [CCL11] and monocyte chemotactic protein 1 [MCP1]) and rejuvenation factors (growth differentiation factor 11 [GDF11], thrombospondin-4 [THBS4], and secreted protein acidic and rich in cysteine like 1 [SPARCL1]) were evaluated in all participants. Results Despite normal performance on standard 30-min delayed testing, mutation carriers exhibited accelerated forgetting of verbal and visual material over 7 days in comparison with matched non-carriers. In the whole sample, lower plasma THBS4 was associated with accelerated long-term forgetting in list recall (β = −0.46, p = 0.002), figure recall (β = −0.44, p = 0.004), and list recognition (β = −0.37, p = 0.010). Additionally, higher plasma GDF11 and CCL11 were both associated with accelerated long-term forgetting (GDF11 versus figure recall: β = 0.39, p = 0.007; CCL11 versus list recognition: β = 0.44, p = 0.002). Conclusions Accelerated long-term forgetting is a cognitive feature of presymptomatic AD. Senescence-related blood-borne factors, especially THBS4, GDF11, and CCL11, may be promising biomarkers for the prediction of accelerated long-term forgetting.

BackgroundThere is an urgent need for cost-effective, easy-to-measure biomarkers to identify subjects who will develop Alzheimer’s disease (AD), especially at the pre-symptomatic stage. This stage can be determined in autosomal dominant AD (ADAD) which offers the opportunity to observe the dynamic biomarker changes during the life-course of AD stages. This study aimed to investigate serum biomarkers during different AD stages and potential novel protein biomarkers of presymptomatic AD.MethodsIn the first stage, 32 individuals [20 mutation carriers including 10 with AD, and 10 with mild cognitive impairment (MCI), and 12 healthy controls] from ADAD families were analyzed. All subjects underwent a complete clinical evaluation and a comprehensive neuropsychological battery. Serum samples were collected from all subjects, and antibody arrays were used to analyze 170 proteins in these samples. The most promising biomarkers were identified during this screening and were then measured in serum samples of 12 subjects with pre-MCI and 20 controls.ResultsThe serum levels of 13 proteins were significantly different in patients with AD or MCI compared to controls. Of the 13 proteins, cathepsin D, immunoglobulin E, epidermal growth factor receptor (EGFR), matrix metalloproteinase-9 (MMP-9), von Willebrand factor (vWF), haptoglobin, and phosphorylated Tau-181 (p-Tau181) correlated with all cognitive measures (R2 = −0.69–0.76). The areas under the receiver operating characteristic curve of these seven proteins were 0.71–0.93 for the classification of AD and 0.57–0.95 for the classification of MCI. Higher levels of p-Tau181 were found in the serum of pre-MCI subjects than in the serum of controls. The p-Tau181 serum level might detect AD before symptoms occur (area under the curve 0.85, sensitivity 75%, specificity 81.67%).ConclusionsA total of 13 serum proteins showed significant differences between subjects with AD and MCI and healthy controls. The p-Tau181 serum level might be a broadly available and cost-effective biomarker to identify individuals with preclinical AD and assess the severity of AD.

Alzheimer disease (AD) is the most common cause of dementia and creates a significant burden on society. As a result, the investigation of hub genes for the discovery of potential therapeutic targets and candidate biomarkers is warranted. In this study, we used the ComBat method to merge three gene expression datasets of AD from the Gene Expression Omnibus (GEO). During combined analysis, we identified 850 differentially expressed genes (DEGs) from the temporal cortex of AD and cognitively normal (CN) samples. We performed weighted gene coexpression network analysis to build gene coexpression networks incorporating these DEGs to identify key modules and hub genes. We found one module most strongly correlated with AD onset as the key module and 19 hub genes in the key module that were down-regulated in AD brains. According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, DEGs were mostly enriched in synapse function, and genes in the key module were mostly related to learning and memory. We selected five little-studied genes, AP3B2, GABRD, GPR158, KIAA0513, and MAL2, to validate their expression in AD mouse model by performing quantitative real-time polymerase chain reaction. We found that all of them were down-regulated in cortices of 8-month 5xFAD mice compared to those of wild-type mice. We then further investigated their correlations with β-secretase activity and Aβ42 levels in AD samples of different Braak stages. We found that all five hub genes had significant negative associations with β-secretase activity and that AP3B2 and KIAA0513 had significant negative associations with Aβ42 levels. We tested the differential expressions of the five hub genes in two AD GEO datasets from the blood and found that KIAA0513 was significantly up-regulated in patients with both mild cognitive impairment (MCI) and AD and was able to differentiate MCI and AD from CN in the two datasets. In conclusion, these five novel vulnerable genes were involved in AD progression, and KIAA0513 was a promising candidate biomarker for early diagnosis of AD.

Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder–decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird’s eye view) object detection compared with some previous state-of-the-art methods.

3D object detection in LiDAR point clouds has been extensively used in autonomous driving, intelligent robotics, and augmented reality. Although the one-stage 3D detector has satisfactory training and inference speed, there are still some performance problems due to insufficient utilization of bird’s eye view (BEV) information. In this paper, a new backbone network is proposed to complete the cross-layer fusion of multi-scale BEV feature maps, which makes full use of various information for detection. Specifically, our proposed backbone network can be divided into a coarse branch and a fine branch. In the coarse branch, we use the pyramidal feature hierarchy (PFH) to generate multi-scale BEV feature maps, which retain the advantages of different levels and serves as the input of the fine branch. In the fine branch, our proposed pyramid splitting and aggregation (PSA) module deeply integrates different levels of multi-scale feature maps, thereby improving the expressive ability of the final features. Extensive experiments on the challenging KITTI-3D benchmark show that our method has better performance in both 3D and BEV object detection compared with some previous state-of-the-art methods. Experimental results with average precision (AP) prove the effectiveness of our network.

White matter (WM) degeneration has been found during the course of cognitive decline in both Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI), however, it is unclear whether there are different WM microstructural abnormalities between two subtypes of aMCI, including single domain aMCI (aMCI-s) and multiple domain aMCI (aMCI-m). Thirty-two patients of aMCI single-domain (aMCI-s), twenty-three patients of aMCI multiple-domain (aMCI-m) and twenty-three healthy normal controls (NC) participated in this study. Neuropsychological measures and diffusion tensor imaging (DTI) data were acquired from each subject and tract-based spatial statistics (TBSS) was implemented. It was found that both aMCI groups showed significantly reduced fractional anisotropy (FA) in the right superior longitudinal fasciculus (SLF) than NC. It was also identified that, as compared to aMCI-m, aMCI-s showed significantly decreased FA in the left SLF, left uncinate fasciculus (UF) and left inferior longitudinal fasciculus (ILF), while significantly increased FA in the left anterior thalamic radiation (ATR). The correlation analysis showed that FA values in the regions with group difference were significantly correlated with cognitive functions as measured by Boston naming test and trail making test. These results suggested that the variations of aMCI may be differentiated by FA indexes and DTI may help to understand why specific signs and symptoms occur in patients.

20(S)-Ginsenoside Rg3 (GRg3) has various bioactivities including anti-cancer effects and inhibition of autophagy. However, no reports have investigated the appearance of autophagy or the connection between autophagy and apoptosis in HeLa cells treated with 20(S)-GRg3. Cell viability was measured by CCK-8 (cell counting kit-8) assays. Apoptosis and the cell cycle were analyzed by Hoechst 33342 staining and flow cytometry. Apoptotic pathways were examined by ROS (reactive oxygen species) determination and rhodamine 123 assays. Western blot analysis was used to determine changes in protein levels. Autophagy induction was monitored by acidic vesicular organelle staining and EGFP-LC3 transfection. 20(S)-GRg3 inhibited autophagy of cells in a starved state, making it impossible for cells to maintain a steady state through autophagy, and then induced apoptosis. 20(S)-GRg3 blocked the late stage of autophagy (fusion of lysosomes and degradation of autophagic lysosomes), including a decrease in acidic vesicular organelle fluorescence, increased LC3 I−II conversion, accumulation of EGFP-LC3 fluorescence, GFP-mRFP-LC3 red-green fluorescence ratio, degradation of the substrate p62, and loss of the balance between autophagy and apoptosis, which induced apoptosis. ROS increased, the mitochondrial membrane potential decreased, apoptotic inducer AIF was released from mitochondria, and nuclear transfer occurred, triggering a series of subsequent apoptotic events. Autophagy inducer rapamycin inhibited the apoptosis induced by 20(S)-GRg3, whereas autophagy inhibitor BA1 promoted apoptosis induced by 20(S)-GRg3. Therefore, 20(S)-GRg3 promoted HeLa cell apoptosis by regulating autophagy. In the autophagic state, 20(S)-GRg3 can be used as a novel autophagy inhibitor in synergy with tumor-blocking therapies such as chemotherapy, which supports its application in the medical field.

