Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on the effect of nontarget types on name based auditory event-related potentials

Exploring the brain response to stimuli of healthy people in passive state is helpful to understand the brain response mechanism of unresponsive people. Event-related potential (ERP) can reflect the time synchronization of potentials, which is a feasible objective electrophysiological index reflecting the functional status of the brain. In this paper, we used the subjects’ own name (SON) as target stimuli and compared with the nontarget stimuli (others’ name) of Three Chinese Characters (3CC) and Two Chinese Characters (2CC) with the same stimuli duration (600ms) and inter stimuli interval (500ms-800ms）. Thirteen healthy subjects attended in this study with four conditions ( [active, passive]×[3CC, 2CC] ). We compared the ERP waveforms, the behavior performance, and the classification of four different conditions. ERP results show that the P300 amplitude of conditions with 3CC nontargets is higher than that of conditions with 2CC nontargets. Behavioral results show that the grand accuracy is 97% when the nontargets are 3CC, while the grand accuracy is only 94% when the nontargets are 2CC. The reaction time is also different from the two nontargets (605ms with 3CC vs 635ms with 2CC). Classification results illustrate that in active condition, the accuracy rate is 82.1% when the nontarget is 3CC, and that is 80.9% in passive condition, which are 4.2% and 6.4% higher than the accuracy rate under 2CC cases in both active and passive conditions. This study can provide a scheme for grading diagnosis of consciousness detection, and further applying to clinical evaluation.Clinical Relevance— This study can provide a better paradigm basis for clinical evaluation of unresponsive patients (such as disorder of consciousness, DOC) and may become an effective auxiliary means for clinical rating scales.
Cardiac Chamber Segmentation Using Deep Learning on Magnetic Resonance Images from Patients Before and After Atrial Septal Occlusion Surgery

We propose a robust technique for segmenting magnetic resonance images of post-atrial septal occlusion intervention in the cardiac chamber. The technique can be used to determine the surgical outcomes of atrial septal defects before and after implantation of a septal occluder, which intends to provide volume restoration of the right and left atria. A variant of the U-Net architecture is used to perform atrial segmentation via a deep convolutional neural network. The method was evaluated on a dataset containing 550 two-dimensional image slices, outperforming conventional active contouring regarding the Dice similarity coefficient, Jaccard index, and Hausdorff distance, and achieving segmentation in the presence of ghost artifacts that occlude the atrium outline. Moreover, the proposed technique is closer to manual segmentation than the snakes active contour model. After segmentation, we computed the volume ratio of right to left atria, obtaining a smaller ratio that indicates better restoration. Hence, the proposed technique allows to evaluate the surgical success of atrial septal occlusion and may support diagnosis regarding the accurate evaluation of atrial septal defects before and after occlusion procedures.
Extending the Optimality Range of Multi-Antenna Coded Caching with Shared Caches

This work considers the cache-aided multiple-input single-output broadcast channel (MISO BC) where an L-antenna transmitter serves K receiving users, each assisted by one of Λ < K caches with normalized capacity γ. For this setting it was known that in the range of L ∈ [K/Λ, Kγ], one can achieve a Degrees-of-Freedom (DoF) performance of L + Kγ. This very restrictive constraint that L ≤ Kγ excluded shared- cache settings with a large number of antennas from achieving the maximum DoF; for the more realistic regime of L > Kγ, all existing coded caching schemes suffer substantially reduced caching or multiplexing gains.Our work provides a novel coded caching scheme that achieves the exact best known, near optimal, DoF L+Kγ, and does so even if L > Kγ, thus covering an important hole in identifying the optimal performance for the multi-antenna shared-cache problem. Therefore, our work reveals that shared-cache systems with many transmit antennas can also enjoy both full multiplexing gains (L) as well as full caching gains (Kγ) despite the sharing of the caches. A side benefit of this scheme is its applicability in multi-antenna settings with dedicated users caches, where it can offer the advantage of reducing the subpacketization without sacrificing the DoF performance.
Influence of organic manures in combination with chemical fertilizers on production, quality and economic feasibility of soybean in typic haplustert of jabalpur

A field experiment WaS carried out to study the effective combination of organic and inorganic nutrient sources on the availability, uptake. quality and yield of soybean (JS 335) in a Typic Haplustert soil of Jabalpur, Among the nutrient sources 50% of recommended dose of NPK + 10 t FYM ha−1 enhanced the availability N, P. K and S in soil and uptake by soybean as well as recorded the highest grain (16.00 q ha−1) and straw (30.75 q ha−1) yields. Same combination also recorded highest energy yield (127.1 MJ ha−1), protein content (49.96%) oil content (21.28%) and oil production (337.2 kg ha-1). Among the organic sources, enriched compost was found to be best to improve crop productivity and soil health in their individual addition. The elevated cost of cultivation (6046 Rs. ha−1), cost of production (Rs. 3.96 kg−1 grain) and cost benefit ratio (1:2.51) was calibrated in 100% NPK treatment while the maximum net profit (Rs. 7581 ha−1) was obtained where only 50% NPK was added with organic manures.
Balancing Convergence and Diversity in Multiobjective Immune Algorithm

Recently, multiobjective immune algorithms (MOIAs) become popular, which are designed for multiobjective optimization problems (MOPs). However, most existing MOIAs put more attention on maintaining diversity as the used clonal selection strategy will allocate more cloning for the sparse areas, which may hamper the convergence to speed to the optimal Pareto front, especially for some complicated MOPs. To alleviate the phenomenon mentioned above, we propose a dynamic mechanism into traditional MOIAs in this paper, aiming to balance convergence and diversity, called BCD-MOIA. First, MOP will be decomposed into several single subproblems by decomposition method, and then these subproblems will be optimized simultaneously. Second, we propose a novel measure metric instead of the crowding distance to assign the clone number for each solution, which includes two main parts. The first part focuses on the diversity performance, i.e., the perpendicular distance between solution and its associated weight vectors. The second part uses the aggregated function values quantified by the decomposition method, which is more efficient for accelerating the convergence speed and maintaining diversity as well. Moreover, a dynamic mechanism is performed during the whole evolutionary process, focusing on diversity and convergence at different stages. By this way, our proposed algorithm can tradeoff the performance on convergence and diversity dynamically. The effectiveness of our proposed algorithm BCD-MOIA is validated by comparing with three competitive MOIAs and three multi-objective evolutionary algorithms for tackling two sets of complicated problems.
A novel Graph Attention Network Architecture for modeling multimodal brain connectivity

While Deep Learning methods have been successfully applied to tackle a wide variety of prediction problems, their application has been mostly limited to data structured in a grid-like fashion. However, the study of the human brain "connectome" involves the representation of the brain as a graph with interacting nodes. In this paper, we extend the Graph Attention Network (GAT), a novel neural network (NN) architecture acting on the features of the nodes of a binary graph, to handle a set of graphs provided with node features and non-binary edge weights. We demonstrate the effectiveness of our architecture by training it multimodal data collected from a large homogeneous fMRI dataset (n=1003 individuals with multiple fMRI sessions per subject) made publicly available by the Human Connectome Project (HCP), demonstrating good performance and seamless integration of multimodal neuroimaging data. Our adaptation provides a powerful and flexible deep learning tool to integrate multimodal neuroimaging connectomics data in a predictive context.
The Lung Cancer Associated MicroRNAs and Single Nucleotides Polymorphisms: a Mendelian Randomization Analysis

Lung cancer is a major public health burden and among the highest incidence and mortality rates of the cancers. MicroRNAs (miRNAs) play an important role in the development of lung cancer. The aim of this study was to investigate whether there was a potential causal relation between miRNAs and non-small-cell lung cancer (NSCLC). 1,026 patients with NSCLC from The Cancer Genome Atlas (TCGA) were analyzed. NSCLC associated SNPs’ allele scores were established, and candidate miRNAs were filtered from differential expression analysis. Mendelian randomization (MR) analysis was conducted for 5 candidate miRNA (hsa-miR-135b, hsa-miR-142, hsa-miR-182, hsa-miR-183 and hsa-miR-3607) and 76 candidate SNPs in lung adenocarcinoma (LUAD) group. According to the core assumptions of MR, there was no clear evidence of a causal relation between the 5 candidate miRNAs and LUAD. The reads per million miRNAs mapped (RPM) level of candidate miRNAs changed less than 3% per allele score. To our knowledge, this is the first study using the TCGA data set to investigate the causal relation between miRNAs and lung cancer using the MR approach, and also one of the first MR studies to use miRNA expression as an exposure factor, with the SNPs as instrumental variables.
Test Procedure for Determining the Stabilisation Time of Lamps and Other Household Appliances

Proper stabilisation is one important requirement for the measurement of harmonic emission of electrical appliances. A set of modern household appliances is used to study the change of their electrical and thermal parameters after connection to the power source and the time for their entire stabilisation in a laboratory setup. Based on a novel approach, which is more robust and less time-consuming compared to other approaches, the stabilisation process for different types of appliances is determined. The impact of different test conditions, like the mounting orientation or ambient temperature on the stabilisation time is studied. The results suggest that the stabilisation times should be individually determined for a tested device and the applied test conditions has to be carefully selected to adequately reflect reality.
On Impact of Imperfect CSI over SWIPT Device-to-Device (D2D) MIMO Relay Systems

Simultaneous wireless information and power transfer (SWIPT) is an efficient solution for the power scarce wireless communications. In this paper, we consider a relay assisted multi-input and multi-output (MIMO) device-to-device (D2D) communications with a practical scenario of imperfect channel state information (CSI) over a generalized Nakagami-m fading channels. Further, at the relay node, energy from the received radio-frequency (RF) signals is harvested by adopting time-switch (TS) protocol for broadcasting a signal to the destination. In a resource limited environment, it is deterrent to use all the MIMO antennas due to increased system complexity with the dedicated RF chain for each active antenna. Thus, transmit antenna selection strategy (TAS) is considered in this work. Framework for the outage probability and asymptotic outage probability of TAS based MIMO D2D relay system is provided. It is observed that the diversity order of the system gets affected with small variation in imperfect CSI correlation coefficient and throughput of the system gets affected severely with increasing rates with imperfect CSI. Further, Monte-Carlo simulations are performed to validate the derived analytical expressions.
Realistic Mathematical Model of Retinal Outer Plexiform Layer for Edge Detection

Edge information is essential for object recognition and motion detection. It is reported that photoreceptors, horizontal cells and bipolar cells in the outer retina involved for edge detection. Moreover, it is known that the center and the surround receptive field structure found in the bipolar cell layer is thought to be related to an initial process of edge detection. In the present study, we constructed retinal network models including photoreceptors, horizontal cells, and bipolar cells using single-compartment neurons to investigate those contributions for edge detection. We simulate fixation of a natural image with changing the size of the horizontal cell receptive field and confirmed that the constructed model successfully extracts edges in the image. Furthermore, most of the edge in the scene is extracted when the size of the horizontal cell receptive field matched with that reported in anatomical evidence. To evaluate the performance of edge detection, we compare the result of edge detection with the Canny algorithm. As a result, we conformed that the model well detects fine edges similar to the Canny edge detection.
The ontogeny and functional distribution of novel, neurochemically-defined columns in mammalian visual cortex

The synaptic organization of the mammalian visual cortex is susceptible to activityand experience-dependent modifications during a distinct temporal window of development. In an effort to understand the synaptic mechanisms responsible for initiating and maintaining this period of plasticity, the studies presented in this thesis describe the ontogenic distribution of specific molecular indices of serotonergic and glutamatergic neurotransmission in the developing kitten visual cortex and evaluate their patterns of expression following manipulations of early visual experience. The distributions of serotonin (5-HT) 1A, 1C, 2 and 3 receptor subtypes and the 5-HT uptake site were assessed in postnatal cat visual cortex using in vitro autoradiographic methods. Each 5-HT receptor subtype exhibited a unique temporal, regional and laminar pattern of expression. The density of 5-HTiA receptors was highest within superficial and deep cortical layers around postnatal day (PD) 30, while 5-HTic and 5-HT2 receptors exhibited peak levels in middle cortical layers at PD50 and PD120, respectively. Between PD30 and PD90, 5-HTic & 5-HT2 receptors were expressed at high levels within the same columnar compartments, but within different geniculate-recipient layers of area 17. These columns of 5-HT receptors measured about 400 [t,m wide with a centre-to-centre spacing of approximately 900 Inn. The ontogenic distribution of synaptic zinc (Zn), a vesicular component of a subset of glutamatergic-neuron terminals, was also examined in the cat visual cortex. In the adult, intense Zn-staining was highest in superficial and deep layers. The relative absence of Zn in layer IV conspicuously distinguished visual cortical areas 17 and 18 from adjacent cortical regions. The earliest Zn-positive staining in visual cortex was apparent by PD2 and was restricted to a thin layer at the bottom of the cortical plate. By PD 10, and continuing through PD20, synaptic zinc formed a trilaminar pattern of dense staining in areas 17 and 18, which included the top of layer I, and layers III and V. The laminar pattern of synaptic zinc in visual cortex appeared mature by PD30, except that the distribution of zinc in layer IV was not uniform. When examined in the tangential plane at
On the Capacity of Quantum Private Information Retrieval from MDS-Coded and Colluding Servers

In quantum private information retrieval (QPIR), a user retrieves a classical file from multiple servers by downloading quantum systems without revealing the identity of the file. The QPIR capacity is the maximal achievable ratio of the retrieved file size to the total download size. In this paper, the capacity of QPIR from MDS-coded and colluding servers is studied for the first time. Two general classes of QPIR, called stabilizer QPIR and dimension-squared QPIR induced from classical strongly linear PIR are defined, and the related QPIR capacities are derived. For the non-colluding case, the general QPIR capacity is derived when the number of files goes to infinity. A general statement on the converse bound for QPIR with coded and colluding servers is derived showing that the capacities of stabilizer QPIR and dimension-squared QPIR induced from any class of PIR are upper bounded by twice the classical capacity of the respective PIR class. The proposed capacity-achieving scheme combines the star-product scheme by Freij-Hollanti et al. and the stabilizer QPIR scheme by Song et al. by employing (weakly) self-dual Reed--Solomon codes.
Automated movement detection reveals features of maturation in preterm infants

