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In this paper, the humidity sensitive material ZIF-67 with rhombic dodecahedron structure was prepared by the co-precipitation method. The crystal structure, elemental composition, and morphology were comprehensively characterized utilizing diverse analytical techniques. The humidity sensing properties were investigated at room temperature (25 °C). The ZIF-67 humidity sensor exhibited preferable humidity sensing performance with a wide linear detection range (11% − 95% relative humidity (RH)), rapid response/recovery time (34 s/211 s), high response (120.05), good stability, and high resolution with a minimum detection limit of 1% relative humidity. The sensor also exhibited good repeatability and stability, demonstrating its potential applications in environmental monitoring, medical practice, aerospace technology, and other domains that require high-precision measurement. In addition, this study proposes a promising candidate material for humidity sensing, exhibiting vital potential in a specific area or application and having the potential to develop cutting-edge humidity sensors. [ABSTRACT FROM AUTHOR]

Rubber plantations are an important component of tropical forest ecosystems and are emerging as crucial contributors to carbon sequestration in the tropics. However, ecosystem respiration (RECO), which constitutes an essential constraint on the carbon fixation capacity of rubber plantations, and its driving mechanisms remains unclear. Therefore, this study developed a data-driven semi-empirical model to simulate rubber plantations RECO utilising eddy covariance flux measurements and was upscaled to Hainan Island using remote sensing images and climatic data. Numerical simulations experiments analysed direct and indirect of climatic factors impacts on rubber plantations RECO. The results showed that the model accurately captured RECO trends and seasonality (R2 = 0.87, RMSE = 1.27 g C m−2 d−1); in the past 19 years, RECO showed a noticeable increase, particularly in the late rainy season and the seasonality of RECO has shown a delayed pattern. The RECO in the central region (52% area) exhibited multimodal enhancement while northern and southern regions (37% area) showed oscillations or decreases. Temporally, RECO is higher in the rainy season compared to the dry season, and spatially, RECO is higher in the southern region than in the northern region. Among the climatic factors, water conditions (rainfall and air humidity) have been emerged as dominant factors (5.38%) influencing RECO, surpassing temperature (3.96%) and radiation (3.81%). In addition, climatic factors make a positive overall contribution during the dry season but perform oppositely in the rainy season. This study offers theoretical and technical insights into high carbon sink management in rubber plantations and carbon sequestration in tropical forests ecosystems. [ABSTRACT FROM AUTHOR]

Industrialization and agricultural demand have both improved human life and led to environmental contamination. Especially the discharge of a lot of poisonous and harmful gases, including ammonia, ammonia pollution has become a pressing problem. High concentrations of ammonia can pose significant threats to both the environment and human health. Therefore, accurate monitoring and detection of ammonia gas are crucial. To address this challenge, we have developed an ammonia gas sensor using In(OH)3/Ti3C2Tx nanocomposites through an in-situ electrostatic self-assembly process. This sensor was thoroughly characterized using advanced techniques like XRD, XPS, BET, and TEM. In our tests, the I/M-2 sensor exhibited remarkable performance, achieving a 16.8% response to 100 ppm NH3 at room temperature, which is a 3.5-fold improvement over the pure Ti3C2Tx MXene sensor. Moreover, it provides swift response time (20 s), high response to low NH3 concentrations (≤ 10 ppm), and excellent long-term stability (30 days). These exceptional characteristics indicate the immense potential of our In(OH)3/Ti3C2Tx gas sensor in ammonia detection. [ABSTRACT FROM AUTHOR]

Introduction and Hypothesis: Myofascial pelvic pain (MFPP), characterized by sensitive trigger points in the pelvic floor muscles, leads to chronic pain and affects various aspects of life. Despite the availability of different treatment modalities, there is limited comparative research on their effectiveness. This study compares radiofrequency (RF) therapy and myofascial manual therapy (MMT) in treating MFPP. We aimed to evaluate pelvic floor muscle strength changes, clinical symptoms, and patient comfort during treatment. Methods: The study involved 176 participants, divided equally into RF and MMT groups. We assessed pelvic floor pain using the Visual Analogue Scale (VAS), muscle strength using the Modified Oxford Scale (MOS) and surface electromyography (sEMG), clinical symptom improvement through questionnaires, and patient discomfort during treatment. Results: Both RF and MMT groups significantly reduced pelvic floor and paraurethral muscle pain (VAS scores, p < 0.001). RF treatment significantly decreased vaginal laxity in its group (p < 0.001), with no notable change in the MMT group (p = 0.818). RF therapy also resulted in greater patient comfort than MMT (p < 0.001). Although both treatments improved clinical symptoms, there was no significant difference between the two (p = 0.692). MOS scores and pelvic floor sEMG values showed no significant differences between the groups before and after treatment (p > 0.05). Conclusions: Both RF and MMT effectively alleviate pelvic floor pain and improve clinical symptoms in MFPP patients. RF therapy, however, offers additional benefits in reducing vaginal laxity and enhancing treatment comfort. [ABSTRACT FROM AUTHOR]

Due to the well-known domain shift problem, directly deploying a trained multi-modal classifier to a new environment usually leads to poor performance. The existing multi-modal domain adaption methods not only lack the fine-grained information of cross-modal data distribution, but also lack the cross-modal correlation research. Therefore, this paper proposes a multi-modal domain adaption method based on parameter fusion and two-step alignment (PFTS) to solve the related problems. The consistency of network parameters is used to enhance the correlation among modalities, and a higher-order moment measurement is introduced to improve the alignment of data distribution at the fine-grained level. In addition, the weighting of each modality is further carried out to achieve focused transfer. Comprehensive experiments based on multi-modal datasets with different domain adaption settings have been conducted, the results show that the precision of PFTS is 5.38% higher than state-of-the-art multi-modal domain adaption methods. [ABSTRACT FROM AUTHOR]

Soil microbial communities are engineers of important biogeochemical processes and play a critical role in regulating the functions and stability of forest ecosystem. However, few studies have assessed microbial interactions during forest conversion, which is essential to the understanding of the structure and function of soil microbiome. Herein, we investigated the co-occurrence network pattern and putative functions of fungal and bacterial communities in forest-transforming areas (five sites that cover the typical forests) using high-throughput sequencing of the ITS genes and 16S rRNA. Our study showed that the bacterial network had higher average connectivity and more links than fungal network, which might indicate that the bacterial community had more complex internal interactions compared with fungal one. Alphaproteobacteria_unclassfied, Telmatobacter, 0319-6A21 and Latescibacteria_unclassfied were the keystone taxa in bacterial network. For the fungal community network, the keystone taxon was Ceratobasidium. A structural equation model indicated that the available potassium and total organic carbon were important soil environmental factors, which affected all microbial modules, including bacterial and fungi. Total nitrogen had significant effects on the bacterial module that contains a relatively rich group of nitrogen cycling functions, and pH influenced the bacterial module which have higher potential functions of carbon cycling. And, more fungal modules were directly affected by forest structure (S Tree) compared with bacterial ones. This study provides new insights into our understanding of the feedback of underground creatures to forest conversion and highlights the importance of microbial modules in the nutrient cycling process. [ABSTRACT FROM AUTHOR]

Background: Musculoskeletal disorders after childbirth are common, but current studies often have a narrow focus, concentrating on particular areas and neglecting a thorough evaluation of pain locations and overall severity. This research aimed to determine the occurrence, spread, severity, and root causes of musculoskeletal discomfort in females during the 6–8 week period after giving birth, focusing on investigating the link between pain and posture. Methods: This study collected data from 432 postpartum women, 6–8 weeks post-delivery, focusing on ten posture angles captured photographically and analysed using Exbody software. Participants also filled out structured questionnaires on pregnancy history, the Short Form McGill Pain Questionnaire (SF-MPQ) scores, physical activity patterns, and involvement in household and neonatal care tasks. Results: In our research, 49.8% of the respondents experienced pain after childbirth in different regions of their bodies. Utilising SF-MPQ, the mean Pain Rating Index was 7.35 (SD = 5.93) and Present Pain Intensity and Visual Analog Scale was 3.13 (SD = 2.09). Among the evaluated postural angles, only the Q-angle exhibited a noteworthy correlation with knee discomfort. Individuals with less involvement in household and newborn care tasks had a significantly lower occurrence of postpartum pain, with a decrease of 76% (OR = 0.243, p = 0.001). Similarly, those who shared these responsibilities had a 53% decreased likelihood (OR = 0.468, p = 0.008) of experiencing postpartum pain. Conclusion: Many postpartum women experience moderate-intensity pain in various body regions. Pain's correlation with posture was limited. Reducing physical strain during infant care notably decreased postpartum pain, underscoring the need for holistic support for postpartum women. [ABSTRACT FROM AUTHOR]

