Contemporary trends of leadless pacemaker implantation in the United States.

INTRODUCTION: Leadless pacemakers (LPM) have established themselves as the important therapeutic modality in management of selected patients with symptomatic bradycardia. To determine real-world utilization and in-hospital outcomes of LPM implantation since its approval by the Food and Drug Administration in 2016. METHODS: For this retrospective cohort study, data were extracted from the National Inpatient Sample database from the years 2016-2020. The outcomes analyzed in our study included implantation trends of LPM over study years, mortality, major complications (defined as pericardial effusion requiring intervention, any vascular complication, or acute kidney injury), length of stay, and cost of hospitalization. Implantation trends of LPM were assessed using linear regression. Using years 2016-2017 as a reference, adjusted outcomes of mortality, major complications, prolonged length of stay (defined as >6 days), and increased hospitalization cost (defined as median cost >34 098$) were analyzed for subsequent years using a multivariable logistic regression model. RESULTS: There was a gradual increased trend of LPM implantation over our study years (3230 devices in years 2016-2017 to 11 815 devices in year 2020, p for trend <.01). The adjusted mortality improved significantly after LPM implantation in subsequent years compared to the reference years 2016-2017 (aOR for the year 2018: 0.61, 95% CI: 0.51-0.73; aOR for the year 2019: 0.49, 95% CI: 0.41-0.59; and aOR for the year 2020: 0.52, 95% CI: 0.44-0.62). No differences in adjusted rates of major complications were demonstrated over the subsequent years. The adjusted cost of hospitalization was higher for the years 2019 (aOR: 1.33, 95% CI: 1.22-1.46) and 2020 (aOR: 1.69, 95% CI: 1.55-1.84). CONCLUSION: The contemporary US practice has shown significantly increased implantation rates of LPM since its approval with reduced rates of inpatient mortality.

Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimization.

Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact.

Fabrication of Efficient and Non-Enzymatic Electrochemical Sensors for the Detection of Sucrose.

Molecularly imprinted polymers have been used for the creation of an electrochemical sensor for the detection of sucrose, which are modified by using functionalized graphene (fG). Using AIBN as the free radical initiator and sucrose as the template, imprinted polymers are synthesized. The monomer, 4,4'-diisocyanatodiphenylmethane (DPDI), has both proton donor groups (N-H or O-H) and lone-pair donor groups (C=O). By creating H-bonds with electron donor groups (C=O), the proton donor group in this polymer may interact with the sugar molecule serving as its template. The sensor signals have improved as a result of the interaction between the monomer and the template. Thermogravimetric and differential thermal analysis (TGA/DTA) curves, scanning electron microscopy (SEM), and FT-IR spectroscopy have been employed to characterize the fabricated receptors. The fabricated sensor has exhibited a limit of detection of 16 ppb for the target analyte that is highly sensitive, linear, reversible, regenerative, and selective. Moreover, the sensor's stability, reproducibility, and reusability have been evaluated for six months, following the device's manufacturing, and the results revealed similar responses with the percentage error of less than 1%. Most importantly, this sensor has demonstrated a quick response time, which is very sensitive, stable, and selective.

Designing Electron-Deficient Diketone Unit Based Non-Fused Ring Acceptors with Amplified Optoelectronic Features for Highly Efficient Organic Solar Cells: A DFT Study.

