nils-herrmann/clean_scirepeval huggingface dataset:
Identification of Optogenetically Activated Striatal Medium Spiny Neurons by Npas4 Expression

Optogenetics is a powerful neuromodulatory tool with many unique advantages to explore functions of neuronal circuits in physiology and diseases. Yet, interpretation of cellular and behavioral responses following in vivo optogenetic manipulation of brain activities in experimental animals often necessitates identification of photoactivated neurons with high spatial resolution. Although tracing expression of immediate early genes (IEGs) provides a convenient approach, neuronal activation is not always followed by specific induction of widely used neuronal activity markers like c-fos, Egr1 and Arc. In this study we performed unilateral optogenetic stimulation of the striatum in freely moving transgenic mice that expressed a channelrhodopsin-2 (ChR2) variant ChR2(C128S) in striatal medium spiny neurons (MSNs). We found that in vivo blue light stimulation significantly altered electrophysiological activity of striatal neurons and animal behaviors. To identify photoactivated neurons we then analyzed IEG expression patterns using in situ hybridization. Upon light illumination an induction of c-fos was not apparent whereas another neuronal IEG Npas4 was robustly induced in MSNs ipsilaterally. Our results demonstrate that tracing Npas4 mRNA expression following in vivo optogenetic modulation can be an effective tool for reliable and sensitive identification of activated MSNs in the mouse striatum.
Prevention of gut leakiness by a probiotic treatment leads to attenuated HPA response to an acute psychological stress in rats

Intestinal barrier impairment is incriminated in the pathophysiology of intestinal gut disorders associated with psychiatric comorbidity. Increased intestinal permeability associated with upload of lipopolysaccharides (LPS) translocation induces depressive symptoms. Gut microbiota and probiotics alter behavior and brain neurochemistry. Since Lactobacillus farciminis suppresses stress-induced hyperpermeability, we examined whether (i) L. farciminis affects the HPA axis stress response, (ii) stress induces changes in LPS translocation and central cytokine expression which may be reversed by L. farciminis, (iii) the prevention of "leaky" gut and LPS upload are involved in these effects.



At the end of the following treatments female rats were submitted to a partial restraint stress (PRS) or sham-PRS: (i) oral administration of L. farciminis during 2 weeks, (ii) intraperitoneal administration of ML-7 (a specific myosin light chain kinase inhibitor), (iii) antibiotic administration in drinking water during 12 days. After PRS or sham-PRS session, we evaluated LPS levels in portal blood, plasma corticosterone and adrenocorticotropic hormone (ACTH) levels, hypothalamic corticotropin releasing factor (CRF) and pro-inflammatory cytokine mRNA expression, and colonic paracellular permeability (CPP).



PRS increased plasma ACTH and corticosterone; hypothalamic CRF and pro-inflammatory cytokine expression; CPP and portal blood concentration of LPS. L. farciminis and ML-7 suppressed stress-induced hyperpermeability, endotoxemia and prevented HPA axis stress response and neuroinflammation. Antibiotic reduction of luminal LPS concentration prevented HPA axis stress response and increased hypothalamic expression of pro-inflammatory cytokines.



The attenuation of the HPA axis response to stress by L. farciminis depends upon the prevention of intestinal barrier impairment and decrease of circulating LPS levels.
DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks

In this paper, we investigate the use of recurrent neural networks (RNNs) in the context of search-based online advertising. We use RNNs to map both queries and ads to real valued vectors, with which the relevance of a given (query, ad) pair can be easily computed. On top of the RNN, we propose a novel attention network, which learns to assign attention scores to different word locations according to their intent importance (hence the name DeepIntent). The vector output of a sequence is thus computed by a weighted sum of the hidden states of the RNN at each word according their attention scores. We perform end-to-end training of both the RNN and attention network under the guidance of user click logs, which are sampled from a commercial search engine. We show that in most cases the attention network improves the quality of learned vector representations, evaluated by AUC on a manually labeled dataset. Moreover, we highlight the effectiveness of the learned attention scores from two aspects: query rewriting and a modified BM25 metric. We show that using the learned attention scores, one is able to produce sub-queries that are of better qualities than those of the state-of-the-art methods. Also, by modifying the term frequency with the attention scores in a standard BM25 formula, one is able to improve its performance evaluated by AUC.
An Overview of Classifier Fusion Methods

