Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation and Application of Lipofuscin-Based Age Determination for Chesapeake Bay Blue Crabs Callinectes sapidus

Abstract Quantifying lipofuscin (LF), a metabolic byproduct that accumulates in postmitotic cells, serves as one of the principal approaches for aging crustaceans, but the accuracy of this method remains an important issue. Here, we quantified LF accumulation as a function of chronological age and temperature (degree-days) in an economically important crustacean, the blue crab Callinectes sapidus, to test the accuracy of LF-based age estimates and determine the age-specific partial recruitment of juveniles to summer and fall commercial fisheries. Three known-age juvenile cohorts (63-83 d) were reared in ponds up to 1.8 years of age. Field collections were conducted in two subestuaries of Chesapeake Bay from June to October during 2003 and 2004. Lipofuscin accumulation oscillated seasonally in known-age cohorts. Significant (log e transformed) LF accumulation occurred at average intervals of 2.5 months, with the exception of winter months (mean temperature = 8°C). Seasonalized von Bertalanffy functions acc.
A new multiscale finite element method for high-contrast elliptic interface problems

We introduce a new multiscale finite element method which is able to accurately capture solutions of elliptic interface problems with high contrast coefficients by using only coarse quasiuniform meshes, and without resolving the interfaces. A typical application would be the modelling of flow in a porous medium containing a number of inclusions of low (or high) permeability embedded in a matrix of high (respectively low) permeability. Our method is H^1- conforming, with degrees of freedom at the nodes of a triangular mesh and requiring the solution of subgrid problems for the basis functions on elements which straddle the coefficient interface but which use standard linear approximation otherwise. A key point is the introduction of novel coefficientdependent boundary conditions for the subgrid problems. Under moderate assumptions, we prove that our methods have (optimal) convergence rate of O(h) in the energy norm and O(h^2) in the L_2 norm where h is the (coarse) mesh diameter and the hidden constants in these estimates are independent of the “contrast” (i.e. ratio of largest to smallest value) of the PDE coefficient. For standard elements the best estimate in the energy norm would be O(h^(1/2−e)) with a hidden constant which in general depends on the contrast. The new interior boundary conditions depend not only on the contrast of the coefficients, but also on the angles of intersection of the interface with the element edges.
A Quantitative Comparison of Single-Dye Tracking Analysis Tools Using Monte Carlo Simulations

Single-particle tracking (SPT) is widely used to study processes from membrane receptor organization to the dynamics of RNAs in living cells. While single-dye labeling strategies have the benefit of being minimally invasive, this comes at the expense of data quality; typically a data set of short trajectories is obtained and analyzed by means of the mean square displacements (MSD) or the distribution of the particles’ displacements in a set time interval (jump distance, JD). To evaluate the applicability of both approaches, a quantitative comparison of both methods under typically encountered experimental conditions is necessary. Here we use Monte Carlo simulations to systematically compare the accuracy of diffusion coefficients (D-values) obtained for three cases: one population of diffusing species, two populations with different D-values, and a population switching between two D-values. For the first case we find that the MSD gives more or equally accurate results than the JD analysis (relative errors of D-values <6%). If two diffusing species are present or a particle undergoes a motion change, the JD analysis successfully distinguishes both species (relative error <5%). Finally we apply the JD analysis to investigate the motion of endogenous LPS receptors in live macrophages before and after treatment with methyl-β-cyclodextrin and latrunculin B.
Rifaximin in the management of colonic diverticular disease

Rifaximin is a rifamycin derivative that acts by inhibiting bacterial RNA synthesis. Since it is virtually unabsorbed after oral administration, its bioavailability within the GI tract is high, with intraluminal and fecal drug concentrations largely exceeding the minimum inhibitory concentration values observed in vitro against a broad spectrum of bacteria, including Gram-positive and Gram-negative bacteria, both aerobes and anaerobes. The GI tract, therefore, represents the primary therapeutic target and the disorders in which intestinal bacteria have a pathogenic role represent the main indication. This is the case with colonic diverticular disease. As a consequence, the broad antibacterial activity of rifaximin appears to be of value in the treatment of this clinical condition. Clinical trials have provided evidence of the substantial benefit of rifaximin in diverticular disease. Indeed, available data show the efficacy of the drug in achieving symptomatic relief in patients with uncomplicated disease. A therapeutic gain of approximately 30%, compared with fiber supplementation only, can be expected after cyclic administration of rifaximin for 12 months. However, its value in the prevention of inflammatory complications of the disease needs to be further explored. Recent studies have shown some evidence of synergy between rifaximin and mesalazine and suggest that a combined treatment could be worthwhile in selected subsets of patients with diverticular disease.
Scaling laws for secrecy capacity in cooperative wireless networks

