Search

Intellectual Developmental Disorder (IDD) is a neurodevelopmental disorder involving impairment of general cognitive abilities. This disorder impacts the conceptual, social, and practical skills adversely. There is a growing interest in exploring the neurological behavior associated with these disorders. Assessment of functional brain connectivity and graph theory measures have emerged as powerful tools to aid these research goals. The current research contributes by comparing brain connectivity patterns of IDD individuals to those typical controls. Considering the intellectual deficits linked to the IDD population, we hypothesized an atypical connectivity pattern in the IDD group. Brain signals were recorded by a dry-electrode Electroencephalography (EEG) system during the rest and music states observed by the subjects. We studied a group of seven IDD subjects and seven healthy controls to understand the connectivity within the human brain during the resting-state vis-a-vis while listening to music. Findings of this research emphasize (1) hyper-connected functional brain networks and increased modularity as potential characteristics of the IDD group, (2) the ability of soothing music to reduce the resting state hyper-connected pattern in the IDD group, and (3) the effect of soothing music in the lower frequency bands of the control group compared to the higher frequency bands of the IDD group.

Background The human monoclonal antibody otilimab inhibits granulocyte-macrophage colony-stimulating factor (GM-CSF), a key driver in immune-mediated inflammatory conditions. We aimed to evaluate the efficacy, safety, and key patient-reported outcomes related to pain in patients with active rheumatoid arthritis receiving otilimab. Methods This phase 2b, dose-ranging, multicentre, placebo-controlled study was done at 64 sites across 14 countries. Patients aged 18 years or older with rheumatoid arthritis who were receiving stable methotrexate were randomly assigned (1:1:1:1:1:1) to subcutaneous placebo or otilimab 22·5 mg, 45 mg, 90 mg, 135 mg, or 180 mg, plus methotrexate, once weekly for 5 weeks, then every other week until week 50. The randomisation schedule was generated by the sponsor, and patients were assigned to treatment by interactive response technology. Randomisation was blocked (block size of six) but was not stratified. Investigators, patients, and the sponsor were blinded to treatment. An unblinded administrator prepared and administered the study drug. The primary endpoint was the proportion of patients who achieved disease activity score for 28 joints with C-reactive protein (DAS28-CRP) 3·2 (week 24) escaped to otilimab 180 mg. Patients who escaped were treated as non-responders in their original assigned group. Safety endpoints were incidence of adverse events and serious adverse events, infections, and pulmonary events. Efficacy and safety outcomes were assessed in the intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT02504671. Findings Between July 23, 2015, and Dec 29, 2017, 222 patients were randomly assigned (37 to each group). 86 (49%) of 175 escaped to otilimab 180 mg at week 12 and 57 (69%) of 83 at week 24. At week 24, the proportion of patients with DAS28-CRP Interpretation Otilimab plus methotrexate was well tolerated and, despite not achieving the primary endpoint of DAS28-CRP remission, there were improvements compared with placebo in disease activity scores. Of note, patients reported significant improvement in pain and physical function, supporting further clinical development of otilimab in rheumatoid arthritis.

Senor nodes either deployed for environment monitoring or health monitoring are battery operated, and thus energy constrained. This paper proposes a novel method for energy harvesting for the Internet of things (IoT) based applications by exploiting the inherent inadequacies of the sampling process. The sensor nodes generate a continuous-time signal corresponding to the sensed parameters, which sampled and digitized for further processing. However, the traditional sampling process discarded most of the input signal energy, which could be harvested to supplement the sensor node itself. The sampling interval (i.e., the duration between two samples) of the sampling process, is composed of acquisition and hold time. During acquisition time, the continuous-time input signal is connected to a capacitor for acquiring the input signal value, while in hold time, the capacitor holds the acquired value and disconnects from the input signal. Therefore, during the hold time, the input signal energy is wasted, making the overall sampling process inadequate. This paper overcomes these inadequacies by modifying the circuitry of the sampling process during the hold time. It thus harvests the energy from the input signal obtained through a sensing element without affecting the overall performance of the system. To validate the proposed method, a hardware setup has been designed to sample and harvest the energy for both the environment as well as health monitoring application.

This article presents a collection of electroencephalographic (EEG) data recorded from 14 participants, that includes 7 participants with Intellectual and Developmental Disorder (IDD) and 7 Typically Developing Controls (TDC) under resting-state and under music stimuli. The EEG data were acquired using the EMOTIV EPOC+ device that is a 14-channel dry electrode device. The article provides two types of data: (1) Raw EEG data under resting-state and with music stimuli (i.e., task based data) and (2) pre-processed EEG data under resting state and with music stimuli. Alongside this data, we provide a robust and fully automated pre-processing pipeline for EEG data. The pipeline performs filtering, line noise removal, data selection, ICA, and automatic artefact removal.

Accurate nuclei identification is an important step in diagnosis of several diseases. The problem is complex due to heterogeneity in structure, color, and texture among the different categories of cells. The problem is further complicated due to overlapped/clustered nuclei. To address these challenges, we propose an Encoder-Decoder based Convolutional Neural Network (CNN) with Nested-Feature Concatenation (EDNFC-Net) for automatic nuclei segmentation. The feature concatenation cell (FCC) of the EDNFC-Net is made up of two stacks of convolutional filters combined with non-linearity, followed by a concatenation of features. Apart from intra-FCC feature concatenation, a mechanism is also provided for inter-FCC feature concatenation. This arrangement leads to better feature flow and feature-reusability. Similarly, direct feature flow is provided between the encoder and decoder module that preserves the context information. A new loss function with better-penalizing capability is also proposed that helps in the better background and foreground separation. Qualitative and quantitative results are provided on two datasets to validate the proposed architecture and loss function.

