Miracle-dz/UniFAR-FTUs huggingface dataset:
Mercury-induced Conformational Changes and Identification of Conserved Surface Loops in Plasma Membrane Aquaporins from Higher Plants

Aquaporins are integral membrane proteins occurring in mammals, plants, and microorganisms, which serve as channels that permit the bidirectional passage of water through cellular membranes. Higher plants contain abundant levels of aquaporins in both the tonoplast and plasma membrane. Aquaporins contain six transmembrane segments with three surface loops located at the apoplastic face of the membrane and two loops at the cytosolic side. In this study, we probed the topology of plasma membrane aquaporins to determine the effects of divalent cations on aquaporin conformation, and to identify structural features that distinguish plasma membrane intrinsic proteins from tonoplast intrinsic proteins. Plasma membrane vesicles from storage tissue of Beta vulgaris L. were subjected to limited proteolysis, and proteolytic fragmentation patterns were detected using affinity-purified antibodies recognizing aquaporins of 31-kDa. In its native membrane-associated state, the 31-aquaporin band, PMIP31, was refractory to proteolysis by trypsin. However, mercuric compounds specifically induced a conformational change resulting in the exposure of a proteolytic cleavage site and formation of a unique 22-kDa proteolytic fragment (p22). N-terminal sequence analysis of p22 established its identity as an aquaporin-derived fragment. Topological studies using sealed right-side-out plasma membrane vesicles established that the proteolytic cleavage site is located at surface loop C, the second apoplastic loop, immediately preceding the sequence Gly-Gly-Gly-Ala-Asn. The Gly-Gly-Gly-Ala-Asn-X-X-X-X-Gly-Tyr motif of loop C and a 14 amino acid motif in apoplastic loop E, Thr-Gly-Ile/Thr-Asn-Pro-Ala-Arg-Ser-Leu/Phe-Gly-Ala-Ala-Ile/Val-Ile/Val-Phe/Tyr-Asn are completely conserved in all known higher plant aquaporins of plasma membrane origin and are not present in any of the known tonoplast intrinsic proteins. These results demonstrate that the two highly conserved plasma membrane intrinsic protein surface loops are structural features that clearly distinguish plasma membrane from tonoplast aquaporins.
What’s in a Name? Evaluating the Effects of the “Sex Offender” Label on Public Opinions and Beliefs

Particularly over the past two decades, the terms sex offender and juvenile sex offender (JSO) have attained increasingly common usage in media and public policy discourse. Although often applied as factual descriptors, the labels may evoke strong subconscious associations with a population commonly presumed to be compulsive, at high risk of re-offense, and resistant to rehabilitation. Such associations, in turn, may exert considerable impact on expressions of support for certain policies as well as public beliefs and opinions about adults and youth who have perpetrated sexual offenses. The current study systematically evaluated the impact of the “sex offender” and “JSO” labels through series of items administered to a nationally stratified and matched sample from across the United States. The study employed an experimental design, in which one group of participants (n = 498) ranked their levels of agreement with a series of statements utilizing these labels, and a control group (n = 502) responded to a matched set of statements substituting the labels with more neutral descriptive language. Findings support the hypothesis that use of the “sex offender” label strengthens public support for policies directed at those who have perpetrated sexual crimes, including public Internet disclosure, residency restrictions, and social networking bans. The “JSO” label is demonstrated to produce particularly robust effects, enhancing support for policies that subject youth to public Internet notification and affecting beliefs about youths’ propensity to re-offend as adults. Implications for public policy, media communication, and research are explored and discussed.
SWIM: scalable weakly-consistent infection-style process group membership protocol

Several distributed peer-to-peer applications require weakly-consistent knowledge of process group membership information at all participating processes. SWIM is a generic software module that offers this service for large scale process groups. The SWIM effort is motivated by the unscalability of traditional heart-beating protocols, which either impose network loads that grow quadratically with group size, or compromise response times or false positive frequency w.r. t. detecting process crashes. This paper reports on the design, implementation and performance of the SWIM sub-system on a large cluster of commodity PCs. Unlike traditional heart beating protocols, SWIM separates the failure detection and membership update dissemination functionalities of the membership protocol. Processes are monitored through an efficient peer-to-peer periodic randomized probing protocol. Both the expected time to first detection of each process failure, and the expected message load per member do not vary with group size. Information about membership changes, such as process joins, drop-outs and failures, is propagated via piggybacking on ping messages and acknowledgments. This results in a robust and fast infection style (also epidemic or gossip-style) of dissemination. The rate of false failure detections in the SWIM system is reduced by modifying the protocol to allow group members to suspect a process before declaring it as failed - this allows the system to discover and rectify false failure detections. Finally, the protocol guarantees a deterministic time bound to detect failures. Experimental results from the SWIM prototype are presented. We discuss the extensibility of the design to a WAN-wide scale.
Discovering Dynamic Logical Blog Communities Based on Their Distinct Interest Profiles

This paper addresses the problem of identifying dynamic logical blog communities based on the distinct interests shared by blogs in the communities. This paper is motivated by the facts that the blog space is highly dynamic both in the participating bloggers and in the interests/issues of concern to them in their blogs, and that many organizations are interested in identifying the evolution/emergence of various blog communities and the current interests/issues of concern to the blogs of those communities. Using dynamic shared distinct interests to define logical blog communities allows us to better identify and track the issues of concern to the bloggers than using statically chosen keywords or statically defined blog communities. The paper discusses algorithms to solve the above problem, which attempt to maximize discriminativeness and diversity of the distinct interests of the blog communities. Experiments are reported to evaluate the performance of the algorithms and to demonstrate their utility. Keywords-distinct interest; clustering with description; weblog community discovery; community description; contrast pattern.
Sudden Unexpected Death in Epilepsy

Objective To develop and validate a tool for individualized prediction of sudden unexpected death in epilepsy (SUDEP) risk, we reanalyzed data from 1 cohort and 3 case–control studies undertaken from 1980 through 2005. Methods We entered 1,273 epilepsy cases (287 SUDEP, 986 controls) and 22 clinical predictor variables into a Bayesian logistic regression model. Results Cross-validated individualized model predictions were superior to baseline models developed from only average population risk or from generalized tonic-clonic seizure frequency (pairwise difference in leave-one-subject-out expected log posterior density = 35.9, SEM ± 12.5, and 22.9, SEM ± 11.0, respectively). The mean cross-validated (95% bootstrap confidence interval) area under the receiver operating curve was 0.71 (0.68–0.74) for our model vs 0.38 (0.33–0.42) and 0.63 (0.59–0.67) for the baseline average and generalized tonic-clonic seizure frequency models, respectively. Model performance was weaker when applied to nonrepresented populations. Prognostic factors included generalized tonic-clonic and focal-onset seizure frequency, alcohol excess, younger age at epilepsy onset, and family history of epilepsy. Antiseizure medication adherence was associated with lower risk. Conclusions Even when generalized to unseen data, model predictions are more accurate than population-based estimates of SUDEP. Our tool can enable risk-based stratification for biomarker discovery and interventional trials. With further validation in unrepresented populations, it may be suitable for routine individualized clinical decision-making. Clinicians should consider assessment of multiple risk factors, and not focus only on the frequency of convulsions.
Neuroactive Steroids in Acute Ischemic Stroke: Association with Cognitive, Functional, and Neurological Outcomes

Abstract Despite several scientific and technological advances, there is no single neuroprotective treatment that can reverse the brain damage after acute ischemic stroke (AIS). Neuroactive steroids are cholesterol-derived hormones that have the ability to modulate the normal and pathologic nervous system employing genomic and nongenomic mechanisms. In this work, we first investigated if AIS affects the plasma concentration of 5 neuroactive steroids (cortisol, estradiol, progesterone, testosterone, and 3α-androstenediol glucuronide). Second, we studied if levels of circulating steroids associate with neurological, cognitive, and functional outcome in a cohort of 60- to 90 year-old male and female patients with AIS. For this purpose, we recruited patients who were hospitalized at the Emergency Room of the Central Military Hospital within the first 24 h after stroke onset. We designed 2 experimental groups, each one composed of 30 control subjects and 30 AIS patients, both males and females. The assessment of neurological deficit was performed with the NIHSS and the tests used for the functional and cognitive status were: (1) modified Rankin Scale; (2) Photo test, and (3) abbreviated Pfeiffer’s mental status questionnaire. We observed a significant difference in plasma concentration of cortisol and estradiol between both experimental groups. In the AIS group, higher levels of these neuroactive steroids were associated with more pronounced neurological, cognitive and functional deficits in women compared to men. We propose that in elderly patients, high levels of circulating neuroactive steroids like cortisol and estradiol could potentiate AIS-mediated neuropathology in the ischemic and penumbra areas.
RELEVANCE FEEDBACK IN FACIAL-RECOGNITION-BASED IMAGE RETRIEVAL

