Suite Version Research Articles

Efficiency and Simulation of Thailand's Chiang Mai University Model for COVID-19 Mass Vaccination Hub (CMU-MVH Model).

Mass vaccination is crucial for achieving herd immunity and controlling pandemics. Simulation models predict vaccination outcomes. This study evaluates efficiency and designs a tailored vaccination plan for Thailand. Data was derived from a COVID-19 Mass Vaccination Hub (MVH) in Chiang Mai, Thailand. It was taken between June 2021 and December 2021. The discreet event simulation model is developed in Arena Simulation Enterprise Suite version 14.0. The operational efficiency of Chiang Mai University Mass Vaccination Hub (CMU-MVH) was 1,073 vaccinations per 8 hours with 38 service staff. The average processing times (APT) were 2.5 minutes for the registration station, 12.2 minutes for the assessment station, 8.3 minutes for vaccination, and 28.2 minutes for observation. With 100% staff availability and staff utilization ranging from 0.5 to 0.7, the CMU-MVH could handle 180 to 230 vaccinees per hour or 1,227 to 1,527 vaccinees per day. Reduced staff availability leads to an increase in average processing time, especially when staff availability is lower or equal to 60%. The CMU-MVH could handle approximately a thousand vaccinations within 8 working hours. This capability could assist policymakers in developing more effective strategies and planning for future mass vaccinations. The data that support the findings of this study are available from the corresponding author, upon reasonable request.

A short-list of pairing-friendly curves resistant to the Special TNFS algorithm at the 192-bit security level

For more than two decades, pairings have been a fundamental tool for designing elegant cryptosystems, varying from digital signature schemes to more complex privacy-preserving constructions. However, the advancement of quantum computing threatens to undermine public-key cryptography. Concretely, it is widely accepted that a future large-scale quantum computer would be capable to break any public-key cryptosystem used today, rendering today's public-key cryptography obsolete and mandating the transition to quantum-safe cryptographic solutions. This necessity is enforced by numerous recognized government bodies around the world, including NIST which initiated the first open competition in standardizing post-quantum (PQ) cryptographic schemes, focusing primarily on digital signatures and key encapsulation/public-key encryption schemes. Despite the current efforts in standardizing PQ primitives, the landscape of complex, privacy-preserving cryptographic protocols, e.g., zkSNARKs/zkSTARKs, is at an early stage. Existing solutions suffer from various disadvantages in terms of efficiency and compactness and in addition, they need to undergo the required scrutiny to gain the necessary trust in the academic and industrial domains. Therefore, it is believed that the migration to purely quantum-safe cryptography would require an intermediate step where current classically secure protocols and quantum-safe solutions will co-exist. This is enforced by the report of the Commercial National Security Algorithm Suite version 2.0, mandating transition to quantum-safe cryptographic algorithms by 2033 and suggesting to incorporate ECC at 192-bit security in the meantime. To this end, the present paper aims at providing a comprehensive study on pairings at 192-bit security level. We start with an exhaustive review in the literature to search for all possible recommendations of such pairing constructions, from which we extract the most promising candidates in terms of efficiency and security, with respect to the advanced Special TNFS attacks. Our analysis is focused, not only on the pairing computation itself, but on additional operations that are relevant in pairing-based applications, such as hashing to pairing groups, cofactor clearing and subgroup membership testing. We implement all functionalities of the most promising candidates within the RELIC cryptographic toolkit in order to identify the most efficient pairing implementation at 192-bit security and provide extensive experimental results.

Design, molecular docking, synthesis and in vitro evaluation of anti-influenza activity of oseltamivir carboxamides and their hybrid molecules with hydroxamic acid

Background The influenza virus is a highly contagious respiratory disease that causes seasonal outbreaks and occasionally, unpredictable pandemics with high morbidity and mortality rates. This problem is exacerbated by the lack of drugs with potential antiviral activity against all types of influenza strains, including resistant strains. Therefore, there is an urgent need to develop novel antiviral agents. Methods The synthesis of new oseltamivir carboxamides with amino acids and the subsequent synthesis of hybrid molecules with hydroxamic acid were considered. Two series are presented as series one; oseltamivir carboxamides with L-serine, L-isoleucine, L-phenylalanine, L-tyrosine and series two included hydroxamates of series one. This approach may provide promising candidates with potential anti-influenza activity. The in vitro cytotoxic activity against Madin-Darby Canine Kidney (MDCK), type (NBL-2) - CCL-34 cells using the MTT ((3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay was used to determine the half maximal inhibitory concentrations (IC50) of the investigated compounds. The percent inhibition of neuraminidase was plotted against concentrations and the IC50 values were calculated by non-linear logistic curve fitting. Results The compounds were subjected to molecular docking using the GOLD suite (version 5.7.1) to predict the binding affinities for neuraminidase (3CL0). The docking scores are presented as PLP fitness and are comparable to those of oseltamivir. Oseltamivir-Phenylalanine recorded the highest docking score (72.23 kcal/mol), while, oseltamivir acid was recorded (56.24 kcal/mol). The ADMET parameters were generated using the Swiss ADME server to predict successful candidates with reasonable oral absorption and safety margins. All compounds are safer than oseltamivir and their IC50 values for neuraminidase inhibition were variable. The hybrids showed a lower percentage of viable cells. Oseltamivir-phenylalanine had the highest inhibitory activity against neuraminidase (3.03 μM), when compared with oseltamivir (67.22 μM). Conclusion Oseltamivir-phenylalanine showed remarkable and very significant activity, and the hybrid molecules were surprisingly less effective on neuraminidase than oseltamivir carboxamides.

Deep learning guided prediction modeling of dengue virus evolving serotype

Evolution remains an incessant process in viruses, allowing them to elude the host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Emerging and re-emerging diseases are among the significant public health concerns globally. The revival of dengue is mainly due to the potential for naturally arising mutations to induce genotypic alterations in serotypes. These transformations could lead to future outbreaks, underscoring the significance of studying DENV evolution in endemic regions. Predicting the emerging Dengue Virus (DENV) genome is crucial as the virus disrupts host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging DENV serotypes. While Recurrent Neural Networks (RNN) were initially designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing the contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generated sequences were analyzed along with available DENV serotype sequences to find conserved motifs in the genome through MEME Suite (version 5.5.5). The generative model showed an accuracy of 93 %, and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score, all to be around 99.00 %, demonstrating the classification model's reliability. Our model classified the generated sequences as DENV-4, exhibiting 65.99 % similarity to DENV-4 and around 63–65 % similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum of 90 % similarity underscored their uniqueness.

Design and implementation of domestic dual-SIM telesecurity alarm system using voice code recognition

Violent crime cases which include, robbery, rape, and homicide are terribly on the rise, and the role of security in combating this menace cannot be overemphasized. This research presents a security device that aims at fighting violent crimes using voice recognition technology. The work also tends to solve the issue of network downtime when the user is out of reach for help in time of attack. In this work, a voice processing unit which comprises the condenser microphone, an amplifier, a shift register, and a timer was designed. The processing unit circuit was incorporated into microcontrollers which create Human-Device interaction and the GSM communication unit which is made up of two GSM modules. The two microcontrollers used in the design are PIC18F4520 and PIC16F873A. The microcontrollers were programmed with C++ using the MPLAB IDE software and the circuit simulation was done using Proteus Design Suite version 8. The result shows that the appropriate authority receives SMS whenever the pre-recorded code is mentioned. The result also shows that during network downtime, the second GSM module sends an SMS to the appropriate authority. Evaluating the performance of this work, it was observed that the device works best in a calm area compared to a noisy area. This work is designed to work in domestic areas like homes, offices, malls, and mainly areas free from so much noise. Therefore, this work has successfully reduced the crime rate in emergencies.

Computational studies and structural insights for discovery of potential natural aromatase modulators for hormone-dependent breast cancer.

