Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Neuroprotective Effects of Dexamethasone and Minocycline during Hepatic Encephalopathy

Objective. Encephalopathy and brain edema are serious complications of acute liver injury and may lead to rapid death of patients. The present study was designed to investigate the role of the inflammatory mediators and oxidative stress in the cytotoxic brain oedema and the neuroprotective effects of both minocycline and dexamethasone. Methods. 48 male albino rats were divided into 4 groups: control group, acute liver injury (ALI) group, minocycline pretreated ALI group, and dexamethasone pretreated ALI group. 24 hours after acute liver injury serum ammonia, liver enzymes, brain levels of heme oxygenase-1 gene, iNOS gene expression, nitrite/nitrate, and cytokines were measured. In addition, the grades of encephalopathy and brain water content were assessed. Results. ALI was associated with significant increases in all measured inflammatory mediators, oxidative stress, iNOS gene expression, and nitrite/nitrate. Both minocycline and dexamethasone significantly modulated the inflammatory changes and the oxidative/nitrosative stress associated with ALI. However, only minocycline but not dexamethasone significantly reduced the cytotoxic brain oedema. Conclusion. Both minocycline and dexamethasone could modulate inflammatory and oxidative changes observed in brain after ALI and could be novel preventative therapy for hepatic encephalopathy episodes.
Games with Externalities and Delegation to a Common Agent

I present a model in which the players of a game have the option to delegate parts of their strategies to a third party who has an interest in the outcome of the game. I analyze whether the game with delegation to a common agent improves over the equilibrium of the original game. This paper contributes to the literature on private common agency and to the failure of the revelation principle with multiple principals. One contribution of this paper is the characterization of the complete set of equilibrium outcomes for the game with delegation, including the asymmetric outcomes. I also provide an answer to the question whether the results of the existing models of private common agency are robust to mixed strategy deviations and shed light on the persistence of the failure of revelation principle.
Inflation Expectations and Consumption Expenditure

We document a positive cross-sectional association between households' inflation expectations and their willingness to purchase durable consumption goods. Households that expect inflation to increase are 8% more likely to have a positive spending attitude compared to households that expect constant or decreasing inflation. This positive association is higher for more educated households, working-age households, high-income households, and urban households. We use novel German survey data for the period from 2000 to 2013 to establish these facts. To obtain identification, we exploit an unexpected shock to households' inflation expectations: the newly-appointed administration unexpectedly announced in November 2005 a three percentage point increase in the value-added tax (VAT) effective in 2007. The unexpected VAT increase led to an exogenous increase in inflation expectations which had a large positive effect on the willingness to spend on durables. Our findings suggest that fiscal and monetary policy measures that engineer higher inflation expectations may be successful in stimulating consumption expenditures.
Blue Collar Blues: Is Trade to Blame for Rising US Income Inequality?

International trade accounts for only a small share of growing income inequality and labor-market displacement in the United States. Lawrence deconstructs the gap in real blue-collar wages and labor productivity growth between 1981 and 2006 and estimates how much higher these wages might have been had income growth been distributed proportionately and how much of the gap is due to measurement and technical factors about which little can be done. * While increased trade with developing countries may have played some part in causing greater inequality in the 1980s, surprisingly, over the past decade the impact of such trade on inequality has been relatively small. Many imports are no longer produced in the United States, and US goods and services that do compete with imports are not particularly intensive in unskilled labor. Rising income inequality and slow real wage growth since 2000 reflect strong profit growth, much of which may be cyclical, and dramatic income gains for the top 1 percent of wage earners, a development that is more closely related to asset-market performance and technological and institutional innovations rather than conventional trade in goods and services. The minor role of trade, therefore, suggests that any policy that focuses narrowly on trade to deal with wage inequality and job loss is likely to be ineffective. Instead, policymakers should (a) use the tax system to improve income distribution and (b) implement adjustment policies to deal more generally with worker and community dislocation.
Measurement Algorithm for Available Bandwidth with Real Time Rate

The packet transmission rate (PTR) algorithm is an active probing algorithm, which sends several probing packets to detect background traffic. The PTR algorithm compares the time interval at the source with that of destination, and uses the time interval to estimate the available bandwidth. But the PTR algorithm cannot consider the instability of the background traffic and the losses of the probing packets have been lost by the transmission process. In this paper, we improve the PTR algorithm. We first adjust the transmission rate and fix the time interval for probing packets, and second we match probing packet number and record time interval of probing packets with source and destination to resolve the problem of packets lost. Finally, we do experiments to compare our algorithm with PTR algorithm, the result shows our algorithm is more accurate then PTR algorithm.
Globalization and capital markets: implications for inflation and the yield curve: a speech at the Center for Financial Stability (CEF), Buenos Aires, Argentina, May 16, 2007

