Search

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]

We present a discriminant metric study of stone projectile points (n = 422) from 21 archaeological sites in the Andes of South America (16–37°S). We use comparative datasets critically, since darts may have been smaller than previously thought. We assess the use-life of each point and tie them to reliable chronological sequences, in order to increase the reliability of our data. Our results show that in the Lake Titicaca Basin and northwestern Argentina, bows had replaced spearthrowers by 1780–950 cal BP, prior to the development of complex societies such as Wari and Tiwanaku. South of 29°S, the results suggest that bow technology was transmitted from north to south, since the earliest arrow-sized points at 29°S date to 3000 cal BP and at 37°S, 1800–1000 cal BP. North of 34°S, the continental limit of domesticated plants and animals, there is a gradual abandonment of the spearthrower. South of 34°S in northern Patagonia, both weapon systems coexisted. We suggest that bow and arrow technology was not an independent invention in the southern Andes, but instead, it appeared in new groups via macroregional technological borrowing among Andean herders. [ABSTRACT FROM AUTHOR]

The invention or adoption of bows is one of the most significant global shifts in humanity's history, and tracking this in the archaeological record depends on metrically distinguishing dart and arrow projectile points. Given the importance of comparative databases in this endeavor, this paper presents an expanded compilation of measurements on 85 hafted points from North America. For South America, we present unpublished data for 22 hafted points and 61 foreshafts. A clear metric gap in point width at 14–15 mm separates arrows and darts. This reflects the weapons' different physical requirements, since these points come from historically independent regions and periods. These patterns are not replicated in ethnographic arrows, which are consistently larger than archaeological arrows. We suggest they not be used in archaeological comparisons. Hafted darts are notably larger than unhafted darts from archaeological sites, which is mostly due to use-life reductions. We suggest that there is no universally applicable data set, nor derived formula or index, that can be used to identify archaeological points as darts or arrows. We recommend nonparametric comparisons focusing on natural breaks in cross-cultural archaeological data. [ABSTRACT FROM AUTHOR]

Rationale/Purpose: The purpose of this study was to identify and explore a selection of the tools (alcohol, song/chants, signs) that darts fans use at live Professional Darts Corporation (PDC) events and explain how the fans use these tools in the PDC darts arenas. Design/methodology/approach: A three-year empirical qualitative study, using a range of ethnographic methods, was conducted to understand how the use of these tools has made the live PDC darts events one of the ultimate sites of performative fandom. Findings: The carnivalesque atmosphere created by fans at live PDC events has made the events a televisual sports spectacle. This article explores some of the specific tools that are used to create a spectacle by the fans.These fan performances, using the tools, have been critical to the increases in the media viewership of professional darts tournaments and live attendances at the PDC events since the mid-2000s. Research Contribution: This article provides new insight into how a sports governing body utilises fans to create atmospheres at a niche sports event. By analysing darts fans' use of the selected tools, this article examines how a sport can be managed and marketed primarily via event atmospheres rather than the sporting action on the stage or field. [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.

In this study, we conduct a comprehensive history matching study for the FluidFlower benchmark model. This benchmark was prepared and organized by the University of Bergen, the University of Stuttgart, and Massachusetts Institute of Technology, for promoting understanding of the complex physics of geological carbon storage (GCS) through in-house experiments and numerical simulations. This paper synthesizes the experiences of history matching the benchmark data encountered by the Delft-DARTS and CSIRO participants. History matching is first performed based on a low-dimensional-zonated structured model using a simple Poisson-like solver. The permeability of six facies and two faults is inferred in this stage to match the digitized concentration data. The history matching is then further enhanced to richer spatial and physical models to capture the spatial variation of permeability and buoyancy effects, using an unstructured grid. Efficient adjoint methods are used to evaluate the gradient used in the optimization of data misfits or equivalent Bayesian log-likelihoods. With efficient optimization methods available for both maximum a posteriori model inference and Randomized Maximum Likelihood methods for model uncertainty, we perform history matching using both binary and continuous concentration observations. The results show that the tracer plumes in the enriched model match the experimental plumes more accurately compared with the results from the parsimonious-zonated model. The history matching results based on the concentration observations provide more similar plume shapes compared with the case based on the binary observations. The permeability difference between the model before and after history matching reveals that the tracer plume zone and the high permeable zone are the regions of high sensitivity in terms of data misfit between the model response and observations. Surprisingly, CO 2 concentration plume forecasts based on these history-matched models were not especially sensitive to the improvements observed in the enhanced model. [ABSTRACT FROM AUTHOR]

Following the split in darts during the 1990s and a resulting Tomlin order (where terms were agreed in a court of law without admission of liability from either side) in 1997, two governing bodies were recognised as the leading darts organisations: the British Darts Organisation (BDO), and the Professional Darts Corporation (PDC – the new name for the World Darts Council, originally formed in 1992). Though both organisations were initially regarded as equals, the PDC rapidly transformed into the leading version of professional darts in the late 2000s. The multi-million-pound circuit of live PDC darts events held across the globe have been widely celebrated in sports media since the early 2010s. However, what has not been explored in scholarly literature are the key catalysts that led to the rapid changes in the PDC which led to the organisation becoming the preeminent version of professional darts during the 2010s. Using a range of semi-structured interviews and archival information with a variety of professional players, media figures and governance linked to the PDC, this article explicates the key catalysts involved in the development of the PDC as an organisation during the 2000s. [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.

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]

The nature of prosumption is one of the most important areas of debate within the study of contemporary consumer culture. David L. Andrews and George Ritzer highlighted how the majority of literature in the 21st century has focused on the sociology of sport as a form of consumer culture, and that there is a distinct lack of research into the exploration of fan atmospheres in regard to physical prosumption at live sports events as opposed to virtual spaces. We seek to address this through utilising findings from a 3-year ethnographic study at a range of live Professional Darts Corporation (PDC) events across the globe. Our findings extend existing knowledge on prosumer fandom, providing valuable insights for researchers on how sports organisations, when promoting their sporting event, utilise the fans as both producers and consumers to help create fervent atmospheres and market their sport to associated media outlets. [ABSTRACT FROM AUTHOR]

Background: Dart sport has become a sport that has recently become widespread among sports branches and its popularity is increasing rapidly day by day. With this popularization, research on darts is increasing. It is stated that athletes can do the training they do at any time of the day with a much better performance at a different time, as in many changing conditions. In addition, at a different time of day, fatigue may be reached later or recovery may occur more quickly. For these reasons, knowing the effect of training at different times of the day is important for both the coach and the athlete. Objectives: The aim of this study was to investigate the effect of different times of the day on throwing performance in darts, an indoor skill in which focus, attention, concentration are at the forefront and environmental factors are less effective than outdoor skill. Methods: In this study, 16 dart athletes (8 boys, 8 girls) with a sport age of 2 - 3 years and an age of 12 - 14 years, who did not have any health problems in Mardin province, participated voluntarily: 2 m and 2.37 m from two different distances at 8.00 and 17.00, 1 day apart. Before the dart throws, the Daily Rhythm Determination Scale for Children was administered to the participants. The center of the dartboard was placed at eye level for each subject. After the throws, the Perceived Difficulty Scale was applied. In the analysis examining the relationship between perceived degree of difficulty, morningness-eveningness and throwing, the subjects' evening throwing scores and evening perceived degree of difficulty scores were examined; a negative and significant relationship was observed in the eveningist group (r = -1, P = 0.01). No significant relationship was observed in the morning group. Results: In the study, a statistically significant difference was found between the subjects' morning and evening throws at 2 m and 2.37 cm. In both morning and evening groups, it was determined that the throwing performance increased in the evening measurements. Conclusions: As a result, this study concluded that there is an effect of time of day in dart sport and that dart throws made in the evening give better results than dart throws made in the morning. Based on these results, it is recommended that a program should be made by the coaches considering the school programs and daily routines to reduce the effect of time of day. It is thought that this study will be important for chronobiological researches to reveal the factors affecting performance as well as the effect of time of day in darts sport. [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) plays an important role in many computer vision tasks. However, the high computational cost of forward and backward propagations during the search, process restricts its practical application. In this paper, we present the search process as a multi-armed bandit problem, where we take into account the uncertainty caused by the contradiction between the huge search space and limited number of trials. Bandit NAS optimizes the trade-off between exploitation and exploration for a highly efficient search. Specifically, we sampled from a set of operations in one trial, where each operation was weighted by its trial performance and a bias to allow operations with less training to be selected. We further reduced the search space by abandoning the operation with the lowest potential, significantly reducing the search cost. Experimental results on the CIFAR-10 dataset show that the resulting architecture achieves the most advanced precision with a search speed approximately two times faster than that of partially connected differentialble architecture search. On ImageNet, we attained the most advanced top-1 accuracy of 75.3% with a search time of 1.8 GPU days. [ABSTRACT FROM AUTHOR]

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.

