Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle size distribution of silver nanoparticles in different water conditions

The study measured the effect of different water conditions on the size distribution of silver nanoparticles. The particle size distribution of silver nanoparticles shifted towards the large size of silver nanoparticle aggregates with increasing concentration of electrolytes. Divalent cations could increase the size of silver nanoparticles more effectively compared with monovalent electrolyte. As a result, particle size of silver nanoparticles are much higher in seawater than in surface fresh water.
Acidic Laminin: Molecular Mechanisms and Potential for Nervous Tissue Repair

Biomaterials have shown promise for treatment of injuries to the nervous system. Laminin, a glycoprotein, forms distinct polymers under neutral (pH 7; neutral laminin, nLam) or acidic (pH 4; acidic laminin, aLam) conditions (1, 2). aLam promotes significant axonal growth (2), making it of interest as a therapeutic for nervous tissue injuries. In this thesis, instead of as a substrate, we evaluate unbound aLam. In Chapter 2.1, we use an in vitro model system to investigate the mechanisms underlying aLam growth promotion. Results indicate: 1) laminin can act as a signaling molecule promoting outgrowth of adult neurons in vitro; 2) aLam is a more efficient promoter of outgrowth than nLam; 3) both polymers signal through α1 and α3 integrins without increasing their expression; 4) aLam, increases α3 integrins when α1 integrins are blocked; 6) aLam increases vinculin, a focal adhesion complex protein. These findings indicate that aLam promotes outgrowth by increasing integrin activation to enhance neurite outgrowth. In Chapter 2.2 microcontact printing and live imaging were combined to evaluate aLam’s effects on growth dynamics. Our results suggest: 1) neurons will adhere to stamps and grow in a directional manner in culture; 2) cells did not adhere or grow well during live imaging. Results indicate potential for directing neuronal outgrowth, but optimization is necessary to assess growth dynamics. Peripheral nerve injury (PNI) and spinal cord injury (SCI) are devastating. In Chapter 3.1 we investigate aLam’s treatment potential in PNI. Results indicate that aLam treatment: 1) increased presence of larger diameter axons; 2) facilitated compliance in treadmill walking; 3) alleviated autophagia; 4) did not affect motor function, axon number or myelination. These data show that aLam treatment elicits an axon growth response without affecting motor function recovery. Further research is needed to optimize treatment for functional improvements. Chapter 3.2 evaluates aLam treatment after SCI. Results show that aLam treatment: 1) did not affect axon regeneration; 2) decreased astrocyte activation; 3) did not affect neuropathic pain or motor outcomes. The data indicate treatment did not lead to functional improvements. Further research is needed to investigate the potential of aLam for SCI repair.
Accelerating Deep Learning Frameworks with Micro-Batches

cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN, the decision is performed on a per-layer basis, and thus it often resorts to slower algorithms that fit the workspace size constraints. We present u-cuDNN, a thin wrapper library for cuDNN that transparently divides layers' mini-batch computation into multiple micro-batches, both on a single GPU and a heterogeneous set of GPUs. Based on Dynamic Programming and Integer Linear Programming (ILP), u-cuDNN enables faster algorithms by decreasing the workspace requirements. At the same time, u-cuDNN does not decrease the accuracy of the results, effectively decoupling statistical efficiency from the hardware efficiency. We demonstrate the effectiveness of u-cuDNN for the Caffe and TensorFlow frameworks, achieving speedups of 1.63x for AlexNet and 1.21x for ResNet-18 on the P100-SXM2 GPU. We also show that u-cuDNN achieves speedups of up to 4.54x, and 1.60x on average for DeepBench's convolutional layers on the V100-SXM2 GPU. In a distributed setting, u-cuDNN attains a speedup of 2.20x when training ResNet-18 on a heterogeneous GPU cluster over a single GPU. These results indicate that using micro-batches can seamlessly increase the performance of deep learning, while maintaining the same overall memory footprint.
Keeping Secrets from Friends: Design Guidelines for Multiplexed Graphical Passwords

www.aodr. org 49 Copyright : This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/bync/3.0/), which permits unrestricted educational and non-commercial use, provided the original work is properly cited. Keeping Secrets from Friends: Design Guidelines for Multiplexed Graphical Passwords Ian Oakley, Andrea Bianchi 1 Ulsan National Institute of Science and Technology, Department of Human and Systems Engineering, Ulsan, Korea. 2 Sungkyunkwan Unversity, School of Information and Communication Engineering, Suwon, Korea.
Learning objects from RGB-D sensors using point cloud-based neural networks

