Altered thalamus functional connectivity in patients with acute unilateral vestibulopathy: a resting-state fMRI study.

Objective: Acute unilateral vestibulopathy (AUVP) is the second leading cause of peripheral vestibular vertigo. Full recovery of AUVP is related to sufficient central vestibular compensation. It has been confirmed that the vestibular nucleus and vestibular cortex are involved in the process of vestibular compensatory in AUVP patients. However, few studies have focused on the functional compensation of thalamus in patients with AUVP. This study aimed to explore the alterations of resting-state functional connectivity (FC) focused on thalamus using functional magnetic resonance imaging (fMRI) in AUVP patients. Methods: Data of 3D-T1 and resting-state fMRI were collected from 40 AUVP patients and 35 healthy controls (HC). Seeds-based (bilateral thalamus) FC was analyzed to investigate the changes in FC between the two groups. Furthermore, we evaluated the associations between altered thalamus FC and clinical features in AUVP patients using Pearson's partial correlation. Results: Compared with HC, AUVP patients showed decreased FC between bilateral thalamus and left insula. We also observed decreased FC between right thalamus and left supramarginal gyrus. Additionally, we found increased FC between left thalamus and right postcentral gyrus (PCG), as well as increased FC between right thalamus and regions of bilateral PCG, right middle frontal gyrus and right middle occipital gyrus in AUVP patients. Furthermore, the FC between left thalamus and left insula was negatively correlated with values of canal paresis in patients with AUVP (p = 0.010, r = -0.434). Conclusion: Our results provided first evidence for the decreased thalamo-vestibular cortex pathway, as well as increased thalamo-somatosensory and thalamo-visual cortex pathway in AUVP patients. These findings help us better understand the underlying mechanisms of central dynamic compensatory following an acute unilateral peripheral vestibular damage.

A one-year relapse prediction model for acute ischemic stroke (AIS) based on clinical big data.

Objective: To develop and evaluate a nomogram prediction model for recurrence of acute ischemic stroke (AIS) within one year. Method: Patients with AIS treated at the second affiliated hospital of Xuzhou Medical University from August 2017 to July 2019 were enrolled. Clinical data such as demographic data, risk factors, laboratory tests, TOAST etiological types, MRI features, and treatment methods were collected. Cox regression analysis was done to determine the parameters for entering the nomogram model. The performance of the model was estimated by receiver operating characteristic curves, decision curve analysis, calibration curves, and C-index. Result: A total of 645 patients were enrolled in this study. Side of hemisphere (SOH, Bilateral, HR = 0.35, 95 % CI = 0.15-0.84, p = 0.018), homocysteine (HCY, HR = 1.38, 95 % CI = 1.29-1.47, p < 0.001), c-reactive protein (CRP, HR = 1.04, 95 % CI = 1.01-1.07, p = 0.013) and stroke severity (SS, HR = 3.66, 95 % CI = 2.04-6.57, p < 0.001) were independent risk factors. The C-index of the nomogram model was 0.872 (se = 0.016). The area under the receiver operating characteristic (ROC)curve at one-year recurrence was 0.900. Calibration curve, decision curve analysis showed good performance of the nomogram. The cutoff value for low or high risk of recurrence score was 1.73. Conclusion: The nomogram model for stroke recurrence within one year developed in this study performed well. This useful tool can be used in clinical practice to provide important guidance to healthcare professionals.

MECOM: A Meta-Completion Network for Fine-Grained Recognition With Incomplete Multi-Modalities.