Principal component analysis (PCA) and independent component analysis (ICA) are widely used to recognize faces, while relevance feedback (RF) is broadly taken to improve the performance of content-based image retrieving (CBIR) systems. Generally, variations due to illumination, camera geometry and pose have a great effect upon the result of face recognition. In this paper, above methods are combined to promote the detection and recognition rate of faces. Rather than performing PCA/ICA with holistic image, we take advantages of facial components in a facial image to reduce variations and matching complexity. Furthermore, when the component positions are well aligned, the variation due to geometrical factors can be compensated, and it results in further accuracy improvement. Finally, we propose relevance feedback to retrieve the defective in face detection and recognition.
A Simple and Fast Term Selection Procedure for Text Clustering

Text clustering is a theme that is receiving considerable attention nowadays in areas such as text mining and information retrieval. A starting point for clustering methods applied on unstructured document collection is the creation of a vector-space model usually known as bag-ofwords model [1J. Documents are then usually described by a matrix which happens to be huge and extremely sparse which is due to the exceeding number of terms describing the set of documents. Although several techniques can be employed to reduce this number, the final figure is still high thus leading to a feature space of high dimensionality. This paper presents a simple procedure that not only considerably reduces the dimensionality of the feature space and hence the processing time, but also produces clustering performances comparable or even better when confronted with the full set of terms.
Analysis of the Spatio-Temporal Patterns of Water Pollution and Source Contribution Using the MODIS Sensor Products and Multivariate Statistical Techniques

Analysis of the spatio-temporal patterns and source apportionment of water pollution is important for proper management and protection of water resources. In this paper, different multivariate statistical methods were used to explore the spatio-temporal patterns of water pollution, and quantitative relationships between the important pollution parameters and environmental variables. Eleven significant parameters measured in 22 monitoring sites were preprocessed spanning between 2001 and 2007. Results of the hierarchical clustering analysis (HCA) demonstrated that this method had high flexibility for efficient classification of the monitoring sites. Results of discriminant analysis (DA) revealed that a high number of parameters contributed in discrimination of classes in the spring and summer seasons, especially in the April and September months. Recorded data of river water temperature (RWT), runoff, and two products of the MODIS sensor including the monthly Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) for the period of 2002-2007 were used as the explanatory variables. Test of NDVI and LST was based on extraction of their average values in different buffers of 250 up to 1500 m around the streams. Monthly data of group 1, a group with the highest number of monitoring sites resulting from the clustering procedure, was used for the analysis. Rotated principal component analysis (rotated PCA) was used for exploration of the quantitative relationships between the pollution parameters and environmental variables. Absolute principal component score- multivariate linear regression (APCS-MLR) was applied to quantify the source contributions for each pollution parameter. Results showed that NDVI and runoff can be considered as the efficient indicators of the non-point pollution sources such as the agricultural activities and surface weathering. NDVI showed an important role in reduction of TDS and No3-. Multiple buffers of NDVI showed temporally variable relations with different pollution sources. LST showed high discrimination potentials for distinguishing pollutions related to biochemical activities. Although the tested environmental variables revealed some relationships with those of the water pollution sources, nevertheless for more detailed analysis of the water pollution problem, the role of other latent environmental variables should be taken into consideration.
The Rebirth of the U.S.-Mexico Border

Law enforcement along the U.S.-Mexico border region has significantly changed since the 1970s. Currently, Latinas/os make up more than half of the agents who patrol the southern border region. The Texas Lower Rio Grande Valley, in particular, has transformed from a predominantly Anglo police establishment to one with a heavy presence of Mexican American agents within local and federal agencies. Though responsible for managing the flows of bodies, narcotics, and would-be terrorists from illegally crossing into the United States, the institutional and social vestiges of this region’s racialized past continues to inform practices of agents in the field, including those of Mexican American descent. In this article, we draw from ethnographic fieldwork, participant observations, and in-depth qualitative interviews with enforcement agents situated in the Texas Lower Rio Grande Valley to investigate what we describe as a phenomenology of racial power among enforcement agents, or the tethering of power, subjectivity, and embodied racialized practices in their day-to-day lives.
Optimized Selection Sort Algorithm for Two Dimensional array

The main idea of Optimized Selection Sort Algorithm (OSSA) is based on the already existing selection sort algorithm, with a difference that old selection sort; sorts one element either smallest or largest in a single iteration while optimized selection sort, sorts both the elements at the same time i.e smallest and largest in a single iteration. In this study we have developed a variation of OSSA for two-dimensional array and called it Optimized Selection Sort Algorithms for Two-Dimensional arrays OSSA2D. The hypothetical and experimental analysis revealed that the implementation of the proposed algorithm is easy. The comparison shows that the performance of OSSA2D is better than OSSA by four times and when compared with old Selection Sort algorithm the performance is improved by eight times (i.e if OSSA can sort an array in 100 seconds, OSSA2D can sort it in 24.55 Seconds, and similarly if Selection Sort takes 100 Seconds then OSSA2D take only 12.22 Seconds). This performance is remarkable when the array size is very large. The experiential results also demonstrate that the proposed algorithm has much lower computational complexity than the one dimensional sorting algorithm when the array size is very large.
Space Vector Modulation with DC-Link Voltage Balancing Control for Three-Level Inverters

The DC-link voltage balancing scheme for three-level inverter is proposed in this paper. Dependence of the DC-link capacitor voltage deviation on DC-link current and inverter switching states is established for proposed three-level inverter. Pulse pattern rearrangements for space vector PWM (SVPWM) using degree of freedom available in choice of redundant space vectors, sequencing of vectors, and splitting of duty cycles of vector are best exploited. Self neutral-point voltage deviation control in feed forward and simplified closed loop scheme are proposed in this paper. The effectiveness of proposed scheme is verified by computer simulations.
Image2Weather: A Large-Scale Image Dataset for Weather Property Estimation

To facilitate weather property estimation from images, a large-scale image dataset associated with rich weather information is developed. Based on the taken time and geographical information of an image, we associate it with weather properties obtained from a weather forecast website. Through data filtering like indoor/outdoor classification and sky region detection, a clean and large-scale image-weather dataset (named Image2Weather dataset) consisting of more than 180,000 photos is built to promote related researches. In addition to reporting statistical characteristics of this dataset, we investigate the relationship between several visual features and weather properties, which then serve as the foundation of interesting applications like weather type classification and temperature estimation. We show effectiveness of weather property estimation based on the Image2Weather dataset, and discuss how it can be leveraged to facilitate related studies in the future.
SELINDA: A secure, scalable and light-weight data collection protocol for smart grids

Security in the smart grid is a challenge as an increasing number of sensors and measurement devices are connected to the power grid. General purpose security protocols are not suitable for providing data security to devices with limited memory, computational power and network connectivity. In this paper, we develop a secure and light-weight scalable security protocol that allows a power system operator (PO) to collect data from measurement devices (MDs) using data collectors (DCs). The security protocol trades off between computations and device memory requirements and provides flexible association between DC and MDs. These features allow data to be securely transferred from MDs to PO via mobile or untrustworthy DCs. We analyze the complexity and security of the protocol and validate its performance using experiments. Our results confirm that our proposed protocol collects data in a secure, fast and efficient manner.
A common framework for perception and action: Neuroimaging evidence

In recent years, neurophysiological evidence has accumulated in favor of a common coding between perception and execution of action. We review findings from recent neuroimaging experiments in the action domain with three complementary perspectives: perception of action, covert action triggered by perception, and reproduction of perceived action (imitation). All studies point to the parietal cortex as a key region for body movement representation, both observed and performed.
An Agent-based simulation model for the market diffusion of a second generation biofuel

Second generation biofuels are widely considered a promising energy alternative to conventional (fossil) fuels. Although they will not completely replace fossil fuels (e.g., due to the limited availability of biomass), these high-quality biofuels can contribute to reducing emissions and strengthening a country's energy autonomy. In Austria, a team at the Vienna University of Technology is developing a biomass-to-liquid technology based on the Fischer-Tropsch synthesis. While the remaining technical obstacles are expected to be overcome in due time, the market introduction of the novel biofuel also requires substantial investments. In this context, we developed an agent-based simulation model that can provide potential investors with forecasts for the biofuel's market diffusion. This paper describes the model and presents simulation results.
Neutrophils Infiltrate the Spinal Cord Parenchyma of Rats with Experimental Diabetic Neuropathy

Spinal glial cell activation and cytokine secretion have been implicated in the etiology of neuropathic pain in a number of experimental models, including diabetic neuropathy. In this study, streptozotocin- (STZ-) induced diabetic rats were either untreated or treated with gabapentin (50 mg/kg/day by gavage for 2 weeks, from 6 weeks after STZ). At 8 weeks after STZ, hypersensitivity was confirmed in the untreated diabetic rats as a reduced response threshold to touch, whilst mechanical thresholds in gabapentin-treated diabetic rats were no different from controls. Diabetes-associated thermal hypersensitivity was also ameliorated by gabapentin. We performed a cytokine profiling array in lumbar spinal cord samples from control and diabetic rats. This revealed an increase in L-selectin, an adhesion molecule important for neutrophil transmigration, in the spinal cord of diabetic rats but not diabetic rats treated with gabapentin. Furthermore, we found an increase in the number of neutrophils present in the parenchyma of the spinal cord, which was again ameliorated in gabapentin-treated diabetic rats. Therefore, we suggest that dysregulated spinal L-selectin and neutrophil infiltration into the spinal cord could contribute to the pathogenesis of painful diabetic neuropathy.