Organic solar cells (OSCs) made of electron-acceptor and electron-donor materials have significantly developed in the last decade, demonstrating their enormous potential in cutting-edge optoelectronic applications. Consequently, we designed seven novel non-fused ring electron acceptors (NFREAs) (BTIC-U1 to BTIC-U7) using synthesized electron-deficient diketone units and reported end-capped acceptors, a viable route for augmented optoelectronic properties. The DFT and TDDFT approaches were used to measure the power conversion efficiency (PCE), open circuit voltage (Voc), reorganization energies (λh, λe), fill factor (FF), light harvesting efficiency (LHE) and to evaluate the potential usage of proposed compounds in solar cell applications. The findings confirmed that the photovoltaic, photophysical, and electronic properties of the designed molecules BTIC-U1 to BTIC-U7 are superior to those of reference BTIC-R. The TDM analysis demonstrates a smooth flow of charge from the core to the acceptor groups. Charge transfer analysis of the BTIC-U1:PTB7-Th blend revealed orbital superposition and successful charge transfer from HOMO (PTB7-Th) to LUMO (BTIC-U1). The BTIC-U5 and BTIC-U7 outperformed the reference BTIC-R and other developed molecules in terms of PCE (23.29% and 21.18%), FF (0.901 and 0.894), normalized Voc (48.674 and 44.597), and Voc (1.261 eV and 1.155 eV). The proposed compounds enclose high electron and hole transfer mobilities, making them the ideal candidate for use with PTB7-Th film. As a result, future SM-OSC design should prioritize using these constructed molecules, which exhibit excellent optoelectronic properties, as superior scaffolds.

Detection of HPV16 viral load in L2 gene as a related predictor of cervical cancer among women in Dhi-Qar province by qRT-PCR.

BACKGROUND: The most common infection among young women that increases the risk of developing cervical cancer (CC) is human papillomavirus (HPV). In this study, we are going to assess whether HPV16 DNA concentration helps indicate cervical cancer progression ,As well as for age groups and their relationship to cervical cancer. METHODS: Present study included 93 adult females suffering from cervical cancer during the period from 2017 to 2020. Molecular detection of HPV was done using amplification of the L2 gene (minor capsid protein). RESULTS: Present results showed that 60 (65%) of the patients from 93 cervical cancer cases were infected by HPV16 while only 5 (8%) of healthy patients from the control group were positive for HPV16. So, the current study revealed high HPV16 load in cervical cancer ranged from 1.09 × 102 IU/ml to 5.07 × 103 IU/ml with a mean ± SD of viral load was 1043.25 ± 8.50 IU/ml while in healthy individuals very low viral load ranging from 88 IU/ml to 101 IU/ml and mean ± SD of viral load was 91.25 ± 2.90 IU/ml was reported. CONCLUSION: HPV16 viral load is significantly associated with cervical carcinoma among women in Dhi-Qar Province.

No-reference image quality metrics for color domain modified images.

Predicting the quality of natural images without using a reference image has always been a challenging task. Numerous approaches have been proposed in the past, but they mainly focused on spatial and frequency domain degradations like blur, noise, and compression. Image quality metrics (IQMs) in literature perform with quite a high accuracy for such types of degraded images. However, their performances are not good on the images modified in the color domain. In this study, psychophysical experiments were conducted to assess the quality of the color domain images. A new dataset was developed for this purpose. Additionally, a second dataset consisting of color domain modified images from the three previously published datasets were used in the psychophysical experiments. The newly developed dataset was then used to develop three IQMs based on absolute values, relative values, and statistical analysis of image color appearance attributes. Their performances were then evaluated together with five spatial domain IQMs from the literature using cross-database evaluation methodology. The results showed that the color-domain IQMs outperformed the other models. The absolute and relative attributes-based models, when combined, achieved the best performance. The present results suggest that more effort is needed to improve the performance of color domain IQMs for image quality estimation.

Method for developing and using high quality reference images to evaluate tone mapping operators.

With the advancement of imaging technology, high dynamic range (HDR) images can now be captured and displayed to produce realistic effects. Tone mapping operators (TMOs) are used to map HDR radiance to the displayable range. A reliable TMO would play a significant role in the accurate reproduction of HDR scenes. The present study aimed to establish an image quality metric based on external references to evaluate various TMOs. Two psychophysical experiments were conducted to develop reference images and to investigate the performance of TMOs. In experiment 1, a set of high quality reference images was developed by rendering image features in terms of contrast, sharpness, and colorfulness, to achieve good rendering for each image. The images were used as reference in experiment 2 to evaluate the performance of 14 TMOs using a six-point categorical judgment method. The TMOs were evaluated using four scales, i.e., contrast, sharpness, colorfulness, and overall performance. The hierarchical relationship among TMOs was established. The results were further compared with previous studies, and high correlation was found between the current experiments and previous studies.

