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Central nervous system (CNS) injuries, including stroke, traumatic brain injury, and spinal cord injury, are leading causes of long-term disability. It is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated limb. Previous studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote recovery. However, the ability to increase plasticity in the injured brain is restricted and difficult to improve. Therefore, over several decades, researchers have been prompted to enhance the compensation by the unaffected hemisphere. Animal experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor function. In addition, several clinical treatments have been designed to restore ipsilateral motor control, including brain stimulation, nerve transfer surgery, and brain–computer interface systems. Here, we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.

Promoter is a key DNA element located near the transcription start site, which regulates gene transcription by binding RNA polymerase. Thus, the identification of promoters is an important research field in synthetic biology. Nannochloropsis is an important unicellular industrial oleaginous microalgae, and at present, some studies have identified some promoters with specific functions by biological methods in Nannochloropsis, whereas few studies used computational methods. Here, we propose a method called DNPPro (DenseNet-Predict-Promoter) based on densely connected convolutional neural networks to predict the promoter of Nannochloropsis. First, we collected promoter sequences from six Nannochloropsis strains and removed 80% similarity using CD-HIT for each strain to yield a reliable set of positive datasets. Then, in order to construct a robust classifier, within-group scrambling method was used to generate negative dataset which overcomes the limitation of randomly selecting a non-promoter region from the same genome as a negative sample. Finally, we constructed a densely connected convolutional neural network, with the sequence one-hot encoding as the input. Compared with commonly used sequence processing methods, DNPPro can extract long sequence features to a greater extent. The cross-strain experiment on independent dataset verifies the generalization of our method. At the same time, T-SNE visualization analysis shows that our method can effectively distinguish promoters from non-promoters.

The ability to meet higher effluent quality requirements and the reduction of energy consumption are the biggest challenges in wastewater treatment worldwide. A large proportion of the energy generated during wastewater treatment processes is neglected and lost in traditional wastewater treatment plants. As a type of energy harvesting system, triboelectric nanogenerators (TENGs) can extensively harvest the microscale energies generated from wastewater treatment procedures and auxiliary devices. This harvested energy can be utilized to improve the removal efficiency of pollutants through photo/electric catalysis, which has considerable potential application value in wastewater treatment plants. This paper gives an overall review of the generated potential energies (e.g., water wave energy, wind energy, and acoustic energy) that can be harvested at various stages of the wastewater treatment process and introduces the application of TENG devices for the collection of these neglected energies during wastewater treatment. Furthermore, the mechanisms and catalytic performances of TENGs coupled with photo/electric catalysis (e.g., electrocatalysis, photoelectric catalysis) are discussed to realize higher pollutant removal efficiencies and lower energy consumption. Then, a thorough, detailed investigation of TENG devices, electrode materials, and their coupled applications is summarized. Finally, the intimate coupling of self-powered photoelectric catalysis and biodegradation is proposed to further improve removal efficiencies in wastewater treatment. This concept is conducive to improving knowledge about the underlying mechanisms and extending applications of TENGs in wastewater treatment to better solve the problems of energy demand in the future.

Restoring limb movements after central nervous system injury remains a substantial challenge. Recent studies proved that crossing nerve transfer surgery could rebuild physiological connectivity between the contralesional cortex and the paralyzed arm to compensate for the lost function after brain injury. However, the neural mechanism by which this surgery mediates motor recovery remains still unclear. Here, using a clinical mouse model, we showed that this surgery can restore skilled forelimb function in adult mice with unilateral cortical lesion by inducing cortical remapping and promoting corticospinal tract sprouting. After reestablishing the ipsilateral descending pathway, resecting of the artificially rebuilt peripheral nerve did not affect motor improvements. Furthermore, retaining the sensory afferent, but not the motor efferent, of the transferred nerve was sufficient for inducing brain remapping and facilitating motor restoration. Thus, our results demonstrate that surgically rebuilt sensory input triggers neural plasticity for accelerating motor recovery, which provides an approach for treating central nervous system injuries.

