Miracle-dz/UniFAR-FTUs huggingface dataset:
Striatal structure and function predict individual biases in learning to avoid pain

Significance Our ability to learn how to avoid harm is critical for maintaining physical and mental health. However, excessive harm avoidance can be maladaptive, as evident in psychiatric disorders such as avoidant personality disorder and obsessive-compulsive disorder. We therefore investigated the neural factors underlying individual imbalances in harm avoidance behavior. Our findings show that such imbalances can be predicted by the function and structure of an individual’s striatum, a brain region that is critical for goal-directed decisionmaking. Moreover, the neural signals expressed in this region revealed key processes through which individuals learn to avoid harm. These findings highlight a neural basis for imbalanced harm avoidance behavior, extreme forms of which may contribute to psychiatric pathology. Pain is an elemental inducer of avoidance. Here, we demonstrate that people differ in how they learn to avoid pain, with some individuals refraining from actions that resulted in painful outcomes, whereas others favor actions that helped prevent pain. These individual biases were best explained by differences in learning from outcome prediction errors and were associated with distinct forms of striatal responses to painful outcomes. Specifically, striatal responses to pain were modulated in a manner consistent with an aversive prediction error in individuals who learned predominantly from pain, whereas in individuals who learned predominantly from success in preventing pain, modulation was consistent with an appetitive prediction error. In contrast, striatal responses to success in preventing pain were consistent with an appetitive prediction error in both groups. Furthermore, variation in striatal structure, encompassing the region where pain prediction errors were expressed, predicted participants’ predominant mode of learning, suggesting the observed learning biases may reflect stable individual traits. These results reveal functional and structural neural components underlying individual differences in avoidance learning, which may be important contributors to psychiatric disorders involving pathological harm avoidance behavior.
An Environment for Guidelinebased Decision Support Systems for Outpatients Monitoring

Summary Objectives: We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic. Methods: We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2. Results: The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient’s conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients. Conclusions: Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients’ needs, in our work the Decision Support System also helps the physicians in properly configuring the mobile application. Then the Decision Support System is also continuously fed by patient-reported outcomes.
Altered hypermetabolic response to cortical spreading depolarizations after traumatic brain injury in rats

Spreading depolarizations are waves of near-complete breakdown of neuronal transmembrane ion gradients, free energy starving, and mass depolarization. Spreading depolarizations in electrically inactive tissue are associated with poor outcome in patients with traumatic brain injury. Here, we studied changes in regional cerebral blood flow and brain oxygen (PbtO2), glucose ([Glc]b), and lactate ([Lac]b) concentrations in rats, using minimally invasive real-time sensors. Rats underwent either spreading depolarizations chemically triggered by KCl in naïve cortex in absence of traumatic brain injury or spontaneous spreading depolarizations in the traumatic penumbra after traumatic brain injury, or a cluster of spreading depolarizations triggered chemically by KCl in a remote window from which spreading depolarizations invaded penumbral tissue. Spreading depolarizations in noninjured cortex induced a hypermetabolic response characterized by a decline in [Glc]b and monophasic increases in regional cerebral blood flow, PbtO2, and [Lac]b, indicating transient hyperglycolysis. Following traumatic brain injury, spontaneous spreading depolarizations occurred, causing further decline in [Glc]b and reducing the increase in regional cerebral blood flow and biphasic responses of PbtO2 and [Lac]b, followed by prolonged decline. Recovery of PbtO2 and [Lac]b was significantly delayed in traumatized animals. Prespreading depolarization [Glc]b levels determined the metabolic response to clusters. The results suggest a compromised hypermetabolic response to spreading depolarizations and slower return to physiological conditions following traumatic brain injury-induced spreading depolarizations.
On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope

Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or “breeding” values of individuals are generated by substitution effects, which involve both “biological” additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the “genotypic” value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts.
On switch factor based analysis of coupled RC interconnects