The aromatase enzyme plays an important role in the progress of hormone-dependent breast cancer, especially in estrogen receptor-positive (ER+) breast cancers. In case of postmenopausal women, the aromatization of androstenedione to estrone in adipose tissue is the most important source of estrogen. Generally 60%-75% of pre- and post-menopausal women suffer from estrogen-dependent breast cancer, and thus suppressing estrogen has been recognized to be a successful therapy. Hence, to limit the stimulation of estrogen, aromatase inhibitors (AIs) are used in the second-line treatment of breast cancer. The present computational study employed an in silico approach in the identification of natural actives targeting the aromatase enzyme from a structurally diverse set of natural products. Molecular docking, QSAR studies and pharmacophore modeling were carried out using the VLife Molecular Design Suite (version 4.6). The stability of the compounds was confirmed by molecular dynamics. From molecular docking and analysis of interactions with the amino acid residues of the binding cavity, it was found that the amino acid residues interacting with the non-steroidal inhibitors exhibited π-stacking interactions with PHE134, PHE 221, and TRP 224, while the steroidal drug exemestane lacked π-stacking interactions. QSAR studies were performed using the flavonoid compounds, in order to identify the structural functionalities needed to improve the anti-breast cancer activity. Molecular dynamics of the screened hits confirmed the stability of compounds with the target in the binding cavity. Moreover, pharmacophore modelling presented the pharmacophoric features of the selected scaffolds for aromatase inhibitory activity. The results presented 23 hit compounds that can be developed as anti-breast cancer modulating agents in the near future. Additionally, anthraquinone compounds with minor structural modification can also serve to be potential aromatase inhibitors. The in silico protocol utilised can be useful in the drug discovery process for development of new leads from structurally diverse set of natural products that are comparable to the drugs used clinically in breast cancer therapy.

Improved prediction of PFAS partitioning with PPLFERs and QSPRs.

Per- and polyfluoroalkyl substances (PFAS) are chemicals of high concern and are undergoing hazard and risk assessment worldwide. Reliable physicochemical property (PCP) data are fundamental to assessments. However, experimental PCP data for PFAS are limited and property prediction tools such as quantitative structure-property relationships (QSPRs) therefore have poor predictive power for PFAS. New experimental data from Endo 2023 are used to improve QSPRs for predicting poly-parameter linear free energy relationship (PPLFER) descriptors for calculating water solubility (SW), vapor pressure (VP) and the octanol-water (KOW), octanol-air (KOA) and air-water (KAW) partition ratios. The new experimental data are only for neutral PFAS, and the QSPRs are only applicable to neutral chemicals. A key PPLFER descriptor for PFAS is the molar volume and this work compares different versions and makes recommendations for obtaining the best PCP predictions. The new models are included in the freely available IFSQSAR package (version 1.1.1), and property predictions are compared to those from the previous IFSQSAR (version 1.1.0) and from QSPRs in the US EPA's EPI Suite (version 4.11) and OPERA (version 2.9) models. The results from the new IFSQSAR models show improvements for predicting PFAS PCPs. The root mean squared error (RMSE) for predicting log KOWversus expected values from quantum chemical calculations was reduced by approximately 1 log unit whereas the RMSE for predicting log KAW and log KOA was reduced by 0.2 log units. IFSQSAR v.1.1.1 has an RMSE one or more log units lower than predictions from OPERA and EPI Suite when compared to expected values of log KOW, log KAW and log KOA for PFAS, except for EPI Suite predictions for log KOW which have a comparable RMSE. Recommendations for future experimental work for PPLFER descriptors for PFAS and future research to improve PCP predictions for PFAS are presented.

Emotional and behavioural functioning in children with tyrosinaemia type 1.

Tyrosinaemia type I is arare hereditary metabolic disease caused by deficiency of the enzyme involved in the breakdown of tyrosine. Since the use of nitisinone in addition to diet in 1992, survival rates have increased significantly, but more and more socio-emotional problems have become apparent. The aim of the study was the assessment the relationship between variations in serum tyrosine and phenylalanine levels and measurements of socio-emotional functioning and determination of patients' IQs. was the assessment the relationship between variations in serum tyrosine and phenylalanine levels and measurements of socio-emotional functioning and determination of patients' IQs. Twelve children were studied, from asingle centre, born between 1994 and 2012, treated with nitisinone and alow-phenylalanine and -tyrosine diet. The psychological evaluation was conducted using the parent form of the Child Behaviour Checklist (CBCL)/4-18. Additionally, the patients' IQs were measured using the Stanford-Binet 5 (SB5) Intelligence Scale. Statistical analyses were performed using PAWS software suite version 26. We found that phenylalanine variability over time correlated with measures of emotional and behavioural functioning. This relationship holds true for externalising behaviour, associated with the experience of maladjustment and aggression. Total score intellectual and cognitive function was within the norm for all patients. To maintain better quality of life for patients and their families in terms of emotional and behavioural functioning, it may be important to avoid spikes (significant fluctuations) in phenylalanine levels. Regular, detailed psychological evaluations are recommended to detect potential problems and implement interventions aimed at achieving the best possible individual development and realise the intellectual and behavioural potential, thereby improving the patient's and her family's quality of life.