We investigate large wireless networks subject to security constraints. In contrast to point-to-point, interference-limited communications considered in prior works, we propose active cooperative relaying based schemes. We consider a network with n<sub>l</sub> legitimate nodes and n<sub>e</sub> eavesdroppers, and path loss exponent α ≥ 2. As long as n<sub>e</sub><sup>2</sup>(log(n<sub>e</sub>))<sup>γ</sup> = o(n<sub>l</sub>) holds for some positive γ, we show one can obtain unbounded secure aggregate rate. This means zero-cost secure communication, given a fixed total power constraint for the entire network. We achieve this result with (i) the source using Wyner randomized encoder and a serial (multi-stage) block Markov scheme, to cooperate with the relays, and (ii) the relays acting as a virtual multi-antenna to apply beamforming against the eavesdroppers. Our simpler parallel (two-stage) relaying scheme can achieve the same unbounded secure aggregate rate when n<sub>e</sub><sup>α/2 + 1</sup> (log(n<sub>e</sub>))<sup>γ+δ(α/2+1)</sup> = o(n<sub>l</sub>) holds, for some positive γ, δ.
Establishing a Probabilistic Depth Map from Focused Plenoptic Cameras

In this paper we propose a novel method for depth estimation based on a single recording of a focused plenoptic camera. The presented algorithm is based on multiple stereo-observations within the multi-view micro images of the focused plenoptic camera. Here, pixel correspondences are found based on local intensity error minimization. Since our algorithm works directly on the micro images, no sub-aperture or epipolar plane images have to be synthesized. Due to the fact that we perform stereo matching based on local criteria we only estimate depth for pixels with sufficient gradient. Thus, we reduce the complexity of the problem, while neglecting uncertain stereo correspondences. Our algorithm incorporates multiple stereo-observations of the same point in a probabilistic depth map. We will show, that this (inverse) depth map can be modeled as a map of Gaussian distributed random variables. Thus, each depth pixel consists of an estimated depth and a corresponding variance, which gives a measure for the uncertainty of the estimation. This uncertainty information can be used in subsequent filtering methods.
Interpersonal psychotherapy delivered over the telephone to recurrent depressives. A pilot study

Rates of recurrent depression peak among women in their childbearing years, yet a large number of these women are not treated or do not seek maintenance treatment between episodes. Barriers to treatment include time constraints, demands of child care, and finances. A method for improving access to treatment of depression may be delivery of psychotherapy over the telephone. A 12‐week pilot controlled clinical trial with random assignment was conducted to test the feasibility and efficacy of Interpersonal Psychotherapy delivered over the telephone (IPT‐T) for depression in women (N=15 IPT‐T and N=15 no treatment) with a life‐time history of recurrent depression and low rates of previous treatment. Patients were interviewed at baseline and at 12 weeks by a blind and independent clinical evaluator to assess their symptom level and social functioning. IPT‐T as compared to no treatment was significantly better in lowering symptoms of depression as measured by the Hamilton Rating Scale for Depression (p<.02), improving global functioning (GAS) (p<.02), and improving work and social functioning (p<.03–.05, respectively). These preliminary findings support the feasibility and helpfulness of IPT‐T in preventing an increase in depressive symptoms among women with recurrent depression but current mild to moderately levels of depression. Depression and Anxiety 16:114–117, 2002. © 2002 Wiley‐Liss, Inc.
Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp.
Frequency Domain Packet Scheduling with MIMO for 3GPP LTE Downlink

In this paper, we formalize a general Frequency Domain Packet Scheduling (FDPS) problem for 3GPP LTE Downlink (DL). The DL FDPS problem incorporates the Single-User Multiple Input Multiple Output (SU-MIMO) technique, and can express various scheduling policies, including the Proportional-Fair metric, the MaxWeight scheduling, etc. For LTE DL SU-MIMO, the constraint of selecting only one MIMO mode (transmit diversity or spatial multiplexing) per user in each transmission time interval (TTI) increases the hardness of the FDPS problem. We prove the problem is MAX SNP-hard, which implies approximation algorithms with constant approximation ratios are the best we can expect. Subsequently, we propose an approximation algorithm of polynomial runtime. The solution is based on a greedy method for maximizing a non-decreasing submodular function over a matroid. The algorithm can solve the general DL FDPS problem with an approximation ratio of 4. We implement the proposed algorithm and compare its performance with other well-known schedulers.
THE SCIENCE OF WELL-BEING

People are innately resilient and able to create their own well-being if they learn to live in ways that are healthy, happy, and good. Current emphasis on costly care of discrete diseases for profit neglects more effective means of health promotion and disease prevention. Such emphasis has caused unsustainable damage to both the providers and recipients of profit-driven disease care, which fails to reduce the burden of disease efficiently and equitably. In this article, health and well-being are defined and ways to measure them are provided to facilitate clinical care and planning of effective health promotion and disease prevention. The physical, mental, social, and spiritual aspects of well-being are all strongly dependent on human personality; they are strongly intertwined with one another because health is highly dependent on personal behaviors and the socioeconomic influences. In turn, human personality depends on three systems of learning and memory: associative conditioning, intentionality, and self-awareness. Consequently, health and well-being are creative adaptive processes that can be deliberately enhanced so that individuals and their communities can learn to flourish. In contrast, the medical model of diseases as discrete entities with specific causes is approximate, imprecise, and fails to inform clinicians how they can optimally treat any individual person. Health promotion, disease prevention, and personalized disease care are each necessary components of health care. The science of well-being provides the essential facts and principles that can allow us to prioritize efforts to serve the well-being of others in ways that are effective, efficient, and equitable.
Local word confidence measure using word graph and n-best list

This paper presents some confidence measures for large vocabulary speech recognition which are based on word graph or N-Best List structures. More and more applications need fast estimation of any measures in order to stay real-time. We propose some simple and fast measures, locally computed, that can be directly used within the first decoding recognition process. We also define some other measures combining word graph and N-Best List. Experimental results on a French broadcast news corpus are also presented.
An evidential approach in ensembles

In this paper, we describe an approach to modeling the general process of combining decisions involved in ensembles of classifiers as an evidential reasoning process. This work proposes a novel structure, theoretical properties and manipulation mechanisms for representing classifier decisions as pieces of evidence. The advantage of the representation formalism is that it not only facilitates the distinguishing of trivial focal elements from important ones, resulting in the improvement of the ensemble performance, but it also effectively reduces the computation-time from exponential (as required in the conventional process of combining multiple pieces of evidence) to linear. We have conducted a comparative analysis on the effectiveness of the proposed evidence representation formalism in the text categorization domain. By comparing this method with majority voting and the previous results, we also demonstrate the advantage of this novel approach in combining classifiers.
A critical role for the anterior hippocampus in relational memory: Evidence from an fMRI study comparing associative and item recognition

Neuroscientific research has established that the hippocampal formation, a structure within the medial temporal lobe (MTL), plays a critical role in memory for facts and events (declarative memory) (Milner et al., 1998 ). However, its precise role remains unclear. According to one view, the hippocampus has a special role in relating or binding together previously unrelated pieces of information, while another view proposes that the hippocampus is equally involved in all forms of declarative memory, regardless of their demands on relational processing. Using functional magnetic resonance imaging (fMRI), we show that hippocampal activation is modulated by the extent to which a retrieval task depends on relational processing. © 2003 Wiley‐Liss, Inc.
Hyperproteic hypocaloric enteral nutrition in the critically ill patient: A randomized controlled clinical trial

Introduction: Our aim was to evaluate the impact of hyperproteic hypocaloric enteral feeding on clinical outcomes in critically ill patients, particularly on severity of organic failure measured with the Sequential Organ Failure Assessment (SOFA). Materials and Methods: In a double blind clinical trial, 80 critically ill adult patients were randomized to hyperproteic hypocaloric or to isocaloric enteral nutrition; all patients completed follow-up of at least 4 days. Prescribed caloric intake was: Hyperproteic hypocaloric enteral nutrition (15 kcal/kg with 1.7 g/kg of protein) or isocaloric enteral nutrition (25 kcal/kg with 20% of the calories as protein). The main outcome was the differences in delta SOFA at 48 h. Secondary outcomes were intensive care unit (ICU) length of stay, days on ventilator, hyperglycemic events, and insulin requirements. Results: There were no differences in SOFA score at baseline (7.5 (standard deviation (SD) 2.9) vs 6.7 (SD 2.5) P = 0.17). The total amount of calories delivered was similarly low in both groups (12 kcal/kg in intervention group vs 14 kcal/kg in controls), but proteic delivery was significantly different (1.4 vs 0.76 g/kg, respectively P ≤ 0.0001). The intervention group showed an improvement in SOFA score at 48 h (delta SOFA 1.7 (SD 1.9) vs 0.7 (SD 2.8) P = 0.04) and less hyperglycemic episodes per day (1.0 (SD 1.3) vs 1.7 (SD 2.5) P = 0.017). Discussion: Enteral hyperproteic hypocaloric nutrition therapy could be associated with a decrease in multiple organ failure measured with SOFA score. We also found decreased hyperglycemia and a trend towards less mechanical ventilation days and ICU length of stay.
Anti-inflammatory properties of montelukast, a leukotriene receptor antagonist in patients with asthma and nasal polyposis.

BACKGROUND Leukotrienes, especially LTC4, are important inflammatory mediators in allergic and nonallergic inflammation of the entire airways. Of particular interest are numerous theories regarding the pathogenesis of aspirin intolerance with subsequent hyperproduction of leukotrienes and inhibition of cyclooxygenase. OBJECTIVE To examine the influence of the cysteinyl-leukotriene receptor antagonist montelukast on clinical symptoms and inflammatory markers in nasal lavage fluid in patients with bronchial asthma and nasal polyps, and determine its dependency on aspirin sensitization. METHODS Twenty-four patients (7 women, 17 men; median age, 55.5 years) with nasal polyps and controlled asthma (n=12 with aspirin intolerance) were treated with 10 mg montelukast once daily for 6 weeks in a blinded, placebo-controlled fashion. The placebo phase was randomly assigned 4 weeks before (n=12) or after treatment (n=12). Symptom score, rhinoendoscopy, rhinomanometry, smears for eosinophils, and nasal lavages for the determination of different mediators were performed. RESULTS Compared to placebo, there were significant improvements in the nasal symptom score and airflow limitation as well as a reduction in the inflammatory mediators in nasal lavage fluid after treatment. Furthermore, reduced eosinophils in nasal smears and peripheral blood were observed 2 and 6 weeks after treatment. CONCLUSION Leukotriene 1 receptor blockade led to a significant decrease in eosinophil inflammation accompanied by a reduction in other mediators such as neurokinin A and substance P in the nasal lavage fluid of patients with nasal polyps and asthma, with or without aspirin intolerance.
Microscopic mechanisms of laser ablation of organic solids in the thermal and stress confinement irradiation regimes

The results of large-scale molecular dynamics simulations demonstrate that the mechanisms responsible for material ejection as well as most of the parameters of the ejection process have a strong dependence on the rate of the laser energy deposition. For longer laser pulses, in the regime of thermal confinement, a phase explosion of the overheated material is responsible for the collective material ejection at laser fluences above the ablation threshold. This phase explosion leads to a homogeneous decomposition of the expanding plume into a mixture of liquid droplets and gas phase molecules. The decomposition proceeds through the formation of a transient structure of interconnected liquid clusters and individual molecules and leads to the fast cooling of the ejected plume. For shorter laser pulses, in the regime of stress confinement, a lower threshold fluence for the onset of ablation is observed and attributed to photomechanical effects driven by the relaxation of the laser-induced pressure. Larger and .
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas

We investigate the approximability of several classes of real-valued functions by functions of a small number of variables (juntas). Our main results are tight bounds on the number of variables required to approximate a function f:{0, 1}<sup>n</sup> → [0,1] within ℓ<sub>2</sub>-error ϵ over the uniform distribution: If f is sub modular, then it is ϵ-close to a function of O(1/ϵ<sup>2</sup> log 1/ϵ) variables. This is an exponential improvement over previously known results FeldmanKV:13. We note that Ω(1/ϵ<sup>2</sup>) variables are necessary even for linear functions. If f is fractionally sub additive (XOS) it is &epsi;-close to a function of 2<sup>O(1/ϵ2)</sup> variables. This result holds for all functions with low total ℓ<sub>1</sub>-influence and is a real-valued analogue of Fried gut's theorem for boolean functions. We show that 2<sup>Ω(1/ϵ)</sup> variables are necessary even for XOS functions. As applications of these results, we provide learning algorithms over the uniform distribution. For XOS functions, we give a PAC learning algorithm that runs in time 2<sup>1/poly(ϵ)</sup> poly(n). For sub modular functions we give an algorithm in the more demanding PMAC learning model BalcanHarvey:[12] which requires a multiplicative (1 + γ) factor approximation with probability at least 1 - ϵ over the target distribution. Our uniform distribution algorithm runs in time 2<sup>1/poly(γϵ)</sup> poly(n). This is the first algorithm in the PMAC model that can achieve a constant approximation factor arbitrarily close to 1 for all sub modular functions (even over the uniform distribution). It relies crucially on our approximation by junta result. As follows from the lower bounds in FeldmanKV:13 both of these algorithms are close to optimal. We also give applications for proper learning, testing and agnostic learning with value queries of these classes.
Performance analysis of AODV , DSR & TORA Routing Protocols

The field of Mobile Ad hoc Networks (MANETs) has gained an important part of the interest of researchers and become very popular in last few years. MANETs can operate without fixed infrastructure and can survive rapid changes in the network topology. They can be studied formally as graphs in which the set of edges varies in time. The main method for evaluating the performance of MANETs is simulation. This paper is subjected to the on-demand routing protocols with identical loads and environment conditions and evaluates their relative performance with respect to the two performance metrics: average End-to-End delay and packet delivery ratio. We investigated various simulation scenarios with varying pause times. From the detailed simulation results and analysis, a suitable routing protocol can be chosen for a specified network and goal.