Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Room Maternity Care Versus Traditional Maternity Care: A Cross-Sectional Study Examining Differences in Mothers’ Perceptions of Readiness for Discharge and Satisfaction and Health Outcomes

Background Single room maternity care (SRMC) includes all aspects of the birth process (labour, delivery, postpartum) in a single room with a consistent team of healthcare providers. Traditional maternity care (TMC) involves having mothers labouring and delivering their baby in one room and then transferring to a room on another unit, which also means a transition in providers. Although many hospitals have transitioned to SRMC, there has been limited evidence to support their development. Methods This study was conducted in two large hospitals (one offering SRMC, the other TMC) in Western Canada. A cross-sectional between-subjects design was used to compare differences between SRMC and TMC. New mothers were asked to complete validated questionnaires. Health information was collected from administrative and health databases. The main outcomes included readiness for hospital discharge, mothers’ satisfaction, newborn length of stay, and mother length of stay. Several covariates were examined. Results In total, 506 (292 SRMC; 214 TMC) mothers participated. Readiness for discharge and maternal satisfaction were significantly higher in SRMC. Although newborn and mother length of stay were significantly reduced in SRMC compared to TMC for univariate tests, mother length of stay was not significantly different when adjusting for other variables. Conclusions There are positive health and psychosocial outcomes for mothers and newborns in the SRMC model of care compared to TMC. Since readiness for discharge and satisfaction are associated with positive maternal-infant interactions and transitions to community, SRMC could be the better approach. Further research should examine healthcare provider outcomes and implementation costs.
Health-Related Quality of Life in People Living With HIV With Cognitive Symptoms: Assessing Relevant Domains and Associations

This study aimed to validate and assess a comprehensive set of illness-specific health-related quality of life (HRQL) domains in people living with HIV (PLWH) with cognitive symptoms. One hundred and three HIV patients with cognitive symptoms (n = 93 male, 90.3%) were identified from two UK HIV clinics and complete a series of validated scales measuring seven HRQL domains identified as important to HRQL by PLWH with cognitive impairment. These included: physical functioning, cognition, social connectedness, self-concept, HIV stigma, acceptance of and perceived control over cognitive health, and physical and mental health and wellbeing. Exploratory factor analysis confirmed that domain total scores loaded onto one main factor, representing HRQL. Scale cut-off scores revealed a significant proportion of patients scored outside the normal range on single domains (between 26.2% and 79.6%), and many patients on multiple domains (40.8% on 4 or more domains). We found evidence of poor HRQL across domains in the majority of PLWH with cognitive symptoms and identified domains driving these experiences. This provides targets for intervention development and clinical action to maintain or improve HRQL in PLWH with cognitive symptoms or impairment.
The influence of N95 and FFP2 masks on cardiorespiratory variables in healthy individuals during aerobic exercise: a systematic review and meta-analysis

ABSTRACT Objective: In view of the current COVID-19 pandemic, the objective of this study was to determine, through a systematic review and meta-analysis, whether the use of N95/FFP2 masks during aerobic exercise has a significant impact on HR, RR, SpO2, and blood pressure (BP) in healthy individuals. Methods: We searched the MEDLINE database for studies published in English between 2005 and 2021. To reduce bias and increase reliability, only randomized controlled trials and randomized crossover clinical trials were considered for inclusion. The selected outcomes included HR, RR, SpO2, and BP, with perceived exertion being evaluated by means of the Borg scale. Results: Eight controlled trials were included in the meta-analysis. Seven evaluated HR (p > 0.05), five evaluated RR (p > 0.05), five evaluated SpO2 and BP (p > 0.05 for both), and six evaluated perceived exertion, presenting controversial results such as risk ratios that were grouped for each variable. Conclusions: This study suggests that N95 and FFP2 masks do not have significant effects on HR, RR, SpO2, and BP during aerobic exercise in healthy individuals.
Associations between maternal and infant selenium status and child growth in a birth cohort from Dhaka, Bangladesh

