Miracle-dz/UniFAR-FTUs huggingface dataset:
Greig Cephalopolysyndactyly Syndrome: Phenotypic Variability Associated with Variants in Two Different Domains of GLI3

Abstract Background GLI3 is a transcriptional regulator of several genes involved in mammalian skeletal development. Mutations in the pleiotropic gene GLI3 may result in different inherited disorders including Greig cephalopolysyndactyly syndrome (GCPS). GCPS is characterized by mild to severe craniofacial and limb malformations. Methods and Results Here, we report clinical and molecular study of 3 families with GCPS originated in different regions of Pakistan. Sanger sequencing revealed two novel variants including a frameshift [c. 3790_3791InsC, p.(Gly1236Argfs*11)] and a missense [c.1692A>G, p.(His536Arg)], and one previously reported variant [c. 1965_1966delAT, p.(His627Glufs*48)] located in 2 different domains of the GLI3. Conclusion This study not only expanded spectrum of the mutations in the GLI3 but also highlighted phenotypic variability in the GCPS patients. This will facilitate diagnosis and genetic counseling of families with same and related disorders in the Pakistani population.
Associations of elevated weight status with symptom severity and treatment outcomes in binge/purge eating disorders.

OBJECTIVE Binge-eating and purging behaviors commonly co-occur with overweight. However, little is known about the potential associations of elevated weight status with eating disorder severity or treatment outcomes. Thus, the present study compared binge-eating and purging patients with low, normal, and high weight statuses on eating disorder and mood symptoms at treatment admission, and tested whether weight status was associated with symptom change over treatment. METHOD The sample included 135 adult female patients in an intensive outpatient program, who completed self-assessments at admission and discharge. MANOVAs compared the groups at treatment admission, and multilevel models examined changes over time. RESULTS At admission, the high-weight group reported greater fasting frequency than the normal-weight group, and higher shape and weight concerns than the low-weight group. Over time, the high-weight group additionally showed higher eating disorder psychological symptom severity than the normal-weight group. The groups did not differ on mood symptoms at admission. Longitudinal results indicated that the groups showed comparable symptom improvements over treatment. DISCUSSION These findings highlight the severity of higher-weight patients with bulimia nervosa. Additionally, although these patients may present with more severe symptoms, their response to an intensive treatment may be comparable to that of normal- or lower-weight groups.
Does learning history shape the associability of outcomes? Further tests of the outcome predictability effect

In recent years, several studies of human predictive learning demonstrated better learning about outcomes that have previously been experienced as consistently predictable compared to outcomes previously experienced as less predictable, namely the outcome predictability effect. As this effect may have wide-reaching implications for current theories of associative learning, the present study aimed to examine the generality of the effect with a human goal-tracking paradigm, employing three different designs to manipulate the predictability of outcomes in an initial training phase. In contrast to the previous studies, learning in a subsequent phase, when every outcome was equally predictable by novel cues, was not reliably affected by the outcomes’ predictability in the first phase. This lack of an outcome predictability effect provides insights into the parameters of the effect and its underlying mechanisms.
Strong survival selection on seasonal migration versus residence induced by extreme climatic events.

1. Elucidating the full eco-evolutionary consequences of climate change requires quantifying the impact of extreme climatic events (ECEs) on selective landscapes of key phenotypic traits that mediate responses to changing environments. Episodes of strong ECE-induced selection could directly alter population composition, and potentially drive micro-evolution. However, to date, few studies have quantified ECE-induced selection on key traits, meaning that immediate and longer-term eco-evolutionary implications cannot yet be considered. 2. One widely-expressed trait that allows individuals to respond to changing seasonal environments, and directly shapes spatio-seasonal population dynamics, is seasonal migration versus residence. Many populations show considerable among-individual phenotypic variation, resulting in 'partial migration'. However, variation in the magnitude of direct survival selection on migration versus residence has not been rigorously quantified, and empirical evidence of whether seasonal ECEs induce, intensify, weaken or reverse such selection is lacking. 3. We designed full-annual-cycle multi-state capture-recapture models that allow estimation of seasonal survival probabilities of migrants and residents from spatio-temporally heterogeneous individual resightings. We fitted these models to nine years of geographically extensive year-round resighting data from partially migratory European shags (Phalacrocorax aristotelis). We thereby quantified seasonal and annual survival selection on migration versus residence across benign and historically extreme non-breeding season (winter) conditions, and tested whether selection differed between females and males. 4. We show that two of four observed ECEs, defined as severe winter storms causing overall low survival, were associated with very strong seasonal survival selection against residence. These episodes dwarfed the weak selection or neutrality evident otherwise, and hence caused selection through overall annual survival. The ECE that caused highest overall mortality and strongest selection also caused sex-biased mortality, but there was little overall evidence of sex-biased selection on migration versus residence. 5. Our results imply that seasonal ECEs and associated mortality can substantially shape the landscape of survival selection on migration versus residence. Such ECE-induced phenotypic selection will directly alter migrant and resident frequencies, and thereby alter immediate spatio-seasonal population dynamics. Given underlying additive genetic variation, such ECEs could potentially cause micro-evolutionary changes in seasonal migration, and thereby cause complex eco-evolutionary population responses to changing seasonal environments.
Association of OCT Angiography Parameters With Age in Cognitively Healthy Older Adults.

BACKGROUND AND OBJECTIVE To evaluate the association of changes in retinal anatomy and microvasculature with age and sex in cognitively healthy older adults. PATIENTS AND METHODS Cross-sectional study of cognitively healthy subjects aged 50 years and older who underwent optical coherence tomography angiography (OCTA) to estimate the association between age and sex with ganglion cell layer-inner plexiform layer (GC-IPL); central subfield thickness (CST); subfoveal choroidal thickness (CT); foveal avascular zone (FAZ) size; and superficial (SCP), deep (DCP), and whole capillary plexus (WCP) vessel density (VD) and perfusion density (PD) measured in the ETDRS 3-mm and 6-mm circle and rings. RESULTS Among 141 older adults (72.9% female; median age: 69 years), 282 eyes were imaged. Females had a greater CT, GC-IPL thickness, and FAZ size and a lower CST than males. After controlling for sex, both CT (P = .001) and GC-IPL thickness (P < .001) decreased with age, whereas FAZ size and CST did not. There was a reduction in VD and PD in SCP, DCP, and WCP with age in the 3-mm circle, 3-mm ring, and 6-mm circle (all P < .05). CONCLUSIONS There is a significant reduction in both VD and PD, as well as decreased choroidal and GC-IPL thickness associated with aging, even beyond the fifth decade, in cognitively healthy adults. [Ophthalmic Surg Lasers Imaging Retina. 2020;51:706-714. ].
Psychometric Properties of NASA-TLX and Index of Cognitive Activity as Measures of Cognitive Workload in Older Adults

Cognitive workload is increasingly recognized as an important determinant of performance in cognitive tests and daily life activities. Cognitive workload is a measure of physical and mental effort allocation to a task, which can be determined through self-report or physiological measures. However, the reliability and validity of these measures have not been established in older adults with a wide range of cognitive ability. The aim of this study was to establish the test–retest reliability of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) and Index of Cognitive Activity (ICA), extracted from pupillary size. The convergent validity of these measures against event-related potentials (ERPs) was also investigated. A total of 38 individuals with scores on the Montreal Cognitive Assessment ranging between 17 and 30 completed a working memory test (n-back) with three levels of difficulty at baseline and at a two-week follow-up. The intraclass correlation coefficients (ICC) values of the NASA-TLX ranged between 0.71 and 0.81, demonstrating good to excellent reliability. The mean ICA scores showed fair to good reliability, with ICCs ranging between 0.56 and 0.73. The mean ICA and NASA-TLX scores showed significant and moderate correlations (Pearson’s r ranging between 0.30 and 0.33) with the third positive peak of the ERP at the midline channels. We conclude that ICA and NASA-TLX are reliable measures of cognitive workload in older adults. Further research is needed in dissecting the subjective and objective constructs of cognitive workload.
Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks

Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc. However, research studies conducted with deep neural networks in these fields are not abundant. Therefore, in this paper, we aim to propose and evaluate several state-of-the-art neural network models in these fields. We first review the basics of representative models, namely long short-term memory and its variants, the temporal convolutional network and the generative adversarial network. Then, long short-term memory with autoencoder and attention-based models, the temporal convolutional network and the generative adversarial model are proposed and applied to time series classification and forecasting. Gaussian sliding window weights are proposed to speed the training process up. Finally, the performances of the proposed methods are assessed using five optimizers and loss functions with the public benchmark datasets, and comparisons between the proposed temporal convolutional network and several classical models are conducted. Experiments show the proposed models’ effectiveness and confirm that the temporal convolutional network is superior to long short-term memory models in sequence modeling. We conclude that the proposed temporal convolutional network reduces time consumption to around 80% compared to others while retaining the same accuracy. The unstable training process for generative adversarial network is circumvented by tuning hyperparameters and carefully choosing the appropriate optimizer of “Adam”. The proposed generative adversarial network also achieves comparable forecasting accuracy with traditional methods.
High-Performance Two-Dimensional Perovskite Ca2Nb3O10 UV Photodetectors.

We first report two-dimensional (2D) perovskite Ca2Nb3O10 ultraviolet photodetectors (UV PDs), which are prepared via a facile calcination-exfoliation method. The 2D Ca2Nb3O10 PDs demonstrate high performance at 3 V at 280 nm, high responsivity (14.94 A W-1), high detectivity (8.7 × 1013 Jones), high spectral selectivity (R280/R400 = 8.84 × 103), fast speed (0.08/5.6 ms), and long-term stability, exceeding those of most reported UV PDs. Furthermore, the Ca2Nb3O10 PDs integrated with poly(ethylene terephthalate) (PET) show excellent flexibility and have high linear dynamic range (96 dB). Our work provides a general strategy for searching new UV PDs based on numerous layered niobates. The Ca2Nb3O10 nanosheets may be one of the optimum semiconductor materials for next-generation high-performance UV PDs.
Preparation and Characterization of Nonwoven Fibrous Biocomposites for Footwear Components

Chromium-tanned leathers used in the manufacture of footwear and leather goods pose an environmental problem because they contain harmful chemicals and are very difficult to recycle. A solution to this problem can be composite materials from tree leaves, fruit residues and other fibrous agricultural products, which can replace chromium-tanned leather. The present study describes the preparation of biocomposite leather-like materials from microbial cellulose and maple leave fibers as bio-fillers. The formulation was optimized by design of experiment and the prepared biocomposites characterized by tensile test, FTIR, DMA, SEM, adhesion test, volume porosity, water absorptivity, surface wettability and shape stability. From the viewpoint of future use in the footwear industry, results obtained showed that the optimized material was considerably flexible with tensile strength of 2.13 ± 0.29 MPa, elastic modulus of 76.93 ± 1.63 MPa and porosity of 1570 ± 146 mL/min. In addition, the material depicted good shape stability and surface adhesive properties. The results indicate that a suitable treatment of biomass offers a way to prepare exploitable nonwoven fibrous composites for the footwear industry without further burdening the environment.
Effect of daily periodic human movement on dengue dynamics: the case of the 2010 outbreak in Hermosillo, Mexico.

The human movement plays an important role in the spread of infectious diseases. On an urban scale, people move daily to workplaces, schools, among others. Here, we are interested in exploring the effect of the daily local stay on the variations of some characteristics of dengue dynamics such as the transmission rates and local basic reproductive numbers. For this, we use a two-patch mathematical model that explicitly considers the daily mobility of people and real data from the 2010 dengue outbreak in Hermosillo, Mexico. Based on a preliminary cluster analysis, we divide the city into two regions, the south and north sides, which determine each patch of the model. We use a Bayesian approach to estimate the transmission rates and local basic reproductive numbers of some urban mobility scenarios where residents of each patch spend daily the 100% (no human movement between patches), 75% and 50% of their day at their place of residence. For the north side, estimates of transmission rates do not vary and it is more likely that the local basic reproductive number to be greater than one for all three different scenarios. On the contrary, tranmission rates of the south side have more weight in lower values when consider the human movement between patches compared to the uncoupled case. In fact, local basic reproductive numbers less than 1 are not negligible for the south side. If information about commuting is known, this work might be useful to obtain better estimates of some contagion local properties of a patch, such as the basic reproductive number.
Detection of SARS-CoV2 antigen in human saliva may be a reliable tool for large scale screening

Introduction: SARS-CoV2, the aetiological agent of the current COVID-19 pandemic, has been detected in saliva and recently implicated in several oral diseases. Collection of nasopharyngeal swabs (NPS) and detection by reverse transcriptase-polymerase chain reaction (RT-PCR) requires medical / technical expertise. A reliable and easy to handle point-of-care (POC) test is highly desirable, especially to curb transmission. Therefore, in this study, we evaluated a commercially available POC rapid antigen test (RAT) for the detection of SARS-CoV2 antigens in the saliva of RT-PCR confirmed positive and negative patients. Methods: Thirty saliva samples of 10 saliva RT-PCR negative and 20 saliva RT-PCR positive patients were tested by RAT. Results: RAT was negative in 10/10 (100%) RT-PCR-negative samples; positive in 9/20 (45%) RT-PCR-positive samples; concordance was 63% (p=0.001). Patients with positive RAT had higher virus copies in their NPS samples compared to the RAT-negative patients. This difference was also statistically significant (p=0.01). Conclusion: Thus, the POC RAT may be used to detect SARS-CoV2 as a reliable tool for self-testing, large-scale population screening and emergency medical/dental screening. Patients negative by RAT should be confirmed by RT-PCR.
Longitudinal Analysis of COVID-19 Patients Shows Age-Associated T Cell Changes Independent of Ongoing Ill-Health

Objectives The immunological and inflammatory changes following acute COVID-19 are hugely variable. Persistent clinical symptoms following resolution of initial infection, termed long COVID, are also hugely variable, but association with immunological changes has not been described. We investigate changing immunological parameters in convalescent COVID-19 and interrogate their potential relationships with persistent symptoms. Methods We performed paired immunophenotyping at initial SARS-CoV-2 infection and convalescence (n=40, median 68 days) and validated findings in 71 further patients at median 101 days convalescence. Results were compared to 40 pre-pandemic controls. Fatigue and exercise tolerance were assessed as cardinal features of long COVID using the Chalder Fatigue Scale and 6-minute-walk test. The relationships between these clinical outcomes and convalescent immunological results were investigated. Results We identify persistent expansion of intermediate monocytes, effector CD8+, activated CD4+ and CD8+ T cells, and reduced naïve CD4+ and CD8+ T cells at 68 days, with activated CD8+ T cells remaining increased at 101 days. Patients >60 years also demonstrate reduced naïve CD4+ and CD8+ T cells and expanded activated CD4+ T cells at 101 days. Ill-health, fatigue, and reduced exercise tolerance were common in this cohort. These symptoms were not associated with immune cell populations or circulating inflammatory cytokines. Conclusion We demonstrate myeloid recovery but persistent T cell abnormalities in convalescent COVID-19 patients more than three months after initial infection. These changes are more marked with age and are independent of ongoing subjective ill-health, fatigue and reduced exercise tolerance.
A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions

Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on patient populations with certain diseases. However, it is unclear whether these specialized ML models-trained on patient subpopulations with certain diseases or defined by other clinical characteristics-are more accurate than a general ML model trained on an unrestricted hospital cohort. In this study based on an electronic health record cohort of consecutive inpatient cases of a single tertiary care center, we demonstrate that accurate prediction of hospital readmissions may be obtained by general, disease-independent, ML models. This general approach may substantially decrease the cost of development and deployment of respective ML models in daily clinical routine, as all predictions are obtained by the use of a single model.
Basomedial amygdala activity in mice reflects specific and general aversion uncontrollability