A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided.
Encoding words into Cloud models from interval-valued data via fuzzy statistics and membership function fitting

When constructing the model of a word by collecting interval-valued data from a group of individuals, both interpersonal and intrapersonal uncertainties coexist. Similar to the interval type-2 fuzzy set (IT2 FS) used in the enhanced interval approach (EIA), the Cloud model characterized by only three parameters can manage both uncertainties. Thus, based on the Cloud model, this paper proposes a new representation model for a word from interval-valued data. In our proposed method, firstly, the collected data intervals are preprocessed to remove the bad ones. Secondly, the fuzzy statistical method is used to compute the histogram of the surviving intervals. Then, the generated histogram is fitted by a Gaussian curve function. Finally, the fitted results are mapped into the parameters of a Cloud model to obtain the parametric model for a word. Compared with eight or nine parameters needed by an IT2 FS, only three parameters are needed to represent a Cloud model. Therefore, we develop a much more parsimonious parametric model for a word based on the Cloud model. Generally a simpler representation model with less parameters usually means less computations and memory requirements in applications. Moreover, the comparison experiments with the recent EIA show that, our proposed method can not only obtain much thinner footprints of uncertainty (FOUs) but also capture sufficient uncertainties of words.
Lessons Learned in Deploying the World s Largest Scale Lustre File System

The Spider system at the Oak Ridge National Laboratory’s Leadership Computing Facility (OLCF) is the world’s largest scale Lustre parallel file system. Envisioned as a shared parallel file system capable of delivering both the bandwidth and capacity requirements of the OLCF’s diverse computational environment, the project had a number of ambitious goals. To support the workloads of the OLCF’s diverse computational platforms, the aggregate performance and storage capacity of Spider exceed that of our previously deployed systems by a factor of 6x 240 GB/sec, and 17x 10 Petabytes, respectively. Furthermore, Spider supports over 26,000 clients concurrently accessing the file system, which exceeds our previously deployed systems by nearly 4x. In addition to these scalability challenges, moving to a center-wide shared file system required dramatically improved resiliency and fault-tolerance mechanisms. This paper details our efforts in designing, deploying, and operating Spider. Through a phased approach of research and development, prototyping, deployment, and transition to operations, this work has resulted in a number of insights into large-scale parallel file system architectures, from both the design and the operational perspectives. We present in this paper our solutions to issues such as network congestion, performance baselining and evaluation, file system journaling overheads, and high availability in a system with tens of thousands of components. We also discuss areas of continued challenges, such as stressed metadata performance and the need for file system quality of service alongside with our efforts to address them. Finally, operational aspects of managing a system of this scale are discussed along with real-world data and observations.
Floridi's "Open Problems in Philosophy of Information", Ten Years Later

In his article Open Problems in the Philosophy of Information (Metaphilosophy 2004, 35 (4)), Luciano Floridi presented a Philosophy of Information research programme in the form of eighteen open problems, covering the following fundamental areas: information definition, information semantics, intelligence/cognition, informational universe/nature and values/ethics. We revisit Floridi’s programme, highlighting some of the major advances, commenting on unsolved problems and rendering the new landscape of the Philosophy of Information (PI) emerging at present. As we analyze the progress of PI we try to situate Floridi’s programme in the context of scientific and technological development that have been made last ten years. We emphasize that Philosophy of Information is a huge and vibrant research field, with its origins dating before Open Problems, and its domains extending even outside their scope. In this paper, we have been able only to sketch some of the developments during the past ten years. Our hope is that, even if fragmentary, this review may serve as a contribution to the effort of understanding the present state of the art and the paths of development of Philosophy of Information as seen through the lens of Open Problems.
Importance of mind-muscle connection during progressive resistance training

