Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis.

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples' transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with l2,1 -norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

MTMG: A multi-task model with multi-granularity information for drug-drug interaction extraction.

Drug-drug interactions (DDIs) extraction includes identifying drug entities and interactions between drug pairs from the biomedical corpus. The discovery of potential DDIs aids in our understanding of the mechanisms underlying adverse reactions or combination therapy to improve patient safety. The manual extraction of DDIs is very time-consuming and expensive; therefore, computer-aided extraction of DDIs is vital. Many neural network-based methods have been proposed and achieved good efficiency in the extraction of DDIs over the years. However, most studies improved the performance of DDIs extraction with various external drug features while directly using golden drug entities, leading to error propagation and low universality in practical application. In this paper, we propose a new multi-task framework called MTMG, which changes DDIs extraction from a sentence-level classification task to a sequence labeling task named Drug-Specified Token Classification (DSTC). The proposed approach, MTMG, jointly trains DSTC with drug named entity recognition (DNER) and two sentence-level auxiliary tasks we designed. We aim to improve the performance of the entire DDIs extraction pipeline by better using the correlation between entities and relationships and, to the extent possible, using the information of varying granularity implied in the dataset. Experimental results show that MTMG can both improve the accuracy of DNER and DDIs extraction and outperforms state-of-the-art technique.

Predicting in-hospital outcomes of patients with acute kidney injury.

Acute kidney injury (AKI) is prevalent and a leading cause of in-hospital death worldwide. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. We develop a deep learning model based on a nationwide multicenter cooperative network across China that includes 7,084,339 hospitalized patients, to dynamically predict the risk of in-hospital death (primary outcome) and dialysis (secondary outcome) for patients who developed AKI during hospitalization. A total of 137,084 eligible patients with AKI constitute the analysis set. In the derivation cohort, the area under the receiver operator curve (AUROC) for 24-h, 48-h, 72-h, and 7-day death are 95·05%, 94·23%, 93·53%, and 93·09%, respectively. For dialysis outcome, the AUROC of each time span are 88·32%, 83·31%, 83·20%, and 77·99%, respectively. The predictive performance is consistent in both internal and external validation cohorts. The model can predict important outcomes of patients with AKI, which could be helpful for the early management of AKI.

[Application of intermittent flap opening in L-shaped surgical incision of calcaneal fracture].

OBJECTIVE: To explore clinical effect of intermittent flap opening technique in L-shaped incision of calcaneal fracture. METHODS: From January 2017 to January 2019, 48 patients with Sanders typeⅡ to Ⅳ calcaneal fractures were treated by open reduction and internal fixation. According to different flap opening techniques, the patients were divided into control group and observation group, 24 patients in each group. In observation group, there were 17 males and 7 females, aged from 20 to 60 years old with an average of(45.12±9.56) years old;7 patients were typeⅡ, 10 patients were type Ⅲ and 7 patients were type Ⅳ according to Sanders classification;3 patients were C0, 16 patients were C1 and 5 patients were C2 according to Tscherne-Gotzen soft-tissue assessment;treated with intermittent flap technique. In control group, there were 19 males and 5 females aged from 20 to 60 years old with an average of (47.32±10.67) years old;7 patients were typeⅡ, 11 patients were type Ⅲ and 6 patients were type Ⅳ according to Sanders classification;2 patients were C0, 18 patients were C1 and 4 patients were C2 according to Tschemc-Gotzen soft-tissue assessment;treated with static flap opening technique. Operation time, flap retraction time, changes of Böhler angle and Gissane angle before and after operation at 3 days, and occurrence of incision complications were observed and compared between two groups. RESULTS: All patients were followed up from 3 to 6 months with an average of(4.52±1.01) months. There were no significant differences in operation time, changes of Böhler angle and Gissane angle before and after operation at 3 days between the two groups(P>0.05);there was statistical difference in flap retraction time between two groups(P<0.05). Occurrence of incision complications in observation group was significantly lower than that in control group (P<0.05). CONCLUSION: Intermittent flap opening technique is superior to static opening technique in reducing incision complications of lateral "L" approach of calcaneus. Single Kirschner wire opening does not affect the exposure, reduction and fixation of fracture during operation.