Defining the Drivers of Idelalisib-Related Early Autoimmune Toxicity in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) patients with disease resistant to BTK and BCL-2 inhibitors can potentially be treated with PI3K inhibitors to overcome resistance and improve outcomes. PI3K inhibitors such as idelalisib and duvelisib have shown promising efficacy in CLL patients, but severe toxicities remain a significant hurdle to their implementation in the clinic (PMID: 24615777). By investigating the molecular mechanisms that lead to severe early autoimmune toxicity with idelalisib, we hope to predict or prevent toxicity, thus expanding the use of this efficacious PI3K inhibitor class of drugs. To this end, the Brown lab showed that idelalisib-induced hepatitis is autoimmune. CyTOF analysis in CLL patients with early severe toxicity in a front-line idelalisib clinical trial cohort showed a significant decrease in CD4+ T regulatory cells (Treg) with a relative shift in favor of T effectors in peripheral blood. In addition, the Brown lab showed that the Th17 pathway is more active at baseline, with a further increase in patients with toxicity (PMID: 27247136, 36732326). Since the CYTOF data is restricted to fewer mostly T-cell markers in our studies, an unbiased single cell RNA sequencing (scRNA-seq) approach is warranted to explore the full effects of idelalisib on immune cells and CLL cells in patients with and without autoimmune toxicity. The question remains what molecular mechanisms drive the idelalisib-induced decrease in the Treg population, its effect on natural killer (NK) and CD8 T-cell function, and the activation of the Th17 pathway in CLL patients with early toxicity. We therefore carried out the first systematic effort to understand the mechanisms of idelalisib toxicity in molecular detail using combined scRNA and scTCR-seq. In this study, we profile T-cell enriched PBMCs of matched pre-treatment and on-idelalisib treatment samples from CLL patients with early severe toxicity and without any toxicity (n=4 CLL patients from each group, a total of 16 samples) using 10X single cell transcriptomic and TCR profiling. Raw sequencing data files were processed using the Cell Ranger suite (version 7.1.0) from 10x Genomics. Sample identification based on SNP information was done using the Popscle suite. Seurat package (version 4.9.9.9) was used for the single cell analysis. After filtering cells with fewer than 600 features, less than 1000 UMIs, mitochondrial content higher than 15%, 51,487 cells were left for the downstream analysis. To explore transcriptional heterogeneity and perform initial cell clustering, principal component analysis and nonlinear dimensional reduction using UMAP were applied using 20 dimensions. Leiden algorithm was used for cluster identification. Cell types were identified using Azimuth with the PBMC reference dataset and confirmed with expression plots. The initial observations of the cell proportions show that baseline samples from patients with early idelalisib toxicity have fewer Tregs, NK, and CD4+ cytotoxic T (CTL) cells compared to patients without toxicity. In addition, we observed a decline in Tregs, gamma-delta (γδ) T, and CD4+ CTL cells and a concurrent increase in proliferative CD4+ and CD8+ T-cells and proliferative NK on idelalisib treatment in the early toxicity group. On the contrary, CD4+ CTL and CD4+ T effector memory (TEM) cells are enriched with idelalisib therapy in patients without toxicities. At the same time, more CD4+ T-Cell Memory (TCM), γδ T, CD8+ naive T, CD8+ TCM, and Mucosal-associated invariant T (MAIT) cells are present in pretreatment samples from patients with toxicity compared to patients without toxicity. Furthermore, proliferative T-cells are depleted with idelalisib therapy in patients without toxicity whereas they are enriched in patients with early toxicity. To further characterize idelalisib-induced depletion of Tregs, we reclustered them to identify distinct subgroups and found particular clusters of Treg were depleted in patients with early toxicity. In the non-toxicity group, more diversity of Tregs was identified with depletion of some clusters but also an increase in a cluster. At present, we are integrating the scRNA & scTCR-seq data to characterize the cellular diversity and heterogeneous phenotypic states of the T-cell populations and updated results unraveling molecular mechanisms that drive early severe toxicity in CLL patients with idelalisib treatment will be presented at the meeting.

