nils-herrmann/clean_scirepeval huggingface dataset:
Cycloastragenol is a potent telomerase activator in neuronal cells: implications for depression management.

Cycloastragenol (CAG) is an aglycone of astragaloside IV. It was first identified when screening Astragalus membranaceus extracts for active ingredients with antiaging properties. The present study demonstrates that CAG stimulates telomerase activity and cell proliferation in human neonatal keratinocytes. In particular, CAG promotes scratch wound closure of human neonatal keratinocyte monolayers in vitro. The distinct telomerase-activating property of CAG prompted evaluation of its potential application in the treatment of neurological disorders. Accordingly, CAG induced telomerase activity and cAMP response element binding (CREB) activation in PC12 cells and primary neurons. Blockade of CREB expression in neuronal cells by RNA interference reduced basal telomerase activity, and CAG was no longer efficacious in increasing telomerase activity. CAG treatment not only induced the expression of bcl2, a CREB-regulated gene, but also the expression of telomerase reverse transcriptase in primary cortical neurons. Interestingly, oral administration of CAG for 7 days attenuated depression-like behavior in experimental mice. In conclusion, CAG stimulates telomerase activity in human neonatal keratinocytes and rat neuronal cells, and induces CREB activation followed by tert and bcl2 expression. Furthermore, CAG may have a novel therapeutic role in depression.
Design , development and characterization of serratiopeptidase loaded albumin nanoparticles

Article history: Received on: 18/11/2014 Revised on: 09/12/2014  In recent years, albumin nanoparticles have been widely studied for delivery of various active pharmaceuticals with enhanced accumulation at the site of inflammation. Albumin is a versatile carrier to prepare nanoparticles and nanospheres due to its easy availability in pure form, biodegradability non-toxic and non-immunogenic nature. The mechanism of action of Serratiopeptidase appears to be hydrolysis of histamine, bradykinin and serotonin. Serratiopeptidase also has a proteolytic and fibrinolytic effect. Protein i.e. bovine serum albumin was used to entrap serratiopeptidase enzyme. Protease activity of the enzyme was checked and method was validated to access the active enzyme concentration during formulation. Solvent desolvation method was used for the preparation of BSA nanoparticles. Effect of buffer pH was checked on the enzyme activity. Chloroform was selected and used as solvent for nanoparticle preparation. Effect of various variables such as concentration of BSA, agitation rate, glutarldehyde concentration, time of crosslinking etc. on the formulation was studied. Formed nanoparticles were characterized for drug content, in-vitro release, entrapment efficiency, particle size and size distribution. The formed serratiopeptidase loaded albumin nanoparticles may be used for the treatment of arthritis.
A programmable CMOS voltage controlled ring oscillator for radio-frequency diathermy on-chip circuit

In this work we present the design of a digitally controlled ring type oscillator in 0.5 μm CMOS technology for a low-cost and portable radio-frequency diathermy (RFD) device. The oscillator circuit is composed by a low frequency ring oscillator (LFRO), a voltage controlled ring oscillator (VCRO), and a logic control. The digital circuit generates an input signal for the LFO, which generates a voltage ramp that controls the oscillating output signal of the VCRO in the range of 500 KHz to 1 MHz. Simulation results show that the proposed circuit exhibits controllable output characteristics in the range of 500 KHz–1 MHz, with low power consumption and low phase noise, making it suitable for a portable RFD device.
Matching large ontologies: A divide-and-conquer approach

Ontologies proliferate with the progress of the Semantic Web. Ontology matching is an important way of establishing interoperability between (Semantic) Web applications that use different but related ontologies. Due to their sizes and monolithic nature, large ontologies regarding real world domains bring a new challenge to the state of the art ontology matching technology. In this paper, we propose a divide-and-conquer approach to matching large ontologies. We develop a structure-based partitioning algorithm, which partitions entities of each ontology into a set of small clusters and constructs blocks by assigning RDF Sentences to those clusters. Then, the blocks from different ontologies are matched based on precalculated anchors, and the block mappings holding high similarities are selected. Finally, two powerful matchers, V-DOC and GMO, are employed to discover alignments in the block mappings. Comprehensive evaluation on both synthetic and real world data sets demonstrates that our approach both solves the scalability problem and achieves good precision and recall with significant reduction of execution time.
Signals from the gut microbiota to distant organs in physiology and disease

The ecosystem of the human gut consists of trillions of bacteria forming a bioreactor that is fueled by dietary macronutrients to produce bioactive compounds. These microbiota-derived metabolites signal to distant organs in the body, which enables the gut bacteria to connect to the immune and hormone system, to the brain (the gut–brain axis) and to host metabolism, as well as other functions of the host. This microbe–host communication is essential to maintain vital functions of the healthy host. Recently, however, the gut microbiota has been associated with a number of diseases, ranging from obesity and inflammatory diseases to behavioral and physiological abnormalities associated with neurodevelopmental disorders. In this Review, we will discuss microbiota–host cross-talk and intestinal microbiome signaling to extraintestinal organs. We will review mechanisms of how this communication might contribute to host physiology and discuss how misconfigured signaling might contribute to different diseases.
A SAMPLING THEOREM FOR STATIONARY ( WIDE SENSE ) STOCHASTIC PROCESSES

where g is of bounded variation on [ — h~x/2, h~l/2\ and the jumps of g at the endpoints, if any, are equal . This result, or some variant of it, is known in the communications art as the sampling theorem ; it is widely used in information theory [4]. In the present paper we seek conditions under which the random variables x(t) of a stationary (wide sense) stochastic process \x(t), — oo <t< «> } are determined linearly by the "sample" random variables \x(nh), — oo <«< oo }. (The sample spacing h>0 is to be regarded throughout as a fixed preassigned number.) The problem is a simple one, intuitively speaking. Suppose process x contains a component aie2wiXlt at frequency Xi; this component contributes to the samples the quantities
Weakly Supervised Object Localization Using Things and Stuff Transfer

We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations. The source and target classes might share similar appearance (e.g. bear fur is similar to cat fur) or appear against similar background (e.g. horse and sheep appear against grass). To exploit this, we acquire three types of knowledge from the source set: a segmentation model trained on both thing and stuff classes; similarity relations between target and source classes; and cooccurrence relations between thing and stuff classes in the source. The segmentation model is used to generate thing and stuff segmentation maps on a target image, while the class similarity and co-occurrence knowledge help refining them. We then incorporate these maps as new cues into a multiple instance learning framework (MIL), propagating the transferred knowledge from the pixel level to the object proposal level. In extensive experiments, we conduct our transfer from the PASCAL Context dataset (source) to the ILSVRC, COCO and PASCAL VOC 2007 datasets (targets). We evaluate our transfer across widely different thing classes, including some that are not similar in appearance, but appear against similar background. The results demonstrate significant improvement over standard MIL, and we outperform the state-of-the-art in the transfer setting.
A Formal Theory of Justifications

We develop an abstract theory of justifications suitable for describing the semantics of a range of logics in knowledge representation, computational and mathematical logic. A theory or program in one of these logics induces a semantical structure called a justification frame. Such a justification frame defines a class of justifications each of which embodies a potential reason why its facts are true. By defining various evaluation functions for these justifications, a range of different semantics are obtained. By allowing nesting of justification frames, various language constructs can be integrated in a seamless way. The theory provides elegant and compact formalisations of existing and new semantics in logics of various areas, showing unexpected commonalities and interrelations, and creating opportunities for new expressive knowledge representation formalisms.
FreeWalker: a smart insole for longitudinal gait analysis

Gait analysis is an important diagnostic measure to investigate the pattern of walking. Traditional gait analysis is generally carried out in a gait lab, with equipped force and body tracking sensors, which needs a trained medical professional to interpret the results. This procedure is tedious, expensive, and unreliable and makes it difficult to track the progress across multiple visits. In this paper, we present a smart insole called FreeWalker, which provides quantitative gait analysis outside the confinement of traditional lab, at low- cost. The insole consists of eight pressure sensors and two motion tracking sensors, i.e. 3-axis accelerometer and 3-axis gyroscope. This enables measurement of under-foot pressure distribution and motion sequences in real-time. The insole is enabled with onboard SD card as well as wireless data transmission, which help in continuous gait-cycle analysis. The data is then sent to a gateway, for analysis and interpretation of data, using a user interface where gait features are graphically displayed. We also present validation result of a subject's left foot, who was asked to perform a specific task. Experiment results show that we could achieve a data-sampling rate of over 1 KHz, transmitting data up to a distance of 20 meter and maintain a battery life of around 24 hours. Taking advantage of these features, FreeWalker can be used in various applications, like medical diagnosis, rehabilitation, sports and entertainment.
Contribution LIVER STEATOSIS CLASSIFICATION USING HIGH-FREQUENCY ULTRASOUND

High-frequency B-mode images of 19 fresh human liver samples were obtained to evaluate their usefulness in determining the steatosis grade. The images were acquired by a mechanically controlled singlecrystal probe at 25 MHz. Image features derived from gray-level concurrence and nonseparable wavelet transform were extracted to classify steatosis grade using a classifier known as the support vector machine. A subsequent histologic examination of each liver sample graded the steatosis from 0 to 3. The four grades were then combined into two, three and four classes. The classification results were correlated with histology. The best classification accuracies of the two, three and four classes were 90.5%, 85.8% and 82.6%, respectively, which were markedly better than those at 7 MHz. These results indicate that liver steatosis can be more accurately characterized using high-frequency B-mode ultrasound. Limitations and their potential solutions of applying high-frequency ultrasound to liver imaging are also discussed.
Wide-band CMOS low noise amplifier for applications in radio astronomy

A CMOS low noise amplifier (LNA) is proposed for a proof-of-concept design of the square kilometer array (SKA) radio telescope. A novel variation on a well-known source-degenerated LNA topology is introduced that allows tuning of the power match centre frequency independently from the frequency at which the LNAs noise figure approaches its minimum noise. The 0.7-1.4 GHz LNA designed in 0.18mum CMOS achieves better than 11 dB return loss with a noise temperature of 40 K, provides a power gain of 17 dB and IP1dB of -10dBm while consuming 50 mW of power from a 1.8 V supply. The design procedure of the LNA is presented and performance of the LNA is compared with previously published results
The femoral calcar: a computed tomography anatomical study.

The femoral calcar is a dense internal septum reaching from the femoral neck to the distal part of the lesser trochanter. Our study aimed at providing an exhaustive radio-anatomical description of this structure. One hundred pelvic computed tomography examinations were retrospectively selected to bilaterally evaluate the shape, dimensions, and density of the femoral calcar. Then, its relation to the femoral cavity was assessed by recording the dimensions of the medullary canal at the level of the greatest length of the spur. The femoral calcar exhibited a variable shape classified as ridge-type 17% (34/200), spur-type 66.5% (133/200), and septum-type 16.5% (33/200). Its mean dimensions were: height = 33.03 mm (20-46), length = 9.94 mm (5-16), and thickness = 2.71 mm (1-4). These dimensions were positively correlated to the height and weight of the individuals (P < 0.001) and were higher in males (P < 0.001). Its mean density was 788.5 Hounsfield units (530-1,200). The longest oblique and anteroposterior diameters of the femoral cavity were respectively 38.74 mm (28-51) and 22.04 mm (17-27). The femoral cavity dimensions were positively correlated to the height and weight of the individuals (P < 0.001), to the femoral calcar dimensions (P < 0.001) and were higher in males (P < 0.001). The femoral calcar was constantly identified as a vertical plate of compact bone exhibiting a consistent anatomical pattern, which suggests a significant mechanical function within the upper femur. Our results may lead to a greater understanding of the hip fracture patterns and to alternative designs for hip arthroplasties.
Development of a novel robot-mediated adaptive response system for joint attention task for children with autism

With Centers for Disease Control and Prevention prevalence estimates for children with autism spectrum disorder (ASD) at 9.1 per 1,000 (1 in 110), identification and effective treatment of ASD is often characterized as a public health emergency. Emerging technology, especially robotic technology, has been shown to be appealing to these children and such interest can be harnessed to address the limitations while providing intervention services to young children with ASD. Generally the spectrum nature of autism calls for intensive, individualized intervention. However, existing robot-mediated systems tend to have limited adaptive capability that limits individualization. Our current work seeks to bridge this gap by developing a novel adaptive and individualized robot-mediated technology for children with ASD. The system is composed of a humanoid robot with its vision being augmented by several wall-mounted cameras for real-time head tracking using a distributed architecture. Based on the cues from the child's head movement, the robot intelligently adapts itself in an individualized manner to promote joint attention. The developed system is validated with two typically developing children. The validation results of the head tracker and the closed-loop nature of interaction are presented.
Prenatal developmental origins of behavior and mental health: The influence of maternal stress in pregnancy

