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As an effective measure for retaining paddy drainage and control agricultural non-point source pollution, the paddy field-ditch-pond system can buffer nitrogen and phosphorus pollutants in field drainage. Few studies have investigated the spatiotemporal dynamics of nitrogen and phosphorus across an integrated paddy field-ditch-pond system in polder landscapes. So in this paper, water quality monitoring through a paddy field-ditch-pond system were conducted in a typical polder area of southern China. Total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH 4 + -N), nitrate nitrogen (NO 3 − -N) and orthophosphate (PO 4 3− ) of the paddy field-ditch-pond system were measured during the growth periods of paddy rice. The temporal and spatial distribution characteristics of nitrogen and phosphorus were analyzed. The results showed that the concentrations of TN and TP ranged from 1.31 to 3.54 mg·L − 1 and 0.038 to 0.14 mg·L − 1 , respectively, in the drainage ditches during the paddy rice growth period. The concentrations of TN and TP varied between 0.11 ~ 4.90 mg·L − 1 and 0.02 ~ 0.22 mg·L − 1 in the ponds, respectively. The concentrations of TN, NH 4 + -N, NO 3 − -N, TP and PO 4 3− in paddy fields, drainage ditches and ponds showed a decreasing trend from paddy fields to drainage ditches and ponds. The ditch-pond subsystem had a certain interception and purification effect on nitrogen and phosphorus of paddy field drainage water. In the study area, setting up ecological drainage ditches, adopting controlled drainage and growing aquatic plants in drainage ditches can be taken to improve water quality. Findings from this research may provide scientific basis for precise prevention and control of agricultural non-point source pollution in the polder area.

Accurate estimation of reference evapotranspiration (ETo) is key to agricultural irrigation scheduling and water resources management in arid and semiarid areas. This study evaluates the capability of coupling a Bat algorithm with the XGBoost method (i.e., the BAXGB model) for estimating monthly ETo in the arid and semiarid regions of China. Meteorological data from three stations (Datong, Yinchuan, and Taiyuan) during 1991–2015 were used to build the BAXGB model, the multivariate adaptive regression splines (MARS), and the gaussian process regression (GPR) model. Six input combinations with different sets of meteorological parameters were applied for model training and testing, which included mean air temperature (Tmean), maximum air temperature (Tmax), minimum air temperature (Tmin), wind speed (U), relative humidity (RH), and solar radiation (Rs) or extraterrestrial radiation (Ra, MJ m−2·d−1). The results indicated that BAXGB models (RMSE = 0.114–0.412 mm·d−1, MAE = 0.087–0.302 mm·d−1, and R2 = 0.937–0.996) were more accurate than either MARS (RMSE = 0.146–0.512 mm·d−1, MAE = 0.112–0.37 mm·d−1, and R2 = 0.935–0.994) or GPR (RMSE = 0.289–0.714 mm·d−1, MAE = 0.197–0.564 mm·d−1, and R2 = 0.817–0.980) model for estimating ETo. Findings of this study would be helpful for agricultural irrigation scheduling in the arid and semiarid regions and may be used as reference in other regions where accurate models for improving local water management are needed.

Pine wilt disease (PWD) is a serious forest disease caused by pine wood nematode (PWN). To examine the relationship between coniferous endophytes and PWD resistance, this study investigated endophytic bacterial and fungal communities in five conifer species: two Japanese black pine populations (Pinus thunbergii from Qingdao University, PQ, and Fushan Forest Park, PF), Chinese arborvitae (Platycladus orientalis, PO), cedar (Cedrus deodara, CD), and Masson pine (Pinus massoniana, PM). Results showed a strong correlation between endophytic microbial diversity and PWD resistance. PO with high PWD resistance hosted the most unique bacterial species, while PM with low PWD resistance had the fewest unique bacteria and significantly lower ACE and Shannon indices. At the bacterial genus level, dominant genera in resistant conifers often showed high nematocidal activity, whereas those in susceptible plants boosted nematode reproduction. PQ featured the unique dominant genus Pantoea, and PO’s unique Acinetobacter and the shared genus Bacillus (with CD) both displayed high toxicity to PWNs. In contrast, PF’s Pseudomonas and PM’s Stenotrophomonas significantly promoted nematode reproduction. Fungal community analysis revealed that the unique endophytic fungi in PQ are more abundant than those in PF, and the Shannon index of its endophytic fungi is comparable to that of CD and significantly higher than that of PF. PF’s dominant fungal genus Pestalotiopsis might facilitate nematode invasion, and its fungal Shannon index is significantly lower than PQ’s. Eight bacterial strains were isolated from these five conifer plants, with six highly nematocidal strains originating from PQ, CD, and PO. This study offers evidence that endophytic microbial communities critically influence PWD resistance, offering a microbial basis for developing resistant conifer cultivars through microbiome engineering.

