Inverse problems in statistical physics are motivated by the challenges of ‘big data’ in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we focus on the inverse Ising problem and closely related problems, namely how to infer the coupling strengths between spins given observed spin correlations, magnetizations, or other data. We review applications of the inverse Ising problem, including the reconstruction of neural connections, protein structure determination, and the inference of gene regulatory networks. For the inverse Ising problem in equilibrium, a number of controlled and uncontrolled approximate solutions have been developed in the statistical mechanics community. A particularly strong method, pseudolikelihood, stems from statistics. We also review the inverse Ising problem in the non-equilibrium case, where the model parameters must be reconstructed based on non-equilibrium statistics.
Background: Postoperative pain is a significant yet inadequately managed complication following surgery, and auriculotherapy to alleviate acute postoperative pain (APP) and reduce the use of opioids remains controversial. Methods: We searched the MEDLINE, Web of Science, Embase, Cochrane Library, CINAHL Complete, and ClinicalTrials.gov from inception to January 23, 2024 for all randomized controlled trials (RCTs) of auriculotherapy in the treatment of APP. The extracted data underwent risk of bias assessment, meta-analysis, subgroup analyses, sensitivity analysis, meta-regression analysis, and evidence rating. Results: A total of 24 studies involving 2131 patients were included in the meta-analysis. Low-quality evidence indicated that auriculotherapy was effective in reducing pain intensity at 24 [MD(95 %CI)=-0.64(-1.09, −0.19), I2=77 %, P＜0.01], 48 [MD(95 %CI)=-0.49(-0.97, 0.00), I2=71 %, P=0.05], and 72 [MD(95 %CI)=-0.80(-1.32, −0.28), I2=52 %, P＜0.01] hours after surgery, while moderate-quality evidence showed a decrease in total opioid consumption [MD(95 %CI)=-24.41 OME (-38.28, −10.54), I2=95 %, P＜0.01]. However, no significant effects were observed in reducing postoperative nausea or vomiting [RR(95 %CI)=0.61(0.32, 1.16), I2=71 %, P=0.13; RR(95 %CI)=0.32(0.09, 1.18), I2=71 %, P=0.09; RR (95 %CI)=0.34(0.11, 1.06), I2=28 %, P=0.06; for postoperative nausea and vomiting (PONV), postoperative nausea or postoperative vomiting respectively], with evidence ranging from moderate to very low. Additionally, two RCTs found that auriculotherapy could delay the time to the first request for analgesia. Conclusions: The summary estimates indicate that auriculotherapy may be beneficial in reducing APP and opioid consumption in specific surgeries based on low-to-moderate quality evidence. However, high-quality RCTs are still further studied in different surgical populations. Systematic Review Registration: PROSPERO database, CRD42024506989.
Background. Yoga, an ancient practice rooted in Indian culture, has gained global recognition for its physical and mental health benefits. Among its practices, Surya Namaskar (SN) stands out as a holistic yogic Sun Salutation exercise combining postures, breathing, and mindfulness, offering physical vitality, mental calmness, and a practical solution to the challenges posed by modern sedentary lifestyles.
Objectives. The objective of the present systematic review was to analyze the effect of SN on overall health and wellness of healthy adults.
Materials and methods. A comprehensive search was conducted in five major databases, namely Scopus, PubMed, PubMed Central, Web of Science, and ScienceDirect, using the terms such as “Surya Namaskar”, “Sun Salutation”,“Surya Namaskar and physical fitness”, “Surya Namaskar for adults”, “Sun Salutation for overall health and wellness”,and “Surya Namaskar and sedentary lifestyle”. The articles published in English between 2011 and 2024 were considered in the current review. The systematic search and reporting adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Quality Assessment Tool for Quantitative Studies was used to analyze the methodological quality of the included articles.
Results. Based on the inclusion and exclusion criteria, 117 articles were initially retrieved, out of which 11 were finally included, encompassing data from 445 healthy adults from three countries, aged between 18 and 65 years. The duration of the SN intervention varied from four to 24 weeks, with session frequency ranging from three days per week to daily, and a diverse number of cycles. The methodological quality analysis revealed that two articles were of strong, six of moderate, and the remaining three of weak quality.
Conclusions. This systematic review concludes that the practice of the yogic Sun Salutation exercise (SN) is beneficial for improving and maintaining physical fitness, physiological health, and psychological well-being, which determine the overall health and wellness of healthy adults.