Our work focuses on tackling the problem of fine-grained recognition with incomplete multi-modal data, which is overlooked by previous work in the literature. It is desirable to not only capture fine-grained patterns of objects but also alleviate the challenges of missing modalities for such a practical problem. In this paper, we propose to leverage a meta-learning strategy to learn model abilities of both fast modal adaptation and more importantly missing modality completion across a variety of incomplete multi-modality learning tasks. Based on that, we develop a meta-completion method, termed as MECOM, to perform multimodal fusion and explicit missing modality completion by our proposals of cross-modal attention and decoupling reconstruction. To further improve fine-grained recognition accuracy, an additional partial stream (as a counterpart of the main stream of MECOM, i.e., holistic) and the part-level features (corresponding to fine-grained objects' parts) selection are designed, which are tailored for fine-grained nature to capture discriminative but subtle part-level patterns. Comprehensive experiments from quantitative and qualitative aspects, as well as various ablation studies, on two fine-grained multimodal datasets and one generic multimodal dataset show our superiority over competing methods. Our code is open-source and available at https://github.com/SEU-VIPGroup/MECOM.

Increased functional connectivity between default mode network and visual network potentially correlates with duration of residual dizziness in patients with benign paroxysmal positional vertigo.

Objective: To assess changes in static and dynamic functional network connectivity (sFNC and dFNC) and explore their correlations with clinical features in benign paroxysmal positional vertigo (BPPV) patients with residual dizziness (RD) after successful canalith repositioning maneuvers (CRM) using resting-state fMRI. Methods: We studied resting-state fMRI data from 39 BPPV patients with RD compared to 38 BPPV patients without RD after successful CRM. Independent component analysis and methods of sliding window and k-means clustering were adopted to investigate the changes in dFNC and sFNC between the two groups. Additionally, temporal features and meta-states were compared between the two groups. Furthermore, the associations between fMRI results and clinical characteristics were analyzed using Pearson's partial correlation analysis. Results: Compared with BPPV patients without RD, patients with RD had longer duration of BPPV and higher scores of dizziness handicap inventory (DHI) before successful CRM. BPPV patients with RD displayed no obvious abnormal sFNC compared to patients without RD. In the dFNC analysis, patients with RD showed increased FNC between default mode network (DMN) and visual network (VN) in state 4, the FNC between DMN and VN was positively correlated with the duration of RD. Furthermore, we found increased mean dwell time (MDT) and fractional windows (FW) in state 1 but decreased MDT and FW in state 3 in BPPV patients with RD. The FW of state 1 was positively correlated with DHI score before CRM, the MDT and FW of state 3 were negatively correlated with the duration of BPPV before CRM in patients with RD. Additionally, compared with patients without RD, patients with RD showed decreased number of states and state span. Conclusion: The occurrence of RD might be associated with increased FNC between DMN and VN, and the increased FNC between DMN and VN might potentially correlate with the duration of RD symptoms. In addition, we found BPPV patients with RD showed altered global meta-states and temporal features. These findings are helpful for us to better understand the underlying neural mechanisms of RD and potentially contribute to intervention development for BPPV patients with RD.

Comprehensive assessment of 11 de novo HiFi assemblers on complex eukaryotic genomes and metagenomes.

Pacific Biosciences (PacBio) HiFi sequencing technology generates long reads (>10 kbp) with very high accuracy (<0.01% sequencing error). Although several de novo assembly tools are available for HiFi reads, there are no comprehensive studies on the evaluation of these assemblers. We evaluated the performance of 11 de novo HiFi assemblers on (1) real data for three eukaryotic genomes; (2) 34 synthetic data sets with different ploidy, sequencing coverage levels, heterozygosity rates, and sequencing error rates; (3) one real metagenomic data set; and (4) five synthetic metagenomic data sets with different composition abundance and heterozygosity rates. The 11 assemblers were evaluated using quality assessment tool (QUAST) and benchmarking universal single-copy ortholog (BUSCO). We also used several additional criteria, namely, completion rate, single-copy completion rate, duplicated completion rate, average proportion of largest category, average distance difference, quality value, run-time, and memory utilization. Results show that hifiasm and hifiasm-meta should be the first choice for assembling eukaryotic genomes and metagenomes with HiFi data. We performed a comprehensive benchmarking study of commonly used assemblers on complex eukaryotic genomes and metagenomes. Our study will help the research community to choose the most appropriate assembler for their data and identify possible improvements in assembly algorithms.

Altered parietal operculum cortex 2 functional connectivity in benign paroxysmal positional vertigo patients with residual dizziness: A resting-state fMRI study.

AIMS: To investigate changes in functional connectivity (FC) focusing on parietal operculum cortex 2 (OP2) in benign paroxysmal positional vertigo (BPPV) patients with residual dizziness (RD) after successful canalith repositioning procedure (CRP). METHODS: High-resolution three-dimensional T1 and resting-state functional magnetic resonance imaging (fMRI) were performed on 55 healthy controls (HCs), 55 BPPV patients with RD, and 55 patients without RD after successful CRP. Seed-based (bilateral OP2) FC was calculated to investigate the changes in FC among the three groups. Additionally, we further explored the associations between abnormal FC and clinical symptoms. RESULTS: One-way analysis of covariance showed significant FC differences among the three groups. Post-hoc analysis showed that patients with RD exhibited decreased FC between left OP2 and regions of left angular gyrus (AG), thalamus, precuneus, middle frontal gyrus (MFG), and right cerebellum posterior lobe (CPL) in comparison with HCs. In addition, compared with patients without RD, patients with RD showed decreased FC between left OP2 and regions of left MFG, AG, middle temporal gyrus, and right CPL. Moreover, in patients with RD, the FC between left thalamus and OP2 was negatively correlated with duration of RD, and the FC between left AG and OP2 was negatively correlated with duration of BPPV. CONCLUSION: BPPV patients with RD showed reduced FC between brain regions involved in vestibular processing and spatial cognition; These results suggested that BPPV patients with RD might have diminished central processing of vestibular information and impaired spatial cognition.

MiR129-5p-loaded exosomes suppress seizure-associated neurodegeneration in status epilepticus model mice by inhibiting HMGB1/TLR4-mediated neuroinflammation.

BACKGROUND: Neuroinflammation contributes to both epileptogenesis and the associated neurodegeneration, so regulation of inflammatory signaling is a potential strategy for suppressing epilepsy development and pathological progression. Exosomes are enriched in microRNAs (miRNAs), considered as vital communication tools between cells, which have been proven as potential therapeutic method for neurological disease. Here, we investigated the role of miR129-5p-loaded mesenchymal stem cell (MSC)-derived exosomes in status epilepticus (SE) mice model. METHODS: Mice were divided into four groups: untreated control (CON group), kainic acid (KA)-induced SE groups (KA group), control exosome injection (KA + Exo-con group), miR129-5p-loaded exosome injection (KA + Exo-miR129-5p group). Hippocampal expression levels of miR129-5p, HMGB1, and TLR4 were compared among groups. Nissl and Fluoro-jade B staining were conducted to evaluate neuronal damage. In addition, immunofluorescence staining for IBA-1 and GFAP was performed to assess glial cell activation, and inflammatory factor content was determined by ELISA. Hippocampal neurogenesis was assessed by BrdU staining. RESULTS: The expression of HMGB1 was increased after KA-induced SE and peaking at 48 h, while hippocampal miR129-5p expression decreased in SE mice. Exo-miR129-5p injection reversed KA-induced upregulation of hippocampal HMGB1 and TLR4, alleviated neuronal damage in the hippocampal CA3, reduced IBA-1 + and GFAP + staining intensity, suppressed SE-associated increases in inflammatory factors, and decreased BrdU + cell number in dentate gyrus. CONCLUSIONS: Exosomes loaded with miR129-5p can protect neurons against SE-mediated degeneration by inhibiting the pro-inflammatory HMGB1/TLR4 signaling axis.

Neoadjuvant chemotherapy combined with immunotherapy versus neoadjuvant chemoradiotherapy in patients with locally advanced esophageal squamous cell carcinoma.

