scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks.

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC) shows great promise for studying cellular heterogeneity in epigenetic landscapes, but there remain important challenges in the analysis of scATAC data due to the inherent high dimensionality and sparsity. Here we introduce scBasset, a sequence-based convolutional neural network method to model scATAC data. We show that by leveraging the DNA sequence information underlying accessibility peaks and the expressiveness of a neural network model, scBasset achieves state-of-the-art performance across a variety of tasks on scATAC and single-cell multiome datasets, including cell clustering, scATAC profile denoising, data integration across assays and transcription factor activity inference.

Novel extragenic genomic safe harbors for precise therapeutic T-cell engineering.

Cell therapies that rely on engineered immune cells can be enhanced by achieving uniform and controlled transgene expression in order to maximize T-cell function and achieve predictable patient responses. Although they are effective, current genetic engineering strategies that use γ-retroviral, lentiviral, and transposon-based vectors to integrate transgenes, unavoidably produce variegated transgene expression in addition to posing a risk of insertional mutagenesis. In the setting of chimeric antigen receptor (CAR) therapy, inconsistent and random CAR expression may result in tonic signaling, T-cell exhaustion, and variable T-cell persistence. Here, we report and validate an algorithm for the identification of extragenic genomic safe harbors (GSH) that can be efficiently targeted for DNA integration and can support sustained and predictable CAR expression in human peripheral blood T cells. The algorithm is based on 7 criteria established to minimize genotoxicity by directing transgene integration away from functionally important genomic elements, maximize efficient CRISPR/Cas9-mediated targeting, and avert transgene silencing over time. T cells engineered to express a CD19 CAR at GSH6, which meets all 7 criteria, are curative at low cell dose in a mouse model of acute lymphoblastic leukemia, matching the potency of CAR T cells engineered at the TRAC locus and effectively resisting tumor rechallenge 100 days after their infusion. The identification of functional extragenic GSHs thus expands the human genome available for therapeutic precision engineering.

Arg177 and Asp159 from dog prion protein slow liquid-liquid phase separation and inhibit amyloid formation of human prion protein.

Prion diseases are a group of transmissible neurodegenerative diseases primarily caused by the conformational conversion of prion protein (PrP) from α-helix-dominant cellular prion protein (PrPC) to ß-sheet-rich pathological aggregated form of PrPSc in many mammalian species. Dogs exhibit resistance to prion diseases, but the mechanism behind the phenomenon remains poorly understood. Compared with human PrP and mouse PrP, dog PrP has two unique amino acid residues, Arg177 and Asp159. Because PrPC contains a low-complexity and intrinsically disordered region in its N-terminal domain, it undergoes liquid-liquid phase separation (LLPS) in vitro and forms protein condensates. However, little is known about whether these two unique residues modulate the formation of PrPC condensates. Here, using confocal microscopy, fluorescence recovery after photobleaching assays, thioflavin T binding assays, and transmission electron microscopy, we report that Arg177 and Asp159 from the dog PrP slow the LLPS of full-length human PrPC, shifting the equilibrium phase boundary to higher protein concentrations and inhibit amyloid formation of the human protein. In sharp contrast, His177 and Asn159 from the human PrP enhance the LLPS of full-length dog PrPC, shifting the equilibrium phase boundary to lower protein concentrations, and promote fibril formation of the canid protein. Collectively, these results demonstrate how LLPS and amyloid formation of PrP are inhibited by a single residue Arg177 or Asp159 associated with prion disease resistance, and how LLPS and fibril formation of PrP are promoted by a single residue His177 or Asn159. Therefore, Arg177/His177 and Asp159/Asn159 are key residues in modulating PrPC liquid-phase condensation.

Identifying Human miRNA Target Sites via Learning the Interaction Patterns between miRNA and mRNA Segments.

miRNAs (microRNAs) target specific mRNA (messenger RNA) sites to regulate their translation expression. Although miRNA targeting can rely on seed region base pairing, animal miRNAs, including human miRNAs, typically cooperate with several cofactors, leading to various noncanonical pairing rules. Therefore, identifying the binding sites of animal miRNAs remains challenging. Because experiments for mapping miRNA targets are costly, computational methods are preferred for extracting potential miRNA-mRNA fragment binding pairs first. However, existing prediction tools can have significant false positives due to the prevalent noncanonical miRNA binding behaviors and the information-biased training negative sets that were used while constructing these tools. To overcome these obstacles, we first prepared an information-balanced miRNA binding pair ground-truth data set. A miRNA-mRNA interaction-aware model was then designed to help identify miRNA binding events. On the test set, our model (auROC = 94.4%) outperformed existing models by at least 2.8% in auROC. Furthermore, we showed that this model can suggest potential binding patterns for miRNA-mRNA sequence interacting pairs. Finally, we made the prepared data sets and the designed model available at http://cosbi2.ee.ncku.edu.tw/mirna_binding/download.

