Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Oxidative Stress and the Glutathione System in Loss of Dopamine Neurons Due to Impairment of Energy Metabolism

Abstract: Alterations in the glutathione system and impairment in energy metabolism have both been implicated in the loss of dopamine neurons in Parkinson's disease. This study examined the importance of cellular glutathione and the involvement of oxidative stress in the loss of mesencephalic dopamine and GABA neurons due to inhibition of energy metabolism with malonate, the reversible, competitive inhibitor of succinate dehydrogenase. Consistent with previous findings, exposure to malonate for 24 h followed by 48 h of recovery caused a dose‐dependent loss of the dopamine population with little effect on the GABA population. Toxicity was assessed by simultaneous measurement of the high‐affinity uptake of [3H]dopamine and [14C]GABA. Total glutathione content in rat mesencephalic cultures was decreased by 65% with a 24‐h pretreatment with 10 µM buthionine sulfoxamine. This reduction in glutathione level greatly potentiated damage to both the dopamine and GABA populations and removed the differential susceptibility between the two populations in response to malonate. These findings point to a role for oxidative stress occurring during energy impairment by malonate. Consistent with this, several spin‐trapping agents, α‐phenyl‐tert‐butyl nitrone and two cyclic nitrones, MDL 101,002 and MDL 102,832, completely prevented malonate‐induced damage to the dopamine neurons in the absence of buthionine sulfoxamine. The spin‐trapping agents also completely prevented toxicity to both the dopamine and GABA populations when cultures were exposed to malonate after pretreatment with buthionine sulfoxamine to reduce glutathione levels. Counts of tyrosine hydroxylase‐positive neurons verified enhancement of cell loss by buthionine sulfoxamine plus malonate and protection against cell loss by the spin‐trapping agents. NMDA receptors have also been shown to play a role in malonate‐induced dopamine cell loss and are associated with the generation of free radicals. Consistent with this, toxicity to the dopamine neurons due to a 1‐h exposure to 50 µM glutamate was attenuated by the nitrone spin traps. These findings provide evidence for an oxidative challenge occurring during inhibition of energy metabolism by malonate and show that glutathione is an important neuroprotectant for midbrain neurons during situations when energy metabolism is impaired.
Notes on the Truncated Fourier Transform

In a previous paper [vdH04], we introduced a truncated version of the classical Fast Fourier Transform. When applied to polynomial multiplication, this algorithm has the nice property of eliminating the “jumps” in the complexity at powers of two. When applied to the multiplication of multivariate polynomials or truncated multivariate power series, a non-trivial asymptotic factor was gained with respect to the best previously known algorithms. In the present note, we correct two errors which slipped into the previous paper and we give a new application to the multiplication of polynomials with real coefficients. We also give some further hints on how to implement the TFT in practice.
Lexicalized Context-Fee Grammars

Lexicalized context-free grammar(LCFG) is an attractive compromise between the parsing efficiency of context-free grammar (CFG) and the elegance and lexical sensitivity of lexicalized tree adjoining grammar (LTAG). LCFG is a restricted form of LTAG that can only generate context-free languages and can be parsed in cubic time. However, LCFG supports much of the elegance of LTAG's analysis of English and shares with LTAG the ability to lexicalize CFGS without changing the trees generated.
Is the Nucleus Pulposus a Solid or a Fluid? Mechanical Behaviors of the Nucleus Pulposus of the Human Intervertebral Disc

Study Design. A new technique to measure the viscoelastic behavior of the nucleus pulposus in shear was used to assess its solid and fluid characteristics. Objectives. To review existing knowledge on mechanical behaviors of the nucleus pulposus, and to develop a new technique to study the viscoelastic behaviors of isolated nucleus pulposus samples in torsional (pure) shear under transient and dynamic conditions. Summary of Background Data. Numerous studies have investigated the swelling behavior of the nucleus and found the swelling pressure to range approximately 0.05‐3 MPa, depending on loading conditions. Very few studies, however, have investigated the load‐deformational behaviors of the nucleus pulposus. Methods. Thirteen nondegenerate samples of nucleus pulposus were harvested from lumbar discs and tested in torsional shear under transient and dynamic test conditions. A linear viscoelastic law with variable amplitude relaxation spectrum was used to model the stress relaxation and dynamic frequency sweep experiments. The coefficients of the viscoelastic law were determined from the stress relaxation experiments, whereas the dynamic shear modulus and phase shift angle were determined from the frequency sweep. Results. The nucleus exhibits significant viscoelastic effects in shear. Under transient conditions, the stress relaxed to values near zero, which is indicative of the “fluid‐like” behaviors of the nucleus. Under dynamic conditions, however, the material parameters for the nucleus, magnitude of the complex modulus (7‐21 kPa), and phase angle (23‐31°) were more characteristic of a viscoelastic solid. The authors' proposed stress‐strain law exhibited excellent agreement with the viscoelastic data. Conclusions. In response to shear deformations, the nucleus pulposus exhibited significant viscoelastic effects, characteristic of a fluid and a solid. Whether the nucleus pulposus behaves more as a fluid or a solid in vivo depends on the rate of loading.
Predictors of PTSD Treatment Response Trajectories in a Sample of Childhood Sexual Abuse Survivors: The Roles of Social Support, Coping, and PTSD Symptom Clusters