Purpose: Gene expression data generated from microarray technology is often analyzed for disease diagnostics and treatment. However, this data suffers with missing values that may lead to inaccurate findings. Since data capture is expensive, time consuming, and is required to be collected from subjects, it is worthwhile to recover missing values instead of re-collecting the data. In this paper, a novel but simple method, namely, DSNN (Doubly Sparse DCT domain with Nuclear Norm minimization) has been proposed for imputing missing values in microarray data. Extensive experiments including pathway enrichment have been carried out on four blood cancer dataset to validate the method as well as to establish the significance of imputation. Methods: A new method, namely, DSNN, was proposed for missing value imputation on gene expression data. Method was validated on four dataset, CLL, AML, MM (Spanish data), and MM (Indian data). All the dataset were downloaded from GEO repository. Missing values were introduced in the original data from 10 to 90% in steps of 10% because method validation requires ground truth. Quantitative results on normalized mean square error (NMSE) between the ground truth and imputed data were computed. To further validate and establish the significance of the proposed imputation method, two experiments were carried out on the data imputed with the proposed method, data imputed with the state-of-art methods, and data with missing values. In the first experiment, classification of normal vs. cancer subjects was carried out. In the second experiment, biological significance of imputation was ascertained by identifying top candidate tumor drivers using the existing state-of-the-art SPARROW algorithm, followed by gene list enrichment analysis on top candidate drivers. Results: Quantitative NMSE results of the DSNN method were compared with three state-of-the-art imputation methods. DSNN method was observed to perform better compared to these other methods both at high as well as low observable data. Experiment-1 demonstrated superior results on classification with imputation compared to that performed on missing data matrix as well as compared to classification on imputed data with existing methods. In experiment-2, cancer affected pathways were discovered with higher significance in the data imputed with the proposed method compared to those discovered with the missing data matrix. Conclusion: Missing value problem in microarray data is a serious problem and can adversely influence downstream analysis. A novel method, namely, DSNN is proposed for missing value imputation. The method is validated quantitatively on the application of classification and biologically by performing pathway enrichment analysis.

This paper establishes connection between discrete cosine transform (DCT) and the discrete-time fractional Brownian motion process (dfBm). It is proved that the eigenvectors of the auto-covariance matrix of a dfBm can be approximated by DCT basis vectors in the asymptotic sense. This shows that DCT basis acts as discrete Karhunen–Loeve transform (DKLT) for these processes in the approximate sense. Analytic perturbation theory of linear operators is used to prove this result. This result will be of great practical significance in applications where one is looking for an appropriate basis to work with signals that can perhaps be modeled as belonging to fBm processes. The utility of the proposed work has been illustrated with two real-life data (a) on compressive sampling based reconstruction of financial time-series and (b) in denoising gravitational wave event GW150914 data obtained from a binary black hole merger.

This paper proposes and analyzes impulse radio ultra-wideband (IR-UWB) system for wireless sensor networks (WSNs) using massive antenna arrays (MAAs) at fusion center (FC) for distributed detection. The coherent- and energy-based fusion rules are analyzed for the proposed WSN over multiple access channels. The tradeoff between the performance and the implementation complexity of the coherent- and energy-based fusion is studied. Furthermore, we show that MAAs at FC with various levels of channel knowledge can enhance the performance of energy-based detector in a UWB sensor network with simple system implementation and signal processing. Performance of the proposed UWB sensor network with reference to probability of detection, false alarm, and error are analyzed over the standard IEEE 802.15.4a multipath channels and results are validated using simulations. We have also analyzed the impact of various design parameters, such as the number of sensors, receiver antennas, sensor quality, and integration interval, on the system performance.

Noncoherent impulse radio ultrawideband (UWB) receivers do not require channel state information and are also robust against pulse shape distortions. However, their performance is suboptimal, especially at low signal-to-noise ratios. In this article, we propose simple noncoherent UWB receivers for wireless sensor network applications using massive antenna arrays. Two receivers are proposed: based on instantaneous received UWB signal energy and based on weighted instantaneous received UWB signal energy. The proposed receivers use the law of large numbers resulting from the massive receiver arrays and utilize the large degrees of freedom available in a UWB multipath channel. Furthermore, the proposed receivers do not require training symbols for the estimation of decision threshold. Both these receivers outperform the existing noncoherent receivers as verified using simulation results on the standardized IEEE 802.15.4a UWB multipath channel.

In this letter, we propose a novel robust impulse radio-ultra-wide band (IR-UWB) receiver design that utilizes the received UWB signal cluster sparsity characteristics to mitigate impulse noise (IN). The IN samples are easily detected and removed in the proposed receiver. Furthermore, multiple UWB signal clusters (due to multipath channel), in the proposed receiver design, reduce UWB signal blanking loss as compared with the single cluster (single path channel). The proposed receiver’s bit error rate (BER) performance is theoretically analyzed and compared with six popular existing non-linear receivers in the presence of IN over standardized IEEE 802.15.4a channel models. Unlike the existing receivers, the proposed receiver does not require a priori knowledge of the amplitude, the probability of occurrence of IN, or channel estimation.

