Activity Cliff Prediction: Dataset and Benchmark

Activity cliffs (ACs), which are generally defined as pairs of structurally similar molecules that are active against the same bio-target but significantly different in the binding potency, are of great importance to drug discovery. Up to date, the AC prediction problem, i.e., to predict whether a pair of molecules exhibit the AC relationship, has not yet been fully explored. In this paper, we first introduce ACNet, a large-scale dataset for AC prediction. ACNet curates over 400K Matched Molecular Pairs (MMPs) against 190 targets, including over 20K MMP-cliffs and 380K non-AC MMPs, and provides five subsets for model development and evaluation. Then, we propose a baseline framework to benchmark the predictive performance of molecular representations encoded by deep neural networks for AC prediction, and 16 models are evaluated in experiments. Our experimental results show that deep learning models can achieve good performance when the models are trained on tasks with adequate amount of data, while the imbalanced, low-data and out-of-distribution features of the ACNet dataset still make it challenging for deep neural networks to cope with. In addition, the traditional ECFP method shows a natural advantage on MMP-cliff prediction, and outperforms other deep learning models on most of the data subsets. To the best of our knowledge, our work constructs the first large-scale dataset for AC prediction, which may stimulate the study of AC prediction models and prompt further breakthroughs in AI-aided drug discovery. The codes and dataset can be accessed by https://drugai.github.io/ACNet/

Uncertainty and Explainable Analysis of Machine Learning Model for Reconstruction of Sonic Slowness Logs

Logs are valuable information for oil and gas fields as they help to determine the lithology of the formations surrounding the borehole and the location and reserves of subsurface oil and gas reservoirs. However, important logs are often missing in horizontal or old wells, which poses a challenge in field applications. In this paper, we utilize data from the 2020 machine learning competition of the SPWLA, which aims to predict the missing compressional wave slowness and shear wave slowness logs using other logs in the same borehole. We employ the NGBoost algorithm to construct an Ensemble Learning model that can predicate the results as well as their uncertainty. Furthermore, we combine the SHAP method to investigate the interpretability of the machine learning model. We compare the performance of the NGBosst model with four other commonly used Ensemble Learning methods, including Random Forest, GBDT, XGBoost, LightGBM. The results show that the NGBoost model performs well in the testing set and can provide a probability distribution for the prediction results. In addition, the variance of the probability distribution of the predicted log can be used to justify the quality of the constructed log. Using the SHAP explainable machine learning model, we calculate the importance of each input log to the predicted results as well as the coupling relationship among input logs. Our findings reveal that the NGBoost model tends to provide greater slowness prediction results when the neutron porosity and gamma ray are large, which is consistent with the cognition of petrophysical models. Furthermore, the machine learning model can capture the influence of the changing borehole caliper on slowness, where the influence of borehole caliper on slowness is complex and not easy to establish a direct relationship. These findings are in line with the physical principle of borehole acoustics

Emergency tracheal intubation in 202 patients with COVID-19 in Wuhan, China:lessons learnt and international expert recommendations

Tracheal intubation in coronavirus disease 2019 (COVID-19) patients creates a risk to physiologically compromised patients and to attending healthcare providers. Clinical information on airway management and expert recommendations in these patients are urgently needed. By analysing a two-centre retrospective observational case series from Wuhan, China, a panel of international airway management experts discussed the results and formulated consensus recommendations for the management of tracheal intubation in COVID-19 patients. Of 202 COVID-19 patients undergoing emergency tracheal intubation, most were males (n=136; 67.3%) and aged 65 yr or more (n=128; 63.4%). Most patients (n=152; 75.2%) were hypoxaemic (Sao2 <90%) before intubation. Personal protective equipment was worn by all intubating healthcare workers. Rapid sequence induction (RSI) or modified RSI was used with an intubation success rate of 89.1% on the first attempt and 100% overall. Hypoxaemia (Sao2 <90%) was common during intubation (n=148; 73.3%). Hypotension (arterial pressure <90/60 mm Hg) occurred in 36 (17.8%) patients during and 45 (22.3%) after intubation with cardiac arrest in four (2.0%). Pneumothorax occurred in 12 (5.9%) patients and death within 24 h in 21 (10.4%). Up to 14 days post-procedure, there was no evidence of cross infection in the anaesthesiologists who intubated the COVID-19 patients. Based on clinical information and expert recommendation, we propose detailed planning, strategy, and methods for tracheal intubation in COVID-19 patients

A reference-grade wild soybean genome

Wild relatives of crop plants are invaluable germplasm for genetic improvement. Here, Xie et al. report a reference-grade wild soybean genome and show that it can be used to identify structural variation and refine quantitative trait loci

A reference-grade wild soybean genome

Efficient crop improvement depends on the application of accurate genetic information contained in diverse germplasm resources. Here we report a reference-grade genome of wild soybean accession W05, with a final assembled genome size of 1013.2 Mb and a contig N50 of 3.3 Mb. The analytical power of the W05 genome is demonstrated by several examples. First, we identify an inversion at the locus determining seed coat color during domestication. Second, a translocation event between chromosomes 11 and 13 of some genotypes is shown to interfere with the assignment of QTLs. Third, we find a region containing copy number variations of the Kunitz trypsin inhibitor (KTI) genes. Such findings illustrate the power of this assembly in the analysis of large structural variations in soybean germplasm collections. The wild soybean genome assembly has wide applications in comparative genomic and evolutionary studies, as well as in crop breeding and improvement programs

