Design and Mechanical Compatibility of Nylon Bionic Cancellous Bone Fabricated by Selective Laser Sintering

In order to avoid the stress shielding phenomenon in orthopedic bionic bone implantation, it is necessary to consider the design of mechanical compatible implants imitating the host bone. In this study, we developed a novel cancellous bone structure design method aimed at ensuring the mechanical compatibility between the bionic bone and human bone by means of computer-aided design (CAD) and finite element analysis technology (specifically, finite element modeling (FEM)). An orthogonal lattice model with volume porosity between 59% and 96% was developed by means of CAD. The effective equivalent elastic modulus of a honeycomb structure with square holes was studied by FEM simulation. With the purpose of verifying the validity of the cancellous bone structure design method, the honeycomb structure was fabricated by selective laser sintering (SLS) and the actual equivalent elastic modulus of the honeycomb structure was measured with a uniaxial compression test. The experimental results were compared with the FEM values and the predicted values. The results showed that the stiffness values of the designed structures were within the acceptable range of human cancellous bone of 50-500 MPa, which was similar to the stiffness values of human vertebrae L1 and L5. From the point of view of mechanical strength, the established cellular model can effectively match the elastic modulus of human vertebrae cancellous bone. The functional relationship between the volume porosity of the nylon square-pore honeycomb structure ranging from 59% to 96% and the effective elastic modulus was established. The effect of structural changes related to the manufacture of honeycomb structures on the equivalent elastic modulus of honeycomb structures was studied quantitatively by finite element modeling

Research on the Food Security Condition and Food Supply Capacity of Egypt

Food security is chronically guaranteed in Egypt because of the food subsidy policy of the country. However, the increasing Egyptian population is straining the food supply. To study changes in Egyptian food security and future food supply capacity, we analysed the historical grain production, yield per unit, grain-cultivated area, and per capita grain possession of Egypt. The GM (1,1) model of the grey system was used to predict the future population. Thereafter, the result was combined with scenario analysis to forecast the grain possession and population carrying capacity of Egypt under different scenarios. Results show that the increasing population and limitations in cultivated land will strain Egyptian food security. Only in high cultivated areas and high grain yield scenarios before 2020, or in high cultivated areas and mid grain yield scenarios before 2015, can food supply be basically satisfied (assurance rate ≥ 80%) under a standard of 400 kg per capita. Population carrying capacity in 2030 is between 51.45 and 89.35 million. Thus, we propose the use of advanced technologies in agriculture and the adjustment of plant structure and cropping systems to improve land utilization efficiency. Furthermore, urbanization and other uses of cultivated land should be strictly controlled to ensure the planting of grains

Screening of deafness-causing DNA variants that are common in patients of European ancestry using a microarray-based approach

The unparalleled heterogeneity in genetic causes of hearing loss along with remarkable differences in prevalence of causative variants among ethnic groups makes single gene tests technically inefficient. Although hundreds of genes have been reported to be associated with nonsyndromic hearing loss (NSHL), GJB2, GJB6, SLC26A4, and mitochondrial (mt) MT-RNR1 and MTTS are the major contributors. In order to provide a faster, more comprehensive and cost effective assay, we constructed a DNA fluidic array, CapitalBioMiamiOtoArray, for the detection of sequence variants in five genes that are common in most populations of European descent. They consist of c.35delG, p.W44C, p.L90P, c.167delT (GJB2); 309kb deletion (GJB6); p.L236P, p.T416P (SLC26A4); and m.1555A>G, m.7444G>A (mtDNA). We have validated our hearing loss array by analyzing a total of 160 DNAs samples. Our results show 100% concordance between the fluidic array biochip-based approach and the established Sanger sequencing method, thus proving its robustness and reliability at a relatively low cost

