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The construction of an energy distribution network can improve the system's ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network needs accurate distribution network model parameters as support. At present, the installation of PMU equipment in China's distribution network synchronous phase angle measurement unit is limited, which brings challenges to the parameter correction of the distribution network. In this paper, an automatic correction algorithm of distribution network parameters based on the CIM model and measurement data is proposed for the distribution network system without PMU. Firstly, the distribution network topology construction technology based on XML files and key fields of the distribution network is proposed, and the non/small impedance devices (such as switches) are merged and reduced. The breadth-first traversal algorithm is used to verify the connectivity of the constructed topology. Then, based on the topology construction and the least squares method, an iterative parameter correction technique is constructed. Finally, the accuracy and effectiveness of the proposed algorithm are verified in a standard IEEE 33-bus system distribution network and an example of the China Southern Power Grid. The topology connections constructed based on the CIM model have significantly enhanced the efficiency of parameter correction. [ABSTRACT FROM AUTHOR]

Simple Summary: Spirometra mansoni is a neglected human-infective tapeworm. Its motile larva can invade humans, causing a parasitic zoonosis known as sparganosis. Cestoda parasites live in an oxygen-deprived environment in the gastrointestinal tract of their hosts; they mainly depend on energy production from glucose to lactate catalyzed by a series of enzymes under anaerobic conditions. Meanwhile, lactate dehydrogenase (LDH) is a complicated multi-gene family and plays key roles in oxidative production capacity and detoxification pathways in organisms. The purpose of this study was to investigate the gene diversity, phylogenetic patterns and molecular characteristics of the S. mansoni lactate dehydrogenase (SmLDH) protein family to explore the potential important roles of SmLDH in the growth and development of S. mansoni. The plerocercoid of Spirometra mansoni can parasitize both human and animals, resulting in sparganosis. Lactate dehydrogenase (LDH) is an important enzyme in parasites. However, our knowledge of the LDH family in S. mansoni is still inadequate. This work identified 19 new LDH members in S. mansoni. Clustering analysis demonstrated that all SmLDHs were divided into two main groups, which is consistent with the patterns of conserved motif organization. According to RT-qPCR, 2 LDHs were highly expressed in the plerocercoid stage and 17 LDHs were highly expressed in the adult stage. The evolutionary tree showed a high level of diversity of both cestode and trematode LDHs. SmLDHs contained both conserved family members and members in the process of further diversification. rSmLDH has a NAD-binding domain and a substrate-binding domain. The protein was immunolocalized in the epidermis of the pleroceroid and in the tegument, uterus and egg shell of adult worms. The optimum activity for rSmLDH in the pyruvate reduction reaction was found to be pH 4.5 and 37 °C. In the oxidation reaction, optimal values for pH and temperature were 9.0 and 30 °C, respectively. Gossypol was found to be the most powerful inhibitor in both reduction and oxidation reactions. The results provide a basis for the further study of the biological roles of LDHs in S. mansoni and other LDH-containing taxa. [ABSTRACT FROM AUTHOR]

Introduction: Winter wheat plays a crucial role in ensuring food security and sustainable agriculture. Accurate identification and recognition of winter wheat in remote sensing images are essential for monitoring crop growth and yield estimation. In recent years, attention-based convolutional neural networks have shown promising results in various image recognition tasks. Therefore, this study aims to explore the application of attention-based convolutional neural networks for winter wheat identification on GF-2 high-resolution images and propose improvements to enhance recognition accuracy. Method: This study built a multi-band winter wheat sample dataset based on GF-2 images. In order to highlight the characteristics of winter wheat, this study added two bands, NDVI and NDVIincrease, to the dataset and proposed a SUNet network model. In this study, the batch normalization layer was added to the basic structure of the UNet convolutional network to speed up network convergence and improve accuracy. In the jump phase, shuffle attention was added to the shallow features extracted from the coding structure for feature optimization and spliced with the deep features extracted by upsampling. The SUNet made the network pay more attention to the important features to improve winter wheat recognition accuracy. In order to overcome the sample imbalance problem, this study used the focus loss function instead of the traditional cross-entropy loss function. Result: The experimental data show that its mean intersection over union, overall classification accuracy, recall, F1 score and kappa coefficient are 0.9514, 0.9781, 0.9707, 0.9663 and 0.9501, respectively. The results of these evaluation indicators are better than those of other comparison methods. Compared with the UNet, the evaluation indicators have increased by 0.0253, 0.0118, 0.021, 0.0185, and 0.0272, respectively. Conclusion: The SUNet network can effectively improve winter wheat recognition accuracy in multi-band GF-2 images. Furthermore, with the support of a cloud platform, it can provide data guarantee and computing support for winter wheat information extraction. [ABSTRACT FROM AUTHOR]