This study evaluates whether focusing on using specific muscles during bench press can selectively activate these muscles. Altogether 18 resistance-trained men participated. Subjects were familiarized with the procedure and performed one-maximum repetition (1RM) test during the first session. In the second session, 3 different bench press conditions were performed with intensities of 20, 40, 50, 60 and 80 % of the pre-determined 1RM: regular bench press, and bench press focusing on selectively using the pectoralis major and triceps brachii, respectively. Surface electromyography (EMG) signals were recorded for the triceps brachii and pectoralis major muscles. Subsequently, peak EMG of the filtered signals were normalized to maximum maximorum EMG of each muscle. In both muscles, focusing on using the respective muscles increased muscle activity at relative loads between 20 and 60 %, but not at 80 % of 1RM. Overall, a threshold between 60 and 80 % rather than a linear decrease in selective activation with increasing intensity appeared to exist. The increased activity did not occur at the expense of decreased activity of the other muscle, e.g. when focusing on activating the triceps muscle the activity of the pectoralis muscle did not decrease. On the contrary, focusing on using the triceps muscle also increased pectoralis EMG at 50 and 60 % of 1RM. Resistance-trained individuals can increase triceps brachii or pectarilis major muscle activity during the bench press when focusing on using the specific muscle at intensities up to 60 % of 1RM. A threshold between 60 and 80 % appeared to exist.
Combined malonic and methylmalonic aciduria with normal malonyl-coenzyme A decarboxylase activity: A case supporting multiple aetiologies

We identified a patient who excreted large amounts of methylmalonic acid and malonic acid. In contrast to other patients who have been described with combined methylmalonic and malonic aciduria, our patient excreted much larger amounts of methylmalonic acid than malonic acid. Since most previous patients with this biochemical phenotype have been reported to have deficiency of malonyl-CoA decarboxylase, we assayed malonyl-CoA decarboxylase activity in skin fibroblasts derived from our patient and found the enzyme activity to be normal. We examined four isocaloric (2000 kcal/day) dietary regimes administered serially over a period of 12 days with 3 days devoted to each dietary regimen. These diets were high in carbohydrate, fat or protein, or enriched with medium-chain triglycerides. Diet-induced changes in malonic and methylmalonic acid excretion became evident 24–36 h after initiating a new diet. Total excretion of malonic and methylmalonic acid was greater (p<0.01) during a high-protein diet than during a high-carbohydrate or high-fat diet. A high-carbohydrate, low-protein diet was associated with the lowest levels of malonic and methylmalonic acid excretion. Perturbations in these metabolites were most marked at night. On all dietary regimes, our patient excreted 3–10 times more methylmalonic acid than malonic acid, a reversal of the ratios reported in patients with malonyl-CoA decarboxylase deficiency. Our data support a previous observation that combined malonicand methylmalonic aciduria has aetiologies other than malonyl-CoA decar-boxylase deficiency. The malonic acid to methylmalonic acid ratio in response to dietary intervention may be useful in identifying a subgroup of patients with normal enzyme activity.
NoCBench : A Benchmarking Platform for Network on Chip

This paper describes NoCBench, a benchmarking platform for evaluating the performance of Network-on-chip enabled Systems-on-chip. NoCBench includes an initial set of standardized processing cores, NoC components, and application benchmarks for system-level design exploration and analysis. It uses the NoCSim network on-chip simulator as the core simulation engine to execute these models and applications. We also present a simple case study to demonstrate the capability of the simulation environment.
The Design and Implementation of the FreeBSD Operating System

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Dysfunctional attitudes, loneliness, and depression in college students

The role of dysfunctional attitudes in loneliness among college students was investigated. Subjects were 50 introductory psychology volunteers (20 male, 30 female) who completed measures of loneliness severity, depression, and dysfunctional attitudes. The results showed a strong predictive relationship between dysfunctional attitudes and loneliness even after level of depression was statistically controlled. Lonely college students' thinking is dominated by doubts about ability to find satisfying romantic relationships and fears of being rejected and hurt in an intimate pairing. Lonely individuals also experience much anxiety in interpersonal encounters and regard themselves as undesirable to others. Generally, a negative evaluation of self, especially in the social realm, is present. Implications of the results for treatment planning for lonely clients are discussed.
Efficient BVH-based Collision Detection Scheme with Ordering and Restructuring

Bounding volume hierarchy (BVH) has been widely adopted as the acceleration structure in broad-phase collision detection. Previous state-of-the-art BVH-based collision detection approaches exploited the spatio-temporal coherence of simulations by maintaining a bounding volume test tree (BVTT) front. A major drawback of these algorithms is that large deformations in the scenes decrease culling efficiency and slow down collision queries. Moreover, for front-based methods, the inefficient caching on GPU caused by the arbitrary layout of BVH and BVTT front nodes becomes a critical performance issue. We present a fast and robust BVH-based collision detection scheme on GPU that addresses the above problems by ordering and restructuring BVHs and BVTT fronts. Our techniques are based on the use of histogram sort and an auxiliary structure BVTT front log, through which we analyze the dynamic status of BVTT front and BVH quality. Our approach efficiently handles interand intra-object collisions and performs especially well in simulations where there is considerable spatio-temporal coherence. The benchmark results demonstrate that our approach is significantly faster than the previous BVH-based method, and also outperforms other state-of-the-art spatial subdivision schemes in terms of speed. CCS Concepts •Computing methodologies → Collision detection; Physical simulation;
Multiple output of dual half bridge LLC resonant converter using PFM-PD control

This paper proposes the multiple output of dual half bridge LLC resonant converter using PFM-PD control. For the main output, dual half bridge LLC resonant converter controlled by pulse frequency modulation (PFM) is used. For the sub output, multiple outputs scheme is configured and operated by phase delay (PD) control. Since the control variables, PFM and PD, have little mutual effect, the sub output voltage can be regulated for a wide input and load range. All MOSFETs achieved ZVS and all rectifier diodes attained ZCS for the whole load range. The modes of operation are investigated and then steady state characteristics of the proposed converter are analyzed. A 320V- 400V input, 24V/20A, 5V/16A hardware prototype is realized with dsPIC33FJ16GS502 and tested to verify the performances of the proposed multiple output converters‥
Predicting Sepsis Severity from Limited Temporal Observations

Sepsis, an acute systemic inflammatory response syndrome caused by severe infection, is one of the leading causes of in-hospital mortality. Our recent work provides evidence that mortality rate in sepsis patients can be significantly reduced by Hemoadsorption (HA) therapy with duration determined by a data-driven approach. The therapy optimization process requires predicting high-mobility group protein B-1 concentration 24 hours in the future. However, measuring sepsis biomarkers is very costly, and also blood volume is limited such that the number of available temporal observations for training a regression model is small. The challenge addressed in this study is how to balance the trade-off of prediction accuracy versus the limited number of temporal observations by selecting a sampling protocol (biomarker selection and frequency of measurements) appropriately for the prediction model and measurement noise level. In particular, to predict HMGB1 concentration 24 hours ahead when limiting the number of blood drawings before therapy to three, we found that the accuracy of observing HMGB1 and three other cytokines (Lsel, TNF-alpha, and IL10) was comparable to observing eight cytokines that are commonly used sepsis biomarkers. We found that blood drawings 1-hour apart are preferred when measurements are noise free, but in presence of noise, blood drawings 3 hours apart are preferred. Comparing to the data-driven approaches, the sampling protocol obtained by using domain knowledge has a similar accuracy with the same cost, but half of the number of blood drawings.
Global Updates on the Future Directions of Neurotoxins and Fillers

Neurotoxins and fillers continue to remain in high demand, comprising a large part of the growing business of cosmetic minimally invasive procedures. Multiple Food and Drug Administration-approved safe yet different products exist within each category, and the role of each product continues to expand. The authors review the literature to provide an overview of the use of neurotoxins and fillers and their future directions.
Traffic Modeling and prediction using ARIMA/GARCH model

The predictability of network traffic is a significant interest in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD) and self-similarity in the large time scale, multifractal in small time scale. In this paper we propose a new network traffic prediction model based on nonlinear time series ARIMA/GARCH. This model combines linear time series ARIMA model with non-linear GARCH model. We provide a parameters estimation procedure for our proposed ARIMA/GARCH model. We then evaluate a scheme for our models’ prediction. We show that our model can capture prominent traffic characteristics, not only in large time scale but also in small time scale. Compare with existing FARIMA model, our model have better prediction accuracy.
Airport risk assessment : a probabilistic approach

Risk reduction is one of the key objectives pursued by transport safety policies. Particularly, the formulation and implementation of transport safety policies needs the systematic assessment of the risks, the specification of residual risk targets and the monitoring of progresses towards those ones. Risk and safety have always been considered critical in civil aviation. The purpose of this paper is to describe and analyse safety aspects in civil airports. An increase in airport capacity usually involves changes to runways layout, route structures and traffic distribution, which in turn effect the risk level around the airport. For these reasons third party risk becomes an important issue in airports development. To avoid subjective interpretations and to increase model accuracy, risk information are colleted and evaluated in a rational and mathematical manner. The method may be used to draw risk contour maps so to provide a guide to local and national authorities, to population who live around the airport, and to airports operators.  Risk Management, Risk assessment methodology, Safety Civil aviation.