Clinical domain knowledge-derived template improves post hoc AI explanations in pneumothorax classification.

OBJECTIVE: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. METHOD: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of the explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). RESULTS: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. CONCLUSIONS: In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving model explanations. Our approach not only aligns model explanations more closely with clinical insights but also exhibits extensibility to other thoracic diseases. We anticipate that our template guidance will forge a novel approach to elucidating AI models by integrating clinical domain expertise.

The effect of gonadal hormones on the gene expression of brain-pituitary in protandrous black porgy, Acanthopagrus schlegelii.

In black porgy (Acanthopagrus schlegelii), the brain-pituitary-testis (Gnrh-Gths-Dmrt1) axis plays a vital role in male fate determination and maintenance, and then inhibiting female development in further (puberty). However, the feedback of gonadal hormones on regulating brain signaling remains unclear. In this study, we conducted short-term sex steroid treatment and surgery of gonadectomy to evaluate the feedback regulation between the gonads and the brain. The qPCR results show that male phase had the highest gths transcripts; treatment with estradiol-17ß (E2) or 17α-methyltestosterone (MT) resulted in the increased pituitary lhb transcripts. After surgery, apart from gnrh1, there is no difference in brain signaling genes between gonadectomy and sham fish. In the diencephalon/mesencephalon transcriptome, de novo assembly generated 283,528 unigenes; however, only 443 (0.16%) genes showed differentially expressed between sham and gonadectomy fish. In the present study, we found that exogenous sex steroids affect the gths transcription; this feedback control is related to the gonadal stage. Furthermore, gonadectomy may not affect gene expression of brain signaling (Gnrh-Gths axis). Our results support the communication between ovotestis and brain signaling (Gnrh-Gths-testicular Dmrt1) for the male fate.

[Chemical constituents from roots of Kadsura heteroclita].

The dichloromethane fraction of Kadsura heteroclita roots was separated and purified by chromatographic techniques(e.g., silica gel, Sephadex LH-20, ODS, MCI column chromatography) and semi-preparative HPLC. Twenty compounds were isolated from K. heteroclita, and their structures were identified by NMR, MS, UV, and X-ray single crystal diffraction techniques. Twenty compounds were isolated from K. heteroclita, which were identified as xuetongdilactone G(1), mallomacrostin C(2), 3,4-seco(24Z)-cychmrt-4(28),24-diene-3,26-dioic acid 3-methyl ester(3), nigranoic acid(4), methyl ester schizanlactone E(5), schisandronic acid(6), heteroclic acid(7), wogonin(8),(2R,3R)-4'-O-methyldihydroquercetin(9), 15,16-bisnor-13-oxo-8(17),11E-labdadien-19-oic acid(10), stigmast-4-ene-6ß-ol-3-one(11), psoralen(12),(1R,2R,4R)-trihydroxy-p-menthane(13), homovanillyl alcohol(14), 2-(4-hydroxyphenyl)-ethanol(15), coniferaldehyde(16),(E)-7-(4-hydroxy-3-methoxyphenyl)-7-methylbut-8-en-9-one(17), acetovanillone(18), vanillic acid(19) and vanillin(20). Compound 1 is a new compound named xuetongdilactone G. Compounds 2-3 and 8-20 are isolated from K. heteroclita for the first time.

Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures.

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

O-GlcNAcylation of TDP-43 suppresses proteinopathies and promotes TDP-43's mRNA splicing activity.

Pathological TDP-43 aggregation is characteristic of several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD-TDP); however, how TDP-43 aggregation and function are regulated remain poorly understood. Here, we show that O-GlcNAc transferase OGT-mediated O-GlcNAcylation of TDP-43 suppresses ALS-associated proteinopathies and promotes TDP-43's splicing function. Biochemical and cell-based assays indicate that OGT's catalytic activity suppresses TDP-43 aggregation and hyperphosphorylation, whereas abolishment of TDP-43 O-GlcNAcylation impairs its RNA splicing activity. We further show that TDP-43 mutations in the O-GlcNAcylation sites improve locomotion defects of larvae and adult flies and extend adult life spans, following TDP-43 overexpression in Drosophila motor neurons. We finally demonstrate that O-GlcNAcylation of TDP-43 promotes proper splicing of many mRNAs, including STMN2, which is required for normal axonal outgrowth and regeneration. Our findings suggest that O-GlcNAcylation might be a target for the treatment of TDP-43-linked pathogenesis.

Prevalence and the associated factors of cognitive impairment among people living with HIV in Taiwan: a cross-sectional study.

Cognitive impairment impacts the quality of life and increases morbidity and mortality rates. The prevalence of and factors associated with cognitive impairment have become important issues as the age of people living with HIV(PLWH) increases. In 2020, We conducted a cross-sectional study to survey the cognitive impairment among PLWH in three hospitals in Taiwan with Alzheimer Disease-8 (AD8) questionnaire. The average age of 1,111 individuals was 37.54 ± 10.46 years old, and their average duration to live with HIV was 7.12 ± 4.85 years. The rate of impaired cognitive function was 2.25% (N = 25) when AD8 score ≥ 2 was a positive finding for cognitive impairment. Aging (p = .012), being less educated (p = 0.010), and having a longer duration to live with HIV (p = .025) were significantly associated with cognitive impairment. Multivariate logistic regression analysis revealed that only the duration of living with HIV was a significant factor related to the tendency of cognitive impairment (p = .032). The risk of cognitive impairment increased by 1.098 times for every additional year to live with HIV. In conclusion, the prevalence of cognitive impairment among PLWH in Taiwan was 2.25%. Healthcare personnel should be sensitive to the changes in PLWH's cognitive function as they age.

FedScore: A privacy-preserving framework for federated scoring system development.

OBJECTIVE: We propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. MATERIALS AND METHODS: The FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore's performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a pre-existing score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. RESULTS: We compared the acquired FedScore model's performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. CONCLUSION: This study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability.

Association between subclinical epileptiform discharge and the behavioral and psychological symptoms in patients with Alzheimer's dementia.

OBJECTIVES: Behavioral and psychological symptoms of dementia (BPSD) are highly prevalent in patients with Alzheimer's disease (AD), causing burdens on caregivers. Behavioral and psychological symptoms of dementia and subclinical epileptiform discharge (SED) increased with the disease course of AD. However, the interaction between them was still unknown. The present study aimed to evaluate the associations between SED and BPSD. METHODS/DESIGN: Patients with AD from Kaohsiung Municipal Ta-tung Hospital were included in this study. International 10-20 system scalp electroencephalography (EEG) for 13 min was performed to detect SED. Behavioral and psychological symptoms of dementia was assessed by neuropsychiatric inventory (NPI) questionnaires. The occurrence of BPSD subsyndromes was compared between patients with and without SED. RESULTS: Two hundred sixty-three adult patients qualified for the inclusion criteria and were enrolled in this study. The mean age of patients was 80.2 years, and approximately 62% were women. 17.1% of patients showed SED on EEG. Apathy was the most commonly reported BPSD subsyndrome in this cohort. There was no significant difference in the prevalence of BPSD between patients with and without SED. (75.6% vs. 67.4%, p = 0.2806). However, the NPI score of irritability subsyndrome was significantly higher in the SED (+) group (2.6 ± 3.7 vs. 1.2 ± 2.7, p = 0.0028). In addition, subclinical epileptiform discharge in the frontal lobe was associated with a considerably higher occurrence of hyperactivity subsyndrome, including irritability. CONCLUSIONS: SED may not be a direct cause of BPSD, but the presence of SED may affect the manifestation of BPSD.

Globin vector regulatory elements are active in early hematopoietic progenitor cells.