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins in eukaryotes, with nearly 800 genes coding for these proteins. They are involved in many physiological processes, such as light perception, taste and smell, neurotransmitter, metabolism, endocrine and exocrine, cell growth and migration. Importantly, GPCRs and their ligands are the targets of approximately one third of all marketed drugs. GPCRs are traditionally known for their role in transmitting signals from the extracellular environment to the cell's interior via the plasma membrane. However, emerging evidence suggests that GPCRs are also localized on mitochondria, where they play critical roles in modulating mitochondrial functions. These mitochondrial GPCRs (mGPCRs) can influence processes such as mitochondrial respiration, apoptosis, and reactive oxygen species (ROS) production. By interacting with mitochondrial signaling pathways, mGPCRs contribute to the regulation of energy metabolism and cell survival. Their presence on mitochondria adds a new layer of complexity to the understanding of cellular signaling, highlighting the organelle's role as not just an energy powerhouse but also a crucial hub for signal transduction. This expanding understanding of mGPCR function on mitochondria opens new avenues for research, particularly in the context of diseases where mitochondrial dysfunction plays a key role. Abnormalities in the phase conductance pathway of GPCRs located on mitochondria are closely associated with the development of systemic diseases such as cardiovascular disease, diabetes, obesity and Alzheimer's disease. In this review, we examined the various types of GPCRs identified on mitochondrial membranes and analyzed the complex relationships between mGPCRs and the pathogenesis of various diseases. We aim to provide a clearer understanding of the emerging significance of mGPCRs in health and disease, and to underscore their potential as therapeutic targets in the treatment of these conditions. [Display omitted] •GPCRs existing on mitochondrial membrane affect the physiological function of mitochondria.•Dysfunction in mGPCRs is linked to cardiovascular disorders and neurodegenerative disorders.•Targeting mGPCRs offers therapeutic potential for treating these conditions.

In order to explore the appropriate irrigation schedule for summer maize, a field experiment was conducted in 2013 in Lubotan of Shaanxi Province. Soil water content, soil salinity, soil hydraulic parameters, crop growth parameters and summer maize yield were measured in the experiment. The SWAP model was calibrated based on field experiment observation data in 2013. The SWAP model was used to simulate and optimize irrigation schedule for summer maize after calibration. The results showed that model simulation results of soil water content, soil salinity and summer maize yield agreed well with the measured values. The Root Mean Square Error (RMSE) and Mean Relative Error (MRE) were within the allowable error ranges. The RMSE values were all lower than 0.05 cm3/cm3 and the MRE values were lower than 15% in soil water content calibration. The RMSE values were all lower than 0.1 mg/cm3 and the MRE values were lower than 20% in soil salinity calibration. The RMSE and MRE values were 1299.6 kg/hm2 and 15.26% in summer maize yield calibration. The model parameters suitable for the study area were obtained in calibration. The SWAP model could be used to simulate and optimize irrigation schedule for summer maize after calibration. The SWAP model was used to simulate soil water-salt balance, summer maize yield and water use efficiency under different irrigation schedules. The model simulation results for different irrigation schedules indicated that the optimal irrigation schedules of summer maize were three times each for jointing stage (July 5), heading stage (August 5) and grain filling stage (August 30) with irrigation amount of 128 mm, 128 mm and 96 mm, respectively. The optimal irrigation quota was 352.0 mm for summer maize in the study area.

Oral squamous cell carcinoma (OSCC) urgently requires innovative therapeutic strategies due to its severity and stagnant five-year survival rate. Targeting glutamine metabolism, a promising approach, is hampered by tumor cells’ profound metabolic plasticity. Through a series of experiments, we uncovered heterogeneous responses of OSCC cell lines to glutamine deprivation: While most cells maintained growth, HSC3 cells showed a marked reduction in proliferation. Subsequent experiments have shown that this slowdown was not due to programmed cell death. Metabolomics and biochemical assays revealed elevated cholesterol ester (ChE) levels in glutamine-tolerant cells, not due to enhanced endogenous synthesis (HMGCS1/SQLE expression decreased) but via upregulated low-density lipoprotein receptor (LDLR)-mediated LDL uptake. Moreover, we found that fatty acid oxidation during glutamine deprivation not only supplied substrates for the tricarboxylic acid (TCA) cycle but also accelerated energy metabolism and potentially increased lipid synthesis for membrane structure and signaling. RNA sequencing identified robust enrichment of the peroxisome proliferator-activated receptor α (PPARα) pathway in tolerant cells. Reactive oxygen species (ROS) accumulation and activating transcription factor 5 (ATF5) nuclear translocation suggested the activation of multiple stress response pathways to maintain survival and growth under glutamine deprivation. Our study reveals a survival mechanism in OSCC: cells enhance exogenous lipid utilization to bypass glutamine dependence. Combined inhibition of LDL uptake and PPARα signaling may overcome metabolic plasticity, providing a rationale for precision therapies targeting metabolic heterogeneity in OSCC. Graphical abstract

The field hydrology model-DRAINMOD has several options for evapotranspiration (ET) calculation. The choice of each calculation method may affect modeling results because ET is one of the most important hydrologic components in field hydrology. ET calculation method may also have significant impact on crop yield predictions. As variable ET estimation methods are available nowadays, this paper presents a study of different ET inputs on simulated crop yields and drainage volume based on weather and soil data from a subhumid climate zone in China. The study results show that the effect of ET estimate on drainage and crop yield prediction is affected by crop species and growing period; more accurate ET estimate is also critical in simulating the effect of controlled drainage on field hydrology and crop production. The results of this study indicate that the effort contributed to better ET estimate is very important in model applications.

Creating a form that is attractive to the intended market audience is one of the greatest challenges in product development given the subjective nature of preference and heterogeneous market segments with potentially different product preferences. Accordingly, product designers use a variety of qualitative and quantitative research tools to assess product preferences across market segments, such as design theme clinics, focus groups, customer surveys, and design reviews; however, these tools are still limited due to their dependence on subjective judgment, and being time and resource intensive. In this dissertation, we focus on a key research question: how can we understand and predict more reliably the preference for a future product in heterogeneous markets, so that this understanding can inform designers' decision-making? We present a number of data-driven approaches to model product preference. Instead of depending on any subjective judgment from human, the proposed preference models investigate the mathematical patterns behind users’ choice and behavior. This allows a more objective translation of customers' perception and preference into analytical relations that can inform design decision-making. Moreover, these models are scalable in that they have the capacity to analyze large-scale data and model customer heterogeneity accurately across market segments. In particular, we use feature representation as an intermediate step in our preference model, so that we can not only increase the predictive accuracy of the model but also capture in-depth insight into customers' preference. We tested our data-driven approaches with applications in visual aesthetics preference. Our results show that the proposed approaches can obtain an objective measurement of aesthetic perception and preference for a given market segment. This measurement enables designers to reliably evaluate and predict the aesthetic appeal of their designs. We also quantify the relative importance of aesthetic attributes when both aesthetic attributes and functional attributes are considered by customers. This quantification has great utility in helping product designers and executives in design reviews and selection of designs. Moreover, we visualize the possible factors affecting customers' perception of product aesthetics and how these factors differ across different market segments. Those visualizations are incredibly important to designers as they relate physical design details to psychological customer reactions. The main contribution of this dissertation is to present purely data-driven approaches that enable designers to quantify and interpret more reliably the product preference. Methodological contributions include using modern probabilistic approaches and feature learning algorithms to quantitatively model the design process involving product aesthetics. These novel approaches can not only increase the predictive accuracy but also capture insights to inform design decision-making.

We present a similar image retrieval (SIR) platform that is used to quickly discover visually similar products in a catalog of millions. Given the size, diversity, and dynamism of our catalog, product search poses many challenges. It can be addressed by building supervised models to tagging product images with labels representing themes and later retrieving them by labels. This approach suffices for common and perennial themes like \"white shirt\" or \"lifestyle image of TV\". It does not work for new themes such as \"e-cigarettes\", hard-to-define ones such as \"image with a promotional badge\", or the ones with short relevance span such as \"Halloween costumes\". SIR is ideal for such cases because it allows us to search by an example, not a pre-defined theme. We describe the steps - embedding computation, encoding, and indexing - that power the approximate nearest neighbor search back-end. We also highlight two applications of SIR. The first one is related to the detection of products with various types of potentially objectionable themes. This application is run with a sense of urgency, hence the typical time frame to train and bootstrap a model is not permitted. Also, these themes are often short-lived based on current trends, hence spending resources to build a lasting model is not justified. The second application is a variant item detection system where SIR helps discover visual variants that are hard to find through text search. We analyze the performance of SIR in the context of these applications.

Background: Olaparib, the world's first poly ADP-ribose polymerase (PARP) inhibitor (PARPi), has been approved for treatment of ovarian cancer, breast cancer, pancreatic cancer and prostate cancer by FDA. The current study was to assess olaparib-related adverse events (AEs) of real-world through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). Methods: Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma Poisson shrinker (MGPS) algorithms were employed to quantify the signals of olaparib-associated AEs. Results: Out of 8,450,009 reports collected from the FAERS database, 6402 reports of olaparib-associated AEs were identified. A total of 118 significant disproportionality preferred terms (PTs) conforming to the four algorithms simultaneously were retained. The most common AEs included anemia, thrombocytopenia, nausea, decreased appetite, blood creatinine increased and dermatomyositis, which were corresponding to those reported in the specification and clinical trials. Unexpected significant AEs as interstitial lung disease, Pneumocystis jirovecii pneumonia, folate deficiency, renal impairment and intestinal obstruction might also occur. The median onset time of olaparib-related AEs was 61 days (interquartile range [IQR] 14-182 days), and most of the cases occurred within the first 1 month after olaparib initiation. Conclusion: Results of our study were consistent with clinical observations, and we also found potential new and unexpected AEs signals for olaparib, suggesting prospective clinical studies were needed to confirm these results and illustrate their relationship. Our results could provide valuable evidence for further safety studies of olaparib. Keywords: olaparib, PARP inhibitor, pharmacovigilance, data mining, FAERS