nils-herrmann/clean_scirepeval huggingface dataset:
Angiotensin-(1-7) Attenuates Angiotensin II-Induced ICAM-1, VCAM-1, and MCP-1 Expression via the MAS Receptor Through Suppression of P38 and NF-κB Pathways in HUVECs.

Atherosclerosis is a chronic inflammatory disease. Intracellular adhesion molecule-1 (ICAM-1), vascular cellular adhesion molecule-1 (VCAM-1), and monocyte chemoattractant protein-1 (MCP-1) play important roles in inflammatory processes. P38 mitogen-activated protein kinase (MAPK) and nuclear factor (NF)-κB signaling regulate ICAM-1, VCAM-1, and MCP-1 expression. Angiotensin (Ang) II upregulates ICAM-1, VCAM-1, and MCP-1 expression through the P38 MAPK and NF-κB pathways. Ang-(1-7) may oppose the actions of Ang II. We investigated whether Ang-(1-7) prevents Ang II-induced ICAM-1, VCAM-1, and MCP-1 expression in human umbilical vein endothelial cells (HUVECs).



ICAM-1, VCAM-1, and MCP-1 expression was estimated by real-time polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA); P38, NF-κB, and p-IκB-α expression was estimated by western blotting.



Ang-(1-7) inhibited Ang II-induced ICAM-1, VCAM-1, and MCP-1 expression and secretion in HUVECs. Ang II sharply increased P38 MAPK phosphorylation, which was inhibited by pretreatment with Ang-(1-7). Moreover, Ang-(1-7) significantly inhibited Ang II-induced IκB-α phosphorylation and NF-κB P65 nuclear translocation. The MAS receptor antagonist A-779 abolished the suppressive effects of Ang-(1-7).



Ang-(1-7) attenuates Ang II-induced ICAM-1, VCAM-1, and MCP-1 expression via the MAs receptor by suppressing the P38 and NF-κB pathways in HUVECs. Ang-(1-7) might delay the progression of inflammatory diseases, including atherosclerosis.
An International Urogynecological Association (IUGA)/International Continence Society (ICS) joint report on the terminology for female pelvic floor dysfunction.

Next to existing terminology of the lower urinary tract, due to its increasing complexity, the terminology for pelvic floor dysfunction in women may be better updated by a female-specific approach and clinically based consensus report.



This report combines the input of members of the Standardization and Terminology Committees of two international organizations, the International Urogynecological Association (IUGA), and the International Continence Society (ICS), assisted at intervals by many external referees. Appropriate core clinical categories and a subclassification were developed to give an alphanumeric coding to each definition. An extensive process of 15 rounds of internal and external review was developed to exhaustively examine each definition, with decision-making by collective opinion (consensus).



A terminology report for female pelvic floor dysfunction, encompassing over 250 separate definitions, has been developed. It is clinically based with the six most common diagnoses defined. Clarity and user-friendliness have been key aims to make it interpretable by practitioners and trainees in all the different specialty groups involved in female pelvic floor dysfunction. Female-specific imaging (ultrasound, radiology, and MRI) has been a major addition while appropriate figures have been included to supplement and help clarify the text. Ongoing review is not only anticipated but will be required to keep the document updated and as widely acceptable as possible.



A consensus-based terminology report for female pelvic floor dysfunction has been produced aimed at being a significant aid to clinical practice and a stimulus for research.
PonyGE2: grammatical evolution in Python

Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process.  It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout. As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided. A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.
Designing Parallel MetaHeuristic Methods

Meta-heuristic methods represent very powerful tools for dealing with hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in "reasonable" computing times. Moreover, a major issue in metaheuristic design and calibration is to make them robust, i.e., to provide high performance solutions for a variety of problem settings. Parallel meta-heuristics aim to address both issues. The objective of this chapter is to present a state-of-the-art survey of the main parallel meta-heuristic ideas and strategies, and to discuss general design principles applicable to all meta-heuristic classes. To achieve this goal, we explain various paradigms related to parallel meta-heuristic development, where communications, synchronization and control aspects are the most relevant. We also discuss implementation issues, namely the influence of the target architecture on parallel execution of meta-heuristics, pointing out the characteristics of shared and distributed memory multiprocessor systems. All these topics are illustrated by examples from recent literature. These examples are related to the parallelization of various meta-heuristic methods, but we focus here on Variable Neighborhood Search and Bee Colony Optimization.
Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.
Slice Finder: Automated Data Slicing for Model Validation

As machine learning (ML) systems become democratized, it becomes increasingly important to help users easily debug their models. However, current data tools are still primitive when it comes to helping users trace model performance problems all the way to the data. We focus on the particular problem of slicing data to identify subsets of the validation data where the model performs poorly. This is an important problem in model validation because the overall model performance can fail to reflect that of the smaller subsets, and slicing allows users to analyze the model performance on a more granularlevel. Unlike general techniques (e.g., clustering) that can find arbitrary slices, our goal is to find interpretable slices (which are easier to take action compared to arbitrary subsets) that are problematic and large. We propose Slice Finder, which is an interactive framework for identifying such slices using statistical techniques. Applications include diagnosing model fairness and fraud detection, where identifying slices that are interpretable to humans is crucial.
A randomized clinical trial of acceptance and commitment therapy versus progressive relaxation training for obsessive-compulsive disorder.

Effective treatments for obsessive-compulsive disorder (OCD) exist, but additional treatment options are needed. The effectiveness of 8 sessions of acceptance and commitment therapy (ACT) for adult OCD was compared with progressive relaxation training (PRT).



Seventy-nine adults (61% female) diagnosed with OCD (mean age = 37 years; 89% Caucasian) participated in a randomized clinical trial of 8 sessions of ACT or PRT with no in-session exposure. The following assessments were completed at pretreatment, posttreatment, and 3-month follow-up by an assessor who was unaware of treatment conditions: Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Beck Depression Inventory-II, Quality of Life Scale, Acceptance and Action Questionnaire, Thought Action Fusion Scale, and Thought Control Questionnaire. Treatment Evaluation Inventory was completed at posttreatment.



ACT produced greater changes at posttreatment and follow-up over PRT on OCD severity (Y-BOCS: ACT pretreatment = 24.22, posttreatment = 12.76, follow-up = 11.79; PRT pretreatment = 25.4, posttreatment = 18.67, follow-up = 16.23) and produced greater change on depression among those reporting at least mild depression before treatment. Clinically significant change in OCD severity occurred more in the ACT condition than PRT (clinical response rates: ACT posttreatment = 46%-56%, follow-up = 46%-66%; PRT posttreatment = 13%-18%, follow-up = 16%-18%). Quality of life improved in both conditions but was marginally in favor of ACT at posttreatment. Treatment refusal (2.4% ACT, 7.8% PRT) and dropout (9.8% ACT, 13.2% PRT) were low in both conditions.



ACT is worth exploring as a treatment for OCD.
Control law design for haptic interfaces to virtual reality

The goal of control law design for haptic displays is to provide a safe and stable user interface while maximizing the operator’s sense of kinesthetic immersion in a virtual environment. This paper outlines a control design approach which guarantees the stability of a haptic interface when coupled to a broad class of human operators and virtual environments. Two-port absolute stability criteria are used to develop explicit control law design bounds for two different haptic display implementations: impedance display and admittance display. The strengths and weaknesses of each approach are illustrated through numerical and experimental results for a three degree-of-freedom device. The example highlights the ability of the proposed design procedure to handle some of the more difficult problems in control law synthesis for haptics, including structural flexibility and non-collocation of sensors and actuators. The authors are with the Department of Electrical Engineering University of Washington, Box 352500 Seattle, WA 98195-2500 * corresponding author submitted to
Statistics and Causal Inference : A Review

This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis.
Fully Porous 3 D Printed Titanium Femoral Stem to Reduce Stress-Shielding Following Total Hip Arthroplasty