Abstract Deficiency of essential trace element, Se, has been implicated in adverse birth outcomes and in child linear growth because of its important role in redox biology and associated antioxidant effects. We used data from a randomised controlled trial conducted among a cohort of pregnant and lactating women in Dhaka, Bangladesh to examine associations between Se biomarkers in whole blood (WBSe), serum and selenoprotein P (SEPP1) in maternal delivery and venous cord (VC) blood. Associations between Se biomarkers, birth weight and infant growth outcomes (age-adjusted length, weight, head circumference and weight-for-length z-scores) at birth, 1 and 2 years of age were examined using regression analyses. WB and serum Se were negatively associated with birth weight (adjusted β, 95 % CI, WBSe delivery: −26·6 (–44·3, −8·9); WBSe VC: −19·6 (–33·0, −6·1)); however, delivery SEPP1 levels (adjusted β: −37·5 (–73·0, −2·0)) and VC blood (adjusted β: 82·3 (30·0, 134·7)) showed inconsistent and opposite associations with birth weight. Positive associations for SEPP1 VC suggest preferential transfer from mother to fetus. We found small associations between infant growth and WBSe VC (length-for-age z-score β, 95 % CI, at birth: −0·05 (–0·1, −0·01)); 12 months (β: −0·05 (–0·08, −0·007)). Weight-for-age z-score also showed weak negative associations with delivery WBSe (at birth: −0·07 (–0·1, −0·02); 12 -months: −0·05 (–0·1, −0·005)) and in WBSe VC (at birth: −0·05 (–0·08, −0·02); 12 months: −0·05 (–0·09, −0·004)). Given the fine balance between essential nutritional and toxic properties of Se, it is possible that WB and serum Se may negatively impact growth outcomes, both in utero and postpartum.
A robotic honeycomb for interaction with a honeybee colony

Robotic technologies have shown the capability to interact with living organisms and even to form integrated mixed societies composed of living and artificial agents. Biocompatible robots, incorporating sensing and actuation capable of generating and responding to relevant stimuli, can be a tool to study collective behaviors previously unattainable with traditional techniques. To investigate collective behaviors of the western honeybee (Apis mellifera), we designed a robotic system capable of observing and modulating the bee cluster using an array of thermal sensors and actuators. We initially integrated the system into a beehive populated with about 4000 bees for several months. The robotic system was able to observe the colony by continuously collecting spatiotemporal thermal profiles of the winter cluster. Furthermore, we found that our robotic device reliably modulated the superorganism’s response to dynamic thermal stimulation, influencing its spatiotemporal reorganization. In addition, after identifying the thermal collapse of a colony, we used the robotic system in a “life-support” mode via its thermal actuators. Ultimately, we demonstrated a robotic device capable of autonomous closed-loop interaction with a cluster comprising thousands of individual bees. Such biohybrid societies open the door to investigation of collective behaviors that necessitate observing and interacting with the animals within a complete social context, as well as for potential applications in augmenting the survivability of these pollinators crucial to our ecosystems and our food supply. Description Robotic interaction with a honeybee colony influenced self-organized collective behaviors, forming a biohybrid society.
Inhibition of a signaling modality within the gp130 receptor enhances tissue regeneration and mitigates osteoarthritis

Adult mammals are incapable of multitissue regeneration, and augmentation of this potential may shift current therapeutic paradigms. We found that a common co-receptor of interleukin 6 (IL-6) cytokines, glycoprotein 130 (gp130), serves as a major nexus integrating various context-specific signaling inputs to either promote regenerative outcomes or aggravate disease progression. Via genetic and pharmacological experiments in vitro and in vivo, we demonstrated that a signaling tyrosine 814 (Y814) within gp130 serves as a major cellular stress sensor. Mice with constitutively inactivated Y814 (F814) were resistant to surgically induced osteoarthritis as reflected by reduced loss of proteoglycans, reduced synovitis, and synovial fibrosis. The F814 mice also exhibited enhanced regenerative, not reparative, responses after wounding in the skin. In addition, pharmacological modulation of gp130 Y814 upstream of the SRC and MAPK circuit by a small molecule, R805, elicited a protective effect on tissues after injury. Topical administration of R805 on mouse skin wounds resulted in enhanced hair follicle neogenesis and dermal regeneration. Intra-articular administration of R805 to rats after medial meniscal tear and to canines after arthroscopic meniscal release markedly mitigated the appearance of osteoarthritis. Single-cell sequencing data demonstrated that genetic and pharmacological modulation of Y814 resulted in attenuation of inflammatory gene signature as visualized by the anti-inflammatory macrophage and nonpathological fibroblast subpopulations in the skin and joint tissue after injury. Together, our study characterized a molecular mechanism that, if manipulated, enhances the intrinsic regenerative capacity of tissues through suppression of a proinflammatory milieu and prevents pathological outcomes in injury and disease. Description The gp130 Y814 signaling module acts as a cellular stress sensor that hinders tissue regeneration through promotion of a pro-inflammatory milieu. Reinvigorating tissue regeneration Enhancing the regenerative capacity of adult tissues could improve wound healing and resistance to chronic degenerative diseases. Here Shkhyan and colleagues identified a tyrosine residue at position 814 (Y814) of glycoprotein 130 (gp130) that functioned as a cellular stress sensor responsible for inflammatory responses. By replacing the tyrosine with a phenylalanine (F814), the authors were able to achieve downregulation of pro-inflammatory and pro-fibrotic signaling cascades after stimulation in vitro. F814 mice demonstrated increased resistance to development of osteoarthritis and enhanced skin regeneration after wounding, and a small molecule that modulated gp130 receptor pro-inflammatory signaling also improved symptoms of osteoarthritis in rodents and dogs. These findings suggest that targeting Y814 in gp130 may potentially improve tissue regeneration. —MN
IScore, a useful prognostic tool for patients with acute ischemic stroke treated with intravenous thrombolysis: a validation study