Isolation of Thioinosine and Butenolides from a Terrestrial Actinomycetes sp. GSCW-51 and Their in Silico Studies for Potential against SARS-CoV-2.

In our continuous screening for bioactive microbial natural products, the culture extracts of a terrestrial Actinomycetes sp. GSCW-51 yielded two new metabolites, i. e., 5-hydroxymethyl-3-(1-hydroxy-6-methyl-7-oxooctyl)dihydrofuran-2(3H)-one (1), 5-hydroxymethyl-3-(1,7-dihydroxy-6-methyloctyl)dihydrofuran-2(3H)-one (2), and two known compounds; 5'-methylthioinosine (3), and 5'-methylthioinosine sulfoxide (4), which are isolated first time from any natural source, along with four known compounds (5-8). The structures of the new compounds were deduced by HR-ESI-MS, 1D and 2D NMR data, and in comparison with related compounds from the literature. Additionally, owing to the current COVID-19 pandemic situation, we also computationally explored the therapeutic potential of our isolated compounds against SARS-CoV-2. Compound 4 showed the best binding energies of -6.2 and -6.6 kcal/mol for Mpro and spike proteins, respectively. The intermolecular interactions were also studied using 2-D and 3-D imagery, which also supported the binding energies as well as put several insights under the spotlight. Furthermore, Lipinski's rule of 5 was used to predict the drug likeness of compounds 1-4, which indicated all compounds obey Lipinski's rule of 5. The study of bioavailability radars of the compounds 1-4 also confirmed their drug likeness properties where all the five crucial drug likeness parameters are in color area, which is safe to be used as drugs. Our isolation and computational findings highly encourage the scientific community to do further in vitro and in vivo studies of compounds 1-4.

Influence of Mn Ions' Insertion in Pseudo-Tetragonal Phased CaBi4Ti4O15-Based Ceramics for Highly Efficient Energy Storage Devices and High-Temperature Piezoelectric Applications.

In the present era of advanced technology, the surge for suitable multifunctional materials capable of operating above 300 °C has increased for the utilization of high-temperature piezoelectric devices. For this purpose, a pseudo-tetragonal phased CaBi4Ti3.98 (Nb0.5Fe0.5)0.02O15:xwt%MnO2 (CBTNF:xMn), with x = 0-0.20, ceramic system has been engineered for the investigation of structural, ferroelectric, dielectric and high-temperature-dependent piezoelectric properties. XRD analysis confirms that low-content Mn-ion insertion at the lattice sites of CBTNF does not distort the pseudo-tetragonal phase lattice of CBTNF:xMn ceramics, but enhances the functional behavior of the ceramic system, specifically at x = 0.15 wt%Mn. Compared to pure CBT and CBTNF ceramics, CBTNF:0.15Mn has demonstrated a highly dense relative density (~96%), a saturated polarization (PS) of 15.89 µC/cm2, a storage energy density (WST) of ~1.82 J/cm3, an energy-conversion efficiency (ƞ) of ~51% and an upgraded piezoelectric behavior (d33) of 27.1 pC/N at room temperature. Sharp temperature-dependent dielectric constant (εr) peaks display the solid ferroelectric behavior of the CBTNF:0.15Mn sample with a Curie temperature (TC) of 766 °C. The thermally stable piezoelectric performance of the CBTNF:0.15Mn ceramic was observed at 600 °C, with just a 0.8% d33 loss (25 pC/N). The achieved results signify that multi-valence Mn ions have effectively intercalated at the lattice sites of the pseudo-tetragonal phased CBTNF counterpart and enhanced the multifunctional properties of the ceramic system, proving it to be a durable contender for utilization in energy-storage applications and stable high-temperature piezoelectric applications.