Design and Fabrication of a Novel Corona-Shaped Metamaterial Biosensor for Cancer Cell Detection.

The early detection and diagnosis of cancer presents significant challenges in today's healthcare. So, this research, suggests an original experimental biosensor for cell cancer detection using a corona-shaped metamaterial resonator. This resonator is designed to detect cancer markers with high sensitivity, selectivity, and linearity properties. By exploiting the unique properties of the corona metamaterial structure in the GHz regime, the resonator provides enhanced interaction of electromagnetic waves and improved detection skills. Through careful experimental, simulation, and optimization studies, we accurately demonstrate the resonator's ability to detect cancer. The proposed detection system is capable of real-time non-invasive cancer detection, allowing for rapid intervention and better patient outcomes. The sensitivity value was confirmed through simulation, estimated at 0.1825 GHz/RIU. The results of two different simulation methods are used: the simulation software CST Studio Suite (version 2017) based on the finite element method (FEM), and the simulation software ADS (version 2019) based on the equivalent circuit method, thereby increasing confidence in the convergence of simulation and measurement results. This work opens new avenues for developing advanced detection technologies in the field of oncology, and paves the way for more effective cancer diagnosis. The experimental study verified that this realized sensor has very small frequency shifts, significantly small electrical dimension and miniaturization, high sensitivity, and good linearity. The suggested configurations showed a capacity for sensing cancer cells in the GHz regime.

Phytoligands analysis of aqueous seed extract of Momordica charantia Linn. via GC-MS for its antidiabetic activity

In India, Momordica charantia Linn. is utilized in juices, health supplements and colouring agents. The aim of the current study is to analyze phytoligands by utilizing gas chromatography mass spectroscopy of aqueous seed extract of Momordica charantia Linn (MCA). The MCA was prepared by macerating the powdered seeds for 72 hrs followed by filtration and lyophilization followed by an in vitro α-glucosidase inhibitory activity. MCA was the potent inhibitor of α-glucosidase with IC50 value 73.75± 0.05 μg/ml. The analysis through GC-MS investigated 60 phytoconstituents which were validated using Schrodinger suite version 2020-3 against α-glucosidase (PDB ID- 5NN5). As per docking results, the existing phytochemicals azetidin-2-one 3,3-dimethyl-4-(1-aminoethyl)-, alpha-l-rhamnopyranose, piperidine, 3-methyl-, 1,4-Dioxane-2,5-dione,3,6-dimethyl and citronellol epoxide (R or S) from MCA, got the highest d-score of -7.230, -5.918, -5.643, -5.312 and -5.185 kcal/mol and could be promising antidiabetic drug candidates.

Translation of Monoclonal Antibodies Pharmacokinetics from Animal to Human Using Physiologically Based Modeling in Open Systems Pharmacology (OSP) Suite: A Retrospective Analysis of Bevacizumab.

Interspecies translation of monoclonal antibodies (mAbs) pharmacokinetics (PK) in presence of target-mediated drug disposition (TMDD) is particularly challenging. Incorporation of TMDD in physiologically based PK (PBPK) modeling is recent and needs to be consolidated and generalized to provide better prediction of TMDD regarding inter-species translation during preclinical and clinical development steps of mAbs. The objective of this study was to develop a generic PBPK translational approach for mAbs using the open-source software (PK-Sim® and Mobi®). The translation of bevacizumab based on data in non-human primates (NHP), healthy volunteers (HV), and cancer patients was used as a case example for model demonstration purpose. A PBPK model for bevacizumab concentration-time data was developed using data from literature and the Open Systems Pharmacology (OSP) Suite version 10. PK-sim® was used to build the linear part of bevacizumab PK (mainly FcRn-mediated), whereas MoBi® was used to develop the target-mediated part. The model was first developed for NHP and used for a priori PK prediction in HV. Then, the refined model obtained in HV was used for a priori prediction in cancer patients. A priori predictions were within 2-fold prediction error (predicted/observed) for both area under the concentration-time curve (AUC) and maximum concentration (Cmax) and all the predicted concentrations were within 2-fold average fold error (AFE) and average absolute fold error (AAFE). Sensitivity analysis showed that FcRn-mediated distribution and elimination processes must be accounted for at all mAb concentration levels, whereas the lower the mAb concentration, the more significant the target-mediated elimination. This project is the first step to generalize the full PBPK translational approach in Model-Informed Drug Development (MIDD) of mAbs using OSP Suite.

Anti-inflammatory effects of dexamethasone in COVID-19 patients: Translational population PK/PD modeling and simulation.

Dexamethasone (DEX) given at a dose of 6 mg once-daily for 10 days is a recommended dosing regimen in patients with coronavirus disease 2019 (COVID-19) requiring oxygen therapy. We developed a population pharmacokinetic and pharmacodynamic (PopPK/PD) model of DEX anti-inflammatory effects in COVID-19 and provide simulations comparing the expected efficacy of four dosing regimens of DEX. Nonlinear mixed-effects modeling and simulations were performed using Monolix Suite version 2021R1 (Lixoft, France). Published data for DEX PK in patients with COVID-19 exhibited moderate variability with a clearance of about half that in healthy adults. No accumulation of the drug was expected even with daily oral doses of 12 mg. Indirect effect models of DEX inhibition of TNFα, IL-6, and CRP plasma concentrations were enacted and simulations performed for DEX given at 1.5, 3, 6, and 12 mg daily for 10 days. The numbers of individuals that achieved specified reductions in inflammatory biomarkers were compared among the treatment groups. The simulations indicate the need for 6 or 12 mg daily doses of DEX for 10 days for simultaneous reductions in TNFα, IL-6, and CRP. Possibly beneficial is DEX given at a dose of 12 mg compared to 6 mg. The PopPK/PD model may be useful in the assessment of other anti-inflammatory compounds as well as drug combinations in the treatment of cytokine storms.

A Flexible and General-Purpose Platform for Heterogeneous Computing

In the big data era, processing large amounts of data imposes several challenges, mainly in terms of performance. Complex operations in data science, such as deep learning, large-scale simulations, and visualization applications, can consume a significant amount of computing time. Heterogeneous computing is an attractive alternative for algorithm acceleration, using not one but several different kinds of computing devices (CPUs, GPUs, or FPGAs) simultaneously. Accelerating an algorithm for a specific device under a specific framework, i.e., CUDA/GPU, provides a solution with the highest possible performance at the cost of a loss in generality and requires an experienced programmer. On the contrary, heterogeneous computing allows one to hide the details pertaining to the simultaneous use of different technologies in order to accelerate computation. However, effective heterogeneous computing implementation still requires mastering the underlying design flow. Aiming to fill this gap, in this paper we present a heterogeneous computing platform (HCP). Regarding its main features, this platform allows non-experts in heterogeneous computing to deploy, run, and evaluate high-computational-demand algorithms following a semi-automatic design flow. Given the implementation of an algorithm in C with minimal format requirements, the platform automatically generates the parallel code using a code analyzer, which is adapted to target a set of available computing devices. Thus, while an experienced heterogeneous computing programmer is not required, the process can run over the available computing devices on the platform as it is not an ad hoc solution for a specific computing device. The proposed HCP relies on the OpenCL specification for interoperability and generality. The platform was validated and evaluated in terms of generality and efficiency through a set of experiments using the algorithms of the Polybench/C suite (version 3.2) as the input. Different configurations for the platform were used, considering CPUs only, GPUs only, and a combination of both. The results revealed that the proposed HCP was able to achieve accelerations of up to 270× for specific classes of algorithms, i.e., parallel-friendly algorithms, while its use required almost no expertise in either OpenCL or heterogeneous computing from the programmer/end-user.

