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Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula protoxin-2 (PTx2) has high selectivity for hNaV1.7 and serves as a valuable scaffold to design novel therapeutics to treat pain. Here, we used computational modeling methods to study the molecular mechanisms by which PTx2 exhibits state-dependent binding to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic structural models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to VSD II and IV in the deactivated conformations. These state-specific interactions are mediated primarily by PTx2 R22, K26, and K27 residues with the VSD S3-S4 loop, and L23, W24 with the S1-S2 loop. The surrounding membrane lipids facilitated the binding process. Our work revealed important protein-protein contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.SummaryVoltage-gated sodium channel NaV1.7 plays a major role in pain perception and is selectively inhibited by tarantula venom protoxin-2, used to design pain therapeutics. Here, Ngoet alapplied computational modeling to assess state-dependent protoxin-2 - NaV1.7 binding.

Achiral and chiral isomers of dipyrenoheteroles were synthesized via alkyne benzannulation. The electronic properties of these compounds were examined using cyclic voltammetry and differential pulse voltammetry. The enantiomers of the chiral isomers were separated, and their optical properties were examined in circular dichroism and circularly polarized luminescence studies. The chiral isomers exhibited a large bathochromic shift, relative to the achiral isomer, in both absorbance and fluorescence, resulting from decreased symmetry, rather than a change in the size of the backbone.

The fault location problem is one of the most important issues in power system operation and control. To obtain an experimental system for teaching and training electrical engineering students, this paper performed a study to design and implement a real-time fault location laboratory-scale model from practical hardware components. The impedance-based fault location method is embedded in the system to determine the distance to fault in the transmission line. Furthermore, the monitoring and controlling program is designed by the Matlab App Designer – A new professional app to create the graphical user interface and use the integrated editor quickly. Several fault types including three-phase fault, phase-to-phase fault, phase-to-ground fault are created to evaluate the performance of the fault location experimental system. The real-time measurement results which are acquired and observed on the user guide interface of the program confirm the effectiveness of the experimental system; therefore, the system can be considered a powerful tool for electrical engineering students.

Osteoarthritis (OA), a progressive degenerative disease of cartilage in joints, is the most common cause of chronic disability in older adults. While OA is mostly considered an age-related pathology, women have a 1.5-fold higher risk of developing OA relative to men and experience more severe symptoms. Yet, they remain underrepresented in musculoskeletal research and clinical trials. Responsible for cartilage formation, articular chondrocytes experience physiological changes in OA, but the functional implications of such alterations remain largely unexplored due to difficulties in acquiring the data experimentally. Through reparameterization, we expand a mathematical chondrocyte model to investigate sex-specific OA pathogenesis. We performed sensitivity analysis to address the impact of ion channel activity in healthy and OA chondrocyte populations. Simulations show that in healthy female chondrocytes, the resting membrane potential is more depolarized than in healthy male chondrocytes, suggesting potential sex-specific emergent physiological differences in articular chondrocytes. In both sexes, the resting membrane potential of healthy chondrocytes is most sensitive to 𝐼𝐶𝑎−𝐴𝑇𝑃, 𝐼𝑁𝑎−𝑏, 𝐼𝑁𝑎𝐾 and 𝐼𝐾−𝑏, but in OA it depolarizes and becomes sensitive to 𝐼𝐾𝐷𝑅, 𝐼𝑁𝑎𝐾 and 𝐼𝐾−𝑏. Developed and evaluated against experimental data, our articular chondrocyte OA electrophysiological model can be used to further study OA pathology and sex-specific pathological OA changes.