I am delighted to be back in Argentina. Today I want to talk about some important macroeconomic and financial developments around the world. Inflation rates and interest rates are at historically low levels, and yield curves are relatively flat nearly everywhere. In addition, until recently, many emerging-market countries simply did not have yield curves because there was effectively no market for debt issued in the domestic currency beyond a very short term. I believe that the taming of inflation and the improved credibility of monetary policy have been crucial to the deepening of domestic capital markets, which is typically associated with higher economic growth. I will explore some possible explanations for these developments, focusing on the prospects for, and risks to, the long-term inflation outlook, particularly in emerging-market economies. Before I continue, please note that the opinions I express here today are my own and not necessarily shared by my colleagues on the Federal Open Market Committee.1
VeriSHIM A BDD Verifier for SHIM

Concurrent programming languages have become more popular with the advent of multi-core systems. Shared memory is read or written atomically, by concurrent processes to prevent races. A typical mechanism is the use of locks. However, if locks are not acquired and released in the correct order, the result is a deadlock. In this paper, we propose a deadlock detection tool for a deterministic, concurrent language, SHIM. SHIM is race free but not deadlock free. A deadlock can easily be detected while the program is running, but here we use static approaches to find deadlocks in a program. Enumerating all possible states is not feasible because there will be explosion of states. We exploit the deterministic property of SHIM and do a BDD based reacha-bility analysis after abstracting parts of the program. We run the deadlock detector on the the JPEG decoder and report the results.
The Martin Report and Its Aftermath

The confluence of the institutionalization of the equity market and of the development of computer technology is having a drastic organizational impact on the American stock market. The Institutional Investor Study called for a competitive central market system. The Martin Report accelerated the development of such a system, but it is unlikely that the system that will evolve will bear much resemblance to the one recommended in that Report. The first commission rate system seems to have little chance of surviving. Full-fledged institutional membership is almost inevitable. The central market system is on the horizon. That might not have been true if the Martin Report had not been written. The Report was to be the solution to the market's ills. Unfortunately, the Report diagnosed healthy competition as a cancerous growth. Fortunately, the error of the diagnosis was recognized, and there is now hope that for the first time in history, the hallmark of American capitalism, the American stock market, may be truly competitive.
Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty

In this chapter, we present the multistage stochastic programming framework for sequential decision making under uncertainty and stress its differences with Markov Decision Processes. We describe the main approximation technique used for solving problems formulated in the multistage stochastic programming framework, which is based on a discretization of the disturbance space. We explain that one issue of the approach is that the discretization scheme leads in practice to ill-posed problems, because the complexity of the numerical optimization algorithms used for computing the decisions restricts the number of samples and optimization variables that one can use for approximating expectations, and therefore makes the numerical solutions very sensitive to the parameters of the discretization. As the framework is weak in the absence of efficient tools for evaluating and eventually selecting competing approximate solutions, we show how one can extend it by using machine learning based techniques, so as to yield a sound and generic method to solve approximately a large class of multistage decision problems under uncertainty. The framework and solution techniques presented in the chapter are explained and illustrated on several examples. Along the way, we describe notions from decision theory that are relevant to sequential decision making under uncertainty in general.
A CMOS programmable gain amplifier with a novel DC-offset cancellation technique

A programmable gain amplifier (PGA) with a novel DC offset cancellation (DCOC) technique for IEEE 802.11b/g wireless LAN direct-conversion receiver (DCR) is presented. An operational amplifier (OPAMP) utilizing an improved Miller compensation approach is adopted in this PGA design. A gain tuning range of 0 dB to 56 dB with 2 dB per step is achieved. The DCOC loop is based on a voltage-current negative feedback that includes a switchable bandwidth algorithm to speed up the settling time of DCOC. The proposed approach requires no external components and demonstrates excellent DCOC capability in measurement. Fabricated in a 0.13 µm CMOS technology, this PGA dissipates 9.7 mW from a 1.2 V supply voltage and occupies an area of 0.17 mm2.
Serological study of bovine herpes virus type 1 in dairy herds of Hamedan province, Iran

A cross-sectional study with a random cluster sampling design was carried out to estimate the seroprevalence of bovine herpesvirus type 1 (BHV-1) in non-vaccinated dairy herds in Hamedan province, west of Iran. Simple random sampling was used for selection of cattle in each herd. Informative data about each herd and selected animals were recorded by the farm manager in a provided questionnaire. Blood samples were collected from 492 animals in 41 industrial herds. A commercial indirect ELISA test was used to determine the seropositivity against BHV-1. The individual and herd seroprevalence for BHV-1 were 58.74% and 82.93%, respectively. The intra-herd prevalences were ranged from 16.70% to 100%. Geographical characteristics of Hamedan province may explain the high sero-prevalence rates found in this study compared to those of others obtained from different parts of the country. The proportion of seropositive cows were increased with age (p <0.05). Animals from large and moderate sized herds had higher odds of seropositivity than those of small size herds. These findings could be related to the presence of a considerable number of BHV-1 carriers in this region. The high herd and animal prevalence found in the present study suggested necessity of implementing an intensive control program for reducing BHV-1 infection rates.
Ani9, A Novel Potent Small-Molecule ANO1 Inhibitor with Negligible Effect on ANO2