In this paper we presen t a rank-one update method for updating reduced-order models of interconnect parasitics when driv e resistances or load capacitances change, as commonly occurs during timing analysis. These rank-one updates are extremely inexpensive, do not require reexamining the original in terconnect netw ork, and most importantly are provably equivalent to rereducing the original netw ork. This abstract contains the proof only for the case of varying the driver resistance, but examples are given to sho w that the exactness holds more generally. In particular, a cross-talk case is examined where the conductance matrix is singular.
Plasmodium falciparum, anaemia and cognitive and educational performance among school children in an area of moderate malaria transmission: baseline results of a cluster randomized trial on the coast of Kenya

Objectives Studies have typically investigated health and educational consequences of malaria among school‐aged children in areas of high malaria transmission, but few have investigated these issues in moderate transmission settings. This study investigates the patterns of and risks for Plasmodium falciparum and anaemia and their association with cognitive and education outcomes on the Kenyan coast, an area of moderate malaria transmission.
Adaptive Tactics: Terrorist Targeting and Regime Type

Most work on terrorism in political science has focused on the propensity of different regime types to become targets of terrorist attacks. These studies have presented a debate that has indicated that democracies are safe because their institutions and norms largely negate the need for political violence (Eyerman, 1998), while others have stated that democracies are more likely to be targeted because of their accommodation of diverse political views (Gurr, 1979) and their openness (Marighela, 1971). However, these studies do not evaluate the selection of terrorist targets in terms of the target state's regime type. Using the ITERATE dataset, I attempt to determine whether terrorist targets differ between various regime types by adopting the argument advanced by Bueno de Mesquita et al. (2005) regarding selection institutions. Namely, I seek to show that terrorists in democratic states will target the general population while terrorists in autocratic states will target those institutions, like the military, that keep the leader in power.
Why Do Early Mathematics Skills Predict Later Reading? The Role of Mathematical Language

A growing body of evidence indicates that the development of mathematics and literacy skills is highly related. The importance of literacy skills—specifically language—for mathematics development has been well rationalized. However, despite several prominent studies indicating that mathematics skills are highly predictive of literacy development, the reason for this relation is not well understood. The purpose of this study was to identify how and why early mathematics is predictive of early literacy development. Participants included 125 preschool children 3–5 years old (M = 4 years 3 months). Participants were assessed on mathematics, literacy, and cognitive measures in both the fall and spring of their preschool year. Mediation analyses indicated that the relation between early mathematics and literacy skills is mediated by children’s mathematical language skills. These findings suggest that, in prior research identifying mathematical performance as a significant predictor of later literacy skills, mathematical performance may have acted only as a proxy measure for more complex language skills such as those assessed on a mathematical language measure.
An Unwelcomed Digital Visitor in the Classroom: The Longitudinal Impact of Online Racial Discrimination on Academic Motivation

Abstract. Online racial discrimination experiences often reflect attacks on the humanity and intelligence of members of specific racial groups (e.g., African Americans and Latinos). Such experiences may have detrimental effects on academic outcomes over time. Changes in reports of online racial discrimination and academic motivation were examined among a sample of 418 African American (n = 257) and Latino (n = 161) youth in Grades 6–12. Latent growth models with parallel processes revealed that adolescents reported increases in online racial discrimination over time yet relative stability in academic motivation. Elevated rates of online racial discrimination were related to decreases in adolescents' academic motivation. This was the case even after adjusting for teacher discrimination and baseline grade point average. In addition, high initial levels of academic motivation were related to increases in adolescents' reports of online racial discrimination. Findings highlight the importance of understanding racial discrimination in online contexts when examining how race-related experiences affect the academic adjustment of adolescents of color.
A critique of comparative studies of brain size

In recent years, there have been over 50 comparative analyses carried out in which social or ecological variables have been used to explain variation in whole brain size, or a part thereof, in a range of vertebrate species. Here, we review this body of work, pointing out that there are a number of substantial problems with some of the assumptions that underpin the hypotheses (e.g. what brain size means), with the data collection and with the ways in which the data are combined in the analyses. These problems are particularly apparent in those analyses in which attempts are made to correlate complex behaviour with parts of the brain that carry out multiple functions. We conclude that now is the time to substantiate these results with data from experimental manipulations.
Optimal Rates of Convergence for Noisy Sparse Phase Retrieval via Thresholded Wirtinger Flow

