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EELS-DARTS is a simulator designed for autonomy development and analysis of large degree of freedom snake-like robots for space exploration. A detailed description of the EELS-DARTS simulator design is presented. This includes the versatile underlying multibody dynamics representation used to model a variety of distinct snake robot configurations as well as an anisotropic friction model for describing screw–ice interaction. Additional simulation components such as graphics, importable terrain, joint controllers, and perception are discussed. Methods for setting up and running simulations are discussed, including how a snake robot's autonomy stack closes the commands and information loop with the simulation via ROS. Multiple use cases are described to illustrate how the simulation is used to aid and inform robot design, autonomy development, and field test use throughout the project's life cycle. A validation analysis of the screw–ice contact model is performed for the surface mobility case. Lastly, an overview of simulation use for planning operations during a recent field test to the Athabasca Glacier in Canada is discussed. [ABSTRACT FROM AUTHOR]

HeliCAT-DARTS is a high-fidelity rotorcraft dynamics simulator developed for the design and development of rotorcraft for planetary exploration. While initially developed for the life cycle use of the Ingenuity Mars Helicopter mission, the simulator now supports a broad range of rotorcraft configurations and applications. HeliCAT provides a GNC testbed and aerial mobility analysis platform for rotorcraft design, closed-loop flight software development, verification and validation (V&V), and mission operations. This article discusses the design and use of the HeliCAT simulator and results from technology demonstrations and missions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to a global shutdown of sporting activities. While professional sports competitions restarted in mid-2020, spectators were usually not allowed. This paper investigates the effect of absent fans and reduced social pressure on performance in professional darts – a setting where individual player performances can be well observed. Considering almost five years of tournament data, we use Bayesian multilevel models to investigate potential heterogeneity across players concerning reduced social pressure. For our analysis, we consider the two main performance measures in darts: the three-dart average and the checkout performance. Our results indicate that the effect of reduced social pressure on performance varies substantially across players. We further find experienced players to be less affected by social pressure compared to relatively inexperienced players. [ABSTRACT FROM AUTHOR]

This research focuses on the target deconvolution of the natural compound myrianthic acid, a triterpenoid characterized by an ursane skeleton isolated from the roots of Myrianthus arboreus and from Oenothera maritima Nutt. (Onagraceae), using MS-based chemical proteomic techniques. Application of drug affinity responsive target stability (DARTS) and targeted-limited proteolysis coupled to mass spectrometry (t-LiP-MS) led to the identification of the enzyme fatty acid synthase (FAS) as an interesting macromolecular counterpart of myrianthic acid. This result, confirmed by comparison with the natural ursolic acid, was thoroughly investigated and validated in silico by molecular docking, which gave a precise picture of the interactions in the MA/FAS complex. Moreover, biological assays showcased the inhibitory activity of myrianthic acid against the FAS enzyme, most likely related to its antiproliferative activity towards tumor cells. Given the significance of FAS in specific pathologies, especially cancer, the myrianthic acid structural moieties could serve as a promising reference point to start the potential development of innovative approaches in therapy. [ABSTRACT FROM AUTHOR]

Exercise training integrating physical and cognitive activities is gaining attention because of its potential benefits for brain health. This study focuses on exercise training using a dart game called Wellness Darts. Wellness Darts is a sport involving throwing darts and walking to pull them out of the board, memorizing the score, and subtracting this from the total score, thus requiring the simultaneous performance of two tasks: exercise and calculation. This is expected to maintain and improve cognitive function, and whether this continual darts training affects brain function is of great interest. Before conducting the longitudinal study revealing its effect on brain function, we aimed to cross-sectionally confirm the difference in hemispheric lateralization between expert and non-expert players. Functional near-infrared spectroscopy (fNIRS) was used to measure brain activity for three groups: an expert older group who practiced darts continually, a non-expert older control group, and a non-expert younger control group. Their brain activity patterns were quantified by the lateralization index (LI) and compared between groups. The results showed that the younger and the expert older groups had significantly higher LI values than the non-expert older group, and there was no difference between the expert older and the younger groups. Our results suggest that the Wellness Darts game possibly promotes hemispheric lateralization. [ABSTRACT FROM AUTHOR]

Neural Architecture Search (NAS) has proven valuable in many applications such as computer vision. However, its true potential is unveiled when applied to less-explored domains. In this work, we study NAS for addressing problems in diverse fields where we expect to apply deep neural networks in the real world domains. We introduce D2NAS, Differential and Diverse NAS, leveraging techniques such as Differentiable ARchiTecture Search (DARTS) and Diverse-task Architecture SearcH (DASH) for architecture discovery. Our approach outperforms existing models, including Wide ResNet (WRN) and DASH, when evaluated on NAS-Bench-360 tasks, which include 10 numbers of diverse tasks for 1D, 2D, 2D Dense (tightly interconnected data) tasks. Compared to DASH, D2NAS reduces average error rates by 12.2%, while achieving an 85.1% reduction in average parameters (up to 97.3%) and a 91.3% reduction in Floating Point Operations (FLOPs, up to 99.3%). Therefore, D2NAS enables the creation of lightweight architectures that exhibit superior performance across various tasks, extending its applicability beyond computer vision to include mobile applications.