Comprehensive comparative genomic and microsatellite analysis of SARS, MERS, BAT-SARS, and COVID-19 coronaviruses.

The coronavirus disease 2019 (COVID-19) pandemic has spread around the globe very rapidly. Previously, the evolution pattern and similarity among the COVID-19 causative organism severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and causative organisms of other similar infections have been determined using a single type of genetic marker in different studies. Herein, the SARS-CoV-2 and related ß coronaviruses Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV,  bat coronavirus (BAT-CoV) were comprehensively analyzed using a custom-built pipeline that employed phylogenetic approaches based on multiple types of genetic markers including the whole genome sequences, mutations in nucleotide sequences, mutations in protein sequences, and microsatellites. The whole-genome sequence-based phylogeny revealed that the strains of SARS-CoV-2 are more similar to the BAT-CoV strains. The mutational analysis showed that on average MERS-CoV and BAT-CoV genomes differed at 134.21 and 136.72 sites, respectively, whereas the SARS-CoV genome differed at 26.64 sites from the reference genome of SARS-CoV-2. Furthermore, the microsatellite analysis highlighted a relatively higher number of average microsatellites for MERS-CoV and SARS-CoV-2 (106.8 and 107, respectively), and a lower number for SARS-CoV and BAT-CoV (95.8 and 98.5, respectively). Collectively, the analysis of multiple genetic markers of selected ß viral genomes revealed that the newly born SARS-COV-2 is closely related to BAT-CoV, whereas, MERS-CoV is more distinct from the SARS-CoV-2 than BAT-CoV and SARS-CoV.

Pomegranate as a source of bioactive constituents: a review on their characterization, properties and applications.

Increasing awareness about the use of compounds obtained from natural sources exerting health-beneficial properties, including antimicrobial and antioxidant effects, led to increased number of research papers focusing on the study of functional properties of target compounds to be used as functional foods or in preventive medicine. Pomegranate has shown positive health properties due to the presence of bioactive constituents such as polyphenols, tannins, and anthocyanins. Punicalagin is the major antioxidant, abundantly found in pomegranate's peel. Research has shown that pomegranate polyphenols not only have a strong antioxidant capacity but they also inhibit the growth of pathogenic bacteria like V. cholera, P. aeruginosa and S. aureus, B. cereus, E. coli, and S. virulence factor, and inhibits fungi such as A. Ochraceus, and P. citrinum. Compounds of natural origin inhibit the growth of various pathogens by extending the shelf life of foodstuffs and assuring their safety. Therefore, the need to find compounds to be used in combination with antibiotics or as new antimicrobial sources, such as plant extracts. On the basis of the above discussion, this review focuses on the health benefits of pomegranate, by summarizing the current body of research focusing on pomegranate bioactive constituents and their therapeutic potential against some pathogenic microbes.

GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification.

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at1 and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.

Representation of women, older patients, ethnic, and racial minorities in trials of atrial fibrillation.

BACKGROUND: Representation trends of women, older adults, and ethnic/racial minorities in randomized controlled trials (RCTs) of atrial fibrillation (AF) are uncertain. METHODS: We systematically reviewed 134 AF related RCTs (phase II and III) encompassing 149,162 participants using Medline and ClinicalTrials.gov through April 2019 to determine representation trends of women, older patients (≥75 years), and ethnic/racial minorities. Weighted data on the prevalence of AF from epidemiological studies were used to compare the representation of the studied groups of interest in AF RCTs to their expected burden of the disease. RESULTS: Only 18.7% of the RCTs reported proportion of older patients, and 12.7% RCTs reported ethnic/racial minorities. The proportions of women in RCTs versus general population were 35.2% and 35.1%, of Hispanics were 11.9% and 5.2%, of Blacks were 1.2% and 5.7%, of American Indian/Alaskans were 0.2% and 0.2%, of Asians were 14.2% and 2.4%, of native Hawaiian/Pacific Islanders were 0.05% and 0.1% and of non-Whites were 19.5% and 22.5%, respectively. The weighted mean age (SD) across the trials was 65.3 (3.2) years which was less than the corresponding weighted mean age of 71.1 (4.5) years in the comparative epidemiological data. CONCLUSION: The reporting of older patients and ethnic/racial minorities was poor in RCTs of AF. The representation of women and American Indian/Alaskan natives matched their expected population share of disease burden. Hispanics and Asians were over-represented and Blacks, native Hawaiian/Pacific Islanders and non-Whites were under-represented in RCTs of AF.

