Nonsyndromic cleft lip with or without a cleft palate (NSCL/P) is among the most common human congenital birth defects and imposes a substantial physical and financial burden on affected individuals. Here, we conduct a case-control-based GWAS followed by two rounds of replication; we include six independent cohorts from China to elucidate the genetic architecture of NSCL/P in Chinese populations. Using this combined analysis, we identify a new locus at 16p13.3 associated with NSCL/P: rs8049367 between CREBBP and ADCY9 (odds ratio ¼ 0.74, P ¼ 8.98 Â 10 À 12 ). We confirm that the reported loci at 1q32.2, 10q25.3, 17p13.1 and 20q12 are also involved in NSCL/P development in Chinese populations. Our results provide additional evidence that the rs2235371-related haplotype at 1q32.2 could play a more important role than the previously identified causal variant rs642961 in Chinese populations. These findings provide information on the genetic basis and mechanisms of NSCL/P.

An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi's experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.

Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related radiographic findings on PXRs. Here, we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic curve (AUROC) of 0.973 (95% CI, 0.960–0.983) and an area under the precision-recall curve (AUPRC) of 0.963 (95% CI, 0.948–0.974) in the clinical population test set of 1888 PXRs. The accuracy, sensitivity, and specificity at the cutoff value are 0.924 (95% CI, 0.912–0.936), 0.908 (95% CI, 0.885–0.908), and 0.932 (95% CI, 0.919–0.946), respectively. PelviXNet demonstrates comparable performance with radiologists and orthopedics in detecting pelvic and hip fractures.

Genome-wide association studies (GWAS) have reported a number of loci harboring common variants that influence risk of colorectal cancer (CRC) in European descent. But all the SNPs identified explained a small fraction of total heritability. To identify more genetic factors that modify the risk of CRC, especially Chinese Han specific, we conducted a three-stage GWAS including a screening stage (932 CRC cases and 966 controls) and two independent validations (Stage 2: 1,759 CRC cases and 1,875 controls; Stage 3: 943 CRC cases and 1,838 controls). In the combined analyses, we discovered two novel loci associated with CRC: rs12522693 at 5q23.3 (CDC42SE2-CHSY3, OR = 1.31, P = 2.08 × 10−8) and rs17836917 at 17q12 (ASIC2-CCL2, OR = 0.75, P = 4.55 × 10−8). Additionally, we confirmed two previously reported risk loci, rs6983267 at 8q24.21 (OR = 1.17, P = 7.17 × 10−7) and rs10795668 at 10p14 (OR = 0.86, P = 2.96 × 10−6) in our cohorts. These results bring further insights into the CRC susceptibility and advance our understanding on etiology of CRC.