The ability to track the changes of the surrounding environment is critical for humans and animals to adapt their behaviors. In high-dimensional environments, the interactions between each dimension need to be estimated for better perception and decision making, for example in volatile or social cognition tasks. We develop a hierarchical Bayesian model for inferring and decision making in multi-dimensional volatile environments. The hierarchical Bayesian model is composed of a hierarchical perceptual model and a response model. Using the variational Bayes method, we derived closed-form update rules. These update rules also constitute a complete predictive coding scheme. To validate the effectiveness of the model in multi-dimensional volatile environments, we defined a probabilistic gambling task modified from a two-armed bandit. Simulation results demonstrated that an agent endowed with the proposed hierarchical Bayesian model is able to infer and to update its internal belief on the tendency and volatility of the sensory inputs. Based on the internal belief of the sensory inputs, the agent yielded near-optimal behavior following its response model. Our results pointed this model a viable framework to explain the temporal dynamics of human decision behavior in complex and high dimensional environments. [ABSTRACT FROM AUTHOR]

Spiral ganglion neurons (SGNs) are important for hearing, and their peripheral and central processes connect sensory cells of the Corti organ to the central nervous system. The resulting network forms a point-to-point auditory conduction. As a cardiac hormone, brain natriuretic peptide (BNP) binds to natriuretic peptide receptor type A leading to diuresis, vasodilatation, inhibition of renin and aldosterone production, and cardiac and vascular myocyte growth. This study primarily aimed to explore the expression and function of BNP in the rat's inner ear and elucidate its regulatory mechanism. We determined the expression and function of BNP and found that the vitamin D receptor (VDR) could upregulate the expression of BNP and enhance its function. In SGNs of the rat inner ear, BNP promotes neuron survival and prolongs neurite length through the cGMP-PKG signaling pathway, which could be regulated by VDR and provide a novel approach for neuronal regeneration therapy. To the best of our knowledge, this is the first study to report this potential transcriptional regulatory relationship and will act as a reference for research on neuronal regeneration therapy for SGNs injury. [ABSTRACT FROM AUTHOR]

Simple Summary: In two cohorts of vaccinated cancer patients and healthcare workers, 5% had COVID-19 infection after vaccination. These infections occurred more frequently in younger cancer patients with gastrointestinal cancer, gynecological or breast cancer, or a localized cancer and in patients receiving chemotherapy or targeted therapy when vaccinated. In both cohorts, these breakthrough infections occurred early after initiation of vaccination (Alpha SARS-CoV-2 variant) or several months after the end of vaccination (Omicron SARS-CoV-2 variant). In both cohorts these COVID-19 cases in vaccinated individuals were not severe, with only four cancer patients requiring oxygen therapy. In a multicenter prospective cohort of cancer patients (CP; n = 840) and healthcare workers (HCWs; n = 935) vaccinated against COVID-19, we noticed the following: i/after vaccination, 4.4% of HCWs and 5.8% of CP were infected; ii/no characteristic was associated with post-vaccine COVID-19 infections among HCWs; iii/CP who developed infections were younger, more frequently women (NS), more frequently had gastrointestinal, gynecological, or breast cancer and a localized cancer stage; iv/CP vaccinated while receiving chemotherapy or targeted therapy had (NS) more breakthrough infections after vaccination than those vaccinated after these treatments; the opposite was noted with radiotherapy, immunotherapy, or hormonotherapy; v/most COVID-19 infections occurred either during the Alpha wave (11/41 HCW, 20/49 CP), early after the first vaccination campaign started, or during the Omicron wave (21/41 HCW, 20/49 CP), more than 3 months after the second dose; vi/risk of infection was not associated with values of antibody titers; vii/the outcome of these COVID-19 infections after vaccination was not severe in all cases. To conclude, around 5% of our CPs or HCWs developed a COVID-19 infection despite previous vaccination. The outcome of these infections was not severe. [ABSTRACT FROM AUTHOR]