Background Multiple Myeloma (MM) is preceded by the premalignant stage of Monoclonal Gammopathy of Undetermined Significance (MGUS) and therefore, it is important to identify the genetic factors responsible for progression of MGUS to MM. We have built machine learning (ML) models to identify pivotal genetic biomarkers that distinguish MM and MGUS. Methods Tumor normal matched paired Whole Exome Sequencing (WES) data of 1174 patients of MM and 61 patients of MGUS were analyzed. The data were obtained from dbGaP (phs000748; phs000348), AIIMS, Delhi, India, and EGA (EGAD00001001901). Variants were identified using four variant callers, namely, MuSE, Mutect2, VarScan2, and Somatic-Sniper and; SNVs were annotated using ANNOVAR. Pooled genomic annotations obtained were analyzed to derive significantly mutated genes with ‘dndscv’ tool. Union of top ranked 250 significantly mutated genes from each variant caller yielded 1316 genes. For each gene, variant count and (maximum, mean, median, and standard deviation of) VAF and AD were used as features which were reduced by principal component analysis (PCA) and only top-3 principal components were selected for each gene. Next, 5 ML classifiers (random forest, decision tree, logistic regression, XGBoost, and SVM) were used to distinguish MM from MGUS. Imbalance of data (95% MM and 5% MGUS cases) was handled by the cost-sensitive loss function in the classifiers. Permutation based feature importance was carried out on top two performing models to infer the most significant features that were mapped back to genes to obtain the top ranking genes for MM and MGUS. Results Cost-sensitive SVM outperformed the rest of the models in balanced accuracy, weighted F1-score, Mathews correlation coefficient (MCC), precision, recall and area under curve (AUC) with values 95.5%, 94.82%, 0.8162, 76.49%, 98.33% and 95.5%, respectively. Top ranking genes identified for MM were: HLA-DQB1 IRF1, MUC6, FGFR3, MUC4, HOXA1, ITPR3, HIST1H1E, MUC12, ITGA2, HLA-DQA2, HUWE1, IGLL5, HLA-DRB5, HLA-DQB2, ILK. Top ranking genes identified for MGUS were: MUC3A, HLA-A, HLA-C, IRF4, JAK1, HDAC2, HLA-DQA1, FRG1, HS6ST1, H2AFV, and HLA-DRB1. HLA-DQB1, IRF1, ITPR3, HOXA1, HIST1H1E, HUWE1, IGLL5, HIPK3, HLA-DQA2, HLA-DRB5, and ILK were found significant for MM; and HLA-A, HLA-C, IRF4, JAK1, HDAC2 HLA-DQA1, HS6ST1, H2AFV, and HLA-DRB1 were found significant for MGUS by the top two ML classifiers. All these genes were found significant in the literature for MM and MGUS. Conclusion MGUS and MM share many common features such as genomic biomarkers, structural variants etc. with the difference of having less impact of mutations in MGUS. Thus, it is challenging to distinguish MM from MGUS. Here, we utilized ML classifiers to distinguish MM from MGUS. Our classifiers are able to identify the significant genes that are helpful in MM vs. MGUS classification that can lead to a better understanding of progression from MGUS to MM.

Highlights • An AI-enabled risk staging method, MRS, is developed using easy-to-acquire parameters. • Genomic tests cannot be performed owing to economical or geographical constraints. • MRS does not use cytogenetic abnormalities for risk stage prediction unlike RISS. • K-adaptive partitioning (KAP) used to find new thresholds for the parameters., Introduction : An efficient readily employable risk prognostication method is desirable for MM in settings where genomics tests cannot be performed owing to geographical/economical constraints. In this work, a new Modified Risk Staging (MRS) has been proposed for newly diagnosed Multiple Myeloma (NDMM) that exploits six easy-to-acquire clinical parameters i.e. age, albumin, β2-microglobulin (β2M), calcium, estimated glomerular filtration rate (eGFR) and hemoglobin. Materials and Methods : MRS was designed using a training cohort of 716 NDMM patients of our inhouse MM Indian (MMIn) cohort and validated on MMIn (n=354) cohort and MMRF (n=900) cohort. K-adaptive partitioning (KAP) was used to find new thresholds for the parameters. Risk staging rules, obtained via training a J48 classifier, were used to build MRS. Results : New thresholds were identified for albumin (3.6 g/dL), β2M (4.8 mg/L), calcium (11.13 mg/dL), eGFR (48.1 mL/min), and hemoglobin (12.3 g/dL) using KAP on the MMIn dataset. On the MMIn dataset, MRS outperformed ISS for OS prediction in terms of C-index, hazard ratios, and its corresponding p-values, but performs comparable in prediction of PFS. On both MMIn and MMRF datasets, MRS performed better than RISS in terms of C-index and p-values. A simple online tool was also designed to allow automated calculation of MRS based on the values of the parameters. Discussion : Our proposed ML-derived yet simple staging system, MRS, although does not employ genetic features, outperforms RISS as confirmed by better separability in KM survival curves and higher values of C-index on both MMIn and MMRF datasets. Funding : Grant: BT/MED/30/SP11006/2015 (Department of Biotechnology, Govt. of India), Grant: DST/ICPS/CPS-Individual/2018/279(G) (Department of Science and Technology, Govt. of India), UGC-Senior Research Fellowship.