Microprocessor (MP) is a complex involved in initiating the biogenesis of microRNAs (miRNAs) by cleaving primary microRNAs (pri-miRNAs). miRNAs are small single-stranded RNAs that play a key role in the post-transcriptional regulation of gene expression. Thus, understanding the molecular mechanism of MP is critical for interpreting the roles of miRNAs in normal cellular processes and during the onset of various diseases. MP comprises a ribonuclease enzyme, DROSHA, and a dimeric RNA-binding protein, which is called DGCR8 in humans and Pasha in Caenorhabditis elegans. DROSHA cleaves stem-loop structures located within pri-miRNAs to generate pre-miRNAs. Although the molecular mechanism of human MP (hMP; hDROSHA-DGCR8) is well understood, that of Caenorhabditis elegans MP (cMP; cDrosha-Pasha) is still largely unknown. Here, we reveal the molecular mechanism of cMP and show that it is distinct from that of hMP. We demonstrate that cDrosha and Pasha measure ∼16 and ∼25 bp along a pri-miRNA stem, respectively, and they work together to determine the site of cMP cleavage in pri-miRNAs. We also demonstrate the molecular basis for their substrate measurement. Thus, our findings reveal a previously unknown molecular mechanism of cMP; demonstrate the differences between the mechanisms of hMP and cMP; and provide a foundation for revealing the mechanisms regulating miRNA expression in different animal species.The Microprocessor complex that initiates miRNA biogenesis was discovered in animals in 2004. However, the molecular mechanism of C. elegans Microprocessor (cMP) has remained elusive since its discovery 18 years ago. In this study, we revealed the unique molecular mechanism of cMP by conducting high-throughput pri-miRNA cleavage assays. We demonstrated that cMP, consisting of cDrosha and Pasha, each can measure the stem lengths of pri-miRNAs. cDrosha measures ∼16 bp of the lower stem length, whereas Pasha measures ∼25 bp of the upper stem in pri-miRNAs. In addition, we identified the cleavage sites and cleavage efficiency of cMP in C. elegans pri-miRNAs. These results will be helpful for future studies of miRNA biogenesis in C. elegans.

In this study, extracts of shallot skin, flesh and whole bulbs were evaluated their inhibitory effect on xanthine oxidase activity in vitro. The results showed that the effect of shallot skin extract was superior to the extracts from flesh and whole bulb. The extraction of flavonoid from shallot skin was comprehensively investigate using one-factor-at-a-time and response surface methodology based on multivariable linear regression and artificial neural network. Four established models accurately illustrated the quantitative relationship between temperature, time of extraction, solvent and total flavonoids content, yield of extraction. The optimal conditions were determined as: temperature of 85 °C, time of 120 min and ethanol concentration of 50%. The IC50 values on xanthine oxidase of the extracts in optimal and sub-optimal conditions were 3.23 and 21.09 µg/mL while the total flavonoids contents were 20.26% and 13.89%, respectively. The docking results suggested that isorhamnetin-3-glucoside and other flavonoids in shallot skin might play a key role in xanthine oxidase activity of shallot extract.

Celery seed is a potential medicinal material for the development of anti-gout agents because of its uric acid-lowering, xanthine oxidase inhibitory, anti-inflammatory and analgesic effects. In this study, the process of extraction of flavonoid from celery seed was optimized using response surface methodology. Various algorithms were used in order to model the relationship between output response (yield of extraction, total flavonoid content) and input parameters (ethanol concentration, temperature and solvent/solid ratio). The best model was established using multiple linear regression (MLR) algorithm. The optimal conditions were determined as follows: ethanol concentration: 90%; temperature: 90°C and the solvent/solid ratio: 4 ml/g. At these conditions, the yield of extraction was 11.57 ± 1.46% and the total flavonoid content was 4.922 ± 0.312 mg/g. The xanthine oxidase inhibitory effect of apigenin - the main flavonoid of celery seed, was evaluated with the IC50 value of 0.247 µg/ml (95% CI 0.129–0.617 µg/ml). It was suggested that flavonoid might play a key role in the xanthine oxidase inhibitory activity of celery seed extract.

Moisturizers play an essential role in maintaining the integrity of the skin barrier by increasing stratum corneum hydration (SCH) and reducing transepidermal water loss (TEWL). According to dermatology and allergy guidelines, moisturizers should be applied on the skin within 3 min after bathing or showering. However, there is very little evidence supporting this recommendation. This study aimed to investigate the effectiveness of immediate and delayed moisturizing after bathing/washing on the improvement of SCH and TEWL.This was a crossover study of 60 healthy Vietnamese volunteers aged 18-25 years. In each subject, SCH and TEWL levels were measured at three areas: non-moisturized, immediate moisturizing after washing, and delayed moisturizing at 30 min after washing.In non-moisturized skin, SCH and TEWL levels were significantly different from the baseline at 60 min after washing, while significantly decreased TEWL levels were observed immediately after moisturizing. In addition, moisturized skin had significantly higher SCH and lower TEWL levels compared with non-moisturized areas at every time point (p 0.05). Interestingly, the percentage changes of SCH and TEWL levels from baseline did not differ between immediately and delayed moisturized areas.Tested moisturizer helped increase SCH and decrease TEWL; however, there was no difference in moisturizing effectiveness between immediate and delayed moisturizing in healthy skin. The recommendation of immediate application of moisturizers after bathing/washing should be reconsidered, and more studies are needed to establish a stronger recommendation.