The aggregation of nanoscale zero-valent iron (NZVI) is one of the biggest challenges for its application when treating contaminants in aquatic environment. We report a study on synthesis of sodium carbonate-modified biochar (BC-600) combined with sodium alginate (SA)-modified NZVI (SA/NZVI@BC-600) for the removal of tetracycline (TC). When the initial concentration of TC was 20 mg/L, 100% TC was removed by SA/NZVI@BC-600 at an initial pH of 7 under room temperature of 25 °C within 90 min. In addition, the reactivity of the SA/NZVI@BC-600 composites toward TC removal was not obviously declined after 4 cycles. SA/NZVI@BC-600 shows high reactivity, stability, and reusability. This excellent performance of SA/NZVI@BC-600 was related to the addition of SA and BC-600. The best performance of the SA/NZVI@BC-600 system was observed under weakly acidic and neutral conditions. Increasing the initial concentration and lowering the reaction temperature had a slight negative effect on the removal of TC by SA/NZVI@BC-600. In addition, the presence of CO32− and HCO3− had a significant negative effect on the degradation of TC. Meanwhile, ·OH and ·O2− played the leading role in TC degradation. This study not only reported a novel strategy of synthesizing an excellent BC modified NZVI based catalyst but also evaluated its promising application for antibiotic degradation in aqueous solution. [ABSTRACT FROM AUTHOR]

Emphatic temporal-difference (ETD) learning (Sutton et al. 2016) is a pioneering off-policy reinforcement learning method involving the use of the followon trace. The recently proposed gradient emphasis learning (GEM, Zhang et al. 2020) algorithm is used to fix the problems of unbounded variance and large emphasis approximation error introduced by the followon trace from the perspective of stochastic approximation. In this paper, we rethink GEM and introduce a novel generalized GEM(β ) algorithm to learn the true emphasis. The key to the construction of the generalized GEM(β ) algorithm is introducing a tunable hyperparameter β that is not necessarily the same as the discount factor γ to the GEM operator. We then apply the emphasis estimated by the proposed GEM(β ) algorithm to the value estimation gradient and the policy gradient, respectively, yielding the corresponding ETD variant for off-policy evaluation (OPE) and actor-critic algorithm for off-policy control. Finally, we empirically demonstrate the advantages of the proposed algorithms on the diagnostic linear OPE benchmark as well as classic control tasks with neural network function approximators. [ABSTRACT FROM AUTHOR]

Antenatal exposure to air pollutants is thought to be associated with a variety of maternal blood markers as well as adverse birth outcomes. However, the dysgenic influence of air pollutants on the antiphospholipid syndrome (APS) in mothers and their pregnancy outcomes remains unclear. In the current study, 371 mother-infant pairs (189 healthy: 182 APS) from Nanjing Maternal and Child Health Hospital as well as air pollutants concentration from their living environment were used to investigate correlations between air pollution with maternal blood indicators and fetal birth weight in the groups of APS and healthy mothers. Generalized linear model was used to evaluate the contributions of air pollutant exposure during pregnancy to the blood indicators variation. The relationships between birth weight with specific air pollutant and blood index were analyzed using ridge regression. Results showed that APS fetal birth weight was significantly impacted by air pollutant exposure during pregnancy, in particular, the birth weight decreased significantly along with increasing fine particulate matter 2.5 (PM2.5) and fine particulate matter 10 (PM10) exposure concentrations throughout pregnancy. In contrast, birth weight increased significantly with sulfur dioxide (SO2) exposure. In addition, APS-related blood indicators comprised of platelet distribution width (PDW), total bilirubin (TBIL), mean platelet volume (MPV), platelet-larger cell ratio (P_LCR), homocysteine (HCY), alkaline phosphatase (ALP), direct bilirubin (DBIL), basophilic granulocyte (BAS), platelet thrombocytocrit (PCT), preprandial glucose levels (OGTT0), monocytes (MON), and monocytes ratio (MON_ratio) were also strongly related with prenatal exposure to PM2.5 and PM10, in which PDW levels showed most strongly negative impaction on fetal birth weight. Together, we showed that prenatal exposure to air pollutant (PM2.5 and PM10) may exacerbate the poor birth outcomes of low birth weight by impacting APS maternal blood indicators especially for PDW. [ABSTRACT FROM AUTHOR]

Off-policy learning, where the goal is to learn about a policy of interest while following a different behavior policy, constitutes an important class of reinforcement learning problems. It is well-known that emphatic temporal-difference (TD) learning is a pioneering off-policy reinforcement learning method involving the use of the followon trace. Although the gradient emphasis learning (GEM) algorithm has recently been proposed to fix the problems of unbounded variance and large emphasis approximation error introduced by the followon trace from the perspective of stochastic approximation. This approach, however, is limited to a single gradient-TD2-style update instead of considering the update rules of other GTD algorithms. Overall, it remains an open question on how to better learn the emphasis for off-policy learning. In this paper, we rethink GEM and investigate introducing a novel two-time-scale algorithm called TD emphasis learning with gradient correction (TDEC) to learn the true emphasis. Further, we regularize the update to the secondary learning process of TDEC and obtain our final TD emphasis learning with regularized correction (TDERC) algorithm. We then apply the emphasis estimated by the proposed emphasis learning algorithms to the value estimation gradient and the policy gradient, respectively, yielding the corresponding emphatic TD variants for off-policy evaluation and actor-critic algorithms for off-policy control. Finally, we empirically demonstrate the advantage of the proposed algorithms on a small domain as well as challenging Mujoco robot simulation tasks. Taken together, we hope that our work can provide new insights into the development of a better alternative in the family of off-policy emphatic algorithms. [ABSTRACT FROM AUTHOR]

Particle-attached (PA) and free-living (FL) bacterial communities play essential roles in the biogeochemical cycling of essential nutrients in aquatic environments. However, little is known about the factors that drive the differentiation of bacterial lifestyles, especially in flooding lake systems. Here we assessed the compositional and functional similarities between the FL and PA bacterial fractions in a typical flooding lake—the Poyang Lake (PYL) of China. The results revealed that PA communities had significantly different compositions and functions from FL communities in every hydrological period, and the diversity of both PA and FL communities was affected mainly by the water regime rather than bacterial lifestyles. PA communities were more diverse and enriched with Proteobacteria and Bacteroidetes, while FL communities had more Actinobacteria. There was a higher abundance of photosynthetic and nitrogen-cycling bacterial groups in PA communities, but a higher abundance of members involved in hydrocarbon degradation, aromatic hydrocarbon degradation, and methylotrophy in FL communities. Water properties (e.g., temperature, pH, total phosphorus) significantly regulated the lifestyle variations of PA and FL bacteria in PYL. Collectively, our results have demonstrated a clear ecological differentiation of PA and FL bacterial communities in flooding lakes, suggesting that the connectivity between FL and PA bacterial fractions is water property-related rather than water regime-related. [ABSTRACT FROM AUTHOR]

Laportea bulbifera, a Miao medicine grown in karst areas, has exerted a unique curative effect on skin itching in the elderly, with an annual sales of > 100 million Yuan. Owing to the shortage of resources and large morphological variations in L. bulbifera, it is difficult to identify the species correctly using only traditional methods, which seriously affects the safety of drug usage for patients. This study obtained the complete high-quality L. bulbifera chloroplast (cp) genome, using second- and third-generation high-throughput sequencing. The cp genome was 149,911 bp in length, with a typical quadripartite structure. A total of 127 genes were annotated, including 83 protein-coding genes, 36 tRNA genes, and 8 rRNA genes. There was an inverted small single copy (SSC) structure in the L. bulbifera cp genome, one large-scale rearrangement of ~ 39 kb excised in the SSC and IR regions. The complete cp genome sequence is used as a potentially effective super-barcode and the highly variable regions (ycf1, matK, and ndhD) can be used as potentially specific barcodes to accurately distinguish L. bulbifera from counterfeits and closely related species. This study is important for the identification of L. bulbifera and lays a theoretical foundation for elucidating the phylogenetic relationship of the species. [ABSTRACT FROM AUTHOR]

Purpose: Bryophytes play an important role in post-mining vegetation succession in metal-mining areas. As a pioneer group, bryophytes can prevent soil erosion, increase soil nutrition, and affect subsequent vegetation succession. This study investigated the diversity and distribution of bryophytes in the ion-type rare earth mining area of southern Jiangxi Province in southeast China to reveal: 1) the key environmental factors influencing bryophytes recovery, 2) the effect of bryophytes recovery through community structure composition, and 3) the most dominant and therefore suitable groups for early vegetation restoration. Methods: Sampling was carried out in five mining areas. In each area, the diversity and distribution of bryophytes in five typical habitats [virgin field (VF), orchard, ammonium deposition leaching (ADL), ammonium in situ leaching (AISL), and cropland] were analyzed. Results: A total of 118 species of bryophytes belonging to 66 genera and 36 families were collected and identified. Endemic species were found in all habitats in the mining area, and the dominant bryophytes were common species in all habitats. The bryophyte diversity showed habitat-related changes, in the following order: cropland > orchard > VF > AISL > ADL. A structural equation model showed that soil moisture and slope location directly affected bryophyte diversity. Bryophyte communities with upright growth were the dominant groups that adapted to the harsh post-mining environment. Edaphic factors had a greater influence on the bryophyte distribution than topographic and biological factors, at both the overall and habitat scale. Soil moisture was the most important factor affecting the overall distribution of bryophytes in the mining area. There were clear differences in the importance of factors affecting the bryophyte community structure in different habitats. Conclusion: Although mining activities significantly reduce bryophyte diversity, bryophyte communities adapt to the lack of water and moisture by adjusting their life-form structure and species composition. The common species in mining areas might be suitable materials for local vegetation restoration. Deliberate manipulation of the terrain, nutrient supplementation, and reducing the concentrations of harmful substances will help accelerate the early succession of vegetation in leached soil. [ABSTRACT FROM AUTHOR]

Purpose: The home-field advantage (HFA) of litter decomposition is widespread in various terrestrial ecosystems, but whether it is present in wetland ecosystems and what the influencing factors are is less well-known. Here we conducted a 165-day transplanted litter decomposition experiment in a wetland ecosystem to explore the HFA effect and its relationships to litter quality and decomposer community structure. Methods: Three types of litter (sedge, silvergrass, and reed leaves) were transplanted in the Poyang Lake Wetlands of China, and the decomposition rate was accessed by periodic recovery of the remained litter samples. The bacterial and eukaryotic decomposer communities were determined by the rRNA gene sequencing method. Extracellular enzyme activities from litters were continuously monitored during decomposition. Co-occurrence networks were constructed to identify the keystone taxa. Results: We found that the HFA of litter decomposition was prevalent in wetlands, with home-field advantage for sedge and silvergrass litters, while home-field disadvantage for reed litter. The primary litter quality (especially TN and cellulose), enzymatic activity, and nutrient release pattern all affected the HFA effect. The most significant variation of decomposer community occurred among decay times, followed by litter types and decay sites. The difference in decomposer community composition among litters decreased with the decomposition time. The bacterial communities were significantly different among decay sites for all litters, while the eukaryotic communities were similar among decay sites until 165 days. The bacterial community was more sensitive to the HFA, while the eukaryotic community mainly contributed to the decomposition rate. Some habitat-specific decomposers contributed to the HFA, including Gammaproteobacteria, Alphaproteobacteria, Firmicutes, Ascomycota, Nematoda, and Ciliophora. The networks for litters with HFA had more nodes, more positive edges and keystone species than the litters without HFA. Conclusion: The initial litter quality and decomposer community (mainly bacteria) collectively drive the HFA of litter decomposition in wetlands, and the network analysis may be used to predict the HFA during litter decomposition. [ABSTRACT FROM AUTHOR]

Highlights: Critical strategies for converting wastes to catalysts are summarized. Applications of waste-derived catalysts in hydrogen evolution reaction, oxygen evolution reaction, and overall water electrolysis are comprehensively reviewed. Perspectives in the development of waste-derived catalysts are analyzed.The sustainable production of green hydrogen via water electrolysis necessitates cost-effective electrocatalysts. By following the circular economy principle, the utilization of waste-derived catalysts significantly promotes the sustainable development of green hydrogen energy. Currently, diverse waste-derived catalysts have exhibited excellent catalytic performance toward hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and overall water electrolysis (OWE). Herein, we systematically examine recent achievements in waste-derived electrocatalysts for water electrolysis. The general principles of water electrolysis and design principles of efficient electrocatalysts are discussed, followed by the illustration of current strategies for transforming wastes into electrocatalysts. Then, applications of waste-derived catalysts (i.e., carbon-based catalysts, transitional metal-based catalysts, and carbon-based heterostructure catalysts) in HER, OER, and OWE are reviewed successively. An emphasis is put on correlating the catalysts’ structure–performance relationship. Also, challenges and research directions in this booming field are finally highlighted. This review would provide useful insights into the design, synthesis, and applications of waste-derived electrocatalysts, and thus accelerate the development of the circular economy-driven green hydrogen energy scheme. [ABSTRACT FROM AUTHOR]