Speech Emotion Recognition (SER) is crucial for enabling computers to understand the emotions conveyed in human communication. With recent advancements in Deep Learning (DL), the performance of SER models has significantly improved. However, designing an optimal DL architecture requires specialised knowledge and experimental assessments. Fortunately, Neural Architecture Search (NAS) provides a potential solution for automatically determining the best DL model. The Differentiable Architecture Search (DARTS) is a particularly efficient method for discovering optimal models. This study presents emoDARTS, a DARTS-optimised joint CNN and Sequential Neural Network (SeqNN: LSTM, RNN) architecture that enhances SER performance. The literature supports the selection of CNN and LSTM coupling to improve performance. While DARTS has previously been used to choose CNN and LSTM operations independently, our technique adds a novel mechanism for selecting CNN and SeqNN operations in conjunction using DARTS. Unlike earlier work, we do not impose limits on the layer order of the CNN. Instead, we let DARTS choose the best layer order inside the DARTS cell. We demonstrate that emoDARTS outperforms conventionally designed CNN-LSTM models and surpasses the best-reported SER results achieved through DARTS on CNN-LSTM by evaluating our approach on the IEMOCAP, MSP-IMPROV, and MSP-Podcast datasets.

One of the challenges in deep learning involves discovering the optimal architecture for a specific task. This is effectively tackled through Neural Architecture Search (NAS). Neural Architecture Search encompasses three prominent approaches—reinforcement learning, evolutionary algorithms, and gradient descent—that have demonstrated noteworthy potential in identifying good candidate architectures. However, approaches based on reinforcement learning and evolutionary algorithms often necessitate extensive computational resources, requiring hundreds of GPU days or more. Therefore, we confine this work to a gradient-based approach due to its lower computational resource demands. Our objective encompasses identifying the optimal gradient-based NAS method and pinpointing opportunities for future enhancements. To achieve this, a comprehensive evaluation of the use of four major Gradient descent-based architecture search methods for discovering the best neural architecture for image classification tasks is provided. An overview of these gradient-based methods, i.e., DARTS, PDARTS, Fair DARTS and Att-DARTS, is presented. A theoretical comparison, based on search spaces, continuous relaxation strategy and bi-level optimization, for deriving the best neural architecture is then provided. The strong and weak features of these methods are also listed. Experimental results for comparing the error rate and computational cost of these gradient-based methods are analyzed. These experiments involved using bench marking datasets CIFAR-10, CIFAR-100 and ImageNet. The results show that PDARTS is better and faster among the examined methods, making it a potent candidate for automating Neural Architecture Search. By effectively conducting a comparative analysis, our research provides valuable insights and future research directions to address the criticism and gaps in the literature.

Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML). Although search methods based on reinforcement learning and evolutionary algorithms can find high-performance architectures, these search methods typically require hundreds of GPU days. Unlike searching in a discrete search space based on reinforcement learning and evolutionary algorithms, the differentiable neural architecture search (DARTS) continuously relaxes the search space, allowing for optimization using gradient-based methods. Based on DARTS, we propose NDARTS in this article. The new algorithm uses the Implicit Function Theorem and the Neumann series to approximate the hyper-gradient, which obtains better results than DARTS. In the simulation experiment, an ablation experiment was carried out to study the influence of the different parameters on the NDARTS algorithm and to determine the optimal weight, then the best performance of the NDARTS algorithm was searched for in the DARTS search space and the NAS-BENCH-201 search space. Compared with other NAS algorithms, the results showed that NDARTS achieved excellent results on the CIFAR-10, CIFAR-100, and ImageNet datasets, and was an effective neural architecture search algorithm. [ABSTRACT FROM AUTHOR]

One of the challenges in deep learning involves discovering the optimal architecture for a specific task. This is effectively tackled through Neural Architecture Search (NAS). Neural Architecture Search encompasses three prominent approaches—reinforcement learning, evolutionary algorithms, and gradient descent—that have demonstrated noteworthy potential in identifying good candidate architectures. However, approaches based on reinforcement learning and evolutionary algorithms often necessitate extensive computational resources, requiring hundreds of GPU days or more. Therefore, we confine this work to a gradient-based approach due to its lower computational resource demands. Our objective encompasses identifying the optimal gradient-based NAS method and pinpointing opportunities for future enhancements. To achieve this, a comprehensive evaluation of the use of four major Gradient descent-based architecture search methods for discovering the best neural architecture for image classification tasks is provided. An overview of these gradient-based methods, i.e., DARTS, PDARTS, Fair DARTS and Att-DARTS, is presented. A theoretical comparison, based on search spaces, continuous relaxation strategy and bi-level optimization, for deriving the best neural architecture is then provided. The strong and weak features of these methods are also listed. Experimental results for comparing the error rate and computational cost of these gradient-based methods are analyzed. These experiments involved using bench marking datasets CIFAR-10, CIFAR-100 and ImageNet. The results show that PDARTS is better and faster among the examined methods, making it a potent candidate for automating Neural Architecture Search. By effectively conducting a comparative analysis, our research provides valuable insights and future research directions to address the criticism and gaps in the literature. [ABSTRACT FROM AUTHOR]