Sustaining Teaching with Technology after the Quarantine: Evidence from Chinese EFL Teachers’ Technological, Pedagogical and Content Knowledge

Given that little is known about English teachers’ technological, pedagogical and content knowledge (TPACK), this study examined teachers’ TPACK of using interactive whiteboards (IWBs) by contextualizing the research in the Chinese EFL context. Surveys and multi-case interviews were conducted among secondary school EFL teachers. The results revealed that Chinese EFL teachers generally perceived themselves to be competent in TPACK, with content knowledge achieving the highest value (5.545) and technological knowledge having the lowest value (5.147). In addition, teachers with higher professional titles perceived themselves as having lower TPACK. Barriers to using IWBs in English teaching include a lack of using efficacy regarding IWBs, traditional teaching beliefs, insufficient technical support and training, defects in IWBs for English teaching and time constraints. This study enriched technology adoption literature and informed policymakers and educational institutions of the necessity to provide specialized training to improve teachers’ TPACK and take measures to reduce teachers’ non-teaching-related tasks to ensure sustainable technology adoption in English teaching

Mitogen-activated protein kinase inhibition-induced modulation of epidermal growth factor receptor signaling in human head and neck squamous cell carcinoma

Background: Epidermal growth factor receptor (EGFR) overexpression is one of the most notable characteristics in head and neck squamous cell carcinoma (HNSCC). The MAPK kinase (MEK) inhibitor trametinib has shown efficacy to treat HNSCC; however, the molecular mechanism remains unclear. Methods: HNSCC lines, mouse models, Western blot, and flow cytometry were employed to analyze the anticancer effects of trametinib. Results: The JHU-011, JHU-022, and JHU-029 HNSCC cells with different genetic alterations were highly susceptible to trametinib. Trametinib effectively reduced EGFR expression, which was accompanied by the reduction of pro-survival protein MYC, and the increased expression of a MYC-targeted cyclin-dependent kinase inhibitor p27kip1 and pro-apoptotic protein BIM. Trametinib resulted in G1 arrest of the cells, markedly reduced cell numbers in S phase, and significantly increased apoptosis. In mouse models, trametinib strongly inhibited tumors growth. Conclusions: The MAPK–ERK signaling inhibition by trametinib may target EGFR and the downstream proteins against HNSCC

Promoting Effect of Choline-Phosphate Cytidylyltransferase Gene (<i>pcyt-1</i>) on Departure of Pinewood Nematode from <i>Monochamus alternatus</i>

In order to study the key gene in internal causes of pinewood nematode (PWN), Bursaphelenchus xylophilus, a departure from its vector beetle, Monochamus alternatus, we collected PWNs extracted from newly emerged M. alternatus and beetles 7 days after emergence. The total RNAs of the two groups of PWNs were extracted, transcriptomes sequencing was performed, and gene expression differences between the two groups of PWN were analyzed. It was found that the expression of the choline-phosphate cytidylyltransferase gene (pcyt-1) was markedly up-regulated. After inhibition of pcyt-1 expression by RNA interference, the rate of lipid degradation in PWN decreased significantly, and the motility of PWN also decreased significantly. The analysis identified that phosphatidylcholine could promote the emulsification and degradation of neutral lipid granules in PWN, which provides sufficient energy for PWN departure from M. alternatus. The up-regulation of the gene pcyt-1 is an important internal factor for PWN departure from its vector

Research on the Simplified Method of Nonlinear Finite Element Analysis for CFS-SPR Connections

This study reviewed some simplified methods of finite element analysis (FEA) for connections in cold-formed steel (CFS) structure, and summarized eight simplified methods divided into three categories. Shear performance tests were performed for six groups of self-piercing riveted (SPR) connection in CFS. A constitutive model of shear behavior for SPR connections was proposed, which was simplified from the load–displacement curve of shear performance test results. The models of SPR connection were established in ABAQUS by the eight simplified methods, and then the FEA results and the test results were compared. The applicable scope of each simplified model was explored, and a simplified method of FEA that was most suitable for the shear behavior of the CFS-SPR connection was proposed. Moreover, the shear performance test of the CFS shear wall with SPR was conducted by considering the rivet spacing, and failure modes and load–deformation curves were obtained. On this basis, numerical models of the CFS-SPR connection shear wall were established. By comparing the test results and the FEA results for the CFS-SPR connection shear wall, the feasibility of a simplified method of FEA applied to the CFS-SPR connection was verified. The main failure modes of the CFS-SPR connection were that the rivet tail pulled out from the bottom sheet and the rivet head pulled over from the top sheet. The SPR connection of the CFS frame could be simplified with a pin or a fastener element, and the SPR connection between the steel frame and the sheathing could be simulated by a Cartesian connector or a Spring2 element. The FEA results were highly similar to the test results for the CFS-SPR shear wall