Current hip replacement femoral implants are made of fully solid materials which all have stiffness considerably higher than that of bone. This mechanical mismatch can cause significant bone resorption secondary to stress shielding, which can lead to serious complications such as peri-prosthetic fracture during or after revision surgery. In this work, a high strength fully porous material with tunable mechanical properties is introduced for use in hip replacement design. The implant macro geometry is based off of a short stem taper-wedge implant compatible with minimally invasive hip replacement surgery. The implant micro-architecture is fine-tuned to locally mimic bone tissue properties which results in minimum bone resorption secondary to stress shielding. We present a systematic approach for the design of a 3D printed fully porous hip implant that encompasses the whole activity spectrum of implant development, from concept generation, multiscale mechanics of porous materials, material architecture tailoring, to additive manufacturing, and performance assessment via in vitro experiments in composite femurs. We show that the fully porous implant with an optimized material micro-structure can reduce the amount of bone loss secondary to stress shielding by 75% compared to a fully solid implant. This result also agrees with those of the in vitro quasi-physiological experimental model and the corresponding finite element model for both the optimized fully porous and fully solid implant. These studies demonstrate the merit and the potential of tuning material architecture to achieve a substantial reduction of bone resorption secondary to stress shielding.  2017.
Connecting Automatic Generation Control and Economic Dispatch From an Optimization View

Automatic generation control (AGC) regulates mechanical power generation in response to load changes through local measurements. Its main objective is to maintain system frequency and keep energy balanced within each control area in order to maintain the scheduled net interchanges between control areas. The scheduled interchanges as well as some other factors of AGC are determined at a slower time scale by considering a centralized economic dispatch (ED) problem among different generators. However, how to make AGC more economically efficient is less studied. In this paper, we study the connections between AGC and ED by reverse engineering AGC from an optimization view, and then we propose a distributed approach to slightly modify the conventional AGC to improve its economic efficiency by incorporating ED into the AGC automatically and dynamically.
Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis.

Statistical mediation methods provide valuable information about underlying mediating psychological processes, but the ability to infer that the mediator variable causes the outcome variable is more complex than widely known. Researchers have recently emphasized how violating assumptions about confounder bias severely limits causal inference of the mediator to dependent variable relation. Our article describes and addresses these limitations by drawing on new statistical developments in causal mediation analysis. We first review the assumptions underlying causal inference and discuss three ways to examine the effects of confounder bias when assumptions are violated. We then describe four approaches to address the influence of confounding variables and enhance causal inference, including comprehensive structural equation models, instrumental variable methods, principal stratification, and inverse probability weighting. Our goal is to further the adoption of statistical methods to enhance causal inference in mediation studies.
Language networks: Their structure, function, and evolution

SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at SFI, inspired by an invited visit to or collaboration at SFI, or funded by an SFI grant.  It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may be reposted only with the explicit permission of the copyright holder.
Calcium polysulfide remediation of hexavalent chromium contamination from chromite ore processing residue.

Past disposal of high-lime chromite ore processing residue (COPR) from a chemical works in S.E. Glasgow, UK, has led to continuing release of toxic and carcinogenic hexavalent chromium (Cr(VI)) to groundwaters which are highly contaminated with Cr(VI)O4(2-). Traditional methods of remediating Cr(VI)-contaminated land, e.g. using ferrous sulfate and organic matter, have had limited success in converting Cr(VI) to less harmful and insoluble Cr(III). This paper describes the first application of calcium polysulfide (CaS(x)) to the remediation of contaminated groundwater and high-lime COPR in a series of laboratory experiments, which have demonstrated the effectiveness of the treatment in quantitatively and rapidly reducing Cr(VI) to Cr(III) over the pH range (8-12.5) typically found at the sites. Cr(III)-organic complexes, present in groundwater at one location, were also effectively precipitated upon treatment with CaS(x). The potential for large-scale use of CaS(x) in the remediation of Cr(VI) from COPR is also discussed.
SolMiner: Mining Distinct Solutions in Programs