Two series of pleuromutilin derivatives were designed and synthesized as inhibitors against Staphylococcus aureus (S. aureus). 6-chloro-4-amino-1-R-1H-pyrazolo[3,4-d]pyrimidine or 4-(6-chloro-1-R-1H-pyrazolo[3,4-d]pyrimidine-4-yl)amino-phenylthiol were connected to pleuromutilin. A diverse array of substituents was introduced at the N-1 position of the pyrazole ring. The in vitro antibacterial activities of these semisynthetic derivatives were evaluated against two standard strains, Methicillin-resistant Staphylococcus aureus (MRSA) ATCC 43300, Staphylococcus aureus (S. aureus), ATCC 29213 and two clinical S. aureus strains (144, AD3) using the broth dilution method. Compounds 12c, 19c and 22c (MIC = 0.25 μg/mL) manifested good in vitro antibacterial ability against MRSA which was similar to that of tiamulin (MIC = 0.5 μg/mL). Among them, compound 22c killed MRSA in a time-dependent manner and performed faster bactericidal kinetics than tiamulin in time–kill curves. In addition, compound 22c exhibited longer PAE than tiamulin, and showed no significant inhibition on the cell viability of RAW 264.7, Caco-2 and 16-HBE cells at high doses (≤8 μg/mL). The neutropenic murine thigh infection model study revealed that compound 22c displayed more effective in vivo bactericidal activity than tiamulin in reducing MRSA load. The molecular docking studies indicated that compound 22c was successfully localized inside the binding pocket of 50S ribosomal, and four hydrogen bonds played important roles in the binding of them. [ABSTRACT FROM AUTHOR]

Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series characteristics, while CNN only collects waveform characteristics, and only uses CNN for gait recognition, this leads to a certain lack of time-series characteristics. LSTM can collect time-series characteristics, but LSTM results in performance degradation when processing long sequences. However, using CNN can compress the length of feature vectors. In this paper, a sequential convolution LSTM network for gait recognition using multimodal wearable inertial sensors is proposed, which is called SConvLSTM. Based on 1D-CNN and a bidirectional LSTM network, the method can automatically extract features from the raw acceleration and gyroscope signals without a manual feature design. 1D-CNN is first used to extract the high-dimensional features of the inertial sensor signals. While retaining the time-series features of the data, the dimension of the features is expanded, and the length of the feature vectors is compressed. Then, the bidirectional LSTM network is used to extract the time-series features of the data. The proposed method uses fixed-length data frames as the input and does not require gait cycle detection, which avoids the impact of cycle detection errors on the recognition accuracy. We performed experiments on three public benchmark datasets: UCI-HAR, HuGaDB, and WISDM. The results show that SConvLSTM performs better than most of those reporting the best performance methods, at present, on the three datasets. [ABSTRACT FROM AUTHOR]

Simple Summary: Vaccination against COVID-19 was a major weapon against current epidemics. In a multicenter cohort study conducted in cancer patients (CP) and health care workers (HCW) we demonstrated that: (i) vaccination was well accepted in both cohorts; (ii) the seropositivity rate was high after the first and second injections among HCW and among CP this was only after the second injection; (iii) similar patterns in antibody response followed the second dose; in both groups, antibody levels waned 3 months after the second dose. Overall, two-dose COVID-19 vaccination was effective in both populations studied, but could not totally prevent a few vaccine breakthrough infections, owing to the continuously emerging novel variants. In this prospective, real-life cohort study, we followed 523 cancer patients (CP) and 579 healthcare workers (HCW) from two cancer centers to evaluate the biological and clinical results of the COVID-19 vaccination campaign. Seventy percent of the CP and 90% of the HCW received an mRNA vaccine or the AZD1222 vaccine. Seropositivity was high after the first vaccine among HCW and poor among CP. The second dose resulted in almost 100% seropositivity in both cohorts. Antibody response was higher after the second injection than the first in both populations. Despite at least two doses, 8 CP (1.5%) and 14 HCW (2.4%) were infected, corresponding either to a weak level of antibody or a new strain of virus (particularly the Omicron variant of concern). Sixteen CP and three HCW were hospitalized but none of them died from COVID-19. To conclude, this study showed that two doses of COVID-19 vaccines were crucially necessary to attain sufficient seropositivity. However, the post-vaccination antibody level declines in individuals from the two cohorts and could not totally prevent new SARS-CoV-2 infections. [ABSTRACT FROM AUTHOR]

Background: The presence of the blood–brain barrier (BBB) uniquely distinguishes the brain from other organs, and various brain pathologies, including cancer, can disrupt or breach the BBB. The specific implications of BBB alterations in glioma have not been sufficiently clarified. Methods: In this study, statistical analysis of the TCGA pan-glioma dataset and four other validation cohorts was used to investigate the infiltration of BBB constituent cells (endothelial cells, pericytes and astrocytes) in the glioma tumor microenvironment (TME). Results: We found that the infiltration proportions of the three BBB constituent cell types were highly collinear, which implied alteration of the BBB. Hence, we developed an index, the BBB score, which is calculated based on the infiltration proportion of BBB constituent cells. Furthermore, we observed that patients with higher BBB scores were more likely to be diagnosed with more malignant entities in the TCGA database according to significant molecular features, such as IDH mutation status and 1p/19q deletion. The BBB score was also strikingly positively correlated with WHO grade in other cohorts. More importantly, a higher BBB score correlated with shorter survival time and unfavorable prognosis in glioma patients. Finally, we showed that TME-related pathways may regulate BBB alterations and that coinhibitory immune checkpoints were enriched in samples with higher BBB scores. Conclusions: We showed that TME-related pathways may regulate BBB alterations and that coinhibitory immune checkpoints were enriched in samples with higher BBB scores. Assessing BBB alterations may help elucidate the complex role of the glioma TME and suggest new combination treatment strategies. [ABSTRACT FROM AUTHOR]

Recognizing the importance of incorporating different risk measures in the portfolio management model, this paper examines the dynamic mean-risk portfolio optimization problem using both variance and value at risk (VaR) as risk measures. By employing the martingale approach and integrating the quantile optimization technique, we provide a solution framework for this problem. We demonstrate that, under a general market setting, the optimal terminal wealth may exhibit different patterns. When the market parameters are deterministic, we derive the closed-form solution for this problem. Examples are provided to illustrate the solution procedure of our method and demonstrate the benefits of our dynamic portfolio model compared to its static counterpart. [ABSTRACT FROM AUTHOR]

In recent years, the exercise behavior of Chinese adolescents has been on the decline, which is extremely detrimental to their physical and mental health development. However, few studies have explored the mechanisms by which exercise cognition influences Chinese adolescents' exercise behavior. The present study aimed to investigate the relationship between exercise cognition and exercise behavior among Chinese adolescents and the mediating role of satisfying basic psychological needs for exercise. The study consisted of 996 adolescents (44.6% males, 55.4% females) between the ages of 12 and 15 (M = 13.34, SD = 1.059). Participants' exercise behaviors and the satisfaction of basic psychological needs for exercise data were collected via surveys. Structure equation modeling (SEM) was performed to examine the direct and indirect effects. The results were as follows: (1) adolescents' perceptions of exercise were significantly associated with exercise behavior and (2) the mediation model suggests that the satisfaction of basic psychological needs for exercise is an important mechanism by which exercise cognition influences the occurrence of exercise behavior. Therefore, it is crucial to help adolescents form good exercise cognition. Schools, families, and society should take responsibility for adolescents' formation of good exercise cognition and satisfy adolescents' basic psychological needs for exercise so as to enhance adolescents' exercise behaviors and thereby develop good exercise habits. [ABSTRACT FROM AUTHOR]

Simple Summary: COVID-19 has some clinical manifestations that are similar to the side effects of cancer treatments such that cancer patients may fail to distinguish COVID-19 symptoms from those of their treatments. The PAPESCO-19 study investigated 13 COVID-19 symptoms and confirmed that in combination with anorexia, fever, headache, and rhinorrhea, anosmia has a strong association with COVID-19 for cancer patients while dysgeusia/ageusia does not. Background: Cancer patients may fail to distinguish COVID-19 symptoms such as anosmia, dysgeusia/ageusia, anorexia, headache, and fatigue, which are frequent after cancer treatments. We aimed to identify symptoms associated with COVID-19 and to assess the strength of their association in cancer and cancer-free populations. Methods: The multicenter cohort study PAPESCO-19 included 878 cancer patients and 940 healthcare workers (HCWs). At baseline and quarterly thereafter, they reported the presence or absence of 13 COVID-19 symptoms observed over 3 months and the results of routine screening RT-PCR, and they were systematically tested for SARS-CoV-2-specific antibodies. We identified the symptom combinations significantly associated with COVID-19. Results: Eight percent of cancer patients were COVID-19 positive, and 32% were symptomatic. Among the HCWs, these proportions were 9.5 and 52%, respectively. Anosmia, anorexia, fever, headache, and rhinorrhea together accurately discriminated (c-statistic = 0.7027) COVID-19 cases from cancer patients. Anosmia, dysgeusia/ageusia, muscle pain, intense fatigue, headache, and chest pain better discriminated (c-statistic = 0.8830) COVID-19 cases among the HCWs. Anosmia had the strongest association in both the cancer patients (OR = 7.48, 95% CI: 2.96–18.89) and HCWs (OR = 5.71, 95% CI: 2.21–14.75). Conclusions: COVID-19 symptoms and their diagnostic performance differ in the cancer patients and HCWs. Anosmia is associated with COVID-19 in cancer patients, while dysgeusia/ageusia is not. Cancer patients deserve tailored preventive measures due to their particular COVID-19 symptom pattern. [ABSTRACT FROM AUTHOR]

Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB). [ABSTRACT FROM AUTHOR]

The morphology of the active layer plays a crucial role in determining device performance and stability for organic solar cells. All-polymer solar cells (All-PSCs), showing robust and stable morphologies, have been proven to give better thermal stability than their fullerene counterparts. However, outstanding thermal stability is not always the case for polymer blends, and the limiting factors responsible for the poor thermal stability in some All-PSCs, and how to obtain higher efficiency without losing stability, still remain unclear. By studying the morphology of poly [2,3-bis (3-octyloxyphenyl) quinoxaline-5,8-diyl-alt-thiophene-2,5-diyl](TQ1)/poly[4,8-bis[5-(2-ethylhexyl)-2-thienyl]benzo[1,2-b:4,5-b′]dithiophene-alt-(4-(2-ethylhexyl)-3-fluorothieno[3,4-b]thiophene-)-2-carboxylate-2-6-diyl]] (PCE10)/PNDI-T10 blend systems, we found that the rearranged molecular packing structure and phase separation were mainly responsible for the poor thermal stability in devices containing PCE10. The TQ1/PNDI-T10 devices exhibited an improved PCE with a decreased π–π stacking distance after thermal annealing; PCE10/PNDI-T10 devices showed a better pristine PCE, however, thermal annealing induced the increased π–π stacking distance and thus inferior hole conductivity, leading to a decreased PCE. Thus, a maximum PCE could be achieved in a TQ1/PCE10/PNDI-T10 (1/1/1) ternary system after thermal annealing resulting from their favorable molecular interaction and the trade-off of molecular packing structure variations between TQ1 and PCE10. This indicates that a route to efficient and thermal stable All-PSCs can be achieved in a ternary blend by using material with excellent pristine efficiency, combined with another material showing improved efficiency under thermal annealing. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is an innovative technique, which can provide cloud-computing near mobile devices on the edge of networks. Based on the MEC architecture, this paper proposes an ARIMA-BP-based Selective Offloading (ABSO) strategy, which minimizes the energy consumption of mobile devices while meeting the delay requirements. In ABSO, we exploit an ARIMA-BP model for estimating computation capacity of the edge cloud, and then design a Selective Offloading Algorithm for obtaining offloading strategy. Simulation results reveal that the ABSO can apparently decrease the energy consumption of mobile devices in comparison with other offloading methods. [ABSTRACT FROM AUTHOR]

To study the contribution of moisture from different straw-treated and irrigated soil layers to the water consumption of winter wheat in dry farming, a 2-year straw treatment and regulated deficit irrigation experiment was implemented. The field experiment was carried out with 0% (S0), 1% (S1), and 2% (S2) straw returning amounts, and 75 mm (V3), 60 mm (V2), and 45 mm (V1) irrigation volumes. This experiment involved nine treatments, used to quantitatively analyze the ratio and variation of soil water use from different soil layers via the direct contrast method (DCM) and the multiple linear mixed model (MLMM). The results show the following: (1) The distribution of precipitation isotope compositions displayed a repeated trend of first decreasing and then increasing during the study period. Regression analysis showed that the local meteoric water line (LMWL): δD = 6.37δ18O − 3.77 (R2 = 0.832). (2) With increasing soil depth, the δ18O value decreased gradually, and the maximum δ18O value of the soil water within each growth period was distributed at 10 cm. (3) Under the same irrigation amount, δ 18O increased with increasing straw return at 0–20 cm and decreased with increasing straw return at 20–80 cm. (4) The comparison results of the DCM and MLMM were consistent. During the jointing and flowering stages, 0–30 cm soil water was the main source of water for winter wheat. The contribution of soil water below 30 cm had a decreasing trend from the jointing stage to the flowering stage. The average contribution rates of the 0–30 cm soil layer during the jointing and flowering stages were 23.07% and 23.15%, respectively. These findings have important implications for studying the soil water cycle in the context of farming. [ABSTRACT FROM AUTHOR]

The soil-water characteristic curve is the basic constitutive relation to express the water-holding characteristics of ion-absorbed rare earth. The simulated solution mining test and pressure plate apparatus tests were carried out, the relationship between matric suction and water content under different leaching actions was obtained. Using the Fredlund and Xing four-parameter model, the soil-water characteristic curves of ion-absorbed rare earth under different leaching actions were obtained, and the changing trend of each parameter was analyzed, and the influence of different leaching methods on the water-holding characteristic of soil was obtained. For different types of leaching solutions, the water-holding capacity of the soil varies from strong to weak for pure water, the 3% magnesium sulfate solution, and the 3% ammonium sulfate solution; as the concentration of the leaching solution increases, the water-holding capacity of the sample gradually decreases, and it decreases most significantly from 0% to 2%. Moreover, the saliency of the "hysteresis effect" of the soil-water characteristic curve was from high to low for pure water, the 3% magnesium sulfate solution, and the 3% ammonium sulfate solution, and the "hysteresis effect" showed a decreasing trend with the increase in the concentration. [ABSTRACT FROM AUTHOR]

The natural adsorption material montmorillonite (MMT) was selected, and cellulose acetate (CA) was used as the loading substrate to design and prepare a kind of green and environment-friendly recyclable porous composite fiber membrane with good heavy metal ion adsorption performance. Acetic acid modified montmorillonite (HCl-MMT), sodium dodecyl sulfonate modified montmorillonite (SDS-MMT), and chitosan modified montmorillonite (CTS-MMT) were prepared by inorganic modification and organic modification, and the porous MMT/CA composite fiber membrane was constructed by centrifugal spinning equipment. The morphological and structural changes of MMT before and after modification and their effects on porous composite fiber membranes were investigated. The morphology, structure, and adsorption properties of the composite fibers were characterized by scanning electron microscopy (SEM) and atomic absorption spectrometry (ASS). The experimental results showed that the maximum adsorption capacity of Cu2+ on the prepared 5 wt% CTS-MMT composite fiber membrane was 60.272 mg/g after 10 h static adsorption. The adsorption of Cu2+ by a porous composite fiber membrane conforms to the quasi-second-order kinetic model and Langmuir isothermal adsorption model. The main factor of the Cu2+ adsorption rate is chemical adsorption, and the adsorption mechanism is mainly monolayer adsorption. [ABSTRACT FROM AUTHOR]

Hashing has been widely used for large-scale remote sensing image retrieval due to its outstanding advantages in storage and search speed. Recently, deep hashing methods, which produce discriminative hash codes by building end-to-end deep convolutional networks, have shown promising results. However, training these networks requires numerous labeled images, which are scarce and expensive in remote sensing datasets. In order to solve this problem, we propose a deep unsupervised hashing method, namely deep contrastive self-supervised hashing (DCSH), which uses only unlabeled images to learn accurate hash codes. It eliminates the need for label annotation by maximizing the consistency of different views generated from the same image. More specifically, we assume that the hash codes generated from different views of the same image are similar, and those generated from different images are dissimilar. On the basis of the hypothesis, we can develop a novel loss function containing the temperature-scaled cross-entropy loss and the quantization loss to train the developed deep network end-to-end, resulting in hash codes with semantic similarity preserved. Our proposed network contains four parts. First, each image is transformed into two different views using data augmentation. After that, they are fed into an encoder with the same shared parameters to obtain deep discriminate features. Following this, a hash layer converts the high-dimensional image representations into compact binary codes. Lastly, a novel hash function is introduced to train the proposed network end-to-end and thus guide generated hash codes with semantic similarity. Extensive experiments on two popular benchmark datasets of the UC Merced Land Use Database and the Aerial Image Dataset have demonstrated that our DCSH has significant superiority in remote sensing image retrieval compared with state-of-the-art unsupervised hashing methods. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD), one of the most common types of chronic liver disease, is strongly correlated with obesity, insulin resistance, metabolic syndrome, and genetic components. The pathological progression of NAFLD, consisting of non-alcoholic fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), and liver cirrhosis, is characterized by a broad spectrum of clinical phenotypes. Although patients with mild NAFL are considered to show no obvious clinical symptoms, patients with long-term NAFL may culminate in NASH and further liver fibrosis. Even though various drugs are able to improve NAFLD, there are no FDA-approved medications that directly treat NAFLD. In this paper, the pathogenesis of NAFLD, the potential therapeutic targets, and their underlying mechanisms of action were reviewed. [ABSTRACT FROM AUTHOR]