Purpose: To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC).A total of 196 resectable patients with ESCC undergoing surgery were retrospectively enrolled, among whom 25% had sMLN. 146 out of 196 patients (from hospital 1) were randomly divided into the training (n = 116) and testing cohorts (n = 30) at an 8:2 ratio, while the remaining 50 patients from hospital 2 constituted the external validation cohort. Least absolute shrinkage and selection operator binary logistic regression was employed for radiomics feature dimensionality reduction and selection, and multivariable logistic regression analysis was used to construct the radiomics prediction model. The clinical features were statistically selected to develop the clinical model. And both the selected radiomics and clinical features were used to develop the combined model. The predictive value of models was assessed using the area under the receiver operating characteristic curves (AUC).The LN radiomics model was constructed with 9 radiomics features, the clinical model was developed with 3 clinical features, and the combined model was developed using both the LN radiomics and clinical features. However, no statistical radiomics features from ESCC were extracted in dimensionality reduction. Compared to the clinical model, the combined model exhibited superior predictive ability (AUC: 0.893 vs. 0.766, P = 0.003), and the LN radiomics model showed slightly better predictive ability (AUC: 0.860 vs. 0.766, P = 0.153). It was validated in the test and external validation cohorts.The combined model could assist in preoperatively identifying sMLN in resectable ESCC. It is beneficial for more accurate N staging and clinical comprehensive staging of ESCC, thereby facilitating the clinical physician to make more personalized and standardized treatment strategies.Methods: To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC).A total of 196 resectable patients with ESCC undergoing surgery were retrospectively enrolled, among whom 25% had sMLN. 146 out of 196 patients (from hospital 1) were randomly divided into the training (n = 116) and testing cohorts (n = 30) at an 8:2 ratio, while the remaining 50 patients from hospital 2 constituted the external validation cohort. Least absolute shrinkage and selection operator binary logistic regression was employed for radiomics feature dimensionality reduction and selection, and multivariable logistic regression analysis was used to construct the radiomics prediction model. The clinical features were statistically selected to develop the clinical model. And both the selected radiomics and clinical features were used to develop the combined model. The predictive value of models was assessed using the area under the receiver operating characteristic curves (AUC).The LN radiomics model was constructed with 9 radiomics features, the clinical model was developed with 3 clinical features, and the combined model was developed using both the LN radiomics and clinical features. However, no statistical radiomics features from ESCC were extracted in dimensionality reduction. Compared to the clinical model, the combined model exhibited superior predictive ability (AUC: 0.893 vs. 0.766, P = 0.003), and the LN radiomics model showed slightly better predictive ability (AUC: 0.860 vs. 0.766, P = 0.153). It was validated in the test and external validation cohorts.The combined model could assist in preoperatively identifying sMLN in resectable ESCC. It is beneficial for more accurate N staging and clinical comprehensive staging of ESCC, thereby facilitating the clinical physician to make more personalized and standardized treatment strategies.Results: To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC).A total of 196 resectable patients with ESCC undergoing surgery were retrospectively enrolled, among whom 25% had sMLN. 146 out of 196 patients (from hospital 1) were randomly divided into the training (n = 116) and testing cohorts (n = 30) at an 8:2 ratio, while the remaining 50 patients from hospital 2 constituted the external validation cohort. Least absolute shrinkage and selection operator binary logistic regression was employed for radiomics feature dimensionality reduction and selection, and multivariable logistic regression analysis was used to construct the radiomics prediction model. The clinical features were statistically selected to develop the clinical model. And both the selected radiomics and clinical features were used to develop the combined model. The predictive value of models was assessed using the area under the receiver operating characteristic curves (AUC).The LN radiomics model was constructed with 9 radiomics features, the clinical model was developed with 3 clinical features, and the combined model was developed using both the LN radiomics and clinical features. However, no statistical radiomics features from ESCC were extracted in dimensionality reduction. Compared to the clinical model, the combined model exhibited superior predictive ability (AUC: 0.893 vs. 0.766, P = 0.003), and the LN radiomics model showed slightly better predictive ability (AUC: 0.860 vs. 0.766, P = 0.153). It was validated in the test and external validation cohorts.The combined model could assist in preoperatively identifying sMLN in resectable ESCC. It is beneficial for more accurate N staging and clinical comprehensive staging of ESCC, thereby facilitating the clinical physician to make more personalized and standardized treatment strategies.Conclusion: To propose and validate a CT radiomics model utilizing radiomic features from lymph nodes (LNs) with maximum short axis diameter (MSAD) < 1 cm for predicting small metastatic LN (sMLN) in patients with resectable esophageal squamous cell carcinoma (ESCC).A total of 196 resectable patients with ESCC undergoing surgery were retrospectively enrolled, among whom 25% had sMLN. 146 out of 196 patients (from hospital 1) were randomly divided into the training (n = 116) and testing cohorts (n = 30) at an 8:2 ratio, while the remaining 50 patients from hospital 2 constituted the external validation cohort. Least absolute shrinkage and selection operator binary logistic regression was employed for radiomics feature dimensionality reduction and selection, and multivariable logistic regression analysis was used to construct the radiomics prediction model. The clinical features were statistically selected to develop the clinical model. And both the selected radiomics and clinical features were used to develop the combined model. The predictive value of models was assessed using the area under the receiver operating characteristic curves (AUC).The LN radiomics model was constructed with 9 radiomics features, the clinical model was developed with 3 clinical features, and the combined model was developed using both the LN radiomics and clinical features. However, no statistical radiomics features from ESCC were extracted in dimensionality reduction. Compared to the clinical model, the combined model exhibited superior predictive ability (AUC: 0.893 vs. 0.766, P = 0.003), and the LN radiomics model showed slightly better predictive ability (AUC: 0.860 vs. 0.766, P = 0.153). It was validated in the test and external validation cohorts.The combined model could assist in preoperatively identifying sMLN in resectable ESCC. It is beneficial for more accurate N staging and clinical comprehensive staging of ESCC, thereby facilitating the clinical physician to make more personalized and standardized treatment strategies. [ABSTRACT FROM AUTHOR]

Potato is a typical shallow-root crop and a good understanding of its root distribution is critical in potato water management, which is especially important for potato production in areas such as Inner Mongolia where water resource is limited. We conducted field experiments to investigate the dynamic distribution of potato root in the soil, then used this as a basis to design a new irrigation method for potato production in the region; effects of the improved irrigation and conventional method were analyzed as well by measuring plant growth, yield, water productivity and nitrogen use efficiency. The results showed that large difference existed in root distribution of potato plants at different growth stages in Inner Mongolia, suggesting the present water management strategy of uniform irrigation during potato production is inefficient and has great potential for improvement. Adjusting irrigation amount so that the wet soil zone depth covered 80% of root distribution during potato growth significantly increased water productivity, decreased N leaching, improved N absorption, nitrogen use efficiency and plant growth, leading to increased tuber yield. Therefore, the improved water management method can be used extensively. [ABSTRACT FROM AUTHOR]

In previous study, ectopic expression of GhSAMDC1 improved vegetative growth and early flowering in tobacco, which had been explained through changes of polyamine content, polyamines and flowering relate genes expression. To further disclose the transcript changes of ectopic expression of GhSAMDC1 in tobacco, the leaves from wild type and two transgenic lines at seedling (30 days old), bolting (60 days old) and flowering (90 days old) stages were performed for transcriptome analysis. Compared to wild type, a total of 938 differentially expressed genes (DEGs) were found to be up- or down-regulated in the two transgenic plants. GO and KEGG analysis revealed that tobacco of wild-type and transgenic lines were controlled by a complex gene network, which regulated multiple metabolic pathways. Phytohormone detection indicate GhSAMDC1 affect endogenous phytohormone content, ABA and JA content are remarkably increased in transgenic plants. Furthermore, transcript factor analysis indicated 18 transcript factor families, including stress response, development and flowering related transcript factor families, especially AP2-EREBP, WRKY, HSF and Tify are the most over-represented in those transcript factor families. In conclusion, transcriptome analysis provides insights into the molecular mechanism of GhSAMDC1 involving rapid vegetative growth and early flowering in tobacco. [ABSTRACT FROM AUTHOR]

In the economically important red macroalga Pyropia, carbohydrates and proteins contents are usually used to evaluate the nutritional status and commercial value. As main component elements of carbohydrates and proteins, C, N and S contents can be the suitable indicators. However, efficient and nondestructive estimation of these elements have not been well established in macroalgae, which is definitely needed in high-throughput phenotyping and selective breeding. In the current study, hyperspectral imaging was used to estimate the C, N and S contents in thalli of Pyropia yesoensis. Based on spectral information acquired by two hyperspectral cameras with range of 400 nm to 1700 nm, two machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish prediction models following different preprocessing methods. The result showed that SVR model following Savitzky-Golay (S-G) smoothing preprocessing performed the best for N content with excellent accuracy (RTest2 = 0.94, RMSE = 0.331, MAPE = 5.47%, RPD = 3.44). Both of PLSR and SVR models could not yet meet satisfactory prediction for C and S content with RTest2 < 0.4 and RPD < 1.5. Further validation on field samples corroborated accuracy and robustness of the optimal estimation model for N. This study demonstrates that hyperspectral imaging is effective for estimating N contents of P.yesoensis thalli. It unveils the capability of hyperspectral imaging with machine learning models for estimating macroelement contents in macroalgae thalli, and offers a convenient, non-destructive, and effective method for the phenotyping and selective breeding of macroalgae. [ABSTRACT FROM AUTHOR]

Effective antitumour immunity depends on the orchestration of potent T cell responses against malignancies1. Regression of human cancers has been induced by immune checkpoint inhibitors, T cell engagers or chimeric antigen receptor T cell therapies2–4. Although CD8 T cells function as key effectors of these responses, the role of CD4 T cells beyond their helper function has not been defined. Here we demonstrate that a trispecific antibody to HER2, CD3 and CD28 stimulates regression of breast cancers in a humanized mouse model through a mechanism involving CD4-dependent inhibition of tumour cell cycle progression. Although CD8 T cells directly mediated tumour lysis in vitro, CD4 T cells exerted antiproliferative effects by blocking cancer cell cycle progression at G1/S. Furthermore, when T cell subsets were adoptively transferred into a humanized breast cancer tumour mouse model, CD4 T cells alone inhibited HER2+ breast cancer growth in vivo. RNA microarray analysis revealed that CD4 T cells markedly decreased tumour cell cycle progression and proliferation, and also increased pro-inflammatory signalling pathways. Collectively, the trispecific antibody to HER2 induced T cell-dependent tumour regression through direct antitumour and indirect pro-inflammatory/immune effects driven by CD4 T cells.A trispecific antibody targeting HER2 and T cells stimulates CD4 T cell-dependent inhibition of of breast cancer cell proliferation in vitro and tumor regression in a humanized mouse model. [ABSTRACT FROM AUTHOR]