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features

Loop closure detection is a key challenge in visual simultaneous localization and mapping (SLAM) systems, which has attracted significant research interest in recent years. It entails correctly determining whether a scene has previously been visited by a mobile robot and completely establishing the consistent maps of motion. There are many loop closure detection methods that have been proposed, but most of these algorithms are handcrafted features-based and perform weak robustness to illumination variations. In this paper, we investigate a Siamese Convolutional Neural Network (SCNN) to solve the task of loop closure detection in RGB-D SLAM. Firstly, we use a pre-trained SCNN model to extract features as image descriptors; then, the L2 norm distance is adopted as a similarity metric between descriptors. In terms of the learned features for matching, there are two key issues for discussion: (1) how to define an appropriate loss as supervision (utilizing the cross-entropy loss, the contrastive loss, or the combination of two); and (2) how to combine the appearance information in RGB images and position information in depth images (utilizing early fusion, mid-level fusion or late fusion). We compare our proposed method of different baseline by experiments carried out on two public datasets (New College and NYU), and our performance outperforms the state-of-the-art

Microarray-based genotyping and detection of drug-resistant HBV mutations from 620 Chinese patients with chronic HBV infection

Background: Research has shown that hepatitis B virus (HBV) genotypes are closely linked to the clinical manifestations, treatment, and prognosis of the disease.Objective: To study the association between genotype and drug-resistant HBV mutations in 620 Chinese patients with chronic HBV infection.Methods: HBV DNA levels were determined using real-time quantitative PCR in plasma samples. Microarrays were performed for the simultaneous detection of HBV genotypes (HBV/B, C, and D) and drug-resistance-related hotspot mutations. A portion of the samples analyzed using microarrays was selected randomly and the data were confirmed using direct DNA sequencing.Results: Most samples were genotype C (471/620; 76.0%), followed by genotype B (149/620; 24.0%). Among the 620 patient samples, 17 (2.7%) had nucleotide analogs (NA) resistance-related mutations. Of these, nine and eight patients carried lamivudine (LAM)-/telbivudine (LdT)-resistance mutations (rtL180M, rtM204I/V) and adefovir (ADV)-resistance mutations (rtA181T/V, rtN236T), respectively. No patients had both lamivudine (LAM)- and either ade-fovir (ADV) or entecavir (ETV) resistance mutations. Additionally, out of the 620 patient samples, 64.0% (397/620) were also detected with the precore stop-codon mutation (G1896A) by microarray assay.Conclusion: The results of the current study revealed that the prevalence of nucleotide analogs (NA)-resistance in Chinese hospitalized HBV-positive patients was so low that intensive nucleotide analogs (NA)-resistance testing before nucleotide analog (NA) treatment might not be required. In addition, the present study suggests that chronic HBV patients with genotype C were infected with fitter viruses and had an increased prevalence of nucleotide analogs (NA)-resistance mutations compared to genotype B virus

Contamination Event Detection Method Using Multi-Stations Temporal-Spatial Information Based on Bayesian Network in Water Distribution Systems

As a core part of protecting water quality safety in water distribution systems, contamination event detection requires high accuracy. Previously, temporal analysis-based methods for single sensor stations have shown limited performance as they fail to consider spatial information. Besides, abundant historical data from multiple stations are still underexploited in causal relationship modelling. In this paper, a contamination event detection method is proposed, in which both temporal and spatial information from multi-stations in water distribution systems are used. The causal relationship between upstream and downstream stations is modelled by Bayesian Network, using the historical water quality data and hydraulic data. Then, the spatial abnormal probability for one station is obtained by comparing its current causal relationship with the established model. Meanwhile, temporal abnormal probability is obtained by conventional methods, such as an Autoregressive (AR) or threshold model for the same station. The integrated probability that is calculated employed temporal and spatial probabilities using Logistic Regression to determine the final detection result. The proposed method is tested over two networks and its detection performance is evaluated against results obtained from traditional methods using only temporal analysis. Results indicate that the proposed method shows higher accuracy due to its increased information from both temporal and spatial dimensions

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems