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The practice of a musical instrument requires fine dexterity, repetitive, fast, and precise movements, as well as important efforts to set the instrument into vibration, while adopting postures often unnatural for the human body. As a result, musicians are often subject to pain and musculoskeletal disorders. In the case of plucked string instruments and especially the concert harp, the plucking force is directly related to the strings' tension. Consequently, the choice of the strings has to be made based on both, the musician feel while playing, and the musculoskeletal consequences. This paper investigates how the string properties and the playing dynamics affect the finger and wrist muscle activity during harp playing. This study first emphasized the noteworthy recruitment of the flexor and extensor muscles (42% and 29% of MVC, respectively). Findings outlined further that the fingering choice, the adopted playing dynamics and the string's material govern the muscular activity level and the playing control. Such results are a first step to better understand how the harp ergonomics may affect the player's integrity and help them decide the most suitable stringing for their practice. [ABSTRACT FROM AUTHOR]

We introduce DeepNash, an autonomous agent that plays the imperfect information game Stratego at a human expert level. Stratego is one of the few iconic board games that artificial intelligence (AI) has not yet mastered. It is a game characterized by a twin challenge: It requires long-term strategic thinking as in chess, but it also requires dealing with imperfect information as in poker. The technique underpinning DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego through self-play from scratch. DeepNash beat existing state-of-the-art AI methods in Stratego and achieved a year-to-date (2022) and all-time top-three ranking on the Gravon games platform, competing with human expert players.

Stable ovarian function is a key factor in the performance of the reproductive system. In contrast, some ovarian function-related diseases, such as polycystic ovarian syndrome, premature ovarian failure (POF), and ovarian cancer, are the main cause of infertility and death of women around the world. Despite multiple attempts, there are no effective tools against these conditions; however, mesenchymal stem cell-based therapy, especially using adipose tissue, has attracted much attention in medicine in light of its advantages such as easy isolation and accessibility. Conversely, it has been suggested that MSC-conditioned medium (CM) can restore injured tissues and has high immunocompatibility. So, here, we will summarize the effects of administration of MSCs and CM derived from adipose tissue on ovarian functions and related diseases.

Learning to combine control at the level of joint torques with longer-term goal-directed behavior is a long-standing challenge for physically embodied artificial agents. Intelligent behavior in the physical world unfolds across multiple spatial and temporal scales: Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals that are defined on much longer time scales and that often involve complex interactions with the environment and other agents. Recent research has demonstrated the potential of learning-based approaches applied to the respective problems of complex movement, long-term planning, and multiagent coordination. However, their integration traditionally required the design and optimization of independent subsystems and remains challenging. In this work, we tackled the integration of motor control and long-horizon decision-making in the context of simulated humanoid football, which requires agile motor control and multiagent coordination. We optimized teams of agents to play simulated football via reinforcement learning, constraining the solution space to that of plausible movements learned using human motion capture data. They were trained to maximize several environment rewards and to imitate pretrained football-specific skills if doing so led to improved performance. The result is a team of coordinated humanoid football players that exhibit complex behavior at different scales, quantified by a range of analysis and statistics, including those used in real-world sport analytics. Our work constitutes a complete demonstration of learned integrated decision-making at multiple scales in a multiagent setting.

A cadmium(II) complex containing dppt ligand with the formula [CdCl 2 (dppt) 2 ], where dppt is 5,6-diphenyl-3-(2-pyridyl)-1,2,4-triazine was synthesized, elucidated and submitted to in vitro cytotoxicity studies against human breast (MCF-7), glioblastoma (U-87), and lung (A549) cancer cell lines as well as mouse embryo normal cell line (NIH/3T3), in comparison with cisplatin employing MTT assay over 24 and 48 h. The complex exhibited the highest cytotoxic effect against MCF-7 cells among the other three cell lines with IC 50 values of 8.7 ± 0.5 (24 h) and 1.2 ± 0.7 µM (48 h). Significantly, flow cytometric assessment of the complex-treated MCF-7 and U-87 cells demonstrated a dose-dependent induced apoptotic cell death. The cellular morphological changes were in concord with cytotoxicity and flow cytometric results. The results of comet assay showed that the complex is able to induce DNA damage in MCF-7 cells. These observations are of importance, as sustained damage to cellular DNA could lead to apoptotic cell death. The results of DNA-binding studies indicated that the complex fits into the DNA minor groove and interacts with DNA via a partial intercalation. Moreover, the complex was able to efficiently cleave pUC19 DNA through a hydrolytic mechanism. The binding affinity between the complex and apoptosis-relevant protein targets including APAF1, Bax, Bcl-2, Cas3, Cas7, and Cas9 was evaluated through molecular docking studies. In silico virtual studies revealed the complex's strong affinity towards apoptosis-related proteins; therefore the complex can act as a potential apoptosis inducer. Physicochemical, pharmacokinetics, lipophilicity, drug-likeness, and medicinal chemistry properties of the complex were also predicted through in silico absorption, distribution, metabolism and excretion studies., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

In multiagent worlds, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment. These interactions, combined with the potential stochasticity of the agents' dynamic behaviors, make such systems complex and interesting to study from a decision-making perspective. Significant research has been conducted on learning models for forward-direction estimation of agent behaviors, for example, pedestrian predictions used for collision-avoidance in self-driving cars. In many settings, only sporadic observations of agents may be available in a given trajectory sequence. In football, subsets of players may come in and out of view of broadcast video footage, while unobserved players continue to interact off-screen. In this paper, we study the problem of multiagent time-series imputation in the context of human football play, where available past and future observations of subsets of agents are used to estimate missing observations for other agents. Our approach, called the Graph Imputer, uses past and future information in combination with graph networks and variational autoencoders to enable learning of a distribution of imputed trajectories. We demonstrate our approach on multiagent settings involving players that are partially-observable, using the Graph Imputer to predict the behaviors of off-screen players. To quantitatively evaluate the approach, we conduct experiments on football matches with ground truth trajectory data, using a camera module to simulate the off-screen player state estimation setting. We subsequently use our approach for downstream football analytics under partial observability using the well-established framework of pitch control, which traditionally relies on fully observed data. We illustrate that our method outperforms several state-of-the-art approaches, including those hand-crafted for football, across all considered metrics., (© 2022. The Author(s).)

Background: The short stability window of several hours from blood collection to measuring basophil activation has limited the use of flow cytometry-based basophil activation assays in clinical settings. We examine if it is possible to extend this window to 1 day allowing for shipment of samples between laboratories. Several options exist for reporting the results including reporting all the measured values directly, calculating ratios and reporting a single value covering all measured results. Each of these options have different stability and value to the physician., Methods: Whole blood samples from peanut allergic patients were stimulated with four different peanut concentrations at Day 0, Day 1, and Day 2. Samples were stored under temperature-controlled conditions. Flow cytometry was used to analyze the samples. The basophil activation and degranulation were measured as percentage of positive CD63 basophils and CD203c MFI fold change. Shipped samples were transported under ambient conditions., Results: The results show that CD63 is a stable marker at Day 1. The CD203c ratio decreases significantly at Day 1. Calculating the CD63/IgE ratio proves to be more stable than CD63 alone. The most stable readouts are the semi-quantitative results and the trajectory of the dose response curve. Finally, we confirmed that the stability can be extended to samples shipped overnight to the laboratory., Conclusions: It is possible to extend the stability of the basophil activation assay to 1 day for samples stored at 18-25°C as well as samples shipped under ambient conditions as long as the temperature is within the 2-37°C range., (© 2021 The Authors. Cytometry Part B: Clinical Cytometry published by Wiley Periodicals LLC on behalf of International Clinical Cytometry Society.)

Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence. Traditionally, researchers have focused on using well-known games to build strong agents. This progress, however, can be better informed by characterizing games and their topological landscape. Tackling this latter question can facilitate understanding of agents and help determine what game an agent should target next as part of its training. Here, we show how network measures applied to response graphs of large-scale games enable the creation of a landscape of games, quantifying relationships between games of varying sizes and characteristics. We illustrate our findings in domains ranging from canonical games to complex empirical games capturing the performance of trained agents pitted against one another. Our results culminate in a demonstration leveraging this information to generate new and interesting games, including mixtures of empirical games synthesized from real world games.

We introduce α-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs). The approach leverages continuous-time and discrete-time evolutionary dynamical systems applied to empirical games, and scales tractably in the number of agents, in the type of interactions (beyond dyadic), and the type of empirical games (symmetric and asymmetric). Current models are fundamentally limited in one or more of these dimensions, and are not guaranteed to converge to the desired game-theoretic solution concept (typically the Nash equilibrium). α-Rank automatically provides a ranking over the set of agents under evaluation and provides insights into their strengths, weaknesses, and long-term dynamics in terms of basins of attraction and sink components. This is a direct consequence of the correspondence we establish to the dynamical MCC solution concept when the underlying evolutionary model's ranking-intensity parameter, α, is chosen to be large, which exactly forms the basis of α-Rank. In contrast to the Nash equilibrium, which is a static solution concept based solely on fixed points, MCCs are a dynamical solution concept based on the Markov chain formalism, Conley's Fundamental Theorem of Dynamical Systems, and the core ingredients of dynamical systems: fixed points, recurrent sets, periodic orbits, and limit cycles. Our α-Rank method runs in polynomial time with respect to the total number of pure strategy profiles, whereas computing a Nash equilibrium for a general-sum game is known to be intractable. We introduce mathematical proofs that not only provide an overarching and unifying perspective of existing continuous- and discrete-time evolutionary evaluation models, but also reveal the formal underpinnings of the α-Rank methodology. We illustrate the method in canonical games and empirically validate it in several domains, including AlphaGo, AlphaZero, MuJoCo Soccer, and Poker.

Diazinon, a commonly used organophosphorus pesticide, has been widely used throughout the world in agriculture and horticulture to control insects that feed on crops, ornamentals, lawns, fruits, vegetables and other food products. The toxicity of the DZN causes adverse effects on many organs. The purpose of this study was to examine the protective effect of vitamins A, E and C on liver enzymes alanine transaminase (ALT), Aspartate aminotransferase (AST) and Lactate Dehydrogenase (LDH) in rats exposed to diazinon. In this study, male wistar rats were randomly divided into 10 different groups. The groups were administered normal saline, soybean oil (as the solvent for diazinon and fat-soluble vitamins), diazinon, (30 mg kg(-1), vitamins E, C and A (100, 500 mg kg(-1) and 400 IU kg(-1), respectively) and a combination of diazinon with the same dose of each vitamin intraperitoneally i.p.daily for 14 days. Seven days after the final injection, the animals were anesthetized and blood samples were taken. The photometric method was used to measure the activity of the enzymes. The activities of ALT and AST in the diazinon group were significantly higher than that observed in the control group; however, the diazinon/vitamin E, A, C group displayed significant reduction in ALT and AST activities compared to the diazinon group. The lowest level of LDH enzyme activity was observed in the dazinon/vitamin C group and this was statistically lower than the diazinon group. The results of this study revealed that vitamin E, A and C have a potent protective effect against diazinon-induced hepatotoxicity in rats, which may be due to the scavenging of free radicals and increased antioxidant status.