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The membrane fouling issue has aroused great concern. To improve their antifouling properties, surface grafting with oxidative deposition were employed to amend a polyvinylidene fluoride (PVDF) membrane. The modifiers were amino-modified graphene oxide (AMGO), dopamine (DPA) and 1,3-diaminoguanidine hydrochloride (DAGH). To take bovine serum albumin (BSA, 1 g/l) as an example of organic materials, BSA interception rate and pure water flux recovery rate increased to 93.65% and 66.74%, respectively, while the corresponding values for the original membrane were much lower (72.82% and 31.72%). The optimum synthesis conditions were found to be 1.5 mg/ml of DPA, 1 wt% of DAGH, 2 mg/ml of AMGO, 4 h of DPA oxidation deposition time and 1 h of AMGO grafting time. Many functional groups like C = N, -NH2, C = O and -OH improved the membrane surface hydrophilicity leading to a higher resistance to organic pollution. Dopamine and guanidyl facilitated the antimicrobial performance of the modified membrane, whose antimicrobial rate was up to 96%, while the raw membrane had no antimicrobial activity. The amended membrane possessed 40% higher mechanical strength than the initial one. It could withstand a high pumping suction force. The noteworthy property was that the irreversible fouling rate decreased by 55%. Therefore, the amended membrane could restore its flux much more easily.

The genus Conidiobolus s.s. (Conidiobolaceae, Entomophthorales) has been delimited to accommodate members that produce microspores. Herein, morphological studies, combined with phylogenetic analysis based on the nuclear large subunit of rDNA (nucLSU), the mitochondrial small subunit of rDNA (mtSSU), and the elongation-factor-like gene (EFL) revealed two Conidiobolus s.s. species isolated from plant debris in China. Conidiobolus longiconidiophorus sp. nov. is mainly characterised by its long primary conidiophores, while Conidiobolus polysporus sp. nov. is diagnosed by 2–3 primary conidia arising from branched primary conidiophores. Phylogenetically, the former is grouped into a separate clade, while the latter is closely related to C. incongruus, but is morphologically distinguished by its larger primary conidia and branched conidiophores.

Introducction: Mobile Robot is a kind of robot system consisting of sensors, remote control operators and automatic control mobile carriers. It is a product of integrated application of integrated disciplines developed in recent years. In the research of mobile robot related technology, navigation technology is its core, and path planning is an important link and subject of navigation research. Objective: An improved firefly algorithm is proposed for path planning of Mobile Robots in this paper. Methods: In this paper, an improved firefly algorithm is proposed. Compared with traditional firefly algorithm, this algorithm has three main improvements: (1) using Sobol sequence to initialize population; (2) adding dynamic disturbance coefficient to enhance the global search ability of the algorithm; (3) considering the uncertainty of search, the attraction between individuals is strong. Fuzzy control is carried out by setting membership function. Results: The new algorithm takes advantage of the uniformity of Sobol sequence sampling and starts to optimize in a wider range, which makes the initial path of the algorithm longer, but because the new algorithm introduces the dynamic disturbance coefficient and the fuzzy control strategy, the average running time is shorter. Conclusion: In the simulation experiment of mobile robot path planning problem, the improved firefly algorithm proposed in this paper is easier to jump out of local optimum than the traditional firefly algorithm, and has more robust search ability. Discussion: It is obvious from the graph that in 100 iterations, the FaFA algorithm takes advantage of the uniformity of Sobol sequence sampling and starts to optimize in a wider range, which makes the initial path of the algorithm longer, but because the FaFA algorithm introduces the dynamic disturbance coefficient and the fuzzy control strategy, it makes the algorithm able.

During the process of drug discovery, exploring drug-protein interactions (DPIs) is a key step. With the rapid development of biological data, computer-aided methods are much faster than biological experiments. Deep learning methods have become popular and are mainly used to extract the characteristics of drugs and proteins for further DPIs prediction. Since the prediction of DPIs through machine learning cannot fully extract effective features, in our work, we propose a deep learning framework that uses variational autoencoders and attention mechanisms; it utilizes convolutional neural networks (CNNs) to obtain local features and attention mechanisms to obtain important information about drugs and proteins, which is very important for predicting DPIs. Compared with some machine learning methods on the C.elegans and human datasets, our approach provides a better effect. On the BindingDB dataset, its accuracy (ACC) and area under the curve (AUC) reach 0.862 and 0.913, respectively. To verify the robustness of the model, multiclass classification tasks are performed on Davis and KIBA datasets, and the ACC values reach 0.850 and 0.841, respectively, thus further demonstrating the effectiveness of the model.

Deep convolutional neural networks have been successfully applied to face detection recently. Despite making remarkable progress, most of the existing detection methods only localize each face using a bounding box, which cannot segment each face from the background image simultaneously. To overcome this drawback, we present a face detection and segmentation method based on improved Mask R-CNN, named G-Mask, which incorporates face detection and segmentation into one framework aiming to obtain more fine-grained information of face. Specifically, in this proposed method, ResNet-101 is utilized to extract features, RPN is used to generate RoIs, and RoIAlign faithfully preserves the exact spatial locations to generate binary mask through Fully Convolution Network (FCN). Furthermore, Generalized Intersection over Union (GIoU) is used as the bounding box loss function to improve the detection accuracy. Compared with Faster R-CNN, Mask R-CNN, and Multitask Cascade CNN, the proposed G-Mask method has achieved promising results on FDDB, AFW, and WIDER FACE benchmarks.

Additional file 2. Full length gels and blots with membrane edges.

Many recent machine learning models show dynamic shape characteristics. However, existing AI compiler optimization systems suffer a lot from problems brought by dynamic shape models, including compilation overhead, memory usage, optimization pipeline and deployment complexity. This paper provides a compiler system to natively support optimization for dynamic shape workloads, named DISC. DISC enriches a set of IR to form a fully dynamic shape representation. It generates the runtime flow at compile time to support processing dynamic shape based logic, which avoids the interpretation overhead at runtime and enlarges the opportunity of host-device co-optimization. It addresses the kernel fusion problem of dynamic shapes with shape propagation and constraints collecting methods. This is the first work to demonstrate how to build an end-to-end dynamic shape compiler based on MLIR infrastructure. Experiments show that DISC achieves up to 3.3x speedup than TensorFlow/PyTorch, and 1.8x than Nimble.

The scaling up of deep neural networks has been demonstrated to be effective in improving model quality, but also encompasses several training challenges in terms of training efficiency, programmability, and resource adaptability. We present Whale, a general and efficient distributed training framework for giant models. To support various parallel strategies and their hybrids, Whale generalizes the programming interface by defining two new primitives in the form of model annotations, allowing for incorporating user hints. The Whale runtime utilizes those annotations and performs graph optimizations to transform a local deep learning DAG graph for distributed multi-GPU execution. Whale further introduces a novel hardware-aware parallel strategy, which improves the performance of model training on heterogeneous GPUs in a balanced manner. Deployed in a production cluster with 512 GPUs, Whale successfully trains an industry-scale multimodal model with over ten trillion model parameters, named M6, demonstrating great scalability and efficiency.

The last decade has witnessed growth in the computational requirements for training deep neural networks. Current approaches (e.g., data/model parallelism, pipeline parallelism) parallelize training tasks onto multiple devices. However, these approaches always rely on specific deep learning frameworks and requires elaborate manual design, which make it difficult to maintain and share between different type of models. In this paper, we propose Auto-MAP, a framework for exploring distributed execution plans for DNN workloads, which can automatically discovering fast parallelization strategies through reinforcement learning on IR level of deep learning models. Efficient exploration remains a major challenge for reinforcement learning. We leverage DQN with task-specific pruning strategies to help efficiently explore the search space including optimized strategies. Our evaluation shows that Auto-MAP can find the optimal solution in two hours, while achieving better throughput on several NLP and convolution models.

The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets. Minibatch stochastic gradient descent (SGD) algorithm requires workers to halt forward/back propagations, to wait for gradients aggregated from all workers, and to receive weight updates before the next batch of tasks. This synchronous execution model exposes the overheads of gradient/weight communication among a large number of workers in a distributed training system. We propose a new SGD algorithm, DaSGD (Local SGD with Delayed Averaging), which parallelizes SGD and forward/back propagations to hide 100% of the communication overhead. By adjusting the gradient update scheme, this algorithm uses hardware resources more efficiently and reduces the reliance on the low-latency and high-throughput inter-connects. The theoretical analysis and the experimental results show its convergence rate O(1/sqrt(K)), the same as SGD. The performance evaluation demonstrates it enables a linear performance scale-up with the cluster size.