In this paper we present a scene understanding approach for assistive robotics based on learning to recognize diﬀerent objects from RGB-D devices. Using the depth information it is possible to compute descriptors that capture the geometrical relations among the points that constitute an object or extract features from multiple viewpoints. We developed a framework for testing diﬀerent neural models that receive this depth information as input. Also, we propose a novel approach using three-dimensional RGB-D information as input to Convolutional Neural Networks. We found F1-scores greater than 0 . 9 for the majority of the objects tested, showing that the adopted approach is eﬀective as well for classiﬁcation.
A review of biomass densification systems to develop uniform feedstock commodities for bioenergy application

Developing uniformly formatted, densified feedstock from lignocellulosic biomass is of interest to achieve consistent physical properties such as size and shape, bulk and unit density, and durability, which significantly influence storage, transportation and handling characteristics, and, by extension, feedstock cost and quality. A variety of densification systems are considered for producing a uniform format feedstock commodity for bioenergy applications, including (i) pellet mill, (ii) cuber, (iii) screw extruder, (iv) briquette press, (v) roller press, (vi) tablet press, and (vii) agglomerator. Each of these systems has varying impacts on feedstock chemical and physical properties, and energy consumption. This review discusses the suitability of these densification systems for biomass feedstocks and the impact these systems have on specific energy consumption and end‐product quality. For example, a briquette press is more flexible in terms of feedstock variables where higher moisture content and larger particles are acceptable for making good quality briquettes; or among different densification systems, a screw press consumes the most energy because it not only compresses but also shears and mixes the material. Pre‐treatment options like pre‐heating, grinding, steam explosion, torrefaction, and ammonia fiber explosion (AFEX) can also help to reduce specific energy consumption during densification and improve binding characteristics. Binding behavior can also be improved by adding natural binders, such as proteins, or commercial binders, such as lignosulfonates. The quality of the densified biomass for both domestic and international markets is evaluated using PFI (United States standard) or CEN (European standard). Published in 2011 by John Wiley & Sons, Ltd
Evolutionary psychology: A Primer

The goal of research in evolutionary psychology is to discover and understand the design of the human mind. Evolutionary psychology is an approach to psychology, in which knowledge and principles from evolutionary biology are put to use in research on the structure of the human mind. It is not an area of study, like vision, reasoning, or social behavior. It is a way of thinking about psychology that can be applied to any topic within it.
Dental and periodontal disease in hemodialysis patients is associated with inflammation; a study from North India

Introduction: Poor oral health and periodontitis is an important problem in chronic kidney disease (CKD) patients on hemodialysis (HD) awaiting kidney transplantation. This could contribute to increased morbidity and mortality as a result of infections and consequences of inflammation. Objectives: To study the oral health status and assess its impact in inducing inflammation in CKD patients on HD. Patients and Methods: Fifty CKD patients on HD (group I) were compared with 50 healthy controls (group-II) in a prospective cohort study. Oral health assessment was done with the WHO oral health assessment form with a mouth mirror and a community periodontal index (CPI) probe by a trained dentist. The demographic clinical data of oral health and periodontitis was collected in dialysis patients and healthy controls. CRP levels were assayed as markers of inflammation. Results: Data shows that patients on dialysis have evidence of increased inflammation as indicated by raised CRP values. Our study shows the poor oral and mouth hygiene and periodontitis and inflammation was much more in dialysis patients as compared to control group (P<0.05). Amongst dialysis patients with higher inflammation (CRP values), there was poorer oral health and more periodontitis (P<0.05). This indicates that higher inflammation could result from poor dental hygiene and periodontitis. Conclusion: Poor oral and mouth hygiene was associated with inflammation as indicated by high CRP values in CKD patients on dialysis with poor dental hygiene. Periodontitis was significantly associated with development of inflammation.
Parametric analysis of continuous high shear wet granulation

