Miracle-dz/UniFAR-FTUs huggingface dataset:
Racial and Educational Disparities in Cognitive Life Expectancies.

OBJECTIVES We estimate life expectancy with and without dementia for Americans 65 years and older by education and race to examine how these stratification systems combine to shape disparities in later-life cognitive health. METHOD Based on the Health and Retirement Study (2000-2014), we use a multivariate, incidence-based life table approach to estimate life expectancy by cognitive health status for race-education groups. The models also simulate group differences in the prevalence of dementia implied by these rates. RESULTS The life table results document notable race-education differences in dementia and dementia-free life expectancy, as well as stark differences in implied dementia prevalence. At each education level, blacks can expect to live more years with dementia and they have significantly higher rates of dementia prevalence. This distribution of disparities in the older population is anchored by two groups -- blacks without a high school diploma and whites with some college or more. DISCUSSION Dementia experience and dementia burden differ dramatically along race-education lines. Race and education combine to exaggerate disparities and they both have enduring effects. Future research should explicitly consider how race and education combine to influence dementia in the older American population.
Intrusive memories and rumination in patients with post-traumatic stress disorder: A phenomenological comparison

The aim of the study was to investigate the phenomenological differences between intrusive memories and rumination in PTSD. The study population consisted of 31 patients with PTSD referred for cognitive behavioural therapy to specialist services. A semi-structured interview was used to examine the characteristics of the most prominent intrusive memory and rumination. Intrusive memories were predominantly sensory experiences of short duration, whereas rumination was predominantly a thought process of longer duration. Shame was associated more with rumination than with intrusive memories. Anxiety, helplessness, numbness, and threat were greater at the time of the trauma than when experiencing the intrusive memory. In contrast, feelings like anger and sadness were greater when experiencing intrusive memories than at the time of the event. The distinction between intrusive memories and rumination is of clinical importance as intrusive memories usually decrease with imaginal reliving of the trauma, whereas rumination may require different therapeutic strategies, such as rumination-focused or mindfulness-based cognitive therapy.
Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-hospital cardiac arrest (OHCA) patients. ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms. Recently, deep learning architectures based on 1D Convolutional Neural Networks (CNN) have been proposed for this task. This study introduces a deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF. Two datasets were used, one from public repositories of Holter recordings captured at the onset of the arrhythmia, and a second from OHCA patients obtained minutes after the onset of the arrest. Data was partitioned patient-wise into training (80%) to design the classifiers, and test (20%) to report the results. The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier. The algorithms were evaluated in terms of balanced accuracy (BAC), the unweighted mean of the sensitivity (Se) and specificity (Sp). The BAC, Se, and Sp of the architecture for 4-s ECG segments was 99.3%, 99.7%, and 98.9% for the public data, and 98.0%, 99.2%, and 96.7% for OHCA data. The proposed architecture outperformed all other classifiers by at least 0.3-points in BAC in the public data, and by 2.2-points in the OHCA data. The architecture met the 95% Sp and 90% Se requirements of the American Heart Association in both datasets for segment lengths as short as 3-s. This is, to the best of our knowledge, the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.
Transcript and metabolite changes during the early phase of abscisic acid-mediated induction of crassulacean acid metabolism in Talinum triangulare

Abstract Crassulacean acid metabolism (CAM) has evolved as a water-saving strategy, and its engineering into crops offers an opportunity to improve their water use efficiency. This requires a comprehensive understanding of the regulation of the CAM pathway. Here, we use the facultative CAM species Talinum triangulare as a model in which CAM can be induced rapidly by exogenous abscisic acid. RNA sequencing and metabolite measurements were employed to analyse the changes underlying CAM induction and identify potential CAM regulators. Non-negative matrix factorization followed by k-means clustering identified an early CAM-specific cluster and a late one, which was specific for the early light phase. Enrichment analysis revealed abscisic acid metabolism, WRKY-regulated transcription, sugar and nutrient transport, and protein degradation in these clusters. Activation of the CAM pathway was supported by up-regulation of phosphoenolpyruvate carboxylase, cytosolic and chloroplastic malic enzymes, and several transport proteins, as well as by increased end-of-night titratable acidity and malate accumulation. The transcription factors HSFA2, NF-YA9, and JMJ27 were identified as candidate regulators of CAM induction. With this study we promote the model species T. triangulare, in which CAM can be induced in a controlled way, enabling further deciphering of CAM regulation.
“It Was as Though My Spirit Left, Like They Killed Me”: The Disruptive Impact of an HIV-Positive Diagnosis among Women in the Dominican Republic

An HIV diagnosis may be associated with severe emotional and psychological distress, which can contribute to delays in care or poor self-management. Few studies have explored the emotional, psychological, and psychosocial impacts of an HIV diagnosis on women in low-resource settings. We conducted in-depth interviews with 30 women living with HIV in the Dominican Republic. Interviews were audio-recorded, transcribed, and analyzed using the biographical disruption framework. Three disruption phases emerged (impacts of a diagnosis, postdiagnosis turning points, and integration). Nearly all respondents described the news as deeply distressful and feelings of depression and loss of self-worth were common. Several reported struggling with the decision to disclose—worrying about stigma. Postdiagnosis turning points consisted of a focus on survival and motherhood; social support (family members, friends, HIV community) promoted integration. The findings suggest a need for psychological resources and social support interventions to mitigate the negative impacts of an HIV diagnosis.
Dental Composite Formulation Design with Bioactivity on Protein Adsorption Combined with Crack-Healing Capability

Short Title Protein-repellent dental composite with crack-healing ability Abstract Fracture and secondary caries are the primary reasons for the failure of dental restorations. To face this omnipresent problem, we report the formulation design and synthesis of a protein-resistant dental composite composed of 2-methacryloyloxyethyl phosphorylcholine (MPC) that also can self-repair damage and recover the load-bearing capability via microencapsulated triethylene glycol dimethacrylate (TEGDMA) and N,N-dihydroxy ethyl-p-toluidine (DHEPT). The bioactivity of the resulting MPC-microencapsulated TEGDMA-DHEPT was evaluated on protein adsorption through early bacterial attachment. Its mechanical properties were also investigated, including self-healing assessment. Microcapsules of poly (urea-formaldehyde) (PUF) were synthesized by incorporating a TEGDMA-DHEPT healing liquid. A set of composites that contained 7.5% of MPC, 10% of microcapsules, and without MPC/microcapsules were also prepared as controls. The two distinct characteristics of strong protein repellency and load-bearing recovery were achieved by the combined strategies. The novel dual composite with a combination of protein-repellent MPC and PUF microcapsules for restoring microcracks is a promising strategy for dental restorations to address the two main challenges of fracture and secondary caries. The new dual composite formulation design has the potential to improve the longevity of dental restorations significantly.
Evaluating and Enhancing the Generalization Performance of Machine Learning Models for Physical Activity Intensity Prediction From Raw Acceleration Data

Purpose: To evaluate and enhance the generalization performance of machine learning physical activity intensity prediction models developed with raw acceleration data on populations monitored by different activity monitors. Method: Five datasets from four studies, each containing only hip- or wrist-based raw acceleration data (two hip- and three wrist-based) were extracted. The five datasets were then used to develop and validate artificial neural networks (ANN) in three setups to classify activity intensity categories (sedentary behavior, light, and moderate-to-vigorous). To examine generalizability, the ANN models were developed using within dataset (leave-one-subject-out) cross validation, and then cross tested to other datasets with different accelerometers. To enhance the models’ generalizability, a combination of four of the five datasets was used for training and the fifth dataset for validation. Finally, all the five datasets were merged to develop a single model that is generalizable across the datasets (50% of the subjects from each dataset for training, the remaining for validation). Results: The datasets showed high performance in within dataset cross validation (accuracy 71.9–95.4%, Kappa K = 0.63–0.94). The performance of the within dataset validated models decreased when applied to datasets with different accelerometers (41.2–59.9%, K = 0.21–0.48). The trained models on merged datasets consisting hip and wrist data predicted the left-out dataset with acceptable performance (65.9–83.7%, K = 0.61–0.79). The model trained with all five datasets performed with acceptable performance across the datasets (80.4–90.7%, K = 0.68–0.89). Conclusions: Integrating heterogeneous datasets in training sets seems a viable approach for enhancing the generalization performance of the models. Instead, within dataset validation is not sufficient to understand the models’ performance on other populations with different accelerometers.
Transcriptomic Analysis of Leaf Sheath Maturation in Maize

The morphological development of the leaf greatly influences plant architecture and crop yields. The maize leaf is composed of a leaf blade, ligule and sheath. Although extensive transcriptional profiling of the tissues along the longitudinal axis of the developing maize leaf blade has been conducted, little is known about the transcriptional dynamics in sheath tissues, which play important roles in supporting the leaf blade. Using a comprehensive transcriptome dataset, we demonstrated that the leaf sheath transcriptome dynamically changes during maturation, with the construction of basic cellular structures at the earliest stages of sheath maturation with a transition to cell wall biosynthesis and modifications. The transcriptome again changes with photosynthesis and lignin biosynthesis at the last stage of sheath tissue maturation. The different tissues of the maize leaf are highly specialized in their biological functions and we identified 15 genes expressed at significantly higher levels in the leaf sheath compared with their expression in the leaf blade, including the BOP2 homologs GRMZM2G026556 and GRMZM2G022606, DOGT1 (GRMZM2G403740) and transcription factors from the B3 domain, C2H2 zinc finger and homeobox gene families, implicating these genes in sheath maturation and organ specialization.
The language of character strengths: Predicting morally valued traits on social media

Abstract Objective Social media is increasingly being used to study psychological constructs. This study is the first to use Twitter language to investigate the 24 Values in Action Inventory of Character Strengths, which have been shown to predict important life domains such as well‐being. Method We use both a top‐down closed‐vocabulary (Linguistic Inquiry and Word Count) and a data‐driven open‐vocabulary (Differential Language Analysis) approach to analyze 3,937,768 tweets from 4,423 participants (64.3% female), who answered a 240‐item survey on character strengths. Results We present the language profiles of (a) a global positivity factor accounting for 36% of the variances in the strengths, and (b) each of the 24 individual strengths, for which we find largely face‐valid language associations. Machine learning models trained on language data to predict character strengths reach out‐of‐sample prediction accuracies comparable to previous work on personality (r median = 0.28, ranging from 0.13 to 0.51). Conclusions The findings suggest that Twitter can be used to characterize and predict character strengths. This technique could be used to measure the character strengths of large populations unobtrusively and cost‐effectively.
3D Bioprinted Nanocellulose-Based Hydrogels for Tissue Engineering Applications: A Brief Review

Nanocellulosic materials, such as cellulose nanocrystals, cellulose nanofibers, and bacterial nanocellulose, that display high surface area, mechanical strength, biodegradability, and tunable surface chemistry have attracted great attention over the last decade for biomedical applications. Simultaneously, 3D printing is revolutionizing the field of biomedical engineering, which enables the fast and on-demand printing of customizable scaffolds, tissues, and organs. Nanocellulosic materials hold tremendous potential for 3D bioprinting due to their printability, their shear thinning behavior, their ability to live cell support and owing to their excellent biocompatibility. The amalgamation of nanocellulose-based feedstocks and 3D bioprinting is therefore of critical interest for the development of advanced functional 3D hydrogels. In this context, this review briefly discusses the most recent key developments and challenges in 3D bioprinting nanocellulose-based hydrogel constructs that have been successfully tested for mammalian cell viability and used in tissue engineering applications.
Characteristics of Waveform Shape in Parkinson’s Disease Detected with Scalp Electroencephalography

Abstract Neural activity in the β frequency range (13–30 Hz) is excessively synchronized in Parkinson’s disease (PD). Previous work using invasive intracranial recordings and non-invasive scalp electroencephalography (EEG) has shown that correlations between β phase and broad-band γ (>50 Hz) amplitude [i.e., phase amplitude coupling (PAC)] are elevated in PD, perhaps a reflection of this synchrony. Recently, it has also been shown, in invasive human recordings, that non-sinusoidal features of β oscillation shape also characterize PD. Here, we show that these features of β waveform shape also distinguish PD patients on and off medication using non-invasive recordings in a dataset of 15 PD patients with resting scalp EEG. Specifically, β oscillations over sensorimotor electrodes in PD patients off medication had greater sharpness asymmetry and steepness asymmetry than on medication (sign rank, p < 0.02, corrected). We also showed that β oscillations over sensorimotor cortex most often had a canonical shape, and that using this prototypical shape as an inclusion criteria increased the effect size of our findings. Together, our findings suggest that novel ways of measuring β synchrony that incorporate waveform shape could improve detection of PD pathophysiology in non-invasive recordings. Moreover, they motivate the consideration of waveform shape in future EEG studies.
The role of military chaplains in mental health care of the deployed service member.

This research utilized a cross-sectional design secondarily analyzing data from active duty military health care personnel who anonymously completed the "2005 Department of Defense Survey of Health Related Behaviors Among Active Duty Military Personnel. " Sample for this analysis of Operation Iraqi Freedom/Operation Enduring Freedom deployed mental health seeking service members was N = 447. Religiosity/spirituality and psychological distress experienced by active duty military personnel who sought help from military mental health providers (MH), military chaplains (CHC) or both (CHC & MH) were explored and compared. Greater psychosocial distress seen in the CHC & MH group could be a reflection of a successful collaborative model for mental health care that is currently promoted by the military where chaplains are first line providers in an effort to provide services to those in greatest need and ultimately provide them with care from a trained mental health professional. Research and evaluation of chaplain training programs and collaborative models is recommended.
Stream Processing using Grammars and Regular Expressions

In this dissertation we study regular expression based parsing and the use of grammatical specifications for the synthesis of fast, streaming string-processing programs. In the first part we develop two linear-time algorithms for regular expression based parsing with Perl-style greedy disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle and action machines, and a finite-state specialization of the streaming parsing algorithm presented in the first part. In the second part we also develop a new linear-time streaming parsing algorithm for parsing expression grammars (PEG) which generalizes the regular grammars of Kleenex. The algorithm is based on a bottom-up tabulation algorithm reformulated using least fixed points and evaluated using an instance of the chaotic iteration scheme by Cousot and Cousot.
Protease-Activated Receptor 1 Deletion Causes Enhanced Osteoclastogenesis in Response to Inflammatory Signals through a Notch2-Dependent Mechanism

Key Points PAR1 is transiently expressed in osteoclast precursor cells during differentiation. PAR1 deletion enhances osteoclastogenesis, whereas PAR1 overexpression inhibits it. The enhanced osteoclastogenesis of PAR1-deletion is mediated by Notch2. We found that protease-activated receptor 1 (PAR1) was transiently induced in cultured osteoclast precursor cells. Therefore, we examined the bone phenotype and response to resorptive stimuli of PAR1-deficient (knockout [KO]) mice. Bones and bone marrow–derived cells from PAR1 KO and wild-type (WT) mice were assessed using microcomputed tomography, histomorphometry, in vitro cultures, and RT-PCR. Osteoclastic responses to TNF-α (TNF) challenge in calvaria were analyzed with and without a specific neutralizing Ab to the Notch2-negative regulatory region (N2-NRR Ab). In vivo under homeostatic conditions, there were minimal differences in bone mass or bone cells between PAR1 KO and WT mice. However, PAR1 KO myeloid cells demonstrated enhanced osteoclastogenesis in response to receptor activator of NF-κB ligand (RANKL) or the combination of RANKL and TNF. Strikingly, in vivo osteoclastogenic responses of PAR1 KO mice to TNF were markedly enhanced. We found that N2-NRR Ab reduced TNF-induced osteoclastogenesis in PAR1 KO mice to WT levels without affecting WT responses. Similarly, in vitro N2-NRR Ab reduced RANKL-induced osteoclastogenesis in PAR1 KO cells to WT levels without altering WT responses. We conclude that PAR1 functions to limit Notch2 signaling in responses to RANKL and TNF and moderates osteoclastogenic response to these cytokines. This effect appears, at least in part, to be cell autonomous because enhanced osteoclastogenesis was seen in highly purified PAR1 KO osteoclast precursor cells. It is likely that this pathway is involved in regulating the response of bone to diseases associated with inflammatory signals.
NURR1 activation in skeletal muscle controls systemic energy homeostasis

Significance Skeletal muscle is the major tissue involved in regulating glucose uptake and body metabolism. In response to exercise, glycogen is metabolized in the liver to glucose, which is taken up by muscle to provide fuel for contraction. We show that NURR1, an orphan nuclear receptor, is up-regulated in response to exercise and strongly stimulates glucose uptake. Genetic overexpression of Nurr1 in skeletal muscle of mice enhances physical performance. In obese mice, overexpression of Nurr1 or treatment with putative chemical activators of NURR1 prevents hyperglycemia and hepatic steatosis and mimics the effect of exercise. Our work provides insights into a transcriptional link between exercise and metabolism, and identifies NURR1 agonists as potential therapeutics to ameliorate obesity and other metabolic abnormalities. Skeletal muscle plays a central role in the control of metabolism and exercise tolerance. Analysis of muscle enhancers activated after exercise in mice revealed the orphan nuclear receptor NURR1/NR4A2 as a prominent component of exercise-responsive enhancers. We show that exercise enhances the expression of NURR1, and transgenic overexpression of NURR1 in skeletal muscle enhances physical performance in mice. NURR1 expression in skeletal muscle is also sufficient to prevent hyperglycemia and hepatic steatosis, by enhancing muscle glucose uptake and storage as glycogen. Furthermore, treatment of obese mice with putative NURR1 agonists increases energy expenditure, improves glucose tolerance, and confers a lean phenotype, mimicking the effects of exercise. These findings identify a key role for NURR1 in governance of skeletal muscle glucose metabolism, and reveal a transcriptional link between exercise and metabolism. Our findings also identify NURR1 agonists as possible exercise mimetics with the potential to ameliorate obesity and other metabolic abnormalities.