Disinfection byproducts (DBPs) are initially formed in the process of chlorination in the drinking water treatment plants (DWTPs), then further formed in the distribution system due to the presence of residual chlorine and reactive organic matters. However, in China, DBPs are monitored in the effluent from the DWTPs, but less is known about concentrations of DBPs in tap water since they are usually monitored once per half a year. The smart water service system is establishing real-time monitoring of water indices, although DBPs are an urgent need, they are difficult to monitor in real-time due to their diversity and complicated detection methods. If the correlation between DBP concentration and routinely real-time monitored water quality parameters (e.g., pH value, residual chlorine, ammonia) can be evaluated, the concentration of DBPs can be predicted, which will strengthen the control of tap water safety. This article comprehensively assessed the physicochemical parameters and the occurrence of DBP formation in the tap water with an 18-month investigation in Z city (China). DBP formation in tap water of different seasons and different water sources were compared. Based on the relationship between DBPs and physicochemical parameters, linear prediction and nonlinear prediction models of trihalomethanes (THMs), haloacetonitriles (HANs) and haloacetic acids (HAAs) were established, and the accuracy of these models was verified by measured data. Finally, the toxicity and carcinogenic and non-carcinogenic health risk assessment of DBPs in tap water were analyzed.

Greenhouse cover pollution with green shading composed of dust, microalgae and bacteria is a severe problem in tropical areas. The shading results in lower greenhouse indoor light intensity reducing the yield and quality of protected horticulture crops. However, few studies have focused on environmentally efficient ways to remove green shading to increase greenhouse production. In this study, five purified microalgae were isolated from the green shading of three greenhouse roofs and were identified using morphological and molecular assessments. The effects of Ultraviolet-C irradiation (UV-C, 254 nm) at doses of 100, 200 and 300 mJ cm

This paper investigates estimation of semiparametric varying-coefficient spatial autoregressive models in which the dependent variable is missing at random. An inverse propensity score weighted sieve two-stage least squares (S-2SLS) estimation with imputation is proposed. The proposed estimators are shown to be consistent, no matter the initial value is taken as the naive S-2SLS estimate or the naive nonlinear least squares estimate, and the asymptotic distribution of the latter is also derived. Simulation studies are carried out to investigate the performance of the proposed estimator. The method is finally exemplified with one real data set on Boston housing prices.

In this study, we examined the prognostic factors affecting outcomes following nerve grafting in high radial nerve injuries. Thirty-three patients with radial nerve injuries at a level distal to the first branch to the triceps and proximal to the posterior interosseous nerve were retrospectively studied. After a follow-up of at least 1 year, 24 patients (73%) obtained M3+ wrist extension, 16 (48%) obtained M3+ finger extension and only ten (30%) obtained M3+ thumb extension. Univariate, multivariate and receiver operating characteristic analyses showed that a delay in the repair of less than 6 months, a defect length of less than 5 cm or when grafted with three or more donor nerve cables achieved better recovery. Number of cables used was related to muscle strength recovery but not time to reinnervation. Nerve grafting for high radial nerve injury achieved relatively good wrist extension but poor thumb extension and is affected by certain prognostic factors. Level of evidence: IV

Macrophages promote early host responses to infection by releasing pro-inflammatory cytokines, and they are crucial to combat amoebiasis, a disease affecting millions of people worldwide. Macrophages elicit pro-inflammatory responses following direct cell/cell interaction of Entamoeba histolytica, inducing NLRP3 inflammasome activation with high-output IL-1β/IL-18 secretion. Here, we found that trophozoites could upregulate peroxiredoxins (Prx) expression and abundantly secrete Prxs when encountering host cells. The C-terminal of Prx was identified as the key functional domain in promoting NLRP3 inflammasome activation, and a recombinant C-terminal domain could act directly on macrophage. The Prxs derived from E. histolytica triggered toll-like receptor 4-dependent activation of NLRP3 inflammasome in a cell/cell contact-independent manner. Through genetic, immunoblotting or pharmacological inhibition methods, NLRP3 inflammasome activation was induced through caspase-1-dependent canonical pathway. Our data suggest that E. histolytica Prxs had stable and durable cell/cell contact-independent effects on macrophages following abundantly secretion during invasion, and the C-terminal of Prx was responsible for activating NLRP3 inflammasome in macrophages. This new alternative pathway may represent a potential novel therapeutic approach for amoebiasis, a global threat to millions.