The rational improvement of the enzyme catalytic activity is one of the most significant challenges in biotechnology. Most conventional strategies used to engineer enzymes involve selecting mutations to increase their thermostability. Determining good criteria for choosing these substitutions continues to be a challenge. In this work, we combine bioinformatics, electrostatic analysis, and molecular dynamics to predict beneficial mutations that may improve the thermostability of XynA from

Systemic sclerosis is a complex, often progressive, multisystem autoimmune disease. It is commonly categorized into limited cutaneous or diffuse cutaneous systemic sclerosis. There is near universal involvement of skin fibrosis and gastrointestinal dysfunction, but lung disease is not only common but also a most serious complication. Severe lung disease is the top cause of mortality, displacing scleroderma renal crisis as the leading cause of death. Whether there is limited cutaneous or diffuse cutaneous manifestations can be predictive of what type of lung disease that can present in the patient. Limited cutaneous systemic sclerosis patients tend to have pulmonary hypertension whereas diffuse cutaneous systemic sclerosis patients tend to have interstitial lung disease. There are more rare phenotypes associated with antibodies Th/To and U3RNP that can have both pulmonary hypertension and interstitial lung disease concomitantly. There are inherent challenges in the management for both pulmonary hypertension and interstitial lung disease but with the focus on early diagnosis for each of these lung complications, treatment may have a higher chance of efficacy.

Systemic sclerosis is a complex, often progressive, multisystem autoimmune disease. It is commonly categorized into limited cutaneous or diffuse cutaneous systemic sclerosis. There is near universal involvement of skin fibrosis and gastrointestinal dysfunction, but lung disease is not only common but also a most serious complication. Severe lung disease is the top cause of mortality, displacing scleroderma renal crisis as the leading cause of death. Whether there is limited cutaneous or diffuse cutaneous manifestations can be predictive of what type of lung disease that can present in the patient. Limited cutaneous systemic sclerosis patients tend to have pulmonary hypertension whereas diffuse cutaneous systemic sclerosis patients tend to have interstitial lung disease. There are more rare phenotypes associated with antibodies Th/To and U3RNP that can have both pulmonary hypertension and interstitial lung disease concomitantly. There are inherent challenges in the management for both pulmonary hypertension and interstitial lung disease but with the focus on early diagnosis for each of these lung complications, treatment may have a higher chance of efficacy.

For the multiscale simulation of advanced materials with hierarchical, heterogeneous, and random microstructures such as fibrous composites by finite element method, mathematical homogenization method has been extended to the stochastic multiscale method. In this paper, a stochastic perturbation-based homogenization method was used to predict homogenized properties of short fiber reinforced composites with random fiber length, random orientation, and random mechanical properties of fiber and matrix resin. For demonstration, a random short fiber reinforced plastic was investigated considering thirteen random physical parameters and two ways of approximation of statistically measured fiber length distributions. The influences of each uncertainty factor on the homogenized properties of the material were observed. The results show that the homogenization properties are likely orthotropic even though the fiber and matrix are considered as isotropic materials. The material is stronger in the direction where the fibers are mostly longitudinal. Each component of the homogenized properties is resulted in a probability density function under the influence of the random physical parameters. Besides, the scatter of the properties was predicted due to very slight difference between two fiber length distributions.

International audience; Word alignments identify translational correspondences between words in a parallel sentence pair and are used, for instance, to learn bilingual dictionaries, to train statistical machine translation systems or to perform quality estimation. Variational autoencoders have been recently used in various of natural language processing to learn in an unsupervised way latent representations that are useful for language generation tasks. In this paper, we study these models for the task of word alignment and propose and assess several evolutions of a vanilla variational autoencoders. We demonstrate that these techniques can yield competitive results as compared to Giza++ and to a strong neural network alignment system for two language pairs.

