Physical States Research Articles

Exploring the experiences of dancers who have achieved peak performance: on-stage, pre-stage, and post-stage

The aim of this study is to identify and classify the different attributes that contribute to peak performance among professional dancers, and to understand how these attributes change over time. We conducted an analysis using inductive content analysis on open-ended survey data collected from 42 formally trained professional dancers. Additionally, we analyzed interview data from seven professional dancers who demonstrated outstanding achievements in the field among the survey participants. The main themes that emerged were related to various temporal events of peak performance experience: pre-stage, on-stage, and post-stage. During the on-stage, peak performance was perceived by both internal and external factors. During the pre-stage, emphasis was placed on technical, cognitive, and artistic strategies during practice, whereas just before going on the stage, attention shifted to psychological and physical strategies. During the post-stage, dancers reported immediate changes in their psychological and physical states following the peak performance experience, and thereafter, the peak performance experience was noted to influence psychological, technical, and cognitive aspects. These findings provide valuable insights into the key characteristics that emerge throughout a series of peak performance experiences and are consistent with previous research.

Short-Term Preexposure to Novel Enriched Environment Augments Hippocampal Ripples in Urethane-Anesthetized Mice.

Learning and memory are affected by novel enriched environment, a condition where animals play and interact with a variety of toys and conspecifics. Exposure of animals to the novel enriched environments improves memory by altering neural plasticity during natural sleep, a process called memory consolidation. The hippocampus, a pivotal brain region for learning and memory, generates high-frequency oscillations called ripples during sleep, which is required for memory consolidation. Naturally occurring sleep shares characteristics in common with general anesthesia in terms of extracellular oscillations, guaranteeing anesthetized animals suitable to examine neural activity in a sleep-like state. However, it is poorly understood whether the preexposure of animals to the novel enriched environment modulates neural activity in the hippocampus under subsequent anesthesia. To ask this question, we allowed mice to freely explore the novel enriched environment or their standard environment, anesthetized them, and recorded local field potentials in the hippocampal CA1 area. We then compared the characteristics of hippocampal ripples between the two groups and found that the amplitude of ripples and the number of successive ripples were larger in the novel enriched environment group than in the standard environment group, suggesting that the afferent synaptic input from the CA3 area to the CA1 area was higher when the animals underwent the novel enriched environment. These results underscore the importance of prior experience that surpasses subsequent physical states from the neurophysiological point of view.

Coherently excited superresolution using intensity product of phase-controlled quantum erasers via polarization-basis projection measurements

Recently, the delayed-choice quantum eraser has been applied for coherently excited superresolution using phase-controlled projection measurements of laser light to overcome the diffraction limit in classical physics as well as to solve the limited order N of the N00N state in quantum physics. Here, a general scheme of the phase-controlled quantum eraser-based superresolution is proposed for quantum sensing satisfying the Heisenberg limit, and its general solution is derived for an arbitrary Nth-order intensity correlation. Furthermore, phase quantization of the proposed superresolution is discussed to better understand the wave nature of quantum mechanics. Unlike other methods of superresolution in quantum sensing, the proposed method is for the intensity products between phase-controlled quantum erasers and thus is compatible with most conventional sensing metrologies.

Unlocking the full potential of mesenchymal stromal cell therapy for osteoarthritis through machine learning-based in silico trials

Despite the potential of mesenchymal stromal cells (MSCs) in osteoarthritis (OA) treatment, the challenge lies in addressing their therapeutic inconsistency. Clinical trials revealed significantly varied therapeutic outcomes among patients receiving the same allogenic MSCs but different treatment regimens. Therefore, optimizing personalized treatment strategies is crucial to fully unlock MSCs’ potential and enhance therapeutic consistency. We employed the XGBoost algorithm to train a self-collected database comprising 37 published clinical reports to create a model capable of predicting the probability of effective pain relief and Western Ontario and McMaster Universities (WOMAC) index improvement in OA patients undergoing MSC therapy. Leveraging this model, extensive in silico simulations were conducted to identify optimal personalized treatment strategies and ideal patient profiles. Our in silico trials predicted that the individually optimized MSC treatment strategies would substantially increase patients’ chances of recovery compared to the strategies used in reported clinical trials, thereby potentially benefiting 78.1%, 47.8%, 94.4% and 36.4% of the patients with ineffective short-term pain relief, short-term WOMAC index improvement, long-term pain relief and long-term WOMAC index improvement, respectively. We further recommended guidelines on MSC number, concentration, and the patients’ appropriate physical (body mass index, age, etc.) and disease states (Kellgren-Lawrence grade, etc.) for OA treatment. Additionally, we revealed the superior efficacy of MSC in providing short-term pain relief compared to platelet-rich plasma therapy for most OA patients. This study represents the pioneering effort to enhance the efficacy and consistency of MSC therapy through machine learning applied to clinical data. The in silico trial approach holds immense potential for diverse clinical applications.

Calmodulin‐Based Dynamic Protein Hydrogels with Three Distinct Mechanical Stiffness

AbstractStimuli‐responsive hydrogels that leverage protein conformational changes are of significant interest in the design of dynamic materials applicable in a myriad of fields, such as drug delivery, actuators, biosensors, and microfluidics. The small calcium binding protein calmodulin (CaM), which undergoes three‐stage conformational changes upon successive binding with Ca2+ and specific ligands, offers a mechanism to create dynamic hydrogels with three distinct physical states. In this work, a CaM‐based recombinant protein hydrogel is engineered using [Ru(bpy)3]2+‐mediated photo‐crosslinking. This hydrogel displays the ability to reversibly increase its Young's modulus by 1.5‐fold and ∼7‐fold, respectively, upon binding with Ca2+ and subsequent interaction with trifluoperazine. The magnitude of stiffness changes is tunable by adjusting the length proportion of dynamic and static domains and modifying protein content. This tunable and reversible control over hydrogel mechanics is further utilized to engineer shape‐morphing materials, highlighting the versatile potential of this CaM‐based protein hydrogel for diverse applications.

Exploring BMI recognition through resting state, free imagination, and visual stimulation-induced EEG

In recent years, the development of Brain-Computer Interface (BCI) technology has attracted significant attention, particularly in the acquisition and application of electroencephalogram (EEG) signals. Most pertinent studies have utilized EEG to monitor neural activity states. Given that EEG does not provide direct insights into the physical states of body structures, many researchers have refrained from employing EEG to assess structural body states such as Body Mass Index (BMI). This study, for the first time, employs portable EEG detection devices combined with machine learning algorithms such as Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) to process EEG features, enabling the application of EEG in identifying BMI. We collected EEG data from three different states: resting state, free imagination, and viewing pictures. The results demonstrate that these EEG signals can effectively differentiate subjects with higher BMI. Particularly under visual stimulation, EEG features exhibited optimal performance when subjects viewed images of high-calorie foods, with the model achieving an average accuracy of 80% and an F1 score of 75%. From the perspective of frequency bands, within a single frequency band, the β band performed notably well in classification. However, the model constructed by integrating features from various frequency bands yielded the best classification performance. These comprehensive paradigms are expected to fill the current research gap in the application of EEG and provide new methodological perspectives for further studies.

The Emergence of Consciousness in a Physical Universe

Consciousness appears so mysterious and hard to formulate within physical sciences because the present day scientific thinking excludes an element of reality and a general mechanics of its processing from its consideration. The primary missing element is the reality of information in the physical universe as an intrinsic causal correlate of observable physical states. Moreover, there exists a general formalism of information processing that is universally applicable to the processing resulting from each physical interaction. As shown, the formalism further enables a general mechanism to construct arbitrary structured and abstract semantics or object description in modular hierarchy as well as a powerful mechanism of population coding to represent arbitrary precision and variation in object description resolving the combinatorial problem. Here, a semantic value, or simply semantics, is equivalent (\(\equiv\)) to the content of information of causal correlation, and treated as a value to enable its formal processing. The primary motive here is to lay down a formal account of information (semantic) processing that leads to bridging the conceptual gap between the objectively observable elements in nature and the subjective consciousness. It is shown that the qualities we associate with consciousness are causally correlated semantics of relation that a represented agency holds with other objects within a dynamically evolving semantic structure, where the state of the population of physical systems (neurons) correlating with the structure holds causal powers to effect appropriate behavior. Since the information (semantic value) arises from natural causal dependence, the correlation based consciousness forms an undeniable reality of existence. It is derived here how a semantic value equivalent to ‘a self as an observer of objects and controller of actions’ is constructed. If the semantic components of a conscious experience, such as the self, the objects of experience, and the relation of experience attributing the self as the owner or experiencer, causally correlate with a system’s state having causal influence in action, then it suffices to bridge the gap between the objective reality and the subjective consciousness. That is, the semantic value corresponding to the thoughts and senses is the reality of nature the semantics of self relates to as the owner. Moreover, the semantics of ‘self as an observer and controller of action’ is itself shown to form a part of observed objects giving rise to self awareness.

Good husbandry of the cow vet

As a new graduate, we often think that honing our technical skills is the number one priority. While being a technically-skilled veterinarian is incredibly important for serving our clients and animals, we also need to develop the skills to take care of ourselves. We can assess our mental and physical states as cattle veterinarians through the lens of the 5 Freedoms and 5 Domains of Animal Welfare. We will discuss how goal setting is a habit of highly successful people and how we often overlook the value of intention setting. As cattle veterinarians, we can combat imposter syndrome through setting boundaries, recog­nizing burnout and investing in relationships. Cattle veterinar­ians, like our bovine patients, are herd animals too.

Momentum space oscillation properties of vortex states collision

A qualitative calculation and discussion of two-vortex-state collisions are given in the scalar ϕ4 model. Three kinds of vortex states—Bessel, general monochromatic, and Laguerre-Gaussian—are considered. It is found that the total final-momentum distribution in the collision of physical vortex states displays general topological structures, which depend on the initial vortex states’ topological charges, which are proportional to the orbital angular momenta. This peculiar matching provides a novel observable, the topological number of momentum distribution, and it may represent a new fascinating research direction in particle physics. We also find that the situation when the angular momenta of the two colliding Laguerre-Gaussian states combine to zero can be recognized by the total final-momentum distribution close to the collision axis. Both features can be used to measure the orbital angular momentum of vortex states. Published by the American Physical Society 2024

The synergistic and dynamic relationship between learning design and learning analytics

The synergistic relationship between learning design and learning analytics has the potential for improving learning and teaching in near real-time. The potential for integrating the newly available and dynamic information from ongoing analysis into learning design requires new perspectives on learning and teaching data processing and analysis as well as advanced theories, methods, and tools for supporting dynamic learning design processes. Three perspectives of learning analytics design provide summative, real-time, and predictive insights. In a case study with 3,550 users, the navigation sequence and network graph analysis demonstrate the potential of learning analytics design. The study aims to demonstrate how the analysis of navigation patterns and network graph analysis could inform the learning design of self-guided digital learning experiences. Even with open-ended freedom, only 608 sequences were evidenced by learners out of a potential number of hundreds of millions of sequences. Advancements of learning analytics design have the potential for mapping the cognitive, social and even physical states of the learner and optimise their learning environment on the fly.

A scoping review of the use of bioacoustics to assess various components of farm animal welfare

Recording and analyzing animal sounds, including vocal and non-vocal cues, is a relevant tool to access and monitor different aspects of animal physiology, behaviour and general welfare in a remote, non-invasive and continuous fashion. This field, known as bioacoustics, is considerably growing, notably due to the development of machine learning techniques. Yet, applications of these methods in animal husbandry remain limited, and their potential may not be fully acknowledged. This review aims to present the potential of bioacoustic tools to monitor various aspects of the welfare of farm animals, and focuses on studies published since 2010. The first section of the review introduces the concept of bioacoustics, describes the different parameters that can be extracted from the sounds of animals, and explains how they can be analysed. The potential of these measures to inform about the physical characteristics, individuality, health, physiology, behaviour, and affective states of different species of farm animals are then presented, through a contemporary overview of the research field. Although this analysis identifies a variety of applications of bioacoustics tools in relation to the assessment and monitoring of farm animal welfare, it also points out the current scarcity of methods implemented in practice and available to farm management. Barriers to the development of bioacoustics measures in conventional farming are also discussed, and future directions for this field of research are suggested.

Quantum physics: A better model to understand consciousness-related brain functions

With the development of quantum mechanics, it is applied to different fields, including biology. As intricate as human brains, quantum physics is replacing classical physics in explaining consciousness-related brain functions. The bilayer phospholipid membrane enables neurons in the brain to store and protect quantum information, and the abundance of 1/2-spin phosphorous creates potential for quantum entanglement that allows information to transfer along long distances and process consciousness. Scientists have used Schrdinger's cat thought experiment to explain how the uncertain and superimposed states in quantum physics can be applied to our decision-making behavior with conditions of "Yes" or "No." Scientists also conducted experiments to witness the quantum entanglement of particles in the brain. The observation of the phenomenon broke the pre-assumption that quantum entanglement is too fragile to occur in the chaotic environment in human brains, and it allows the possibility of ongoing conscious processing there. To further understand the decision-making mechanism, physicists should also integrate the knowledge in neuroscience, psychology, sociology, and other interdisciplinary subjects.

Biological activity of probiotic microorganisms depending on cultivation conditions

Metabiotics, a new generation of probiotics based on biologically active components (metabolites), are currently the subject of interest and study in the scientific community. This is a more advanced and promising form of probiotic preparations, among the properties of which high bioavailability, safety, and 'versatility in use are especially noted. The article presents a study of the antagonistic activity and biofilm formation of opportunistic strains of microorganisms under the influence of metabolites of probiotic microorganisms grown on 'various nutrient media and in different physical states. The objectives posed during the study answer questions about the biological activity of probiotic microorganisms grown on different nutrient media and in various physical states. The data obtained will form the basis for further research of active components that allow obtaining a clinical effect and will also help formulate an approach to developing the most effective ready-made forms of metabiotics. The place of research is the microbiological laboratory of the Testing Center of the Testing Laboratory Complex of the Federal State Budgetary Educational Institution of Higher Education Novosibirsk State Agrarian University.

Fermented Gracilaria lemaneiformis polysaccharides alleviate food allergy by regulating Treg cells and gut microbiota

Food allergy has a significant impact on the health and well-being of individuals, affecting both their physical and mental states. Research on natural bioactive compounds, such as polysaccharides extracted from seaweeds, holds great promise in the treatment of food allergies. In this study, fermented Gracilaria lemaneiformis polysaccharides (F-GLSP) were prepared using probiotic fermentation. Probiotic fermentation of Gracilaria lemaneiformis reduces the particle size of polysaccharides. To compare the anti-allergic activity of F-GLSP with unfermented Gracilaria lemaneiformis polysaccharides (UF-GLSP), an OVA-induced mouse food allergy model was established. F-GLSP exhibited a significant reduction in OVA-specific IgE and mMCP levels in allergic mice. Moreover, it significantly inhibited Th2 differentiation and IL-4 production and significantly promoted Treg differentiation and IL-10 production in allergic mice. In contrast, UF-GLSP only reduced OVA-specific IgE and mMCP in the serum of allergic mice. Furthermore, F-GLSP demonstrated a more pronounced regulation of intestinal flora abundance compared to UF-GLSP, significantly influencing the populations of Firmicutes, Bacteroidetes, Lactobacillus, and Clostridiales in the intestines of mice with food allergy. These findings suggest that F-GLSP may regulate food allergies in mice through multiple pathways. In summary, this study has promoted further development of functional foods with anti-allergic properties based on red algae polysaccharides.

QED corrections to the thermal neutrino interaction rate

Motivated by precision computations of neutrino decoupling at MeV temperatures, we show how QED corrections to the thermal neutrino interaction rate can be related to the electron-positron spectral function as well as an effective \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\overline{\ u }\ u \\gamma $$\\end{document} vertex. The spectral function is needed both in a timelike and in a spacelike domain, and for both of its physical polarization states (transverse and longitudinal with respect to spatial momentum). Incorporating an NLO evaluation of this spectral function, an estimate of the \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\overline{\ u }\ u \\gamma $$\\end{document} vertex, and HTL resummation of scatterings mediated by soft Bose-enhanced t-channel photons, we compute the interaction rate as a function of the neutrino momentum and flavour. Effects on the – (0 . 2)% level are found, noticeably smaller than a previous estimate of a related quantity.

Matter relative to quantum hypersurfaces

We explore the canonical description of a scalar field in 1+1 dimensional Minkowski space as a parametrized field theory on an extended phase space that includes additional embedding fields that characterize spacetime hypersurfaces X relative to which the scalar field is described. This theory is quantized via the Dirac prescription and physical states of the theory are used to define conditional wave functionals |ψϕ[X]⟩ interpreted as the state of the field relative to the hypersurface X, thereby extending the Page-Wootters formalism to quantum field theory. It is shown that this conditional wave functional satisfies the Tomonaga-Schwinger equation, thus demonstrating the formal equivalence between this extended Page-Wootters formalism and standard quantum field theory. We also construct relational Dirac observables and define a quantum deparametrization of the physical Hilbert space leading to a relational Heisenberg picture, which are both shown to be unitarily equivalent to the Page-Wootters formalism. Moreover, by treating hypersurfaces as quantum reference frames, we extend recently developed quantum frame transformations to changes between classical and nonclassical hypersurfaces. This allows us to exhibit the transformation properties of a quantum field under a larger class of transformations, which leads to a frame-dependent particle creation effect. Published by the American Physical Society 2024

Intelligent explainable optical sensing on Internet of nanorobots for disease detection

Abstract Combining deep learning (DL) with nanotechnology holds promise for transforming key facets of nanoscience and technology. This synergy could pave the way for groundbreaking advancements in the creation of novel materials, devices, and applications, unlocking unparalleled capabilities. In addition, monitoring psychological, emotional, and physical states is challenging, yet recent advancements in the Internet of Nano Things (IoNT), nano robot technology, and DL show promise in collecting and processing such data within home environments. Using DL techniques at the edge enables the processing of Internet of Things device data locally, preserving privacy and low latency. We present an edge IoNT system that integrates nanorobots and DL to identify diseases, generating actionable reports for medical decision-making. Explainable artificial intelligence enhances model transparency, aiding clinicians in understanding predictions. Intensive experiments have been carried out on Kvasir dataset to validate the applicability of the designed framework, where the accuracy of results demonstrated its potential for in-home healthcare management.

Diminished representational momentum for physical states in patients with depressive disorder

The representational momentum for physical state changes refers to the fact that we remember objects as more changed in physical state than they actually were. It has been well documented that depressive disorder is associated with impairment of time perception. Thus, the present work was conducted with the aim to investigate the representational momentum for physical state changes in patients with depressive disorder, firstly. Forty patients with depressive disorder and forty-two healthy controls were administrated with the task of representational momentum for physical state changes and task of time perception. The stimulus comprises 10 videos showing object state changes (e.g., ice melting). During playing, the video would stop and immediately be obscured by mosaics at a specific point. Participants select from three detection images (the exact frame of the video just be masked, the frame after that, and the frame before that) to test representational momentum and estimate the duration of the video in seconds to test time perception. The depressive group showed diminished representational momentum (i.e., lower scores on the task of representational momentum) than normal controls (U = 215.00, Z = −5.83, p < 0.001). In addition, the scores on the task of representational momentum were significantly positively related with the scores on the task of time perception among depressive group (r = 0.446, p = 0.004). The findings indicate that patients with depressive disorder may exhibit diminished representational momentum for physical states, which may be correlated with their impairment of time perception.

Recovery of DNA from acetaminophen exploring physical state and sampling methods

Research into the recovery of DNA from illicit drug samples has shown it is possible to get forensically useful profiles from such substrates. However, it is not yet known if the different physical states that drugs can be found in influences the quantity and quality of DNA that can be recovered or what is the best sampling method to adopt for powdered samples. This research used acetaminophen in four different states – large crystalline, powder, in solution, or residue – to determine the efficacy of current DNA technology in recovery and analysis of the resulting sample. Five replicates of each were prepared. Human blood was deposited on or mixed with the drug and left for 1 hour. The surface of the drug was sampled by wet/dry swabbing (where appropriate), or the entire sample was deposited in a tube, and the DNA then extracted using DNA-IQ™. The amount of DNA recovered (ng), degradation index, number of PCR cycles (Ct) required for the IPC to reach threshold, number of alleles in the DNA profile and average peak height (APH) were assessed. All samples, irrespective of the physical state they were collected from, returned full DNA profiles that corresponded to the DNA profile of the blood donor, with no degradation or inhibition detected. It was also found the wet/dry swabbing method returned higher levels of DNA than inclusion of the entire sample into the tube for powdered acetaminophen and the appropriate method to use will be dependent on casework circumstances. The findings of this research further develops our understanding of the recovery of DNA from drugs, and supports the need for further investigation to understand under what conditions DNA can be recovered from illicit substances.

Hamiltonian Computational Chemistry: Geometrical Structures in Chemical Dynamics and Kinetics.

The common geometrical (symplectic) structures of classical mechanics, quantum mechanics, and classical thermodynamics are unveiled with three pictures. These cardinal theories, mainly at the non-relativistic approximation, are the cornerstones for studying chemical dynamics and chemical kinetics. Working in extended phase spaces, we show that the physical states of integrable dynamical systems are depicted by Lagrangian submanifolds embedded in phase space. Observable quantities are calculated by properly transforming the extended phase space onto a reduced space, and trajectories are integrated by solving Hamilton's equations of motion. After defining a Riemannian metric, we can also estimate the length between two states. Local constants of motion are investigated by integrating Jacobi fields and solving the variational linear equations. Diagonalizing the symplectic fundamental matrix, eigenvalues equal to one reveal the number of constants of motion. For conservative systems, geometrical quantum mechanics has proved that solving the Schrödinger equation in extended Hilbert space, which incorporates the quantum phase, is equivalent to solving Hamilton's equations in the projective Hilbert space. In classical thermodynamics, we take entropy and energy as canonical variables to construct the extended phase space and to represent the Lagrangian submanifold. Hamilton's and variational equations are written and solved in the same fashion as in classical mechanics. Solvers based on high-order finite differences for numerically solving Hamilton's, variational, and Schrödinger equations are described. Employing the Hénon-Heiles two-dimensional nonlinear model, representative results for time-dependent, quantum, and dissipative macroscopic systems are shown to illustrate concepts and methods. High-order finite-difference algorithms, despite their accuracy in low-dimensional systems, require substantial computer resources when they are applied to systems with many degrees of freedom, such as polyatomic molecules. We discuss recent research progress in employing Hamiltonian neural networks for solving Hamilton's equations. It turns out that Hamiltonian geometry, shared with all physical theories, yields the necessary and sufficient conditions for the mutual assistance of humans and machines in deep-learning processes.