Deep learning neural networks based on the manual design for image classification tasks usually require a large amount of a priori knowledge and experience from experts; thus, research on designing neural network architectures automatically has been widely performed. The neural architecture search (NAS) method based on the differentiable architecture search (DARTS) ignores the interrelationships within the searched network architecture cells. The optional operations in the architecture search space lack diversity, and the large parametric and non-parametric operations in the search space make the search process inefficient. We propose a NAS method based on a dual attention mechanism (DAM-DARTS). An improved attention mechanism module is introduced to the cell of the network architecture to deepen the interrelationships between the important layers within the architecture by enhancing the attention between them, which improves the accuracy of the architecture and reduces the architecture search time. We also propose a more efficient architecture search space by adding attention operations to increase the complex diversity of the searched network architectures and reduce the computational cost consumed in the search process by reducing non-parametric operations. Based on this, we further analyze the impact of changing some operations in the architecture search space on the accuracy of the architectures. Through extensive experiments on several open datasets, we demonstrate the effectiveness of the proposed search strategy, which is highly competitive with other existing neural network architecture search methods.

The impact of innovative technologies on the target discovery has been employed here to characterize the interactome of STIRUR 41, a promising 3-fluoro-phenyl-5-pyrazolyl-urea derivative endowed with anti-cancer activity, on neuroblastoma-related cells. A drug affinity responsive target stability-based proteomic platform has been optimized to elucidate the molecular mechanism at the basis of STIRUR 41 action, together with immunoblotting analysis and in silico molecular docking. Ubiquitin Specific Protease 7 (USP-7), one of the deubiquitinating enzymes which protect substrate proteins from proteasomal degradation, has been identified as the most affine STIRUR 41 target. As further demonstrated by in vitro and in-cell assays, STIRUR 41 was able to inhibit both the enzymatic activity of USP-7 and its expression levels in neuroblastoma-related cells, thus laying an encouraging base for the blockade of USP-7 downstream signaling. [ABSTRACT FROM AUTHOR]

Abstract Although YOLOv4 has a high detection accuracy, its backbone network CSPDarknet53 requires a large number of parameters, which reduces the inference speed of the detection network to some extent. To address this drawback, a high‐efficiency detection network search strategy is proposed. First, the concept of Differentiable Architecture Search (DARTS) is used to search networks with a small number of parameters on the COCO2017 datasets; then, the backbone of YOLOv4 is redesigned by stacking cells. This strategy can reduce the number of parameters of the network. Second, the authors use ResNet101 as the teacher network and DD‐YOLO searched by DARTS as the student network to refine the knowledge model and improve the detection accuracy of the model. The proposed model is evaluated by conducting experiments on the COCO2017 test‐dev datasets. The results show that DD‐YOLO achieves 43.5% mean average precision and 2.3% model accuracy improvement. Meanwhile, the model complexity can be reduced to 61.4% of the original. Moreover, compared with YOLOv4, DD‐YOLO is more suitable for mobile deployment.

Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML). Although search methods based on reinforcement learning and evolutionary algorithms can find high-performance architectures, these search methods typically require hundreds of GPU days. Unlike searching in a discrete search space based on reinforcement learning and evolutionary algorithms, the differentiable neural architecture search (DARTS) continuously relaxes the search space, allowing for optimization using gradient-based methods. Based on DARTS, we propose NDARTS in this article. The new algorithm uses the Implicit Function Theorem and the Neumann series to approximate the hyper-gradient, which obtains better results than DARTS. In the simulation experiment, an ablation experiment was carried out to study the influence of the different parameters on the NDARTS algorithm and to determine the optimal weight, then the best performance of the NDARTS algorithm was searched for in the DARTS search space and the NAS-BENCH-201 search space. Compared with other NAS algorithms, the results showed that NDARTS achieved excellent results on the CIFAR-10, CIFAR-100, and ImageNet datasets, and was an effective neural architecture search algorithm.

We present a Model Uncertainty-aware Differentiable ARchiTecture Search ( $\mu $ DARTS) that optimizes neural networks to simultaneously achieve high accuracy and low uncertainty. We introduce concrete dropout within DARTS cells and include a Monte-Carlo regularizer within the training loss to optimize the concrete dropout probabilities. A predictive variance term is introduced in the validation loss to enable searching for architecture with minimal model uncertainty. The experiments on CIFAR10, CIFAR100, SVHN, and ImageNet verify the effectiveness of $\mu $ DARTS in improving accuracy and reducing uncertainty compared to existing DARTS methods. Moreover, the final architecture obtained from $\mu $ DARTS shows higher robustness to noise at the input image and model parameters compared to the architecture obtained from existing DARTS methods.

BackgroundThe most aggressive subtype of breast cancer, triple-negative breast cancer (TNBC), has a worse prognosis and a higher probability of relapse since there is a narrow range of treatment options. Identifying and testing potential therapeutic targets for the treatment of TNBC is of high priority.MethodsUsing a transcriptional signature of triple-negative breast cancer collected from Gene Expression Omnibus (GEO), CMap was utilized to reposition compounds for the treatment of TNBC. CCK8 and colony formation experiments were performed to detect the effect of the candidate drug on the proliferation of TNBC cells. Meanwhile, transwell and wound healing assay were implemented to detect cell metastasis change caused by the candidate drug. Moreover, the proteomic approach was presently ongoing to evaluate the underlying mechanism of the candidate drug in TNBC. Furthermore, drug affinity responsive target stability (DARTS) coupled with LC-MS/MS was carried out to explore the potential drug target candidate in TNBC cells.ResultsWe found that the most widely used medication, eugenol, reduced the growth and metastasis of TNBC cells. According to the underlying mechanism revealed by proteomics, eugenol could inhibit TNBC cell proliferation and metastasis via the NOD1-NF-κB signaling pathway. DARTS experiment further revealed that eugenol may bind to NF-κB in TNBC cells.ConcludesOur findings pointed out that eugenol was a potential candidate drug for the treatment of TNBC.

Although YOLOv4 has a high detection accuracy, its backbone network CSPDarknet53 requires a large number of parameters, which reduces the inference speed of the detection network to some extent. To address this drawback, a high‐efficiency detection network search strategy is proposed. First, the concept of Differentiable Architecture Search (DARTS) is used to search networks with a small number of parameters on the COCO2017 datasets; then, the backbone of YOLOv4 is redesigned by stacking cells. This strategy can reduce the number of parameters of the network. Second, the authors use ResNet101 as the teacher network and DD‐YOLO searched by DARTS as the student network to refine the knowledge model and improve the detection accuracy of the model. The proposed model is evaluated by conducting experiments on the COCO2017 test‐dev datasets. The results show that DD‐YOLO achieves 43.5% mean average precision and 2.3% model accuracy improvement. Meanwhile, the model complexity can be reduced to 61.4% of the original. Moreover, compared with YOLOv4, DD‐YOLO is more suitable for mobile deployment. [ABSTRACT FROM AUTHOR]

Prevailing deep methods for image recognition require massive labeled samples in each visual category for training. However, large amounts of data annotations are timeconsuming, and some uncommon categories only have rare samples available. For this issue, we focus on more challenging few-shot learning (FSL) task, where just few labeled images are used in the training stage. Existing FSL models are constructed with various convolutional neural networks (CNNs), which are trained on an auxiliary base dataset and evaluated for new fewshot predictions on a novel dataset. The performance of these models is difficult to break through because of the domain shift between base and novel datasets and the monotonous network architectures. Considering that, we propose a novel automatic attribute consistent network called Auto-ACNet to overcome the above problems. On one hand, Auto-ACNet utilizes the attribute information about base and novel categories to guide the procedure of representation learning. It introduces the consistent and non-consistent subnets to capture the common and different attributes of image pair, which helps to mitigate the domain shift problem. On the other hand, the architecture of Auto-ACNet is searched with the popular neural architecture search (NAS) technique DARTS, for obtaining a superior FSL network automatically. And the DARTS’s search space is improved by adding the position-aware module to extract the attribute characteristics better. Extensive experimental results on two datasets indicate that the proposed Auto-ACNet achieves significant improvement over the state-of-the-art competitors in this literature. [ABSTRACT FROM AUTHOR]

The current evolution of deep learning requires further optimization in terms of accuracy and time. From the perspective of new requirements, AutoML is an area that could provide possible solutions. AutoML has a neural architecture search (NAS) field. DARTS is a widely used approach in NAS and is based on gradient descent; however, it has some drawbacks. In this study, we attempted to overcome some of the drawbacks of DARTS by improving the accuracy and decreasing the search cost. The DARTS algorithm uses a mixed operation that combines all operations in the search space. The architecture parameter of each operation comprising a mixed operation is trained using gradient descent, and the operation with the largest architecture parameter is selected. The use of a mixed operation causes a problem called vote dispersion: similar operations share architecture parameters during gradient descent; thus, there are cases where the most important operation is disregarded. In this selection process, vote dispersion causes DARTS performance to degrade. To cope with this problem, we propose a new algorithm based on DARTS called DG-DARTS. Two search stages are introduced, and the clustering of operations is applied in DG-DARTS. In summary, DG-DARTS achieves an error rate of 2.51% on the CIFAR10 dataset, and its search cost is 0.2 GPU days because the search space of the second stage is reduced by half. The speed-up factor of DG-DARTS to DARTS is 6.82, which indicates that the search cost of DG-DARTS is only 13% that of DARTS. [ABSTRACT FROM AUTHOR]