Given a programming problem, because of a variety of data structures and algorithms that can be applied and different tradeoffs, such as space-time, to be considered, there may be many distinct solutions. By comparing his/her solution against others' and learning from the distinct solutions, a learner may quickly improve programming skills and gain experience in making trade-offs. Meanwhile, on the Internet many websites provide venues for programming practice and contests. Popular websites receive hundreds of thousands of submissions daily from novices as well as advanced learners. While these websites can automatically judge the correctness of a submission, none extracts distinct solutions from the submissions and provides them to learners. How to automatically identify distinct solutions from a large number of submissions is a challenging and unresolved problem. Due to diverse coding styles and high programming flexibility, submissions implementing the same solution may appear very different from each other; in addition, dealing with submissions at scale imposes extra challenges. We propose SolMiner, a solution miner, that automatically mines distinct solutions from a large number of submissions. SolMiner leverages static program analysis, data mining, and machine learning to automatically measure the similarity between submissions and identify distinct solutions. We have built a prototype of SolMiner and evaluated it. The evaluation shows that the technique is effective and efficient.
Efficient Object Detection and Segmentation for Fine-Grained Recognition

We propose a detection and segmentation algorithm for the purposes of fine-grained recognition. The algorithm first detects low-level regions that could potentially belong to the object and then performs a full-object segmentation through propagation. Apart from segmenting the object, we can also `zoom in' on the object, i.e. center it, normalize it for scale, and thus discount the effects of the background. We then show that combining this with a state-of-the-art classification algorithm leads to significant improvements in performance especially for datasets which are considered particularly hard for recognition, e.g. birds species. The proposed algorithm is much more efficient than other known methods in similar scenarios. Our method is also simpler and we apply it here to different classes of objects, e.g. birds, flowers, cats and dogs. We tested the algorithm on a number of benchmark datasets for fine-grained categorization. It outperforms all the known state-of-the-art methods on these datasets, sometimes by as much as 11%. It improves the performance of our baseline algorithm by 3-4%, consistently on all datasets. We also observed more than a 4% improvement in the recognition performance on a challenging large-scale flower dataset, containing 578 species of flowers and 250,000 images.
Impedance, bandwidth, and Q of antennas

To address the need for fundamental universally valid definitions of exact bandwidth and quality factor (Q) of tuned antennas, as well as the need for efficient accurate approximate formulas for computing this bandwidth and Q, exact and approximate expressions are found for the bandwidth and Q of a general single-feed (one-port) lossy or lossless linear antenna tuned to resonance or antiresonance. The approximate expression derived for the exact bandwidth of a tuned antenna differs from previous approximate expressions in that it is inversely proportional to the magnitude |Z'/sub 0/(/spl omega//sub 0/)| of the frequency derivative of the input impedance and, for not too large a bandwidth, it is nearly equal to the exact bandwidth of the tuned antenna at every frequency /spl omega//sub 0/, that is, throughout antiresonant as well as resonant frequency bands. It is also shown that an appropriately defined exact Q of a tuned lossy or lossless antenna is approximately proportional to |Z'/sub 0/(/spl omega//sub 0/)| and thus this Q is approximately inversely proportional to the bandwidth (for not too large a bandwidth) of a simply tuned antenna at all frequencies. The exact Q of a tuned antenna is defined in terms of average internal energies that emerge naturally from Maxwell's equations applied to the tuned antenna. These internal energies, which are similar but not identical to previously defined quality-factor energies, and the associated Q are proven to increase without bound as the size of an antenna is decreased. Numerical solutions to thin straight-wire and wire-loop lossy and lossless antennas, as well as to a Yagi antenna and a straight-wire antenna embedded in a lossy dispersive dielectric, confirm the accuracy of the approximate expressions and the inverse relationship between the defined bandwidth and the defined Q over frequency ranges that cover several resonant and antiresonant frequency bands.
Polyhedral patterns

We study the design and optimization of polyhedral patterns, which are patterns of planar polygonal faces on freeform surfaces. Working with polyhedral patterns is desirable in architectural geometry and industrial design. However, the classical tiling patterns on the plane must take on various shapes in order to faithfully and feasibly approximate curved surfaces. We define and analyze the deformations these tiles must undertake to account for curvature, and discover the symmetries that remain invariant under such deformations. We propose a novel method to regularize polyhedral patterns while maintaining these symmetries into a plethora of aesthetic and feasible patterns.