The globin genes are archetypal tissue-specific genes that are silent in most tissues but for late-stage erythroblasts upon terminal erythroid differentiation. The transcriptional activation of the ß-globin gene is under the control of proximal and distal regulatory elements located on chromosome 11p15.4, including the ß-globin locus control region (LCR). The incorporation of selected LCR elements in lentiviral vectors encoding ß and ß-like globin genes has enabled successful genetic treatment of the ß-thalassemias and sickle cell disease. However, recent occurrences of benign clonal expansions in thalassemic patients and myelodysplastic syndrome in patients with sickle cell disease call attention to the non-erythroid functions of these powerful vectors. Here we demonstrate that lentivirally encoded LCR elements, in particular HS1 and HS2, can be activated in early hematopoietic cells including hematopoietic stem cells and myeloid progenitors. This activity is position-dependent and results in the transcriptional activation of a nearby reporter gene in these progenitor cell populations. We further show that flanking a globin vector with an insulator can effectively restrain this non-erythroid activity without impairing therapeutic globin expression. Globin lentiviral vectors harboring powerful LCR HS elements may thus expose to the risk of trans-activating cancer-related genes, which can be mitigated by a suitable insulator.

Association between pain and cognitive and daily functional impairment in older institutional residents: a cross-sectional study.

BACKGROUND: Pain is often neglected in disabled older population, especially in Taiwan where the population of institutional residents is rapidly growing. Our study aimed to investigate pain prevalence and associated factors among institutional residents to improve pain assessment and management. METHODS: This nationwide study recruited 5,746 institutional residents in Taiwan between July 2019 and February 2020. Patient self-report was considered the most valid and reliable indicator of pain. A 5-point verbal rating scale was used to measure pain intensity, with a score ranging from 2 to 5 indicating the presence of pain. Associated factors with pain, including comorbidities, functional dependence, and quality of life, were also assessed. RESULTS: The mean age of the residents was 77.1 ± 13.4 years, with 63.1% of them aged over 75 years. Overall, 40.3% of the residents reported pain, of whom 51.2% had moderate to severe pain. Pain was more common in residents with comorbidities and significantly impacted emotions and behavior problems, and the mean EQ5D score, which is a measure of health-related quality of life (p < .001). Interestingly, pain was only related to instrumental activities of daily living (IADL) and not activities of daily living (ADL). On the other hand, dementia was significantly negatively associated with pain (p < .001), with an estimated odds of 0.63 times (95% CI: 0.53-0.75) for the presence of pain when compared to residents who did not have dementia. CONCLUSIONS: Unmanaged pain is common among institutional residents and is associated with comorbidities, IADL, emotional/behavioral problems, and health-related quality of life. Older residents may have lower odds of reporting pain due to difficulty communicating their pain, even through the use of a simple 5-point verbal rating scale. Therefore, more attention and effort should be directed towards improving pain evaluation in this vulnerable population .

A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control.

Due to the outstanding characteristics of the large structural flexibility and strong dexterity of soft robots, they have attracted great attention. However, the dynamic modeling and precise control of soft robots face huge challenges. Traditional model-based and model-free control methods find it difficult to obtain a balance between complexity and accuracy. In this paper, a dynamic model of a three-chamber continuous pneumatic manipulator is established based on the modal method. Moreover, a hybrid controller integrating model predictive control (MPC) and iterative learning control (ILC) is proposed, which can simultaneously perform model parameter learning and trajectory tracking control. Experimental results show that the proposed control method can optimize the parameters of the dynamic model in real time with less iterations than the traditional model-free method and have good control performance in trajectory tracking experiments. In the future, the proposed dynamic model and the hybrid controller should be verified on a multi-section manipulator.

Developing Biomarkers for the Skin: Biomarkers for the Diagnosis and Prediction of Treatment Outcomes of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory decline and cognitive impairment. Research on biomarkers can aid in early diagnosis, monitoring disease progression, evaluating treatment efficacy, and advancing fundamental research. We conducted a cross-sectional longitudinal study to see if there is an association between AD patients and age-matched healthy controls for their physiologic skin characteristics, such as pH, hydration, transepidermal water loss (TEWL), elasticity, microcirculation, and ApoE genotyping. The study used the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating-Sum of the Boxes (CDR-SB) scales as references to quantify the presence of disease, if any. Our findings demonstrate that AD patients have a dominantly neutral pH, greater skin hydration, and less elasticity compared to the control subjects. At baseline, the tortuous capillary percentage negatively correlated with MMSE scores in AD patients. However, AD patients who carry the ApoE E4 allele and exhibit a high percentage of tortuous capillaries and capillary tortuous numbers have shown better treatment outcomes at six months. Therefore, we believe that physiologic skin testing is a rapid and effective way to screen, monitor progression, and ultimately guide the most appropriate treatment for AD patients.