BACKGROUND: To date, few studies have compared effectiveness and survival rates of neoadjuvant chemotherapy combined with immunotherapy (NACI) and conventional neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced esophageal squamous cell carcinoma (ESCC). The present study was conducted to compare therapeutic response and survival between NACI and NCRT. METHODS: The study cohort comprised patients with locally advanced ESCC treated with either NACI or NCRT followed by surgery between June 2018 and March 2021. The 2 groups were compared for treatment response, 3-year overall survival (OS), and disease-free survival (DFS). Survival curves were created using the Kaplan-Meier method, differences were compared using the log-rank test, and potential imbalances were corrected for using the inverse probability of treatment weighting (IPTW) method. RESULTS: Among 202 patients with locally advanced ESCC, 81 received NACI and 121 received conventional NCRT. After IPTW adjustment, the R0 resection rate (85.2% vs 92.3%; P = .227) and the pathologic complete response (pCR) rate (27.5% vs 36.4%; P = .239) were comparable between the 2 groups. Nevertheless, patients who received NACI exhibited both a better 3-year OS rate (91.7% vs 79.8%; P = .032) and a better 3-year DFS rate (87.4% vs 72.8%; P = .039) compared with NCRT recipients. CONCLUSIONS: NACI has R0 resection and pCR rates comparable to those of NCRT and seems to be correlated with better prognosis than NCRT. NACI followed by surgery may be an effective treatment strategy for locally advanced ESCC.

Negatives Make a Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning.

Semi-Supervised Few-Shot Learning (SSFSL) aims to train a classifier that can adapt to new tasks using limited labeled data and a fixed amount of unlabeled data. Various sophisticated methods have been proposed to tackle the challenges associated with this problem. In this paper, we present a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective. We leverage these pseudo-labels to augment the support set, which is typically limited in few-shot tasks, e.g., 1-shot classification. In such label-constrained scenarios, our approach can offer highly accurate negative pseudo-labels. By iteratively excluding negative pseudo-labels one by one, we ultimately derive a positive pseudo-label for each unlabeled sample in our approach. The integration of negative and positive pseudo-labels complements the limited support set, resulting in significant accuracy improvements for SSFSL. Our approach can be implemented in just few lines of code by only using off-the-shelf operations, yet it outperforms state-of-the-art methods on four benchmark datasets. Furthermore, our approach exhibits good adaptability and generalization capabilities when used as a plug-and-play counterpart alongside existing SSFSL methods and when extended to generalized linear models.

A clinical prediction model based on interpretable machine learning algorithms for prolonged hospital stay in acute ischemic stroke patients: a real-world study.

Objective: Acute ischemic stroke (AIS) brings an increasingly heavier economic burden nowadays. Prolonged length of stay (LOS) is a vital factor in healthcare expenditures. The aim of this study was to predict prolonged LOS in AIS patients based on an interpretable machine learning algorithm. Methods: We enrolled AIS patients in our hospital from August 2017 to July 2019, and divided them into the "prolonged LOS" group and the "no prolonged LOS" group. Prolonged LOS was defined as hospitalization for more than 7 days. The least absolute shrinkage and selection operator (LASSO) regression was applied to reduce the dimensionality of the data. We compared the predictive capacity of extended LOS in eight different machine learning algorithms. SHapley Additive exPlanations (SHAP) values were used to interpret the outcome, and the most optimal model was assessed by discrimination, calibration, and clinical utility. Results: Prolonged LOS developed in 149 (22.0%) of the 677 eligible patients. In eight machine learning algorithms, prolonged LOS was best predicted by the Gaussian naive Bayes (GNB) model, which had a striking area under the curve (AUC) of 0.878 ± 0.007 in the training set and 0.857 ± 0.039 in the validation set. The variables sorted by the gap values showed that the strongest predictors were pneumonia, dysphagia, thrombectomy, and stroke severity. High net benefits were observed at 0%-76% threshold probabilities, while good agreement was found between the observed and predicted probabilities. Conclusions: The model using the GNB algorithm proved excellent for predicting prolonged LOS in AIS patients. This simple model of prolonged hospitalization could help adjust policies and better utilize resources.

