Tag: nevin manimala

Proc Natl Acad Sci U S A. 2023 Aug;120(31):e2302930120. doi: 10.1073/pnas.2302930120. Epub 2023 Jul 25.

ABSTRACT

This paper is concerned with the problem of reconstructing an unknown rank-one matrix with prior structural information from noisy observations. While computing the Bayes optimal estimator is intractable in general due to the requirement of computing high-dimensional integrations/summations, Approximate Message Passing (AMP) emerges as an efficient first-order method to approximate the Bayes optimal estimator. However, the theoretical underpinnings of AMP remain largely unavailable when it starts from random initialization, a scheme of critical practical utility. Focusing on a prototypical model called [Formula: see text] synchronization, we characterize the finite-sample dynamics of AMP from random initialization, uncovering its rapid global convergence. Our theory-which is nonasymptotic in nature-in this model unveils the non-necessity of a careful initialization for the success of AMP.

PMID:37490538 | DOI:10.1073/pnas.2302930120

Proc Natl Acad Sci U S A. 2023 Aug;120(31):e2212660120. doi: 10.1073/pnas.2212660120. Epub 2023 Jul 25.

ABSTRACT

Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters and the unobserved hidden variables in generative statistical models. The algorithm-inspired by variational methods used in computational physics-is iterative and can get easily stuck in local minima, even when classical techniques, such as deterministic annealing (DA), are used. We study a VB inference algorithm based on a nontraditional quantum annealing approach-referred to as quantum annealing variational Bayes (QAVB) inference-and show that there is indeed a quantum advantage to QAVB over its classical counterparts. In particular, we show that such better performance is rooted in key quantum mechanics concepts: i) The ground state of the Hamiltonian of a quantum system-defined from the given data-corresponds to an optimal solution for the minimization problem of the variational free energy at very low temperatures; ii) such a ground state can be achieved by a technique paralleling the quantum annealing process; and iii) starting from this ground state, the optimal solution to the VB problem can be achieved by increasing the heat bath temperature to unity, and thereby avoiding local minima introduced by spontaneous symmetry breaking observed in classical physics based VB algorithms. We also show that the update equations of QAVB can be potentially implemented using ⌈logK⌉ qubits and 𝒪(K) operations per step, where K is the number of values hidden categorical variables can take. Thus, QAVB can match the time complexity of existing VB algorithms, while delivering higher performance.

PMID:37490536 | DOI:10.1073/pnas.2212660120

J Chem Theory Comput. 2023 Jul 25. doi: 10.1021/acs.jctc.3c00335. Online ahead of print.

ABSTRACT

Current quantum computing hardware is restricted by the availability of only few, noisy qubits which limits the investigation of larger, more complex molecules in quantum chemistry calculations on quantum computers in the near term. In this work, we investigate the limits of their classical and near-classical treatment while staying within the framework of quantum circuits and the variational quantum eigensolver. To this end, we consider naive and physically motivated, classically efficient product ansatz for the parametrized wavefunction adapting the separable-pair ansatz form. We combine it with post-treatment to account for interactions between subsystems originating from this ansatz. The classical treatment is given by another quantum circuit that has support between the enforced subsystems and is folded into the Hamiltonian. To avoid an exponential increase in the number of Hamiltonian terms, the entangling operations are constructed from purely Clifford or near-Clifford circuits. While Clifford circuits can be simulated efficiently classically, they are not universal. In order to account for missing expressibility, near-Clifford circuits with only few, selected non-Clifford gates are employed. The exact circuit structure to achieve this objective is molecule-dependent and is constructed using simulated annealing and genetic algorithms. We demonstrate our approach on a set of molecules of interest and investigate the extent of our methodology’s reach.

PMID:37490516 | DOI:10.1021/acs.jctc.3c00335

PLoS One. 2023 Jul 25;18(7):e0289166. doi: 10.1371/journal.pone.0289166. eCollection 2023.

ABSTRACT

Technology-based small and medium enterprises (SMEs) are the driving force behind China’s economic and technological development. However, these enterprises often face challenges in financing their research and development (R&D) activities due to limited financing opportunities. Previous research has primarily focused on the resource attributes of government innovation subsidies, which serve as a crucial funding source for these SMEs. This paper aims to explore the impact of government innovation subsidies on firms from a novel perspective, considering the signaling characteristics of these subsidies. The theoretical foundation of this study lies in the asymmetric information theory and the signaling mechanism through which government subsidies send signals about enterprises. The study uses enterprise data from 2012 to 2019 to investigate the effect of government subsidies on the R&D investment of enterprises listed on the SMEs Board in Chinese stock market. The results reveal a significantly positive effect of government subsidies on the R&D investment of SME Board-listed enterprises and verify the mediating role of financing constraints in this effect. The extent to which government subsidies influence the R&D investment of SME Board-listed enterprises is associated with the enterprises’ ownership characteristics, debt ratios, and times interest earned ratios. This study contributes to the literature on the SMEs Board market and may provide the Chinese government insights into developing industry policies that maximize the effectiveness of government subsidies.

PMID:37490503 | DOI:10.1371/journal.pone.0289166

J Med Internet Res. 2023 Jul 25;25:e45658. doi: 10.2196/45658.

ABSTRACT

BACKGROUND: Subtle impairments in instrumental activities of daily living (IADLs) can be a key predictor of disease progression and are considered central to functional independence. Mild cognitive impairment (MCI) is a syndrome associated with significant changes in cognitive function and mild impairment in complex functional abilities. The early detection of functional decline through the identification of IADL impairments can aid early intervention strategies. Digital health technology is an objective method of capturing IADL-related behaviors. However, it is unclear how these IADL-related behaviors have been digitally assessed in the literature and what differences can be observed between MCI and normal aging.

OBJECTIVE: This review aimed to identify the digital methods and metrics used to assess IADL-related behaviors in people with MCI and report any statistically significant differences in digital endpoints between MCI and normal aging and how these digital endpoints change over time.

METHODS: A total of 16,099 articles were identified from 8 databases (CINAHL, Embase, MEDLINE, ProQuest, PsycINFO, PubMed, Web of Science, and Scopus), out of which 15 were included in this review. The included studies must have used continuous remote digital measures to assess IADL-related behaviors in adults characterized as having MCI by clinical diagnosis or assessment. This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS: Ambient technology was the most commonly used digital method to assess IADL-related behaviors in the included studies (14/15, 93%), with passive infrared motion sensors (5/15, 33%) and contact sensors (5/15, 33%) being the most prevalent types of methods. Digital technologies were used to assess IADL-related behaviors across 5 domains: activities outside of the home, everyday technology use, household and personal management, medication management, and orientation. Other recognized domains-culturally specific tasks and socialization and communication-were not assessed. Of the 79 metrics recorded among 11 types of technologies, 65 (82%) were used only once. There were inconsistent findings around differences in digital IADL endpoints across the cognitive spectrum, with limited longitudinal assessment of how they changed over time.

CONCLUSIONS: Despite the broad range of metrics and methods used to digitally assess IADL-related behaviors in people with MCI, several IADLs relevant to functional decline were not studied. Measuring multiple IADL-related digital endpoints could offer more value than the measurement of discrete IADL outcomes alone to observe functional decline. Key recommendations include the development of suitable core metrics relevant to IADL-related behaviors that are based on clinically meaningful outcomes to aid the standardization and further validation of digital technologies against existing IADL measures. Increased longitudinal monitoring is necessary to capture changes in digital IADL endpoints over time in people with MCI.

TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42022326861; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=326861.

PMID:37490331 | DOI:10.2196/45658

JMIR Res Protoc. 2023 Jul 25;12:e36121. doi: 10.2196/36121.

ABSTRACT

BACKGROUND: According to the World Bank, Malaysia reported an estimated 97 tuberculosis cases per 100,000 people in 2021. Chest x-ray (CXR) remains the best conventional method for the early detection of pulmonary tuberculosis (PTB) infection. The intervention of artificial intelligence (AI) in PTB diagnosis could efficiently aid human interpreters and reduce health professionals’ work burden. To date, no AI studies have been evaluated in Malaysia.

OBJECTIVE: This study aims to evaluate the performance of Putralytica and Qure.ai software for CXR screening and PTB diagnosis among the Malaysian population.

METHODS: We will conduct a retrospective case-control study at the Respiratory Medicine Institute, National Cancer Institute, and Sungai Buloh Health Clinic. A total of 1500 CXR images of patients who completed treatments or check-ups will be selected and categorized into three groups: (1) abnormal PTB cases, (2) abnormal non-PTB cases, and (3) normal cases. These CXR images, along with their clinical findings, will be the reference standard in this study. All patient data, including sociodemographic characteristics and clinical history, will be collected prior to screening via Putralytica and Qure.ai software and readers’ interpretation, which are the index tests for this study. Interpretation from all 3 index tests will be compared with the reference standard, and significant statistical analysis will be computed.

RESULTS: Data collection is expected to commence in August 2023. It is anticipated that 1 year will be needed to conduct the study.

CONCLUSIONS: This study will measure the accuracy of Putralytica and Qure.ai software and whether their findings will concur with readers’ interpretation and the reference standard, thus providing evidence toward the effectiveness of implementing AI in the medical setting.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/36121.

PMID:37490330 | DOI:10.2196/36121

JMIR Hum Factors. 2023 Jul 25;10:e42993. doi: 10.2196/42993.

ABSTRACT

BACKGROUND: Highly personalized care is substantially improved by technology platforms that assess and track patient outcomes. However, evidence regarding how to successfully implement technology in real-world mental health settings is limited.

OBJECTIVE: This study aimed to naturalistically monitor how a health information technology (HIT) platform was used within 2 real-world mental health service settings to gain practical insights into how HIT can be implemented and sustained to improve mental health service delivery.

METHODS: An HIT (The Innowell Platform) was naturally implemented in 2 youth mental health services in Sydney, Australia. Web-based surveys (n=19) and implementation logs were used to investigate staff attitudes toward technology before and after implementation. Descriptive statistics were used to track staff attitudes over time, whereas qualitative thematic analysis was used to explore implementation log data to gain practical insights into useful implementation strategies in real-world settings.

RESULTS: After the implementation, the staff were nearly 3 times more likely to agree that the HIT would improve care for their clients (3/12, 25% agreed before the implementation compared with 7/10, 70% after the implementation). Despite this, there was also an increase in the number of staff who disagreed that the HIT would improve care (from 1/12, 8% to 2/10, 20%). There was also decreased uncertainty (from 6/12, 50% to 3/10, 30%) about the willingness of the service to implement the technology for its intended purpose, with similar increases in the number of staff who agreed and disagreed with this statement. Staff were more likely to be uncertain about whether colleagues in my service are receptive to changes in clinical processes (not sure rose from 5/12, 42% to 7/10, 70%). They were also more likely to report that their service already provides the best mental health care (agreement rose from 7/12, 58% to 8/10, 80%). After the implementation, a greater proportion of participants reported that the HIT enabled shared or collaborative decision-making with young people (2/10, 20%, compared with 1/12, 8%), enabled clients to proactively work on their mental health care through digital technologies (3/10, 30%, compared with 2/12, 16%), and improved their response to suicidal risk (4/10, 40% compared with 3/12, 25%).

CONCLUSIONS: This study raises important questions about why clinicians, who have the same training and support in using technology, develop more polarized opinions on its usefulness after implementation. It seems that the uptake of HIT is heavily influenced by a clinician’s underlying beliefs and attitudes toward clinical practice in general as well as the role of technology, rather than their knowledge or the ease of use of the HIT in question.

PMID:37490321 | DOI:10.2196/42993

J Med Internet Res. 2023 Jul 25;25:e46026. doi: 10.2196/46026.

ABSTRACT

BACKGROUND: Some of the most vexing issues with the COVID-19 pandemic were the inability of facilities and events, such as schools and work areas, to track symptoms to mitigate the spread of the disease. To combat these challenges, many turned to the implementation of technology. Technology solutions to mitigate repercussions of the COVID-19 pandemic include tools that provide guidelines and interfaces to influence behavior, reduce exposure to the disease, and enable policy-driven avenues to return to a sense of normalcy. This paper presents the implementation and early evaluation of a return-to-work COVID-19 symptom and risk assessment tool. The system was implemented across 34 institutions of health and education in Alabama, including more than 174,000 users with over 4 million total uses and more than 86,000 reports of exposure risk between July 2020 and April 2021.

OBJECTIVE: This study aimed to explore the usage of technology, specifically a COVID-19 symptom and risk assessment tool, to mitigate exposure to COVID-19 within public spaces. More specifically, the objective was to assess the relationship between user-reported symptoms and exposure via a mobile health app, with confirmed COVID-19 cases reported by the Alabama Department of Public Health (ADPH).

METHODS: This cross-sectional study evaluated the relationship between confirmed COVID-19 cases and user-reported COVID-19 symptoms and exposure reported through the Healthcheck web-based mobile application. A dependent variable for confirmed COVID-19 cases in Alabama was obtained from ADPH. Independent variables (ie, health symptoms and exposure) were collected through Healthcheck survey data and included measures assessing COVID-19-related risk levels and symptoms. Multiple linear regression was used to examine the relationship between ADPH-confirmed diagnosis of COVID-19 and self-reported health symptoms and exposure via Healthcheck that were analyzed across the state population but not connected at the individual patient level.

RESULTS: Regression analysis showed that the self-reported information collected by Healthcheck significantly affects the number of COVID-19-confirmed cases. The results demonstrate that the average number of confirmed COVID-19 cases increased by 5 (high risk: β=5.10; P=.001), decreased by 24 (sore throat: β=-24.03; P=.001), and increased by 21 (nausea or vomiting: β=21.67; P=.02) per day for every additional self-report of symptoms by Healthcheck survey respondents. Congestion or runny nose was the most frequently reported symptom. Sore throat, low risk, high risk, nausea, or vomiting were all statistically significant factors.

CONCLUSIONS: The use of technology allowed organizations to remotely track a population as it is related to COVID-19. Healthcheck was a platform that aided in symptom tracking, risk assessment, and evaluation of status for admitting individuals into public spaces for people in the Alabama area. The confirmed relationship between symptom and exposure self-reporting using an app and population-wide confirmed cases suggests that further investigation is needed to determine the opportunity for such apps to mitigate disease spread at a community and individual level.

PMID:37490320 | DOI:10.2196/46026

JMIR Hum Factors. 2023 Jul 25;10:e46044. doi: 10.2196/46044.

ABSTRACT

BACKGROUND: Patient portals can facilitate patient engagement in care management. Driven by national efforts over the past decade, patient portals are being implemented by hospitals and clinics nationwide. Continuous evaluation of patient portals and reflection of feedback from end users across care settings are needed to make patient portals more user-centered after the implementation.

OBJECTIVE: The aim of this study was to investigate the lived experience of using a patient portal in adult patients recruited from a variety of care settings, focusing on their perceived benefits and difficulties of using the patient portal, and trust and concerns about privacy and security.

METHODS: This qualitative descriptive study was part of a cross-sectional digital survey research to examine the comprehensive experience of using a patient portal in adult patients recruited from 20 care settings from hospitals and clinics of a large integrated health care system in the mid-Atlantic area of the United States. Those who had used a patient portal offered by the health care system in the past 12 months were eligible to participate in the survey. Data collected from 734 patients were subjected to descriptive statistics and content analysis.

RESULTS: The majority of the participants were female and non-Hispanic White with a mean age of 53.1 (SD 15.34) years. Content analysis of 1589 qualitative comments identified 22 themes across 4 topics: beneficial aspects (6 themes) and difficulties (7 themes) in using the patient portal; trust (5 themes) and concerns (4 themes) about privacy and security of the patient portal. Most of the participants perceived the patient portal functions as beneficial for communicating with health care teams and monitoring health status and care activities. At the same time, about a quarter of them shared difficulties they experienced while using those functions, including not getting eMessage responses timely and difficulty finding information in the portal. Protected log-in process and trust in health care providers were the most mentioned reasons for trusting privacy and security of the patient portal. The most mentioned reason for concerns about privacy and security was the risk of data breaches such as hacking attacks and identity theft.

CONCLUSIONS: This study provides an empirical understanding of the lived experience of using a patient portal in adult patient users across care settings with a focus on the beneficial aspects and difficulties in using the patient portal, and trust and concerns about privacy and security. Our study findings can serve as a valuable reference for health care institutions and software companies to implement more user-centered, secure, and private patient portals. Future studies may consider targeting other patient portal programs and patients with infrequent or nonuse of patient portals.

PMID:37490316 | DOI:10.2196/46044

JAMA Netw Open. 2023 Jul 3;6(7):e2325658. doi: 10.1001/jamanetworkopen.2023.25658.

ABSTRACT

IMPORTANCE: Plant-based diets are known to improve cardiometabolic risk in the general population, but their effects on people at high risk of cardiovascular diseases (CVDs) remain inconclusive.

OBJECTIVE: To assess the association of vegetarian diets with major cardiometabolic risk factors, including low-density lipoprotein cholesterol (LDL-C), hemoglobin A1c (HbA1c), systolic blood pressure (SBP), and body weight in people with or at high risk of CVDs.

DATA SOURCES: This meta-analysis was registered before the study was conducted. Systematic searches performed included Embase, MEDLINE, CINAHL, and CENTRAL from inception until July 31, 2021.

STUDY SELECTION: Eligible randomized clinical trials (RCTs) that delivered vegetarian diets in adults with or at high risk of CVDs and measured LDL-C, HbA1c or SBP were included. Of the 7871 records screened, 29 (0.4%; 20 studies) met inclusion criteria.

DATA EXTRACTION AND SYNTHESIS: Two reviewers independently extracted data including demographics, study design, sample size, and diet description, and performed risk of bias assessment. A random-effects model was used to assess mean changes in LDL-C, HbA1c, SBP, and body weight. The overall certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool.

MAIN OUTCOMES AND MEASURES: Mean differences between groups in changes (preintervention vs postintervention) of LDL-C, HbA1c, and SBP; secondary outcomes were changes in body weight and energy intake.

RESULTS: Twenty RCTs involving 1878 participants (range of mean age, 28-64 years) were included, and mean duration of intervention was 25.4 weeks (range, 2 to 24 months). Four studies targeted people with CVDs, 7 focused on diabetes, and 9 included people with at least 2 CVD risk factors. Overall, relative to all comparison diets, meta-analyses showed that consuming vegetarian diets for an average of 6 months was associated with decreased LDL-C, HbA1c, and body weight by 6.6 mg/dL (95% CI, -10.1 to -3.1), 0.24% (95% CI, -0.40 to -0.07), and 3.4 kg (95% CI, -4.9 to -2.0), respectively, but the association with SBP was not significant (-0.1 mm Hg; 95% CI, -2.8 to 2.6). The GRADE assessment showed a moderate level of evidence for LDL-C and HbA1c reduction.

CONCLUSIONS AND RELEVANCE: In this study, consuming a vegetarian diet was associated with significant improvements in LDL-C, HbA1c and body weight beyond standard therapy in individuals at high risk of CVDs. Additional high-quality trials are warranted to further elucidate the effects of healthy plant-based diets in people with CVDs.

PMID:37490288 | DOI:10.1001/jamanetworkopen.2023.25658