Miracle-dz/UniFAR-FTUs huggingface dataset:
Methanogenic Pathway and Archaeal Communities in Three Different Anoxic Soils Amended with Rice Straw and Maize Straw

Addition of straw is common practice in rice agriculture, but its effect on the path of microbial CH4 production and the microbial community involved is not well known. Since straw from rice (C3 plant) and maize plants (C4 plant) exhibit different δ13C values, we compared the effect of these straw types using anoxic rice field soils from Italy and China, and also a soil from Thailand that had previously not been flooded. The temporal patterns of production of CH4 and its major substrates H2 and acetate, were slightly different between rice straw and maize straw. Addition of methyl fluoride, an inhibitor of acetoclastic methanogenesis, resulted in partial inhibition of acetate consumption and CH4 production. The δ13C of the accumulated CH4 and acetate reflected the different δ13C values of rice straw versus maize straw. However, the relative contribution of hydrogenotrophic methanogenesis to total CH4 production exhibited a similar temporal change when scaled to CH4 production irrespectively of whether rice straw or maize straw was applied. The composition of the methanogenic archaeal communities was characterized by terminal restriction fragment length polymorphism (T-RFLP) analysis and was quantified by quantitative PCR targeting archaeal 16S rRNA genes or methanogenic mcrA genes. The size of the methanogenic communities generally increased during incubation with straw, but the straw type had little effect. Instead, differences were found between the soils, with Methanosarcinaceae and Methanobacteriales dominating straw decomposition in Italian soil, Methanosarcinaceae, Methanocellales, and Methanobacteriale in China soil, and Methanosarcinaceae and Methanocellales in Thailand soil. The experiments showed that methanogenic degradation in different soils involved different methanogenic population dynamics. However, the path of CH4 production was hardly different between degradation of rice straw versus maize straw and was also similar for the different soil types.
Proteomic and SAGE profiling of murine melanoma progression indicates the reduction of proteins responsible for ROS degradation

Using 2‐DE of total cell protein extracts, we compared soluble proteins from murine melanoma lines Tm1 and Tm5 with proteins from the nontumoral cell melan‐a from which they were derived. Seventy‐one of the 452 spots (average) detected with CBB were differentially accumulated, i.e., increased or decreased twofold. Forty‐four spots were identified by PMF/MALDI‐TOF, 15 with increased and 29 with decreased protein levels. SAGE showed that 17/34 (50%) of the differentially accumulated proteins, pI range 4–7, presented similar differences at the mRNA level. Major reductions in protein were observed in tumor cells of proteins that degrade reactive oxygen species (ROS). Decreases of ⋛ twofold in GST, superoxide dismutase, aldehyde dehydrogenase, thioredoxin, peroxiredoxin 2, and peroxiredoxin 6 protein were observed. SAGE indicated the reduction of other proteins involved in ROS degradation. As expected, the accumulation of exogenous peroxides was significantly higher in the tumor cells while the levels of glutathionylation were two times lower in the tumor cells compared to melan‐a. The differential accumulation of proteins involved in oncogene/tumor suppressor pathways was observed. Melanoma cells can favor survival pathways activated by ROS by inhibiting p53 pathways and activation of Ras and c‐myc pathways.
Distribution-Free Learning of Bayesian Network Structure in Continuous Domains

In this paper we present a method for learning the structure of Bayesian networks (BNs) without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous domains, where there is little guidance and many choices for the parametric distribution families to be used for the local conditional probabilities of the Bayesian network, and only a few have been examined analytically. We therefore focus on BN structure learning in continuous domains. We address the problem by developing a conditional independence test for continuous variables, which can be readily used by any existing independence-based BN structure learning algorithm. Our test is non-parametric, making no assumptions on the distribution of the domain. We also provide an effective and computationally efficient method for calculating it from data. We demonstrate the learning of the structure of graphical models in continuous domains from real-world data, to our knowledge for the first time using independence-based methods and without distributional assumptions. We also experimentally show that our test compares favorably with existing statistical approaches which use prediscretization, and verify desirable properties such as statistical consistency.
Underdeveloped Other in country-of-origin theory and practices