Yunling cattle are a composite beef cattle breed, combining Brahman (1/2), Murray Grey (1/4) and Local Yunnan cattle (1/4), that was developed in Yunnan, China in the 1980s. Understanding the genetic information of Yunling cattle is of great significance to the development of reasonable breeding strategies for this breed. In the present study, we assessed the current genetic status of Yunling cattle in Yunnan Province (China) by analyzing the variability of the whole mitochondrial genome of 129 individuals. Altogether, 129 sequences displayed 47 different haplotypes. The haplotype diversity and the average number of nucleotide differences were 0.964 and 128.074, respectively. Phylogenetic analyses classified Yunling cattle into seven haplogroups: T1, T2, T3, T4, T6, I1 and I2. Haplogroup I1 was found to be predominant (41.86%), followed by T3 (28.68%). Furthermore, we also identified a novel haplogroup, T6, and defined the sub-haplogroup I1a in Yunling cattle. According to the formation process of Yunling cattle (local Yunnan cattle as the maternal line), the high genetic diversities in the mitochondria of Yunling cattle could be due to the complex maternal origin of local Yunnan cattle. Further studies about local Yunnan breeds are necessary to determine the exact source of haplogroup T6 in Yunling cattle. Our results will be useful for the evaluation and effective management of Yunling cattle.

A wireless seismic network can be effectively used as a tool for subsurface monitoring and imaging. By recording and analyzing ambient noise, a seismic network can image underground infrastructures and provide velocity variation information of the subsurface that can help to detect anomalies. By studying the variation in the noise cross-correlation function of the noise, it is possible to determine the subsurface seismic velocity and image underground infrastructures. Ambient noise imaging can be done in a decentralized fashion using Distributed Spatial Auto-Correlation (dSPAC). In dSPAC over sensor networks, the cross-correlation is the most intensive communication process since nodes need to communicate their data with neighbor nodes. In this paper, a new communication-reduced method for cross-correlation is presented to meet bandwidth and cost of communication constraints in networks while ambient noise imaging is performed using dSPAC method. By applying the proposed communication-reduced method, we show that energy and computational cost of the nodes is also preserved.

In this article, the framework and architecture of a Subsurface Camera (SAMERA) are envisioned and described for the first time. A SAMERA is a geophysical sensor network that senses and processes geophysical sensor signals and computes a 3D subsurface image in situ in real time. The basic mechanism is geophysical waves propagating/reflected/refracted through subsurface enter a network of geophysical sensors, where a 2D or 3D image is computed and recorded; control software may be connected to this network to allow view of the 2D/3D image and adjustment of settings such as resolution, filter, regularization, and other algorithm parameters. System prototypes based on seismic imaging have been designed. SAMERA technology is envisioned as a game changer to transform many subsurface survey and monitoring applications, including oil/gas exploration and production, subsurface infrastructures and homeland security, wastewater and CO2 sequestration, and earthquake and volcano hazard monitoring. System prototypes for seismic imaging have been built. Creating SAMERA requires interdisciplinary collaboration and the transformation of sensor networks, signal processing, distributed computing, and geophysical imaging.

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.

Currently, the genetic variants identified by genome wide association study (GWAS) generally only account for a small proportion of the total heritability for complex disease. One crucial reason is the underutilization of gene-gene joint effects commonly encountered in GWAS, which includes their main effects and co-association. However, gene-gene co-association is often customarily put into the framework of gene-gene interaction vaguely. From the causal graph perspective, we elucidate in detail the concept and rationality of gene-gene co-association as well as its relationship with traditional gene-gene interaction, and propose two Fisher r-to-z transformation-based simple statistics to detect it. Three series of simulations further highlight that gene-gene co-association refers to the extent to which the joint effects of two genes differs from the main effects, not only due to the traditional interaction under the nearly independent condition but the correlation between two genes. The proposed statistics are more powerful than logistic regression under various situations, cannot be affected by linkage disequilibrium and can have acceptable false positive rate as long as strictly following the reasonable GWAS data analysis roadmap. Furthermore, an application to gene pathway analysis associated with leprosy confirms in practice that our proposed gene-gene co-association concepts as well as the correspondingly proposed statistics are strongly in line with reality.