Effects of Tai Chi Chuan on Cognitive Function in Adults 60 Years or Older With Type 2 Diabetes and Mild Cognitive Impairment in China: A Randomized Clinical Trial.

Importance: Type 2 diabetes (T2D) is associated with the progression of cognitive dysfunction. Physical activity benefits cognition, but no evidence from randomized clinical trials has shown whether tai chi chuan has better long-term benefits than fitness walking in cognitive function for patients with T2D and mild cognitive impairment (MCI). Objective: To compare the effectiveness of tai chi chuan, a mind-body exercise, for improving cognitive function in older adults with T2D and MCI, with fitness walking. Design, Setting, and Participants: This randomized clinical trial was conducted between June 1, 2020, and February 28, 2022, at 4 sites in China. Participants included 328 adults (aged ≥60 years) with a clinical diagnosis of T2D and MCI. Interventions: Participants were randomized in a 1:1:1 ratio to a tai chi chuan group, a fitness walking group, or a control group. The tai chi chuan group received 24-form simplified tai chi chuan. The fitness walking group received fitness walking training. Both exercise groups took the training for 60 min/session, 3 times/wk, for 24 weeks in a supervised setting. All 3 groups were provided with a 30-minute diabetes self-management education session, once every 4 weeks for 24 weeks. The participants were followed up for 36 weeks. Main Outcomes and Measures: The primary outcome was the global cognitive function measured at 36 weeks by the Montreal Cognitive Assessment (MoCA). Secondary outcomes included MoCA at 24 weeks and other cognitive subdomain measures and blood metabolic indices at 24 and 36 weeks. Results: A total of 328 participants (mean [SD] age, 67.55 [5.02] years; mean [SD] T2D duration, 10.48 [6.81] years; 167 [50.9%] women) were randomized to the tai chi chuan group (n = 107), fitness walking group (n = 110), or control group (n = 111) and included in the intention-to-treat analysis. At 36 weeks, the tai chi chuan group showed improved MoCA scores compared with the fitness walking group (mean [SD], 24.67 [2.72] vs 23.84 [3.17]; between-group mean difference, 0.84 [95% CI, 0.02-1.66]; P = .046) in the intention-to-treat analysis. The per-protocol analysis data set and subgroup analysis at 36 weeks showed similar results. Based on the generalized linear models, the treatment effects were similar in each group after adjusting for self-reported dietary calories and physical activity. There were 37 nonserious adverse events (tai chi chuan group, 8; fitness walking group, 13; control group, 16) unrelated to the study with no statistically significant difference among the 3 groups (P = .26). Conclusions and Relevance: In this randomized clinical trial including older adults with T2D and MCI, tai chi chuan was more effective than fitness walking in improving global cognitive function. The findings support a long-term benefit, suggesting the potential clinical use of tai chi chuan as an exercise intervention to improve cognitive function for older adults with T2D and MCI. Trial Registration: ClinicalTrials.gov Identifier: NCT04416841.

Clinical evaluation of total hip arthroplasty for osteoarthropathy secondary to septic hip arthritis.

To investigate the clinical efficacy and prognosis of total hip arthroplasty (THA) in patients with osteoarthropathy secondary to septic hip arthritis. A total of 100 patients with osteoarthropathy secondary to septic hip arthritis who underwent surgery in our hospital from December 2010 to December 2021 were selected. Operative data were summarized. Moreover, preoperative and postoperative differences in Harris score, Western Ontario and McMaster Universities Osteoarthritis Index score, Visual Analogue Scale score, hip mobility were compared. Due to hospital transfer, the epidemic of COVID-2019 and other reasons, 10 patients were lost to follow-up and finally 90 patients were recorded. According to the results, the mean operation time (OT) was 1.78 ± 0.32 (hour), the mean intraoperative blood loss (BL) was 586.62 ± 31.66 (mL), the mean red blood cell (RBC) transfusion was 3.84 ± 0.75 (U), and 2 cases were positive, and 88 cases were negative for Trendelenburg sign. Postoperatively, the unequal length of the lower limbs and the Visual Analogue Scale score were significantly lower than that of the preoperative, while the Western Ontario and McMaster Universities Osteoarthritis Index score, Harris score, and hip joint range of motion were significantly higher than those of the preoperative (P < .05). At the last follow-up, all the incisions healed with no deep infections, 1 case suffered from transient sciatic nerve palsy, 2 cases had intermuscular venous thrombosis, and 2 cases who occurred hip dislocation recovered after treatment. THA surgery for osteoarthropathy secondary to septic hip arthritis has a good effect, relieving the patient's pain effectively, improving prognosis and joint mobility. For osteoarthropathy secondary to septic hip arthritis, THA may be meaningful in terms of prognosis.

[Treatment of acute non-displaced scaphoid fracture of wrist with syringe needle-guided percutaneous cannulated headless hollow compression screw internal fixation].

OBJECTIVE: To investigate the clinical efficacy of needle-guided percutaneous cannulated compression screw fixation in the treatment of acute non-displaced scaphoid fracture of wrist. METHODS: The clinic data of twenty-eight patients with acute non-displaced scaphoid fracture from January 2014 to January 2019 were analyzed retrospectively. According to the intraoperative method of placement of cannulated screw, they were divided into Guide group(16 patients)and Conventional group(12 patients). There were 13 males and 3 females in Guide group, aged from 20 to 60 years old with an average of(31.42±9.71)years old;5 patients were classified as type A2, 3 patients were classified as type B1 and 8 patients were classified as type B2 according to Herbert classification;they were treated with percutaneous cannulated compression screw fixation under the guidance of needle. There were 11 males and 1 female in Conventional group, aged from 23 to 61 years old with an average of(30.51±7.52)years old;5 patients were classified as type A2, 2 patients were classified as type B1 and 5 patients were classified as type B2 according to Herbert classification;they were treated with conventional percutaneous cannulated compression screw fixation. The operation time, screw angle relative to the longitudinal axis of the scaphoid and wrist function score were assessed and compared between the two groups. RESULTS: A total of 28 patients were followed up from 20 to 45 months with an average of (33.00±8.72) months. None of patients had intraoperative complication and incision infection. These patients returned to work gradually 2 weeks after operation, and all fractures healed within 12 weeks. The operation time in the Guide group was significantly less than that in the Conventinal group(P<0.05). Screw angle relative to the longitudinal axis of the scaphoid in the Guide group was significantly smaller than that in the Conventional group(P<0.05). There was no significant difference in Mayo wrist function scores at the last follow-up between the two groups(P>0.05). During the follow-up period, none of the 28 patients showed internal fixation displacement, arthritis, scaphoid necrosis and other complications. CONCLUSION: In the treatment of acute non-displaced scaphoid fractures, the operation time of needle-guided percutaneous cannulated headless compression screw fixation is significantly shorter than that of conventional percutaneous screw fixation, and the screw axis is easier to be parallel to the longitudinal axis of the scaphoid.

Identification of bHLH family genes in Agaricus bisporus and transcriptional regulation of arginine catabolism-related genes by AbbHLH1 after harvest.

Basic helix-loop-helix (bHLH) transcription factors (TFs) are widely distributed in eukaryotes and play an important role in biological growth and development. The identification and functional analyses of bHLH genes/proteins in edible mushrooms (Agaricus bisporus) have yet to be reported. In the present study, we identified 10 putative bHLH members carrying the conserved bHLH domains. Phylogenetic analyses revealed that the 10 AbbHLHs were the closest to sequences of species belonging to 7 different fungal subgroups, which was supported by loop length, intron patterns, and key amino acid residues. The substantial increase after harvest and continuously elevated expression of AbbHLH1 during the development until the disruption of mushroom velum, and the preferential expression in cap and gill tissues suggest the important function of AbbHLH1 in postharvest development of A. bisporus. The relationship of arginine catabolism-related genes with the early stage of postharvest continuing development also was revealed by expression determination. Subcellular localization showed that AbbHLH1 could be localized in nucleus. Importantly, the electrophoretic mobility shift and dual-luciferase reporter assays showed that AbbHLH1 activated the promoters of AbOAT, AbSPDS, and AbSAMDC and suppressed the expression of AbARG, AbUREA, and AbODC, probably for the modulation of arginine catabolism and thus control of postharvest mushroom development. Taken together, the available data provide valuable functional insight into the role of AbbHLH proteins in postharvest mushrooms.

