Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Background: Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. Methods: The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. Results: We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. Conclusions: In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

Cobalt-Catalyzed Enantioselective C-H Carbonylation towards Chiral Isoindolinones.

Transition metal-catalyzed enantioselective C-H carbonylation with carbon monoxide, an essential and easily available C1 feedstock, remains challenging. Here, we disclosed an unprecedented enantioselective C-H carbonylation catalyzed by inexpensive and readily available cobalt(II) salt. The reactions proceed efficiently through desymmetrization, kinetic resolution, and parallel kinetic resolution, affording a broad range of chiral isoindolinones in good yields with excellent enantioselectivities (up to 92 % yield and 99 % ee). The synthetic potential of this method was demonstrated by asymmetric synthesis of biological active compounds, such as (S)-PD172938 and (S)-Pazinaclone. The resulting chiral isoindolinones also serve as chiral ligands in cobalt-catalyzed enantioselective C-H annulation with alkynes to construct phosphorus stereocenter.

Degradation of sulfonamide antibiotic via UV/MgO2 system: kinetic, application, and mechanism.

In response to antibiotic residues in the water, a novel advanced oxidation technology based on MgO2 was used to remediate sulfamethazine (SMTZ) pollution in aquatic environments. Upon appropriate regulation, the remarkable removal efficiency of SMTZ was observed in a UV/MgO2 system, and the pseudo-first-order reaction constant reached 0.4074 min-1. In addition, the better performance of the UV/MgO2 system in a weak acid environment was discovered. During the removal of SMTZ, the pathways of SMTZ degradation were deduced, including nitration, ring opening, and group loss. In the mineralization exploration, the further removal of residual products of SMTZ by the UV/MgO2 system was visually demonstrated. The qualitative and quantitative researches as well as the roles of reactive species were valuated, which revealed the important role of ·O2-. Common co-existing substances in actual wastewater such as NO3- HA, Cl-, Fe2+, Co2+, and Mn2+ can slightly inhibit the degradation of SMTZ in the UV/MgO2 system. Finally, the capacity of efficient degradation of SMTZ in actual wastewater by the UV/MgO2 system was proved. The results indicated that the innovative UV/MgO2 system was of great practical application prospect in antibiotic residue wastewater remediation.

Chiral N,N'-Dioxide Ligands Tune Diastereoselectivity in Mg(II)-Catalyzed Asymmetric Ring-Opening Desymmetrization of Azetidiniums.

A diastereodivergent asymmetric desymmetrization of azetidinium salts with benzothiazoleamides as carbon nucleophiles through a chiral N,N'-dioxide/Mg(II) complex-promoted ring-opening reaction is realized by tuning ligands. Both syn- and anti-chiral δ-amino acid derivatives bearing benzothiazole structure were obtained in moderate to good yields and dr and ee values. DFT calculations indicated that the diastereodivergency stems from the different size of the chiral pocket formed by variable substructures of the ligands, leading to the opposite attack direction of the nucleophiles.

A Novel c.3636-4 A>G Mutation in the CCDC88C Plays a Causative Role in Familial Spinocerebellar Ataxia.

INTRODUCTION: Spinocerebellar ataxia (SCA) is an autosomal dominant genetic disease characterized by cerebellar neurological deficits. Specifically, its primary clinical manifestation is ataxia accompanied by peripheral nerve damage. A total of 48 causative genes of SCA have been identified. This study aimed to identify causative genes of autosomal dominant SCA in a four-generation Chinese kindred comprising eight affected individuals. METHODS: Genomic DNA samples were extracted from the pedigree members, and genomic whole-exome sequencing was performed, followed by bidirectional Sanger sequencing, and minigene assays to identify mutation sites. RESULTS: A novel pathogenic heterozygous mutation in the splice region of the coiled-coil domain containing the 88C (CCDC88C) gene (NM_001080414:c.3636-4 A>G) was identified in four affected members. The minigene assay results indicated that this mutation leads to the insertion of CAG bases (c.3636-1_3636-3 insCAG). CONCLUSION: CCDC88C gene mutation leads to SCA40 (OMIM:616053), which is a rare subtype of SCA without symptoms during childhood. Our findings further demonstrated the role of the CCDC88C gene in SCA and indicated that the c.3636-4 A>G (NM_001080414) variant of CCDC88C is causative for a later-onset phenotype of SCA40. Our findings enrich the mutation spectrum of CCDC88C gene and provide a theoretical basis for the genetic counseling of SCA40.

Using proteomics and metabolomics to identify therapeutic targets for senescence mediated cancer: genetic complementarity method.