Efficacy and safety of low dose rivaroxaban in patients with coronary heart disease: a systematic review and meta-analysis.

The mortality effects and risk-benefit profile of low dose rivaroxaban (2.5 mg twice daily) in patients with coronary heart disease are not completely understood. Five randomized controlled trials (26,110 patients) were selected using PubMed and Cochrane library till April 2019. The background antiplatelet therapy was aspirin in 3 trials, P2Y12 inhibitor in 1 trial, and in 1 trial 65% patients received aspirin and 35% were on dual antiplatelet therapy (DAPT). The outcomes of interest were cardiovascular mortality, all-cause mortality, myocardial infarction (MI), stroke and major bleeding events. Random effects hazard ratios (HR) with 95% confidence intervals (CI) were calculated. Low dose rivaroxaban did not reduce the risk of cardiovascular mortality (HR 0.90, 95% CI 0.73-1.11, P = 0.34) or all-cause mortality (HR 0.91, 95% CI 0.74-1.12, P = 0.38) compared with control. However, low dose rivaroxaban was associated with reduction in MI (HR 0.85, 95% CI 0.73-0.99, P = 0.04), and stroke (HR 0.59, 95%CI 0.48-0.73, P < 0.001) at the expense of major bleeding (HR 1.64, 95% CI 1.39-1.94, P < 0.001) compared with control. These effects did not vary according to acute coronary syndrome or stable coronary heart disease (P-interaction > 0.05). The use of low dose rivaroxaban in patients with coronary heart disease predominantly receiving antiplatelet monotherapy did not reduce cardiovascular or all-cause mortality. The benefits of preventing MI and stroke were balanced by increased risk of major bleeding.

A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique.

Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC.

Impact of discharge currents on the intensity of 46.9 nm capillary discharge soft X-ray laser.

In this article, capillary discharge Ne-like argon 46.9nm soft X-ray laser has been firstly manifested with 4.8mm inner diameter alumina capillary for higher discharge currents. We have designed and installed capillary discharge setup for 4.8mm inner diameter alumina capillary to achieve intense 46.9nm laser. One dimensional Langragian Magneto-hydrodynamics (MHD) code was used to simulate the plasma conditions at the lasing time. The MHD code was used to perform the parametric studies of Z-pinch argon plasma, such as electron temperature, electron density and Ne-like argon ion density. The intensities of capillary discharge 46.9nm laser emitted from 4.8mm inner diameter alumina capillary were measured at 30, 36 and 40kA main discharge currents. According to the results, when the main current amplitude was increased from 30kA to 36kA and 40kA, the intensity of laser produced at optimum pressure increased up to 1.5 and 2 times, respectively. Moreover, we also studied the influence of predischarge current by increasing the predischarge current from 25 to 250A and investigated 140A as the best predischarge current for lasing. Hence, increasing the amplitude of main current using a comparatively larger inner diameter capillary is an effective way to improve intensity of capillary discharge 46.9nm soft X-ray laser. The maximum energy of 46.9nm laser was observed approximately 1.5mJ under best discharge conditions. The discussion has been made on the enhancement of 46.9nm laser intensity for higher main discharge currents and best predischarge current with experimental and simulated results. This is the first observation of 46.9nm laser with 4.8mm inner diameter alumina capillary.