In this paper, a sparsity based narrowband interference (NBI) mitigation method for ultra-wide-band (UWB) system is proposed.Proposed receiver design method exploits distinct characteristics of UWB signal and NBI.The proposed NBI mitigation method does not use any limiter. Hence, it is free from any threshold effect unlike existing methods.The proposed receiver design is versatile and is robust against NBI for future large bandwidth unlicensed and licensed coexisting 5G and beyond communications. Display Omitted In real operating environments, the performance of low power large bandwidth ultra wide-band (UWB) communication systems suffers due to narrowband interference (NBI) from high power narrowband interferers. Since noise is not spectrally white in the presence of NBI in UWB systems, conventional matched filter based receivers are not optimum in such scenarios. Although NBI mitigation methods have been proposed using NBI subspace and/or limiters for UWB systems, subspace-based approaches are usually not feasible due to no apriori knowledge of the NBI subspace in a real scenario, and the limiter-based approach is also non-optimum. In this paper, we propose a sparsity-based NBI mitigation method that exploits distinct characteristics of UWB signals and NBI, and does not require a limiter. Improved performance of the proposed receiver has been validated via time hopping binary phase shift keying (TH-BPSK) UWB signal transmission in both additive white Gaussian noise and multipath fading channels.

This paper presents Neyman Pearson (N-P) criterion based detector for ship detection in marine environment. Statistical modeling of ambient noise data with and without ship noise, collected in the shallow waters of the Bay of Bengal, are utilized to build the detector. The noise data with and without ship noise are collected using the hydrophones at the depths of 5 m/15 m and 3 m/5 m, respectively, from the ocean surface. The ambient noise without and with ship noise is shown to have generalized extreme value and generalized Gaussian distribution, respectively. The presence of a ship leads to changes in the statistics of the ambient noise. This statistical characterization is used for designing a N-P criterion based log likelihood ratio test for the detection of presence of a ship. The proposed method detects the presence of a ship with an accuracy of 98.33%.

This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l 1 minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. The proposed OptShrink LR + S method yields good qualitative and quantitative results.

Commonly used Gaussian and Bernoulli distributed measurement matrices are sub-optimum for sparse and localized (SaL) signals. In this paper, we propose a measurement matrix design to enhance the sub-Nyquist rate-based receiver’s performance by exploiting the SaL characteristics of the received signal. The proposed measurement matrix exploits the covariance matrix structure of the underlying received signal and can be optimized off-line or adaptively. Effectiveness of the proposed measurement matrix is verified on ultra-wideband (UWB) signals, since the UWB signals are both sparse and localized. Proposed matrix yields approx 2 dB gain as compared to the conventional Gaussian random matrix for the UWB system over the standardized IEEE 802.15.4a multipath channel.

Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are easily deployable and are cost-effective. Hence, these can enable extensive coverage of cancer diagnostic facilities. However, the development of such a tool for ALL cancer was challenging so far due to the non-availability of a large training dataset. The visual similarity between the malignant and normal cells adds to the complexity of the problem. This paper discusses the recent release of a large dataset and presents a novel deep learning architecture for the classification of cell images of ALL cancer. The proposed architecture, namely, SDCT-AuxNet

Several electroencephalogram (EEG)-based predictive models for automated epilepsy diagnosis have been proposed over more than a decade. However, to the best of our knowledge, none have been evaluated on a holdout/test set. A vast majority of these studies have reported accuracies above 95% on a benchmark EEG dataset, but the dataset has been shown here to have certain limitations when used for building classifiers for epilepsy diagnosis. We implemented two previously reported classifiers trained on the benchmark dataset whose accuracies were observed to drop sharply when evaluated on a test set. We propose a feature, engineered specifically, for epilepsy diagnosis that attempts to characterize the neuronal synchronization using scalp EEG by extending the concept of the impulse response of linear time-invariant systems to matrices. This feature was tested on the EEG of 50 epileptics and 50 healthy subjects and yielded an area under the curve (AUC) of 0.87. It outperforms the existing models implemented by us that gave the AUC of 0.80 when trained and tested on scalp EEG data, thereby, setting the new benchmark for automated epilepsy diagnosis on test set evaluation. The feature has also been shown to have statistical consistency across time and vigilance states with robustness against EEG artifacts.

High dimensional genomics data such as microarray gene expression and RNA sequencing, generally suffers from missing values. Incomplete data can adversely affect the downstream analysis for diagnostics and treatment. Several methods to impute missing values in gene expression data have been developed, but most of these work at high levels of observability. In this paper, we have proposed a nove12-stage method, namely, CT-NNBI of imputing incomplete gene expression matrices using Discrete Cosine Transform Domain Sparsity and Nuclear Norm Constraint with Split Bregman Iteration (CT-NNBI) that yields smaller imputation errors, consistently, at all levels of observability. The proposed method has been compared with the state-of-the-art matrix completion methods on three different cancer dataset and is observed to perform better. The validation of imputed data has been demonstrated on the application of classification.

Most of the existing question answering models can be largely compiled into two categories: i) open domain question answering models that answer generic questions and use large-scale knowledge base along with the targeted web-corpus retrieval and ii) closed domain question answering models that address focused questioning area and use complex deep learning models. Both the above models derive answers through textual comprehension methods. Due to their inability to capture the pedagogical meaning of textual content, these models are not appropriately suited to the educational field for pedagogy. In this paper, we propose an on-the-fly conceptual network model that incorporates educational semantics. The proposed model preserves correlations between conceptual entities by applying intelligent indexing algorithms on the concept network so as to improve answer generation. This model can be utilized for building interactive conversational agents for aiding classroom learning., Comment: Published in the 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2019

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) technique for the fifth generation (5G) and beyond wireless networks. The performance of the SCMA-based NOMA system is sensitive to the codebook design. Since there is no optimum method of codebook design, these are designed, in general, using sub-optimal methods. The high values of the mutual information and the shaping gain of the multidimensional constellation points improve the SCMA codebook's performance. Hence, in this paper, we design an SCMA codebook by optimizing the mutual information and the shaping gain of constellation points with the constraints of fixed minimum Euclidean distance and average power. Simulation results show that the SCMA system with the proposed codebook outperforms those with the existing codebooks. Further, it is observed that the message passing algorithm converges faster to successful detection with the proposed codebook.

