Miracle-dz/UniFAR-FTUs huggingface dataset:
Resurgent sodium current promotes action potential firing in the avian auditory brainstem

Auditory brainstem neurons of all vertebrates fire phase‐locked action potentials (APs) at high rates with remarkable fidelity, a process controlled by specialized anatomical and biophysical properties. This is especially true in the avian nucleus magnocellularis (NM) – the analogue of the mammalian anteroventral cochlear nucleus. In addition to high voltage‐activated potassium (KHVA) channels, we report, using whole cell physiology and modelling, that resurgent sodium current (INaR) of sodium channels (NaV) is equally important and operates synergistically with KHVA channels to enable rapid AP firing in NM. Anatomically, we detected strong NaV1.6 expression near hearing maturation, which was less distinct during hearing development despite functional evidence of INaR, suggesting that multiple NaV channel subtypes may contribute to INaR. We conclude that INaR plays an important role in regulating rapid AP firing for NM neurons, a property that may be evolutionarily conserved for functions related to similar avian and mammalian hearing.
Neoteric AT for the visually challenged

This paper interpolates a unified arrangement which can perform three different functions to bring light in the life of the visually challenged. The data provided by ultrasonic sensor is processed using a microcontroller based embedded system. The advent of MaxSonar sensor and the Ultrasonic sensing devices has led to many useful functions as it has high accuracy. Using MaxSonar sensor and ultrasonic sensor at different levels of human body the obstacles in the path are detected and output is given to the user via earphones. A GPS GSM module is in incorporated into the system to share the location coordinates in state of emergency. When the user approaches a pedestrian traffic signal, the status of the signal is given to the user via earphones. This is achieved with use of RF TX-RX technology.
Segmentation of Pathological Structures by Landmark-Assisted Deformable Models

Computerized segmentation of pathological structures in medical images is challenging, as, in addition to unclear image boundaries, image artifacts, and traces of surgical activities, the shape of pathological structures may be very different from the shape of normal structures. Even if a sufficient number of pathological training samples are collected, statistical shape modeling cannot always capture shape features of pathological samples as they may be suppressed by shape features of a considerably larger number of healthy samples. At the same time, landmarking can be efficient in analyzing pathological structures but often lacks robustness. In this paper, we combine the advantages of landmark detection and deformable models into a novel supervised multi-energy segmentation framework that can efficiently segment structures with pathological shape. The framework adopts the theory of Laplacian shape editing, that was introduced in the field of computer graphics, so that the limitations of statistical shape modeling are avoided. The performance of the proposed framework was validated by segmenting fractured lumbar vertebrae from 3-D computed tomography images, atrophic corpora callosa from 2-D magnetic resonance (MR) cross-sections and cancerous prostates from 3D MR images, resulting respectively in a Dice coefficient of 84.7 ± 5.0%, 85.3 ± 4.8% and 78.3 ± 5.1%, and boundary distance of 1.14 ± 0.49mm, 1.42 ± 0.45mm and 2.27 ± 0.52mm. The obtained results were shown to be superior in comparison to existing deformable model-based segmentation algorithms.
APPLICATION OF CHROMATIC MONITORING AS A PLASMA DIAGNOSTIC TECHNIQUE

This paper describes the application of chromatic monitoring of both laboratory and industrial plasmas. The light emitted from the nitrogen plasma generated in the different plasma sources was measured with an imaging spectrograph. The emission spectra were reduced to data points representing the chromatic coordinates of these spectra. The data compression makes this method attractive for monitoring plasma parameter changes. The aim of this work was to test the possibility of application of chromatic monitoring for plasma processing.
Development of Concrete Mix Design Nomograph Containing Polyethylene Terephtalate (PET) as Fine Aggregate

This paper describes the experimental investigation to develop the concrete mix design Nomograph for concrete containing PET as fine aggregate. The physical and mechanical properties were determined by using mix proportion containing 25%, 50% and 75% of PET with water cement ratio (w/c) 0.45, 0.55 and 0.65. The data obtained showed that the inclusion of PET aggregate reduce the strength performances of concrete. All the data obtained were combined into one single graph to develop a preliminary mix design nomograph for PET concrete. The nomograph consist of ; relationship between compressive strength and water cement ratio; relationship between splitting tensile strength water cement ratio; relationship between splitting tensile strength and PET percentage and relationship between compressive strength and PET percentage. The mix design nomograph can be used to assists in selecting the proper mix proportion parameters based on the criteria required.
Structure refinement of decagonal Al–Ni–Co, superstructure type I

The structure of decagonal Al–Ni–Co, superstructure type I (the so-called Edagawa phase), was refined in physical space. A statistical approach was used to calculate the structure factors based on a rhombic Penrose tiling, with tile edge length 12.1 Å, as reference frame. A total number of 691 parameters was refined against 6843 structure amplitudes, merged in Laue group 10/m.
Optimal Discrete Slicing

Slicing is the procedure necessary to prepare a shape for layered manufacturing. There are degrees of freedom in this process, such as the starting point of the slicing sequence and the thickness of each slice. The choice of these parameters influences the manufacturing process and its result: The number of slices significantly affects the time needed for manufacturing, while their thickness affects the error. Assuming a discrete setting, we measure the error as the number of voxels that are incorrectly assigned due to slicing. We provide an algorithm that generates, for a given set of available slice heights and a shape, a slicing that is provably optimal. By optimal, we mean that the algorithm generates sequences with minimal error for any possible number of slices. The algorithm is fast and flexible, that is, it can accommodate a user driven importance modulation of the error function and allows the interactive exploration of the desired quality/time tradeoff. We demonstrate the practical importance of our optimization on several three-dimensional-printed results.
A comparative study of CNN, BoVW and LBP for classification of histopathological images

Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific characteristics. This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images. We introduce a new dataset, KIMIA Path960, that contains 960 histopathology images belonging to 20 different classes (different tissue types). We make this dataset publicly available. The small size of the dataset and its inter-and intra-class variability makes it ideal for initial investigations when comparing image descriptors for search and classification in complex medical imaging cases like histopathology. We investigate deep features, LBP histograms and BoVW to classify the images via leave-one-out validation. The accuracy of image classification obtained using LBP was 90.62% while the highest accuracy using deep features reached 94.72%. The dictionary approach (BoVW) achieved 96.50%. Deep solutions may be able to deliver higher accuracies but they need extensive training with a large number of (balanced) image datasets.
Radar Image Colorization: Converting Single-Polarization to Fully Polarimetric Using Deep Neural Networks

Numerous radar polarimetry theories and polarimetric synthetic aperture radar (PolSAR) processing methods have been developed. However, the vast majority of SAR images are not fully polarimetric (full-pol). This paper proposes “radar image colorization” to reconstruct a full-pol image from a non-full-pol image, so that existing PolSAR methods, such as model-based decomposition and unsupervised classification, can be directly applied to the reconstructed full-pol SAR images. It proposes to train a specially designed deep neural network to convert a single polarization gray-scale SAR image to full-pol. It consists of two components: a feature extractor network to extract hierarchical multi-scale spatial features of the gray-scale SAR image, followed by a feature translator network to map spatial feature to polarimetric feature with which the polarimetric covariance matrix of each pixel can be reconstructed. Both qualitative and quantitative experiments with real full-pol data are conducted to show the efficacy of the proposed method. The reconstructed full-pol SAR image agrees well with the true full-pol image, not only in the sense of visual similarity but also in the sense of real PolSAR applications, such as target decomposition and terrain classification.
Loss of B Cells in Patients with Heterozygous Mutations in IKAROS.

BACKGROUND Common variable immunodeficiency (CVID) is characterized by late-onset hypogammaglobulinemia in the absence of predisposing factors. The genetic cause is unknown in the majority of cases, and less than 10% of patients have a family history of the disease. Most patients have normal numbers of B cells but lack plasma cells. METHODS We used whole-exome sequencing and array-based comparative genomic hybridization to evaluate a subset of patients with CVID and low B-cell numbers. Mutant proteins were analyzed for DNA binding with the use of an electrophoretic mobility-shift assay (EMSA) and confocal microscopy. Flow cytometry was used to analyze peripheral-blood lymphocytes and bone marrow aspirates. RESULTS Six different heterozygous mutations in IKZF1, the gene encoding the transcription factor IKAROS, were identified in 29 persons from six families. In two families, the mutation was a de novo event in the proband. All the mutations, four amino acid substitutions, an intragenic deletion, and a 4.7-Mb multigene deletion involved the DNA-binding domain of IKAROS. The proteins bearing missense mutations failed to bind target DNA sequences on EMSA and confocal microscopy; however, they did not inhibit the binding of wild-type IKAROS. Studies in family members showed progressive loss of B cells and serum immunoglobulins. Bone marrow aspirates in two patients had markedly decreased early B-cell precursors, but plasma cells were present. Acute lymphoblastic leukemia developed in 2 of the 29 patients. CONCLUSIONS Heterozygous mutations in the transcription factor IKAROS caused an autosomal dominant form of CVID that is associated with a striking decrease in B-cell numbers. (Funded by the National Institutes of Health and others.).
Cooperation in Asymmetric Commons Dilemmas

This paper is a study of collective action in asymmetric access to a common resource. An example is an irrigation system with upstream and downstream resource users. While both contribute to the maintenance of the common infrastructure, the upstream participant has first access to the resource. Results of our two-player asymmetric commons game show that privileged resource access player invest more than the downstream players. Investments by the downstream player into the common resource are rewarded by a higher share from the common resource by the upstream player. Decisions are mainly explained by the levels of trust and trustworthiness. Introducing uncertainty in the production function of the common resource did not affect the results in a significant way.
Wear characteristics of UHMW polyethylene by scratching method

This paper investigates the abrasive wear phenomenon of polymers like ultrahigh molecular weight polyethylene (UHMWPE) by micro-scratching test. Two types of polymers were used: UHMWPE: non-irradiated (conventional) CPE, and gamma-irradiated (cross-linked) XPE with 2 types of indenter scratches under two conditions: dry and wet. The surfaces of polyethylene samples were visually analysed by optical microscopy. The scratch path obtained after the scratching test was investigated by profilometry of the cross-sectional profile, in two different locations of all results by scratches, located in the middle one and the second to the head of scratch path. Calculation was made using graphical estimations of scratching conventional and irradiated polyethylene at dry and wet conditions. The abrasivity factor f ab was calculated using the Zum Gahr method and the value obtained is slightly less than zero.
Emotional Impact of Cardiopulmonary Resuscitation Training on High School Students

Background The American Heart Association (AHA) has implemented several programs to educate the public about cardiopulmonary resuscitation (CPR). A common issue in bystander CPR is the fear of hurting the victim. As a result, the victim may not receive CPR in time. The purpose of this study was to measure the emotional impact of CPR training on high school students using two approved AHA courses. Methods A total of 60 students participated in this study. These students had a mean age of 15.4 ± 1.2 years old and were selected from a high school in Southern California. Subjects were divided into two groups, Basic Life Support (BLS) (n1 = 31) and Hands-Only™ CPR (n2 = 29). Emotional impacts were assessed by having each subject answer a questionnaire based on given scenarios before and after their training session. Results There was a significant difference in both groups when comparing positive-emotion scores before and after the training (BLS: 30.3 ± 6.0 vs. 34.5 ± 6.7, p < 0.001; Hands-Only 27.9 ± 5.0 vs. 32.1 ± 6.5, p < 0.001). In addition, both groups showed significant reductions in negative-emotion scores (BLS: 29.2 ± 6.7 vs. 23.7 ± 6.5, p < 0.001 and Hands-Only: 26.8 ± 6.1vs. 24.8 ± 7.7, p = 0.05). Conclusion Our results indicate that the AHA programs have positive effects on students’ emotional response. We recommend that future studies include an in-depth study design that probes the complexity of students’ emotions after completing an AHA session.
Mitochondria‐targeted antioxidant SkQ1 improves spermatogenesis in Immp2l mutant mice

Previous studies have confirmed that spermatogenesis in homozygous Immp2l mutant male mice was normal at the age of 6 months, but was significantly abnormal at the age of 13 months. Meanwhile, oxidative stress is reported to be involved in spermatogenic impairment in old mutant mice. However, it is unclear whether antioxidant treatment is a suitable intervention for improving spermatogenesis in old mutant mice. This study sought to investigate the effect of mitochondria‐targeted antioxidant SkQ1 on spermatogenesis in homozygous Immp2l mutant mice. Immp2l mutant mice were treated with the mitochondria‐targeted antioxidant SkQ1 from the age of 6 weeks until 13 months. SkQ1 treatment significantly improved spermatogenesis in old Immp2 l mutant mice. Moreover, SkQ1 treatment improved the morphology of testicular seminiferous tubules, significantly reduced the apoptosis of germ cells and increased the level of GPX4 expression in old Immp2 l mutant mice. In conclusion, our data suggest that the mitochondria‐targeted antioxidant SkQ1 is effective in improving spermatogenesis in Immp2 l mutant mice and might be used for the treatment of male infertility.
Hyper-brain connectivity in binge drinking college students: a diffusion tensor imaging study

ABSTRACT The current study used diffusion tensor imaging to examine patterns/degree of brain connectivity in 12 college-aged binge drinking (BD) and 12 moderate drinking individuals. Voxel-level and region-of-interest analyses revealed increased connectivity of the BD brain in the right corona radiata, right external capsule, and both the right and left cingulum. Also, fractional anisotropy and axial diffusivity values of these regions correlated with a number of drinking behaviors of the BD as well as both groups combined. It is hypothesized that increased connectivity in the BD may produce difficulties with regulatory control, contributing to their propensity to binge.
EXPERIMENTAL INVESTIGATION AND PERFORMANCE ANALYSES OF CBN INSERT IN HARD TURNING OF COLD WORK TOOL STEEL (D3)

Long-term wear tests on the CBN tool behaviour during hard turning of AISI D3 (60 HRC) have been investigated. The evolution of surface roughness, cutting forces and temperature has been studied according to the cutting parameters and tool wear. Results show that CBN is resistant to wear despite of aggressiveness of AISI D3 steel. A great part of heat generated is mainly dissipated throughout the chip; at a cutting speed of 320 m/min the chip temperature is found to be 14 times higher than that recorded in the workpiece. The cutting speed range of 90 to 240 m/min has been found suitable to cut this material. The former speed range is appropriate to respect the cutting constraints, whereas beyond 240 m/min a drastic increase in tool wear occurs, causing a remarkable drop in tool life. Roughness measurements reveal a dependence on CBN tool wear. However, although the wear rises up to the allowable flank wear of value 0.3 mm, roughness Ra remains around 1.0 μm. The feed rate remains the most affecting factor on the roughness values. The proposed statistical models are based on the response surface methodology correlating the cutting parameters together with roughness, cutting forces and tool life.
Extracting and Defining Flexibility of Residential Electrical Vehicle Charging Loads

The popularization of electric vehicles raises concerns about their negative impact on the electrical grid. Extracting electric vehicle charging load patterns is a key factor that allows smart grid operators to make intelligent and informed decisions about conserving energy and promoting the stability of the electrical grid. This paper presents an unsupervised algorithm to extract electric vehicle charging load patterns nonintrusively from the smart meter data. Furthermore, a method to define flexibility for the collective electric vehicle charging demand by analyzing the time-variable patterns of the aggregated electric vehicle charging behaviors is presented. Validation results on real residential loads have shown that the proposed approach is a promising solution to extract electric vehicle charging loads and that the approach can effectively mitigate the interference of other appliances that have similar load behaviors as electric vehicles. Furthermore, a case study on real residential data to analyze electric vehicle charging trends and quantify the flexibility achievable from the aggregated electric vehicle load in different time periods is presented.
Big Data Analytics in Industrial IoT Using a Concentric Computing Model

The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time data analytics, data integration, transmission of meaningful data, edge analytics, real-time fusion of streaming data, and security and privacy).
The characteristics of electrical trees in the inner and outer layers of different voltage rating XLPE cable insulation

The statistical initiation and propagation characteristics of electrical trees in cross-linked polyethylene (XLPE) cables with different voltage ratings from 66 to 500 kV were investigated under a constant test voltage of 50 Hz/7 kV (the 66 kV rating cable is from UK, the others from China). It was found that the characteristics of electrical trees in the inner region of 66 kV cable insulation differed considerably from those in the outer region under the same test conditions; however, no significant differences appeared in the 110 kV rating cable and above. The initiation time of electrical trees in both the inner and the outer regions of the 66 kV cable is much shorter than that in higher voltage rating cables; in addition the growth rate of electrical trees in the 66 kV cable is much larger than that in the higher voltage rating cables. By using x-ray diffraction, differential scanning calorimetry and thermogravimetry methods, it was revealed that besides the extrusion process, the molecular weight of base polymer material and its distribution are the prime factors deciding the crystallization state. The crystallization state and the impurity content are responsible for the resistance to electrical trees. Furthermore, it was proposed that big spherulites will cooperate with high impurity content in enhancing the initiation and growth processes of electrical trees via the ‘synergetic effect’. Finally, dense and small spherulites, high crystallinity, high purity level of base polymer material and super-clean production processes are desirable for higher voltage rating cables.
Water transport through reconstituted collagen hollow-fiber membranes

The hydraulic permeability κ of reconstituted collagen hollow-fiber membranes has been determined from measurements of the volumetric flow rate of aqueous solutions through the membrane for various pressure differences across the membrane. Increasing the ultraviolet crosslinking time during manufacture of the membrane markedly decreases κ, whereas decreasing the pH of the aqueous solution increases κ. Applying a tensile strain of only 10% along the fiber axis decreases κ approximately fivefold. These results are in good agreement with those expected from a recent model of the membrane structure in which collagen fibrils, oriented preferentially along the hollow-fiber axis, bend between crosslinks to accept water in a pore structure. If the average fibril volume fraction and diameters are known, estimates can be made of κ. These estimates, based on direct small-angle x-ray scattering measurements of fibril diameters, are in good agreement with the measured κ values.