Improved stability of ß-carotene by encapsulation in SHMP-corn starch aerogels.

This study aimed to prepare a starch-based aerogel with microporous network structure, and to investigate its physicochemical properties after ß-carotene encapsulation. Corn starch aerogels (CSA) prepared with sodium hexametaphosphate (SHMP) as a cross-linking agent and ß-carotene encapsulation were evaluated in terms of morphology, long- and short-range molecular order, bioavailability, and stability. After encapsulating ß-carotene, the morphology of SHMP-CSA showed that the aerogels presented agglomeration, and the relative crystallinity increased from 17.2% to 22.2%. The characteristic bands of ß-carotene were not found in the FT-IR pattern, and the short-range molecular order of aerogel was decreased, proving that ß-carotene was well embedded in the aerogel. During the simulated in vitro release process, ß-carotene was almost completely released. After ultraviolet or light irradiation, the retention rate of ß-carotene was much higher than that in the control group. These results demonstrated that SHMP-CSA encapsulation could effectively improve the stability of ß-carotene.

Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis.

Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.

The influence of (H2O)1-2 in the HOBr + HO2 gas-phase reaction.

The HOBr + HO2 reaction in the absence of water has three different channels for the abstraction of H to generate the corresponding products. The dominant channel is the generation of BrO + H2O2. The introduction of water molecules influences this dominant reaction via the way the reactants interact with the water molecules. The addition of water molecules decreases the energy barrier and increases the rate coefficient of the reaction. Interestingly, water works as a catalyst and we obtain BrO + H2O2, like in the reaction without water, or the water works as a reactant and we obtain products other than BrO + H2O2. The rate coefficients of the HOBr + HO2 reaction in the presence of water are calculated to be faster than the reaction in the absence of water. However, other pathways in the presence of water are slower than the reaction in the absence of water. The water-assisted effective rate coefficients for the HOBr + HO2 reaction are also larger than those for the reaction in the absence of water. The influence of a water dimer is not as important when compared with one water molecule. In summary, a single water molecule has a positive catalytic influence in enhancing the HOBr + HO2 reaction.

The influence of a single water molecule on the reaction of BrO + HONO.

Quantum chemical computations and transition state theory are employed to systematically research the influence of a single molecule water on the BrO + HONO reaction. Two distinct reactions, namely BrO + trans-HONO and BrO + cis-HONO are explored for the reaction in the absence of water, which is mainly decided by the configuration of HONO. With introduction a single water molecule to the reaction, the rate coefficient of the channel starting from BrO + cis-HONO and BrO + trans-HONO are 2.43 × 10-19 and 5.22 × 10-22 cm3 molecule-1 s-1, which is larger than the reaction in the absence of water. For further comprehend the impact of water on the BrO + HONO reaction, it is necessary to compute the effective rate coefficient by taking into account the concentration of water. The water-assisted effective rate coefficients for the BrO + HONO reaction are smaller than that the reaction in the absence of water. The reaction of BrO with cis-HONO is feasible both in absence and existence of water.

Low-rank sparse feature selection with incomplete labels for Alzheimer's disease progression prediction.

BACKGROUND: How to predict the cognitive performance of Alzheimer's disease (AD) and identify the informative neuroimaging markers is essential for timely treatment and possible delay of the disease. However, incomplete labeled samples and noises in neuroimaging data pose challenges to building reliable and robust prediction models. In this paper, we present a model named Low-rank Sparse Feature Selection with Incomplete Labels (LSFSIL) for predicting cognitive performance and identifying informative neuroimaging markers with MRI data and incomplete cognitive scores. METHOD: We propose a sparse matrix decomposition method to decompose the incomplete cognitive score matrix into two parts for recovering missing scores and utilizing incomplete labeled data. The former is the recovered cognitive score matrix without missing values. To make the recovered scores close to the real ones, a manifold regularizer is devised to fit the label distribution for capturing the label correlations locally. The latter is a ℓ1-norm regularized matrix which represents the associated errors. Next, a low-rank regression model that regards the recovered matrix as the target is developed to increase the robustness to noises and outliers. Besides, ℓ2,1-norm is introduced into the objective function as a sparse regularization to identify the important features. RESULTS: Experimental results demonstrate that LSFSIL achieves higher performance and outperforms several state-of-the-art feature selection approaches. Moreover, the neuroimaging markers selected by LSFSIL are consistent with the previous AD studies. CONCLUSIONS: LSFSIL is effective in informative neuroimaging marker identification for cognitive performance prediction with incomplete labeled data.

[Selective U-shaped osteotomy of lateral tibial condyle for the treatment of collapse and comminuted fracture of lateral tibial plateau].

OBJECTIVE: To explore methods and clinical effects of selective U-shaped osteotomy of lateral tibial condyle in treating collapse and comminuted fracture of lateral tibial plateau. METHODS: From January 2014 to October 2019, 15 patients with collapse and comminuted fracture of lateral tibial plateau were treated by selective U-shaped osteotomy of lateral tibial condyle, including 9 males and 6 females. The age of patients ranged from 25 to 70 years old, with an average age of (38.5±7.7) years old. According to ABC classification of condyle fracture of tibial plateau lateral, there were 2 cases of type A, 6 cases of type B, 4 cases of type BC and 3 cases of type C. Five patients were combined with medial plateau fracture, 8 patients were combined with left knee fracture and 7 patients of right knee fracture. The time of treatment after injury ranged from 1 day to 14 days with an average of (3.4±1.2) days. CT of all patients showed that lateral tibial plateau collapsed more than 2 mm, more than 2 pieces of bones were crushed and broken, and lateral tibial condyle cortex was intact. At follow-up of 12 months after operation, Rasmussen's anatomical grading system was used to evaluate fracture reduction. Rasmussen's functional grading system were used to evaluate knee joint function. RESULTS: Selective U-shaped osteotomy was successfully complated in 15 patients at one time, and operation time ranged from 55 to 110 min, with an average time of (85.6±20.0) min. The lateral plateau operation ranged from 20 to 60 min with an average time of(30.5±10.5) min. All patients were followed up for 12 to 24 months with an average of (14.6±2.5) months. Fracture healing time was 12 to 24 weeks, with an average of (13.6±3.6) weeks. At follow-up 12 months after operation, by Rasmussen's grading system, anatomical score of knee joint ranged from 14 to 18 points, with an average score of (17.5±0.3) points, of which 13 cases were excellent and 2 cases were good. The functional score ranged from 13 points to 30 points, with an average score of (26.8±2.5) points. Among them, 12 cases were excellent, 1 case was good, 2 cases were fair. Two patients suffered 2 mm and 4 mm loss of lateral tibial plateau, 1 case of knee joint 5 ° valgus, 1 case of stiff joints (10 ° to 100 °). No common peroneal nerve injury, important vascular injury, postoperative infection, internal fixation failure and other serious complications was found. CONCLUSION: The use of selective lateral tibial condyle "U"- shaped osteotomy approach is an effective and reliable method to treat the collapse and comminuted fracture of the lateral tibial plateau. It has the advantages of simple surgical incision, direct fracture exposure, accurate repositioning and fixation, short operation time and few complications.

Multi-Attribute Discriminative Representation Learning for Prediction of Adverse Drug-Drug Interaction.

