Miracle-dz/UniFAR-FTUs huggingface dataset:
DOC2B Acts as a Calcium Switch and Enhances Vesicle Fusion

Calcium-dependent exocytosis is regulated by a vast number of proteins. DOC2B is a synaptic protein that translocates to the plasma membrane (PM) after small elevations in intracellular calcium concentration. The aim of this study was to investigate the role of DOC2B in calcium-triggered exocytosis. Using biochemical and biophysical measurements, we demonstrate that the C2A domain of DOC2B interacts directly with the PM in a calcium-dependent manner. Using a combination of electrophysiological, morphological, and total internal reflection fluorescent measurements, we found that DOC2B acts as a priming factor and increases the number of fusion-competent vesicles. Comparing secretion during repeated stimulation between wild-type DOC2B and a mutated DOC2B that is constantly at the PM showed that DOC2B enhances catecholamine secretion also during repeated stimulation and that DOC2B has to translocate to the PM to exert its facilitating effect, suggesting that its activity is dependent on calcium. The hypothesis that DOC2B exerts its effect at the PM was supported by the finding that DOC2B affects the fusion kinetics of single vesicles and interacts with the PM SNAREs (soluble NSF attachment receptors). We conclude that DOC2B is a calcium-dependent priming factor and its activity at the PM enables efficient expansion of the fusion pore, leading to increased catecholamine release.
Fine-Grained Sentiment Analysis using Probabilistic Soft Logic CMPS 209 C Project Report

Sentiment analysis is the task of classification of a text as having a positive, negative or neutral sentiment. But for most applications, determining the overall sentiment of an entire text might not be sufficient. It has become more important to do this task at a more fine-grained level in order to establish the sentiment of every opinion in the text. But these segments are not easy to classify independently as they contain very less information. A solution is to make use of more information in the form of relational features between the segments and collectively classify them. This project explores this using Probabilistic Soft Logic [8] by using both attribute and relational features of segments. Attribute features involve integrating various sentiment lexicons, and relational features involve using neighborhood and contrasting relations between segments. The results from this show that the performance is significantly improved by using relational features rather than just attribute features.
A configurational bias Monte Carlo method for linear and cyclic peptides

A new configurational bias Monte Carlo technique is described for the simulation of peptides, focusing on the biologically relevant cases of linear and cyclic peptides. The approach leads to efficient, Boltzmann-weighted sampling of the torsional degrees of freedom in these biological molecules, a feat not possible with previous Monte Carlo and molecular dynamics methods.
Trellis detection for random lattices

In general, lattice problems are simple to describe but rather hard to solve optimally. Several suboptimal solutions have been proposed for the closest vector problem (CVP), which is central in multiple-input multiple-output (MIMO) communication systems. It is known that some lattices have a trellis representation, however, those lattices require very particular geometries that are not found in lattices randomly generated. In this paper we show that for the typical number of dimensions used in MIMO communication, with high probability, there exists a synthetic lattice that is a member of the family of lattices that have a trellis representation and which is sufficiently close to any given random lattice. For that purpose we present a method to find a trellis-oriented basis for a given random lattice. The basis vectors of the synthetic lattice and the basis vectors of the original lattice are close and for finite alphabets the two lattices are roughly the same in the region of interest. Therefore, the optimal decision (Voronoi) regions of both lattices chiefly overlap. A linear transformation then focuses the original lattice onto the synthetic one, known to have a trellis representation. This minimizes the distortion of the Voronoi regions associated with maximum-likelihood detection and therefore the performance attained in the MIMO-CVP is close to optimal.
Distributing trust with the Rampart toolkit

Many mechanisms for enforcing security policy in distributed computer systems rely on trusted nodes, that is, computers and resident software whose correct functioning is essential to implementing the policy. In this article, we describe our research on distributing trust among a group of nodes, so while no single node need be fully trusted, the function performed by the group can be. Central to this effort is a toolkit of group communication protocols called Rampart we developed to simplify construction of such groups. An overarching goal of computer security mechanisms is to limit the extent to which each component of a system must be trusted to implement a desired security policy. Typically, this goal leads to system designs in which components are labeled
Central bank transparency: Examining volatility in output and financial markets