Bruce G. Elmegreen, Daniela Calzetti, Angela Adamo
et al.
Power spectra (PS) of high-resolution images of M51 (NGC 5194) taken with the Hubble Space Telescope and the James Webb Space Telescope (JWST) have been examined for evidence of disk thickness in the form of a change in slope between large scales, which map two-dimensional correlated structures, and small scales, which map three-dimensional correlated structures. Such a slope change is observed here in H α , and possibly Pa α , using average PS of azimuthal intensity scans that avoid bright peaks. The physical scale of the slope change occurs at ∼120 pc and ∼170 pc for these two transitions, respectively. A radial dependence in the shape of the H α PS also suggests that the length scale drops from ∼180 pc at 5 kpc, to ∼90 pc at 2 kpc, to ∼25 pc in the central ∼kpc. We interpret these lengths as comparable to the thicknesses of the star-forming disk traced by H ii regions. The corresponding emission measure is ∼100 times larger than what is expected from the diffuse ionized gas. The PS of JWST Mid-IR Instrument images in eight passbands have more gradual changes in slope, making it difficult to determine a specific value of the thickness for this emission.
As data is increasingly acknowledged as a highly valuable asset, much effort has been put into investigating inter-organisational data sharing, aiming at utilising the value of formerly unused data. Moreover, most researchers agree, that trust between actors is key for successful data sharing activities. However, existing research oftentimes focus on trust from a data provider perspective. Therefore, our work highlights the unbalanced view of trust, addressing it from a data consumer perspective. More specifically, our aim is to investigate trust enhancing measures on a data level, that is data trustworthiness. We found, that existing data trustworthiness enhancing solutions do not meet the requirements of the domain of inter-organisational data sharing. Therefore, our study addresses this gap. Conducting a rigorous design science research approach, this work proposes a new Levels of Assurance for Data Trustworthiness artifact. Built on existing artifacts, we demonstrate, how it addresses the identified challenges within the domain appropriately. We found that our novel approach requires more work to be suitable for adoption. Still, we are confident that our solution can increase consumer trust. We conclude by contributing to the body of design knowledge and emphasise the need for more attention to be put into consumer trust.
Keichi Takahashi, Tomonori Hayami, Yu Mukaizono
et al.
mdx II is an Infrastructure-as-a-Service (IaaS) cloud platform designed to accelerate data science research and foster cross-disciplinary collaborations among universities and research institutions in Japan. Unlike traditional high-performance computing systems, mdx II leverages OpenStack to provide customizable and isolated computing environments consisting of virtual machines, virtual networks, and advanced storage. This paper presents a comprehensive performance evaluation of mdx II, including a comparison to Amazon Web Services (AWS). We evaluated the performance of a 16-vCPU VM from multiple aspects including floating-point computing performance, memory throughput, network throughput, file system and object storage performance, and real-world application performance. Compared to an AWS 16-vCPU instance, the results indicated that mdx II outperforms AWS in many aspects and demonstrated that mdx II holds significant promise for high-performance data analytics (HPDA) workloads. We also evaluated the virtualization overhead using a 224-vCPU VM occupying an entire host. The results suggested that the virtualization overhead is minimal for compute-intensive benchmarks, while memory-intensive benchmarks experienced larger overheads. These findings are expected to help users of mdx II to obtain high performance for their data science workloads and offer insights to the designers of future data-centric cloud platforms.
The increase in emerging and reemerging infectious diseases has underscored the need for the prompt monitoring of intact infectious viruses and the quick assessment of their infectivity. However, molecular techniques cannot distinguish between intact infectious and noninfectious viruses. Here, two distinct methodologies have been developed for the expeditious and dependable quantification of intact infectious H1N1 virus, and several experiments have been conducted to substantiate their efficacy. One is an integrated cell absorption quantitative polymerase chain reaction (qPCR) method (ICA-qPCR), and the other is a combined propidium monoazide qPCR method (PMA-qPCR). The quantification limit is 100 cell culture infective dose 50 % (CCID50)/mL in ICA-qPCR following a 1.5-hour cell absorption or 126 CCID50/mL after a 15-minute incubation. For PMA-qPCR, the limit was 2,512 CCID50/mL. The number of genome copies quantified by the ICA-qPCR and PMA-qPCR methods was strongly correlated with the infectious titer determined by the CCID50 assay, thereby enabling the estimation of virus infectivity. The ICA-qPCR and PMA-qPCR methods are both suitable for the identification and quantification of intact infectious H1N1 virus in inactivated samples, wastewater, and biological materials. In conclusion, the ICA-qPCR and PMA-qPCR methods have distinct advantages and disadvantages, and can be used to quantify intact infectious viruses rapidly. These methodologies can facilitate the identification of the presence of intact infectious viruses in wastewater or on pathogen-related physical surfaces in high-level biosafety laboratories and medical facilities. Furthermore, these methodologies can also be utilized to detect other highly pathogenic pathogens.
Infectious and parasitic diseases, Public aspects of medicine
Issues of limited scene adaptability, inadequate evidence preservation, and low efficiency in traditional digital forensics were addressed by analyzing the feasibility of incorporating decentralized, tamper-resistant blockchain technology into digital forensic practices. Initially, a phased forensic process was proposed based on a hierarchical architecture for blockchain forensic technology, examining the advancements of blockchain at each stage of evidence acquisition, preservation, and presentation. Subsequently, limitations in existing research were analyzed, and a digital forensic framework incorporating comprehensive blockchain involvement was designed by utilizing the distributed advantages of blockchain. This framework integrated evidence information into the on-chain data structure and introduced a complementary graph analysis algorithm to standardize evidence collection across various scenarios. An off-chain distributed database was employed to achieve scalable, efficient storage, while smart contract templates enhance the reusability of contracts for similar forensic transactions. Lastly, potential future directions for the application of blockchain technology in forensic science were explored.
Ruka Setoguchi, Tomoya Sengiku, Hiroki Kono
et al.
Abstract The mechanisms by which the number of memory CD8 T cells is stably maintained remains incompletely understood. It has been postulated that maintaining them requires help from CD4 T cells, because adoptively transferred memory CD8 T cells persist poorly in MHC class II (MHCII)-deficient mice. Here we show that chronic interferon-γ signals, not CD4 T cell-deficiency, are responsible for their attrition in MHCII-deficient environments. Excess IFN-γ is produced primarily by endogenous colonic CD8 T cells in MHCII-deficient mice. IFN-γ neutralization restores the number of memory CD8 T cells in MHCII-deficient mice, whereas repeated IFN-γ administration or transduction of a gain-of-function STAT1 mutant reduces their number in wild-type mice. CD127high memory cells proliferate actively in response to IFN-γ signals, but are more susceptible to attrition than CD127low terminally differentiated effector memory cells. Furthermore, single-cell RNA-sequencing of memory CD8 T cells reveals proliferating cells that resemble short-lived, terminal effector cells and documents global downregulation of gene signatures of long-lived memory cells in MHCII-deficient environments. We propose that chronic IFN-γ signals deplete memory CD8 T cells by compromising their long-term survival and by diverting self-renewing CD127high cells toward terminal differentiation.
Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice. This invited article has undergone anonymous review and is intended as a book chapter for the volume "Frontiers of Statistics and Data Science" edited by Subhashis Ghoshal and Anindya Roy for the International Indian Statistical Association Series on Statistics and Data Science, published by Springer. It covers the material from a short course titled "Artificial Intelligence in Precision and Digital Health" taught by the author Bibhas Chakraborty at the IISA 2022 Conference, December 26-30 2022, at the Indian Institute of Science, Bengaluru.
Aishworya Shrestha, Katarina Hoernke, Thomas Timberlake
et al.
Background Young people will suffer most from climate change yet are rarely engaged in dialogue about it. Citizen science offers a method for collecting policy-relevant data, whilst promoting awareness and capacity building. We tested the feasibility and acceptability of engaging Nepalese adolescents in climate change and health-related citizen science. Methods We purposively selected 33 adolescents from two secondary schools in one remote and one relatively accessible district of Nepal. We contextualised existing apps and developed bespoke apps to survey climate hazards, waste and water management, local biodiversity, nutrition and sociodemographic information. We analysed and presented quantitative data using a descriptive analysis. We captured perceptions and learnings via focus group discussions and analysed qualitative data using thematic analysis. We shared findings with data collectors using tables, graphs, data dashboards and maps. Results Adolescents collected 1667 biodiversity observations, identified 72 climate-change related hazards, and mapped 644 geolocations. They recorded 286 weights, 248 heights and 340 dietary recalls. Adolescents enjoyed learning how to collect the data and interpret the findings and gained an appreciation of local biodiversity which engendered ‘environmental stewardship’. Data highlighted the prevalence of failing crops and landslides, revealed both under- and over-nutrition and demonstrated that children consume more junk foods than adults. Adolescents learnt about the impacts of climate change and the importance of eating a diverse diet of locally grown foods. A lack of a pre-established sampling frame, multiple records of the same observation and spurious nutrition data entries by unsupervised adolescents limited data quality and utility. Lack of internet access severely impacted feasibility, especially of apps which provide online feedback. Conclusions Citizen science was largely acceptable, educational and empowering for adolescents, although not always feasible without internet access. Future projects could improve data quality and integrate youth leadership training to enable climate-change advocacy with local leaders.
