nils-herrmann/clean_scirepeval huggingface dataset:
BIOCAT: a pattern recognition platform for customizable biological image classification and annotation

Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be “chained” in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.
Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study.

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available "apps" that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis. The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject's height or leg length. Absolute measurement errors were least during toe off (3.12 ± 5.44 degrees) compared to heel strike (5.81 ± 5.26 degrees). There were significant (p < 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p > 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle. The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.
AptRank: an adaptive PageRank model for protein function prediction on bi-relational graphs

Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model.



We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training.


Availability and Implementation
The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank .



Supplementary data are available at Bioinformatics online.
A bidirectional flyback converter with cross-coupled non-dissipative snubber circuits

This paper proposed a novel isolated bidirectional flyback converter integrated with two non-dissipative LC snubbers. In the proposed topology, the main flyback transformer and the LC snubbers are crossed-coupled to reduce current circulation and recycle the leakage energy. The proposed isolated bidirectional flyback converter can step-up the voltage of the battery (Vbat = 12V) to a high voltage side (VHV = 200V) for the load demand and vice versa. The main goal of this paper is to demonstrate the performances of this topology to achieve high voltage gain with less switching losses and reduce components stresses. The circuit analysis conferred in detail for Continuous Conduction Mode (CCM). Lastly, a laboratory prototype constructed to compare with simulation result.
3D-printed microfluidic devices.

Microfluidics is a flourishing field, enabling a wide range of biochemical and clinical applications such as cancer screening, micro-physiological system engineering, high-throughput drug testing, and point-of-care diagnostics. However, fabrication of microfluidic devices is often complicated, time consuming, and requires expensive equipment and sophisticated cleanroom facilities. Three-dimensional (3D) printing presents a promising alternative to traditional techniques such as lithography and PDMS-glass bonding, not only by enabling rapid design iterations in the development stage, but also by reducing the costs associated with institutional infrastructure, equipment installation, maintenance, and physical space. With the recent advancements in 3D printing technologies, highly complex microfluidic devices can be fabricated via single-step, rapid, and cost-effective protocols, making microfluidics more accessible to users. In this review, we discuss a broad range of approaches for the application of 3D printing technology to fabrication of micro-scale lab-on-a-chip devices.
An Evaluation of Different Types of Blended Learning Activities in Higher Education

Blended learning has provided benefits to higher education. Therefore, universities tend to introduce blended learning activities to improve their teaching-learning practices. There are different types of blended learning activities such as forum based discussions, online games, and assignments. In order to investigates students' satisfaction towards different types of blended learning activities and their usefulness for students to score well in examinations, an action research was conducted over a period of three years from 2015-2017. The results of the study informed that students preferred to engage with activities that started in classroom and continued online. Also, students found online discussion were less useful than the other types of blended learning activities. Based on the finding of the research paper discusses how blended learning activities should be designed to make them more useful and effective.
Efficacy and safety of mildronate for acute ischemic stroke: a randomized, double-blind, active-controlled phase II multicenter trial.

Mildronate, an inhibitor of carnitine-dependent metabolism, is considered to be an anti-ischemic drug. This study is designed to evaluate the efficacy and safety of mildronate injection in treating acute ischemic stroke.



We performed a randomized, double-blind, multicenter clinical study of mildronate injection for treating acute cerebral infarction. 113 patients in the experimental group received mildronate injection, and 114 patients in the active-control group received cinepazide injection. In addition, both groups were given aspirin as a basic treatment. Modified Rankin Scale (mRS) score was performed at 2 weeks and 3 months after treatment. National Institutes of Health Stroke Scale (NIHSS) score and Barthel Index (BI) score were performed at 2 weeks after treatment, and then vital signs and adverse events were evaluated.



A total of 227 patients were randomized to treatment (n = 113, mildronate; n = 114, active-control). After 3 months, there was no significant difference for the primary endpoint between groups categorized in terms of mRS scores of 0-1 and 0-2 (p = 0.52 and p = 0.07, respectively). There were also no significant differences for the secondary endpoint between groups categorized in terms of NIHSS scores of >5 and >8 (p = 0.98 and p = 0.97, respectively) or BI scores of >75 and >95 (p = 0.49 and p = 0.47, respectively) at 15 days. The incidence of serious adverse events was similar between the two groups.



Mildronate injection is as effective and safe as cinepazide injection in treating acute cerebral infarction.
Vanquishing the XCB Question: The Methodological Discovery of the Last Shortest Single Axiom for the Equivalential Calculus

With the inclusion of an effective methodology, this article answers in detail a question that, for a quarter of a century, remained open despite intense study by various researchers. Is the formula XCB=e(x,e(e(e(x,y),e(z,y)),z)) a single axiom for the classical equivalential calculus when the rules of inference consist of detachment (modus ponens) and substitution Where the function e represents equivalence, this calculus can be axiomatized quite naturally with the formulas e(x,x), e(e(x,y),e(y,x)), and e(e(x,y),e(e(y,z),e(x,z))), which correspond to reflexivity, symmetry, and transitivity, respectively. (We note that e(x,x) is dependent on the other two axioms.) Heretofore, thirteen shortest single axioms for classical equivalence of length eleven had been discovered, and XCB was the only remaining formula of that length whose status was undetermined. To show that XCB is indeed such a single axiom, we focus on the rule of condensed detachment, a rule that captures detachment together with an appropriately general, but restricted, form of substitution. The proof we present in this paper consists of twenty-five applications of condensed detachment, completing with the deduction of transitivity followed by a deduction of symmetry. We also discuss some factors that may explain in part why XCB resisted relinquishing its treasure for so long. Our approach relied on diverse strategies applied by the automated reasoning program OTTER. Thus ends the search for shortest single axioms for the equivalential calculus.
An Intelligent Framework for Auto-filling Web Forms from Different Web Applications

Nowadays, people use on-line services to conduct various tasks such as on-line shopping and holiday trip planning using web applications. Generally users are required to enter information into web forms to interact with the web applications. However they often have to type in the same information to different web applications repetitively. It could be a tedious job for a user to fill in a large amount of web forms with the same information. To save users from typing redundant information, it is critical to propagate and pre-fill the user's previous inputs across different web applications. However, existing software and approaches cannot meet this urgent need. In this position paper, we propose an intelligent framework to propagate user's inputs across different web applications. Our framework collects user's inputs and analyzes the patterns of user's usage. Furthermore it detects the changes of user's contexts by extracting user's contextual information from various sources such as a user's calender. Our framework clusters the user interface (UI) components to form semantic groups of similar UI components based on our proposed clustering approach. Knowing the similarity relation between UI components, the framework can pre-fill the web forms with user's previous inputs. We conduct a preliminary study on effectiveness of our proposed clustering approach. We achieved a precision of 80% and a recall of 87%.
Tendon transmission efficiency of a two-finger haptic glove

This paper presents a two-fingered haptic interface named RML-glove. With this system, the operator can feel the shape and size of virtual 3D objects, and control a robot through force feedback. The tendon driven system makes this haptic glove a lighter and portable system that fits on a bare hand, and adds a haptic sense of force feedback to the fingers without constraining their natural movement. In order to explore the effect of cable friction and frictional losses in this system, experiments were conducted to investigate the impact of different variables including pulleys' active arc, tendon velocity, as well as cable tension and lubrication.
Generalization of Motor Learning Depends on the History of Prior Action

Generalization of motor learning refers to our ability to apply what has been learned in one context to other contexts. When generalization is beneficial, it is termed transfer, and when it is detrimental, it is termed interference. Insight into the mechanism of generalization may be acquired from understanding why training transfers in some contexts but not others. However, identifying relevant contextual cues has proven surprisingly difficult, perhaps because the search has mainly been for cues that are explicit. We hypothesized instead that a relevant contextual cue is an implicit memory of action with a particular body part. To test this hypothesis we considered a task in which participants learned to control motion of a cursor under visuomotor rotation in two contexts: by moving their hand through motion of their shoulder and elbow, or through motion of their wrist. Use of these contextual cues led to three observations: First, in naive participants, learning in the wrist context was much faster than in the arm context. Second, generalization was asymmetric so that arm training benefited subsequent wrist training, but not vice versa. Third, in people who had prior wrist training, generalization from the arm to the wrist was blocked. That is, prior wrist training appeared to prevent both the interference and transfer that subsequent arm training should have caused. To explain the data, we posited that the learner collected statistics of contextual history: all upper arm movements also move the hand, but occasionally we move our hands without moving the upper arm. In a Bayesian framework, history of limb segment use strongly affects parameter uncertainty, which is a measure of the covariance of the contextual cues. This simple Bayesian prior dictated a generalization pattern that largely reproduced all three findings. For motor learning, generalization depends on context, which is determined by the statistics of how we have previously used the various parts of our limbs.
Overview on Biosensors for Detection of Organophosphate Pesticides

Pesticides (herbicides, fungicides or insecticides) play an important role in agriculture to control the pests and increase the productivity to meet the demand of foods by a remarkably growing population. Pesticides application thus became one of the important inputs for the high production of corn and wheat in USA and UK, respectively. It also increased the crop production in China and India [1-4]. Although extensive use of pesticides improved in securing enough crop production worldwide however; these pesticides are equally toxic or harmful to nontarget organisms like mammals, birds etc and thus their presence in excess can cause serious health and environmental problems. Pesticides have thus become environmental pollutants as they are often found in soil, water, atmosphere and agricultural products, in harmful levels, posing an environmental threat. Its residual presence in agricultural products and foods can also exhibit acute or chronic toxicity on human health. Even at low levels, it can cause adverse effects on humans, plants, animals and ecosystems. Thus, monitoring of these pesticide and its residues become extremely important to ensure that agricultural products have permitted levels of pesticides [5-6]. Majority of pesticides belong to four classes, namely organochlorines, organophosphates, carbamates and pyrethroids. Organophosphates pesticides are a class of insecticides, of which many are highly toxic [7]. Until the 21st century, they were among the most widely used insecticides which included parathion, malathion, methyl parathion, chlorpyrifos, diazinon, dichlorvos, dimethoate, monocrotophos and profenofos. Organophosphate pesticides cause toxicity by inhibiting acetylcholinesterase enzyme [8]. It acts as a poison to insects and other animals, such as birds, amphibians and mammals, primarily by phosphorylating the acetylcholinesterase enzyme (AChE) present at nerve endings. This leads to the loss of available AChE and because of the excess acetylcholine (ACh, the impulse-transmitting substance), the effected organ becomes over stimulated. The enzyme is critical to control the transmission of nerve impulse from nerve fibers to the smooth and skeletal muscle cells, secretary cells and autonomic ganglia, and within the central nervous system (CNS). Once the enzyme reaches a critical level due to inactivation by phosphorylation, symptoms and signs of cholinergic poisoning get manifested [9].
A Spectral BSSRDF for Shading Human Skin

We present a novel spectral shading model for human skin. Our model accounts for both subsurface and surface scattering, and uses only four parameters to simulate the interaction of light with human skin. The four parameters control the amount of oil, melanin and hemoglobin in the skin, which makes it possible to match specific skin types. Using these parameters we generate custom wavelength dependent diffusion profiles for a two-layer skin model that account for subsurface scattering within the skin. These diffusion profiles are computed using convolved diffusion multipoles, enabling an accurate and rapid simulation of the subsurface scattering of light within skin. We combine the subsurface scattering simulation with a Torrance-Sparrow BRDF model to simulate the interaction of light with an oily layer at the surface of the skin. Our results demonstrate that this four parameter model makes it possible to simulate the range of natural appearance of human skin including African, Asian, and Caucasian skin types.
Effects of Parenting Style on Children Development

Malaysia is a developing country and government’s urbanization policy in 1980s has encouraged migration of rural population to urban centres, consistent with the shift of economy orientation from agriculture base to industrial base. At present about 60% Malaysian live in urban areas. Live demands and labour shortage in industrial sector have forced mothers to join labour force. At present there are about 65% mothers with children below 15 years of age working fulltime outside homes. Issues related to parenting and children’s development becomes crucial especially in examination oriented society like Malaysia. Using 200 families as sample this study attempted to examine effects of parenting styles of dual-earner families on children behaviour and school achievement. Results of the study indicates that for mothers and fathers authoritative style have positive effects on children behaviour and school achievement. In contrast, the permissive and authoritarian styles have negative effects on children behaviour and school achievement. Effects of findings on children development are discussed.
Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds

We present Science Assistments, an interactive environment, which assesses students’ inquiry skills as they engage in inquiry using science microworlds. We frame our variables, tasks, assessments, and methods of analyzing data in terms of evidence-centered design. Specifically, we focus on the student model, the task model, and the evidence model in the conceptual assessment framework. In order to support both assessment and the provision of scaffolding, the environment makes inferences about student inquiry skills using models developed through a combination of text replay tagging [cf. Sao Pedro et al. 2011], a method for rapid manual coding of student log files, and educational data mining. Models were developed for multiple inquiry skills, with particular focus on detecting if students are testing their articulated hypotheses, and if they are designing controlled experiments. Student-level cross-validation was applied to validate that this approach can automatically and accurately identify these inquiry skills for new students. The resulting detectors also can be applied at run-time to drive scaffolding intervention.