Macroautophagy/autophagy (hereafter autophagy), the process of mass degradation of unnecessary elements within the cell, is often dysregulated in many diseases such as cancer, atherosclerosis, and neurodegenerative diseases. Hence, autophagy modulating agents have a great potential to be therapeutic agents for the autophagy-related diseases. Here we report that an anti-depressant drug sertraline (Sert) is an autophagy-inducing agent. Mechanistically, Sert potentially binds to and antagonizes the mitochondrial VDAC1 (voltage dependent anion channel 1), resulting in reduced cellular ATP (adenosine triphosphate) level, activation of AMP-activated protein kinase (AMPK) and inhibition of its downstream, MTOR (mechanistic target of rapamycin kinase)-RPS6KB1 (ribosomal protein S6 kinase B1) signaling pathway. Cells lacking VDAC1 expression completely abrogate the modulatory effect of Sert on AMPK-MTOR pathway and autophagy-inducing activity. We further show that Sert suppresses tauopathy by promoting the autophagic degradation of MAPT (microtubule associated protein tau) protein via inducing autophagy. Our study demonstrates the potential of Sert as a novel small molecule autophagy-inducing agent and provides a new drug candidate to treat autophagy related diseases by targeting VDAC1. Abbreviations: AMP: adenosine monophosphate; AMPK: AMP-activated protein kinase; ATP: adenosine triphosphate; Baf: bafilomycin A1; BiFC: biomolecular fluorescence complementation; CAMKK2/CAMKKB: calcium/calmodulin dependent protein kinase kinase 2; CC: compound C; DARTS: drug affinity responsive target stability; HUVECs: human umbilical vein endothelial cells; Inda: indatraline; STK11/LKB1: serine/threonine kinase 11; MAPT: microtubule associated protein tau; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; 3-MA: 3-methyladenine; MEFs: mouse embryonic fibroblasts; MTOR: mechanistic target of rapamycin kinase; PI3K: phosphoinositide 3-kinase; Rapa: rapamycin; Sert: sertraline; RPS6KB1: ribosomal protein S6 kinase B1; SQSTM1/p62: sequestosome 1; SLC6A4/SERT1: solute carrier family 6 member 4; TFEB: transcription factor EB; VDAC1: voltage dependent anion channel 1; WT: wild-type; WM: wortmannin. [ABSTRACT FROM AUTHOR]

Introduction. Information about the armament and military arts of nomads of the Early Iron age can be obtained from writings of Greek and Roman authors, who note the warlike temper of Savromatian and Sarmatian tribes. They mention bows, swords and spears among the armament of nomads in their works. This information is confirmed by the archaeological material. Finds of spearheads in burials are quite rare and are ranked third after arrows and swords in percentage. Methods. The typological features of spearheads are based on the shape of their pen, which allows distinguishing two types of spears – leaf-shaped and lence-shaped. Analysis. This article is the beginning of the catalog of spearheads, darts and spear counter-weights found in the Lower Volga Region in Savromatian and Sarmatian monuments. At this moment, the authors have taken into account a sample of 29 spearheads, 3 darts and 3 spear counter-weights and made their typological analysis. Results. The typological analysis, which forms the basis of our classification of spearheads, allows tracing the development of this type of armament in Volgograd region. The further development of this topic will help to find typological interrelationships of spearheads, to clarify their chronological positions and features of mutual occurrence in burials with other types of armament and equipment. In turn, this analysis will help to determine the social status of the buried persons and their role in ethnopolitical processes.

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Copyright of Nigde University Journal of Physical Education & Sport Sciences / Nigde Üniversitesi Beden Egitimi ve Spor Bilimleri Dergisi is the property of Journal of Physical Education & Sports Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Four new compounds were isolated from Cordyceps militaris. • Compound 3 is a rare four-membered lactam. • The protein target of active 3 was identified as C1qbp by chemical biology strategy. Four undescribed compounds including one aromatic glucoside derivative, cordyceglycoside A (1), one new isoleucine derivative inner salt, cordycepisosalt A (2), a rare four-membered lactam, cinerealactam B (3), and one sesquiterpene derivative, cordycepsetp A (4), together with six known compounds were isolated from Cordyceps militaris. The structures including absolute configurations of these new compounds, were unambiguously elucidated by spectroscopic data analysis and single crystal X-ray diffraction. Biological evaluation of compounds 1 – 4 showed that 3 displays anti-renal fibrotic activities in TGF- β 1 induced NRK-52e cells. Furthermore, DARTS coupled with LC-MS/MS analysis was used to identify candidate target proteins for 3. Subsequently, C1qbp knockdown using siRNA allowed us to validate the target protein of 3. [ABSTRACT FROM AUTHOR]

The current evolution of deep learning requires further optimization in terms of accuracy and time. From the perspective of new requirements, AutoML is an area that could provide possible solutions. AutoML has a neural architecture search (NAS) field. DARTS is a widely used approach in NAS and is based on gradient descent; however, it has some drawbacks. In this study, we attempted to overcome some of the drawbacks of DARTS by improving the accuracy and decreasing the search cost. The DARTS algorithm uses a mixed operation that combines all operations in the search space. The architecture parameter of each operation comprising a mixed operation is trained using gradient descent, and the operation with the largest architecture parameter is selected. The use of a mixed operation causes a problem called vote dispersion: similar operations share architecture parameters during gradient descent; thus, there are cases where the most important operation is disregarded. In this selection process, vote dispersion causes DARTS performance to degrade. To cope with this problem, we propose a new algorithm based on DARTS called DG-DARTS. Two search stages are introduced, and the clustering of operations is applied in DG-DARTS. In summary, DG-DARTS achieves an error rate of 2.51% on the CIFAR10 dataset, and its search cost is 0.2 GPU days because the search space of the second stage is reduced by half. The speed-up factor of DG-DARTS to DARTS is 6.82, which indicates that the search cost of DG-DARTS is only 13% that of DARTS.

Abstract Background Daptomycin is a recently introduced, last-resort antibiotic that displays a unique mode of action against Gram-positive bacteria that is not fully understood. Several bacterial targets have been proposed but no human binding partner is known. Methods In the present study we tested daptomycin in cell viability and proliferation assays against six human cell lines, describe the synthesis of biotinylated and fluorescently labeled analogues of daptomycin. Biotinylated daptomycin was used as bait to isolate the human binding partner by the application of reverse chemical proteomics using T7 phage display of five human tumor cDNA libraries. The interaction between the rescued protein and daptomycin was validated via siRNA knockdown, DARTS assay and immunocytochemistry. Results We have found that daptomycin possesses selective growth inhibition of some cancer cell lines, especially MCF7. The unbiased interrogation of human cDNA libraries, displayed on bacteriophage T7, revealed a single human target of daptomycin; ribosomal protein S19. Using a drug affinity responsive target stability (DARTS) assay in vitro, we show that daptomycin stabilizes RPS19 toward pronase. Fluorescently labeled daptomycin stained specific structures in HeLa cells and co-localized with a RPS19 antibody. Conclusion This study provides, for the first time, a human protein target of daptomycin and identifies RPS19 as a possible anticancer drug target for the development of new pharmacological applications and research.

The perspective of perceiving one’s action affects its speed and accuracy. In the present study, we investigated the change in accuracy and kinematics when subjects throw darts from the first-person perspective and the third-person perspective with varying angles of view. To model the third-person perspective, subjects were looking at themselves as well as the scene through the virtual reality head-mounted display (VR HMD). The scene was supplied by a video feed from the camera located to the up and 0, 20 and 40 degrees to the right behind the subjects. The 28 subjects wore a motion capture suit to register their right hand displacement, velocity and acceleration, as well as torso rotation during the dart throws. The results indicated that mean accuracy shifted in opposite direction with the changes of camera location in vertical axis and in congruent direction in horizontal axis. Kinematic data revealed a smaller angle of torso rotation to the left in all third-person perspective conditions before and during the throw. The amplitude, speed and acceleration in third-person condition were lower compared to the first-person view condition, before the peak velocity of the hand in the direction toward the target and after the peak velocity in lowering the hand. Moreover, the hand movement angle was smaller in the third-person perspective conditions with 20 and 40 angle of view, compared with the first-person perspective condition just preceding the time of peak velocity, and the difference between conditions predicted the changes in mean accuracy of the throws. Thus, the results of this study revealed that subject’s localization contributed to the transformation of the motor program.

After spinal cord injury (SCI), reconstruction of neuronal tracts is very difficult because an inhibitory scar is formed at the lesion site, in which several axonal growth inhibitors, such as chondroitin sulfate proteoglycans (CSPG), accumulate. We previously found that matrine, a major alkaloid in Sophora flavescens, enhanced axonal growth in neurons seeded on CSPG coating. The aims of this study were to investigate therapeutic effects of matrine in SCI mice and to clarify the underlying mechanism. Matrine was orally administered to contusion SCI mice. In the matrine-treated mice, motor dysfunction of the hindlimbs was improved, and the density of 5-HT-positive tracts was increased in the injured spinal cord. We explored putative direct binding proteins of matrine in cultured neurons using drug affinity responsive target stability (DARTS). As a result, heat shock protein 90 (HSP90) was identified, and matrine enhanced HSP90 chaperon activity. We then presumed that extracellular HSP90 is a matrine-targeting signaling molecule, and found that specific blocking of extracellular HSP90 by a neutralizing antibody completely diminished matrine-induced axonal growth and SCI amelioration. Our results suggest that matrine enhances axonal growth and functional recovery in SCI mice by direct activation of extracellular HSP90. Matrine could be a significant candidate for therapeutic drugs for SCI with a novel mechanism.

Objectives: The study of sequential effects in aiming tasks might shed light on the organization of repetitive motor performances over time. To date, investigations of such effects in sports have been limited and yielded mixed results. Given the relatively short time intervals between successive attempts, and the absence of defensive interventions, dart throwing provides a potentially fruitful testing ground for examining the presence of sequential performance effects in the motor domain.Methods and Results: A total of 80 competitive darts matches of 10 of the world’s best players were scored from publicly available video footage in terms of sequences of hits and misses of triple 20. In darts, throws are organized in legs, i.e., a rapid succession of three throws by the same player, allowing us to investigate various transitions in performance (throw 1 → 2, 2 → 3, and 3 → 1). The resulting binary sequences were analyzed statistically in terms of independence and stationarity. Across players significant statistical evidence was found for sequential dependence from the first throw in a leg to the second throw, but not for the other transitions. As regards to stationarity, a significant decline in performance was observed in the course of the match.Conclusions: In professional darts, evidence can be found for both sequential dependence as well as for non-stationarity, implying that performance does not, or at least not always, constitute a stationary random independent process. More research is needed on the motor control mechanisms underlying the observed carry-over effects within triplets as well as the possible causes of non-stationarity.

After spinal cord injury (SCI), reconstruction of neuronal tracts is very difficult because an inhibitory scar is formed at the lesion site, in which several axonal growth inhibitors, such as chondroitin sulfate proteoglycans (CSPG), accumulate. We previously found that matrine, a major alkaloid in Sophora flavescens, enhanced axonal growth in neurons seeded on CSPG coating. The aims of this study were to investigate therapeutic effects of matrine in SCI mice and to clarify the underlying mechanism. Matrine was orally administered to contusion SCI mice. In the matrine-treated mice, motor dysfunction of the hindlimbs was improved, and the density of 5-HT-positive tracts was increased in the injured spinal cord. We explored putative direct binding proteins of matrine in cultured neurons using drug affinity responsive target stability (DARTS). As a result, heat shock protein 90 (HSP90) was identified, and matrine enhanced HSP90 chaperon activity. We then presumed that extracellular HSP90 is a matrine-targeting signaling molecule, and found that specific blocking of extracellular HSP90 by a neutralizing antibody completely diminishedmatrine-induced axonal growth and SCI amelioration. Our results suggest that matrine enhances axonal growth and functional recovery in SCI mice by direct activation of extracellular HSP90. Matrine could be a significant candidate for therapeutic drugs for SCI with a novel mechanism. [ABSTRACT FROM AUTHOR]

Objectives: The study of sequential effects in aiming tasks might shed light on the organization of repetitive motor performances over time. To date, investigations of such effects in sports have been limited and yielded mixed results. Given the relatively short time intervals between successive attempts, and the absence of defensive interventions, dart throwing provides a potentially fruitful testing ground for examining the presence of sequential performance effects in the motor domain. Methods and Results: A total of 80 competitive darts matches of 10 of the world's best players were scored from publicly available video footage in terms of sequences of hits and misses of triple 20. In darts, throws are organized in legs, i.e., a rapid succession of three throws by the same player, allowing us to investigate various transitions in performance (throw 1 → 2, 2 → 3, and 3 → 1). The resulting binary sequences were analyzed statistically in terms of independence and stationarity. Across players significant statistical evidence was found for sequential dependence from the first throw in a leg to the second throw, but not for the other transitions. As regards to stationarity, a significant decline in performance was observed in the course of the match. Conclusions: In professional darts, evidence can be found for both sequential dependence as well as for non-stationarity, implying that performance does not, or at least not always, constitute a stationary random independent process. More research is needed on the motor control mechanisms underlying the observed carry-over effects within triplets as well as the possible causes of non-stationarity. [ABSTRACT FROM AUTHOR]

Background: Daptomycin is a recently introduced, last-resort antibiotic that displays a unique mode of action against Gram-positive bacteria that is not fully understood. Several bacterial targets have been proposed but no human binding partner is known. Methods: In the present study we tested daptomycin in cell viability and proliferation assays against six human cell lines, describe the synthesis of biotinylated and fluorescently labeled analogues of daptomycin. Biotinylated daptomycin was used as bait to isolate the human binding partner by the application of reverse chemical proteomics using T7 phage display of five human tumor cDNA libraries. The interaction between the rescued protein and daptomycin was validated via siRNA knockdown, DARTS assay and immunocytochemistry. Results: We have found that daptomycin possesses selective growth inhibition of some cancer cell lines, especially MCF7. The unbiased interrogation of human cDNA libraries, displayed on bacteriophage T7, revealed a single human target of daptomycin; ribosomal protein S19. Using a drug affinity responsive target stability (DARTS) assay in vitro, we show that daptomycin stabilizes RPS19 toward pronase. Fluorescently labeled daptomycin stained specific structures in HeLa cells and co-localized with a RPS19 antibody. Conclusion: This study provides, for the first time, a human protein target of daptomycin and identifies RPS19 as a possible anticancer drug target for the development of new pharmacological applications and research. [ABSTRACT FROM AUTHOR]

While the benefits of both an external focus of attention and of a longer quiet eye duration have been well researched in a wide range of sporting activities, little is known about the interaction of these two phenomena and how a potential interaction might influence performance. It was this study’s aim to investigate the interaction and potential effect on performance by using typical focus of attention instructions in a dart throwing task and examining both the quiet eye and performance outcome. The results replicate neither the benefit of an external focus of attention nor the benefit of a longer quiet eye duration. However, an interaction was observed, as quiet eye was prolonged by an earlier onset and later offset in the internal focus condition only. As the typical effect of a performance benefit due to an external focus could not be replicated, the interaction must be interpreted with caution. The results are discussed and interpreted in light of the inhibition hypothesis and possible avenues for future research are suggested.

Narrowing the genetic base available for future genetic progress is a major concern to plant breeders. In order to avoid this, strategies to characterise and protect genetic diversity in regional breeding pools are required. In this study, 89 winter wheat cultivars released in Croatia between 1936 and 2006 were genotyped using 1,229 DArT (Diversity Array Technology) markers to assess the diversity and population structure. In order to place Croatian breeding pool in a European context, Croatian wheat cultivars were compared to 523 European cultivars from seven countries using a total of 166 common DArT markers. The results show higher genetic diversity in the wheat breeding pool from Central Europe (CE) as compared to that from Northern and Western European (NWE) countries. The most of the genetic diversity was attributable to the differences among cultivars within countries. When the geographical criterion (CE vs. NWE) was applied, highly significant difference between regions was obtained that accounted for 16.19 % of the total variance, revealing that the Croatian breeding pool represents genetic variation not currently captured in elite European wheat. The current study emphasises the important contribution made by plant breeders to maintaining wheat genetic diversity and suggests that regional breeding is essential to the maintenance of this diversity. The usefulness of open-access wheat datasets is also highlighted.

In examining large acontia b-mastigophore and p-amastigophore nematocysts of the sea anemone Metridium senile (Linnaeus, 1761) darts were observed by interference-contrast light microscopy (LM) and scanning electron microscopy (SEM). The darts consist of closely packed spines detached from the shafts, still in three helical rows. Their spines form a hollow cylinder with a sharp tip and indented base, its width similar to that of an undischarged shaft but varying in length. b-mastigophore darts were more common than those of p-mastigophores and many were longer.

Scientific fundamentalses of a procedure of development of accuracy of particular movements in darts are indicated. Positive and negative effect of power load of different character on accuracy of elementary movements is exhibited. The basic motion characteristics of a throw of a dart at initial sportsmen are defined. The conditions hindering development of accuracy of a throw of a dart are revealed. The procedure of formation of accuracy of throwing movements in darts is developed and her efficiency is proved.

While the benefits of both an external focus of attention (FOA) and of a longer quiet eye (QE) duration have been well researched in a wide range of sporting activities, little is known about the interaction of these two phenomena and how a potential interaction might influence performance. It was this study's aim to investigate the interaction and potential effect on performance by using typical FOA instructions in a dart throwing task and examining both the QE and performance outcome. The results replicate neither the benefit of an external FOA nor the benefit of a longer QE duration. However, an interaction was observed, as QE was prolonged by an earlier onset and later offset in the internal focus condition only. As the typical effect of a performance benefit due to an external focus could not be replicated, the interaction must be interpreted with caution. The results are discussed and interpreted in light of the inhibition hypothesis and possible avenues for future research are suggested. [ABSTRACT FROM AUTHOR]

Narrowing the genetic base available for future genetic progress is a major concern to plant breeders. In order to avoid this, strategies to characterize and protect genetic diversity in regional breeding pools are required. In this study, 89 winter wheat cultivars released in Croatia between 1936 and 2006 were genotyped using 1,229 DArT (diversity array technology) markers to assess the diversity and population structure. In order to place Croatian breeding pool (CBP) in a European context, Croatian wheat cultivars were compared to 523 European cultivars from seven countries using a total of 166 common DArT markers. The results show higher genetic diversity in the wheat breeding pool from Central Europe (CE) as compared to that from Northern and Western European (NWE) countries. The most of the genetic diversity was attributable to the differences among cultivars within countries. When the geographical criterion (CE vs. NWE) was applied, highly significant difference between regions was obtained that accounted for 16.19% of the total variance, revealing that the CBP represents genetic variation not currently captured in elite European wheat. The current study emphasizes the important contribution made by plant breeders to maintaining wheat genetic diversity and suggests that regional breeding is essential to the maintenance of this diversity. The usefulness of open-access wheat datasets is also highlighted. [ABSTRACT FROM AUTHOR]