Data science is not a science. It is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery that is not otherwise possible and can be beyond human reasoning. It is changing our world practically and profoundly already widely deployed in tens of thousands of applications in every discipline in an AI Arms Race that, due to its inscrutability, can lead to unfathomed risks. This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving, by exploring and evaluating its remarkable, definitive features. As data science is in its infancy, this initial, speculative axiology is intended to aid in understanding and defining data science to recognize its potential benefits, risks, and open research challenges. AI based data science is inherently about uncertainty that may be more realistic than our preference for the certainty of science. Data science will have impacts far beyond knowledge discovery and will take us into new ways of understanding the world.
Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\cite{goodfellow2014generative}] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods require high computation resources. In this work, we design a fast, fair, flexible and private data generation method. We show the effectiveness of our method theoretically and empirically. We show that models trained on data generated by the proposed method can perform well (in inference stage) on real application scenarios.
Ruddlesden-Popper and reduced Ruddlesden-Popper nickelates are intriguing candidates for mimicking the properties of high-temperature superconducting cuprates. The degree of similarity between these nickelates and cuprates has been the subject of considerable debate. Resonant inelastic x-ray scattering (RIXS) has played an important role in exploring their electronic and magnetic excitations, but these efforts have been stymied by inconsistencies between different samples and the lack of publicly available data for detailed comparison. To address this issue, we present open RIXS data on La4Ni3O10 and La4Ni3O8.
This research proposes a model to predict the location of the most deprived areas in a city using data from the census. Census data is very high-dimensional and needs to be simplified. We use the diffusion map algorithm to reduce dimensionality and find patterns. Features are defined by eigenvectors of the Laplacian matrix that defines the diffusion map. The eigenvectors corresponding to the smallest eigenvalues indicate specific characteristics of the population. Previous work has found qualitatively that the second most important dimension for describing the census data in Bristol, UK is linked to deprivation. In this research, we analyse how good this dimension is as a model for predicting deprivation by comparing it with the recognised measures. The Pearson correlation coefficient was found to be greater than 0.7. The top 10 per cent of deprived areas in the UK, which are also located in Bristol, are extracted to test the accuracy of the model. There are 52 of the most deprived areas, and 38 areas are correctly identified by comparing them to the model. The influence of scores of IMD domains that do not correlate with the models and Eigenvector 2 entries of non-deprived Output Areas cause the model to fail the prediction of 14 deprived areas. The model demonstrates strong performance in predicting future deprivation in the project areas, which is expected to assist in government resource allocation and funding greatly. The codes can be accessed here: https://github.com/junegoo94/diffusion_maps
Kamber Schwarz, Joan Najita, Jennifer Bergner
et al.
The Orbiting Astronomical Satellite for Investigating Stellar Systems (OASIS) is a NASA Astrophysics MIDEX-class mission concept, with the stated goal of following water from galaxies, through protostellar systems, to Earth's oceans. This paper details the protoplanetary disk science achievable with OASIS. OASIS's suite of heterodyne receivers allow for simultaneous, high spectral resolution observations of water emission lines spanning a large range of physical conditions within protoplanetary disks. These observations will allow us to map the spatial distribution of water vapor in disks across evolutionary stages and assess the importance of water, particularly the location of the midplane water snowline, to planet formation. OASIS will also detect the H2 isotopologue HD in 100+ disks, allowing for the most accurate determination of total protoplanetary disk gas mass to date. When combined with the contemporaneous water observations, the HD detection will also allow us to trace the evolution of water vapor across evolutionary stages. These observations will enable OASIS to characterize the time development of the water distribution and the role water plays in the process of planetary system formation.