Allostery is the most direct, rapid and efficient way of regulating protein function, ranging from the control of metabolic mechanisms to signal-transduction pathways. However, an enormous amount of unsystematic allostery information has deterred scientists who could benefit from this field. Here, we present the AlloSteric Database (ASD), the first online database that provides a central resource for the display, search and analysis of structure, function and related annotation for allosteric molecules. Currently, ASD contains 336 allosteric proteins from 101 species and 8095 modulators in three categories (activators, inhibitors and regulators). Proteins are annotated with a detailed description of allostery, biological process and related diseases, and modulators with binding affinity, physicochemical properties and therapeutic area. Integrating the information of allosteric proteins in ASD should allow for the identification of specific allosteric sites of a given subtype among proteins of the same family that can potentially serve as ideal targets for experimental validation. In addition, modulators curated in ASD can be used to investigate potent allosteric targets for the query compound, and also help chemists to implement structure modifications for novel allosteric drug design. Therefore, ASD could be a platform and a starting point for biologists and medicinal chemists for furthering allosteric research. ASD is freely available at http://mdl.shsmu.edu.cn/ASD/.

ABSTRACT:The phosphatidylinositol 3-kinase (PI3K) signaling pathway plays important roles in cell proliferation, growth, and survival. Hyperactivated PI3K is frequently found in a wide variety of human cancers, validating it as a promising target for cancer therapy. We determined the crystal structure of the human PI3Kα−PI103 complex to unravel molecular interactions. Based on the structure, substitution at the R 1 position of the phenol portion of PI103 was demonstrated to improve binding affinity via forming a new H-bond with Lys802 at the bottom of the ATP catalytic site. Interestingly, the crystal structure of the PI3Kα−9d complex revealed that the flexibility of Lys802 can also induce additional space at the catalytic site for further modification. Thus, these crystal structures provide a molecular basis for the strong and specific interactions and demonstrate the important role of Lys802 in the design of novel PI3Kα inhibitors. KEYWORDS: PI3K, PI103, crystal structure, drug design, cancer therapy T he lipid kinase family of phosphatidylinositol 3-kinases (PI3Ks) plays pivotal roles in many cellular processes, including proliferation, survival, differentiation, and metabolism. 1−3 Class I PI3K, the best physiologically, biochemically, and structurally characterized member of the PI3K family, consists of four isoforms, α, β, γ, and δ. Each isoform is a heterodimer that comprises a p110 catalytic subunit and a p85 regulatory subunit. Upon insulin and growth factor stimulation, PI3Ks phosphorylate phosphatidylinositol-3,4-bisphosphate (PIP2) to produce phosphatidylinositol-3,4,5-triphosphate (PIP3). The cellular level of PIP3 is also tightly regulated by phosphatases, such as the phosphatase and tensin homologue (PTEN), which dephosphorylates PIP3 back to PIP2. 4,5 The PI3K pathway is frequently deregulated in a wide range of tumor types as a result of hyperactivation of upstream growth factor signaling, mutation, or loss of PTEN, 6 and oncogenic mutations in PIK3CA, 7 which provides further evidence of the role of PI3K in tumorigenesis. Moreover, accumulating evidence indicates that hyperactivation of PI3Kα is inextricably linked to cancer survival and resistance to existing therapies in a great proportion of human cancers. 8 Therefore, targeting PI3Ks with small-molecular-weight inhibitors provides an attractive opportunity for cancer therapy and for overcoming resistance to current therapies, and thus, significant efforts have recently been made to develop PI3K inhibitors. 9 With multiple ongoing efforts in academic and industrial organizations to develop clinically relevant inhibitors against PI3K, a number of inhibitors have already entered clinical trials. 2,10 PI103 is one of the first synthesized PI3K inhibitors; it belongs to the pyridinylfuranopyrimidine class and inhibits PI3K in an ATP-competitive manner with selectivity toward PI3Kα. 11 PI103 has already demonstrated significant antitumor activity against several human tumor xenografts, especially those with well-established abnormalities in the P...

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

This study investigated whether sick building syndrome (SBS) complaints among office workers were associated with the indoor air quality. With informed consent, 417 employees in 87 office rooms of eight high-rise buildings completed a self-reported questionnaire for symptoms experienced at work during the past month. Carbon dioxide (CO2), temperature, humidity and total volatile organic compounds (TVOCs) in each office were simultaneously measured for eight office hours using portable monitors. Time-averaged workday difference between the indoor and the outdoor CO2 concentrations (dCO2) was calculated as a surrogate measure of ventilation efficiency for each office unit. The prevalence rates of SBS were 22.5% for eye syndrome, 15.3% for upper respiratory and 25.4% for non-specific syndromes. Tiredness (20.9%), difficulty in concentrating (14.6%), eye dryness (18.7%) were also common complaints. The generalized estimating equations multivariate logistic regression analyses showed that adjusted odds ratios (aORs) and 95% confidence interval (CI) per 100 ppm increase in dCO2 were significantly associated with dry throat (1.10, 95% CI = (1.00–1.22)), tiredness (1.16, 95% CI = (1.04–1.29)) and dizziness (1.22, 95% CI = (1.08–1.37)). The ORs for per 100 ppb increases in TVOCs were also associated with upper respiratory symptoms (1.06, 95% CI = (1.04–1.07)), dry throat (1.06, 95% CI = (1.03–1.09)) and irritability (1.02, 95% CI = (1.01–1.04)). In conclusion, the association between some SBS symptoms and the exposure to CO2 and total VOCs are moderate but may be independently significant.

Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.

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a b s t r a c tIncreasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson's r < 0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1-5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield (r > 0.40, p < 0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts.