Miracle-dz/UniFAR-FTUs huggingface dataset:
Possible role of differentially expressing novel protein markers (ligatin and fibulin‐7) in human aqueous humor and trabecular meshwork tissue in glaucoma progression

The pathological mechanism underlying glaucoma has always been a complex aspect of this permanently blinding disease but proteomic studies have been helpful in elucidating it to a great extent in several studies. This study was designed to evaluate the expression and to get an idea about the function of two novel markers (ligatin and fibulin‐7) identified in human aqueous humor (hAH) in relation to glaucomatous progression. A significant increase in the protein content of glaucomatous hAH compared to that of non‐glaucomatous controls (NG‐Ctrls) was observed. Ligatin, fibulin‐7, and its proteolysis were revealed in hAH of primary open angle glaucoma (POAG), primary angle closure glaucoma (PACG) and NG‐Ctrls. Quantification confirmed no significant difference in expression of ligatin, whereas fibulin‐7 was significantly (P < 0.05) low in hAH of PACG in comparison to NG‐Ctrls and POAG. Importantly the immunohistochemical assay for both indicated their possible involvement in the maintenance of the appropriate structure of TM in vivo. Since oxidative stress is a major contributor to glaucomatous pathogenesis, in vitro analysis of nuclear and cytoplasmic fractions indicated intracellular changes in localization and expression of ligatin upon oxidative insult of human trabecular meshwork (TM) cells. While no such changes were found for fibulin‐7 expression. This was also corroborated with the immunocytochemical assay. Though a study with a small sample size, this is the first report which confirms the presence of ligatin and fibulin‐7 in hAH, quantified their differential expression, and indicated the possibility of their involvement in the maintenance of the TM structure.
Finite‐volume time‐domain (FVTD) modelling of a broadband double‐ridged horn antenna

This paper demonstrates the suitability of the finite‐volume time‐domain (FVTD) method to analyse electromagnetic ‘real‐world’ problems. As a challenging example, a 1–18 GHz broadband double‐ridged horn antenna is chosen. The horn antenna consists of non‐orthogonal and curved parts and a small coaxial feeding that is modelled in detail. The simulation results of the far‐field patterns, the return loss and the gain are successfully compared to measurements. They show that the FVTD method—using an inhomogeneous tetrahedral mesh—is very well suited for simulating complex structures. Copyright © 2004 John Wiley & Sons, Ltd.
Parameter estimation of the feedback gain in a stochastic model of renal hemodynamics: differences between spontaneously hypertensive rats and Sprague-Dawley rats Running head: A stochastic model of renal hemodynamics

Proximal tubular pressure shows periodic self-sustained oscillations in normotensive rats, but highly irregular fluctuations in spontaneously hypertensive rats (SHR). Although we have suggested that the irregular fluctuations in SHR represent low dimensional deterministic chaos in tubuloglomerular feedback (TGF) they could also arise from other mechanisms such as intrinsic instabilities in preglomerular vessels, or inputs from neighboring, coupled nephrons. To test this possibility we applied a parameter estimation procedure to a model of TGF where a stochastic process was added to represent mechanisms not included explicitly in the model. In its deterministic version the model can have chaotic dynamics arising from TGF. The model introduces random fluctuations into a parameter which determines the gain of tubuloglomerular feedback. The model shows a rich variety of dynamics ranging from low dimensional deterministic oscillations and chaos to high dimensional random fluctuations. To fit the data from normotensive rats the model must introduce only a small variation in the feedback gain and its estimates of that gain agree well with experimental values. These results support the use of the deterministic model of nephron dynamics in normotensive rats. In contrast, the irregular tubular pressure fluctuations in SHR were best described by a model dominated by random parameter fluctuations. The results point to the failure of simple mathematical models of nephron dynamics adequately to describe processes that are important for the observed irregular tubular pressure fluctuations, and the need to consider other factors, such as differences in vascular function or nephron-nephron interactions, in further work on this problem. keywords: stochastic processes, spectral density, nephron, tubuloglomerular feedback mechanism, deterministic chaos
On the Universal Approximability of Quantized ReLU Neural Networks

Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight. In this paper, we study the representation power of quantized neural networks. First, we prove the universal approximability of quantized ReLU networks. Then we provide upper bounds of storage size given the approximation error bound and the bit-width of weights for function-independent and function-dependent structures. To the best of the authors' knowledge, this is the first work on the universal approximability as well as the associated storage size bound of quantized neural networks.
Visualizing the Emergence of Intermediate Visual Patterns in DNNs: Supplementary Material

Figure 1 shows the projected sample features g and each category direction μc, based on the COCO 2014 dataset and the CUB-200-2011 dataset1. The visualization results revealed the semantic similarity between categories. For example, dining table features were similar to pizza features, and black footed albatross features were similar to laysan albatross features. Furthermore, Figure 2 shows the projected sample feature g at different iterations of training. This illustrated how the discrimination power of sample features gradually increased through the training process.
Computational study of turbulent separating flow in an asymmetric plane diffuser

A computational study has been performed to compare the mean velocity and turbulent quantities with the experimental test case of Obi et ai. (1993a) which involves separation from a plane wall and subsequent reattachment in the downstream duct of an asymmetric diffuser. The diffusing section has a length of 21H, where H is the inlet channel height, and overall expansion ratio 4.7. The Reynolds number, based on an upstream reference velocity and H, is 21200. The incompressible Navier-Stokes equations for fully developed turbulent flow has been solved by using a standard finite volume CFD code. The convection and diffusion terms of all the equations, including the momentum equations in two directions and the model transport equations for turbulence quantities, have been approximated by the higher order differencing scheme. The SIMPLE algorithm has been used to obtain the pressure field. Standard k E model and Hybrid turbulence model (Basara and Jakirlic, 2001) have been tested. The Hybrid turbulence model demonstrates an improved result for the mean velocity and turbulence quantities while standard k E model fails to capture the downstream flow behavior. There are, however some shortcomings in these models particularly in the development of flow after reattachment, which is always predicted to be too slow.
Coherent terahertz radiation from beam-driven upper hybrid wave in magnetostatic plasma

ABSTRACT A scheme for generating tunable terahertz radiation using relativistic electron beam-induced upper hybrid wave immersed in a magnetized plasma is investigated. The ponderomotive force applied to the beam electrons because of electromagnetic radiation wave and pump wave generates the non-linear current density in the high-frequency regime. For an appropriate pump wave frequency, under the condition of phase matching the THz radiation wave is obtained. The enhancement in the radiation power occurs along the axial direction in the vicinity of Cerenkov resonance. Our analytical expression shows that the growth rate in the unstable modes of the THz radiation wave increases with beam density (beam current) and the modulation index. In addition, the growth rate of the instability decreases with an increase in radiation wave frequency and scales as the inverse cube root of radiation frequency. An interesting result obtained from our model is that an increase in cyclotron frequency causes the growth decrement in THz radiation emission, while the efficiency of THz radiation emission increases with the growth rate and the modulation index. Also, the emission of the THz radiation wave becomes larger as the modulation index approaches unity.
Nanometer-resolved chemical analyses of femtosecond laser-induced periodic surface structures on titanium

The chemical characteristics of two different types of laser-induced periodic surface structures (LIPSS), so-called high and low spatial frequency LIPSS (HSFL and LSFL), formed upon irradiation of titanium surfaces by multiple femtosecond laser pulses in air (30 fs, 790 nm, 1 kHz), are analyzed by various optical and electron beam based surface analytical techniques, including micro-Raman spectroscopy, energy dispersive X-ray analysis, X-ray photoelectron spectroscopy, and Auger electron spectroscopy. The latter method was employed in a high-resolution mode being capable of spatially resolving even the smallest HSFL structures featuring spatial periods below 100 nm. In combination with an ion sputtering technique, depths-resolved chemical information of superficial oxidation processes was obtained, revealing characteristic differences between the two different types of LIPSS. Our results indicate that a few tens of nanometer shallow HSFL are formed on top of a ∼150 nm thick graded superficial oxide layer .
Finite-temperature Kondo effect in a realistic 5-fold-degenerate double quantum-dot device

A novel highly correlated state is shown to emerge in a double-quantum-dot device, coupled in parallel to two leads, when the energy difference between the dots exactly compensates the difference between the intra- and inter-dot Coulomb interaction. Due to the five-fold degenerate low-energy space, the ground state is driven towards a special point between the Kondo and the mixed-valence regimes, where the transmission coefficient exhibits a sharp dip at the chemical potential, leading to a non-monotonic dependence of the conductance on temperature or voltage bias and the reemergence of the Kondo effect at finite temperatures. As that energy difference is easily controlled by gate voltage, our results are directly relevant to existing experimental setups.
Abutting Grating Illusion: Cognitive Challenge to Neural Network Models

Even the state-of-the-art deep learning models lack fundamental abilities compared to humans. Multiple comparison paradigms have been proposed to explore the distinctions between humans and deep learning. While most comparisons rely on corruptions inspired by mathematical transformations, very few have bases on human cognitive phenomena. In this study, we propose a novel corruption method based on the abutting grating illusion, which is a visual phenomenon widely discovered in both human and a wide range of animal species. The corruption method destroys the gradient-defined boundaries and generates the perception of illusory contours using line gratings abutting each other. We applied the method on MNIST, high resolution MNIST, and silhouette object images. Various deep learning models are tested on the corruption, including models trained from scratch and 109 models pretrained with ImageNet or various data augmentation techniques. Our results show that abutting grating corruption is challenging even for state-of-the-art deep learning models because most models are randomly guessing. We also discovered that the DeepAugment technique can greatly improve robustness against abutting grating illusion. Visualisation of early layers indicates that better performing models exhibit stronger end-stopping property, which is consistent with neuroscience discoveries. To validate the corruption method, 24 human subjects are involved to classify samples of corrupted datasets.
Using Barbour’s Ephemeris Time, and Padmanabhan’s Inflaton Value, plus Will’s Massive Graviton Velocity to Isolate Rest Energy of Massive Graviton as Compared to Racetrack Inflation Results of Graviton Physics and Modified Wheeler de Witt Results of Wormhole Physics

The idea is to identify via ephemeris time as given by Barbour and an inflaton field as given by Padmanabhan, for scale factor proportional to time to the alpha power and a velocity given by Will for massive gravitons, an initial energy for a massive graviton in space-time. The spatial values for the graviton production could be from the Planckian to Electro weak regime, with a nod to using a worm hole from a prior to a present universe as a delivery font for gravitational energy, as an information carrying bridge from prior universe “information settings” to the present space-time. The number of Gravitons will be set as N, and the initial time, as a tie in with Barbour’s ephemeris time, a constant times Planck time. In setting up the positions, as input into the positions and distributions of gravitons in our model, we will compare results as could be generated by Racetrack inflation, for presumed position of relic gravitons when just produced in the universe, as compared with results given by an adaptation of an argument presented by Crowell, in a modification of the Wheeler de Witt equation he gave germane to worm hole physics. In addition, with this presentation we will discuss entropy generation via graviton production. And compare that with semi classical arguments, as well as Brane-anti brane combinations. The idea will be to in all of this to re set the particulars of massive gravity in such a way as to revisit the outstanding problem of massive gravity: Its predictions do not match those of general relativity in the limit when a massive graviton mass approaches zero In particular, while at small scales, Newton’s gravitational law is recovered, the bending of light is only three quarters of the result Albert Einstein obtained in general relativity
LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity

Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major challenges in analyzing connectivity matrices including the high dimensionality of brain networks, unknown latent sources underlying the observed connectivity, and the large number of brain connections leading to spurious findings. In this paper, we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures. Compared with the existing method that vectorizes connectivity matrices ignoring brain network topology, LOCUS achieves more efficient and accurate source separation for connectivity matrices using low-rank structure. We propose a novel angle-based uniform sparsity regularization that demonstrates better performance than the existing sparsity controls for low-rank tensor methods. We propose a highly efficient iterative Node-Rotation algorithm that exploits the block multi-convexity of the objective function to solve the non-convex optimization problem for learning LOCUS. We illustrate the advantage of LOCUS through extensive simulation studies. Application of LOCUS to Philadelphia Neurodevelopmental Cohort neuroimaging study reveals biologically insightful connectivity traits which are not found using the existing method.
Microstructure and piezoelectric properties of 0.95(Na0.5K0.5)NbO3–0.05BaTiO3 ceramics

For 0.95(Na0.5K0.5)NbO3–0.05BaTiO3 (0.95NKN-0.05BT) ceramics sintered at 1040–1075°C, abnormal grain growth occurred but the grain size decreased when the sintering temperature exceeded 1075°C. The dielectric constant (ϵ3T∕ϵ3), electromechanical coupling factor (kp), and piezoelectric constant (d33) were considerably increased with increasing relative density and grain size. Evaporation of Na2O deteriorated the piezoelectric properties by decreasing the resistivity. To minimize Na2O evaporation, specimens were muffled with 0.95NKN-0.05BT powders during the sintering. Improved piezoelectric properties of d33=225pC∕N, kp=36%, and ϵ3T∕ϵ3=1058 were obtained for specimen sintered at 1060°C for 2h with muffling.
Association of anti-ribosomal P protein antibodies with neuropsychiatric systemic lupus erythematosus.

OBJECTIVE Although it has been reported that anti-ribosomal P protein antibodies (anti-P) are highly specific for lupus psychosis, there have been discrepancies among the studies regarding the clinical correlation of these antibodies with systemic lupus erythematosus (SLE). The present study was therefore carried out to reappraise the association of anti-P and neuropsychiatric SLE. METHODS Highly purified synthetic ribosomal P peptides of the carboxyl-terminal 22-amino acid sequence were conjugated to human serum albumin (HSA) with the use of glutaraldehyde. Anti-P in sera from 75 patients with SLE (26 without central nervous system disease [non-CNS], 28 with lupus psychosis, and 21 with nonpsychotic CNS involvement [nonpsychotic CNS lupus]) were analyzed with an enzyme-linked immunosorbent assay (ELISA), using HSA-ribosomal P peptide conjugates as antigens. Anti-P levels were quantitated by subtracting the nonspecific binding activities to HSA. RESULTS The ELISA was found to be specific for anti-P, as determined by comparison with the results of Western blotting using extracts of HEp-2 cells. Serum anti-P levels were significantly elevated in patients with lupus psychosis, including organic brain syndrome and nonorganic psychosis, compared with those with non-CNS SLE or those with nonpsychotic CNS lupus. There were no significant differences in serum anti-P levels between patients with organic brain syndrome and those with nonorganic psychosis. Anti-P antibodies were not detected in the cerebrospinal fluid of patients with either lupus psychosis or nonpsychotic CNS lupus. CONCLUSION Our results, obtained from the highly specific ELISA using HSA-ribosomal P peptide conjugates, confirm the correlation of serum anti-P with lupus psychosis. The data also suggest that differences in the purity of the ribosomal P peptides used might be a major reason for the conflicting results in the literature regarding the association of anti-P with lupus psychosis.
Droplet Growth Dynamics during Atmospheric Condensation on Nanopillar Surfaces

ABSTRACT The Gibbs free energy barrier for heterogeneous nucleation of a condensed droplet on a rough surface changes significantly with changes of humidity content in the condensing environment. The influence of environmental factors (ambient temperature and relative humidity) and substrate characteristics (topology, surface chemistry, and substrate temperature) on atmospheric condensation phenomenon is very important to elucidate the condensed droplet wetting state and condensate harvesting applications. Condensation from the humid air has been reported for plain silicon and fabricated nanopillar surfaces to facilitate condensate harvesting. Droplet growth and size distributions were recorded for 90 min. Spherical droplets condensed on the silicon surfaces and irregular-shaped droplets were observed on the nanopillar surfaces due to the pinning effect of the pillars. The effect of droplet pinning on coalescence events has been described based on the energy balance for the condensed droplets. A mathematical model reveals that certain dimensional combinations (pillar pitch, pillar diameter, and pillar height) of the nanopillar geometry are required to exhibit the pinning mechanism for condensed droplets. Regeneration of droplets was observed at void spaces generated from coalescence events. The growth of individual droplets was tracked over multiple time and length scales, starting from nucleation to get further insight into the direct growth and coalescence mechanisms. Abbreviation: ESEM: Environmental Scanning Electron Microscope; HCP: Hexagonal Closed-Packed; MPL: Microsphere Photolithography; RH: Relative Humidity
Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins 1

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a subproblem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in sitedirected mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteinerich’ environment, where as Prokaryotic bonded examples lack it. 1. PhD thesis in Computer Science, XXII Cycle. Coordinator: Prof. Simone Martini, Thesis Supervisor: Prof. Rita Casadio, Tutor: Prof. Luciano Margara. 2. Department of Computer Science, University of Bologna, Mura Anteo Zamboni 7, 40127 Bologna, Italy. 3. Bologna Biocomputing Group, CIRB/Dept. of Biology, University of Bologna, Via San Giacomo 9/2 40126 Bologna, Italy.