Objective To explore the feasibility of preheating in 41 ℃ water bath for 30 minutes to correct the red blood cell parameters in the specimens containing high-titer cold agglutinins(CAs). Methods Two specimens containing high-titer CAs were selected during work,and the parameters of complete blood count at room temperature or after preheating in 37 ℃ or 41 ℃ water bath were compared.The smears were stained,and the distribution of red blood cells was observed with a microscope.Further,74 specimens without CAs were collected for complete blood count,and then the test results at room temperature and after preheating at 41 ℃ were compared. Results At room temperature,the specimens containing high-titer CAs showed significantly reduced red blood cell count(RBC)and hematocrit(HCT),abnormally increased mean corpuscular hemoglobin(MCH)and mean cell hemoglobin concentration(MCHC),abnormal percents of hemoglobin(HGB)and RBC,and aggregation of a large number of red blood cells.After being preheated at 37 ℃ for a certain time,the specimens demonstrated obviously improved parameters while still aggregation of a small number of red blood cells.After being preheated at 41 ℃ for 30 minutes,the specimens showed significantly increased RBC,normal HCT,MCH,and MCHC,and evenly distributed red blood cells.The 74 specimens without CAs showed the comparability was ≥80% between room temperature and preheating at 41 ℃ for 30 minutes or 60 minutes. Conclusion We can preheat the specimens containing high-titer CAs in a water bath at 41 ℃ to obtain accurate red blood cell parameters.

Linear regression technique is an efficient method to solve face recognition problem. It’s based on the theory that images in the same class will also belong to same linear subspace and they can be represented through a linear equation. However, this method suffers from some misclassification problems for the infinite ductility of regression equation, moreover, it also doesn’t make a proper and full use of the information in each sample. For overcoming these problems, a novel algorithm named the Distance Weighted Regression Classifier (DWLRC) is proposed here. It can be used for face recognition under different expression and illumination conditions through a distance weighted method, and it can also be used for optimizing the error in the final distance calculating stage. Experiments on three benchmarks show the better performance of our DWLRC compared with the traditional LRC and some state-of-art methods.

In this paper, a novel classifier named Kernel Nearest-Farthest Subspace (KNFS) classifier is proposed for face recognition. Inspired by the kernel-based classifier and the Nearest-Farthest Subspace (NFS) classifier, KNFS can make the sample points to be linear separable by utilizing the kernel function to map linear inseparable sample points in low-dimensional space to high-dimensional kernel space. And it can improve the recognition accuracy of crossed sample points between classes. The algorithm provides the highest reported recognition accuracy on AR and AT&T database. The results are comparable with many other state-of-art face recognition algorithms.