Nearly 10% of all births in the United States are preterm. Preterm birth is a major risk for developmental neuromotor disorders. Early characterization of a future developmental outcome is necessary to design early interventions. However, such evaluations are currently subjective and typically happen only several months after birth. The aim of this study was to quantify movement bouts after birth and to determine if features of maturation might be characterized. Four preterm infants were continuously monitored for several months, from a few days after birth until discharge, in the Neonatal Intensive Care Unit. Movement was quantified from the photoplethysmogram using a wavelet-based algorithm. In all 4 infants, maturation was associated with a decrease (p < 0.001) in the occurrence of movement bouts ≤ 30s and an increase (p < 0.001) in longer movement bouts (> 30s). The distribution of movement durations followed a power law function with its exponent defining the characteristic of the distribution. The exponent significantly increased with post-menstrual age. Future research will test whether these maturational changes can predict developmental outcomes.Clinical Relevance— Early identification of changes in features of preterm infant movement may be useful in predicting neuromotor development and potential disorders.
A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two- channel input for the regional-based YOLO method. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and an average number of false positives per subject (FPavg) of 52.18 throughout the five-folds cross-validation. The 3D-CNN based second stage further improved the detection performance by reducing the FPavg to 1.42. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.
Non-Peroxide Antibacterial Activity in Some new Zealand Honeys

SummaryA range of New Zealand monofloral honeys was assayed for antibacterial activity with and without the hydrogen peroxide present (inactivated by the addition of catalase). It was found that in the honeys with high antibacterial activity a large part of this activity was due to a factor other than hydrogen peroxide. The test micro-organism used, Staphylococcus aureus, was not inhibited by the osmolarity or the acidity of the honey. The association of high antibacterial activity with particular floral sources suggests that the non-peroxide antibacterial activity is of floral origin. The activity of manuka honey was tested and found to be heat-stable.
Stress Assessment by Combining Neurophysiological Signals and Radio Communications of Air Traffic Controllers

Air Traffic Control (ATC) has been classified as the fourth most stressful job. In this regard, sixteen controllers were asked to perform ecological ATC simulation during which behavioral (Radio Communications with pilots - RCs), subjective (stress perception) and neurophysiological signals (brain activity and skin conductance - SC) were collected. All the considered parameters reported significant changes under high stress conditions. In particular, the theta, alpha, and beta brain rhythms increased significantly (all p<0.05) all over the brain areas, and both the SC components exhibited higher values (p<0.01). Additionally, the number of speech under high stress decreased significantly (p<10-4) while both the mean and median value of the F0 component of the RC increased (p<0.01). The results can be employed to objectively measure and track the controller’s stress level while dealing with ATC activities to better tailoring the workshift and maintaining high safety levels.
k-Connectivity in Random Graphs induced by Pairwise Key Predistribution Schemes

Random key predistribution schemes serve as a viable solution for facilitating secure communication in Wireless Sensor Networks (WSNs). We analyze reliable connectivity of a heterogeneous WSN under the random pairwise key predistribution scheme of Chan et al. According to this scheme, each of the n sensor nodes is classified as type-1 (respectively, type-2) with probability μ (respectively, 1 − μ) where 0 < μ < 1. Each type-1 (respectively, type-2) node is paired with 1 (respectively, Kn) other node selected uniformly at random; each pair is then assigned a unique pairwise key so that they can securely communicate with each other. A main question in the design of secure and heterogeneous WSNs is how should the parameters n, μ, and Kn be selected such that resulting network exhibits certain desirable properties with high probability. Of particular interest is the strength of connectivity often studied in terms of k-connectivity; i.e., with k = 1, 2, …, the property that the network remains connected despite the removal of any k − 1 nodes or links. In this paper, we answer this question by analyzing the inhomogeneous random K-out graph model naturally induced under the heterogeneous pairwise scheme. It was recently established that this graph is 1-connected asymptotically almost surely (a.a.s.) if and only if Kn = ω(1). Here, we show that for k = 2, 3, …, we need to set ${K_n} = \frac{1}{{1 - \mu }}\left( {\log n + (k - 2)\log \log n + \omega (1)} \right)$ for the network to be k-connected a.a. s. The result is given in the form of a zero-one law indicating that the network is a.a. s. not k-connected when ${K_n} = \frac{1}{{1 - \mu }}\left( {\log n + (k - 2)\log \log n - \omega (1)} \right)$. We present simulation results to demonstrate the usefulness of the results in the finite node regime.