Xanthine oxidase inhibitory activity of celery seed extract was predicted from UV–Vis spectrum using five different algorithms: multiple linear regression, artificial neural network, support vector regression, random forest and partial linear regression. The optimal parameters of each model established by these methods were examined carefully and the best model was selected. The results showed that the model obtained by partial linear regression had the best quality with coefficient of determination R2 = 0.9618, correlation coefficient of leave-one-out cross validation Q2 = 0.8746, average absolute deviation MAD = 0.0671, root mean square error RMSE = 0.0761 and the accuracy of the model with train and test were 99.29% and 96.69%, respectively. Our findings suggest that this model could be an effective method for rapidly determining xanthine oxidase inhibitory activity of herbal medicine, particularly celery seed extract from UV–Vis spectra. However, more investigations are required in order to improve the performance of the partial linear regression model.

Word alignments identify translational correspondences between words in a parallel sentence pair and are used, for instance, to learn bilingual dictionaries, to train statistical machine translation systems or to perform quality estimation. Variational autoencoders have been recently used in various of natural language processing to learn in an unsupervised way latent representations that are useful for language generation tasks. In this paper, we study these models for the task of word alignment and propose and assess several evolutions of a vanilla variational autoencoders. We demonstrate that these techniques can yield competitive results as compared to Giza++ and to a strong neural network alignment system for two language pairs.

This paper proposes and validates a new design for autogenous mobile systems based on the combination of vibration and impact of an oscillating mass with two-side elastic constraints. The proposed system is driven by a two-side impact oscillator which is excited by a periodic pulsed force. The system can move backward and forward as desired by simply reverse the excitation force. A mathematical model has been developed to form a basis of the design process and to choose proper operational parameters. An experimental rig has been implemented to validate the proposed design. The mathematical model of the system with non-dimensional parameters allows extending the results to both large- and micro-scale autogenous drifting applications.

Word alignments identify translational correspondences between words in a parallel sentence pair and is used, for instance, to learn bilingual dictionaries, to train statistical machine translation systems , or to perform quality estimation. In most areas of natural language processing, neural network models nowadays constitute the preferred approach, a situation that might also apply to word alignment models. In this work, we study and comprehensively evaluate neural models for unsupervised word alignment for four language pairs, contrasting several variants of neural models. We show that in most settings, neural versions of the IBM-1 and hidden Markov models vastly outperform their discrete counterparts. We also analyze typical alignment errors of the baselines that our models overcome to illustrate the benefits-and the limitations-of these new models for morphologically rich languages., Comment: The 16th International Workshop on Spoken Language Translation, Nov 2019, Hong Kong, Hong Kong SAR China

In loT-based system, over-the-air (OTA) programming or IoTs reconfiguration plays a vital role, especially in large-scale deployments. OTA programming allows IoTs devices to be replaced and upgraded the running applications or image by the newer versions without effort on manually install one by one device. However, the development of OTA programming also meets many challenges due to the limitations of memory resources, battery energy as well as communication support of small and constraint devices. Therefore, designing OTA programming solutions supported by a strong security mechanism has been still an open issue. In this paper, an integration of AES cryptography into a well-known network programming protocol (Deluge on TinyOS 2.x) is presented. The paper not only explains Deluge protocol's operation, messages format but also provides numerical results when running this enhancement with real IoTs devices (Telosb mote on TinyOS 2.x). The developed solution can be deployed in the real IoT-based systems for securely re-configuring IoT devices and the presented empirical results can be used as a dataset for research community to compare in other environments.

Nowadays, Internet of Things (IoT) is not only a hot research topic but also plays a vital role in the development and deployment of many different domains, e.g., industry, transportation, education, health and agriculture as well. However, the development and deployment of real-time IoT-based systems requires stringent technical challenges on the scalability, the availability and the dynamic adaptability of the IoTs systems and applications. In this paper, we present an IoT-based architecture with a reconfigurable approach to deal with those technical challenges, and the proposed IoT-based reconfigurable system is applied for the environmental monitoring in a Smart Garden application as a case study. By implementing the load-balanced infrastructure and the over-the-air (OTA) programming, our system fulfils the large deployment of many wireless sensor devices. In addition, we have also presented and discussed the empirical results related to the resource usage, the packet transmission and the energy consumption during the reconfiguration and lay out tentative approaches on the application of our proposed system to different application domains.