Shallow water acoustic channel estimation techniques are presented at the intersection of time, frequency, and sparsity. Specifically, a mathematical framework is introduced that translates the problem of channel estimation to non-uniform sparse channel recovery in two-dimensional frequency domain. This representation facilitates disambiguation of slowly varying channel components against high-energy transients, which occupy different frequency ranges and also exhibit significantly different sparsity along their local distribution. This useful feature is exploited to perform non-uniform sampling across different frequency ranges, with compressive sampling across higher Doppler frequencies and close to full-rate sampling at lower Doppler frequencies, to recover both slowly varying and rapidly fluctuating channel components at high precision. Extensive numerical experiments are performed to measure relative performance of the proposed channel estimation technique using non-uniform compressive sampling against traditional compressive sampling techniques as well as sparsity-constrained least squares across a range of observation window lengths, ambient noise levels, and sampling ratios. Numerical experiments are based on channel estimates from the SPACE08 experiment as well as on a recently developed channel simulator tested against several field trials.

To characterize amatuximab pharmacokinetics (PK) and the relationship of amatuximab exposure with response in patients with unresectable malignant pleural mesothelioma (MPM) receiving amatuximab with pemetrexed and cisplatin. A nonlinear mixed effects PK model was built using data from all of the amatuximab studies conducted to date. Patients received amatuximab alone or in combination with chemotherapy. The influence of demographic, laboratory and disease characteristics on PK parameters was assessed. Exposure–response analyses explored relationships between amatuximab exposure and overall survival (OS), progression-free survival (PFS) and safety. Alternative amatuximab dosing regimens were explored with simulations using population PK and parametric survival models. Amatuximab PK was best described by a two-compartment model with parallel linear and nonlinear elimination pathways. Body weight and an antidrug antibodies reaction with the titer >64 affected volume of distribution and clearance, respectively. Exposure–response analyses demonstrated that the amatuximab exposure (C min) showed a significant effect on OS (log-rank test, P = 0.0202). For patients with amatuximab C min above the median (38.2 μg/mL), the median OS was 583 days (90 % CI 418 –NE). For patients with C min ≤ 38.2 μg/mL, the median OS was 375 days (90 % CI 325–486). The amatuximab exposure showed similar significant effect on PFS. Exposure–response analysis for adverse events did not reveal any relationship. In patients with MPM, higher amatuximab exposure in combination with chemotherapy was shown to be associated with longer OS, supporting evaluation of more frequent dosing in future trials to achieve higher exposure and subsequently longer OS.

Ultra wideband (UWB) technology is suitable for high data rate short range wireless communication, localization, and imaging techniques. However, UWB systems require high sampling rate and precise synchronization. In order to reduce the sampling rate, have precise synchronization, and for low power requirement, UWB systems are implemented using compressive or sub-Nyquist rate measured samples by exploiting the sparsity of the UWB signal. Compressive sensing (CS)-based UWB systems are being designed in two ways: 1) signal demodulation or detection is performed in the CS domain without full signal recovery at the front-end. Thus, demodulation or detection works on compressive measurements. However, system performance deteriorates in the CS domain as compared with full Nyquist rate sampling and 2) after, Nyquist rate signal is recovered using efficient algorithms at the front-end, the signal demodulation or detection is performed using the conventional receiver. Thus, one requires an efficient CS/sampling of signal measurement at the front-end for better system performance for both the cases stated earlier. In this paper, we propose a deterministic (partial) UWB waveform-matched measurement matrix. The proposed measurement matrix has a circulant structure and is sparse in nature. The proposed matrix is easy to implement in hardware and is operationally time efficient as needed in a practical system. The bit error rate performance of the corresponding UWB system and the operational time complexity with the proposed measurement matrix are better as compared with the existing measurement matrices in the CS domain for both the above receiver designs. The efficacy of the proposed measurement matrix is verified through extensive simulations in both the additive white Gaussian noise and multipath communication environments. In addition, we have also compared other desirable properties of the proposed measurement matrix with the existing measurement matrices.

SeTeAg glasses are prepared by melt quenching technique. The (Se80Te20)100-xAg*/PMMA (where x =2, 4, 6, 8 and 10) composites are prepared by solution casting method. The bulk samples are characterized by XRD, DSC and SEM. DSC scan reveals that the prepared samples are composites because both phases show their separated glass transition temperature Tg and melting temperature Tm. Complex impedance spectroscopy is used to carry out the electrical measurements in the frequency range 10 Hz to 100 KHz. Frequency dependent of the real and imaginary parts of the impedance plotted in the complex plane, were used to calculate the conductivity of different compositions of (Se80Te20)100-xAg/PMMA composites. It has been observed that the room temperature conductivity of the composite increases with increase in Ag content form 1.31×10-5 to 1.15×10-4 (Q. cm)1, while their corresponding activation energies are decreasing. It has also been concluded that conduction is thermally activated with single activation energy.

In the present work the temperature and humidity effects on electrical properties of KH2PO4/SiO2 composites have been studied. Composite nature of the system has been confirmed by the XRD patterns. Wagner’s polarization method confirms the ionic conduction in the prepared systems. Temperature and humidity dependent ac conductivity was measured by using complex impedance spectroscopy in the frequency range 10 Hz to 105 Hz. In pure KH2PO4 the conductivity is influenced by the hopping of the proton through defects while in KH2PO4/SiO2 composite systems, the conductivity of composite system is governed by hydrophilicity below 130 0C. In the present composite systems, absorption of KH2PO4 takes place at the surface of silica which produce defects near the salt KH2PO4/oxide Sio2 interface. The transition temperature of composite was found to be decreased in comparison to the pure KDP.

Amorphous (Se80Te20)94Ag6 thin films are prepared at room temperature by using thermal evaporation technique. The as-prepared thin films are annealed both at higher and lower temperatures than glass transition temperature Tg. X-ray diffraction of annealed film above Tg confirms the presence of different phases. The glass transition temperature of as-prepared glass is evaluated with the help of DSC measurement. Transmittance measurements are used to find the value of optical parameters and optical band gap Eg. Refractive index and extinction coefficient increases with increase in annealing temperature. The band gap show an increase for the annealing temperature less than Tg and decrease for the temperature greater than Tg. Mott and Davis Model forms the basis for the explanation of change in optical band gap.

Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are easily deployable and are cost-effective. Hence, these can enable extensive coverage of cancer diagnostic facilities. However, the development of such a tool for ALL cancer was challenging so far due to the non-availability of a large training dataset. The visual similarity between the malignant and normal cells adds to the complexity of the problem. This paper discusses the recent release of a large dataset and presents a novel deep learning architecture for the classification of cell images of ALL cancer. The proposed architecture, namely, SDCT-AuxNet$^{\theta}$ is a 2-module framework that utilizes a compact CNN as the main classifier in one module and a Kernel SVM as the auxiliary classifier in the other one. While CNN classifier uses features through bilinear-pooling, spectral-averaged features are used by the auxiliary classifier. Further, this CNN is trained on the stain deconvolved quantity images in the optical density domain instead of the conventional RGB images. A novel test strategy is proposed that exploits both the classifiers for decision making using the confidence scores of their predicted class labels. Elaborate experiments have been carried out on our recently released public dataset of 15114 images of ALL cancer and healthy cells to establish the validity of the proposed methodology that is also robust to subject-level variability. A weighted F1 score of 94.8$\%$ is obtained that is best so far on this challenging dataset., Comment: The final version of this preprint has been published in Medical Image Analysis

In this paper, a new protocol for simultaneous wireless information and power transfer (SWIPT) has been proposed, which randomly switches the signal for information decoding (ID) and energy harvesting (EH). However, the information decoder recovers the complete data by utilizing the sparse behaviour of the real world signal, thus not compromising the system performance, unlike the conventional SWIPT protocols such as time switching and power splitting.

Complex Chromosomal Rearrangements (CCRs) are increasingly being reported as genetic risk factors of clinical significance in cancer owing to their identification using high resolution whole genome profiling technologies. This study employed high resolution CGH + SNP microarrays for whole genome copy number variations (CNV) profiling and identified CCRs in 11/107(10%) newly diagnosed Multiple Myeloma (MM) patients. Six patients exhibited Chromothripsis (CTH) among seven chromosomes that were confirmed with automated CTLPscanner web tool and; five cases displayed chromoplexy (CPL) which involved multiple chromosomes. Presence of chromothripsis in chromosome 17 in three out of six patients indicate a link between TP53 aberrations and incidence of CTH. Multivariable Cox regression model demonstrated a significant association of CTH with poor PFS (HR = 3.09, p = 0.010) and OS (HR = 3.31, p = 0.024) which suggests that CTH is an additional independent prognostic marker in multiple myeloma. Addition of CTH in risk stratification models in clinical setting in multiple myeloma may help in upfront identification of high risk patients for suitable customized therapy.

Analysis of electrocardiogram (ECG) signals helps us in detecting various abnormalities and diseases of heart. These signals commonly suffer from the problems of baseline wander and power-line interference. In this paper, we propose a new approach to eliminate such noises from ECG signals using the Fourier decomposition method. Simulation results are presented to show the efficacy of our method over previously used EMD-based methods. The proposed method has been shown to preserve shape characteristics of ECG signals of heart abnormalities.

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.

Portable single channel EEG recording systems are inexpensive compared to the standard traditional systems. These single-channel EEG recorders are based on dry electrodes and allow for simple recording setup. These systems are still under research for understanding their utility for many user-centric healthy lifestyle applications. This paper attempts to examine single-channel dry electrode based portable Neurosky Mindwave EEG system for its utility in detecting meditation state. In order to achieve this goal, data is recorded with both single-channel EEG system and a 19-channel regular EEG recording system for 20 subjects during meditation. Data is pre-processed for artifact removal via independent component analysis. Next, it is studied via power spectral density and compared for both the systems. Similar spectrum was observed with both the devices for subjects under meditation, although amplitude levels differed across subjects and across devices. Since many subjects may only be in the state of rest and not in meditation, data was explicitly captured during rest with the single electrode recorder to understand and differentiate the two psycho-physiological states of meditation and rest. During meditation, higher energy is observed in the delta band compared to the alpha band, whereas opposite (delta band energy is lower to alpha) is observed in the rest state. Meditation state was also studied during different phases, i.e., at the start, in the middle, and at the end of the meditation. A companion Android app is also developed to detect meditation using the single-channel EEG device.

Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool for plasma cell segmentation from microscopic medical images. These images were captured from bone marrow aspirate slides of patients with MM. PCSeg has a robust pipeline consisting of a pre-processing step, the proposed modified multiphase level set method followed by post-processing steps including the watershed and circular Hough transform to segment clusters of cells of interest and to remove unwanted cells. Our modified level set method utilizes prior information about the probability densities of regions of interest (ROIs) in the color spaces and provides a solution to the minimal-partition problem to segment ROIs in one of the level sets of a two-phase level set formulation. PCSeg tool is tested on a number of microscopic images and provides good segmentation results on single cells as well as efficient segmentation of plasma cell clusters.

Ultra-wide band (UWB) ranging requires precise estimation of time-of-arrival (ToA) of first path (FP) signal. In UWB communication, estimation of ToA is challenging in the presence of impulsive interference and multipath. Generally in literature, ToA is estimated using threshold crossing techniques. Although threshold based ToA methods are simple, optimal threshold value is hard to determine in practice. In this paper, we propose joint estimation of ToA and data symbols by exploiting the cluster-sparsity of the received UWB signal in the presence of impulsive interference. The proposed receiver structure enhances the signal-to- noise ratio at the receiver output by mitigating the impulsive interference and barring inter-clusters noise accumulation. The proposed method is also free from any threshold, training, and optimization process. Robustness of the proposed algorithm is validated for standardized IEEE 802.15.4a channel models in the presence of both additive white Gaussian noise and impulsive interference.

The current study examines how nonresident tuition among public research universities has converged toward a national average over the 1987–2006 time period in the USA. Using dynamic fixed-effect panel modeling estimated via GMM (and instrumental variables fixed-effect model to account for endogeneity), we inquire (1) how do competitive market forces and state higher education governance structures influence nonresident tuition rates at public universities and (2) to what extent is conditional convergence of nonresident tuition rates at public universities occurring. We find that over the past 20 years, there has been a rapid convergence in nonresident tuition at public research universities to a national average, shaped by external market forces such as the demand for education and price competition among neighboring states, tuition for residents, the distribution of higher education appropriations internal to states, and the state higher education governance structures. Nonresident tuition has increased at a faster pace in states with low nonresident tuition as compared to states with high nonresident tuition. Our results have implications for higher education policy guidance as they reveal how a tendency toward “privatization” in certain aspects and segments of public higher education has been constrained in states in which higher education is more regulated via consolidated governing boards.

Of late, there has been a growing interest in studying brain networks, particularly, for understanding spontaneous temporal changes in functional brain networks. Recently, phase synchrony based methods have been proposed to track instantaneous time-resolved functional connectivity without any need of windowing the data. This paper extends one such recently used phase synchrony measure in two steps. First, multiple temporal models are built from four-mode tensor that are further clustered to detect dynamic brain network communities. This clustering is based on spatio-temporal data and hence, is named as Spatio-Temporal Clustering (STC). Second, a method is proposed to rank all the communities allowing the proposed model to deal with multiple communities of differing time evolution. This helps in the comparison of network communities, especially, when available communities are too dense to provide relevant information for comparison. The ranking of communities allows for the dimensionality reduction of communities, while still maintaining the key brain networks. Intrinsic time-varying functional connectivity has been investigated for large scale brain networks, including default-mode network (DMN), visual network (VN), cognitive control network (CCN), auditory network (AN), etc. The proposed method provides a new complementary tool to investigate dynamic network states at a high temporal resolution and is tested on resting-state functional MRI data of 26 typically developing controls (TDC) and 35 autism spectrum disorder (ASD) subjects. Simulation results demonstrate that ASD subjects have altered dynamic brain networks compared to TDC.

A number of reconstruction methods have been proposed recently for accelerated functional Magnetic Resonance Imaging (fMRI) data collection. However, existing methods suffer with the challenge of greater artifacts at high acceleration factors. This paper addresses the issue of accelerating fMRI collection via undersampled k-space measurements combined with the proposed method based on l1-l1 norm constraints, wherein we impose first l1-norm sparsity on the voxel time series (temporal data) in the transformed domain and the second l1-norm sparsity on the successive difference of the same temporal data. Hence, we name the proposed method as Double Temporal Sparsity based Reconstruction (DTSR) method. The robustness of the proposed DTSR method has been thoroughly evaluated both at the subject level and at the group level on real fMRI data. Results are presented at various acceleration factors. Quantitative analysis in terms of Peak Signal-to-Noise Ratio (PSNR) and other metrics, and qualitative analysis in terms of reproducibility of brain Resting State Networks (RSNs) demonstrate that the proposed method is accurate and robust. In addition, the proposed DTSR method preserves brain networks that are important for studying fMRI data. Compared to the existing methods, the DTSR method shows promising potential with an improvement of 10-12 dB in PSNR with acceleration factors upto 3.5 on resting state fMRI data. Simulation results on real data demonstrate that DTSR method can be used to acquire accelerated fMRI with accurate detection of RSNs.

This paper proposes estimation of underwater acoustic channel in the delay-Doppler domain under dynamic sea conditions. The sparsity of the channel in the delay-Doppler domain is exploited via advanced compressive sensing (CS) technique to estimate the channel. The proposed use of CS with prior information takes care of relatively dominant but stable slowly time-varying channel component and the rapidly fluctuating high energy transients. Simulation results are presented on prior estimated channel of SPACE08 experiment, considered as ground truth, for the validation of the theory presented.

Motivated with the concept of transform learning and the utility of rational wavelet transform in audio and speech processing, this paper proposes Rational Wavelet Transform Learning in the Statistical sense (RWLS) for natural images. The proposed RWLS design is carried out via lifting framework and is shown to have a closed form solution. The efficacy of the learned transform is demonstrated in the application of compressed sensing (CS) based reconstruction. The learned RWLS is observed to perform better than the existing standard dyadic wavelet transforms.