A comparative study on the features of breast sclerosing adenosis and invasive ductal carcinoma via ultrasound and establishment of a predictive nomogram.

Objective: To analyze the clinical and ultrasonic characteristics of breast sclerosing adenosis (SA) and invasive ductal carcinoma (IDC), and construct a predictive nomogram for SA. Materials and methods: A total of 865 patients were recruited at the Second Hospital of Shandong University from January 2016 to November 2022. All patients underwent routine breast ultrasound examinations before surgery, and the diagnosis was confirmed by histopathological examination following the operation. Ultrasonic features were recorded using the Breast Imaging Data and Reporting System (BI-RADS). Of the 865 patients, 203 (252 nodules) were diagnosed as SA and 662 (731 nodules) as IDC. They were randomly divided into a training set and a validation set at a ratio of 6:4. Lastly, the difference in clinical characteristics and ultrasonic features were comparatively analyzed. Result: There was a statistically significant difference in multiple clinical and ultrasonic features between SA and IDC (P<0.05). As age and lesion size increased, the probability of SA significantly decreased, with a cut-off value of 36 years old and 10 mm, respectively. In the logistic regression analysis of the training set, age, nodule size, menopausal status, clinical symptoms, palpability of lesions, margins, internal echo, color Doppler flow imaging (CDFI) grading, and resistance index (RI) were statistically significant (P<0.05). These indicators were included in the static and dynamic nomogram model, which showed high predictive performance, calibration and clinical value in both the training and validation sets. Conclusion: SA should be suspected in asymptomatic young women, especially those younger than 36 years of age, who present with small-size lesions (especially less than 10 mm) with distinct margins, homogeneous internal echo, and lack of blood supply. The nomogram model can provide a more convenient tool for clinicians.

Liver Metabolomics Analysis Revealing Key Metabolites Associated with Different Stages of Nonalcoholic Fatty Liver Disease in Hamsters.

BACKGROUND AND AIM: Nonalcoholic fatty liver disease (NAFLD) is not only the top cause of liver diseases but also a hepatic-correlated metabolic syndrome. This study performed untargeted metabolomics analysis of NAFLD hamsters to identify the key metabolites to discriminate different stages of NAFLD. METHODS: Hamsters were fed a high-fat diet (HFD) to establish the NAFLD model with different stages (six weeks named as the NAFLD1 group and twelve weeks as the NAFLD2 group, respectively). Those liver samples were analyzed by untargeted metabolomics (UM) analysis to investigate metabolic changes and metabolites to discriminate different stages of NAFLD. RESULTS: The significant liver weight gain in NAFLD hamsters was observed, accompanied by significantly increased levels of serum triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), alanine aminotransferase (ALT), and aspartate aminotransferase (AST). Moreover, the levels of TG, LDL-C, ALT, and AST were significantly higher in the NAFLD2 group than in the NAFLD1 group. The UM analysis also revealed the metabolic changes; 27 differently expressed metabolites were detected between the NAFLD2 and NAFLD1 groups. More importantly, the levels of N-methylalanine, allantoin, glucose, and glutamylvaline were found to be significantly different between any two groups (control, NAFLD2 and NAFLD1). Receiver operating characteristic curve (ROC) curve results also showed that these four metabolites are able to distinguish control, NAFLD1 and NAFLD2 groups. CONCLUSION: This study indicated that the process of NAFLD in hamsters is accompanied by different metabolite changes, and these key differently expressed metabolites may be valuable diagnostic biomarkers and responses to therapeutic interventions.

[Therapeutic effect of recombinant human growth hormone on children with growth hormone deficiency and different pituitary developmental conditions: a prospective study]. / é‡ç»„äººç”Ÿé•¿æ¿€ç´ æ²»ç–—ä¸åŒåž‚ä½“å‘è‚²çŠ¶å†µç”Ÿé•¿æ¿€ç´ ç¼ºä¹ç—‡æ‚£å„¿ç–—æ•ˆçš„å‰çž»æ€§ç ”ç©¶.

OBJECTIVES: To investigate the therapeutic effect of recombinant human growth hormone (rhGH) on children with growth hormone deficiency (GHD) and different pituitary developmental conditions. METHODS: A prospective study was performed on 90 children with GHD who were admitted to Xuchang Maternity and Child Health Hospital from June 2020 to December 2021. According to pituitary height on the median sagittal plane, they were divided into three groups: pituitary dysplasia group (n=45), normal pituitary group (n=31), and enlarged pituitary growth group (n=14). The changes in body height, growth velocity, height standard deviation score and serum levels of insulin-like growth factor binding protein-3 (IGFBP-3) and insulin-like growth factor-1 (IGF-1) were examined after treatment in the above three groups, and the differences of the above indices before and after treatment were compared among the three groups. RESULTS: After treatment, all three groups had significant increases in body height, growth velocity, height standard deviation score, and the serum levels of IGFBP-3 and IGF-1 (P<0.05). Compared with the normal pituitary group, the pituitary dysplasia group and the enlarged pituitary growth group had significantly higher values in terms of the differences in body height, growth velocity, height standard deviation score, IGF-1, and IGFBP-3 before and after treatment (P<0.05). There was no significant difference in the incidence rate of adverse reactions among the three groups (P>0.05). CONCLUSIONS: In GHD children with different pituitary developmental conditions, rhGH can promote bone growth and increase body height, especially in children with pituitary dysplasia and pituitary hyperplasia, with good safety.

Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke.

Acute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and used to develop machine learning (ML) models. The ML model performance was assessed by area under the receiver operating characteristic curve (AUC) and radar plot. Shapley Additive explanations (SHAP) values were used to interpret the importance of all features included in the predictive model. A total of 677 AIS patients were included in the present study. Poor prognosis was observed in 209 patients (30.9%). Six variables, including neuron specific enolase (NSE), homocysteine (HCY), S-100ß, dysphagia, C-reactive protein (CRP), and anticoagulation were included to establish ML models. Six different ML algorithms were tested, and Random Forest model was selected as the final predictive model with the greatest AUC of 0.908. Moreover, according to SHAP results, NSE impacted the predictive model the most, followed by HCY, S-100ß, dysphagia, CRP and anticoagulation. Based on the RF model, an online tool was constructed to predict the prognosis of AIS patients and assist clinicians in optimizing patient treatment. The present study revealed that NSE, HCY, CRP, S-100ß, anticoagulation, and dysphagia were important factors for poor prognosis in AIS patients. ML algorithms were used to develop predictive models for predicting the prognosis of AIS patients, with the RF model presenting the optimal performance.

Comparison of dexmedetomidine and dexamethasone as adjuvants to the ultrasound-guided interscalene nerve block in arthroscopic shoulder surgery: a systematic review and Bayesian network meta-analysis of randomized controlled trials.

Introduction: Interscalene block (ISB) is widely regarded as the gold standard treatment for acute pain following arthroscopic shoulder surgery. However, a single injection of a local anesthetic for ISB may not offer sufficient analgesia. Various adjuvants have been demonstrated to prolong the analgesic duration of the block. Hence, this study aimed to assess the relative efficacy of dexamethasone and dexmedetomidine as adjuncts to prolong the analgesic duration for a single- shot ISB. Methods: The efficacy of adjuvants was compared using a network meta-analysis. The methodological quality of the included studies was evaluated using the Cochrane bias risk assessment tool. A comprehensive search of the PubMed, Cochrane, Web of Science, and Embase databases was conducted with a search deadline of March 1, 2023. Various adjuvant prevention randomized controlled trials have been conducted in patients undergoing interscalene brachial plexus block for shoulder arthroscopic surgery. Results: Twenty-five studies enrolling a total of 2,194 patients reported duration of analgesia. Combined dexmedetomidine and dexamethasone (MD = 22.13, 95% CI 16.67, 27.58), dexamethasone administered perineurally (MD = 9.94, 95% CI 7.71, 12.17), high-dose intravenous dexamethasone (MD = 7.47, 95% CI 4.41, 10.53), dexmedetomidine administered perineurally (MD = 6.82, 95% CI 3.43, 10.20), and low-dose intravenous dexamethasone (MD = 6.72, 95% CI 3.74, 9.70) provided significantly longer analgesic effects compared with the control group. Discussion: The combination of intravenous dexamethasone and dexmedetomidine provided the greatest effect in terms of prolonged analgesia, reduced opioid doses, and lower pain scores. Furthermore, peripheral dexamethasone in prolonging the analgesic duration and lowering opioid usage was better than the other adjuvants when used a single medication. All therapies significantly prolonged the analgesic duration and reduced the opioid dose of a single-shot ISB in shoulder arthroscopy compared with the placebo.

Exoskeleton-Assisted Sit-to-Stand Training Improves Lower-Limb Function Through Modifications of Muscle Synergies in Subacute Stroke Survivors.

Abnormal muscle synergies during sit-to-stand (STS) transitions have been observed post-stroke, which are associated with deteriorated lower-limb function and mobility. Although exoskeletons have been used in restoring lower-limb function, their effects on muscle synergies and lower-limb motor recovery remain unclear. Here, we characterized normal muscle synergy patterns during STS activity in ten healthy adults as a reference, comparing with pathological muscle synergy patterns in ten participants with subacute stroke. Moreover, we assessed the effects of a 3-week exoskeleton-assisted STS training intervention on muscle synergies and clinical scores in seven stroke survivors. We also investigated correlations between neuromuscular complexity of muscle synergies and clinical scores. Our results showed that the STS task involved three motor modules representing distinct biomechanical functions among healthy subjects. In contrast, stroke participants showed 3 abnormal modules for the paretic leg and 2 modules for the non-paretic leg. After the intervention, muscle synergies partially shifted towards the normal pattern observed in healthy subjects on the paretic side. On the non-paretic side, the synergy modules increased to three and neuromuscular coordination improved. Furthermore, the significant intervention-induced increases in Fugl-Meyer Assessment of Lower Extremity and Berg Balance Scale scores were associated with improved muscle synergies on the non-paretic side. These results indicate that the paretic side demonstrates abnormal changes in muscle synergies post-stroke, while the non-paretic side can synergistically adapt to post-stroke biomechanical deviations. Our data show that exoskeleton-based training improved lower-limb function post-stroke by inducing modifications in muscle synergies.

Corrigendum: A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: a real-world retrospective study.

[This corrects the article DOI: 10.3389/fnins.2023.1130831.].

Attribute-Aware Deep Hashing With Self-Consistency for Large-Scale Fine-Grained Image Retrieval.

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods. Moreover, qualitative results demonstrate that not only the obtained hash codes can strongly correspond to certain kinds of crucial properties of fine-grained objects, but also our self-consistency designs can effectively overcome simplicity bias in fine-grained hashing.

Leveraging DNA-Based Nanostructures for Advanced Error Detection and Correction in Data Communication.

This study demonstrates the implementation of the Hamming code using DNA-based nanostructures for error detection and correction in communication systems. The designed DNA nanostructures conduct logical operations to compute check codes and identify and correct erroneous data based on fluorescence signals. The execution of intricate DNA logic operations requires individuals with specialized training. By interpretation of the fluorescence signals generated by the DNA nanostructures, binary language can be extracted, effectively protecting data security. The findings highlight the potential of DNA as a versatile platform for reliable data transmission.