In this paper, we present a hybrid approach to the paraphrase detection task. The approach takes advantage of both feature-engineering and neural-based methods. First, we represent words and entities in a given sentence by using their pre-trained vectors. Then, those pre-trained vectors are encoded by a bidirectional long-short term memory network. The output matrix is fed into an attention network to obtain an attention vector. The final representation of the sentence is inner product of the matrix and the attention vector. We conduct experiments on the Microsoft Research Paraphrase corpus, a popular dataset used for benchmarking paraphrase detection methods. The experimental results show that our approach achieves competitive results.

Blockchain technology is already being exploited in various ways for saving cost with high security to create and manage the distributed system as well as to record information about transactions without the verification of the third party. In addition, the issue of counterfeit certification is making education managers around the world a particular interest. It is a serious problem up to now. Therefore, deploying the system to issuing and verifying digital certificate based on blockchain technology will solve the problem of counterfeit certification which there is not any prior technology really address it. In this study, we propose and implement an issuing and verifying model called UniCert based on UniCoin which is a digital currency built on the blockchain technology. Our diagram can be extended to address many other issues such as anti-counterfeiting, copyright protection of music products, patents, etc.

This paper presents a development in design, mathematical modeling, and experimental study of a vibro-impact moling device, which was invented by the author before. A vibratory unit deploying electromechanical interactions of a conductor with oscillating magnetic field has been realized and developed. The combination of resonance in an RLC circuit including a solenoid is found to create a relative oscillatory motion between the metal bar and the solenoid. This results in impacts of the solenoid on an obstacle block, which causes the forward motion of the system. Compared to the former model which employs impact from the metal bar, the improved rig can offer a higher progression rate of six times when using the same power supply. The novel geometrical arrangement allows for future optimization in terms of system parametric selection and adaptive control. This implies a very promising deployment of the mechanism in ground moling machines as well as other self-propelled mobile systems. In this paper, insight to the design development based on physical and mathematical models of the rig is presented. The coupled electromechanical equations of motion then are solved numerically, and a comparison between experimental results and numerical predictions is presented.

This paper presents a new approach to developing the torque model in deep hole drilling, both for conventional and ultrasonic assisted drilling processes. The model was proposed as a sum of three components: the cutting, the chip evacuation and the stick-slip torques. Parameters of the new model were carried out by applying the regression analysis technique, with the correlation values higher than 0.999. The data were collected from 36 experimental dry drilling tests, both in conventional and ultrasonic assisted cutting conditions, with the depth-to-diameter of the drilled holes of 7.5. The major advantage of the new model compared to previous models is that the new model of chip-evacuation torque has only one coefficient, thus making it easier to evaluate and compare different deep-drilling processes. The effectiveness of ultrasonic assistance in deep hole drilling was also highlighted using the proposed model. The new model is promising to predict critical depth and torque in deep hole drilling.

Improved settleability has become an essential feature of new wastewater treatment innovations. To accelerate adoption of such new technologies, improved clarifier models are needed to help with designing and predicting improvement in settleability. In general, the level of mathematics of settling clarifier models has gone far beyond the level of existing experimental methods available to support these models. To date, even for simple one-dimensional (1D) clarifier models, no experimental method has been described for flocculent settling coefficient (rp). As a consequence, rp cannot be considered as a sludge characteristic and is used as a calibration parameter to achieve observed effluent quality. In this study, we focused on the development of an empirical function based on a simple and practical experimental approach for the calculation of the rp value from sludge characteristics. This approach provided a similar approach as currently taken for hindered settling coefficient calculations (Veslind equation) and allowed for the model to predict effluent quality, thus increasing the power of the 1D model. The threshold of flocculation (TOF), which describes the collision efficiency of particles, directly correlated with the effluent quality of the five tested activated sludge systems and was selected as experimental method. The proposed empirical function between TOF and rp was validated for four years of validating data with five different sludge types operated under different operational conditions and configurations. The good effluent quality prediction with this approach brings us one step closer in making the clarification models more predictive towards effluent quality and clarifier performance.