This study aimed to (a) identify posttraumatic stress disorder (PTSD) trajectories in a sample of Danish treatment-seeking childhood sexual abuse (CSA) survivors and (b) examine the roles of social support, coping style, and individual PTSD symptom clusters (avoidance, reexperiencing, and hyperarousal) as predictors of the identified trajectories. We utilized a convenience sample of 439 CSA survivors attending personalized psychotherapy treatment in Denmark. Four assessments were conducted on a six monthly basis over a period of 18 months. We used latent class growth analysis (LCGA) to test solutions with one to six classes. Following this, a logistic regression was conducted to examine predictors of the identified trajectories. Results revealed four distinct trajectories which were labeled high PTSD gradual response, high PTSD treatment resistant, moderate PTSD rapid response, and moderate PTSD gradual response. Emotional and detached coping and more severe pretreatment avoidance and reexperiencing symptoms were associated with more severe and treatment resistant PTSD. High social support and a longer length of time since the abuse were associated with less severe PTSD which improved over time. The findings suggested that treatment response of PTSD in CSA survivors is characterized by distinct patterns with varying levels and rates of PTSD symptom improvement. Results revealed that social support is protective and that emotional and detached coping and high pretreatment levels of avoidance and reexperiencing symptoms are risk factors in relation to PTSD severity and course. These factors could potentially identify patients who are at risk of not responding to treatment. Furthermore, these factors could be specifically addressed to increase positive outcomes for treatment-seeking CSA survivors.
Advanced Structured Prediction

The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England. Peter V. Gehler is a Senior Researcher in the Perceiving Systems group at the Max Planck Institute for Intelligent Systems, Tbingen, Germany. Jeremy Jancsary is a Senior Research Scientist at Nuance Communications, Vienna. Christoph H. Lampert is Assistant Professor at the Institute of Science and Technology Austria, where he heads a group for Computer Vision and Machine Learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sbastien Gigure, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, Franois Laviolette, Xinghua Lou, Mario Marchand, Andr F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Pra, Gunnar Rtsch, Amlie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thom Werner, Alan Yuille, Stanislav ivn
Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other “omics” data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.
Triple band circularly polarized cavity backed crossed dipole antenna

In this paper, reference dipole is loaded with non-identical CSRRs in both the arms to achieve a triple band operation. Pair of loaded dipoles is crossed through a vacant ring and is further employed with cavity backed reflector to achieve unidirectional circularly polarized radiation with high gain ( > 6.5 dBic), front to back lobe ratio ( > 20 dB) and enhanced 3 dB axial ratio bandwidth (ARBW) in the three operating bands. ARBW of 7.4% (centered at 1.86 GHz), 2.75% (centered at 2.36 GHz) and 6.02% (centered at 2.98 GHz) are achieved in fabrication.
Towards understanding of the cortical network underlying associative memory

Declarative knowledge and experiences are represented in the association cortex and are recalled by reactivation of the neural representation. Electrophysiological experiments have revealed that associations between semantically linked visual objects are formed in neural representations in the temporal and limbic cortices. Memory traces are created by the reorganization of neural circuits. These regions are reactivated during retrieval and contribute to the contents of a memory. Two different types of retrieval signals are suggested as follows: automatic and active. One flows backward from the medial temporal lobe during the automatic retrieval process, whereas the other is conveyed as a top-down signal from the prefrontal cortex to the temporal cortex during the active retrieval process. By sending the top-down signal, the prefrontal cortex manipulates and organizes to-be-remembered information, devises strategies for retrieval and monitors the outcome. To further understand the neural mechanism of memory, the following two complementary views are needed: how the multiple cortical areas in the brain-wide network interact to orchestrate cognitive functions and how the properties of single neurons and their synaptic connections with neighbouring neurons combine to form local circuits and to exhibit the function of each cortical area. We will discuss some new methodological innovations that tackle these challenges.
A Fusion of Stacking with Dynamic Integration