OF THE THESIS PARAMETRIC ANALYSIS OF CONTINUOUS HIGH SHEAR WET GRANULATION By WEI MENG Thesis Director: Fernando J. Muzzio, Ph.D Wet granulation (WG) is widely used in the pharmaceutical industry. This technology has many advantages such as enhancing compression and powder handling, decreasing ingredient segregation and ensuring the content uniformity. Currently, a high level of interest exists in the continuous version of this technology, both by the US FDA, and by large pharmaceutical manufacturers. Continuous manufacturing can provide significant technical and business advantages relative to batch methods. They are more robust and controllable and can achieve the same production rates as batch processes in much smaller and thus less capital-intensive equipment. In recent years, different continuous WG techniques have been investigated. However, integration of these unit operations into a complete continuous tablet manufacturing line is still a bottleneck due to high production cost and poor understanding of the process. In this work, a continuous highshear wet granulation process is examined based on a placebo formulation comprising of 69.7% ∂∂ -lactose monohydrate, 29.3% microcrystalline cellulose and 1% Magnesium stearate. Two process variables (rotation speed, L/S ratio) and two design parameters (blade configuration and nozzle position) are selected for the I-optimal design. The collected granules are dried in a fluid bed drier to a desirable LOD (~ 3%). Granule
Comparison of the AMPLICOR MYCOBACTERIUM test with microscopy and culture for diagnosis of pulmonary tuberculosis

Tuberculosis (TB) is one of the major public health problems in Taiwan. Effective control of TB depends on rapid and correct diagnosis and appropriate treatment. The aim of this study was to evaluate the performance of the Roche AMPLICOR MYCOBACTERIUM (AMPLICOR MTB) test compared with microscopy and culture for diagnosis of pulmonary TB. A total of 123 specimens obtained from 90 suspected pulmonary TB patients were included in this study. All specimens were processed, stained, cultured and assayed using the AMPLICOR MTB test for identification of M. tuberculosis. All patients were followed up for 1 year to confirm or exclude the diagnosis of pulmonary TB. M. tuberculosis was isolated from 29 of these specimens, and final diagnosis of pulmonary TB was identified in 50 patients according to clinical discrepancies. On initial testing, the sensitivity, specificity, positive and negative predictive values of the AMPLICOR MTB test, compared with cultures, were 75.9, 84, 59.5, and 91.9%, respectively. After resolution of discrepancies by the final clinical diagnosis, the sensitivity, specificity, positive and negative predictive values were 72, 98.6, 97.3, and 83.7%, respectively. For smear-positive specimens, the sensitivity was 84.9%, for smear-negative specimens, the sensitivity was 47.1%. Our study demonstrated that the use of the AMPLICOR MTB test was highly sensitive and specific for rapid diagnosis of pulmonary TB. Further studies are needed to determine the cost-effective use of this test with smear-negative specimens.
Epileptic Seizure Detection: A Deep Learning Approach

Epilepsy is the second most common brain disorder after migraine. Automatic detection of epileptic seizures can considerably improve the patients' quality of life. Current Electroencephalogram (EEG)-based seizure detection systems encounter many challenges in real-life situations. The EEGs are non-stationary signals and seizure patterns vary across patients and recording sessions. Moreover, EEG data are prone to numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges, we introduce the use of a deep learning-based approach that automatically learns the discriminative EEG features of epileptic seizures. Specifically, to reveal the correlation between successive data samples, the time-series EEG data are first segmented into a sequence of non-overlapping epochs. Second, Long Short-Term Memory (LSTM) network is used to learn the high-level representations of the normal and the seizure EEG patterns. Third, these representations are fed into Softmax function for training and classification. The results on a well-known benchmark clinical dataset demonstrate the superiority of the proposed approach over the existing state-of-the-art methods. Furthermore, our approach is shown to be robust in noisy and real-life conditions. Compared to current methods that are quite sensitive to noise, the proposed method maintains its high detection performance in the presence of common EEG artifacts (muscle activities and eye-blinking) as well as white noise.
Productivity of Sunflower Hybrids (Helianthus Annuus L.) Under The Effect of AKM Plant Growth Regulator In The Conditions Low Moisture of Southern Steppe of Ukraine

The results of studies on the influence of AKM plant growth regulator (PGR) on growth, development and yield formation of different sunflower hybrids (Zubr, Odes'kyi 249, Forvard, Yason) in low humidity of the southern Steppe of Ukraine are presented. Presowing treatment of seeds of different sunflower hybrids with AKM plant growth regulator causes an increase in leaf surface area by an average of 7%; increases sunflower plant resistance to abiotic stress and increases productivity by 13 - 31%.
Why semilocal functionals work: Accuracy of the on-top pair density and importance of system averaging

Gradient-corrected density functionals provide a common tool for electronic structure calculations in quantum chemistry and condensed matter physics. This article explains why local and semilocal approximations work for the exchange-correlation energy. We demonstrate the high accuracy of the local spin-density ~LSD! approximation for the on-top pair density, which provides the missing link between real atoms and molecules and the uniform electron gas. Special attention is devoted to the leading correction to exchange in the high-density ~or weakly correlated! limit. We give an improved analytic expression for the on-top pair density in the uniform electron gas, calculating its spin-polarization dependence exactly in the high-density limit. We find the exact form of the gradient expansion for the on-top pair density, using Levy’s scaling of the interacting wave function. We also discuss the importance of system averaging, which unweights spatial regions where the density varies most rapidly. We show how the depth of the on-top hole correlates with the degree of locality of the exchange-correlation energy. Finally, we discuss how well fully nonlocal approximations ~weighted-density, self-interaction correction, and hybrid-exchange ! reproduce the on-top hole. © 1998 American Institute of Physics. @S0021-9606~98! 30534-6#
Trophodynamics in marine ecology: 70 years after Lindeman

The seminal work of Lindeman (1942), 'The trophic-dynamic aspect of ecology' (Ecology 23:399), has been an important starting point for the holistic view of ecosystem tropho - dynamics, but it was initially seldom applied to marine ecosystems. Over the past 70 years, research on marine trophodynamics has become more widespread, producing a variety of analyt- ical methods, and increasing our understanding of marine ecosystem functioning. Yet difficulties remain in transforming this body of knowledge into operational management of marine ecosys- tems and marine resources. This Theme Section on 'Trophodynamics in marine ecology' docu- ments recent advances and lessons learned over the past 70 years, and provides an opportunity to reflect on future directions for marine research.
Middle Class in Africa: Determinants and Consequences

ABSTRACT This study complements the inclusive growth literature by examining the determinants and consequences of the middle class in a continent where economic growth has been relatively high. The empirical evidence is based on a sample of 33 African countries for a 2010 cross-sectional study. Ordinary least squares, two-stage-least squares, three-stage-least squares and seemingly unrelated regressions estimation techniques are employed to regress a plethora of middle class indicators, notably, the: floating, middle-class with floating, middle-class without floating, lower-middle-income and upper-middle-income categories. Results can be classified into two main strands. First, results on determinants broadly show that GDP per capita and education positively affect all middle class dependent variables. However, we establish a negative nexus for the effect of ethnic fragmentation, political stability in general and partially for economic vulnerability. Simple positive correlations have been observed for: the size of the informal sector, openness and democracy. Second, on the consequences, the middle class enables the accumulation of human and infrastructural capital, while its effect is null on political stability and democracy in the short run but positive for governance and modernisation. Policy implications are discussed.
MicroRNA-409-3 p inhibits cell proliferation , migration and invasion by targeting RAB 10 in endometrial carcinoma

MicroRNAs (miRs) are small non-coding RNAs that involved in the development and progression of human cancer by negatively regulating its target genes. As one of them, miR-409-3p has been recently shown to be dysregulated in some malignancies, but its expression and biological function in endometrial carcinoma (EC) remain elusive. In this study, we found miR-409-3p was significantly downregulated in human EC tissues compared with normal endometrial tissues by quantitative real-time PCR (qRT-PCR) analysis. Functional analyses in vitro indicated overexpression of miR-409-3p remarkably suppressed EC cell proliferation, induced cell cycle arrest at G0/G1 phase and apoptosis, and reduced migratory activity, as revealed by CCK-8 assay, colony formation assay, flow cytometry and Transwell assays, respectively. Using bioinformatics and luciferase reporter assay, we identified RAB10 was a target of miR-409-3p. Furthermore, overexpression of miR-409-3p increased the expression of PARP cleavage and E-cadherin and decreased the expression of RAB10, CDK4, Cyclin D1, N-cadherin and Vimentin using Western blot analysis. These findings suggest that miR-409-3p could be a potential tumor suppressor in EC through the direct targeting of RAB10.
Complexity of theorem-proving procedures : some general properties

— Theorem-proving procedures are considérée as a family of partial recursive fonctions satisfying some axioms which express the properties of soundness and of decidability for a solvable class of formulas including ail propositions, i.e. ground sentences, and an infinité class of non ground sentences. By using whatever complexity, measure satisfying Blurn 's axioms it is proved that such a family of functions is n^complete ; contains « difficult » proof-procedures ; is such that every its éléments has some programs having the « oscillation property » ; does not contain a « best » proof-procedure. Moreover a notion of approximation is defined according to which the set of ail theorems of a first-order theory is approximable.
Potential assessment of microalgal oils for biodiesel production : A review

In view of increasing energy demand, climate change, increasing price of petroleum and fast depleting fossil fuel resources, the urgent need of finding alternatives fuels is being felt all over the world. Presently, the microalgae, are viewed as potential 3 rd generation source of biodiesel due to significant advantages over terrestrial seed oil plants. The paper attempts theoretically to assess the potential of oils from six microalgae species available in north part of India for the production of biodiesel on the basis on oxidation stability index and Oxidizability. Based on the fatty acid compositions, APE and BAPE are find theoretically. On the basis of APE and BAPE, oxidation stability index and oxidizability have been assessed. The grading of oils in terms of OSI and OX helps to select the potential feed stocks for biodiesel production and discard the other feed stocks that may require considerable efforts to improve the oxidation stability. It is concluded that the oils can be arranged in the decreasing order of oxidation stability index: Pithophora> Spirogyra> Hydrodictyon> Cladophora> Tolypothrix> Rhizoclonium and on the basis of oxidizability. : Pithophora> Spirogyra> Hydrodictyon> Cladophora> Tolypothrix> Rhizoclonium which means that Pithophora takes more time to oxidize and best suited for biodiesel production while the other oil needs antioxidant to increase their stability for biodiesel production.
CMO-BIRS SANDPILE WORKSHOP OPEN PROBLEM: MONOMIZATIONS OF POWER IDEALS AND AN INTERVAL DECOMPOSITION OF THE ACYCLIC PARTIAL ORIENTATIONS

This note grew out of joint work and discussion with Spencer Backman. Fix G = (V,E), a finite, undirected, connected graph which can have multiple edges but not loops. Designate a distinguished root vertex q ∈ V and set V q := V − {q}. All constructions that follow will depend on the pair (G, q), which we call a pointed graph. For any U ⊆ V and u ∈ U , define dU (u) := #{e = {u, v} ∈ E : v ∈ V − U} and dU := ∑ u∈U dU (u). Let k be some field and R := k[xv : v ∈ V q] the polynomial ring with generators indexed by non-root vertices. For r ≥ −1 define the following power ideal of R: