gebhart/patrepeval huggingface dataset:
A Novel Cubic Convolutional Neural Network for Hyperspectral Image Classification (2020)

Recently, the hyperspectral image (HSI) classification methods based on convolutional neural networks (CNN) have developed rapidly with the advance of deep learning (DL) techniques. In order to more efficiently extract spatial and spectral features, we propose an end-to-end cubic CNN (Cubic-CNN) in this article. The proposed Cubic-CNN is a supervised DL framework that significantly improves classification accuracy and shortens training time. Specifically, Cubic-CNN employs the dimension reduction method combined with principal component analysis and 1-D convolution to remove redundant information from HSIs. Then, convolutions are performed on the planes in different directions of the feature cube data to fully extract spatial and spatial-spectral features and fuse the features from different dimensions. In addition, we performed batch normalization on the data cube after each convolutional layer to improve the performance of the network. Extensive experiments and analysis on standard datasets show that the proposed algorithm can outperform the existing state-of-the-art DL-based methods.
Adaptive motion model selection using a cubic spline based estimation framework (2010)

A block based video coder that supports multiple motion models is proposed. Apart from the typical translational motion model, we employ parametric models to more accurately represent complex motions that occur in video sequences. A novel method for estimating the warping parameters in a rate-constrained way is presented. A cubic spline framework is utilized to obtain fractional-accuracy samples for motion compensation. Efficient motion vector prediction schemes are developed to maintain the continuity of the predictor in spite of different motion models. Bit rate savings up to 11.7% in IPPP and 9.3% in Hierarchical B pictures are shown compared to an improved H.264/AVC reference.
CH Bond Functionalization in Water Catalyzed by Carboxylato Ruthenium(II) Systems (2010)

In water it's better: In-situ-generated [Ru(O2CR)2(arene)] catalysts efficiently perform the direct ortho-arylation of functional arenes with chloroarenes or chloroheterocycles in water (see scheme; KOPiv=potassium pivalate), without the need for a surfactant. The activity of these catalysts is higher in water than in organic solvents.
Stretchable and Self-Healing Graphene Oxide–Polymer Composite Hydrogels: A Dual-Network Design (2013)

Superior mechanical properties and self-healing are two hot topics in hydrogel science due to their tight relationship with the following potential application scopes. Most of the conventional hydrogels do not possess both features at the same time. Herein, we expand the recently proposed intertwined double-network mechanism and prepare a novel class of graphene oxide (GO)/poly(acryloyl-6-aminocaproic acid) (PAACA) composite hydrogels with enhanced mechanical properties and self-healing capability to pH stimulus. Without the use of any conventional organic cross-linkers, the double networks in GO/PAACA hydrogels are triggered by GO nanosheets and calcium ions as cross-linkers. For one thing, Ca2+ induces the formation of the 3D cross-linked network through coordination interactions with both oxygen-containing groups of GO nanosheets and polar groups of PAACA side chains. For another, powerful hydrogen-bonding network is simultaneously interconnected, attributed to the interactions of polar groups of PAACA side chains with both other polar groups of PAACA side chains and oxygen-containing groups of GO nanosheets.
Nimble Algorithms for Cloud Computing (2013)

Cloud computing is a new paradigm where data is stored across multiple servers and the goal is to compute a function of all the data. We consider a simple model where each server uses polynomial time and space, but communication among servers being more expensive is ideally bounded by a polylogarithmic function of the input size. We will dub algorithms that satisfy these types of resource bounds as nimble. The main contribution of the paper is to develop nimble algorithms for several areas which involve massive data and for that reason have been extensively studied in the context of Streaming Algorithms. The areas are approximation of Frequency Moments, Counting bipartite homomorphisms (number of copies of a fixed bipartite graph H in a graph G), Rank-k approximation to a matrix, and Clustering. For frequency moments, we will use a new importance sampling technique based on high powers of the frequencies. We reduce the problem of counting homomorphisms to estimating implicitly defined frequency moments. For rank-k approximations, besides recent results of several authors developed in the Streaming context, we use a new variant of the random projection method. For clustering, we use our rank-k approximation and the small coreset of Chen [15] of size at most polynomial in the dimension. In contrast to our algorithms in the cloud computing model, in the streaming model, known lower bound results for frequency moments and rank-k approximations rule out the existence of algorithms that use polylogarithmic space. Microsoft Research India. Email:kannan@microsoft.com Georgia Tech. Email: vempala@gatech.edu 1
Criteria for the Validation of Quantitative Histochemical Enzyme Techniques (1980)

Some practical criteria are suggested for establishing the precision, reproducibility, validity and specificity of quantitative histochemical techniques used for assaying the activities of enzymes in single cells and tissue sections. To be valid, a technique should ideally pass 12 tests. Principally these involve proving that the mean absorbance or fluorescence of the specific final reaction product (FRP) is related to section thickness, incubation time, substrate concentration and the concentration of enzyme in situ. However, the formation of appreciable amounts of non-specific FRP may interfere in the determination of the true enzyme activity. This and other difficulties are illustrated with data obtained from an investigation of Meijer's semipermeable membrane technique for assaying acid phosphatase in unfixed sections of muscle.
Multicolor Core/Shell‐Structured Upconversion Fluorescent Nanoparticles (2008)

Core/shell-structured silica/NaYF4 nanospheres featuring uniform silica coatings are synthesized using a facile, user-friendly method. Multicolor nanospheres are produced by encapsulation of organic dyes or quantum dots in the silica shell, and upconversion fluorescence is generated based on fluorescence resonance energy transfer (FRET).
The three cysteine residues of cytoplasmic aspartyl‐tRNA synthetase from <i>Saccharomyces cerevisiae</i> are not essential for its activity (1990)

Cytoplasmic aspartyl-tRNA synthetase from Saccharomyces cerevisiae is a dimer made up of identical subunits (Mr 63,000) each of these containing three cysteines (residues 255, 512 and 519 in the amino acid sequence). Thiol-specific probes were used to label these cysteines and study the resulting effect of the modification on the kinetic parameters of both the ATP/PPi exchange and tRNA aminoacylation reactions. Using the classical techniques of protein chemistry it was shown that none of the three cysteines was labelled with iodoacetic acid, whilst N-ethylmaleimide and 5,5'-dithiobis(2-nitrobenzoate) reacted with Cys512 and Cys255, respectively. Only the latter modification was accompanied by a decrease in the rates of both enzyme activities whilst the Km values for the various substrates remained unaffected. Site-directed mutagenesis was also used to replace each of the three cysteines by other residues, either individually or simultaneously. For these experiments the enzyme was expressed in Escherichia coli using an expression vector bearing the structural gene in which the first 13 codons were replaced by the first 14 of the CII lambda gene. The resulting substitution in the amino-terminal part of the expressed enzyme had no effect on the kinetic parameters, compared to those of the enzyme purified from S. cerevisiae. Taking into account the consequences of such substitutions, as well as those of chemical modifications on the two reactions catalysed by the enzyme. ATP/PPi exchange and tRNA aminoacylation, it could be concluded that none of these three cysteines plays any essential role in either substrate binding or catalysis.
Imaging in scattering media using correlation image sensors and sparse convolutional coding (2014)

Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media. The key of our method is a new convolutional sparse coding approach for recovering transient (light-in-flight) images from correlation image sensors. This approach is enabled by an analysis of sparsity in complex transient images, and the derivation of a new physically-motivated model for transient images with drastically improved sparsity.
Photocatalytic oxidation for point-of-use abatement of volatile organic compounds in microelectronics manufacturing (1995)

In gas–solid heterogeneous photocatalytic oxidation (PCO), volatile organic compounds (VOCs) present in process air or air vents can be rapidly and completely oxidized to innocuous by-products over a near-ultraviolet (UV) illuminated titanium dioxide thin film catalyst at room temperature. This class of advanced oxidation processes appears to be well-suited for point-of-use VOC abatement in the microelectronics manufacturing industry. In this article, we review industrial requirements and unresolved technical issues in the context of the recently published Semiconductor Industry Association roadmap. The specific requirements for VOC abatement from a typical photolithography track are presented. Bench-scale PCO kinetics for target VOCs are reviewed to demonstrate the typical process behavior expected.
Business‐community partnerships: understanding the nature of partnership (2011)

Purpose This paper aims to offer a New Zealand perspective on how business and community organisations engage to develop mutually beneficial partnerships to tackle pressing social issues. Specifically, the paper seeks to examine the collaboration motivations for business and community partners involved in seven business‐community partnerships in New Zealand. Design/methodology/approach The paper utilises data from in‐depth, semi‐structured interviews with key business and community managers involved in seven partnerships in New Zealand. The transcripts of the interviews were analysed using elements of content and narrative analysis. Findings to be presented in this paper include: explaining what “partnership” is; understanding a business case; and community organisations' motivations for engaging in partnerships with business. Findings This research finds that, while partnerships involving business and community organisations may ideally be associated with shared societal concerns, in this study there was a very strong focus on individual community organisation goals and a dominance of business priorities. This was not balanced by an interest in the broader meta‐goals of the partnership. Originality/value This paper draws attention to diverse and often competing motivations that characterise business‐community partnerships. The research demonstrates that, while partnerships are often discussed in the context of societal benefits, individual organisations frequently form partnerships primarily for their own instrumental self‐interests. It is hoped that this paper will stimulate understanding of the practical challenges to developing business‐community partnerships, given differences among the partners in goal orientations and expectations.
Changes in the Autolytic Activities of Maize Coleoptile Cell Walls during Coleoptile Growth (1997)

Autolytic activities of coleoptile cell walls were measured in developing maize seedlings. The major neutral sugar components of the cell wall polysaccharides were arabinose, xylose and glucose. The quantities of all these components per coleoptile increased for 5 d after germination, suggesting that levels are augmented by biosynthetic processes during coleoptile growth. However, cell wall preparations isolated from the coleoptiles also revealed increasing rates of autolytic activity directed toward each of the sugar components. This result suggests that the constitutive hydrolytic activities expressed by cell walls also increase as a function of coleoptile age. The proportion of glucose in autolysis products relative to that present in the cell walls specifically increased with coleoptile age, while the ratios for arabinose and xylose decreased. Kinetic analyses of autolysis demonstrated that the reactions specific for pentoses at the early growth stage are transient events and that initial low rates of glucan autolysis increased sharply and persisted longer. In these experiments the products of glucan autolysis were largely monomeric while those of the pentose-specific reactions consisted of both monomeric and polymeric sugars. Based on these results, we concluded that two distinct phases of autolytic activities are expressed in the mediation of cell wall polysaccharide metabolism in situ.
miR-212 promotes pancreatic cancer cell growth and invasion by targeting the hedgehog signaling pathway receptor patched-1 (2014)

These data suggest that miR-212 could facilitate PDAC progression and metastasis through targeting PTCH1, implicating a novel mechanism for the progression of PDAC.
Stimulation of Reductive Dechlorination of Tetrachloroethene in Anaerobic Aquifer Microcosms by Addition of Short-Chain Organic Acids or Alcohols (1992)

The effect of the addition of common fermentation products on the dehalogenation of tetrachloroethene was studied in methanogenic slurries made with aquifer solids. Lactate, propionate, crotonate, butyrate, and ethanol stimulated dehalogenation activity, while acetate, methanol, and isopropanol did not.
Functional Characterization of FH Mutation c.557G>A Underlies Uterine Leiomyomas (2022)

The <i>FH</i> gene encodes the fumarate hydratase of the Krebs cycle and functions as a homotetramer to catalyze the hydration of fumarate to malate. Mutations in <i>FH</i> result in uterine leiomyomas, a rare autosomal dominant inherited metabolic disease. However, how <i>FH</i> mutations result in this disease is poorly understood. Here, the <i>FH</i> mutation c.557G>A (p.S186N) was identified in a family with uterine leiomyomas phenotype. A series of studies were performed to confirm the pathogenicity of this mutation. Results showed that the FH mutant exhibited significantly lower fumarase enzyme activity and increased the fumarates level compared with the wildtype, which might be due to the impaired homotetramer formation in the native gel electrophoresis. Interestingly, the immunofluorescence study revealed that the overexpressed FH mutant exhibited puncta structures compared with the evenly expressed FH wildtype in cytoplasm suggesting that the altered amino acid might result in dysfunctional proteins which were accumulated to reduce its cytotoxicity. Importantly, the cells overexpressing the FH mutant exhibited higher proliferation and extracellular acidification rate value (ECAR) which might be caused by the upregulated HIF-1α indicating the tumor phenotype. Notably, phospho-mTOR was significantly increased and autophagy was inhibited in the FH mutant overexpression cells compared with the wildtype. Our work provides new insight into the <i>FH</i> mutation c.557G>A (p.S186N) underlies uterine leiomyomas and important information for accurate genetic counseling and clinical diagnosis of the disease.
Somatic mutations in neurons during aging and neurodegeneration (2018)

The nervous system is composed of a large variety of neurons with a diverse array of morphological and functional properties. This heterogeneity is essential for the construction and maintenance of a distinct set of neural networks with unique characteristics. Accumulating evidence now indicates that neurons do not only differ at a functional level, but also at the genomic level. These genomic discrepancies seem to be the result of somatic mutations that emerge in nervous tissue during development and aging. Ultimately, these mutations bring about a genetically heterogeneous population of neurons, a phenomenon that is commonly referred to as "somatic brain mosaicism". Improved understanding of the development and consequences of somatic brain mosaicism is crucial to understand the impact of somatic mutations on neuronal function in human aging and disease. Here, we highlight a number of topics related to somatic brain mosaicism, including some early experimental evidence for somatic mutations in post-mitotic neurons of the hypothalamo-neurohypophyseal system. We propose that age-related somatic mutations are particularly interesting, because aging is a major risk factor for a variety of neuronal diseases, including Alzheimer's disease. We highlight potential links between somatic mutations and the development of these diseases and argue that recent advances in single-cell genomics and in vivo physiology have now finally made it possible to dissect the origins and consequences of neuronal mutations in unprecedented detail.
Generalized Sliced Distances for Probability Distributions (2020)

Probability metrics have become an indispensable part of modern statistics and machine learning, and they play a quintessential role in various applications, including statistical hypothesis testing and generative modeling. However, in a practical setting, the convergence behavior of the algorithms built upon these distances have not been well established, except for a few specific cases. In this paper, we introduce a broad family of probability metrics, coined as Generalized Sliced Probability Metrics (GSPMs), that are deeply rooted in the generalized Radon transform. We first verify that GSPMs are metrics. Then, we identify a subset of GSPMs that are equivalent to maximum mean discrepancy (MMD) with novel positive definite kernels, which come with a unique geometric interpretation. Finally, by exploiting this connection, we consider GSPM-based gradient flows for generative modeling applications and show that under mild assumptions, the gradient flow converges to the global optimum. We illustrate the utility of our approach on both real and synthetic problems.
Comparison of gene expression profiles between hepatitis B virus- and hepatitis C virus-infected hepatocellular carcinoma by oligonucleotide microarray data on the basis of a supervised learning method. (2002)

Gene expression profiles of hepatocellular carcinomas (HCCs) associated with hepatitis B virus (HBV) and hepatitis C virus (HCV) were analyzed and compared. Oligonucleotide microarrays containing >6000 genes and subsequent gene selection by a supervised learning method yielded 83 genes for which expression differed between the two types of HCCs. Expression levels of 31 of these 83 genes were increased in HBV-associated HCCs, and expression levels of the remaining 52 genes were increased in HCV-associated HCCs. The 31 genes up-regulated in HBV-associated HCC included imprinted genes (H19 and IGF2) and genes relating to signal transduction, transcription, and metastasis. The 52 genes up-regulated in HCV-associated HCC included a number of genes responsible for detoxification and immune response. These results suggest that HBV and HCV cause hepatocarcinogenesis by different mechanisms and provide novel tools for diagnosis and treatment of HBV- and HCV-associated HCCs.
Optimal Channel Shortening Equalization for MIMO ISI Channels (2008)

We consider the problem of channel shortening equalization to perform reduced complexity detection for MIMO channels with intersymbol interference and additive Gaussian noise, the problem is to find a MIMO equalizer and a partial response MIMO target filter such that the combination produces the best detection performance. We show the existence of an infinite family of optimal equalizers and targets that satisfy the a posteriori equivalence condition. Furthermore, these solutions are intimately related to MIMO decision feedback equalizers designed with the "monk determinant" constraint on the feedback filter. These results generalize some recent results on channel shortening based on a posteriori equivalence to the MIMO setting.
Functional Genomics Reveals Extended Roles of the <i>Mycobacterium tuberculosis</i> Stress Response Factor σ <sup>H</sup> (2009)

Mycobacterium tuberculosis is one of the most successful pathogens of humankind. During infection, M. tuberculosis must cope with and survive against a variety of different environmental conditions. Sigma factors likely facilitate the modulation of the pathogen's gene expression in response to changes in its extracellular milieu during infection. sigma(H), an alternate sigma factor encoded by the M. tuberculosis genome, is induced by thiol-oxidative stress, heat shock, and phagocytosis. In response to these conditions, sigma(H) induces the expression of sigma(B), sigma(E), and the thioredoxin regulon. In order to more effectively characterize the transcriptome controlled by sigma(H), we studied the long-term effects of the induction of sigma(H) on global transcription in M. tuberculosis. The M. tuberculosis isogenic mutant of sigma(H) (Delta-sigma(H)) is more susceptible to diamide stress than wild-type M. tuberculosis. To study the long-term effects of sigma(H) induction, we exposed both strains to diamide, rapidly washed it away, and resumed culturing in diamide-free medium (post-diamide stress culturing). Analysis of the effects of sigma(H) induction in this experiment revealed a massive temporal programming of the M. tuberculosis transcriptome. Immediately after the induction of sigma(H), genes belonging to the functional categories "virulence/detoxification" and "regulatory proteins" were induced in large numbers. Fewer genes belonging to the "lipid metabolism" category were induced, while a larger number of genes belonging to this category were downregulated. sigma(H) caused the induction of the ATP-dependent clp proteolysis regulon, likely mediated by a transcription factor encoded by Rv2745c, several members of the mce1 virulence regulon, and the sulfate acquisition/transport network.
Genomic characterization of a novel SARS-CoV-2 lineage from Rio de Janeiro, Brazil (2020)

Abstract In this study, we report the sequencing of 180 new viral genomes obtained from different municipalities of the state of Rio de Janeiro from April to December 2020. We identified a novel lineage of SARS-CoV-2, originated from B.1.1.28, distinguished by five single-nucleotide variants (SNVs): C100U, C28253U, G28628U, G28975U, and C29754U. The SNV G23012A (E484K), in the receptor-binding domain of Spike protein, was widely spread across the samples. This mutation was previously associated with escape from neutralizing antibodies against SARS-CoV-2. This novel lineage emerged in late July being first detected by us in late October and still mainly restricted to the capital of the state. However, as observed for other strains it can be rapidly spread in the state. The significant increase in the frequency of this lineage raises concerns about public health management and continuous need for genomic surveillance during the second wave of infections. Article Summary Line We identified a novel circulating lineage of SARS-CoV-2 in the state of Rio de Janeiro Brazil originated from B.1.1.28 lineage.