Accumulating research shows that prenatal exposure to maternal stress increases the risk for behavioral and mental health problems later in life. This review systematically analyzes the available human studies to identify harmful stressors, vulnerable periods during pregnancy, specificities in the outcome and biological correlates of the relation between maternal stress and offspring outcome. Effects of maternal stress on offspring neurodevelopment, cognitive development, negative affectivity, difficult temperament and psychiatric disorders are shown in numerous epidemiological and case-control studies. Offspring of both sexes are susceptible to prenatal stress but effects differ. There is not any specific vulnerable period of gestation; prenatal stress effects vary for different gestational ages possibly depending on the developmental stage of specific brain areas and circuits, stress system and immune system. Biological correlates in the prenatally stressed offspring are: aberrations in neurodevelopment, neurocognitive function, cerebral processing, functional and structural brain connectivity involving amygdalae and (pre)frontal cortex, changes in hypothalamo-pituitary-adrenal (HPA)-axis and autonomous nervous system.
A Contactless Slipring System Based on Axially Traveling Magnetic Field

It is a preferable method to transfer power wirelessly using contactless slipring systems for rotary applications. The current single or multiple-unit single-phase systems often have limited power transfer capability, so they may not be able to meet the load requirements. This paper presents a contactless slipring system based on axially traveling magnetic field that can achieve a high output power level. A new index termed mutual inductance per pole is introduced to simplify the analysis of the mutually coupled poly-phase system to a single-phase basis. Both simulation and practical results have shown that the proposed system can transfer 2.7 times more power than a multiple-unit (six individual units) single-phase system with the same amount of ferrite and copper materials at higher power transfer efficiency. It has been found that the new system can achieve about 255.6 W of maximum power at 97% efficiency, compared to 68.4 W at 90% of a multiple-unit (six individual units) single-phase system.
Ensemble of Deep Neural Networks for Estimating Particulate Matter from Images

Particulate matter with diameters less than 2.5 micrometers (PM2.5) is one of the most common air pollutants and may cause many severe diseases. An efficient PM2.5 monitoring system is of great benefit for human health and air pollution control. In this paper, we estimate PM2.5 concentrations using outdoor images by a proposed ensemble of deep neural networks-based regression, which uses a feedforward neural network to combine the PM2.5 predictions yielded by three convolutional neural networks, VGG-16, Inception-v3, and ResNet50, and calculate the final PM2.5 prediction of the image. A PM2.5 image dataset with 1460 photos was used for performance evaluation. The experimental results demonstrated that the proposed ensemble can provide more accurate PM2.5 estimation than all three individual deep learning networks used and, therefore, can be used for image-based PM2.5 monitoring.
Cooperative Object Transport in Multi-Robot Systems: A Review of the State-of-the-Art

In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increasethe visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectively.
The Level of Faculty Members ' Spiritual Leadership ( SL ) Qualities Display According To Students in Faculty of Education

Leadership is a process directing to a target of which followers, the participators are shared. For this reason leadership has an important effect on succeeding organizational targets. More importance is given to the leadership studies in order to increase organizational success each day. One of the leadership researches that attracts attention recently is spiritual leadership. Spiritual leadership (SL) is important for imposing ideal to the followers and giving meaning to the works they do. Focusing on SL that has recently taken its place in leadership literature, this study looks into what extend faculty members teaching at Faculty of Education display SL qualities. The study is in descriptive scanning model. 1819 students studying at Kocaeli University Faculty of Education in 2009-2010 academic year constitute the universe of the study. Observing leadership qualities takes long time. Therefore, the sample of the study is determined by deliberate sampling method and includes 432 students studying at the last year of the faculty. Data regarding faculty members' SL qualities were collected using a questionnaire adapted from Fry's (2003) 'Spiritual Leadership Scale'. Consequently, university students think that academic stuff shows the features of SL and its sub dimensions in a medium level. According to students, academicians show attitudes related to altruistic love rather than faith and vision. It is found that faculty members couldn't display leadership qualities enough according to the students at the end of the study.
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning

Transferring the knowledge learned from large scale datasets (e.g., ImageNet) via fine-tuning offers an effective solution for domain-specific fine-grained visual categorization (FGVC) tasks (e.g., recognizing bird species or car make & model). In such scenarios, data annotation often calls for specialized domain knowledge and thus is difficult to scale. In this work, we first tackle a problem in large scale FGVC. Our method won first place in iNaturalist 2017 large scale species classification challenge. Central to the success of our approach is a training scheme that uses higher image resolution and deals with the long-tailed distribution of training data. Next, we study transfer learning via fine-tuning from large scale datasets to small scale, domain-specific FGVC datasets. We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure. Our proposed transfer learning outperforms ImageNet pre-training and obtains state-of-the-art results on multiple commonly used FGVC datasets.