Anoctamin1 (ANO1)/transmembrane protein 16A (TMEM16A), a calcium-activated chloride channel (CaCC), is involved in many physiological functions such as fluid secretion, smooth muscle contraction, nociception and cancer progression. To date, only a few ANO1 inhibitors have been described, and these have low potency and selectivity for ANO1. Here, we performed a high-throughput screening to identify highly potent and selective small molecule inhibitors of ANO1. Three novel ANO1 inhibitors were discovered from screening of 54,400 synthetic small molecules, and they were found to fully block ANO1 channel activity with an IC50 < 3 μM. Electrophysiological analysis revealed that the most potent inhibitor, 2-(4-chloro-2-methylphenoxy)-N-[(2-methoxyphenyl)methylideneamino]-acetamide (Ani9), completely inhibited ANO1 chloride current with submicromolar potency. Notably, unlike previous small-molecule ANO1 inhibitors identified to date, Ani9 displayed high selectivity for ANO1 as compared to ANO2, which shares a high amino acid homology to ANO1. In addition, Ani9 did not affect the intracellular calcium signaling and CFTR chloride channel activity. Our results suggest that Ani9 may be a useful pharmacological tool for studying ANO1 and a potential development candidate for drug therapy of cancer, hypertension, pain, diarrhea and asthma.
PEER FEEDBACK THROUGH BLOGS: An Effective Tool For Improving Students’ Writing Abilities

The advancement of computer technology and expansion of the Internet has an increasing impact on writing instruction. The modes of peer feedback have shifted from traditional peer feedback to online peer feedback. This study investigated the effects of peer feedback activity through blogs on students’ writing ability and examined their attitudes towards peer feedback activity. The research was conducted using a single group pretest-posttest design. Blog, the website, was used as a medium for peer feedback activity. Participants were 34 second-year students who studied EN 013 course (English for Expressing Ideas) in the first semester of the academic year 2012 at Bangkok University. Two writings tests and a questionnaire were used as instruments for data collection to acquire information. The results revealed that students’ writing scores on the pretest and posttest were significantly different. It can be concluded that peer feedback activity through blogs had a significant role to play in improving students’ writing skill. The students also expressed positive attitudes towards the value of peer feedback activity.
Selective Combinational Encryption of Gray Scale Images using Orthogonal Polynomials based Transformation

Summary In this paper, we propose a new image encryption technique in orthogonal polynomials based transformation domain (OPT) with selective combinational scrambling for secure transmission of images. Normally, the values of the neighboring pixels are highly correlated in most of the images. In order to de-correlate the intelligible information present in the image we propose the use of orthogonal polynomials based transformation. Also, the proposed technique exploits the energy preserving property of the orthogonal polynomials based transform coding which compacts energy in a few low frequency coefficients. These low frequency coefficients are selected as candidates for encryption which significantly reduces the number of bits to be encrypted. Then shuffling of these coefficients and their bits are performed. In addition, the sign bits of the selected low frequency coefficients are encrypted and randomly selected transformed blocks are shuffled. A symmetric key based cryptographically secure pseudo random process controls the entire encryption process. Experimental results reveal that the proposed OPT domain encryption scheme provides very low encryption PSNRs implying effective encryption. Also, security analyses prove that the proposed technique is robust to brute force, statistical and differential attacks.
MicroRNAs in Human Diseases: From Autoimmune Diseases to Skin, Psychiatric and Neurodegenerative Diseases

MicroRNAs (miRNAs) are small noncoding RNA molecules that negatively regulate gene expression via degradation or translational repression of their target messenger RNAs (mRNAs). Recent studies have clearly demonstrated that miRNAs play critical roles in several biologic processes, including cell cycle, differentiation, cell development, cell growth, and apoptosis and that miRNAs are highly expressed in regulatory T (Treg) cells and a wide range of miRNAs are involved in the regulation of immunity and in the prevention of autoimmunity. It has been increasingly reported that miRNAs are associated with various human diseases like autoimmune disease, skin disease, neurological disease and psychiatric disease. Recently, the identification of mi- RNAs in skin has added a new dimension in the regulatory network and attracted significant interest in this novel layer of gene regulation. Although miRNA research in the field of dermatology is still relatively new, miRNAs have been the subject of much dermatological interest in skin morphogenesis and in regulating angiogenesis. In addition, miRNAs are moving rapidly onto center stage as key regulators of neuronal development and function in addition to important contributions to neurodegenerative disorder. Moreover, there is now compelling evidence that dysregulation of miRNA networks is implicated in the development and onset of human neruodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Tourette's syndrome, Down syndrome, depression and schizophrenia. In this review, I briefly summarize the current studies about the roles of miRNAs in various autoimmune diseases, skin diseases, psychoneurological disorders and mental stress.
Observing Hidden Service Directory Spying with a Private Hidden Service Honeynet

