Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Social Media to Predict Food Deserts in the United States: Infodemiology Study of Tweets

Background The issue of food insecurity is becoming increasingly important to public health practitioners because of the adverse health outcomes and underlying racial disparities associated with insufficient access to healthy foods. Prior research has used data sources such as surveys, geographic information systems, and food store assessments to identify regions classified as food deserts but perhaps the individuals in these regions unknowingly provide their own accounts of food consumption and food insecurity through social media. Social media data have proved useful in answering questions related to public health; therefore, these data are a rich source for identifying food deserts in the United States. Objective The aim of this study was to develop, from geotagged Twitter data, a predictive model for the identification of food deserts in the United States using the linguistic constructs found in food-related tweets. Methods Twitter’s streaming application programming interface was used to collect a random 1% sample of public geolocated tweets across 25 major cities from March 2020 to December 2020. A total of 60,174 geolocated food-related tweets were collected across the 25 cities. Each geolocated tweet was mapped to its respective census tract using point-to-polygon mapping, which allowed us to develop census tract–level features derived from the linguistic constructs found in food-related tweets, such as tweet sentiment and average nutritional value of foods mentioned in the tweets. These features were then used to examine the associations between food desert status and the food ingestion language and sentiment of tweets in a census tract and to determine whether food-related tweets can be used to infer census tract–level food desert status. Results We found associations between a census tract being classified as a food desert and an increase in the number of tweets in a census tract that mentioned unhealthy foods (P=.03), including foods high in cholesterol (P=.02) or low in key nutrients such as potassium (P=.01). We also found an association between a census tract being classified as a food desert and an increase in the proportion of tweets that mentioned healthy foods (P=.03) and fast-food restaurants (P=.01) with positive sentiment. In addition, we found that including food ingestion language derived from tweets in classification models that predict food desert status improves model performance compared with baseline models that only include socioeconomic characteristics. Conclusions Social media data have been increasingly used to answer questions related to health and well-being. Using Twitter data, we found that food-related tweets can be used to develop models for predicting census tract food desert status with high accuracy and improve over baseline models. Food ingestion language found in tweets, such as census tract–level measures of food sentiment and healthiness, are associated with census tract–level food desert status.
The past, present, and future of coral reef growth in the Florida Keys

Coral‐reef degradation is driving global‐scale reductions in reef‐building capacity and the ecological, geological, and socioeconomic functions it supports. The persistence of those essential functions will depend on whether coral‐reef management is able to rebalance the competing processes of reef accretion and erosion. Here, we reconstructed census‐based carbonate budgets of 46 reefs throughout the Florida Keys from 1996 to 2019. We evaluated the environmental and ecological drivers of changing budget states and compared historical trends in reef‐accretion potential to millennial‐scale baselines of accretion from reef cores and future projections with coral restoration. We found that historically, most reefs had positive carbonate budgets, and many had reef‐accretion potential comparable to the ~3 mm year−1 average accretion rate during the peak of regional reef building ~7000 years ago; however, declines in reef‐building Acropora palmata and Orbicella spp. corals following a series of thermal stress events and coral disease outbreaks resulted in a shift from positive to negative budgets for most reefs in the region. By 2019, only ~15% of reefs had positive net carbonate production. Most of those reefs were in inshore, Lower Keys patch‐reef habitats with low water clarity, supporting the hypothesis that environments with naturally low irradiance may provide a refugia from thermal stress. We caution that our estimated carbonate budgets are likely overly optimistic; comparison of reef‐accretion potential to measured accretion from reef cores suggests that, by not accounting for the role of nonbiological physical and chemical erosion, census‐based carbonate budgets may underestimate total erosion by ~1 mm year−1 (−1.15 kg CaCO3 m−2 year−1). Although the present state of Florida's reefs is dire, we demonstrate that the restoration of reef‐building corals has the potential to help mitigate declines in reef accretion in some locations, which could allow some key ecosystem functions to be maintained until the threat of global climate change is addressed.
Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

Abstract The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up‐to‐date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone‐beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings—with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state‐of‐the‐art methods utilized in radiation oncology.
Binary Synergistic Combinations of Lavender and Fennel Essential Oils with Amoxicillin