As a research hotspot of computer vision and information security, face detection has been widely developed in the past few decades. However, most of the existing detection methods only realize the location of the bounding box, which leads to background noise in the face features as well as limited accuracy of detection. To overcome these drawbacks, a face detection and segmentation method with Generalized Intersection over Union (GIoU) based on Mask R-CNN is proposed in this paper, which is called G-Mask. In this method, ResNet-101 is used to extract features, RPN is used to generate RoIs, and RoIAlign faithfully retains the exact spatial locations to generate binary mask through Fully Convolution Network. In particular, to achieve better performance in multi-scale face detection tasks, we utilize GIoU as the bounding box loss function. Furthermore, a new face dataset with segmentation annotation information is constructed in this paper to train the model. The experimental results of the well-known benchmark FDDB and AFW show that the proposed G-Mask method achieves promising face detection performance compared with Faster R-CNN and the original Mask R-CNN method, and also can realize the instance-level face information segmentation while detecting.

Taking into consideration the disadvantages of the serial structure controller-slow operation speed, poor grid current quality for the present grid-connected invert, the authors proposed a new grid-connected invert architecture, which is based on SOPC control technology on FPGA platform. It adopts the direct current PI control strategy with grid voltage feed-forward and uses hardware to realize algorithm, which improved the running speed and the overall performance of system. Meanwhile, designed regulator parameters for the control strategy and constructed grid-connected control system IP hard core for the invert in this paper. Experimental results of 1 kW prototype show that this method has good grid current quality and strong synchronous tracking capability.

The flow behavior and processing maps of homogenized AA7085 aluminum alloy were investigated by isothermal hot compression tests in the temperature range of 300∼450 °C with strain rates of 0.01∼10 s−1. Microstructure was characterized using Optical microscopy (OM), electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM). The results show that the superimposed processing maps at different strains exhibit the optimized deformation parameters with the temperature of 390∼450 °C and strain rates lower than 0.1 s−1. Microstructure characterization reveals that dynamic recovery and recrystallization are the main deformation mechanisms in the safe regions. At lower temperature and lower strain rate, the coarsening of particles between ɛ=0.5 and ɛ=0.7 may be responsible for the transformation of processing map from unstability to safe domain.

China is the global leader in mariculture production. Increasing sequestered marine carbon (also known as blue carbon) via mariculture activities is a promising approach for mitigating climate change and promoting the development of a low-carbon economy. Mariculture blue carbon is also considered an important component of China’s “sea granary”. In addition to shellfish and macroalgae yields, which represent carbon removed from mariculture environment, blue carbon also includes other important components, which have been largely neglected in the past, such as the carbon transformed by microbes, dissolved organic carbon (mainly referred to as recalcitrant dissolved organic carbon), and sedimentary particulate carbon. Hence, from different aspects, a comprehensive study on the formation processes and mechanisms of carbon sequestration is of great significance for comprehensively unveiling the carbon sequestration capability in coastal mariculture environment, which will contribute to the sustainable development of the fishery economy and construction of an ecological civilization. Moreover, it may add significant economic benefits to the future carbon-trading market.