Background: Senescence have emerged as potential factors of lung cancer risk based on findings from many studies. However, the underlying pathogenesis of lung cancer caused by senescence is not clear. In this study, we try to explain the potential pathogenesis between senescence and lung cancer through proteomics and metabonomics. And try to find new potential therapeutic targets in lung cancer patients through network mendelian randomization (MR). Methods: The genome-wide association data of this study was mainly obtained from a meta-analysis and the Transdisciplinary Research in Cancer of the Lung Consortium (TRICL), respectively.And in this study, we mainly used genetic complementarity methods to explore the susceptibility of aging to lung cancer. Additionally, a mediation analysis was performed to explore the potential mediating role of proteomics and metabonomics, using a network MR design. Results: GNOVA analysis revealed a shared genetic structure between HannumAge and lung cancer with a significant genetic correlation estimated at 0.141 and 0.135, respectively. MR analysis showed a relationship between HannumAge and lung cancer, regardless of smoking status. Furthermore, genetically predicted HannumAge was consistently associated with the proteins C-type lectin domain family 4 member D (CLEC4D) and Retinoic acid receptor responder protein 1 (RARR-1), indicating their potential role as mediators in the causal pathway. Conclusion: HannumAge acceleration may increase the risk of lung cancer, some of which may be mediated by CLEC4D and RARR-1, suggestion that CLEC4D and RARR-1 may serve as potential drug targets for the treatment of lung cancer.

White blood cell detection using saliency detection and CenterNet: A two-stage approach.

White blood cell (WBC) detection plays a vital role in peripheral blood smear analysis. However, cell detection remains a challenging task due to multi-cell adhesion, different staining and imaging conditions. Owing to the powerful feature extraction capability of deep learning, object detection methods based on convolutional neural networks (CNNs) have been widely applied in medical image analysis. Nevertheless, the CNN training is time-consuming and inaccuracy, especially for large-scale blood smear images, where most of the images are background. To address the problem, we propose a two-stage approach that treats WBC detection as a small salient object detection task. In the first saliency detection stage, we use the Itti's visual attention model to locate the regions of interest (ROIs), based on the proposed adaptive center-surround difference (ACSD) operator. In the second WBC detection stage, the modified CenterNet model is performed on ROI sub-images to obtain a more accurate localization and classification result of each WBC. Experimental results showed that our method exceeds the performance of several existing methods on two different data sets, and achieves a state-of-the-art mAP of over 98.8%.

Geometry-aware view reconstruction network for light field image compression.

Light Field (LF) imaging empowers many attractive applications by simultaneously recording spatial and angular information of light rays. In order to meet the challenges of LF storage and transmission, many view reconstruction-based LF compression methods are put forward. However, occlusion issue and under-exploitation of LF rich structure information limit the view reconstruction qualities, which further influence LF compression efficiency. In order to alleviate these problems, in this paper, we propose a geometry-aware view reconstruction network for LF compression. In our method, only sparsely-sampled LF views are encoded, which are further used as priors to reconstruct the un-sampled LF views at the decoder side. The proposed reconstruction process contains two stages including geometry-aware reconstruction and texture refinement. The geometry-aware reconstruction stage utilizes a multi-stream framework, which can fully explore LF spatial-angular, location and geometry information. The texture refinement stage can adequately fuse such rich LF information to further improve LF reconstruction quality. Comprehensive experimental results validate the superiority of the proposed method. The rate-distortion performance and the perceptual quality of reconstructed views further demonstrate that the proposed method can save more bitrate while increasing LF reconstruction quality.

WBC Image Segmentation Based on Residual Networks and Attentional Mechanisms.

White blood cell (WBC) morphology examination plays a crucial role in diagnosing many diseases. One of the most important steps in WBC morphology analysis is WBC image segmentation, which remains a challenging task. To address the problems of low segmentation accuracy caused by color similarity, uneven brightness, and irregular boundary between WBC regions and the background, a WBC image segmentation network based on U-Net combining residual networks and attention mechanism was proposed. Firstly, the ResNet50 residual block is used to form the main unit of the encoder structure, which helps to overcome the overfitting problem caused by a small number of training samples by improving the network's feature extraction capacity and loading the pretraining weight. Secondly, the SE module is added to the decoder structure to make the model pay more attention to useful features while suppressing useless ones. In addition, atrous convolution is utilized to recover full-resolution feature maps in the decoder structure to increase the receptive field of the convolution layer. Finally, network parameters are optimized using the Adam optimization technique in conjunction with the binary cross-entropy loss function. Experimental results on BCISC and LISC datasets show that the proposed approach has higher segmentation accuracy and robustness.

STA-TSN: Spatial-Temporal Attention Temporal Segment Network for action recognition in video.