With the development of mobile commerce, situational awareness and Internet of things, the boundaries of e-commerce have been greatly expanded, and it has entered a big data era of business information. However, customers are faced with the problem that information is rich but useful information is hard to get. E-commerce is facing the challenge of how to provide personalized information recommendation services for customers and motivate customers to purchase continuously. Therefore, this paper studies the problem of e-commerce recommendation under the condition of large data, and proposes a scenario-based e-commerce recommendation algorithm based on customer interest. Firstly, according to the characteristics of customer interest such as situational sensitivity and diversity in personalized recommendation, a multi-dimensional customer interest feature vector is established by using distributed cognitive theory to differentiate the sensitive scenarios of customer interest. Then, the collaborative filtering recommendation algorithm is used to realize customer similarity judgment and product recommendation in sensitive scenarios. Experimental results show that the method has good customer interest extraction ability. Compared with other recommendation methods, it has higher recommendation accuracy and can adapt to the high-quality commodity recommendation service in the process of customer continuous purchase under complex circumstances. [ABSTRACT FROM AUTHOR]

In the present paper, we focus on the diverging behavior of discrecte hedging error with transaction costs. We added the hedging cost to the error directly. The main idea is to divide the hedging error into two parts: the pure hedging error and transaction cost of rebalance. The later part will be diverged when hedging number n goes to infinity. Firstly we show an upper bound of diverging part, which is O (n) of rebalancing number n, then we prove both the upper bound and the lower bound of discrete hedging error with transaction costs are of n order, finally we give an approximation of hedging error to determine the coefficient in front of n . The main technique in the proof is Itô's formula, L'Hopital's rule and three important lemmas in [Yuri, Kabanov, Mher, Safarian. Markets with Transaction Costs. Springer-Verlag, Berlin, Heidelberg, 2009]. The numerical result support our theoretical conclusion. [ABSTRACT FROM AUTHOR]

Ta–W alloys have unique properties but their applications at high temperatures are restricted due to their poor oxidation resistances. The addition of Al and Ti decreases the oxidation rate of the alloy, as Al-contained protective oxide layer could be formed. In this study, ternary and quaternary Ta–W alloys containing Al and Ti were prepared by hot press sintering. The microstructures and oxidation behaviors of the alloys were studied at 1173 K. The results show that the oxidation kinetics of all alloys follows a parabolic law at short oxidation periods. After cyclic oxidation, the kinetics of quaternary alloys still obeys the parabolic law but that of ternary alloy has been changed into linear law kinetics after 8-h oxidation. For ternary alloy, an outer layer with dense and continuous alumina was formed during the initial stage, and phase transformation caused the oxides to peel off. By comparison, the quaternary alloys depicted continuously solid solution of the oxides layer. Overall, Al and Ti played key roles in oxidation degree and affected the oxidation kinetics of ternary and quaternary alloys. [ABSTRACT FROM AUTHOR]

Purpose: Wetland soils may face more severe water stress under future climate change. Our aim was to assess the change characteristics of wetland soil bacterial community and metabolic potentials under drought and flood conditions. Methods: Wetland soil was incubated under five different water environments (including constant moisture at 30%, natural air-dried, and 3 types of flooding depths) by conducting experimental microcosms. After 1, 21, and 132 days of incubation, the soil bacterial community structure and metabolic potential were examined by the 16S rRNA gene sequencing and Biolog-Ecoplates method, respectively. Results: The results showed that Actinobacteria and Firmicutes were significantly enriched under drought and flooding treatments, respectively. Compared to flooding, drought decreased soil microbial biomass and carbon metabolism more severely. However, the depth of flooding did not significantly change bacterial community composition and carbon metabolism. In addition, the responses of soil metabolic functions to drought were more sensitive than the change of bacterial community composition. When the wetland soil faced water stress (drought and flooding), its metabolic functions showed close correlations with bacterial community composition and were negatively affected by most of environmental factors (such as pH, NO3-N, and NH4-N). Conclusions: The results indicate that the drying condition is more severe and rapid than flooding in threatening soil microbial metabolic activity. In addition, the depth of flooding does not significantly change bacterial community composition and carbon metabolism. [ABSTRACT FROM AUTHOR]

The steganalysis method based on Convolutional Neural Network (CNN) has attracted wide attention in the field of steganalysis. This method typically uses high-pass or derivative filters to pre-process images. Multiple filters can be used to improve the detection accuracy. However, the use of multiple filters may generate redundant residual images and redundant features. Redundant features not only consume computing resources and time but also cause model over-fitting, thus compromising the detection accuracy of the model. Therefore, we proposed a filter subset selection method to develop a well-designed pre-processing layer for CNN-based steganalysis framework. This method discarded many high-pass and derivative filters according to the mechanism of convolution operation and the correlations between pixels. Structural Similarity (SSIM) was used to calculate the similarity between the filtered residual images and arrange them in ascending order. Finally, based on the arranged filters, a series of experiments were conducted to determine the optimal filter subset and the optimal CNN-based steganalysis framework. The experimental results indicate that the proposed method not only guarantees detection accuracy but also improves the training efficiency of the model. Therefore, this method offers an optimized trade-off between computational complexity and detection accuracy. [ABSTRACT FROM AUTHOR]

The intelligent teaching system is the most important in the field of teaching. It uses artificial intelligence technology to bring a lot of help to learners in terms of knowledge and skill acquisition. In this process, human tutors are not required to participate. The student model is the core of the intelligent teaching system. Using the Bayesian network with strong self-learning ability to construct the intelligent teaching system student model can significantly improve the intelligence level of the intelligent teaching system. Firstly, we discussed the basic concepts of the student model of the intelligent teaching system. Then, from the perspective of students' ability teaching, combined with the students' learning status and characteristics, the factors influencing the students' learning process are analyzed. Finally, an intelligent teaching system student model was built based on Bayesian network. This model can objectively evaluate students' cognitive ability and can infer students' next action. In addition, the model is also applicable to the online test system, and the experimental results obtained demonstrate the effectiveness of the modified model. [ABSTRACT FROM AUTHOR]

In this work, a novel bi-modal imaging probe with enhanced CT contrast efficiency and FL brightness was constructed, in which the combination of a binary CT contrast agent BaHoF5 and Cu-doped QDs served as a vehicle; hyaluronic acid (HA) was employed as a tumor-targeting ligand. With its CT contrast efficiency about 2.1- and 3.9-fold higher than PEG-BaHoF5 and Iohexol, the CT contrast efficiency and the fluorescent brightness of the bi-modal probe were both enhanced. Likewise, its fluorescent brightness is almost 6-fold brighter after Cu-doped QDs loading. The most important contribution of this work lies on the proposed strategy. The inherent contradiction of the imaging sensitivity of CT and FL imaging is well balanced and a great CT/FL bi-modal imaging performance is simultaneously obtained even at low concentration (400 μg/mL) of the probe, which was superior to the previous CT/FL bi-modal probes. Moreover, since BaHoF5 as a binary CT contrast agent was introduced instead of conventional Au and Bi2S3, the CT/FL bi-modal probe would be more suitable for different patients under different operation voltages. In addition, the in vitro tumor cell imaging also demonstrated a good photo-stability, FL brightness, and tumor-targeting capability of the probe, indicating its great potential in practical bi-modal imaging for further tumor diagnosis and therapy. [ABSTRACT FROM AUTHOR]