Learning adaptive behaviour to control aversion is a major brain function. Detecting the absence of control is also important, although chronic uncontrollable aversion can impact maladaptively on stimulus processing in general. The mouse basomedial amygdala (BMA) contributes to aversion processing with high BMA activity associated with active behavioural responding. The overall aim of the present study was to investigate the associations between aversion (un)controllability, BMA activity and behaviour. Fibre photometry of GCaMP6‐expressing BMA neuron populations was applied in freely behaving adult male mice during exposure to mild electrical shocks, and effects of specific or general (un)controllability were investigated. In a discrete learned helplessness (LH) effect paradigm, mice underwent discrete sessions of pre‐exposure to either escapable shock (ES) or inescapable shock (IES) followed by an escape test. IES mice acquired fewer escape attempts than ES mice, and this co‐occurred with higher aversion‐related BMA activity in the IES group. After 30 days, ES and IES mice were allocated equally to either chronic social stress (CSS)—exposure to continuous uncontrollable social aversion—or control handling (CON), and on days 5 and 15 underwent an IES session. CSS mice made fewer escape attempts than CON mice, and this was now associated with lower aversion‐related BMA activity in the CSS group. These findings suggest that mouse BMA activity is higher when discrete aversion is uncontrollable but becomes lower following chronic uncontrollable aversion exposure. Therefore, BMA activity could be a neural marker of adaptive and maladaptive states consequent to specific and general uncontrollability, respectively.
Consistent treatment of correlation effects in molecular dissociation studies using randomly chosen configurations

A means of performing configuration interaction calculations by randomly generating expansion vectors has recently been proposed and referred to as Monte Carlo configuration interaction (MCCI). To check the ability of the approach to accurately describe molecular dissociation, a comparison with benchmark full configuration interaction (FCI) calculations is presented. The results of the comparison indicate that the MCCI approach can consistently treat correlation effects in molecular dissociation studies.
The effect of remote ischemic preconditioning on postoperative cardiac and inflammatory biomarkers in pancreatic surgery: a randomized controlled trial

Background Cardiac and inflammatory biomarkers have been associated with adverse outcome after major abdominal surgery. Remote ischemic preconditioning (RIPC) may protect organs from ischemic insults during and after cardiac surgery, but the effect in major abdominal surgery is largely unknown. Objective To study the effect of RIPC on cardiac and inflammatory biomarkers in patients undergoing pancreatic resection. Methods Single-center, double-blind, randomized controlled trial in ninety patients undergoing elective pancreatic resection between March 2017 and February 2019. Three cycles of upper-limb ischemia and reperfusion (each 5 minutes) were applied before surgery. The primary endpoint was the maximum postoperative high-sensitive cardiac troponin (hs-cTn) T concentration within 48 hours after surgery. Secondary endpoints were postoperative myocardial injury (PMI, defined as a postoperative hs-cTnT [≥]14 ng L-1), the maximum concentration of interleukin (IL)-6 within 48 hours after surgery, and postoperative complications within 30-days of surgery. Results RIPC did not reduce the maximum hs-cTnT concentration after surgery (12.6 ng L-1 vs 16.6 ng L-1 in the control group (P=0.23), nor did it lessen the incidence of PMI (15 (33.3%) patients in the RIPC group versus 19 (42.2%) controls, P=0.93). The maximum postoperative IL-6 concentration was 239 pg mL-1 [115-360] in the RIPC group, as compared to 317 pg mL-1 [174-909] in the control group (P=0.13). A postoperative complication occurred in 23 (51%) RIPC patients and 24 (53%) controls. Conclusions Remote ischemic preconditioning did not reduce the maximum postoperative hs-cTnT concentration. Postoperative myocardial injury, IL-6 concentrations and postoperative complications were not statistically different between RIPC patients and controls.