Abstract Microbial resistance is an important problem in modern healthcare systems. In addition to drug resistance, the side effects of current antibiotic applications are also known issues. In this present study, binary combinations of amoxicillin with European Pharmacopoeia quality lavender ( Lavandula angustifolia ) and fennel ( Foeniculum vulgare ) essential oils were evaluated against human pathogenic microbial strains. The checkerboard method was used to quantify the efficacy of the essential oils in combination with amoxicillin. As an initial result, remarkable in vitro antimicrobial activity was observed at relatively low amoxicillin concentrations using different oil combinations against Staphylococcus aureus ATCC 6538, Enterococcus faecalis ATCC 29212, Bacillus cereus ATCC 14579, Escherichia coli NRRL B-3008, Salmonella typhi (clinical isolate), respectively. Fractional inhibitory concentrations were calculated and interpreted in terms of addition, synergy, antagonism, or indifferent. A synergistic interaction with the combination F. vulgare essential oil and amoxicillin (fractional inhibitory concentration index = 8.05 × 10 −4 ) was observed against the pathogens E. faecalis and Escherichia coli . Both essential oils together and in combination with amoxicillin showed a synergistic effect with possible future applications.
Goodness‐of‐fit tests in proportional hazards models with random effects

This paper deals with testing the functional form of the covariate effects in a Cox proportional hazards model with random effects. We assume that the responses are clustered and incomplete due to right censoring. The estimation of the model under the null (parametric covariate effect) and the alternative (nonparametric effect) is performed using the full marginal likelihood. Under the alternative, the nonparametric covariate effects are estimated using orthogonal expansions. The test statistic is the likelihood ratio statistic, and its distribution is approximated using a bootstrap method. The performance of the proposed testing procedure is studied through simulations. The method is also applied on two real data sets one from biomedical research and one from veterinary medicine.
The European Kidney Health Alliance (EKHA) and the Decade of the KidneyTM.

The European Kidney Health Alliance (EKHA) is an advocacy organization that defends the case of the kidney patients and the nephrological community at the level of the European Union (EU), and from there, top to bottom, also at the national level of the EU member states and the EU-associated countries. The Decade of the KidneyTM is a global initiative launched by the American Association of Kidney Patients (AAKP) to create greater awareness and organize patient demands for long overdue innovation in kidney care. This article describes the medical and patient burden of kidney disease, the history of EKHA, its major activities and tools for policy action and the need for innovation. We then describe the Decade of the KidneyTM initiative, the rationale why EKHA joined this activity to emanate parallel action at the European side, the novel professionalized structure of EKHA, and its immediate targets. The final aim is to align all major stakeholders for an action plan on kidney disease comparable to Europe's successful Beating Cancer Plan, with the additional intent that the EKHA model is applied also by the respective national kidney-related societies to create a broad mobilization at all levels. The ultimate aims are that the EU considers chronic kidney disease (CKD) as a major health and health-economic problem, to consequently have CKD included as a key health research target by the European Commission, and to improve quality of life and outcomes for all kidney patients.
The role of lifestyle and nutrition in psoriasis: Current status of knowledge and interventions

Extrinsic environmental factors, including patient lifestyle (alcohol intake, smoking, stress, sleep disturbances, and sedentary habit), diet and single nutrients intake may affect psoriasis clinical presentation, severity, and course. All English language articles dealing with psoriasis and lifestyle factors or diet gathered by an extensive PubMed search were carefully examined in order to explore their impact on the disease. Current authoritative knowledge confirms that low‐calories, Mediterranean, and protein restricted/vegetarian diets may be beneficial. Psoriatic patients are also recommended to engage regular physical activity, to avoid alcohol intake and to consume fish rich in omega‐3 polyunsaturated fatty acids, as well as fruit and vegetables. Prebiotics and probiotics may also provide potential benefit, whereas vitamin D supplementation and gluten‐free diet are useful in selected cases only. Changing of dietary and lifestyle habits alone does not replace conventional treatment, but must be considered as an adjuvant. Physicians may play a crucial role, by adequately acknowledging psoriatic patients on the advantages of proper lifestyle and diet habits as well as providing clues to reliable sources of dietary advice.
BLIND SIGNAL SEPARATION IN COMMUNICATIONS: MAKING USE OF KN OWN SIGNAL DISTRIBUTIONS

We apply maximum likelihood blind source separation [7] to complex valued signals mixed with complex valued nonstationary matrices. This case arises in radio communications with baseband signals. We incorporate known source signal distributions in the adaptation, thus making the algorithms less “blind”. This r esults in drastic reduction of the amount of data needed for successful convergence. Adaptation to rapidly changing signal mixing conditions, such as to fading in mobile communications, becomes now feasible as demonstrated by simulations in noisy conditions.
Fusobacterium nucleatum impairs DNA mismatch repair and stability in patients with squamous cell carcinoma of the head and neck