Consumers and marketers employ extant sociocultural discourses to give meaning to the products they consume or sell. In this paper, we present data and analyses that illustrate the manner by which American consumers and marketers draw upon one such sociocultural discourse, development, in the context of “craft” objects. Beyond the focus on discourse, however, our intent is to apply a post-development perspective to the Otherness inherent in country-of-origin (COO) theory and practices. We critique the COO framework and see it as a ramification of, and further creator of, economic difference and hierarchy.
Euclidean Distance Transform Shadow Mapping

The high-quality simulation of the penumbra effect in real-time shadows is a challenging problem in shadow mapping. The existing shadow map filtering techniques are prone to aliasing and light leaking artifacts which decrease the shadow visual quality. In this paper, we aim to minimize both problems with the Euclidean distance transform shadow mapping. To reduce the perspective aliasing artifacts generated by shadow mapping, we revectorize the hard shadow boundaries using the revectorization-based shadow mapping. Then, an exact normalized Euclidean distance transform is computed in the user-defined penumbra region to simulate the penumbra effect. Finally, a mean filter is applied to further suppress skeleton artifacts generated by the distance transform. The results obtained show that our technique runs entirely on the GPU, produces less artifacts than related work, and provides real-time performance.
Nanoprocessors: Configurable Hardware Accelerators for Embedded Systems

TITLE OF THESIS: NANOPROCESSORS: CONFIGURABLE HARDWARE ACCELERATORS FOR EMBEDDED SYSTEMS Lei Zong, Master of Science, 2003 Thesis directed by: Professor Bruce L. Jacob Department of Electrical and Computer Engineering Today’s consumer market is driven by technology innovations. Many technologies that were not available a few years ago are quickly being adopted into common use. Equipment for these services requires microprocessors inside and can be regarded as embedded systems. Embedded systems are designed to serve a particular functionality at a minimal cost. To improve efficiency and throughput, embedded systems uses hardware peripherals to perform sub-functionality in hardware. Because there are many vendors of embedded systems and each system is designed for a specific purpose, the peripherals differ widely across systems. Applications developed for a particular system is not portable to another system. As a result, many embedded systems incorporate programmable hardware to provide flexibility. One popular use of the programmable hardware is for I/ O devices. In this work, we propose and discuss other uses for the programmable hardware. Specifically, we have identified an I/O controller and a hardware scheduler that can be incorporated into programmable logic. Our result shows that these hardware components improve the system performance significantly at a nominal cost. NANOPROCESSORS: CONFIGURABLE HARDWARE ACCELERATORS FOR EMBEDDED SYSTEMS
A MODIFIED VARIABLE STEP SIZE SQUARE CONTOUR BLIND EQUALIZATION ALGORITHM

Wireless communication is one of the useful communication techniques. The high demand for wireless communication services is the capacity of system. Here main target is increasing the capacity. Then most important solution would be to increase bandwidth. For increasing capacity in wireless communications services has led to developments of new technologies that exploit space selectivity. This is done by smartantenna arrays and the adaptive beam forming algorithms. Smart-antenna systems provide techniques for higher system capacity and improved quality of service among other things. Most of the algorithms can be categorized into two classes according to whether a training signal is used or not. They are Non-Blind Adaptive algorithm and Blind Adaptive algorithm. Adaptive beam forming Algorithm are two types. One is blind beam forming where training signal is not needed. Another is non-blind beam forming where training signal is needed.
Mining fuzzy association rules with weighted items

In most models of mining fuzzy association rules, the items are considered to have equal importance. Due to diverse human interest and preference for items, such models do not work well in many situations. To improve such models, we propose a method to mine fuzzy association rules with weighted items. One of the major problems in data mining research is the development of good measures of interest of discovered rules. The weighted support and weighted confidence for fuzzy association rules are defined. Kohonen self-organized mapping is used to fuzzify the numerical attributes into linguistic terms. A new fuzzy association rule mining algorithm, which generalizes the popular Apriori Gen large itemset based algorithm, is developed. The advantages of the new algorithm are shown by testing it on a census database with 5000 transaction records.
Glutaredoxin-2 Is Required to Control Oxidative Phosphorylation in Cardiac Muscle by Mediating Deglutathionylation Reactions*

Background: Mitochondrial proteins are controlled by glutaredoxin-2 (Grx2)-mediated deglutathionylation reactions. Results: Grx2 deficiency compromises cardiac mitochondrial functions leading to hypertrophy and fibrosis in male mice. This is associated with deregulated glutathionylation reactions and mitochondrial dysfunction. Conclusion: Through deglutathionylation, Grx2 controls mitochondrial oxidative phosphorylation in cardiac muscle. Significance: Deregulated glutathionylation in heart can have pathological consequences. Glutaredoxin-2 (Grx2) modulates the activity of several mitochondrial proteins in cardiac tissue by catalyzing deglutathionylation reactions. However, it remains uncertain whether Grx2 is required to control mitochondrial ATP output in heart. Here, we report that Grx2 plays a vital role modulating mitochondrial energetics and heart physiology by mediating the deglutathionylation of mitochondrial proteins. Deletion of Grx2 (Grx2−/−) decreased ATP production by complex I-linked substrates to half that in wild type (WT) mitochondria. Decreased respiration was associated with increased complex I glutathionylation diminishing its activity. Tissue glucose uptake was concomitantly increased. Mitochondrial ATP output and complex I activity could be recovered by restoring the redox environment to that favoring the deglutathionylated states of proteins. Grx2−/− hearts also developed left ventricular hypertrophy and fibrosis, and mice became hypertensive. Mitochondrial energetics from Grx2 heterozygotes (Grx2+/−) were also dysfunctional, and hearts were hypertrophic. Intriguingly, Grx2+/− mice were far less hypertensive than Grx2−/− mice. Thus, Grx2 plays a vital role in modulating mitochondrial metabolism in cardiac muscle, and Grx2 deficiency leads to pathology. As mitochondrial ATP production was restored by the addition of reductants, these findings may be relevant to novel redox-related therapies in cardiac disease.
A divide-and-conquer approach to estimating minimum/maximum leakage current

Forcing primary inputs and memory elements into certain logic values is a viable method to lower standby leakage current for CMOS circuits. The paper presents a divide-and-conquer approach to finding an input vector that leads a circuit into a low (high) leakage state. With only a small fraction of time, our approach can achieve up to 11% smaller leakage than the lowest and up to 20% larger leakage than the highest, respectively, attainable with 100K random vectors for the large ISCAS benchmark circuits. Our results are as good as those obtained by simulated annealing and genetic algorithms that run for a very long time. Due to its divide-and-conquer nature, it is scalable for even larger circuits.
The Impact of Capacity-Building Programs on Nonprofits

Nonprofit organizations (NPOs) play an important role in delivering social services to those in need. Capacity-building efforts for NPOs derive from funders’ desire to increase NPOs’ effectiveness and redress problems associated with limited administrative and organizational capacity. Through technical assistance, training, and grant funds, funders aim to enhance NPO functioning and ultimately improve client outcomes. Despite a general consensus about the importance of capacity building, little high-quality evidence exists on the impact of capacity-building investments. This article presents the findings from the first random assignment evaluation to be conducted in the field of nonprofit capacity building. The subject of the evaluation was one of country’s largest organizational capacity-building initiatives, the federal government’s Compassion Capital Fund (CCF) Demonstration Program. Findings from the evaluation provide clear evidence that capacity-building efforts increase capacity in each of five critical areas of capacity.
Worst-Case Growth Rates of Some Classical Problems of Combinatorial Optimization

A method is presented for determining the asymptotic worst-case behavior of quantities like the length of the minimal spanning tree or the length of an optimal traveling salesman tour of n points in the unit d-cube. In each of these classical problems, the worst-case lengths are proved to have the exact asymptotic growth rate of $\beta _n^{{{(d - 1)} / d}} $, where $\beta $ is a positive constant depending on the problem and the dimension. These results complement known results on the growth rates for the analogous quantities under probabilistic assumptions on the points, but the results given here are free of any probabilistic hypotheses.
Determining the sample size required to establish whether a medical device is non-inferior to an external benchmark

Objectives The use of benchmarks to assess the performance of implants such as those used in arthroplasty surgery is a widespread practice. It provides surgeons, patients and regulatory authorities with the reassurance that implants used are safe and effective. However, it is not currently clear how or how many implants should be statistically compared with a benchmark to assess whether or not that implant is superior, equivalent, non-inferior or inferior to the performance benchmark of interest. We aim to describe the methods and sample size required to conduct a one-sample non-inferiority study of a medical device for the purposes of benchmarking. Design Simulation study. Setting Simulation study of a national register of medical devices. Methods We simulated data, with and without a non-informative competing risk, to represent an arthroplasty population and describe three methods of analysis (z-test, 1−Kaplan-Meier and competing risks) commonly used in surgical research. Primary outcome We evaluate the performance of each method using power, bias, root-mean-square error, coverage and CI width. Results 1−Kaplan-Meier provides an unbiased estimate of implant net failure, which can be used to assess if a surgical device is non-inferior to an external benchmark. Small non-inferiority margins require significantly more individuals to be at risk compared with current benchmarking standards. Conclusion A non-inferiority testing paradigm provides a useful framework for determining if an implant meets the required performance defined by an external benchmark. Current contemporary benchmarking standards have limited power to detect non-inferiority, and substantially larger samples sizes, in excess of 3200 procedures, are required to achieve a power greater than 60%. It is clear when benchmarking implant performance, net failure estimated using 1−KM is preferential to crude failure estimated by competing risk models.
Exploring the Function of Criminal Law in the Policing of Foreigners

This paper looks at the decision-making process in cases involving immigration-related crimes. Based on interviews with practitioners and immigration staff and the analysis of court-files, it disentangles the criteria guiding both enforcement officers and prosecutors in the decision to criminally proceed against suspected immigration offenders. The reasons behind the policy and practice of prosecuting immigration crimes show that criminal law is but one means to enforce immigration rules. The role that criminal law is called to play in this context is purely instrumental as the main consideration for pursuing a case is the impact that it may have on immigration enforcement. Criminal law is emptied of any condemnatory function and, as such, its primary function of censuring wrongs is disrupted. The examination of the decision-making process in these cases reveals how problematic it is to reconcile retribution and punishment with immigration imperatives.
Carbohydrate ingestion reduces skeletal muscle acetylcarnitine availability but has no effect on substrate phosphorylation at the onset of exercise in man

This study investigated the effect of reduced acetylcarnitine availability on oxidative metabolism during the transition from rest to steady‐state exercise. Eight male subjects completed two randomised exercise trials at 68 % of the peak rate of O2 uptake (V̇O2,peak). On one occasion subjects ingested 1 g (kg body mass)−1 glucose 75 min prior to exercise (CHO), whereas the other trial acted as a control (CON). Muscle samples were obtained pre‐ and 75 min post‐ingestion, and following 1 and 10 min of exercise. Plasma glucose and insulin were elevated (P < 0.05), and plasma free fatty acids (FFA) were lower at the onset of exercise in CHO. Acetylcarnitine (CON, 4.8 ± 1.8; CHO, 1.5 ± 0.9 mmol (kg dry mass (d.m.))−1, P < 0.05) and acetyl CoA (CON, 13.2 ± 2.3; CHO, 6.3 ± 0.6 μmol (kg d.m.)−1, P < 0.05) were lower at rest, whereas pyruvate dehydrogenase activation (PDHa) was greater in CHO compared with CON (CON, 0.78 ± 0.07; CHO, 1.44 ± 0.19 mmol min−1 (kg wet mass (w.m.))−1). Respiratory exchange ratio (RER) was significantly elevated during exercise in CHO. The acetyl groups increased at similar rates at the onset of exercise (1 min) and there was no difference in substrate phosphorylation as determined from lactate accumulation and phosphocreatine degradation between trials. Subsequently, oxidative metabolism during the transition from rest to steady‐state exercise was not affected by prior carbohydrate ingestion. Although exercise resulted in the rapid activation of PDH in both trials, PDHa was greater at 1 min in CHO (CON, 2.36 ± 0.22; CHO, 2.91 ± 0.18 mmol min−1 (kg w.m.)−1). No differences in muscle metabolite levels and PDHa were observed after 10 min of moderate exercise between trials. In summary, at rest, carbohydrate ingestion induced multiple metabolic changes which included decreased acetylcarnitine availability and small increases in PDHa. The prior changes in PDHa and acetylcarnitine availability had no effect on substrate phosphorylation and oxidative metabolism at the onset of exercise. These data suggest that acetylcarnitine availability is unlikely to be the site of metabolic inertia during the transition from rest to steady‐state moderate intensity exercise.
Statistical bounds for Gaussian regression algorithms based on Karhunen-Loève expansions

We consider the problem of estimating functions in a Gaussian regression distributed and nonparametric framework where the unknown map is modeled as a Gaussian random field whose kernel encodes expected properties like smoothness. We assume that some agents with limited computational and communication capabilities collect M noisy function measurements on input locations independently drawn from a known probability density. Collaboration is then needed to obtain a common and shared estimate. When the number of measurements M is large, computing the minimum variance estimate in a distributed fashion is difficult since it requires first to exchange all the measurements and then to invert an M χ M matrix. A common approach is then to circumvent this problem by searching a suboptimal solution within a subspace spanned by a finite number of kernel eigenfunctions. In this paper we analyze this classical distributed estimator, and derive a rigorous probabilistic bound on its statistical performance that returns crucial information on the number of measurements and eigenfunctions needed to obtain the desired level of estimation accuracy.
A Data Structure to Handle Large Sets of Equal Terms

We present a data structure to represent and manipulate large sets of (equal) terms (or expressions). Our initial and main motivation for this data structure is the simplification of expressions with respect to a formal theory, typically, an equational one. However, it happens that the data structure is also efficient to compute the congruence closure of a relation over a set of terms. We provide an abstract definition of the data structure, including a precise semantics, and we explain how to implement it efficiently. We prove the correctness of the proposed algorithms, with a complexity analysis and experimental results. We compare these algorithms with previous algorithms to compute the congruence closure and we also sketch how we use the data structure to tackle the expression simplification problem.
Fast projection algorithm and its step size control

Of the many adaptive filtering algorithms, the normalized LMS (NLMS) algorithm is generally used in practice because of its simplicity. The computational complexity of the NLMS algorithm is low, however, convergence is very slow and tracking is poor for a colored input signal such as speech. The projection algorithm was proposed as a generalization of the NLMS algorithm. This paper provides a fast projection algorithm and a step size control to obtain the same steady-state excess mean squared error (MSE) for various projection orders. Computer simulations for colored noise and speech input signal confirm the effectiveness of the projection algorithm and the step size control.
Genome-Wide Association Study of Serum Creatinine Levels during Vancomycin Therapy

Vancomycin, a commonly used antibiotic, can be nephrotoxic. Known risk factors such as age, creatinine clearance, vancomycin dose / dosing interval, and concurrent nephrotoxic medications fail to accurately predict nephrotoxicity. To identify potential genomic risk factors, we performed a genome-wide association study (GWAS) of serum creatinine levels while on vancomycin in 489 European American individuals and validated findings in three independent cohorts totaling 439 European American individuals. In primary analyses, the chromosome 6q22.31 locus was associated with increased serum creatinine levels while on vancomycin therapy (most significant variant rs2789047, risk allele A, β = -0.06, p = 1.1 x 10-7). SNPs in this region had consistent directions of effect in the validation cohorts, with a meta-p of 1.1 x 10-7. Variation in this region on chromosome 6, which includes the genes TBC1D32/C6orf170 and GJA1 (encoding connexin43), may modulate risk of vancomycin-induced kidney injury.