Medium-chain (C6-C10) chemicals are important components of fuels, commodities and fine chemicals. Numerous exciting achievements have proven reversed β-oxidation cycle as a promising platform to synthesize these chemicals. However, under native central carbon metabolism, energetic and redox constraints limit the efficient operation of reversed β-oxidation cycle. Current fermentative platform has to use different chemically and energetically inefficient ways for acetyl-CoA and NADH biosynthesis, respectively. The characteristics such as supplementation of additional acetate and formate or high ATP requirement makes this platform incompatible with large-scale production. Here, an artificial micro-aerobic metabolism for energy and carbon-efficient conversion of glycerol to MCFAs was constructed to present solutions towards these barriers. After evaluating numerous bacteria pathways under micro-aerobic conditions, one synthetic metabolic step enabling biosynthesis of acetyl-CoA and NADH simultaneously, without any energy cost and additional carbon requirement, and reducing loss of carbon to carbon dioxide-emitting reactions, was conceived and successfully constructed. The pyruvate dehydrogenase from Enterococcus faecalis was identified and biochemically characterized, demonstrating the most suitable characteristics. Furthermore, the carbon and energy metabolism in Escherichia coli was rewired by the clustered regularly interspaced short palindromic repeats interference system, inhibiting native fermentation pathways outcompeting this synthetic step. The present engineered strain exhibited a 15.7-fold increase in MCFA titer compared with that of the initial strain, and produced 15.67 g/L MCFAs from the biodiesel byproduct glycerol in 3-L bioreactor without exogenous feed of acetate or formate, representing the highest MCFA titer reported to date. This work demonstrates this artificial micro-aerobic metabolism has the potential to enable the cost-effective, large-scale production of fatty acids and other value-added reduced chemicals.

Acesulfame-K, aspartame, cyclamate, neotame, saccharin, and sucralose are highly water-soluble artificial sweeteners (ASs) that are widely used in consumer products and represent a billion USD global market in 2020. In part because they are poorly removed by conventional water treatment, and acesulfame and sucralose also resist biodegradation and advanced oxidation, multiple ASs co-occur in tap water across the globe, generally at ng/L concentrations but also at µg/L concentrations. Through a review of the literature, this is the first research to integrate taste threshold concentrations with concentrations measured in drinking water to assess if consumers’ tap water will taste sweet. The taste threshold concentrations for acesulfame-K, aspartame, cyclamate, neotame, saccharin, and sucralose are 8.94, 6.59, 105.5, 0.24, and 3.49 mg/L, respectively. While reported concentration of artificial sweeteners in tap water are below their known taste thresholds for sweetness and range from ng/L to hundreds of µg/L, binary combinations of ASs can have a more intense sweetness than one artificial sweetener alone, and hard water can enhance the sweetness of acesulfame-K. Additionally, global production and consumption of ASs is expected to increase 2-5% annually; this increase, when coupled with degradation resistance and persistence, will allow AS concentrations to increase in the water cycle. Thus, in order to maintain consumer satisfaction and confidence, the drinking water industry must be vigilant concerning tastants in their tap water at levels and combinations detectable by consumers.

Distributions of halogenated disinfection byproducts (DBPs) in a two-source water distribution system (WDS) with enhanced chlorination were investigated. The WDS was divided into different sub-service areas based on different electrical conductivity of two water sources. Results clearly show that the principal halogenated DBPs were trihalomethanes (THMs) (5.06–82.69 μg/L), varying within the concentration range as 2–5 times as the levels of haloacetic acids (HAAs) (1.41–61.48 μg/L) and haloacetonitriles (HANs) (0.21–15.13 μg/L). Different water sources, treatment trains, and enhanced chlorination within the WDS had significant effects on seasonal and spatial variations of the DBP distributions over water conveyance. THM and HAA formation followed the sequence of summer > autumn > winter > spring. On the other hand, the DBP spatial distributions were visualized using the ArcGIS enabled Inverse distance weighting technique. The superposition of different DBP spatial distributions allowed for the identification of the high-risk THMs and HAAs areas based on the average values of THMs (27.49 μg/L) and HAAs (14.06 μg/L). Beyond the comprehensive analyses of DBP distribution in a municipal WDS, the project proposed and validated an innovative methodology to locate the DBP high-risk areas and to reveal the effects of different factors on DBPs distribution in a two-source WDS.

Based on the transfer matrix theory and precise integration method, the precise integration transfer matrix method (PITMM) is implemented to investigate the free vibration characteristics of isotropic coupled conicalcylindrical shells. The influence on the boundary conditions, the shell thickness and the semi-vertex conical angle on the vibration characteristics are discussed. Based on the Flügge thin shell theory and the transfer matrix method, the field transfer matrix of cylindrical and conical shells is obtained. Taking continuity conditions at the junction of the coupled conical-cylindrical shell into consideration, the field transfer matrix of the coupled shell is constructed. According to the boundary conditions at the ends of the coupled shell, the natural frequencies of the coupled shell are solved by the precise integration method. An approach for studying the free vibration characteristics of isotropic coupled conical-cylindrical shells is obtained. Comparison of the natural frequencies obtained using the present method with those from literature confirms the validity of the proposed approach. The effects of the boundary conditions, the shell thickness and the semivertex conical angle on vibration characteristics are presented.

Background: Biopsy is considered the gold-standard technique for prostate cancer diagnosis and is recommended in patients with a high clinical indication of prostate cancer. In this study, we aimed to determine the diagnostic efficacy of a novel positron emission tomography (PET)/computed tomography (CT)-guided percutaneous trans-pararectal space-based approach to targeted prostate biopsy. Methods: PET/CT-guided percutaneous trans-pararectal space prostate biopsies were performed in 14 consecutive patients with indications of prostate cancer. Whole-body 18F-FDG PET/CT indicated the presence of 18F-fluorodeoxyglucose (FDG)-avid focal prostate lesions. Two tissue specimens were obtained from each patient. The final diagnoses were established based on the results of a histopathological analysis and clinical follow-up, and these findings were used to verify the diagnostic accuracy of 18F-FDG PET/CT for prostate cancer. Results: The diagnostic accuracy of 18F-FDG PET/CT for prostate cancer was 81.8%. Further analyses of the two biopsied samples per patient led to confirmed histopathological and immunohistochemical diagnoses of prostate cancer in all 14 patients. Consequently, the success rate of PET/CT-guided percutaneous trans-pararectal space prostate-targeted biopsy for the diagnosis of prostate cancer was 100.0% (14/14). Regarding safety, the average duration of biopsy was 20 min, and no serious complications occurred. Conclusions: PET/CT-guided percutaneous trans-pararectal space prostate biopsy may yield a new approach to targeted prostate biopsy for the diagnosis of prostate cancer. Moreover, this biopsy procedure can be performed safely without complications, and is more cost-effective than conventional trans-rectal and trans-perineal prostate biopsy methods.

Summary Penalised likelihood methods, such as the least absolute shrinkage and selection operator (Lasso) and the smoothly clipped absolute deviation penalty, have become widely used for variable selection in recent years. These methods impose penalties on regression coefficients to shrink a subset of them towards zero to achieve parameter estimation and model selection simultaneously. The amount of shrinkage is controlled by the regularisation parameter. Popular approaches for choosing the regularisation parameter include cross-validation, various information criteria and bootstrapping methods that are based on mean square error. In this paper, a new data-driven method for choosing the regularisation parameter is proposed and the consistency of the method is established. It holds not only for the usual fixed-dimensional case but also for the divergent setting. Simulation results show that the new method outperforms other popular approaches. An application of the proposed method to motif discovery in gene expression analysis is included in this paper.

We propose a Random Splitting Model Averaging procedure, RSMA, to achieve stable predictions in high-dimensional linear models. The idea is to use split training data to construct and estimate candidate models and use test data to form a second-level data. The second-level data is used to estimate optimal weights for candidate models by quadratic optimization under non-negative constraints. This procedure has three appealing features: (1) RSMA avoids model overfitting, as a result, gives improved prediction accuracy. (2) By adaptively choosing optimal weights, we obtain more stable predictions via averaging over several candidate models. (3) Based on RSMA, a weighted importance index is proposed to rank the predictors to discriminate relevant predictors from irrelevant ones. Simulation studies and a real data analysis demonstrate that RSMA procedure has excellent predictive performance and the associated weighted importance index could well rank the predictors.