This research utilizes the seminal index from Dincer and Eichengreen (2013), which includes values for 120 countries from 1998 to 2010, to examine the effects of the level of central bank transparency on output and financial market volatility. In addition, this paper explores whether a degree of optimal transparency exists.
Chapter 3 : Modeling for Verification

ion of the rest of the network (b) Abstraction Fig. 7 Verifying latency through a router in an on-chip network. We wish to verify a bound on the latency through node A. An obvious alternative approach involves abstraction and modeling with nondeterminism. Specifically, as depicted in Fig. 7(b), one can abstract all nodes in the network other than A into an environment model E, where E is a transition system which, at each cycle, non-deterministically selects a port to send an input packet to A, sets the destination port for that packet, and chooses a data payload. If we apply this technique to the problem of verifying a latency bound of 15 through the router, it fails to prove the latency bound using a sequence of input packets that causes contention for the same output port within the router. The question is, however, whether such a pattern can arise in this particular network for the traffic patterns Chapter 3: Modeling for Verification 23 that the architect has in mind. Purely non-deterministic modeling of traffic sources does not permit us to model traffic sources in a more precise manner. Thus, as mentioned in Sec. 2.1, the environment model must be tailored to the type of property to be verified. In this case, in order to prove a particular latency bound, we must come up with a suitable model of traffic sources (and sinks). One formalism for more fine-grained modeling of any channel of NoC traffic (including sources and sinks) is a bounded channel regulator, introduced by Holcomb et al. [37] based on the regulator model described by Cruz [27]. A channel in an NoC is any link between components in the network. A bounded channel regulator TR is a monitor on a channel that checks three constraints: the rate ρ , burstiness σ , and bound B on the traffic that traverses the channel. Figure 8 illustrates a bounded channel regulator. The buffer of size σ begins filled with tokens, and one token is removed whenever a head flit passes through the channel being monitored. Srcρ adds a token to the buffer once every ρ cycles, unless the buffer is already full. During a simulation, Srcρ becomes inactive once it has produced a total of B−σ tokens. If a head flit ever passes through the channel when the regulator queue is empty, then a Boolean flag ai is set to signal that the channel traffic does not conform to the constraints of regulator i. We refer to a regulator with rate ρ , burstiness σ , and bound B as TR(ρ,σ ,B). It is easy to model a bounded regulator in SML using nondeterministic assignment for generating packets, a FIFO buffer for storing tokens, and a counter for enforcing the rate of the traffic regulator.
Markov Models for Limited Feedback MIMO Systems

Multiple antenna wireless systems with feedback of quantized channel information, called "limited feedback" systems, are attractive choices for improving the quality of downlink (DL) transmission. Most work in this area use the block-fading channel model where the DL channel is assumed constant in each block and different blocks uncorrelated. In this paper, we consider limited feedback for a temporally correlated DL channel. Markov models are introduced for characterizing the temporal correlation and probability distribution of the DL channel. Using the Markov models, average feedback rates are derived. Numerical results show that the feedback rates are proportional to the Doppler frequency
Online battery state-of-charge estimation based on sparse gaussian process regression

This paper presents a new online method for state-of charge (SoC) estimation of Lithium-ion (Li-ion) batteries based on sparse Gaussian process regression (GPR). Building upon sparse approximation of the regular GPR, the proposed method is computationally more efficient. The battery SoC is estimated based on measured voltage, current and temperature. The accuracy of the proposed method is verified using LiMn2O4/hard-carbon battery data collected from a constant-current discharge test. In addition, the estimation performance of the proposed method is compared with a SoC estimation method using regular GPR with different covariance functions.
Clathrate hydrates as a sink of noble gases in Titan's atmosphere

We use a statistical thermodynamic approach to determine the composition of clathrate hydrates which may form from a multiple compound gas whose composition is similar to that of Titan’s atmosphere. Assuming that noble gases are initially present in this gas phase, we calculate the ratios of xenon, krypton and argon to species trapped in clathrate hydrates. We find that these ratios calculated for xenon and krypton are several orders of magnitude higher than in the coexisting gas at temperature and pressure conditions close to those of Titan’s present atmosphere at ground level. Furthermore we show that, by contrast, argon is poorly trapped in these ices. This trapping mechanism implies that the gas-phase is progressively depleted in xenon and krypton when the coexisting clathrate hydrates form whereas the initial abundance of argon remains almost constant. Our results are thus compatible with the deficiency of Titan’s atmosphere in xenon and krypton measured by the Huygens probe during its descent on January 14, 2005. However, in order to interpret the subsolar abundance of primordial Ar also revealed by Huygens, other processes that occurred either during the formation of Titan or during its evolution must be also invoked.
Write Like I Write: Herding in the Language of Online Reviews

Our behaviors often converge with those of others, and language within social media is no exception. We consider reviews of tourist attractions at TripAdvisor (TA), the world's largest resource for travel information. Unlike social networking sites, TA review forums do not facilitate direct interaction between participants. Nonetheless, theory suggests that language is guided by writers' conception of their audience, and that their style can shift in response. We implement a model of herding as a local transmission process, exploring the hypothesis that a reviewer is influenced by how preceding reviews manifest a given stylistic feature (e.g., pronouns, paralinguistic devices). We find that reviewers are more likely to use unusual features when such characteristics appear in their local context. The extent to which reviewers are influenced by context is correlated to attributes shared in their profiles, as well as their sentiment toward the attraction reviewed. Our results suggest that language can be influenced by others, even in an asynchronous environment with little to no interpersonal interaction. In other words, our behaviors may be susceptible to manipulation in social media; it may not always be the case that we write like ourselves.
Protective Effects of Beta Glucan and Gliclazide on Brain Tissue and Sciatic Nerve of Diabetic Rats Induced by Streptozosin

There have not been yet enough studies about effects of beta glucan and gliclazide on oxidative stress created by streptozotocin in the brain and sciatic nerve of diabetic rats. The aim of this paper was to investigate the antioxidant effects of gliclazide and beta glucan on oxidative stress and lipid peroxidation created by streptozotosin in brain and sciatic nerve. Total of 42 rats were divided into 6 groups including control, diabetic untreated (DM) (only STZ, diabetic), STZ (DM) + beta glucan, STZ (DM) + gliclazide, only beta glucan treated (no diabetic), and only gliclazide treated (no diabetic). The brain and sciatic nerve tissue samples were analyzed for malondialdehyde (MDA), total oxidant status (TOS), total antioxidant status (TAS), oxidative stress index (OSI), and paraoxonase (PON-1) levels. We found a significant increase in MDA, TOS, and OSI along with a reduction in TAS level, catalase, and PON-1 activities in brain and sciatic nerve of streptozotocin-induced diabetic rats. Also, this study shows that in terms of these parameters both gliclazide and beta glucan have a neuroprotective effect on the brain and sciatic nerve of the streptozotocin-induced diabetic rat. Our conclusion was that gliclazide and beta glucan have antioxidant effects on the brain and sciatic nerve of the streptozotocin-induced diabetic rat.
Luminous Infrared Galaxies in the Local Universe

We study the morphology and star formation properties of 159 local luminous infrared galaxies (LIRGs) using multicolor images from Data Release 2 (DR2) of the Sloan Digital Sky Survey (SDSS). The LIRGs are selected from a cross-correlation analysis between the Infrared Astronomical Satellite (IRAS) survey and SDSS. They are all brighter than 15.9 mag in the r band and below redshift ~0.1, and so can be reliably classified morphologically. We find that the fractions of interacting/merging and spiral galaxies are ~48% and ~40%, respectively. Our results complement and confirm the decline (increase) in the fraction of spiral (interacting/merging) galaxies from z ~ 1 to 0.1, as found by Melbourne et al. About 75% of spiral galaxies in the local LIRGs are barred, indicating that bars may play an important role in triggering star formation rates ≳20 M☉ yr-1 in the local universe. Compared with high-redshift LIRGs, local LIRGs have lower specific star formation rates, smaller cold gas fractions, and a narrower range of stellar masses. Local LIRGs appear to be either merging galaxies forming intermediate-mass elliptical galaxies, or spiral galaxies undergoing high star formation activities regulated by bars.
An Evaluation of Statistical Approaches to Rare Variant Analysis in Genetic Association Studies

Genome‐wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large‐scale re‐sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population‐based genetic studies when data are available from re‐sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re‐sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley‐Liss, Inc.
HER2 Status Determination: Analyzing the Problems to Find the Solutions

AbstractMisdiagnosis in the evaluation of HER2 status in breast cancer may have consequent negative impact on clinical decision-making. Therefore, it has become ever more important to share procedures and interpretation criteria for HER2 testing among laboratories. Herein, we report an interlaboratory survey among 9 hospitals located in the central-south regions of Italy. The centers sent a series of 36 slides, 4 for each HER2 score, to the revising centers. We found a good concordance in HER2 scoring for 0 and 3+ score, but a very low concordance for 1+ and 2+ scores. To focus on factors that may lead to discordant results, we report 4 cases which summarized the most common source of discrepancy in HER2 testing. This methodological approach will help the individual laboratory to minimize technical variables and to reduce the percentage of erroneous interpretations of HER2 status.
Dynamic term selection in learning a query from examples

Learning a query from training examples is an important problem in information retrieval, filtering, and text categorization. We propose two methods that dynamically compute the number of terms to be selected from the training examples and added to an individual query. At the basis of both algorithms lies the observation that, given a set of terms ranked in order of decreasing weight, there seems to be a correlation between the curve of term weights and the average precision. More precisely, the average precision increases as long as the curve of term weights is sharp. When the curve becomes flat, the average precision either decreases or remains constant. The new approach aims to maximize the average precision of each query, while also reducing the number of terms per query and hence decreasing the response time of the system. Experimental results on five collections show that variable length queries generally behave better than fixed length queries.
Environmental Performance and Climate Policy

This study’s ultimate goal is to analyze environmental performance (EP) at firm level and the effectiveness of environmental policy along with other possible determinants. Especially, the empirical analysis aims at exploring the relationship between the actual EP of firms in terms of CO2 emissions per output unit, and one aspect of Swedish environmental policy, the CO2-tax. Since Sweden was the first country to introduce a specific CO2-tax in 1991 we believe that the Swedish case may serve as an appropriate “test bench” for analyzing EP and the effectiveness of environmental policy in general. To achieve our objective we use a panel data of Swedish manufacturing spanning over the period 1990-2004. The results suggest that EP has improved in all sectors of manufacturing. We also see that production increases while emissions decrease in many sectors, indicating a decoupling of economic growth and environmental degradation. Furthermore, firms’ EP responds to changes in the CO2-tax and fossil fuel price, but is more sensitive to the tax, indicating different EP behavior among firms depending on why the cost of fossil fuels change. Several sectors also display a positive tendency over time in EP, which may suggest that EP is to some extent stimulated by an overall boost in environmental awareness in society and firms.
Speaker and language factorization in DNN-based TTS synthesis

We have successfully proposed to use multi-speaker modelling in DNN-based TTS synthesis for improved voice quality with limited available data from a speaker. In this paper, we propose a new speaker and language factorized DNN, where speaker-specific layers are used for multi-speaker modelling, and shared layers and language-specific layers are employed for multi-language, linguistic feature transformation. Experimental results on a speech corpus of multiple speakers in both Mandarin and English show that the proposed factorized DNN can not only achieve a similar voice quality as that of a multi-speaker DNN, but also perform polyglot synthesis with a monolingual speaker's voice.
Hen Egg-White Lysozyme Crystallisation: Protein Stacking and Structure Stability Enhanced by a Tellurium(VI)-Centred Polyoxotungstate

As synchrotron radiation becomes more intense, detectors become faster and structure‐solving software becomes more elaborate, obtaining single crystals suitable for data collection is now the bottleneck in macromolecular crystallography. Hence, there is a need for novel and advanced crystallisation agents with the ability to crystallise proteins that are otherwise challenging. Here, an Anderson–Evans‐type polyoxometalate (POM), specifically Na6[TeW6O24]⋅22 H2O (TEW), is employed as a crystallisation additive. Its effects on protein crystallisation are demonstrated with hen egg‐white lysozyme (HEWL), which co‐crystallises with TEW in the vicinity (or within) the liquid–liquid phase separation (LLPS) region. The X‐ray structure (PDB ID: 4PHI) determination revealed that TEW molecules are part of the crystal lattice, thus demonstrating specific binding to HEWL with electrostatic interactions and hydrogen bonds. The negatively charged TEW polyoxotungstate binds to sites with a positive electrostatic potential located between two (or more) symmetry‐related protein chains. Thus, TEW facilitates the formation of protein–protein interfaces of otherwise repulsive surfaces, and thereby the realisation of a stable crystal lattice. In addition to retaining the isomorphicity of the protein structure, the anomalous scattering of the POMs was used for macromolecular phasing. The results suggest that hexatungstotellurate(VI) has great potential as a crystallisation additive to promote both protein crystallisation and structure elucidation.
Channel Selection Based on Phase Measurement in P300-Based Brain-Computer Interface

Most EEG-based brain-computer interface (BCI) paradigms include specific electrode positions. As the structures and activities of the brain vary with each individual, contributing channels should be chosen based on original records of BCIs. Phase measurement is an important approach in EEG analyses, but seldom used for channel selections. In this paper, the phase locking and concentrating value-based recursive feature elimination approach (PLCV-RFE) is proposed to produce robust-EEG channel selections in a P300 speller. The PLCV-RFE, deriving from the phase resetting mechanism, measures the phase relation between EEGs and ranks channels by the recursive strategy. Data recorded from 32 electrodes on 9 subjects are used to evaluate the proposed method. The results show that the PLCV-RFE substantially reduces channel sets and improves recognition accuracies significantly. Moreover, compared with other state-of-the-art feature selection methods (SSNRSF and SVM-RFE), the PLCV-RFE achieves better performance. Thus the phase measurement is available in the channel selection of BCI and it may be an evidence to indirectly support that phase resetting is at least one reason for ERP generations.