Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxidative Damage and Mutagenesis in Saccharomyces cerevisiae: Genetic Studies of Pathways Affecting Replication Fidelity of 8-Oxoguanine

Oxidative damage to DNA constitutes a major threat to the faithful replication of DNA in all organisms and it is therefore important to understand the various mechanisms that are responsible for repair of such damage and the consequences of unrepaired damage. In these experiments, we make use of a reporter system in Saccharomyces cerevisiae that can measure the specific increase of each type of base pair mutation by measuring reversion to a Trp+ phenotype. We demonstrate that increased oxidative damage due to the absence of the superoxide dismutase gene, SOD1, increases all types of base pair mutations and that mismatch repair (MMR) reduces some, but not all, types of mutations. By analyzing various strains that can revert only via a specific CG → AT transversion in backgrounds deficient in Ogg1 (encoding an 8-oxoG glycosylase), we can study mutagenesis due to a known 8-oxoG base. We show as expected that MMR helps prevent mutagenesis due to this damaged base and that Pol η is important for its accurate replication. In addition we find that its accurate replication is facilitated by template switching, as loss of either RAD5 or MMS2 leads to a significant decrease in accurate replication. We observe that these ogg1 strains accumulate revertants during prolonged incubation on plates, in a process most likely due to retromutagenesis.
Antituberculous effects of certain surface-active polyoxyethylene ethers.

We have previously reported (Cornforth, Hart, Rees, and Stock, 1951) that a commercial surfaceactive agent, the non-ionic " Triton A20 " (Rohm and Haas Co., Philadelphia),* has a suppressive effect on experimental tuberculosis in the mouse, and that the effect is shared by a number of similarly constituted products prepared by ourselves. These observations introduced a new type of antituberculous agent. Furthermore, since the therapeutically active products failed to inhibit the multiplication of tubercle bacilli in vitro, it was suggested that they might in some way modify the host's response to a tuberculous infection (Rees, 1953). The antituberculous properties of Triton A20 have been confirmed by Solotorovsky and Gregory (1952), who also reported a synergistic action with dihydrostreptomycin in a mouse infection. In addition to suppressing acute tuberculosis in mice, these agents can produce appreciable regression and healing of an established infection in the guinea-pig (Rees, 1953). The present paper supplies necessary details of the earlier work and describes further developments. Some of the results were briefly communicated by Cornforth, Hart, Nicholls, and Rees (1953).
ATM technology for corporate networks

Asynchronous transfer mode (ATM) technology is discussed in relation to the requirements of corporate networking. An introduction to ATM switch architecture is presented. The various approaches to ATM switch design that have appeared in previously published literature are reviewed. A discussion is presented of some current issues facing the development of the ATM networks. It is argued that much simpler solutions to many of these issues may be adopted in the context of the corporate network than is permissible for the public broadband network. <<ETX>>
0 Monetary and Fiscal Policy Effectiveness in China : Evidence from a FAVAR Model *

A common concern regarding Chinese GDP data is that it is an unreliable or noisy measure of the true state of Chinese economic activity. In this paper, we use a broad set of Chinese economic indicators and a dynamic factor model framework to estimate Chinese “economic activity” and price conditions as latent variables. Our method allows each economic indicator to have periods of missing data and a potentially different start date, which is important in the Chinese context where new indicators are regularly introduced. We incorporate our Chinese economic activity measure into a factor-augmented VAR (FAVAR) to estimate the effects of Chinese monetary and fiscal policy on economic activity. A FAVAR approach seems particularly well-suited to analysis of the Chinese economy dues to the aforementioned concerns with Chinese data quality, as well as the relative lack of long time series data available and the rapid structural changes that country has undergone. Contrary to much of the literature, we find that interest rate changes in China have substantive impacts on economic activity and prices, while measures of changes in credit conditions, such as shocks to M2 or lending levels, do not. These results are robust to the use of a narrow set of indicators with common start dates to estimate the activity series, as well as the introduction of a government expenditures series as a proxy for fiscal policy. The latter series also fails to have a significant impact on the economic activity series. Overall, our results indicate that the channels of transmission for Chinese monetary policy have become closer to those of Western market economies. JEL Codes: C3, E4, E5
Leveraging Linguistic Structure For Open Domain Information Extraction

Relation triples produced by open domain information extraction (open IE) systems are useful for question answering, inference, and other IE tasks. Traditionally these are extracted using a large set of patterns; however, this approach is brittle on out-of-domain text and long-range dependencies, and gives no insight into the substructure of the arguments. We replace this large pattern set with a few patterns for canonically structured sentences, and shift the focus to a classifier which learns to extract self-contained clauses from longer sentences. We then run natural logic inference over these short clauses to determine the maximally specific arguments for each candidate triple. We show that our approach outperforms a state-of-the-art open IE system on the end-to-end TAC-KBP 2013 Slot Filling task.
New Serological Markers for Inflammatory Bowel Disease Are Associated With Earlier Age at Onset, Complicated Disease Behavior, Risk for Surgery, and NOD2/CARD15 Genotype in a Hungarian IBD Cohort

BACKGROUND:Antibodies to Saccharomyces cerevisiae (S. cerevisiae) (ASCA) and porin protein-C of Escherichia coli (anti-OmpC) are associated with disease phenotype and may be of diagnostic importance in inflammatory bowel diseases (IBD). Our aim was to determine whether a panel of new antibodies against bacterial proteins and carbohydrates could help differentiate among the various forms of IBD, and whether they were associated with particular clinical manifestations in a Hungarian cohort of IBD patients.METHODS:Six hundred fifty-two well-characterized, unrelated, consecutive IBD patients (CD [Crohn's disease] 557, men/women 262/295, duration 8.1 ± 11.3 yr; ulcerative colitis [UC] 95, men/women 44/51, duration 8.9 ± 9.8 yr) and 100 healthy and 48 non-IBD gastrointestinal (GI) controls were investigated. Sera were assayed for anti-OmpC and antibodies against a mannan epitope of S. cerevisiae (gASCA), laminaribioside (ALCA), chitobioside (ACCA), and mannobioside (AMCA). TLR4 and NOD2/CARD15 variants were tested by polymerase chain reaction/restriction fragment length polymorphism (PCR-RFLP). Detailed clinical phenotypes were determined by reviewing the patients' medical charts.RESULTS:Sixty-six percent of the CD patients had at least one of the investigated antibodies. Among glycan antibodies, gASCA or the combination of gASCA and atypical perinuclear antineutrophil cytoplasmic antibodies (pANCA) was most accurate for differentiating between CD and UC. ASCA and gASCA assays performed similarly. Increasing amount and level of antibody responses toward gASCA, ALCA, ACCA, AMCA, and OmpC were associated with more complicated disease behavior (P < 0.0001) and need for surgery in CD (P = 0.023). A serological dosage effect was also observed. gASCA and AMCA antibodies were associated with NOD2/CARD15, in addition to a gene-dosage effect. No serotype-phenotype associations were found in UC.CONCLUSIONS:Antibody response to this new panel of serological markers was associated with complicated disease phenotype, NOD2/CARD15 genotype, and a need for surgery in this eastern European IBD cohort.
Evolving tiles for automated self-assembly design

Self-assembly is a distributed, asynchronous mechanism that is pervasive across natural systems where hierarchical complex structures are built from the bottom-up. The lack of a centralised master plan, no external intervention, and preprogrammed interactions among entities are within its most relevant and technologically appealing properties. This paper tackles the self-assembly Wang tiles designability problem by means of artificial evolution. This research is centred in the use of tiles that are extended with rotation and probabilistic motion, and an evolutionary algorithm using the Morphological Image Analyses method as a fitness function. The obtained results support this approach as a successful engineering mechanism for the computer-aided design of self-assembled patterns.
Leaky Quantum Graphs: A Review

The aim of this review is to provide an overview of a recent work concerning ``leaky'' quantum graphs described by Hamiltonians given formally by the expression $-\Delta -\alpha \delta (x-\Gamma)$ with a singular attractive interaction supported by a graph-like set in $\mathbb{R}^\nu,\: \nu=2,3$. We will explain how such singular Schr\"odinger operators can be properly defined for different codimensions of $\Gamma$. Furthermore, we are going to discuss their properties, in particular, the way in which the geometry of $\Gamma$ influences their spectra and the scattering, strong-coupling asymptotic behavior, and a discrete counterpart to leaky-graph Hamiltonians using point interactions. The subject cannot be regarded as closed at present, and we will add a list of open problems hoping that the reader will take some of them as a challenge.
Improving Mechanical Ventilator Clinical Decision Support Systems with A Machine Learning Classifier for Determining Ventilator Mode

Clinical decision support systems (CDSS) will play increasing role in improving quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have complete information on state of supporting physiologic monitoring devices, which can limit input data available to CDSS. This is especially true in use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS make accurate recommendations related to ventilator mode, we developed a highly performant machine learning model that is able to perform per-breath classification of five of most widely used ventilation modes in USA with average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and is highly robust to missing data caused by software/sensor error.
An adaptive technique for dynamic rollback in concurrent event-driven fault simulation

Both simulation for design verification and fault simulation in conjunction with automatic test pattern generation could take advantage of the possibility of moving the simulation time forward and backward. Up until now, rollback was allowed only by explicit backup/restore commands issued by the user. The paper presents a technique that makes possibles a "run for T" command, where T can be any time value, either positive or negative. It is based on an adaptive mechanism that automatically controls the parameters of an advanced network status recording system. A major feature is that the user may decide the maximum allowable overhead. Experiments show that the resulting rollback time is on the average very short, and therefore this technique can be efficiently used to improve sequential ATPG algorithms. <<ETX>>
Parametric regression scheme for distributions: Analysis of DTI fiber tract diffusion changes in early brain development

Temporal modeling frameworks often operate on scalar variables by summarizing data at initial stages as statistical summaries of the underlying distributions. For instance, DTI analysis often employs summary statistics, like mean, for regions of interest and properties along fiber tracts for population studies and hypothesis testing. This reduction via discarding of variability information may introduce significant errors which propagate through the procedures. We propose a novel framework which uses distribution-valued variables to retain and utilize the local variability information. Classic linear regression is adapted to employ these variables for model estimation. The increased stability and reliability of our proposed method when compared with regression using single-valued statistical summaries, is demonstrated in a validation experiment with synthetic data. Our driving application is the modeling of age-related changes along DTI white matter tracts. Results are shown for the spatiotemporal population trajectory of genu tract estimated from 45 healthy infants and compared with a Krabbe's patient.
Attempts to Transmit Infectious Mononucleosis to Rhesus Monkeys and Marmosets and to Isolate Herpes-Like Virus

Summary Buffy coat cells obtained from the blood of 11 patients with infectious monucleosis were inoculated into selected rhesus monkeys and marmosets. These monkeys were free of detectable complement-fixing antibodies to a herpes-like virus (EBV) considered to be etiologically related to infectious mononucleosis. None of the inoculated animals developed detectable illness, or hematological and serological changes. Attempts to isolate a viral agent from the throats of the 11 patients in cultures of human leukocytes were unsuccessful.
Diversification of Ergot Alkaloids in Natural and Modified Fungi

Several fungi in two different families––the Clavicipitaceae and the Trichocomaceae––produce different profiles of ergot alkaloids, many of which are important in agriculture and medicine. All ergot alkaloid producers share early steps before their pathways diverge to produce different end products. EasA, an oxidoreductase of the old yellow enzyme class, has alternate activities in different fungi resulting in branching of the pathway. Enzymes beyond the branch point differ among lineages. In the Clavicipitaceae, diversity is generated by the presence or absence and activities of lysergyl peptide synthetases, which interact to make lysergic acid amides and ergopeptines. The range of ergopeptines in a fungus may be controlled by the presence of multiple peptide synthetases as well as by the specificity of individual peptide synthetase domains. In the Trichocomaceae, diversity is generated by the presence or absence of the prenyl transferase encoded by easL (also called fgaPT1). Moreover, relaxed specificity of EasL appears to contribute to ergot alkaloid diversification. The profile of ergot alkaloids observed within a fungus also is affected by a delayed flux of intermediates through the pathway, which results in an accumulation of intermediates or early pathway byproducts to concentrations comparable to that of the pathway end product.
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering

Spectral Clustering is one of pioneered clustering methods in machine learning and pattern recognition field. It relies on the spectral decomposition criterion to learn a low-dimensonal embedding of data for a basic clustering algorithm such as the k-means. The recent sparse Spectral clustering (SSC) introduces the sparsity for the similarity in low-dimensional space by enforcing a sparsity-induced penalty, resulting a non-convex optimization, and the solution is calculated through a relaxed convex problem via the standard ADMM (Alternative Direction Method of Multipliers), rather than inferring latent representation from eigen-structure. This paper provides a direct solution as solving a new Grassmann optimization problem. By this way calculating latent embedding becomes part of optmization on manifolds and the recently developed manifold optimization methods can be applied. It turns out the learned new features are not only very informative for clustering, but also more intuitive and effective in visualization after dimensionality reduction. We conduct empirical studies on simulated datasets and several real-world benchmark datasets to validate the proposed methods. Experimental results exhibit the effectiveness of this new manifold-based clustering and dimensionality reduction method.
The Domain-Specific Language Monaco and its Visual Interactive Programming Environment

Monaco is a domain-specific language for machine automation programming. It has been developed with the objective to empower domain experts with limited programming capabilities. Its main language features are an imperative notation for reactive systems, concepts for describing asynchronous event handling in a concise way, and a state-of-the-art component approach. Monaco is a programming language with a Pascal-like syntax, but also comes with a visual programming environment. In this paper we review the language Monaco, show the visual representation scheme, report on the programming environment and compare our visual notation to Statecharts.
The Role of Trust in Promissory Organizations in IS Innovation Adoption - Development of a Research Model

Promissory organizations like IS analysis companies or academic institutions have started to play an increasingly important role in the way organizations make sense of IS innovations. Research has so far neglected how the trust that potential adopters place in these promissory organizations affects institutional pressures that promissory organizations exert on them. In this research-in-progress paper, we develop a research model to analyze the effects of different trusting beliefs – integrity, competence, and benevolence beliefs – in IS analysis companies and IS scholars, and how they affect potential adopters’ performance expectations in the early diffusion stages of an IS innovation. We expect this model to contribute to a more nuanced understanding on the role of promissory organizations as a mechanism of producing institutional trust and the importance of institutional trust for potential adopter firms in IS innovation adoption.
The effect of different diagnostic criteria on the prevalence of dementia.

BACKGROUND There are several widely used sets of criteria for the diagnosis of dementia, but little is known about their degree of agreement and their effects on estimates of the prevalence of dementia. METHODS We examined 1879 men and women 65 years of age or older who were enrolled in the Canadian Study of Health and Aging and calculated the proportion given a diagnosis of dementia according to six commonly used classification systems: the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (DSM), third edition (DSM-III), the third edition, revised of the DSM (DSM-III-R), the fourth edition of the DSM (DSM-IV), the World Health Organization's International Classification of Diseases (ICD), 9th revision (ICD-9) and 10th revision (ICD-10), and the Cambridge Examination for Mental Disorders of the Elderly (CAMDEX). The degree of concordance among classification schemes and the importance of various factors in determining diagnostic agreement or disagreement were examined. RESULTS The proportion of subjects with dementia varied from 3.1 percent when we used the criteria of the ICD-10 to 29.1 percent when the DSM-III criteria were used. The six classification systems identified different groups of subjects as having dementia; only 20 subjects were given a diagnosis of dementia according to all six systems. The classifications based on the various systems differed little according to the patients' age, sex, educational level, or status with respect to institutionalization. The factors that most often caused disagreement in diagnosis between DSM-III and ICD-10 were long-term memory, executive function, social activities, and duration of symptoms. CONCLUSIONS The commonly used criteria for diagnosis can differ by a factor of 10 in the number of subjects classified as having dementia. Such disagreement has serious implications for research and treatment, as well as for the right of many older persons to drive, make a will, and handle financial affairs.
Is age a key factor contributing to the disparity between success of neuroprotective strategies in young animals and limited success in elderly stroke patients? Focus on protein homeostasis

Neuroprotection strategies to improve stroke outcome have been successful in the laboratory but not in clinical stroke trials, and thus have come under scrutiny by the medical community. Experimental stroke investigators are therefore under increased pressure to resolve this problem. Acute ischemic stroke represents a severe form of metabolic stress that activates many pathological processes and thereby impairs cellular functions. Traditionally, neuroprotection strategies were designed to improve stroke outcome by interfering with pathological processes triggered by ischemia. However, stroke outcome is also dependent on the brain’s capacity to restore cellular functions impaired by ischemia, and this capacity declines with age. It is, therefore, conceivable that this age-dependent decline in the brain’s self-healing capacity contributes to the disparity between the success of neuroprotective strategies in young animals, and limited success in elderly stroke patients. Here, prosurvival pathways that restore protein homeostasis impaired by ischemic stress should be considered, because their capacity decreases with increasing age, and maintenance of proteome fidelity is pivotal for cell survival. Boosting such prosurvival pathways pharmacologically to restore protein homeostasis and, thereby, cellular functions impaired by ischemic stress is expected to counterbalance the compromised self-healing capacity of aged brains and thereby help to improve stroke outcome.
BUAA-iCC at ImageCLEF 2015 Scalable Concept Image Annotation Challenge

In this working note, we mainly focus on the image anno- tation subtask of ImageCLEF 2015 challenge that BUAA-iCC research group participated. For this task, we flrstly explore textual similarity information between each test sample and predeflned concept. Subse- quently, two difierent kinds of semantic information are extracted from visual images: visual tags using generic object recognition classiflers and visual tags relevant to human being related concepts. For the former information, the visual tags are predicted by using deep convolutional neural network (CNN) and a set of support vector machines trained on ImageNet, and flnally transferred to textual information. For the latter visual information, human related concepts are extracted via face and facial attribute detection, and flnally transferred to similarity informa- tion by using manually designed mapping rules, in order to enhance the performance of annotating human related concepts. Meanwhile, a late fusion strategy is developed to incorporate aforementioned various kinds of similarity information. Results validate that the combination of the textual and visual similarity information and the adopted late fusion strategy could yield signiflcantly better performance.