This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal $x \in \mathbb{R}^p$ from noisy quadratic measurements $y_j = (a_j' x )^2 + \epsilon_j$, $j=1, \ldots, m$, with independent sub-exponential noise $\epsilon_j$. The goals are to understand the effect of the sparsity of $x$ on the estimation precision and to construct a computationally feasible estimator to achieve the optimal rates. Inspired by the Wirtinger Flow [12] proposed for noiseless and non-sparse phase retrieval, a novel thresholded gradient descent algorithm is proposed and it is shown to adaptively achieve the minimax optimal rates of convergence over a wide range of sparsity levels when the $a_j$'s are independent standard Gaussian random vectors, provided that the sample size is sufficiently large compared to the sparsity of $x$.
Active immunization for prevention of De novo hepatitis B virus infection after adult living donor liver transplantation with a hepatitis B core antigen–positive graft

De novo hepatitis B virus (DNHB) infections may occur in recipients who do not receive prophylaxis after liver transplantation (LT) with antibody to hepatitis B core antigen (anti‐HBc)–positive donor grafts. Active immunization has been shown to prevent DNHB in pediatric recipients. Our aim is to investigate the efficacy of HBV vaccination for preventing DNHB in adult living donor liver transplantation (LDLT). In total, 71 adult antibody to hepatitis B surface antigen (anti‐HBs)–negative LDLT patients who received anti‐HBc+ grafts from 2000 to 2010 were enrolled into this study. Patients were given hepatitis B virus vaccinations with the aim of achieving anti‐HBs > 1000 IU/L before transplant and >100 IU/L after transplant. The cohort was stratified into 3 groups: patients with pretransplant anti‐HBs titer of > 1000 IU/L without the need for posttransplant prophylaxis (group 1, n = 24), patients with pretransplant low titer of <1000 IU/L who were given posttransplant lamivudine prophylaxis and responded appropriately to posttransplant vaccination by maintaining anti‐HBs titers of > 100 IU/L (group 2, n = 30), and low titer nonresponders (anti‐HBs titer of < 100 IU/L despite vaccination), for whom lamivudine was continued indefinitely (group 3, n = 17). All DNHB occurred in group 3 patients with posttransplant anti‐HBs levels of < 100 IU/L, with an incidence rate of 17.6% compared with 0% in patients with posttransplant anti‐HBs levels of > 100 IU/L (P = 0.001). A pretransplant anti‐HBs level of >1000 IU/L was significantly associated with early attainment and a sustained level of posttransplant anti‐HBs of >100 IU/L (P < 0.001). Active immunization is effective in preventing DNHB in adult LDLT if the posttransplant anti‐HBs level is maintained above 100 IU/L with vaccination. Antiviral prophylaxis can be safely discontinued in patients who obtain this immunity. Liver Transplantation 23 1266–1272 2017 AASLD.
A Security-assured Accuracy-maximised Privacy Preserving Collaborative Filtering Recommendation Algorithm

The neighbourhood-based Collaborative Filtering is a widely used method in recommender systems. However, the risks of revealing customers' privacy during the process of filtering have attracted noticeable public concern recently. Specifically, kNN attack discloses the target user's sensitive information by creating k fake nearest neighbours by non-sensitive information. Among the current solutions against kNN attack, the probabilistic methods showed a powerful privacy preserving effect. However, the existing probabilistic methods neither guarantee enough prediction accuracy due to the global randomness, nor provide assured security enforcement against kNN attack. To overcome the problems of current probabilistic methods, we propose a novel approach, Probabilistic Partitioned Neighbour Selection, to ensure a required security guarantee while achieving the optimal prediction accuracy against kNN attack. In this paper, we define the sum of k neighbours' similarity as the accuracy metric α, the number of user partitions, across which we select the k neighbours, as the security metric β. Differing from the present methods that globally selected neighbours, our method selects neighbours from each group with exponential differential privacy to decrease the magnitude of noise. Theoretical and experimental analysis show that to achieve the same security guarantee against kNN attack, our approach ensures the optimal prediction accuracy.
Genetic and Environmental Contributions to the Association Between Cannabis Use and Psychotic-Like Experiences in Young Adult Twins

To investigate contributions of genetic and environmental risk factors and possible direction of causation for the relationship between symptoms of cannabis use disorders (CUD) and psychotic-like experiences (PLEs), a population-based sample of 2793 young adult twins (63.5% female, mean [range] age 28.2 [19-36] y) were assessed for symptoms of CUD and PLEs using the Composite International Diagnostic Interview. Latent risk of having symptoms of CUD or PLEs was modeled using Item Response Theory. Co-twin control analysis was performed to investigate effect of familiar confounding for the association between symptoms of CUD and PLEs. Biometric twin models were fitted to estimate the heritability, genetic and environmental correlations, and direction for the association. Lifetime use of cannabis was reported by 10.4 % of the twins, and prevalence of PLEs ranged from 0.1% to 2.2%. The incidence rate ratio of PLEs due to symptoms of CUD was 6.3 (95% CI, 3.9, 10.2) in the total sample and 3.5 (95% CI, 1.5, 8.2) within twin pairs. Heritability estimates for symptoms of CUD were 88% in men and women, and for PLEs 77% in men and 43% in women. The genetic and environmental correlations between symptoms of CUD and PLEs were 0.55 and 0.52, respectively. The model allowing symptoms of CUD to cause PLEs had a better fit than models specifying opposite or reciprocal directions of causation. The association between symptoms of CUD and PLEs is explained by shared genetic and environmental factors and direct effects from CUD to risk for PLEs.
The matroid median problem

In the classical k-median problem, we are given a metric space and would like to open k centers so as to minimize the sum (over all the vertices) of the distance of each vertex to its nearest open center. In this paper, we consider the following generalization of the problem: instead of opening at most k centers, what if each center belongs to one of T different types, and we are allowed to open at most ki centers of type i (for each i=1,2,.,T). The case T = 1 is the classical k-median, and the case of T = 2 is the red-blue median problem for which Hajiaghayi et al. [ESA 2010] recently gave a constant-factor approximation algorithm. Even more generally, what if the set of open centers had to form an independent set from a matroid? In this paper, we give a constant factor approximation algorithm for such matroid median problems. Our algorithm is based on rounding a natural LP relaxation in two stages: in the first step, we sparsify the structure of the fractional solution while increasing the objective function value by only a constant factor. This enables us to write another LP in the second phase, for which the sparsified LP solution is feasible. We then show that this second phase LP is in fact integral; the integrality proof is based on a connection to matroid intersection. We also consider the penalty version (alternately, the so-called prize collecting version) of the matroid median problem and obtain a constant factor approximation algorithm for it. Finally, we look at the Knapsack Median problem (in which the facilities have costs and the set of open facilities need to fit into a Knapsack) and get a bicriteria approximation algorithm which violates the Knapsack bound by a small additive amount.
Routing and Efficient Evaluation Techniques for Multi-hop Mobile Wireless Networks

ACKNOWLEDGMENTS First of all, I would like to express my hearty gratitude to my advisor, Dr. George Riley, for the kind assistance and guidance he has showed me during the pursuit of my PhD degree. He has provided an excellent environment and valuable feedback for my PhD research. Without his help, I could not have completed this dissertation successfully. kind help and support. Finally, I would like to express my sincere gratitude to my parents, Sang-Keun and Sook-Hee Lee, and my younger brother, Young-Jin, for their love, encouragement, and prayer. Without their love and support, I could not have begun my graduate study and finished my PhD degree successfully.
Reliable Hardware Barrier SynchronizationSchemes

Barrier synchronization is a crucial operation for parallel systems. Many schemes have been proposed in the literature to achieve fast barrier through software, hardware, or a combination of these mechanisms. However, none of these schemes emphasize fault-tolerant barrier operations. In this paper, we describe inexpensive support that can be added to network switches for achieving reliable hardware-based barrier synchronization while recovering from lost or corrupted messages. Necessary modiications to the switch architecture and the associated fault-tolerant message-passing protocols are presented. The protocols are optimized for the no-fault case while providing means to detect the failure of any step of the operation and to recover from it. The proposed scheme is evaluated with and without specialized support at the network interface and compared with similar approaches using software-based schemes. It promises signiicant potential to be applied to switch-based parallel systems, especially the emerging networks of workstations.