Transient brain-wide coactivations and structured transitions revealed in hemodynamic imaging data.

Brain-wide patterns in resting human brains, as either structured functional connectivity (FC) or recurring brain states, have been widely studied in the neuroimaging literature. In particular, resting-state FCs estimated over windowed timeframe neuroimaging data from sub-minutes to minutes using correlation or blind source separation techniques have reported many brain-wide patterns of significant behavioral and disease correlates. The present pilot study utilized a novel whole-head cap-based high-density diffuse optical tomography (DOT) technology, together with data-driven analysis methods, to investigate recurring transient brain-wide patterns in spontaneous fluctuations of hemodynamic signals at the resolution of single timeframes from thirteen healthy adults in resting conditions. Our results report that a small number, i.e., six, of brain-wide coactivation patterns (CAPs) describe major spatiotemporal dynamics of spontaneous hemodynamic signals recorded by DOT. These CAPs represent recurring brain states, showing spatial topographies of hemispheric symmetry, and exhibit highly anticorrelated pairs. Moreover, a structured transition pattern among the six brain states is identified, where two CAPs with anterior-posterior spatial patterns are significantly involved in transitions among all brain states. Our results further elucidate two brain states of global positive and negative patterns, indicating transient neuronal coactivations and co-deactivations, respectively, over the entire cortex. We demonstrate that these two brain states are responsible for the generation of a subset of peaks and troughs in global signals (GS), supporting the recent reports on neuronal relevance of hemodynamic GS. Collectively, our results suggest that transient neuronal events (i.e., CAPs), global brain activity, and brain-wide structured transitions co-exist in humans and these phenomena are closely related, which extend the observations of similar neuronal events recently reported in animal hemodynamic data. Future studies on the quantitative relationship among these transient events and their relationships to windowed FCs along with larger sample size are needed to understand their changes with behaviors and diseased conditions.

Brain-wide neural co-activations in resting human.

Spontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of  < 0.1 Hz. However, understanding of fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and transitional characteristics remain limited due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain functions. Using the state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and transitional functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6 Hz, 5 Hz, and 10 Hz) were embedded in the nonstationary dynamics of these functional states. We further identified a superstructure that regulated between-state immediate and long-range transitions involving the entire set of identified cCAPs and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich dynamic structures of brain-wide human neural activations.

Electrophysiological resting state brain network and episodic memory in healthy aging adults.

Recent studies have emphasized the changes in large-scale brain networks related to healthy aging, with the ultimate purpose to aid in differentiating normal neurocognitive aging from neurodegenerative disorders that also arise with age. Emerging evidence from functional Magnetic Resonance Imaging (fMRI) indicates that connectivity patterns within specific brain networks, especially the Default Mode Network (DMN), distinguish those with Alzheimer's disease from healthy individuals. In addition, disruptive alterations in the large-scale brain systems that support high-level cognition are shown to accompany cognitive decline at the behavioral level, which is commonly observed in the aging populations, even in the absence of disease. Although fMRI is useful for assessing functional changes in brain networks, its high costs and limited accessibility discourage studies that need large populations. In this study, we investigated the aging-effect on large-scale networks of the human brain using high-density electroencephalography and electrophysiological source imaging, which is a less costly and more accessible alternative to fMRI. In particular, our study examined a group of healthy subjects in the age range from middle- to older-aged adults, which is an under-studied range in the literature. Employing a high-resolution computation model, our results revealed age associations in the connectivity pattern of DMN in a consistent manner with previous fMRI findings. Particularly, in combination with a standard battery of cognitive tests, our data showed that in the posterior cingulate / precuneus area of DMN higher brain connectivity was associated with lower performance on an episodic memory task. The findings demonstrate the feasibility of using electrophysiological imaging to characterize large-scale brain networks and suggest that changes in network connectivity are associated with normal aging.

BindSpace decodes transcription factor binding signals by large-scale sequence embedding.

The decoding of transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF labels into the same space. By training on binding data from hundreds of TFs and embedding over 1 M DNA sequences, BindSpace achieves state-of-the-art multiclass binding prediction performance, in vitro and in vivo, and can distinguish between signals of closely related TFs.