Objectives: The term oral potentially malignant disorders (OPMD) was recommended at the WHO workshop held in 2005 and published in 2007, and increasingly large number of papers related to OPMD are published. The objective of the bibliometric analysis was to investigate the citation characteristics and analyze research trends of OPMD. Methods: A comprehensive search was performed and identified in the Scopus database since 2007 for the bibliometric list of OPMD in the syntax. Results: A total of 795 papers on OPMD were retrieved and the total number of citations was 8870. The mean number of citations is 11.2, with a range of 0 to 580. A substantial increasing tread in the number of citation is observed since 2007. Leukoplakia (42%) and lichen planus (23%) were the two most individual disorders among the general OPMD. A controlled/comparative study (43%) and a retrospective study (16%) were the two most study designs. India (n = 267) and the UK (n = 99) were the two most contributing countries. Tobacco and alcohol use and betel quid chewing (n = 351) are the most common keywords. Notably, early detection and diagnosis of oral cancer (n = 255) and biomarkers of oral carcinogenesis (n = 178) top the list, which may indicate a trend of key topics. Conclusions: The current study for the first time reported the bibliometric characteristics and research trends of the papers on OPMD in the syntax. Clinical relevance: The bibliometric analysis highlights the key topics and studies which have shaped the understanding and management of OPMD. [ABSTRACT FROM AUTHOR]

In this study, we employed pyrosequencing-based analysis of the V4-V6 16S rRNA gene region and community level physiological profiling (CLPP) to characterize and compare bacterial community composition and metabolic activities in order to determine if microbial composition and activity were able to recover in land transitioning from agriculture to natural wetland. The sample sites included natural wetland (with native vegetation), reclaimed for tillage (agriculture), and abandoned tilled (wetland restoration) soils. The entire data set comprised 78,790 good-quality sequences, 62,364 of which were classified in 23 known phyla. The dominant taxonomic groups presented in all samples were Proteobacteria (34.3%), Acidobacteria (15.5%), Verrucomicrobia (6.9%), Chloroflexi (6.3%), Nitrospira (2.6%) and Actinobacteria (2.4%), while 27.1% of the sequences could not be classified to any known phylum. Microbial community composition and activity were found to cluster into two distinct groupings: one associated with agriculture and the other with restored and natural wetlands. The average well colour development (AWCD) based on carbon source utilization pattern was significantly higher in natural and restored wetland soils than in reclaimed soils, suggesting an improvement of soil health. In conclusion, these findings indicated that microbial community composition and activity were affected by land use and may be used to assess the extent of recovery of natural microbial-driven processes during wetland restoration. Furthermore, restored soil microbial communities in this study appear to be tended to a gradual return to those of natural wetland soils with time. [ABSTRACT FROM AUTHOR]

The impacts of different anthropogenic disturbances on the composition and metabolic function of sediment bacterial community in the largest freshwater lake- Poyang Lake of China were studied. A total of 12 surface sediment samples were collected from four sites, including an undisturbed central lake region (SMS) and three disturbed sites in fishery (NJS), industrial (RH) and tourism (BSZ) areas. Sediment physicochemical properties and heavy metal contents were combined with the results from Illumina Miseq and Biolog-Ecoplates to quantify the impact of environmental factors driving differences in sediment bacterial structure and microbial function. Our results showed that the bacterial community diversity (Shannon index) was higher in disturbed than in undisturbed areas, although the most predominant bacterial phyla in four studied sites were similar. However, the average well color development (AWCD) based on carbon source utilization pattern of the disturbed area was lower than undisturbed areas and the preference of carbon sources differed among the four studied areas. Site contaminants contributed to the distribution of distinct phyla which differentiated bacterial communities among sites. For example, the contents of TN (1.80 g/kg) and TOC (12.34 g/kg) in NJS showed positive correlation with the abundance of Deltaproteobacteria and Chloroflexi; while the concentration of TP (0.43 g/kg), Cu (69.22 mg/kg) and Zn (134.89 mg/kg) in RH was positively correlated with the predominance of Firmicutes and Verrucomicrobia. These results indicated that microbial community carbon metabolic profiles changed with the shift of bacterial community structure, and disturbance-induced pollution enhanced the sediment bacterial diversity and reduced carbon metabolic activity. [ABSTRACT FROM AUTHOR]

Objective: It has been demonstrated that preeclampsia is associated with alterations in placental microRNA expression. Previous reports have shown that hsa-miR-181a-5p is overexpressed in human preeclamptic placenta compared with normotensive placenta. The purpose of this study was to explore whether upregulated hsa-miR-181a-5p expression is involved in the ontogenesis of preeclampsia. Methods: Twenty preeclamptic placentas and 20 normotensive placentas were obtained from nulliparous women by cesarean section. Expression of hsa-miR-181a-5p in placenta tissues and human trophoblast cell lines was analyzed by reverse transcription polymerase chain reaction. The trophoblast cell lines (HTR-8/SVneo and JAR) were transfected with specific oligonucleotides to upregulate miR-181a-5p expression. The effect of miR-181a-5p expression on proliferation, cell cycle, apoptosis, and invasion in HTR-8/SVneo and JAR cells was then investigated. Result: It was demonstrated that hsa-miR-181a-5p expression was upregulated in preeclamptic placentas and that it may trigger antiproliferation and inhibition of cell cycle progression, induce apoptosis, and suppress invasion in HTR-8/SVneo and JAR cells. Conclusion: Anomalously upregulated hsa-miR-181a-5p expression could contribute to trophoblast dysfunction and may be a crucial factor in the pathogenesis of preeclampsia. [ABSTRACT FROM AUTHOR]

Purpose: To review the imaging characteristics and evaluate the diagnostic value of echocardiography for diagnosing anomalous origin of the right coronary artery from the pulmonary artery (ARCAPA). Methods: We retrospectively reviewed the echocardiographic records and compared these images with operative findings in six children with ARCAPA. Results: ARCAPA was characterized by dilation of the left coronary artery, inappropriate origin of the right coronary artery from the pulmonary artery, and collaterals within the interventricular septum. The associated malformations included atrial septal defect in four cases. Four of six cases were diagnosed correctly, while the remaining two cases were misdiagnosed: one of fistula between the pulmonary artery and the left coronary artery and another of ARCAPA diagnosed intraoperatively in one case of atrial septal defect. Conclusion: ARCAPA can be diagnosed by echocardiography but is prone to misdiagnosis. This malformation should be distinguished from coronary artery fistula. [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to investigate shifts in the bacterial community structure and soil enzyme activities during long-term reclamation of different land use types in a freshwater wetland. Materials and methods: Illumina 16S rRNA gene amplification and sequencing and soil extracellular enzymes were used to evaluate changes in bacterial community structure and potential function under four different land use types after 38 years of reclamation: abandoned wetland (AW), vegetable field (VF), pond sediment (PS), rice field (RF) and natural wetland (NW). Results and discussion: Although bacterial rRNA gene alpha diversity remained unchanged, the bacterial community composition changed significantly under different land uses according to hydrological conditions. Under drying-wetting cycle conditions, RF harbored a similar bacterial community composition as NW, with Acidobacteriales, Holophagae, Planctomycetes, and Spirochaetae being dominant in both. Additionally, AW and VF as upland soil had significantly higher abundance of Gemmatimonadates and Actinobacteria. The composition of permanently flooded soil (PS) had unique features and significantly enriched levels of Chlorobi. The shifts in bacterial community structure were mainly driven by soil moisture content, as well as total organic C and N. However, variations in potential enzyme activities were better explained by soil properties than microbial community composition, suggesting high plasticity of the resident microbial community to environmental conditions. Conclusions: Rice field harbored a similar bacterial community structure as natural wetland, while the responses of enzyme activities for all land use types differed significantly. The distributions of bacterial community might be influenced by hydrology, while extracellular enzyme activities were closely related to soil properties. Our results suggest that the function of the active microbial community should also be considered in environmental risk models as indicators of ecosystem conversion caused by land use and anthropogenic activity. [ABSTRACT FROM AUTHOR]

Objectives: To determine association of gross tumour volume (GTV) of resectable oesophageal squamous cell carcinoma (SCC) measured on T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI) and diffusion-weighted imaging (DWI) with T category and lymphatic metastasis (LM).Methods: Sixty oesophageal SCC patients underwent fat-suppressed T2WI, CE-T1WI and DWI with b values of 0, 500 and 800 s/mm2. GTV was measured on three sequences. Statistical analyses were performed to determine association of GTV with T category and LM.Results: Spearman's rank correlation analysis showed positive association of GTV with T category and LM (all p values < 0.01). Differences in GTV were found between T1 and T2 or T3 categories shown by Kruskal-Wallis H and one-way ANOVA tests, and between T1/T2 and T3 and between tumours with and without LM by Mann-Whitney U tests (all p values < 0.05). Receiver operating characteristic analyses showed cut-off GTVs of 5.795, 5.276 and 10.11 cm3 on CE-T1WI could better differentiate T1 from T2 categories, T1 from T3, and T1-2 from T3 than those of 7.066, 7.045 and 8.504 cm3 on T2WI, of 5.793, 6.609 and 6.989 cm3 on DWI with b value of 500 s/mm2, and of 4.156, 4.519 and 4.985 cm3 with b value of 800 s/mm2, respectively. Cut-off of 10.462 cm3 on DWI with b value of 500 s/mm2 could better identify LM than of 12.38, 8.793 and 9.600 cm3 on T2WI, CE-T1WI and DWI with b value of 800 s/mm2, respectively.Conclusions: GTVs on T2WI, CE-T1WI and DWI are associated with T category of and LM of oesophageal SCC.Key Points: • GTV is associated with T category and lymphatic metastasis of oesophageal SCC • GTV measured on contrast-enhanced T 1 -weighted imaging better identifies T category • GTV measured on DWI with b value of 500 s/mm 2 better identifies lymphatic metastasis. [ABSTRACT FROM AUTHOR]

Purpose: Doxorubicin is one of the most active agents in the first-line therapy for metastatic breast cancer, but its utility is partially limited by the frequent emergence of doxorubicin resistance. In this study, we aimed to investigate the role of ATP-binding cassette sub-family B, member 4 (ABCB4) in acquired doxorubicin resistance in breast cancer cells, as well as its potential mechanism.Methods: In doxorubicin-sensitive and -resistant breast cancer cell lines MCF-7 and MDA-MB-231, the expression levels of ABCB4 were detected using real-time quantitative PCR and Western blot analysis, the DNA methylation and histone acetylation status of ABCB4 gene were investigated by bisulfite-sequencing PCR (BSP) and chromatin immunoprecipitation (ChIP) assays, and the doxorubicin sensitivity and intracellular doxorubicin accumulation were observed using cell cytotoxicity assay and flow cytometry. In Madin-Darby Canine Kidney (MDCKII) cells, In vitro transport assay was used to assess the ABCB4-mediated transport of doxorubicin.Results: ABCB4 was overexpressed in doxorubicin-resistant breast cancer cells compared to their doxorubicin-sensitive counterparts, which was associated with reduced DNA methylation as well as increased histone acetylation at the ABCB4 promoter. ABCB4 could actively pump doxorubicin out of the cells, and knockdown of ABCB4 increased doxorubicin sensitivity and intracellular accumulation in doxorubicin-resistant breast cancer cells.Conclusions: Our results indicate that ABCB4 is overexpressed in breast cancer cells with acquired doxorubicin resistance, which could be attributed, at least partially, to the epigenetic modifications of ABCB4 gene. ABCB4 mediates the efflux transport of doxorubicin, and contributes to the acquired resistance of doxorubicin in breast cancer cells. [ABSTRACT FROM AUTHOR]

In this paper, spatiotemporal variability of drought in Xilingol grassland during pasture growing season (from April to September) was investigated, using 52 years (1961-2012) of precipitation data recorded at 14 rain gauge stations in the study area. The Standardized Precipitation Index was used to compute the severity of drought. The Mann-Kendall test, the linear trend, and the sequential Mann-Kendall test were applied to standardized precipitation index (SPI) time series. The results indicate that drought has become increasingly serious on the region scale during pasture growing season, and the rate of SPI decreases ranged from −0.112 to −0.013 per decade. As for the MK test, most of the stations, the Z value range is from −1.081 to −0.005 and Kendall’s τ varies from −0.104 to −0.024. Meanwhile, drought is increased obviously from the northwest to the southeast region. Meanwhile, the occurrence probability of each severity class, times for reaching different drought class from any drought severity state, and residence times in each drought class have been obtained with Markov chain. Furthermore, the drought severities during pasture growing season in 2013-2016 are predicted depending on the weighted Markov chain. The results may provide a scientific basis for preventing and mitigating drought disaster. [ABSTRACT FROM AUTHOR]

Anycast is attracting much attention due to the need of scalable and cost-effective data delivery in inter-datacenter elastic optical networks. However, spectrum fragmentation degrades network’s performance and decreases probability of successful anycast delivery significantly. When the idle spectrum block in elastic optical network is not enough to transmit the anycast request, spectrum splitting with multi-path transmitting the anycast is an effective approach to improve the spectrum fragmentation utilization. For improving spectrum utilization and reducing time delay between multiple paths, we propose a spectrum-efficient algorithm based on traffic splitting and merging (Anycast_SA_TSM) transmission to avoid spectrum fragmentation and delay between multiple paths. In order to minimize the time delay between multiple paths, we design a modified scheme to select the multiple paths with minimal time delay to transmit the anycast. During the spectrum allocation phase, a new spectrum block allocation scheme, the exact fit or fragmentation minimal, is put forward. Moreover, when an appropriate size of spectrum block is found for the split anycast request, merging split sub-requests to a single path is activated for minimizing the additional guard bands and improving the spectrum efficiency. Comparing with other two anycast algorithms, simulation results show that the proposed algorithm can get minimal time delay between split multiple paths, the minimal bandwidth blocking probability and the highest spectrum utilization. [ABSTRACT FROM AUTHOR]

A rapid, sensitive, precise, and accurate dispersive-magnetic solid-phase extraction technique combined with flame atomic absorption spectrometry was established for pre-concentration and separation of Pd (II) in soil samples. In the developed system, 5-amine-1,10-phenanthroline was used as synergistic complexant; sodium dodecyl sulfate and 2-(5-bromo-2-pyridylazo)-5-diethyl aminophenol ligand coated on magnetic nanoparticles were synthesized by a chemical precipitation method, and then employed as the efficient magnetic adsorbent with good magnetic properties and dispersibility. Various operational parameters affecting the extraction efficiency has been studied and optimized in details. Under the optimum experimental conditions, the detection limit of the mentioned method for palladium ions was 0.12 μg/L, while the relative standard deviation was 1.8%. Finally, the developed method was applied for the analysis of palladium ions in three kinds of soil samples and quantitative recoveries were achieved over the range of 96.7-104.0%. It can be a powerful alternative applied to the determination of traces of Pd ions from various real samples in further researches. [ABSTRACT FROM AUTHOR]

A method of hollow fiber-stir bar sorptive extraction (HF-SBSE) coupled with gas chromatography-mass spectrometry was proposed to explore the migration of eight organochlorine pesticide residues in environmental and biological matrices. HF-SBSE was a configuration for solid-phase microextraction purposes, combining the features of SBSE and hollow fiber membrane extraction technology. Variables affecting the extraction (salt addition, extraction temperature, and time) and desorption (microwave time, eluted solvent) have been evaluated. Under the optimal conditions, good linearity ( r > 0.999) of all calibration curves was obtained in validation experiments. The recoveries in different sample matrices were in the range from 69.0 to 114.1% with RSD% less than 10.2%. The bio-accumulation of OCPs was tested in samples of soil, underground water, forage grass, and raw milk. It was indicated that HF-SBSE could be applied in simultaneous extraction of hazardous substances in different matrices without sample clean-up. [ABSTRACT FROM AUTHOR]

The effect of increasing salinity on nitrogen removal via simultaneous nitrification and denitrification, microbial activities and extracellular polymeric substances (EPS) were investigated in a hybrid sequencing batch biofilm reactor filled with soft combination carriers. In the influent salinity range from 1.0 to 2.0%, average COD, NH -N and TN removal efficiencies were higher than 97.1, 97.8 and 86.4% at the steady state. When salinity was increased to 2.5 and 3.0%, ammonium oxidation was obviously inhibited in the reactor. For both suspended sludge (S-sludge) and biofilm, specific ammonium oxidation rate, specific nitrite oxidation rate, specific oxygen uptake rate and dehydrogenase activity reduced with the increase of salinity. The quantity of total EPS increased with the increase of salinity from 1.0 to 2.0%. Generally, humic substances were the dominant composition of EPS in both S-sludge and biofilm, with the percentages of 43.9-54.0 and 43.8-64.6% in total EPS. [ABSTRACT FROM AUTHOR]

Aldehydes generated as by-products during the pretreatment of lignocellulose are the key inhibitors to Saccharomyces cerevisiae, which is considered as the most promising microorganism for industrial production of biofuel, xylitol as well as other special chemicals from lignocellulose. S. cerevisiae has the inherent ability to in situ detoxify aldehydes to corresponding alcohols by multiple aldehyde reductases. Herein, we report that an uncharacterized open reading frame YKL071W from S. cerevisiae encodes a novel 'classical' short-chain dehydrogenase/reductase (SDR) protein with NADH-dependent enzymatic activities for reduction of furfural (FF), glycolaldehyde (GA), formaldehyde (FA), and benzaldehyde (BZA). This enzyme showed much better specific activities for reduction of GA and FF than FA and BZA, and displayed much higher Km and Kcat/ Km but lower Vmax and Kcat for reduction of GA than FF. For this enzyme, the optimum pH was 5.5 and 6.0 for reduction of GA and FF, and the optimum temperature was 30 °C for reduction of GA and FF. Both pH and temperature affected stability of this enzyme in a similar trend for reduction of GA and FF. Cu, Zn, Ni, and Fe had severe inhibition effects on enzyme activities of Ykl071wp for reduction of GA and FF. Transcription of YKL071W in S. cerevisiae was significantly upregulated under GA and FF stress conditions, and its transcription is most probably regulated by transcription factor genes of YAP1, CAD1, PDR3, and STB5. This research provides guidelines to identify more uncharacterized genes with reductase activities for detoxification of aldehydes derived from lignocellulose in S. cerevisiae. [ABSTRACT FROM AUTHOR]

For an arbitrary Lévy process X which is not a compound Poisson process, we are interested in its occupation times. We use a quite novel and useful approach to derive formulas for the Laplace transform of the joint distribution of X and its occupation times. Our formulas are compact, and more importantly, the forms of the formulas clearly demonstrate the essential quantities for the calculation of occupation times of X. It is believed that our results are important not only for the study of stochastic processes, but also for financial applications. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges in achieving bio-signal-based design environments. While the main motivation of this paper was to provide a user interface for physically disabled people to express their artistic natures, a special emphasis is given on graphical user interface design where bio-signals are the single input source. Among three bio-signal sources investigated-electromyography, electrooculography and electroencephalography (EEG)-stimulus-based human-computer interaction design (EEG feature extraction method) is found to be the most promising for achieving design environments to perform complex tasks. In the proposed stimulus-based brain-computer-interaction application, the user communication with a computer is achieved by coupling intended functionalities with stimuli signals on the computer screen. Constant focus on the intended command stimulates the brain. In return, the brain releases a response signals (steady state visual evoked potential). In theory, brain's response signals and the stimulus signals are identical. Once successfully identified, the presence of a signal pattern that is identical to the one of the alternative stimulus signals (paired with a command in a user interface) indicates the intention of a user. Since each option is associated with a unique signal pattern, multiple options can simultaneously be offered to users. The main challenge of working with stimulus signals is that the response signals are weak and they are buried inside of highly polluted EEG signals that include brain's natural activities. In this paper, we introduce a signal processing algorithm based on Lorenz systems of differential equations for identifying the source of stimulus signals. Our experiments strongly suggest that bio-signal-based design environments to perform complex tasks, including geometric modeling can be achieved by utilizing stimulus-based signal processing methodology. [ABSTRACT FROM AUTHOR]

A critical component of brand equity is consumer perceived ethicality (CPE) of the brand. Yet, little is known about how to create positive brand CPE. We offer that the starting point for creating brand CPE is with the brand-naming decision. Drawing on sound symbolism theory, we propose that certain brand name characteristics better convey ethicality. Two studies are conducted. Study 1 finds that higher frequency sounds in brand names better convey ethicality than lower frequency sounds. Study 2 finds that brand names can positively impact brand CPE in the presence of additional information, in particular, information that reflects negatively on the brand's ethical behavior. These results suggest that marketers be more involved at the onset of creating an ethical brand image. [ABSTRACT FROM AUTHOR]

Background and Objectives: It is well known that the expression and function of ATP-binding cassette transporter B1 (ABCB1) show high interindividual variability, but the reasons have not yet been fully elucidated. In this study, combined influence of genetic polymorphism and DNA methylation on ABCB1 mRNA expression and digoxin pharmacokinetics in healthy Chinese males was analyzed. Methods: A total of 93 subjects who were homozygous for the ABCB1 1236-2677-3435 TTT or CGC haplotype were enrolled in this study. DNA methylation status of the ABCB1 promoter and ABCB1 mRNA expression level in exfoliated intestinal epithelial cells were analyzed using bisulfite sequencing PCR and real-time PCR. The pharmacokinetics of digoxin in subjects were investigated after administration of a single oral dose of digoxin 0.5 mg. Results: The DNA methylation levels of ABCB1 promoter showed no significant difference between TTT/TTT and CGC/CGC carriers ( P = 0.54). Subjects with TTT/TTT haplotype pair and high methylation status (TTT/TTT-HM) showed a significantly lower ABCB1 mRNA level compared to other subjects. Compared with TTT/TTT-HM subgroup, the area under the plasma concentration-time curve from time zero to 72 h (AUC) of digoxin was decreased by 26.9 %, the maximum plasma concentration ( C ) was decreased by 25 % and the apparent oral clearance (CL/F) was increased by 21.2 % in CGC/CGC-LM subgroup. The values of time to maximum concentration ( t ) and terminal elimination half-life ( t ) showed no significant difference. Conclusions: Both genetic polymorphism and DNA methylation variation should be taken into consideration to explain the interindividual variability in ABCB1 expression and function more clearly. [ABSTRACT FROM AUTHOR]

In this paper, a new synthesis filter is designed for filter bank systems with transmission delay by adapting random time delay or data-missing situations in the communication network. Firstly, we formulate a mathematical description of the data transmission delay and a measurement model. Secondly, the state model and the measurement model are adapted into new forms by expanding their dimensions. In this way, non-white noise can be converted to white noise, which can be dealt with using standard Kalman filtering. Thirdly, a dimensionality reduction method is proposed to overcome the complexity generated by expanding dimensions, and then a synthesis filter is designed with dimensions equal to those of the original system. Finally, a simulation is presented to demonstrate the effectiveness and the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The short-chain dehydrogenase/reductase (SDR) family, the largest family in dehydrogenase/reductase superfamily, is divided into 'classical,' 'extended,' 'intermediate,' 'divergent,' 'complex,' and 'atypical' groups. Recently, several open reading frames (ORFs) were characterized as intermediate SDR aldehyde reductase genes in Saccharomyces cerevisiae. However, no functional protein in the atypical group has been characterized in S. cerevisiae till now. Herein, we report that an uncharacterized ORF YLL056C from S. cerevisiae was significantly upregulated under high furfural (2-furaldehyde) or 5-(hydroxymethyl)-2-furaldehyde concentrations, and transcription factors Yap1p, Hsf1p, Pdr1/3p, Yrr1p, and Stb5p likely controlled its upregulated transcription. This ORF indeed encoded a protein (Yll056cp), which was grouped into the atypical subgroup 7 in the SDR family and localized to the cytoplasm. Enzyme activity assays showed that Yll056cp is not a quinone or ketone reductase but an NADH-dependent aldehyde reductase, which can reduce at least seven aldehyde compounds. This enzyme showed the best Vmax, Kcat, and Kcat/ Km to glycolaldehyde, but the highest affinity ( Km) to formaldehyde. The optimum pH and temperature of this enzyme was pH 6.5 for reduction of glycolaldehyde, furfural, formaldehyde, butyraldehyde, and propylaldehyde, and 30 °C for reduction of formaldehyde or 35 °C for reduction of glycolaldehyde, furfural, butyraldehyde, and propylaldehyde. Temperature and pH affected stability of this enzyme and this influence varied with aldehyde substrate. Metal ions, salts, and chemical protective additives, especially at high concentrations, had different influence on enzyme activities for reduction of different aldehydes. This research provided guidelines for study of more uncharacterized atypical SDR enzymes from S. cerevisiae and other organisms. [ABSTRACT FROM AUTHOR]