A new type of Janus cellulose acetate (CA) fiber membrane was used to separate oil–water emulsions, which was prepared with plasma gas phase grafting by polymerizing octamethylcyclotetrasiloxane (D4) onto a CA fiber membrane prepared by centrifugal spinning. The Janus–CA fiber membrane was described in terms of chemical structure using Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) analysis, energy dispersive X-ray spectroscopy (EDX) analysis and morphology by field emission scanning electron microscopy (FESEM). In this contribution, we examine the influence of spinning solution concentration, spinning speed and nozzle aperture on the centrifugal spinning process and the fiber morphology. Superhydrophobic/hydrophilic Janus–CA fiber membrane was used to separate water and 1,2-dibromoethane mixture and Toluene-in-water emulsion. Unidirectional water transfer Janus–CA fiber membrane was used to separate n-hexane and water mixture. The separation for the first-time interception rate was about 98.81%, 98.76% and 98.73%, respectively. Experimental results revealed that the Janus cellulose acetate (CA) fiber membrane gave a permeate flux of about 43.32, 331.72 and 275.27 L/(m2·h), respectively. The novel Janus–CA fiber membrane can potentially be used for sustainable W/O emulsion separation. We believe that this is a facile strategy for construction of filtration materials for practical oil–water separation. [ABSTRACT FROM AUTHOR]

Cotton is one of the most important fiber and oil crops in the world. Chloroplast genomes harbor their own genetic materials and are considered to be highly conserved. Transfer RNAs (tRNAs) act as "bridges" in protein synthesis by carrying amino acids. Currently, the variation and evolutionary characteristics of tRNAs in the cotton chloroplast genome are poorly understood. Here, we analyzed the structural variation and evolution of chloroplast tRNA (cp tRNA) based on eight diploid and two allotetraploid cotton species. We also investigated the nucleotide evolution of chloroplast genomes in cotton species. We found that cp tRNAs in cotton encoded 36 or 37 tRNAs, and 28 or 29 anti-codon types with lengths ranging from 60 to 93 nucleotides. Cotton chloroplast tRNA sequences possessed specific conservation and, in particular, the Ψ-loop contained the conserved U-U-C-X3-U. The cp tRNAs of Gossypium L. contained introns, and cp tRNAIle contained the anti-codon (C-A-U), which was generally the anti-codon of tRNAMet. The transition and transversion analyses showed that cp tRNAs in cotton species were iso-acceptor specific and had undergone unequal rates of evolution. The intergenic region was more variable than coding regions, and non-synonymous mutations have been fixed in cotton cp genomes. On the other hand, phylogeny analyses indicated that cp tRNAs of cotton were derived from several inferred ancestors with greater gene duplications. This study provides new insights into the structural variation and evolution of chloroplast tRNAs in cotton plants. Our findings could contribute to understanding the detailed characteristics and evolutionary variation of the tRNA family. [ABSTRACT FROM AUTHOR]

The leading cause of heart failure is cardiomyopathy and damage to the cardiomyocytes. Adult mammalian cardiomyocytes have the ability to regenerate, but this cannot wholly compensate for myocardial cell loss after myocardial injury. Studies have shown that exercise has a regulatory role in the activation and promotion of regeneration of healthy and injured adult cardiomyocytes. However, current research on the effects of aerobic exercise in myocardial regeneration is not comprehensive. This review discusses the relationships between aerobic exercise and the regeneration of cardiomyocytes with respect to complex molecular and cellular mechanisms, paracrine factors, transcriptional factors, signaling pathways, and microRNAs that induce cardiac regeneration. The topics discussed herein provide a knowledge base for physical activity-induced cardiomyocyte regeneration, in which exercise enhances overall heart function and improves the efficacy of cardiac rehabilitation. [ABSTRACT FROM AUTHOR]