Three new aaptamines (1–3) together with two known derivatives (4–5) were isolated from the South China Sea sponge Aaptos suberitoides. The structures of all compounds were unambiguously elucidated by spectroscopic analyses as well as the comparison with literature data. All the compounds were evaluated for their cytotoxic activities against five human cancer cell lines including H1299, H520, SCG7901, CNE-2 and SW680 cells. As a result, compounds 3–5 showed moderate cytotoxicities against H1299 and H520 cells with IC50 values ranging from 12.9 to 20.6 μg/mL. Besides, compounds 3–5 also showed potent inhibitory activities toward cyclin-dependent kinase-2 (CDK2) with IC50 values of 14.3, 3.0 and 6.0 μg/mL, respectively. In addition, compounds 3–5 significantly induced G1 arrests of H1299 cells at low concentrations. Drug affinity responsive target stability (DARTS) experiments were carried out and further demonstrated that compound 3 could effectively bind with CDK2 protein and protect it from the degradation by pronase. [ABSTRACT FROM AUTHOR]

This paper evaluates Howe and Leota/Turp's accounts of gamesmanship by examining case studies of gamesmanship from professional darts. While Leota and Turp make some substantial improvements on Howe in reconceptualizing the idea of sporting excellence, I claim that there are points of criticism that must be addressed, notably in their claims that sports do not prescribe necessary skills, and that it is impossible to distinguish between legitimate sporting strategy unaccounted for by the rules on the one hand, and gamesmanship on the other. Leota and Turp criticise Howe's account of rules for being misconceived: rather than rules prescribing necessary skills as Howe claims, rules actually proscribe skills and set limits on behaviour, rather than prescribing skills. I use darts and other sports to make the case that rules actually do both things. Elsewhere, in the phenomenon of 'grouping', I argue that we find a skill completely unaccounted for by the rules of darts and not a necessary skill for playing darts, which nonetheless counts as excellence for darts. This problematizes some claims from Leota and Turp on which their account of gamesmanship hinges. An aspect of Howe, Leota and Turp's accounts on which they all agree is the importance of psychological resilience in sporting endeavours, which I discuss with reference to darts and rule changes in professional sport. The article ends with a discussion of an example from darts of a potential gamesmanship strategy, from a match between Michael van Gerwen and Darius Labanauskas, that unquestionably remains within the rules of the sport and which could not be eradicated through rule changes because it would violate the spirit of the sport. This is an interesting, controversial example for studies of the different forms of gamesmanship and their categorization, and indicates some limitations on what I say in my argument. [ABSTRACT FROM AUTHOR]

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Background& Purpose: The aim of this study was to determine the effect of educational self-talk on the quiet eye period and the accuracy of beginner darts players. Methodology: For this purpose, 30 male students who were beginners in the sport of darts and ranged in age from 19 to 25 were voluntarily selected and randomly divided into two groups of self-talk and control. The study included the pre-test, intervention, and retention stages. In the pre-test stage, participants threw 10 darts and the information of the visual system was recorded using an eye-tracking device. In the intervention phase, participants in both groups threw darts in two sessions (60 darts in each session). At the end of each session, the participants threw 10 darts as a performance. At the intervention stage, the self-talk group was given instructions for throwing darts, while the control group did not receive any instructions. The retention phase was performed 24 hours after the last intervention session and the participants again tried to throw 10 darts while the information of their visual system was recorded simultaneously. Data were analyzed by ANOVA with repeated measures and one-way ANOVA at a significant level of 0.05. Results: The results showed that educational self-talk had a significant effect on increasing the accuracy of dart-throwing and the length of the quiet eye period. Conclusion: According to the results, it can be said that the basic mechanism of attention in selftalk is the quiet eye period, and this educational self-talk can be used as an effective tool in the training of the quiet eye. [ABSTRACT FROM AUTHOR]

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]

Multimodal image fusion is an emergent research area for cancer detection. It provides a wide variety of visual qualities for the accurate medical diagnosis. However, this process requires accurate registration of each image modality for its efficient and effective use. To address the aforementioned issue, a novel synthetic mammogram construction model is proposed. An image enhancement approach is applied to enhance the image quality. Dual-modality structural feature (DMSF) based mapping function is designed to transform a mammogram from a thermal image segment. This paper also proposes modified Differentiable ARchiTecture Search (DARTS) named as (U-DARTS) to detect and classify the breast lesion. In U-DARTS, a stochastic gradient descent optimizer is used. The proposed approach is tested over DMR and INbreast datasets. The results exhibit a significant improvement in the performance of the proposed model over the existing techniques. The validation and testing accuracies of 98% and 91%, respectively, are achieved. Overall, the proposed approach establishes supremacy in so far as the mammogram construction and further lesion detection are concerned. [ABSTRACT FROM AUTHOR]

The interaction between small-molecule compounds of traditional Chinese medicine and their direct targets is the molecular initiation event, which is the key factor for toxicity efficacy. Psoralen, an active component of Fructus Psoraleae, is toxic to the liver and has various pharmacological properties. Although the mechanism of psoralen-induced hepatotoxicity has been studied, the direct target of psoralen remains unclear. Thus, the aim of this study was to discover direct targets of psoralen. To this end, we initially used proteomics based on drug affinity responsive target stability (DARTS) technology to identify the direct targets of psoralen. Next, we used surface plasmon resonance (SPR) analysis and verified the affinity effect of the 'component-target protein'. This method combines molecular docking technology to explore binding sites between small molecules and proteins. SPR and molecular docking confirmed that psoralen and tyrosine-protein kinase ABL1 could be stably combined. Based on the above experimental results, ABL1 is a potential direct target of psoralen-induced hepatotoxicity. Finally, the targets Nrf2 and mTOR, which are closely related to the hepatotoxicity caused by psoralen, were predicted by integrating proteomics and network pharmacology. The direct target ABL1 is located upstream of Nrf2 and mTOR, Nrf2 can influence the expression of mTOR by affecting the level of reactive oxygen species. Immunofluorescence experiments and western blot results showed that psoralen could affect ROS levels and downstream Nrf2 and mTOR protein changes, whereas the ABL1 inhibitor imatinib and ABL1 agonist DPH could enhance or inhibit this effect. In summary, we speculated that when psoralen causes hepatotoxicity, it acts on the direct target ABL1, resulting in a decrease in Nrf2 expression, an increase in ROS levels and a reduction in mTOR expression, which may cause cell death. We developed a new strategy for predicting and validating the direct targets of psoralen. This strategy identified the toxic target, ABL1, and the potential toxic mechanism of psoralen. [Display omitted] • Identification of ABL1, a potentially toxic target protein of psoralen, using a DARTS-based proteomic identification approach. • Integrating proteomics and network pharmacology strategies to identify mechanisms for hepatotoxicity of psoralen. • The mechanism of psoralen-induced hepatotoxicity by inhibiting ABL1 elevated oxidative stress may be similar to imatinib. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak led to the cancellation of sporting events across the world in the spring of 2020 and forced billions of people across the globe to stay home and self-isolate for a number of months. One of the first sports to reposition and adapt their product during the global lockdown in March/April 2020 was professional darts. The Professional Darts Corporation (PDC) took an opportunity to promote their version of darts via an elite online home tournament, the PDC Home Tour, in a vast sports market craving live sporting action. This commentary provides an understanding of how professional darts, a sport that has relied on fan interaction within multi-purpose arenas at live events, attempted to move online to a home-based tournament for a temporary period to fill a sporting void. The commentary discusses the reaction to the tournaments held by the PDC and MODUS and issues encountered, before exploring the reasons for the tournaments being held during the primary global lockdown. As a niche sport, it is hoped that this commentary can provide some insight into the contemporary sport of professional darts, a sport rarely discussed in the scholarly field of sport and leisure. [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]

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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]

We present two novel tests of Wegner's (1994) theory of ironic processes of mental control using a hockey penalty-shooting task (Study 1) and a dart throwing task (Study 2). In Study 1 we aimed to address a significant limitation of ironic effects research in a performance setting by differentiating nonironic performance error from specifically ironic performance error. When instructed not to miss in a specific direction, anxious performers did so a significantly greater number of times; importantly, there was no difference in nonironic error, which provides the first specific support for Wegner's theory in a performance setting. In Study 2, we present the first examination of the precision of ironic errors. When anxious, participants performed not only more ironically but also performed more precisely in the to-be-avoided zone than when they were not anxious. We discuss the results in the context of the importance of specific instructions in coaching environments. [ABSTRACT FROM AUTHOR]