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue, posing a leading cause of hospitalizations and deaths in healthcare systems. This paper proposes a unified Multi-Attribute Discriminative Representation Learning (MADRL) model for ADDI prediction. Unlike the existing works that equally treat features of each attribute without discrimination and do not consider the underlying relationship among drugs, we first develop a regularized optimization problem based on CUR matrix decomposition for joint representative drug and discriminative feature selection such that the selected drugs and features can well approximate the original feature spaces and the critical factors discriminative to ADDIs can be properly explored. Different from the existing models that ignore the consistent and unique properties among attributes, a Generative Adversarial Network (GAN) framework is then designed to capture the inter-attribute shared and intra-attribute specific representations of adverse drug pairs for exploiting their consensus and complementary information in ADDI prediction. Meanwhile, MADRL is compatible with any kind of attributes and capable of exploring their respective effects on ADDI prediction. An iterative algorithm based on the alternating direction method of multipliers is developed for optimization. Experiments on publicly available dataset demonstrate the effectiveness of MADRL when compared with eleven baselines and its six variants.

Model prediction of herbicide residues in soybean oil: Relationship between physicochemical properties and processing factors.

The distribution and processing factors (PFs) of herbicides in cold-/hot-pressed soybean samples (n = 3) were studied on the laboratory scale. The hot-pressing process was found to have a significant effect on herbicide degradation in soybean samples. Specifically, for highly water-soluble pesticides with pKow > 2 in soybean oil, the PF values were generally > 1. Nonlinear curve fitting revealed that the PFs of herbicides in soybean oil were positively correlated with their octanol-water partition coefficients, but negatively correlated with their water solubility and melting points. A principal component analysis confirmed the dominant parameters among the herbicide PFs during soybean oil production. Using the physicochemical parameters of pesticides, the developed multiple linear regression model gave a fitting accuracy of ≥0.80 for predicting the theoretical PF values of pesticides in soybean oil products (0.39 < RMSE < 0.58). Thus, this model may be applicable for safety risk assessments and establishing maximum residue limits for pesticides in processed products.

Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation.

BACKGROUND: For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on feature engineering and machine learning algorithms. Time-domain and frequency-domain features are extracted from original time series data collected by sensor nodes. For high-dimensional data, feature selection plays an important role in improving the performance of motion recognition. Existing feature selection methods can be categorized into filter and wrapper methods. Wrapper methods usually achieve better performance than filter methods; however, in most cases, they are computationally intensive, and the feature subset obtained is usually optimized only for the specific learning algorithm. OBJECTIVE: This study aimed to provide a feature selection method for motion recognition of upper-limb exercises and improve the recognition performance. METHODS: Motion data from 5 types of upper-limb exercises performed by 21 participants were collected by a customized inertial measurement unit (IMU) node. A total of 60 time-domain and frequency-domain features were extracted from the original sensor data. A hybrid feature selection method by combining filter and wrapper methods (FESCOM) was proposed to eliminate irrelevant features for motion recognition of upper-limb exercises. In the filter stage, candidate features were first selected from the original feature set according to the significance for motion recognition. In the wrapper stage, k-nearest neighbors (kNN), Naïve Bayes (NB), and random forest (RF) were evaluated as the wrapping components to further refine the features from the candidate feature set. The performance of the proposed FESCOM method was verified using experiments on motion recognition of upper-limb exercises and compared with the traditional wrapper method. RESULTS: Using kNN, NB, and RF as the wrapping components, the classification error rates of the proposed FESCOM method were 1.7%, 8.9%, and 7.4%, respectively, and the feature selection time in each iteration was 13 seconds, 71 seconds, and 541 seconds, respectively. CONCLUSIONS: The experimental results demonstrated that, in the case of 5 motion types performed by 21 healthy participants, the proposed FESCOM method using kNN and NB as the wrapping components achieved better recognition performance than the traditional wrapper method. The FESCOM method dramatically reduces the search time in the feature selection process. The results also demonstrated that the optimal number of features depends on the classifier. This approach serves to improve feature selection and classification algorithm selection for upper-limb motion recognition based on wearable sensor data, which can be extended to motion recognition of more motion types and participants.

A Core Drug Discovery Framework from Large-Scale Literature for Cold Pathogenic Disease Treatment in Traditional Chinese Medicine.