Tor's location hidden services (HS) are a tool for anonymous publishing, with the feature that the sites cannot be brought down without taking down the whole Tor network. People run HSs for a multitude of reasons. Some like them to be public, but others want to keep them their existence as private. We have run private unannounced HSs to detect whether the HS directory is spied on. Our results show that the hidden service directory is monitored for new addresses. This paper details the observations made from the scanning activity.
Public signs detection in subway station images

It's difficult to find location in indoor environments such as subway station and airport passenger terminal. However, the detection and recognition of public sign make it possible to find out the place to go. Proposed system can help visually impaired persons or mobile robots in unfamiliar places. We propose the detection method of public signs in natural images. Especially, we dealt with several signs in subway station such as numbers, arrows, ticket, elevator, escalator, phone, toilet, and arrows giving the direction of those places.
Nodes localization through data fusion in sensor network

The location of nodes in sensor network is an important problem with application in resource allocation, location sensitive browsing, and emergency communications. A key problem in sensor network location is the creation of a method that is robust to measurement quantization and measurement noise and also has a reasonable implementation cost. The RSS and TDoA are the popular distance measurement methods, but can be easy affected by noise and not independent. This paper explores a covariance intersection (CI) that fuses together location estimations obtained from power propagation loss measurements and propagation time to obtain higher accuracy location estimate. The performances of Kalman filter type estimators are severely affected by the ignored cross covariance. CI algorithm provides a mechanism to fuse two or more random variables with unknown correlation such that the computed covariance of the new estimate is consistent; therefore CI method is quite suitable for sensor network application. In simulation result shows that position estimates are correct within small ranging distance with few initial master nodes of the system.
An Overdetermined System of Transform Equations in Support of Robust DCE-MRI Registration With Outlier Rejection

Quantitative hepatic perfusion parameters derived by fitting dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of liver to a pharmacokinetic model are prone to errors if the dynamic images are not corrected for respiratory motion by image registration. The contrast-induced intensity variations in pre- and postcontrast phases pose challenges for the accuracy of image registration. We propose an overdetermined system of transformation equations between the image volumes in the DCE-MRI series to achieve robust alignment. In this method, we register each volume to every other volume. From the transforms produced by all pairwise registrations, we constructed an overdetermined system of transform equations that was solved robustly by minimizing the L1/2-norm of the residuals. This method was evaluated on a set of 100 liver DCE-MRI examinations from 35 patients by examining the area under spikes appearing in the voxel time–intensity curves. The robust alignment procedure significantly reduced the area under intensity spikes compared with unregistered volumes (P < .001) and volumes registered to a single reference phase (P < .001). Our registration procedure provides a larger number of reliable time–intensity curve samples. The additional reliable samples in the precontrast baseline are important for calculating the postcontrast signal enhancement and thereby for converting intensity to contrast concentration. On the intensity ramp, retained samples help to better describe the uptake dynamics, providing a better foundation for parameter estimation. The presented method also simplifies the analysis of data sets with many patients by eliminating the need for manual intervention during registration.
Therapeutic correction of ApoER2 splicing in Alzheimer's disease mice using antisense oligonucleotides

Apolipoprotein E receptor 2 (ApoER2) is an apolipoprotein E receptor involved in long‐term potentiation, learning, and memory. Given its role in cognition and its association with the Alzheimer's disease (AD) risk gene, apoE, ApoER2 has been proposed to be involved in AD, though a role for the receptor in the disease is not clear. ApoER2 signaling requires amino acids encoded by alternatively spliced exon 19. Here, we report that the balance of ApoER2 exon 19 splicing is deregulated in postmortem brain tissue from AD patients and in a transgenic mouse model of AD. To test the role of deregulated ApoER2 splicing in AD, we designed an antisense oligonucleotide (ASO) that increases exon 19 splicing. Treatment of AD mice with a single dose of ASO corrected ApoER2 splicing for up to 6 months and improved synaptic function and learning and memory. These results reveal an association between ApoER2 isoform expression and AD, and provide preclinical evidence for the utility of ASOs as a therapeutic approach to mitigate Alzheimer's disease symptoms by improving ApoER2 exon 19 splicing.