Tumor associated neutrophils (TANs) play important roles in the progress of CRC. Since tumor microenvironments could influence the phenotypes of TANs, altering the tumor microenvironment to polarize the phenotype of TANs may be a new strategy for tumor treatment. This study aims to investigate the effect of anti-TGF-β on the polarization of TANs from a pro-tumor phenotype towards an anti-tumor phenotype in CRC. In this work, CRC patients had more infiltration of TANs and higher expression of TGF-β in CRC tissue when compared with the controls. In vitro, SW480 cells were co-cultured with primed neutrophils, which simulated the TANs in the tumor microenvironment, and TGF-β was blocked by anti-TGF-β (1D11) in order to polarize TANs. Anti-TGF-β treatment increased the cytotoxicity of TANs and decreased the metastatic chemoattractants secreted by TANs, and ultimately increased the apoptosis of CRC cells significantly while remarkably suppressing the migration of tumor cells. The changes of signaling pathways in the TANs and tumor cells were explored. The results showed that anti-TGF-β attenuated CRC may be partly mediated by suppression of PI3K/AKT signaling pathways in TANs and partly mediated by suppression of TGF-β/Smad signaling pathways in tumor cells. Furthermore, the tumor in the mice treated with 1D11 was obviously smaller and had reverse tumorigenesis compared with the controls, while neutrophil depletion reduced the anti-tumor effect of 1D11. Our data suggest that anti-TGF-β attenuates tumor growth via the polarization of TANs to an anti-tumor phenotype in CRC, which provides new strategies for CRC treatment.

Janus kinase 2 (JAK2) is a prospective drug target for myeloproliferative neoplasms, mainly polycythemia Vera, essential thrombocythemia and primary myelofibrosis disorders...

It is well known that the performance of Least Squares Support Vector Machine (LSSVM) is guaranteed by k-fold cross-validation (k-CS) or other optimized approaches to choose an appropriate setting of a number of parameters including such as the regularization parameter (7) and the kernel parameter (σ) and so on. However, it is mentioned in a large number of research on k-CS that it need more computational time and large computational burden in the process of optimizing parameters for LSSVM. Moreover, the obtained parameters by CS method are fixed and it lead easily to a poor generalization capabilities in various application. In order to avoid k-CS and to implement parameters update online, this paper proposes a novel method which applied Unscented Kalman Filter (UKF) to dynamically implement parameter updating problem for LSSVM. To estimate LSSVM's parameters online, the state and measurement equations of UKF are first constructed by considering LSSVM's parameter choice as state variable and treating LSSVM model as the measurement equation, respectively. Then, the UKF approach is used to update the LSSVM parameters timely according to the last obtained instance. Applying the proposed method, LSSVM parameters are not any more fixed as tuned parameters on the training dataset, but are adjusted dynamically as new measurements arrived. Finally, the viability and superiority of the proposed method are verified by simulation.

Currently, pattern recognition technique is widely applied in human identity recognition while there are shortages remaining in most kinds of such techniques. In order to overcome these problems, a novel algorithm is proposed to apply in identity recognition course in new field-static gait recognition field. Two combined features are going for static gait recognition: the distance between each part of center pressure and overall foot and the side length of outline triangle of human foot. Then through the comparison of different classifier, the best course for recognition can be obtained.

The issue of mixing enhancement of the fuel and oxidizer in the combustor of a ramjet has received more and more attention. Various injection ways were designed and justified to improve the mixing process by producing more multiplescale vortex structures. Under the restriction of compact configuration of the combustor in an integral rocket dual combustion ramjet, a better way to enhance mixing is turning the laminar flow to turbulent flow. The present study focuses on the stability analysis of the boundary/mixing-layer confluent flow affected by the combustion heat release in the combustion chamber. Two types of basic flows are formed for linear stability analysis both from a theoretical model and numerical computation. Eigenvalue spectra and eigenfunctions are obtained and compared. The results show that the heatrelease effect stabilizes the confluent mixing flow.

As the obtained data in many practical applications tend to be uncertain or inaccurate, the conventional modeling methods characterized by the deterministic model for this type of data have become undesirable. Taking linear programming, the T-S fuzzy model and some ideas from 1-norm minimization into consideration, a novel method identifying interval fuzzy model (INFUMO) consisted of the upper and lower T-S fuzzy model (referred to as f U and f L) has been studied in this paper. In order to solve the INFUMO, optimization problems based on minimizing 1-norm with respect to the approximation error corresponding to f U and f Lare constructed. Finally, the optimization problems are solved by the linear programming and INFUMO is thus constructed. To demonstrate its effectiveness, the proposed method is applied to identify the interval T-S model of static and dynamic nonlinear model with noise. The proposed method can not only deal with uncertain data to be usually modeled as the deterministic model, but also has better robustness.