The ultra-wide band (UWB) technology can be used for low cost and low power applications such as body area networks, IoT based applications, wireless sensor networks and high data rate short range wireless communications. In the UWB literature, non-coherent energy detector (ED) receiver is proposed to reduce receiver implementation complexity by avoiding the complex channel estimation process and relaxing the precise synchronization requirement. However, the ED receiver performance is sensitive to the integration interval, and too large or small value of integration interval degrades the UWB receiver performance. In this paper, we propose a non-coherent UWB receiver by exploiting the cluster-sparsity of the received UWB signal. The proposed receiver structure enhances the signal-to-noise ratio at the receiver output as compared to the ED by barring inter-clusters noise accumulation. The bit error rate (BER) performance of the proposed non-coherent receiver for time-hopping binary pulse position modulation UWB system is analyzed using IEEE 802.15.4a channel. The proposed receiver design does not require any training and optimization process, and provides 1–3 dB BER performance gain as compared to the conventional ED receiver.

Convolutional Neural Networks (CNNs) are typically trained in the RGB color space. However, in medical imaging, we believe that pixel stain quantities offer a fundamental view of the interaction between tissues and stain chemicals. Since the optical density (OD) colorspace allows to compute pixel stain quantities from pixel RGB intensities using the Beer-Lambert’s law, we propose a stain deconvolutional layer, hereby named as SD-Layer, affixed at the front of CNN that performs two functions: (1) it transforms the input RGB microscopic images to Optical Density (OD) space and (2) this layer deconvolves OD image with the stain basis learned through backpropagation and provides tissue-specific stain absorption quantities as input to the following CNN layers. With the introduction of only nine additional learnable parameters in the proposed SD-Layer, we obtain a considerably improved performance on two standard CNN architectures: AlexNet and T-CNN. Using the T-CNN architecture prefixed with the proposed SD-Layer, we obtain 5-fold cross-validation accuracy of 93.2% in the problem of differentiating malignant immature White Blood Cells (WBCs) from normal immature WBCs for cancer detection.

A vast researches are concentrated towards the development of EEG based human computer interface to enhance the quality of life for medical applications. There is a recent attraction to wireless EEG devices as they are cheaper and easily available in the market. The devices use dry electrodes and send signals via wireless, thus are easier to use and more comfortable to wear. Such technology can be used in psychology, neuro therapy, and for real-time patients monitoring. One such off-the-shelf device is Neurosky MindWave headset. In this paper, it is utilized to measure electrical activity of user's forehead. The collected data in voltage is wirelessly transmitted to a computer for further processing. After processing the EEG data, signals were categorized to requisite frequency bands of brain waves for feature extraction. The collected signal can be used to differentiate between meditation and attention activities. Examples of attention activities are reading, problem solving, etc. Meditation level obtained directly from MindWave was not observed to be a good feature for classification of different brain activities. The two psycho-physiological states, attention and meditation are of interest to psychological therapies. This paper shows that meditation and other activities related to attention can be detected and differentiated better with frequency and frequency-time analysis of the EEG signal. The EEG signal recorded from MindWave can be effectively used to plot visual feedback of brain activity.

This paper proposes a joint framework wherein lifting-based, separable, image-matched wavelets are estimated from compressively sensed (CS) images and used for the reconstruction of the same. Matched wavelet can be easily designed if full image is available. Also matched wavelet may provide better reconstruction results in CS application compared to standard wavelet sparsifying basis. Since in CS application, we have compressively sensed image instead of full image, existing methods of designing matched wavelet cannot be used. Thus, we propose a joint framework that estimates matched wavelet from the compressively sensed images and also reconstructs full images. This paper has three significant contributions. First, lifting-based, image-matched separable wavelet is designed from compressively sensed images and is also used to reconstruct the same. Second, a simple sensing matrix is employed to sample data at sub-Nyquist rate such that sensing and reconstruction time is reduced considerably without any noticeable degradation in the reconstruction performance. Third, a new multi-level L-Pyramid wavelet decomposition strategy is provided for separable wavelet implementation on images that leads to improved reconstruction performance. Compared to CS-based reconstruction using standard wavelets with Gaussian sensing matrix and with existing wavelet decomposition strategy, the proposed methodology provides faster and better image reconstruction in compressive sensing application.

Pharmacometric investigation of eribulin was undertaken in patients with metastatic breast cancer (MBC) and other advanced solid tumors. A population pharmacokinetic (PK) model used data combined from seven phase 1 studies (advanced solid tumors; n = 129), and one phase 2 (MBC; n = 211), and one phase 3 study (MBC; n = 173). Phase 3 data were also used in a PK/pharmacodynamic (PD) model of efficacy and tumor response (sum of longest diameters of target lesions). All analyses used NONMEM 7.2. Eribulin PK, described by a dose-independent, three-compartment model with allometric relationship for body weight, was similar for all tumor types. Inter-individual variability (IIV) was 52% for both exposure and clearance. Liver function markers (albumin, alkaline phosphatase, bilirubin) significantly influenced eribulin PK (7.3% of IIV in clearance). Tumor shrinkage correlated with eribulin exposure; a 36% decrease in tumor size from baseline was modeled at week 36. No patient/disease factors significantly predicted eribulin's effect on tumor size. At week 6, a decrease in tumor size was associated with longer survival than an increase (P = .0055), suggesting survival may relate indirectly to eribulin exposure. These pharmacometric analyses provide a detailed overview of eribulin exposure-efficacy relationships to inform physicians treating patients with MBC.