Cold pathogenic disease is a widespread disease in traditional Chinese medicine, which includes influenza and respiratory infection associated with high incidence and mortality. Discovering effective core drugs in Chinese medicine prescriptions for treating the disease and reducing patients' symptoms has attracted great interest. In this paper, we explore the core drugs for curing various syndromes of cold pathogenic disease from large-scale literature. We propose a core drug discovery framework incorporating word embedding and community detection algorithms, which contains three parts: disease corpus construction, drug network generation, and core drug discovery. First, disease corpus is established by collecting and preprocessing large-scale literature about the Chinese medicine treatment of cold pathogenic disease from China National Knowledge Infrastructure. Second, we adopt the Chinese word embedding model SSP2VEC for mining the drug implication implied in the literature; then, a drug network is established by the semantic similarity among drugs. Third, the community detection method COPRA based on label propagation is adopted to reveal drug communities and identify core drugs in the drug network. We compute the community size, closeness centrality, and degree distributions of the drug network to analyse the patterns of core drugs. We acquire 4681 literature from China national knowledge infrastructure. Twelve significant drug communities are discovered, in which the top-10 drugs in every drug community are recognized as core drugs with high accuracy, and four classical prescriptions for treating different syndromes of cold pathogenic disease are discovered. The proposed framework can identify effective core drugs for curing cold pathogenic disease, and the research can help doctors to verify the compatibility laws of Chinese medicine prescriptions.

Prognostic Value of miR-1826 in Prostate Cancer and Its Regulatory Effect on Tumor Progression.

PURPOSE: miRNAs can act as oncogenes or tumor suppressors and participate in the development and progression of tumors, thus affecting the prognosis and survival of cancer patients. In this paper, we mainly studied the role of miR-1826 in prostate cancer. PATIENTS AND METHODS: The expression of miR-1826 was studied by quantitative real-time PCR (qRT-PCR). Kaplan-Meier curves were used to analyze the relationship between the expression of miR-1826 and the survival rate of PC patients. Cox regression analysis was used to study the risk factors affecting the prognosis of PC patients. PC cells were transfected with miR-1826 mimic, mimic negative control (mimic NC), miR-1826 inhibitor, or inhibitor NC. The effect of miR-1826 on the proliferation of PC cells was studied by the CCK-8 method and colony formation assay. Transwell assays were used to detect the effect of miR-1826 on the migratory and invasive abilities of tumor cells. RESULTS: The expression of miR-1826 in PC tissues was lower than that in adjacent normal tissues, and that the expression levels of miR-1826 in four PC cell lines were all lower than normal human prostate epithelial cell lines. Patients with low expression of miR-1826 had shorter overall survival compared with those with high expression. The downregulation of miR-1826 promoted PC cell proliferation, migration, and invasion. CONCLUSION: In summary, the low expression of miR-1826 may promote the progression of PC, and the low expression of miR-1826 is also associated with a poor prognosis in PC patients.

Theoretical investigations on mechanisms and kinetics of methylketene with O(3P) reaction in the atmosphere.

The O(3P)-initiated conversion mechanism and dynamics of CH3CHCO were researched in atmosphere by executing density functional theory (DFT) computations. Optimizations of all the species and single-point energy computations were implemented at the B3LYP/6-311++G(d,p) and CCSD(T)/cc-pVTZ level, respectively. The explicit oxidation mechanism was introduced and discussed. The results state clearly that the O(3P) association was more energetically beneficial than the abstraction of H. The rate coefficients over the probable temperature range of 200-3000 K were forecasted by implementing Rice-Ramsperger-Kassel-Marcus (RRKM) theory. Specifically, the total rate coefficient of O(3P) association reactions is 1.19 × 10-11 cm3 molecule-1 s-1 at 298 K, which is consistent with the experimental results (1.16 × 10-11 cm3 molecule-1 s-1). The rate coefficients for the O(3P) with CH2CO, CH3CHCO, and (CH3)2CCO suggest that rate coefficient of ketene derivatives increase with the increase of methylation degree. Graphical abstract.