In this paper we present a novel method that fuses the ensemble meta-techniques of Stacking and Dynamic Integration (DI) for regression problems, without adding any major computational overhead. The intention of the technique is to benefit from the varying performance of Stacking and DI for different data sets, in order to provide a more robust technique. We detail an empirical analysis of the technique referred to as weighted Meta - Combiner (wMetaComb) and compare its performance to Stacking and the DI technique of Dynamic Weighting with Selection. The empirical analysis consisted of four sets of experiments where each experiment recorded the cross-fold evaluation of each technique for a large number of diverse data sets, where each base model is created using random feature selection and the same base learning algorithm. Each experiment differed in terms of the latter base learning algorithm used. We demonstrate that for each evaluation, wMeta-Comb was able to outperform DI and Stacking for each experiment and as such fuses the two underlying mechanisms successfully.
The role of microRNAs in the regulation of cancer stem cells

Cancer stem cells (CSCs) have been reported in many human tumors and are proposed to drive tumor initiation and progression. CSCs share a variety of biological properties with normal somatic stem cells such as the capacity for self-renewal, the propagation of differentiated progeny, and the expression of specific cell surface markers and stem cell genes. However, CSCs differ from normal stem cells in their chemoresistance and tumorigenic and metastatic activities. Despite their potential clinical importance, the regulation of CSCs at the molecular level is not well-understood. MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs that play an important role in the regulation of several cellular, physiological, and developmental processes. Aberrant miRNA expression is associated with many human diseases including cancer. miRNAs have been implicated in the regulation of CSC properties; therefore, a better understanding of the modulation of CSC gene expression by miRNAs could aid the identification of promising biomarkers and therapeutic targets. In the present review, we summarize the major findings on the regulation of CSCs by miRNAs and discuss recent advances that have improved our understanding of the regulation of CSCs by miRNA networks and may lead to the development of miRNA therapeutics specifically targeting CSCs.
When Pornography Use Feels Out of Control: The Moderation Effect of Relationship and Sexual Satisfaction

ABSTRACT There is an understudied, meaningful distinction between high frequency of pornography use and the subjective feeling that this behavior is out of control. We examined whether the quality of a couple's relationship and sex life can strengthen or weaken the association between frequency of Internet pornography use and perceived lack of control over this behavior. In a sample of 1,036 participants, results showed that frequency of pornography use was more strongly associated with feeling out of control when relationship and sexual satisfaction were lower. Findings suggest that couple dissatisfaction puts the individual at risk of reporting out-of-control pornography use.
Rural in-Migrations in Serbia. An Assessment of Social and Economic Implication on Rural Communities

Despite a great need for finding a new niche in deliberation of rural revival, in-migration and its possible role therein has not been assessed in Serbia until now. The aim of this Thesis is exploration of rural in-migrant households in Serbia and their contribution to the rural economy. Sample of 74 rural in-migrant households in two study areas, South-Backa and Zajecar Districts met the definition’s parameters. The response rate was 81.1%. Survey findings suggest that young and educated families in-migrated from towns in Serbia. Participation of active and employed persons is higher compared to that of inactive and unemployed. Environmentalism have motivated almost half of sample. Family property associated with the loss of a job in the town motivated returnees, while housing was more likely to motivate newcomers. Almost 60% of rural in-migrant households’ farms are small, and around 12% are large holdings, sized 10.1 ha (mean). The total available land is dominated by private ownerships (95.6%), and arable land (70.7%). The majority generate income from agriculture to a certain degree, whereas agriculture is the main source of income for one-third. Four-fifths generate some sort of income within the households, dominated by that originating from agricultural activities. More than half of sample is involved in the output sales market. One third of mainly young and educated rural in-migrant households, is involved in private, micro or small business, providing employment for a total of 78 persons. The socio-economic characteristics and the way of involvement in rural economy is territorially specific, dependent on the factors that motivated migrations, and on general demographic and socio-economic characteristics of the household head. Level of satisfaction with the decision to migrate is very high. Better and much better quality of life in the new environment was reported by 83.3% respondents.
A differential perceptual audio coding method with reduced bitrate requirements

A new audio transform coding technique is proposed that reduces the bitrate requirements of the perceptual transform audio coders by utilizing the stationarity characteristics of the audio signals. The method detects the frames that have significant audible content and codes them in a way similar to conventional perceptual transform coders. However, when successive data frames are found to be similar to those sections, then their audible differences only are coded. An error analysis for the proposed method is presented and results from tests on different types of audio material are listed, indicating that an average of 30% in compression gain (over the conventional perceptual audio coders bitrate) can be achieved, with a small deterioration in the audio quality of the coded signal. The proposed method has the advantage of easy adaptation within the perceptual transform coders architecture and adds only a small computational overhead to these systems.
Clozapine N-Oxide Administration Produces Behavioral Effects in Long–Evans Rats: Implications for Designing DREADD Experiments

Abstract Clozapine N-oxide (CNO) is a ligand for a powerful chemogenetic system that can selectively inhibit or activate neurons; the so-called Designer Receptors Exclusively Activated by Designer Drugs (DREADD) system. This system consists of synthetic G-protein-coupled receptors, which are not believed to be activated by any endogenous ligand, but are activated by the otherwise inert CNO. However, it has previously been shown that the administration of CNO in humans and rats leads to detectable levels of the bioactive compounds clozapine and N-desmethylclozapine (N-Des). As a follow-up, experiments were conducted to investigate the effects of CNO in male Long–Evans rats. It was found that 1 mg/kg CNO reduced the acoustic startle reflex but had no effect on prepulse inhibition (PPI; a measure of sensorimotor gating). CNO (2 and 5 mg/kg) had no effect on the disruption to PPI induced by the NMDA antagonist phencyclidine or the muscarinic antagonist scopolamine. In locomotor studies, CNO alone (at 1, 2, and 5 mg/kg) had no effect on spontaneous locomotion, but 5 mg/kg CNO pretreatment significantly attenuated d-amphetamine-induced hyperlocomotion. In line with the behavioral results, fast-scan cyclic voltammetry found that 5 mg/kg CNO significantly attenuated the d-amphetamine-induced increase in evoked dopamine. However, the effects seen after CNO administration cannot be definitively ascribed to CNO because biologically relevant levels of clozapine and N-Des were found in plasma after CNO injection. Our results show that CNO has multiple dose-dependent effects in vivo and is converted to clozapine and N-Des emphasizing the need for a CNO-only DREADD-free control group when designing DREADD-based experiments.
Asymmetric stability property of a sagittal-plane model with a compliant leg and Rolling contact

We report on the stability analysis of the Rolling SLIP (R-SLIP) model, a sagittal-plane and point-mass model with a compliant leg and rolling ground contact. Owing to its asymmetric morphology, it has four different operation modes according to the range of the initial mass speed and leg configuration. Using the return map analysis, the asymmetric stability property of the R-SLIP model was investigated and compared with that of the SLIP model. The analysis results reveal that the R-SLIP model has better but more complicated stability characteristics than the SLIP model. The analysis results also suggest that the R-SLIP model operates better when it has a soft to hard stiffness change. In addition, the effect of the torsion spring position was also investigated.
Robustness of attack-resilient state estimators

The interaction between information technology and phys ical world makes Cyber-Physical Systems (CPS) vulnerable to malicious attacks beyond the standard cyber attacks. This has motivated the need for attack-resilient state estimation. Yet, the existing state-estimators are based on the non-realistic assumption that the exact system model is known. Consequently, in this work we present a method for state estimation in presence of attacks, for systems with noise and modeling errors. When the the estimated states are used by a state-based feedback controller, we show that the attacker cannot destabilize the system by exploiting the difference between the model used for the state estimation and the real physical dynamics of the system. Furthermore, we describe how implementation issues such as jitter, latency and synchronization errors can be mapped into parameters of the state estimation procedure that describe modeling errors, and provide a bound on the state-estimation error caused by modeling errors. This enables mapping control performance requirements into real-time (i.e., timing related) specifications imposed on the underlying platform. Finally, we illustrate and experimentally evaluate this approach on an unmanned ground vehicle case-study.