Most deep learning-based action recognition models focus only on short-term motions, so the model often causes misjudgments of actions that are combined by multiple processes, such as long jump, high jump, etc. The proposal of Temporal Segment Networks (TSN) enables the network to capture long-term information in the video, but ignores that some unrelated frames or areas in the video can also cause great interference to action recognition. To solve this problem, a soft attention mechanism is introduced in TSN and a Spatial-Temporal Attention Temporal Segment Networks (STA-TSN), which retains the ability to capture long-term information and enables the network to adaptively focus on key features in space and time, is proposed. First, a multi-scale spatial focus feature enhancement strategy is proposed to fuse original convolution features with multi-scale spatial focus features obtained through a soft attention mechanism with spatial pyramid pooling. Second, a deep learning-based key frames exploration module, which utilizes a soft attention mechanism based on Long-Short Term Memory (LSTM) to adaptively learn temporal attention weights, is designed. Third, a temporal-attention regularization is developed to guide our STA-TSN to better realize the exploration of key frames. Finally, the experimental results show that our proposed STA-TSN outperforms TSN in the four public datasets UCF101, HMDB51, JHMDB and THUMOS14, as well as achieves state-of-the-art results.

Accumulative Quantization for Approximate Nearest Neighbor Search.

To further improve the approximate nearest neighbor (ANN) search performance, an accumulative quantization (AQ) is proposed and applied to effective ANN search. It approximates a vector with the accumulation of several centroids, each of which is selected from a different codebook. To provide accurate approximation for an input vector, an iterative optimization is designed when training codebooks for improving their approximation power. Besides, another optimization is introduced into offline vector quantization procedure for the purpose of minimizing overall quantization errors. A hypersphere-based filtration mechanism is designed when performing AQ-based exhaustive ANN search to reduce the number of candidates put into sorting, thus yielding better search time efficiency. For a query vector, a self-centered hypersphere is constructed, so that those vectors not lying in the hypersphere are filtered out. Experimental results on public datasets demonstrate that hypersphere-based filtration can improve ANN search time efficiency with no weakening of search accuracy; besides, the proposed AQ is superior to the state of the art on ANN search accuracy.

Intestinal metabolites and the risk of autistic spectrum disorder: A two-sample Mendelian randomization study.

Background: Observational studies have reported a strong association between autistic spectrum disorder (ASD) and intestinal metabolites. However, it is unclear whether this correlation is causally or violated by confounding or backward causality. Therefore, this study explored the potential causal relationship between intestinal metabolites and dependent metabolites on ASD. Methods: We used a two-sample Mendelian random analysis and selected variants closely related to intestinal flora-dependent metabolites as instrumental variables. MR-Egger, inverse variance weighted (IVW), MR-PRESSO, maximum likelihood, and weighted median were performed to reveal their causal relationships. Ten metabolites were studied, which included trimethylamine-N-oxide, betaine, carnitine, choline, glutamate, kynurenine, phenylalanine, serotonin, tryptophan, and tyrosine. Sensitivity tests were also performed to evaluate the robustness of the MR study. Results: The IVW method revealed that serotonin may increase the ASD risk (OR 1.060, 95% CI: 1.006-1.118), while choline could decrease the ASD risk (OR 0.925, 95% CI: 0.868-0.988). However, no definite causality was observed between other intestinal metabolites (e.g., trimethylamine-N-oxide, betaine, and carnitine) with ASD. Additionally, neither the funnel plot nor the MR-Egger test showed horizontal pleiotropy, and the MR-PRESSO test found no outliers. Cochran's Q test showed no significant heterogeneity among the studies, suggesting the robustness of the study. Conclusion: Our study found potential causality from intestinal metabolites on ASD. Clinicians are encouraged to offer preventive measures to such populations.

Light-field compression using a pair of steps and depth estimation.

Advanced handheld plenoptic cameras are being rapidly developed to capture information about light fields (LFs) from the 3D world. Rich LF data can be used to develop dense sub-aperture images (SAIs) that can provide a more immersive experience for users. Unlike conventional 2D images, 4D SAIs contain both the positional and directional information of light rays; the practical applications of handheld plenoptic cameras are limited by the huge volume of data required to capture this information. Therefore, an efficient LF compression method is vital for further application of the cameras. To this end, the pair of steps and depth estimation (PoS&DE) method is proposed in this paper, and the multiview video and depth (MVD) coding structure is used to relieve the LF coding burden. More specifically, a precise depth-estimation approach is presented for SAIs based on the cost function, and an SAI-guided depth optimization algorithm is designed to refine the initial depth map based on pixel variation tendency. Meanwhile, to reduce running time, intermediate SAI synthesis quality and coding bitrates, including the key SAIs selected and cost-computation steps, are set via extensive statistical experiments. In this way, only a limited number of optimally selected SAIs and their corresponding depth maps must be encoded. The experimental results demonstrate that our proposed LF compression solution using PoS&DE can obtain a satisfied coding performance.