Multi-label biomedical question classification for lexical answer type prediction.

Question classification is considered one of the most significant phases of a typical Question Answering (QA) system. It assigns certain answer types to each question which leads to narrow down the search space of possible answers for factoid and list type questions. The process of assigning certain answer types to each question is also known as Lexical Answer Type (LAT) Prediction. Although much work has been done to enhance the performance of question classification into coarse and fine classes in diverse domains, it is still considered a challenging task in the biomedical field. The difficulty in biomedical question classification stems from the fact that one question might have more than one label or expected answer types associated with it (also, referred to as a multi-label classification). In the biomedical domain, only preliminary work is done to classify multi-label questions by transforming them into a single label through copy transformation technique. In this paper, we have generated a multi-labeled corpus (MLBioMedLAT) by exploring the process of Open Advancement of Question Answering (OAQA) system for the task of biomedical question classification. We use 780 biomedical questions from BioASQ challenge and assign them appropriate labels. To annotate these labels, we use the answers for each question and assign the question semantic type labels by leveraging an existing corpus and utilizing OAQA system. The paper introduces a data transformation approach namely Label Power Set with logistic regression (LPLR) for the task of multi-label biomedical question classification and compares its performance with Structured SVM (SSVM), Restricted Boltzmann Machine (RBM), and copy transformation based logistic regression (CLR) (previously used for a similar task in the OAQA system). To evaluate the integrity of the introduced data transformation technique, we use three prominent evaluation measures namely MicroF1, Accuracy, and Hamming Loss. Regarding MicroF1, our introduced technique coupled with a new feature set surpasses CLR, SSVM, and RBM with a margin of 7%, 8%, and 22% respectively.

Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory.

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

Electron Donor and Acceptor Influence on the Nonlinear Optical Response of Diacetylene-Functionalized Organic Materials (DFOMs): Density Functional Theory Calculations.

Herein, we report the quantum chemical results based on density functional theory for the polarizability (α) and first hyperpolarizability (ß) values of diacetylene-functionalized organic molecules (DFOM) containing an electron acceptor (A) unit in the form of nitro group and electron donor (D) unit in the form of amino group. Six DFOM 1-6 have been designed by structural tailoring of the synthesized chromophore 4,4'-(buta-1,3-diyne-1,4-diyl) dianiline (R) and the influence of the D and A moieties on α and ß was explored. Ground state geometries, HOMO-LUMO energies, and natural bond orbital (NBO) analysis of all DFOM (R and 1-6) were explored through B3LYP level of DFT and 6-31G(d,p) basis set. The polarizability (α), first hyperpolarizability (ß) values were computed using B3LYP (gas phase), CAM-B3LYP (gas phase), CAM-B3LYP (solvent DMSO) methods and 6-31G(d,p) basis set combination. UV-Visible analysis was performed at CAM-B3LYP/6-31G(d,p) level of theory. Results illustrated that much reduced energy gap in the range of 2.212-2.809 eV was observed in designed DFOM 1-6 as compared to parent molecule R (4.405 eV). Designed DFOM (except for 2 and 4) were found red shifted compared to parent molecule R. An absorption at longer wavelength was observed for 6 with 371.46 nm. NBO analysis confirmed the involvement of extended conjugation and as well as charge transfer character towards the promising NLO response and red shift of molecules under study. Overall, compound 6 displayed large α and ßtot, computed to be 333.40 (a.u) (B3LYP gas), 302.38 (a.u.) (CAM-B3LYP gas), 380.46 (a.u.) (CAM-B3LYP solvent) and 24708.79 (a.u), 11841.93 (a.u.), 25053.32 (a.u) measured from B3LYP (gas), CAM-B3LYP (gas) and CAM-B3LYP (DMSO) methods respectively. This investigation provides a theoretical framework for conversion of centrosymmetric molecules into non-centrosymmetric architectures to discover NLO candidates for modern hi-tech applications.

A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer's Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.