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Biodegradable magnesium alloys have excellent properties with respect to biodegradability, biocompatibility, and biomechanics, which may indicate a possibility of its application in intestinal stents. Investigation of Mg-Zn-Y-Nd alloy's application in intestinal stents has been performed. This study aims to investigate the degradation behavior of Mg-Zn-Y-Nd alloy intestinal stents coated with poly(L-lactide)/paclitaxel in the intestinal environment and its biocompatibility with intestinal tissue. In this paper, Mg-Zn-Y-Nd alloy's corrosion properties were evaluated by the immersion test in human feces, SEM and XRD, and animal tests. In vitro results showed that when the Mg-Zn-Y-Nd alloy was immersed in human feces for two weeks, its corrosion resistance could be improved by micro arc oxidation (MAO) and poly-l-lactide (PLLA) dual coating. Additionally, this result was also confirmed in vivo experiments by rabbit model. And animal tests also demonstrated that the Mg-Zn-Y-Nd alloy with MAO/PLLA/paclitaxel dual coating drug-eluting stents could inhibit the proliferation of local intestinal tissue around the stents. However, in vivo studies illustrated that the intestinal stents gradually degraded in rabbit model within 12 days. Considering the degradation rate of the stent was faster than expected in rabbits, the support performance of the scaffold requires further improvement.

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) is a technique widely used to investigate genome-wide chromatin accessibility. The recently published Omni-ATAC-seq protocol substantially improves the signal/noise ratio and reduces the input cell number. High-quality data are critical to ensure accurate analysis. Several tools have been developed for assessing sequencing quality and insertion size distribution for ATAC-seq data; however, key quality control (QC) metrics have not yet been established to accurately determine the quality of ATAC-seq data. Here, we optimized the analysis strategy for ATAC-seq and defined a series of QC metrics for ATAC-seq data, including reads under peak ratio (RUPr), background (BG), promoter enrichment (ProEn), subsampling enrichment (SubEn), and other measurements. We incorporated these QC tests into our recently developed ATAC-seq Integrative Analysis Package (AIAP) to provide a complete ATAC-seq analysis system, including quality assurance, improved peak calling, and downstream differential analysis. We demonstrated a significant improvement of sensitivity (20%-60%) in both peak calling and differential analysis by processing paired-end ATAC-seq datasets using AIAP. AIAP is compiled into Docker/Singularity, and it can be executed by one command line to generate a comprehensive QC report. We used ENCODE ATAC-seq data to benchmark and generate QC recommendations, and developed qATACViewer for the user-friendly interaction with the QC report. The software, source code, and documentation of AIAP are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis.

Fluorescent Penetrant Inspection (FPI) is the most widely used Non Destructive Testing (NDT) method in the aerospace industry. FPI is currently a manual visual inspection process, which by means of fluorescent dye, aims to distinguish between relevant indications (associated with defects) and non-relevant indications (due to insufficient wash-off, dust or other non relevant factors). This NDT method is largely influenced by human factors due to its nature, introducing several challenges on inspection consistency and reliability. In this paper, a multi-robot inspection system is presented to automate the FPI process. The system autonomously performs image acquisitions of the part under inspection, guarantees full inspection coverage of the part, analyzes the images to recognize regions of interest (e.g., regions where fluorescent dye leaves certain linear characteristics), executes the wipe-off operation (enabling penetrant bleed-back process) as required by the FPI process, and subsequently distinguishes defects against other non-relevant indications by utilizing deep neural network models. This automated system has achieved an inspection accuracy comparable to a human inspector while providing benefits pertaining to consistency, reliability and productivity. A proof-of-concept system has been deployed in an aviation manufacturing environment, and experimental results have shown the system’s capacity to perform the FPI process and detect defects in aerospace components, hence enabling the automation of the entire FPI line.

The computer-aided diagnosis of prostate ultrasound images can aid in the detection and treatment of prostate cancer. However, the ultrasound images of the prostate sometimes come with serious speckle noise, low signal-to-noise ratio, and poor detection accuracy. To overcome this shortcoming, we proposed a deep learning model that integrates S-Mask R-CNN and Inception-v3 in the ultrasound image-aided diagnosis of prostate cancer in this paper. The improved S-Mask R-CNN was used to realize the accurate segmentation of prostate ultrasound images and generate candidate regions. The region of interest align algorithm was used to realize the pixel-level feature point positioning. The corresponding binary mask of prostate images was generated by the convolution network to segment the prostate region and the background. Then, the background information was shielded, and a data set of segmented ultrasound images of the prostate was constructed for the Inception-v3 network for lesion detection. A new network model was added to replace the original classification module, which is composed of forward and back propagation. Forward propagation mainly transfers the characteristics extracted from the convolution layer pooling layer below the pool_3 layer through the transfer learning strategy to the input layer and then calculates the loss value between the classified and label values to identify the ultrasound lesion of the prostate. The experimental results showed that the proposed method can accurately detect the ultrasound image of the prostate and segment prostate information at the pixel-level simultaneously. The proposed method has higher accuracy than that of the doctor’s manual diagnosis and other detection methods. Our simple and effective approach will serve as a solid baseline and help ease future research in the computer-aided diagnosis of prostate ultrasound images. Furthermore, this work will promote the development of prostate cancer ultrasound diagnostic technology.

The Gleason scoring system is a reliable method for quantifying the aggressiveness of prostate cancer, which provides an important reference value for clinical assessment on therapeutic strategies. However, to the best of our knowledge, no study has been done on the pathological grading of prostate cancer from single ultrasound images. In this work, a novel Automatic Region-based Gleason Grading (ARGG) network for prostate cancer based on deep learning is proposed. ARGG consists of two stages: (1) a region labeling object detection (RLOD) network is designed to label the prostate cancer lesion region; (2) a Gleason grading network (GNet) is proposed for pathological grading of prostate ultrasound images. In RLOD, a new feature fusion structure Skip-connected Feature Pyramid Network (CFPN) is proposed as an auxiliary branch for extracting features and enhancing the fusion of high-level features and low-level features, which helps to detect the small lesion and extract the image detail information. In GNet, we designed a synchronized pulse enhancement module (SPEM) based on pulse-coupled neural networks for enhancing the results of RLOD detection and used as training samples, and then fed the enhanced results and the original ones into the channel attention classification network (CACN), which introduces an attention mechanism to benefit the prediction of cancer grading. Experimental performance on the dataset of prostate ultrasound images collected from hospitals shows that the proposed Gleason grading model outperforms the manual diagnosis by physicians with a precision of 0.830. In addition, we have evaluated the lesions detection performance of RLOD, which achieves a mean Dice metric of 0.815.

Subsea drilling operations are moving into harsher, deeper and less familiar offshore environments. To avoid disruptive events due to uncertainties associated with these environments, a Riser Lifecycle Management System (RLMS) has been developed for near real-time condition monitoring and fatigue estimation of drilling risers. Unlike conventional techniques such as strain gauges for direct strain/stress measurement, the RLMS measures the vibrations using accelerometers at select joints along the drilling riser. It transmits the vibration data via acoustic telemetry in near real-time to a topside data acquisition system on the drilling vessel where fatigue life estimates for all riser joints are made using machine learning techniques. A proof-of-concept of the RLMS system was successfully deployed in the Gulf of Mexico on a semi-submersible drilling rig for a 9-week test.

The purpose of this study was to assess the influence of two suture methods on the postoperative complications of extraction of mandibular third molars (M3M). We searched the MEDLINE (PubMed), Cochrane Library, and Web of Science databases until 18 May 2018 for randomised controlled trials (RCT) that evaluated the influence of any suture techniques on postoperative complications after the removal of impacted M3M. Pain, facial swelling, and trismus were measured for both the early stage (2-3 days) and late stage (5-7 days) after extraction. We identified 655 records, of which five were assessed for eligibility. All trials included had a moderate risk of bias. The analysis showed that the patients whose wounds had been closed primarily had significantly more pain than those whose wounds were closed secondarily (a wedge of mucosa) during the early stage (standardised mean difference (SMD), -0.49; 95% CI -0.71 to -0.27; P

The objective of this study was to comprehensively assess the use of tranexamic acid (TXA) during orthognathic surgery. A systematic review and meta-analysis of randomized controlled trials addressing these issues were carried out. Three electronic databases, included PubMed, Web of Science, and Cochrane Library, were searched until April 30, 2018. Eligible studies were restricted to randomized, controlled trials (RCTs). Weighted mean differences (WMD) for blood loss, operation time, haematocrit, quality of surgical field, and odds ratio (OR) for transfusion rates were pooled for the included studies. Eight randomized, controlled trials were included for analysis. Compared with the control group, the TXA group showed a reduction in intraoperative blood loss of 165.03 ml (p 0.00001; 95% CI, -200.93 to -129.13 ml), a reduction in the drop of haematocrit of 2.32 g/dl (p 0.00001; 95% CI, -3.38 to -1.26 g/dl), and an improved quality of surgical field (p 0.00001; MD, -1.01; 95% CI, -1.23 to -0.80). Tranexamic acid has a limited effect on reducing operative time (p 0.00001; MD, -16.18 min; 95% CI, -19.60 to -12.75 min) and on decreasing the transfusion rates (p = 0.02; OR = 0.33; 95% CI, 0.13 to 0.83).

Titanium possesses a wide prospect due to its attractive combination of mechanical and electrochemical properties, however, the large affinity of hydrogen during industrial application will reduce its service life. Nevertheless, few studies focus on the effect of hydrogenation on microstructure evolution, electrochemical property and deformation behavior of titanium at room temperature. In this work, these effects induced by additional hydrogen were investigated in detail. The results suggest that the additive hydrogen will reduce the grain size, leading to the formation of lamellar or needle structure alpha phase with an increasing volume fraction of titanium hydride. In the meantime, the mechanical properties and corrosion resistances of hydrogenated titanium increase first and then decrease with the increase of hydrogen content. The improvement of mechanical property can be attributed to the occurrence of deformation twins, which will equivalently reduce the grain size, leads to the enhancement of ultimate compressive strength, work hardening rate and dislocation storage capability while the ability of dislocation annihilation diminished, thus results in higher work hardening rate and better compression ratio. Meanwhile, the electrochemical properties of hydrogenated samples enhanced with the increasing corrosion resistance of compact layer, yet, the film structure converts from compact to porous when additive hydrogen content reach to 27.01 at.%, which results in the deterioration of corrosion resistance. Therefore, the work of this research will give some suggestions for the application of titanium.

Scene classification is a significant aspect of computer vision. Convolutional neural networks (CNNs), a development of deep learning, are a well-understood tool for image classification. But training CNNs requires large-scale datasets. Transfer learning addresses this problem and produces a solution for small-scale datasets. Because scene image classification is more complex than common image classification. We propose a novel ResNet based transfer learning model utilizing multi-layer feature fusion, taking full advantage of interlayer discriminating features and fusing them for classification by softmax regression. In addition, a novel data augmentation method with a filter useful for small-scale datasets is presented. New image patches are generated by sliding block cropping of a raw image, which are then filtered to insure that the new images sufficiently represent the original categorization. Our new ResNet based transfer learning model with enhanced data augmentation is evaluated on six benchmark scene datasets (LF, OT, FP, LS, MIT67, SUN397). Extensive experimental results show that on the six datasets our method obtains better accuracy than other state-of-the-art models.

Ideal titanium implants are required to participate in bone repair actively to improve in situ osteointegration. However, the traditional surface functionalization methods of titanium implants are difficult to both achieve the active regulation and long-term stability of bioactive components. Here, a novel functionalized titanium which loaded with thymosin β4 (Tβ4) and covered by a hydrogel coating was designed and evaluated. A strong adhesion between the coating and the titanium substrate was realized by the synergistic action of borate ester bonds and surface topological structure. The hydrogel coating also achieved an in vivo adhesion between implant and tissue through hydrogen bonds and borate bonds. In addition, based on the ROS response property of borate bonds, the implant can release Tβ4 in response to the immune reaction of bone healing by regulating the polarization of macrophages, thereby reducing the fibrosis formation around the implant interface and promoting vascularization and osteointegration of bone defects.

The purpose of this study was to provide an evidence-based evaluation of the impact of surgical drainage after the removal of mandibular third molars.The Medline (PubMed), Cochrane Library, and Web of Science databases were searched to identify randomized controlled trials up to September 1, 2017. Postoperative variables, including facial swelling, trismus, and pain, were calculated early (2 to 3 days) and late (5 to 7 days) after the removal of impacted mandibular third molars. Weighted mean differences for trismus and standardized mean differences (SMDs) for swelling and pain were pooled for the included studies.The samples consisted of 592 extractions (297 with surgical drainage and 295 controls) in 409 participants. The included studies were published from 1988 to 2016. Participants who received surgical drainage had significantly less facial swelling during the early stage (SMD, -0.46; 95% confidence interval [CI], -0.67 to -0.26; P .0001) and the late stage (SMD, -0.36; 95% CI, -0.55 to -0.16; P = .0004) after the removal of an impacted mandibular third molar. They also had better mouth opening than controls during the early and late stages (early MD, 5.55 mm; 95% CI, 2.31-8.79; P = .0008; late MD, 2.38 mm; 95% CI, 1.47-3.29; P .0001). The level of pain was significantly different between the 2 groups in the early stage (SMD, -0.55; 95% CI, -1.00 to -0.10; P = .01); however, there were no significant differences in the late stage (SMD, -0.13; 95% CI, -0.38 to 0.12; P = .30).The use of surgical drainage has an obviously positive effect on postoperative reactions after the removal of a mandibular third molar. Given the need for additional time for clinic visits, surgical drainage presents an alternative for the perioperative management of impacted mandibular third molar surgery, especially in cases of a fully bony tooth with poor drainage.

Huntington's disease (HD) is a fatal neurodegenerative disorder caused by an abnormal expansion of polyglutamine repeats in the huntingtin protein (Htt). Transcriptional dysregulation is an early event in the course of HD progression and is thought to contribute to disease pathogenesis, but how mutant Htt causes transcriptional alterations and subsequent cell death in neurons is not well understood. RNA-Seq analysis revealed that expression of a mutant Htt fragment in primary cortical neurons leads to robust gene expression changes before neuronal death. Basic helix-loop-helix transcription factor Twist1, which is essential for embryogenesis and is normally expressed at low levels in mature neurons, was substantially up-regulated in mutant Htt–expressing neurons in culture and in the brains of HD mouse models. Knockdown of Twist1 by RNAi in mutant Htt–expressing primary cortical neurons reversed the altered expression of a subset of genes involved in neuronal function and, importantly, abrogated neurotoxicity. Using brain-derived neurotrophic factor (Bdnf), which is known to be involved in HD pathogenesis, as a model gene, we found that Twist1 knockdown could reverse mutant Htt–induced DNA hypermethylation at the Bdnf regulatory region and reactivate Bdnf expression. Together, these results suggest that Twist1 is an important upstream mediator of mutant Htt–induced neuronal death and may in part operate through epigenetic mechanisms.

Radiation therapy is one of the most commonly used interventions in cancer therapy. However, innate or acquired radioresistance in several cancers, and toxicity to normal tissues are still serious concerns. In order to enhance radiation response in cancer therapy, metal-based nanoparticles have been widely studied as the radiosensitizers. Recently, more and more studies have demonstrated the potential value of metal-based hybrid nanoparticles (MHNs) as novel radiosensitizers. In this review, we have summarized the applications of MHNs in optimization of radiosensitization, and in combinatorial therapeutic regimes for improving treatment outcomes in cancer therapy.

In the unstructured family environment, robots are expected to provide various services to improve the quality of human life, based on the performance of specific action sequences generated by service planning. This paper focuses on one of the greatest challenges in service planning that is aimed at accomplishing the service tasks by generating appropriate object sequences to guide the robot on searching the corresponding target objects efficiently and reasonably. A well-structured knowledge-based framework of object search is proposed in our approach as well as taking into account the multi-domain knowledge of applying object, scene, and service in design. In order to improve the searching efficiency and reasonability, an ontology-based hierarchical and interrelated knowledge structure is formed to support the implementation of complicated service planning with either single or multiple tasks. The proposed framework is tested by comprehensive experiments, and the performance is evaluated with other mainstream methods in both simulation and real-world environments. The experimental results demonstrate the feasibility and effectiveness of applying this knowledge-based framework to efficient object searching aspect in service planning.

The oxide film of TiH2 and HDH-Ti powder are investigated using X-ray photoelectron spectroscopy (XPS). The XPS depth profiles indicate that there exists mainly Ti2+, Ti3+, Ti4+ and Ti0 on TiH2 and HDH-Ti surface. The intensities of Ti 2p decrease for Ti4+, first increase and then decrease for Ti3+ and Ti2+, and increase all the time for Ti0 in the surface layer of TiH2 and HDH-Ti with the sputtering depth increasing. The relative fractions of TiO2, Ti2O3 and TiO for the Ti 2p of TiH2 and HDH-Ti first decrease and then slow down with the sputtering depth increasing. Meanwhile, the relative fractions of TiO2 and TiO of HDH-Ti are lower than that of TiH2 after the sputtering depth of about 5 nm, and the fraction of Ti2O3 of HDH-Ti is always lower that of TiH2. In addition, the decrease of Ti2O3 is much pronounced, followed by TiO2 and TiO before and after dehydrogenation when the sputtering depth is more than 5 nm. The XPS depth profiles and calculation results suggest that the release of H atoms removes the part of oxygen on TiH2 surface, which results in the thinner oxide layer and low oxygen content of HDH-Ti powder.

Small- and medium-sized manufacturers, as well as large original equipment manufacturers (OEMs), have faced an increasing need for the development of intelligent manufacturing machines with affordable sensing technologies and data-driven intelligence. Existing monitoring systems and prognostics approaches are not capable of collecting the large volumes of real-time data or building large-scale predictive models that are essential to achieving significant advances in cyber-manufacturing. The objective of this paper is to introduce a new computational framework that enables remote real-time sensing, monitoring, and scalable high performance computing for diagnosis and prognosis. This framework utilizes wireless sensor networks, cloud computing, and machine learning. A proof-of-concept prototype is developed to demonstrate how the framework can enable manufacturers to monitor machine health conditions and generate predictive analytics. Experimental results are provided to demonstrate capabilities and utility of the framework such as how vibrations and energy consumption of pumps in a power plant and CNC machines in a factory floor can be monitored using a wireless sensor network. In addition, a machine learning algorithm, implemented on a public cloud, is used to predict tool wear in milling operations.

The lingual-based mucoperiosteal flap, a novel flap, was unclear about the effects on the prognosis of surgery for impacted mandibular third molars. This study aimed to compare the lingual- and buccal-based mucoperiosteal flaps with respect to postoperative responses and complications.A systematic review with a meta-analysis was designed and the PubMed, Cochrane Library, EMBASE, and Web of Science databases and Google Scholar from January 1, 2000 to April 30, 2020 were searched for randomized clinical trials. The predictor variable was buccal- or lingual-based flap in the surgery, and the outcome variables were pain, swelling, trismus, operative time, and wound dehiscence. Other study variables were sex and retention depth of impacted teeth. RevMan 5.3 software was used for data analysis. Mean differences or standardized mean differences and risk ratios were computed to assess associations between 2 variables, where statistical significance was set at P .05.Seven publications met the inclusion criteria, contributing 370 subjects who had 590 teeth removed to sample. The lingual-based flap failed to significantly reduce postoperative discomfort. However, subgroup analysis revealed that subjects who underwent comma flap (a type of lingual-based flap) surgeries complained of milder pain than those who underwent buccal-based flap surgeries on day 1 (mean difference = -1.18, 95% confidence interval [CI] [-1.53, -0.83], P .001) and day 7 (mean difference = -1.80, 95% CI [-2.13, -1.48], P .001) after surgery. Significant differences were also observed on days 1, 3, and 7 regarding postoperative swelling and trismus (P .01). In addition, the lingual-based flap was reported to cause a significantly lower rate of wound dehiscence (relative risk = 0.46, 95% CI [0.30, 0.69], P = .0002).The lingual-based flap was associated with better primary wound closure in third molar removal. The comma flap, as a subtype, was preferable for relieving postoperative pain, swelling, and trismus over the buccal-based flap.

In this paper, Pb0.98La0.02(Zr0.55Sn0.45-xTix)0.995O3 (x = 0.02–0.08) (PLZST) antiferroelectric (AFE) thick films were fabricated by a unique technology of the rolling process, which will contribute to high BDS. It manifests that the energy-storage parameters, such as dielectric constant, BDS, and switching field, are easily tailored by the Ti content variation at B site. It’s found that the BDS and the switching field of phase transition achieve the maximum in the case of 2 mol% Ti of PLZST, which reaches the best recoverable energy density of 5.7 J/cm3. Besides, this work reveals that the switching field has a more essential effect on the energy-storage density than other parameters at the same BDS when the BDS is higher than the switching field. The results show that one idea for promoting the dielectric energy-storage applications is to find dielectric materials with high switching field and providing one solution for some high energy-storage applications with low electric-field.

We propose a scheme for realizing high-precision three-dimensional (3D) atom localization by using V-type three-level atoms in which atoms interact with a weak probe field, a weak control field together with three mutually perpendicular standing-wave fields. Our numerical results show that the precision of 3D atom localization in volumes can be improved via three-wave mixing (TWM) in the presence of the control switch field but made instead to be reduced when the TWM channel is off in absence of the switch field. As a result, assisting with TWM, a single position information of the atom in the 3D space can be achieved, and the atom can be localized in volumes that are substantially smaller than a cubic optical wavelength. The effect of spontaneously generated coherence (SGC) as well as the relative phase of the applied fields on the precision of 3D atom localization are also discussed.

The degradation of polycarbonate (PC) varies with different service environments. To predict the degradation of PC in atmospheric environments, a back propagation artificial neural networks (BP-ANNs) model was constructed based on datasets from long-term exposure tests in 13 representative cities of China. Based on the analysis by Pearson correlation method and factor analysis, as well as the ranking of environment parameters that influence degradation performance by grey correlation method, 4 key environment parameters were identified. To obtain the optimized model, BP-ANNs with different input environmental parameters, middle layers and training precision were compared. The high prediction accuracy and generalization ability of the well-trained BP-ANN were verified using datasets from atmospheric weathering conducted in three new locations. Furthermore, a high-resolution predictive map for PC degradation was drawn based on the yellow indices which were predicted by inputting the 4 key environment parameters of 804 cites in China. Results showed PC degrades most seriously in the tropical monsoon climate area and plateau climate area, but slightly in the temperate monsoon climate area in the northeastern, and subtropical monsoon climate area in the southwestern basin of China. The model developed in this study would benefit to the rapid design, selection and evaluation of PC-based components in atmospheric service environments.

Cell-fate conversion generally presumes the activity of transcription factors to introduce fate programs of the target cell type in the midst of a pre-existing genetic network. Here, we reveal novel insights into cellular reprogramming in which microRNAs, miR-9/9* and miR-124 (miR-9/9*-124), orchestrate direct conversion of human fibroblasts by first eradicating fibroblast identity and promoting uniform transition to a neuronal state in sequence. Among the direct target genes of miR-9/9*-124, we identify KLF-family transcription factors whose repression is critical for erasing fibroblast fate. The subsequent upregulation of a small nuclear RNA, RN7SK induces chromatin reconfiguration and neuronal gene activation, pushing the reprogramming cells to a neuronal state and allowing for neuronal maturation and subtype-specification. Our study defines deterministic components in the reprogramming cascade by microRNAs.

Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc and cup from fundus images. However, most existing techniques fail to obtain satisfactory segmentation performance because a significant number of pixel-level annotated data are often unavailable during training. To cope with this limitation, in this paper, we propose an effective joint optic disc and cup segmentation method based on semi-supervised conditional Generative Adversarial Nets (GANs). Our architecture consists of a segmentation net, a generator and a discriminator, to learn a mapping between the fundus images and the corresponding segmentation maps. Additionally, we employ both labeled and unlabeled data to improve the segmentation performance. The extensive experiments show that our method achieves state-of-the-art optic disc and cup segmentation results on both ORIGA and REFUGE datasets.

Metaheuristics have been widely utilized for solving NP-hard optimization problems. However, these algorithms usually perform differently from one problem to another, i.e., one may be effective on a problem but performs badly on another problem. Therefore, it is difficult to choose the best algorithm in advance for a given problem. In contrast to selecting the best algorithm for a problem, selection hyper-heuristics aim at performing well on a set of problems (instances). This paper proposes a selection hyper-heuristic based algorithm for multi-objective optimization problems. In the proposed algorithm, multiple metaheuristics exhibiting different search behaviors are managed and controlled as low-level metaheuristics in an algorithm pool, and the most appropriate metaheuristic is selected by means of a performance indicator at each search stage. To assess the performance of the proposed algorithm, an implementation of the algorithm containing four metaheuristics is proposed and tested for solving multi-objective unconstrained binary quadratic programming problem. Experimental results on 50 benchmark instances show that the proposed algorithm can provide better overall performance than single metaheuristics, which demonstrates the effectiveness of the proposed algorithm.

In-situ monitoring of human physical activity under free-living conditions is key to establishing correlation between activity and health. This paper presents the design and performance evaluation of a multi-sensor integrated measurement system (IMS) for quantifying human physical activity. The IMS consists of two accelerometers, one UV sensor, and one respiration sensor to track and record body movement and the level of exertion of human body, to enable an objective assessment of the activities that human test subjects engage in during daily lives. Detailed analysis and simulations were performed leading up to a parametric design of the IMS unit. Experiments performed on human subjects in a clinical study have demonstrated that the multi-sensing system working in conjunction with a supervised learning algorithm is effective in recognizing activities types of varying intensity.

In order to study the potential role of cysteinyl residues in catalysis and inhibition of human aldose reductase, mutants containing cysteine to serine substitution at positions 80 (ALR2:C80S), 298 (ALR2:C298S), and 303 (ALR2:C303S) were constructed. Mutation of Cys298 resulted in the most profound changes, as ALR2:C298S displayed 4- to 5-fold elevation in K'm(NADPH), K'm(DL-glyceraldehyde), and kcat(DL-glyceraldehyde) relative to wild type aldose reductase as well as a 10-fold higher Ki for the aldose reductase inhibitor sorbinil. Wild type and mutant reductases were equally sensitive to tolrestat, a structurally different reductase inhibitor. Carboxymethylation of the wild type enzyme or the C80S and C303S mutants led to a modest decrease in kcat as well as an increase in K'm(DL-glyceraldehyde) and Ki(sorbinil). These parameters were not significantly changed when ALR2:C298S was subjected to carboxymethylation. Lithium sulfate caused activation of ALR2:WT, C80S, and C303S but did not significantly affect the activity of ALR2:C298S. The differential sensitivity of wild type and mutant reductases to inhibition by sorbinil and tolrestat, before and after carboxymethylation, indicates that these inhibitors bind at different sites. These results suggest that Cys-298 is present near the active site and constitutes a regulatory group which controls the catalytic activity and inhibitor sensitivity of the enzyme.