Purpose: The study was performed to determine the effect of Dipsacus asperoides (roots), commonly known as Xu Duan, on rats with spinal cord injury (SCI).Methods: All the animals were separated into 3 groups: normal control; a group that received Dipsacus asperoides treatment after spinal injury, and a group that received phosphate-buffered saline after SCI. These groups allowed for determination of the effect of Dipsacus asperoides treatment on SCI-injured rats. Evaluation of locomotor function restoration based on Basso–Beattie–Bresnahan score was carried out while expressions of I-kBα and NF-kB p65 were estimated. Evaluation of pro-apoptotic protein Bax and anti-apoptotic protein Bcl-2 levels was also carried by Western blotting and densitometry.Results: The results showed that 10-mg/kg/day dose of Dipsacus asperoides restored locomotor function in rats after a period of 4 weeks. The treatment also decreased expressions of I-kBα, NF-kB p65 and Bax, but significantly increased expression of Bcl-2 (p < 0.01) in treated rats, compared to untreated rats.Conclusion: The results indicate that treatment with Dipsacus asperoides extract effectively mitigates spinal cord injury by attenuating inflammation and apoptosis in SCI rats.Keywords: Spinal cord injury, IKK-NFkB pathway, Bax, Bcl-2, Xu Duan, Apoptosis

In this study, fast atrazine degradation by the mixed bacterial cultures from sewage sludge was investigated. The acquired activated cultures showed great capability in atrazine degradation. The biodegradation process was well fitted into a pseudo-first reaction kinetic model. Atrazine could inhibit the propagation of ammonium oxidation bacteria and nitrite oxidation bacteria, decreasing the ammonium removal rate and the accumulation of nitrite. Only 162–172 reads of Nitrosomonadaceae and no Nitrospirales were detected after atrazine was exposed to the mixed cultures. The bacterial community structures in the cultures under different inoculation conditions (with or without atrazine) were investigated to explore the mechanism of atrazine degradation. Our results show that the genera Thiobacillus and Ferruginibacter were the most possible candidates responsible for the degradation of atrazine.

Process modeling and fault detection using a new Takagi-Sugeno (T-S) fuzzy method which is obtained by decomposing structural risk minimization has been proposed in this paper. The proposed method makes use of some ideas on Least square support vector regression (LSSVR). First of all, a novel cost function consisted of R terms is constructed and used for solving consequent parameters of T-S fuzzy model based on process data. Then, the normal model corresponding to no fault is used to obtain nonlinear process models for the process running in normal operation. When a fault occurs, fault detection is performed using the residuals. Finally, several types of faults have been considered on the benchmark Tennessee Eastman Process data. The effectiveness of the obtained simulation results indicate that the developed method is capable to correctly detect various faults by simulations.

To further expand the application of anammox biotechnology, a novel zero-valent iron-assembled upflow anaerobic sludge bed reactor was employed to strengthen anammox performance under low temperature and shock load. Packed with sponge iron and polyester sponge, this novel reactor could speed up the recovery of anammox activity in 12 days and improve the adaptability of anammox bacteria at the temperature of 10–15 °C. The high nitrogen loading rate of 1109.2 mg N/L/day could be adapted in 27 days and the new nitrogen pathway under the effect of sponge iron was clarified by batch experiment. Moreover, the real-time quantitative PCR analysis and Illumina MiSeq sequencing verified the dominant status of Candidatus Kuenenia stuttgartiensis and planctomycete KSU-1, as well as demonstrated the positive role of sponge iron on anammox microorganisms’ proliferation. The findings might be beneficial to popularize anammox-related processes in municipal and industrial wastewater engineering.

A travelling wave ultrasonic by the clutch coupling longitudinal and bending vibration modes is proposed to improve the output performance of the current ultrasonic clutch. The proposed ultrasonic clutch mechanism is a crucial component and it mainly includes a driving friction plate, a slave friction plate, driving and slave Langevin transducers, driving and driven shafts. The proposed ultrasonic clutch has the connection and disconnection function. In addition, a driving travelling wave is generated by compositing the longitudinal and bending vibration modes of transducer and friction plate for realizing the regulation of output performance. The matching frequencies of two vibration modes are obtained by modal simulation analysis using finite element method. A prototype of ultrasonic clutch is manufactured and the vibration characteristics are tested. The results indicate that the travelling wave ultrasonic clutch coupling by longitudinal and bending vibration modes is feasible.

The lesions of fibrous dysplasia usually stabilize after adolescence, and the surgical treatment of adult patient remains the mainstay. However, the surgical treatment alone may be not enough for patient with polyostotic fibrous dysplasia. We present the case of a surgically treated 36-year-old man with a diagnosis of progressive polyostotic fibrous dysplasia in the craniomaxillofacial region. The patients presented the swelling symptoms originally in the parietal bone at the age of 8 years, and extended gradually to almost all of the craniomaxillofacial bones during the following 28 years without specific treatment for socio-economic reasons. The symptom impelled the patient visit our department in 2009 was the rapidly progressive swelling in the chin during the last 3 years, which severely impacted his speech and feeding. The radiographs showed the typical intramedullary located and ill-defined lesions. The patient was treated with segmental mandibulectomy and reconstruction with vascularized fibular myocutaneous flap; the deformities in other craniomaxillofacial bones were not treated simultaneously. The local recurrence was not present in the chin. The visual acuity of right eye severely deteriorated and the left mandibular ramus continued expanding gradually when the patient was followed up through telephone 5 years later. A combination of surgical and medical treatment may be considered for patients with polyostotic craniomaxillofacial fibrous dysplasia.

This paper mainly solves the issue of subspace aided data-driven fault detection for LTI systems, identifying the residual generator without the specific model. By the input and output data, Akaike information criterion and singular value decomposition are used to determined the system order firstly and a comparison between them is made. Then based on a certain algorithm, we can get some useful subspace to construct a residual generator for fault detection effectively. Finally, we apply the theoretical method into simulation studies to show its feasibility and effectiveness.

The Ginkgo biloba is one of ancient trees that exists from billions of years ago, its leaf and nut are used as herbs and foods in China, while so far its pollen does not have any application except pollination. In order to evaluate the antioxidant activity of Ginkgo biloba pollen, and rapidly screen its antioxidative components, the 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging ability, total flavonoid, total phenol, and proanthocyanidin of Ginkgo biloba pollen were determined and compared with those of Ginkgo biloba leaf and nut, and the off-line DPPH-HPLC-PAD and HPLC-ESI-MS2 were applied for screening and identifying the antioxidant flavonoids in Ginkgo biloba pollen. The results showed that the DPPH scavenging ability of Ginkgo biloba pollen was much higher than Ginkgo biloba nut, but lower than Ginkgo biloba leaf, while the total content of flavonoid in Ginkgo biloba pollen was approximately 4.37 times higher than in Ginkgo biloba leaf. Further studies found that the major flavonol aglycone in Ginkgo biloba pollen was kaempferol, which accounted for 96.71% of the total aglycones (includes quercetin, kaempferol and isorhamnetin), and the main flavonoid components in Ginkgo biloba pollen were flavonoid glycosides. Finally, ten antioxidant peaks were screened and identified to be flavonoids (including kaempferol and nine flavonoid glycosides), so flavonoids were likely to be the main antioxidant components in GP, and among them, three novel kaempferol glycosides (peaks 1, 2, and 3) were found in Ginkgo biloba pollen for the first time, which had never been found in Ginkgo biloba.

Traditional manual fiber defect detection is inefficient and imprecise when the fiber moves fast, to solve the problem, a real-time optical fiber defect detection system based on machine vision is designed and developed. Detection system by three industrial cameras captures images of 0°, 120°, 240° angle in space which are transmitted to IPC to classify fiber defect. Fiber defects are defined to establish classification database and criterion. Common AdaBoost classifier is effective for this problem but wastes too much time, so an advanced AdaBoost cascade classifier based on morphological characteristics is designed. Detection results under industry condition show that the system meets the requirement of real-time detection and has high detecting accuracy of more than 99%.

Peanut shell, a byproduct in oil production, is rich in natural antioxidants. Here, a rapid and efficient method using DPPH-HPLC-DAD-TOF/MS was used for the first time to screen antioxidants in peanut shell. The method is based on the hypothesis that upon reaction with 1, 1-diphenyl-2-picrylhydrazyl (DPPH), the peak areas of compounds with potential antioxidant activities in the HPLC chromatogram will be significantly reduced or disappeared, and the identity confirmation could be achieved by HPLC-DAD-TOF/MS technique. With this method, three compounds possessing potential antioxidant activities were found abundantly in the methanolic extract of peanut shell. They were identified as 5,7-dihydroxychromone, eriodictyol, and luteolin. The contents of these compounds were 0.59, 0.92, and 2.36 mg/g, respectively, and luteolin possessed the strongest radical scavenging capacity. DPPH-HPLC-DAD-TOF/MS assay facilitated rapid identification and determination of natural antioxidants in peanut shell, which may be helpful for value-added utilization of peanut processing byproducts.

The US student population is growing rapidly in higher education. More and more people choose to go to college and get a degree before they enter the workforce. In October 2006, 65.8 percent of high school graduates were enrolled in colleges or universities, according to data released by the U.S. Department of Labor's Bureau of Labor Statistics. Since 2001, the college enrollment rate for recent high school graduates has been trending upward. Among these graduates entering college, a unique group of students may also need some extra help in order to succeed academically in their college careers. These are learning disabled students with health situations in addition to academics. When they cannot keep a good balance of health needs and academic demands, students with LDs are easily susceptible to health problems due to stress and/or even academic failure. However, most learning disabled students grow up with parents communicating with health care providers directly for them, according to Mr. Phillipe, director of Students Disability Services at Texas Tech University. Therefore, college is very likely their first time to have to face health problems alone. They may not be confident enough to communicate their symptoms, needs and feelings to their healthcare providers. Therefore, they may not make full use of healthcare due to poor communication as well as the fear of communicating with healthcare providers. This problem may be mainly caused by a low sense of self-efficacy, which is defined as people’s beliefs about their capabilities to have influence over events that affect their lives (Bandura, 1994). Self-efficacy may have a big impact on how students seek help and handle challenges and problems when having health issues. So the purpose of this study was to explore students’, especially students with LDs, health self-efficacy, raising awareness and uncovering themes to be further explored in subsequent research. This study included two parts: first, I administered 289 surveys to test the reliability and utility of the adapted health self-efficacy scale, which was later used to measure the general health self-efficacy level of students with learning disabilities; second, I conducted nine individual interviews with students with learning disabilities. These individual interviews allowed me to capture the “essence” of health self-efficacy in students with LDs which would not have been obtained by distributing surveys alone. Overall, I explored the experiences that students had with their doctors with regard to their health treatments and health beliefs. More specifically, it was interesting to see how health self-efficacy was formed and developed over time and how it manifested itself in the patient-provider relationship. I used the constant comparative method to do line-by-line analysis of the transcripts produced from the individual interviews. The themes were emerged from the data. In the end, the study provides valuable insights and contributions to the current literature regarding health self-efficacy.

GlcNAcase is a glycosyl hydrolase located in the lysosomes of numerous organisms. Levels of the protein, β-N-acetylglucosaminidase 2 (GlcNAcase2), which is a member of the GlcNAcase family, are different in two strains of the silkworm Bombyx mori that have different resistance to Bombyx mori nucleopolyhedroviruses (BmNPVs). We identified six single-nucleotide differences in the GlcNAcase2 coding sequence between the 306 and NB strains. Five are silent changes, but one is a nonsynonymous mutation. Reverse transcription-polymerase chain reaction analysis showed that GlcNAcase2 mRNA levels in the NB strain were nearly 2.57 times higher compared with those in the 306 strain. In addition, GlcNAcase2 enzyme activity was much higher in the NB strain compared with that in the 306 strain. Together, these results indicate that GlcNAcase2 may be involved in variable BmNPV resistance in B. mori.

With the research on the mechanical vibration testing system structure and the typical data acquisition architecture for real-time signal processing, we present a new type of data acquisition board based on general PC developed for a mechanical vibration generator that is of our own copyright. Both the principles and the designing scheme of high-speed data acquisition are introduced. The system mainly consists of a 5-channel vibration signals processing module and a 2-channel output module. The board enables the host computer to acquire rough vibration signals and output drive signals through its ISA bus transparently. Its high performance makes it possible for the host computer to process large amount of vibration signals and compute drive signals in real time.