Design and Implementation of Smart Power Voltage and Frequency Synchroniser in Power Distribution System

Voltage and frequency are usually considered and assessed independently in the design and operation of electrical networks. However, these two are linked. Each and every malfunctioning electrical system has an impact on both voltage and frequency. This paper presents the opportunity for monitoring the distributed electrical energy by means of a system that monitors, controls, and provides a breakpoint based on high or low voltage and frequency tripping mechanism that avoids any damage to the load. The designed system comprised a switch mode power supply (SMPS), a direct digital synthesizer (DDS), and PIC16F876A microcontroller techniques for stable voltage and frequency outputs. Proteus design suite version 8.11 software and Benchcope SDS1102CN were used for modeling and simulation. The hardware prototype was implemented at a telecom cell site for data capturing and analysis. Test results showed that the implementation of the prototype provided stable and constant outputs of 222 V/50 Hz and 112 V/60 Hz which constituted 99% and 99.8% efficiency for voltage and frequency performance respectively. The paper also discusses different technologies that can be adopted by the system to mitigate voltage and frequency effects on customer appliances.

In silico prediction of persistent, mobile, and toxic pharmaceuticals (PMT): A case study in São Paulo Metropolitan Region, Brazil

Computational modelling (in silico) methods based on quantitative structure-activity relationship ((Q)SAR) models, are powerful tools for the assessment of the potential “persistency, mobility, and toxicity” (PMT) of pharmaceuticals compounds. Moreover, the use of (Q)SAR models, is recommended by European Union’s REACH Regulation. In this context, the aims of this research were estimating, for the first time and based by REACH guidelines, the PMT potentials of 115 most sold pharmaceuticals in São Paulo Metropolitan Region (a megacity with 21 million of Brazilian), through five (Q)SAR updated models, namely: the OPERA QSAR; the VEGA QSAR (Version 1.1.5); the EPI Suite (Version 4.11); the ECOSAR (Version, 2.0); and the QSAR Toolbox (Version 4.5). This study prioritized the in-silico predictions from the OPERA and the VEGA, because both QSARs can generate reliable predictions, i.e., they have detailed information about the applicability domains. In silico predictions were performed considering ten endpoints: (i) Molecular weight (MW); (ii) “STP total removal”: Sewage Treatment Plant; (iii) Octanol-water partition coefficient (KOW); (iv) Predicted ready biodegradability; (v) Soil organic adsorption coefficient (KOC); (vi) “Short-term and long-term ecological assessments”; (vii) “Carcinogenicity”; (viii) “Mutagenicity”; (ix) “Estrogen receptor binding”; (x) “Cramer decision tree”. The main results showed that: (a) These 115 pharmaceuticals cover a wide range of so-called small molecules (range from 100 to 600 MW); (b) In STP, a predicted removal lower than 10 % was found for 76 pharmaceuticals; (c) Additionally, 101 chemicals has low (Log KOW <2.5), or medium sorption potential (2.5< log KOW <4.0); (d) Ultimately, 36 PPCPs were considered “persistent” after a weight-of-evidence assessment. In addiction, 17 among these 36 persistent chemicals, were classified as “very mobile” in water (log KOC <3). Finally, only three among 17 PPCPs, namely ciprofibrate, fluconazole and metoclopramide, exhibited one or more toxic characteristics (described in items vi – x). These results it will be possible to alert about the potential risks arising from the indiscriminate disposal of these PPCPs along the water sources of this Brazilian mega metropolis.

Acral lamellar ichthyosis with amino acid substitution in the C-terminus of keratin 2.

Most cases of hereditary ichthyoses present with generalized scaling and skin dryness. However, in some cases skin involvement is restricted to particular body regions as in acral lamellar ichthyosis. We report on the genetic basis of acral ichthyosis in two families presenting with a similar phenotype. Genetic testing was performed by targeted next generation sequencing and whole-exome sequencing. For identity-by-descent analysis, the parents were genotyped and data analysis was performed with the Chromosome Analysis Suite Software. RT-PCR with RNA extracted from skin samples was used to analyse the effect of variants on splicing. Genetic testing identified a few heterozygous variants, but only the variant in KRT2 c.1912 T > C, p.Phe638Leu segregated with the disease and remained the strongest candidate. Pairwise identity-by-descent analysis revealed no indication of family relationship. Phenylalanine 638 is the second last amino acid upstream of the termination codon in the tail of K2, and substitution to leucine is predicted as probably damaging. Assessment of the variant is difficult, in part due to the lack of crystal structures of this region. Altogether, we show that a type of autosomal dominant acral ichthyosis is most probably caused by an amino acid substitution in the C-terminus of keratin 2.