Dysbiosis of the laryngeal microbiota has been demonstrated to the development of head and neck squamous cell carcinoma (HNSCC), but the association of Fusobacterium and Fusobacterium nucleatum (F. nucleatum) with DNA mismatch repair (MMR) and microsatellite instability (MSI) has not been investigated.
Subsurface phase imaging of tapping‐mode atomic force microscopy at phase resonance

The phase image of tapping‐mode atomic force microscopy (TM‐AFM) contains energy dissipation, which is related to the sample information on the physical properties such as the sample Young's modulus, adhesion, surface morphology and subsurface morphology. When TM‐AFM is used for sample measurement, the frequency near the first resonance peak of probe is usually selected to drive the probe vibration. When the probe vibration is driven by the frequency, the probe has a high amplitude sensitivity, but the phase sensitivity is relatively low. In this paper, the frequency at the probe phase resonance peak was selected for driving the probe vibration to measure the sample, which improved the image resolution. Phase imaging was performed on three uniform photoresist samples with different thicknesses and the same structure. When the scanning parameters were fixed and the probe setpoint value was changed alone, it was found that with the decrease of setpoint value the horizontal resolution of the phase subsurface image was decreased, and the depth sensitivity was increased first and then decreased. The result shows that TM‐AFM working at the phase resonance peak can better realise the subsurface imaging of samples at different depths. Phase subsurface imaging at the resonance can be used to quantitatively obtain subsurface phase images of different depths.
Long-term cognitive and behavioral outcomes following early exposure to general anesthetics

Purpose of review Nearly 100 clinical studies have been published evaluating neurodevelopmental outcomes in children following surgery and anesthesia. These studies have reported mixed results, likely attributable at least in part to significant heterogeneity in their study designs, types and numbers of exposures, patient populations evaluated, and most importantly, the outcomes that were assessed. This review aims to summarize the results from clinical studies evaluating behavioral outcomes in children exposed to surgery and anesthesia. Recent findings Children with early exposure to surgery and anesthesia were found to have limited to no differences in intelligence when compared with unexposed children. However, several studies have reported more behavioral problems in children exposed to general anesthesia. An increased incidence of attention-deficit hyperactivity disorder has also been reported in anesthetic exposed children, particularly after multiple exposures. Summary Nearly all clinical studies of anesthetic neurotoxicity are observational in nature, so the associations between anesthetic exposure and behavioral deficits cannot yet be directly attributed to the anesthetic medication. However, the finding of deficits in some neurodevelopmental domains and not others will help guide the selection of appropriate outcomes in future studies of anesthetic neurotoxicity that can further evaluate whether anesthetic medications have an impact on neurodevelopment in children.
Visualizing synaptic dopamine efflux with a 2D composite nanofilm

Chemical neurotransmission constitutes one of the fundamental modalities of communication between neurons. Monitoring release of these chemicals has traditionally been difficult to carry out at spatial and temporal scales relevant to neuron function. To understand chemical neurotransmission more fully, we need to improve the spatial and temporal resolutions of measurements for neurotransmitter release. To address this, we engineered a chemi-sensitive, two-dimensional composite nanofilm that facilitates visualization of the release and diffusion of the neurochemical dopamine with synaptic resolution, quantal sensitivity, and simultaneously from hundreds of release sites. Using this technology, we were able to monitor the spatiotemporal dynamics of dopamine release in dendritic processes, a poorly understood phenomenon. We found that dopamine release is broadcast from a subset of dendritic processes as hotspots that have a mean spatial spread of ≈ 3.2 µm (full width at half maximum [FWHM]) and are observed with a mean spatial frequency of one hotspot per ≈ 7.5 µm of dendritic length. Major dendrites of dopamine neurons and fine dendritic processes, as well as dendritic arbors and dendrites with no apparent varicose morphology participated in dopamine release. Remarkably, these release hotspots co-localized with Bassoon, suggesting that Bassoon may contribute to organizing active zones in dendrites, similar to its role in axon terminals.
Urolithin B suppresses osteoclastogenesis via inhibiting RANKL‐induced signalling pathways and attenuating ROS activities

Osteoporosis (OP) has severely affected human health, which is characterized by abnormal differentiation of osteoclasts. Urolithin B (UB), as a potential natural drug, has been reported to exhibit numerous biological activities including antioxidant and anti‐inflammatory but its effects on OP, especially on RANKL‐stimulated osteoclast formation and activation, are still understood. In our study, we have demonstrated for the first time that UB inhibits RANKL‐induced osteoclast differentiation and explored its potential mechanisms of action. The RAW264.7 cells were cultured and induced with RANKL followed by UB treatment. Then, the effects of UB on mature osteoclast differentiation were evaluated by counting tartrate‐resistant acid phosphatase (TRAP)‐positive multinucleated cells and F‐actin ring analysis. Moreover, the effects of UB on RANKL‐induced reactive oxygen species (ROS) were measured by 2′, 7′‐dichlorodihydrofluorescein diacetate (DCFH‐DA) staining. Further, we explored the potential mechanisms of these downregulation effects by performing Western blotting and quantitative RT‐PCR examination. We found that UB represses osteoclastogenesis, F‐actin belts formation, osteoclast‐specific gene expressions and ROS activity in a time‐ and concentration‐dependent manner. Mechanistically, UB attenuates intracellular ROS levels by upregulation of Nrf2 and other ROS scavenging enzymes activation. Furthermore, UB also inhibited RANKL‐induced NF‐κB, MAPK and Akt signalling pathway and suppressed expression of c‐Fos and nuclear factor of activated T cells 1 (NFATc1), which is the master transcription factor of osteoclast differentiation. Taken together, our findings confirm that UB is a polyphenolic compound that can be a potential therapeutic treatment for osteoclast‐related bone diseases such as osteoporosis.
Highlights in ASCVD Primary Prevention for 2021

Abstract This review examines key studies published in 2021 that are related to primary prevention of atherosclerotic cardiovascular disease (ASCVD). Major randomized clinical trials (RCTs) concerning traditional risk factors or ASCVD events, meta‐analyses, and key observational studies related to primary prevention of ASCVD were considered. The review includes interventions for weight loss, cardiometabolic and renal disease, blood pressure control, diet, and the occurrence of cardiovascular disease events. A few studies considered both primary and secondary prevention populations. The review is not exhaustive. We did not include studies that focused on heart failure or clinical presentations that may be difficult to classify, such as acute or chronic ischemic cardiovascular disease without myocardial infarction. Our purpose was to highlight recent research that will help the reader stay abreast of the changing field of cardiovascular prevention.
The double-edged impacts of COVID-19 epidemic on influenza vaccination uptake in nurses in Hong Kong

Abstract Background Worldwide, the COVID-19 pandemic has disrupted influenza seasonality and impacted influenza vaccination behaviours. This study examines the patterns of influenza vaccination uptake in nurses during the pre-pandemic seasons and amidst the pandemic in 2020/21 in subtropical Hong Kong. Methods In April 2021, a cross-sectional survey was conducted in practising and student nurses. Six patterns were differentiated by their influenza vaccination status in 2020/21 and preceding five seasons: (a) resistance throughout, (b) negative change, (c) decreased tendency, (d) increased tendency (e) positive change, and (f) adherence throughout. Participants’ characteristics and reasons for vaccination/non-vaccination and their associations with each vaccination pattern were explored using multivariable multinomial regression. Results Among 1323 nurses (88% female with a median age of 39 years), no substantial change in vaccination uptake was noted from 2019/20 (41%) to 2020/21 (42%). Some 4% were positive-changers who were older at age of ≥50 years (aOR 4.77) and more likely to anticipate a reduced risk of severe disease with COVID-19/influenza co-infection following vaccination (aOR 8.76). Negative-changers made up 3% and were more inclined to perceive an unlikelihood of widespread influenza outbreaks amidst the pandemic (aOR 3.67). Some 26 and 43% remained adherent and resistant to influenza vaccination throughout respectively notwithstanding the COVID-19 outbreak. Conclusions In contrast to the increased uptake elsewhere, the stable influenza vaccination coverage among nurses in Hong Kong could be explained by the ubiquity of strict physical distancing regulations, which have deterred vaccination in some nurses that offsets the new uptakes induced by the mild COVID-19 outbreaks.
Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis

Abstract Aim The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis. Materials and Methods Using national survey data from Taiwan (n = 3453) and the United States (n = 3685), predictors of periodontitis were extracted from the datasets and pre‐processed, and then 10 machine learning algorithms were trained to develop predictive models. The models were validated both internally (bootstrap sampling) and externally (alternative country's dataset). The algorithms were compared across six performance metrics ([i] area under the curve for the receiver operating characteristic [AUC], [ii] accuracy, [iii] sensitivity, [iv] specificity, [v] positive predictive value, and [vi] negative predictive value) and two methods of data pre‐processing ([i] machine‐learning‐based feature selection and [ii] dimensionality reduction into principal components). Results Many algorithms showed extremely strong performance during internal validation (AUC > 0.95, accuracy > 95%). However, this was not replicated in external validation, where predictive performance of all algorithms dropped off drastically. Furthermore, predictive performance differed according to data pre‐processing methodology and the cohort on which they were trained. Conclusions Larger sample sizes and more complex predictors of periodontitis are required before machine learning can be leveraged to its full potential.