A Π-shaped slotted patch antenna with a partial ground structure for lower 5G/WiFi/WiMAX applications

In this research work, a π-shaped slotted microstrip patch antenna (MPA) with a partial ground structure for lower fifth generation (5G)/wireless fidelity (WiFi)/worldwide interoperability for microwave access (WiMAX) applications is presented. The proposed MPA is optimized and designed by using the computer simulation technology (CST) microwave studio (MWS) suite version of 2018. The π-shaped slotted MPA is mounted on low loss dielectric material Rogers RT5880 with a height of 0.79 mm and relative permittivity of 2.2. The length (L) and the width (W) of the π-slotted antenna are 35 mm and 31 mm respectively, which covers the operating frequency range 2.87–5.47 GHz. The MPA has a wide impedance bandwidth (2600 MHz), high radiation efficiency (90.88%), acceptable gain (2.647 dB) and low return loss (simulated: -36.81, measured: -25 dB). The VSWR over the entire operating frequency is regarded as 1 < voltage standing wave ratio (VSWR) < 2 and VSWR is 1.0293 at centre frequency 3.47 GHz. The current distribution on the surface as well as the input impedance of the π-shaped slotted MPA are also favorable. The MPA shows an omni-directional property over the entire operating frequency band. Since the time domain, frequency domain and fabricated results are buttressed by each other, the designed π-slotted MPA is one of the best candidates for high speed lower 5G/WiFi/WiMAX communication applications.

Field programmable gate array implementation of multiwavelet transform based orthogonal frequency division multiplexing system

This article offers an efficient design and implementation of a discrete multiwavelet critical-sampling transform based orthogonal frequency division multiplexing (DMWCST-OFDM) transceiver using field programmable gate array (FPGA) platform. The design uses 16-point discrete multiwavelet critical-sampling transform (DMWCST) and its inverse as main processing modules. All modules were designed using a part of Vivado&lt;sup&gt;®&lt;/sup&gt; Design Suite version (2015.2), which is Xilinx system generator (XSG), and is compatible with MATLAB Simulink version R2013b. The FPGA implementation is carried out on a Zynq (XC7Z020-1CLG484) evaluation board with joint test action group (JTAG) hardware co-simulation. According to the results obtained from the implementation tools, the implemented system is efficient in terms of resource utilization and could support the real-time operations.

Neuroimaging Markers for Determining Former American Football Players at Risk for Alzheimer's Disease.

NFL players, by virtue of their exposure to traumatic brain injury (TBI), are at higher risk of developing dementia and Alzheimer's disease (AD) than the general population. Early recognition and intervention before the onset of clinical symptoms could potentially avert/delay the long-term consequences of these diseases. Given that AD is thought to have a long pre-clinical incubation period, the aim of the current research was to determine whether former NFL players show evidence of incipient dementia in their structural imaging before diagnosis of AD. To identify neuroimaging markers of AD, against which former NFL players would be compared, we conducted a whole-brain volumetric analysis using a cohort of AD patients (ADNI clinical database) to produce a set of brain regions demonstrating sensitivity to early AD pathology (i.e., the “AD fingerprint”). A group of 46 former NFL players' brain magnetic resonance images were then interrogated using the AD fingerprint, that is, the former NFL subjects were compared volumetrically to AD patients using a T1-weighted magnetization-prepared rapid gradient echo sequence. The FreeSurfer image analysis suite (version 6.0) was used to obtain volumetric and cortical thickness data. The Automated Neuropsychological Assessment Metric-Version 4 was used to assess current cognitive functioning. A total of 55 brain regions demonstrated significant atrophy or ex vacuo dilatation bilaterally in AD patients versus controls. Of the 46 former NFL players, 41% demonstrated a greater than expected number of atrophied/dilated AD regions compared with age-matched controls, presumably reflecting AD pathology.