Abstract Background Stroke is one of the major causes of disability and mortality worldwide. Up to 30% of individuals who experience stroke die within 30 days, and more than 50% of those who survive will have some degree of disability. There are some predetermining factors based on admission data that could be used to objectively assess the odds of poor outcomes, including the Ischemic Stroke Predictive Risk Score (IScore). Objective To analyze and validate the IScore in patients undergoing intravenous thrombolysis for stroke and compare the results of this predictor with actual death and disability outcomes. Methods In a retrospective study, data were collected from a database housed at the Stroke Unit of the Teaching Hospital of Universidade Federal do Paraná, Southern Brazil. The IScore was applied to admission data from 239 patients, and the results were compared with actual outcomes (death and disability) within 30 days and 1 year after the stroke event. Data analysis was performed using an analysis of the receiver operating characteristic (ROC) curve to determine the sensitivity and specificity of the IScore in the study population. Results The IScore demonstrated moderate sensitivity and high specificity in patients with stroke who underwent thrombolysis when evaluated after 30 days and 1 year of the event. Conclusions The IScore can be applied to in stroke patients undergoing thrombolysis; therefore, it may be used as an objective prognostic tool to guide clinical decision-making. Understanding the prognosis of patients in the acute phase can assist clinicians in making the best therapeutic decisions and enable better end-of-life care.
Kernel Fisher Dictionary Transfer Learning

Dictionary learning is an efficient knowledge representation method that can learn the essential features of data. Traditional dictionary learning methods are difficult to obtain nonlinear information when processing large-scale and high-dimensional datasets. While most dictionary learning algorithms are based on the assumption that the training data and test data have the same feature distribution, which is not always true in practical applications. To address the above problems, we propose the Kernel Fisher Dictionary Transfer Learning (KFDTL) algorithm. First, we map each sample to high-dimensional space through kernel mapping and use any dictionary learning algorithm to learn the essential features. Then, the feature-based transfer learning method is performed to predict the labels of the target samples. This method includes three main contributions: (1) KFDTL constructs a discriminative Fisher embedding model to make the same class samples have similar coding coefficients; (2) Based on the relationship between profiles and atoms, KFDTL constructs an adaptive model that adapts source domain samples to target domain samples; (3) The kernel method is used to efficiently solve nonlinear problems. Experiments on a large number of public image datasets have proved the effectiveness of the proposed method. The source code of the proposed method is available at https://github.com/zzfan3/KFDTL.
Learning from the Implicit Functional Hierarchy in an Analog Netlist

Analog circuit design is characterized by a plethora of implicit design and technology aspects available to the experienced designer. In order to create useful computer-aided design methods, this implicit knowledge has to be captured in a systematic and hierarchical way. A key approach to this goal is to "learn" the knowledge from the netlist of an analog circuit. This requires a library of structural and functional blocks for analog circuits together with their individual constraints and performance equations, graph homomorphism techniques to recognize blocks that can have different structural implementations and I/O pins, as well as synthesis methods that exploit the learned knowledge. In this contribution, we will present how to make use of the functional and structural hierarchy of operational amplifiers. As an application, we explore the capabilities of machine learning in the context of structural and functional properties and show that the results can be substantially improved by pre-processing data with traditional methods for functional block analysis. This claim is validated on a data set of roughly 100,000 readily sized and simulated operational amplifiers.
Stochastic Games with Synchronization Objectives

We consider two-player stochastic games played on a finite graph for infinitely many rounds. Stochastic games generalize both Markov decision processes (MDP) by adding an adversary player, and two-player deterministic games by adding stochasticity. The outcome of the game is a sequence of distributions over the graph states, representing the evolution of a population consisting of a continuum number of identical copies of a process modeled by the game graph. We consider synchronization objectives, which require the probability mass to accumulate in a set of target states, either always, once, infinitely often, or always after some point in the outcome sequence; and the winning modes of sure winning (if the accumulated probability is equal to 1) and almost-sure winning (if the accumulated probability is arbitrarily close to 1). We present algorithms to compute the set of winning distributions for each of these synchronization modes, showing that the corresponding decision problem is PSPACE-complete for synchronizing once and infinitely often and PTIME-complete for synchronizing always and always after some point. These bounds are remarkably in line with the special case of MDPs, while the algorithmic solution and proof technique are considerably more involved, even for deterministic games. This is because those games have a flavor of imperfect information, in particular they are not determined and randomized strategies need to be considered, even if there is no stochastic choice in the game graph. Moreover, in combination with stochasticity in the game graph, finite-memory strategies are not sufficient in general.
Optimization of AI SoC with Compiler-assisted Virtual Design Platform

As deep learning keeps evolving dramatically with rapidly increasing complexity, the demand for efficient hardware accelerators has become vital. However, the lack of software/hardware co-development toolchains makes designing AI SoCs (artificial intelligent system-on-chips) considerably challenging. This paper presents a compiler-assisted virtual platform to facilitate the development of AI SoCs from the early design stage. The electronic system-level design platform provides rapid functional verification and performance/energy analysis. Cooperating with the neural network compiler, AI software and hardware can be co-optimized on the proposed virtual design platform. Our Deep Inference Processor is also utilized on the virtual design platform to demonstrate the effectiveness of the architectural evaluation and exploration methodology.
Examining cognitive determinants of planning future routine events: a pilot study in school-age Italian children (Análisis de los determinantes cognitivos de la planificación de eventos de rutina futuros: un estudio piloto con niños italianos en edad escolar)

ABSTRACT Previous research addressed the cognitive antecedents of children’s ability to plan future routine events mainly in terms of executive functioning. Additionally, most studies assessed planning through ‘high structure’ tools (e.g., the Tower of London), whereas little research employed ecological ‘low structure’ paper-pencil tasks, such as the Key Search Task (KST). The primary aim of the current research was to investigate, in a sample of 45 Italian school-age children (meanage = 5.08; SD = 0.80; rangeage = 5–8 years), the role of fluid intelligence (Gf), core executive functions — CEFs (working memory, inhibition and shifting), as well as their interactions in planning performance, using the KST. Results revealed that Gf was the main predictor of planning, whereas only inhibition moderated the Gf-planning link. These findings suggested that better competencies in inhibition promote children’s ability to reason and solve problems requiring systematic and ecological plans of action. Implications and limits were also considered.
Dopamine neurons govern olfactory-gated infant begging behavior

Altricial young of many species beg parents for the nutrients required for healthy development. From human crying to begging chicks, young expend precious energy reserves to communicate their hunger. Despite repeated independent evolutionary origins, the neural basis of parent-directed communication by infants is unknown. Here, we examined the sensory and neural basis of begging behavior in tadpoles of the monogamous and biparental Mimetic poison frog (Ranitomeya imitator). In this species, tadpoles beg parents for egg meals by dancing. We used this robust motor display to determine that tadpoles use multimodal cues for caregiver recognition, where olfactory cues are necessary for caregiver recognition while visual cues provide an orientation goal. We found that dopamine related brain regions have higher neural activity in begging tadpoles and that dopamine signaling had opposing modulatory effects through D1 and D2 family receptors, similar to swimming behavior in other tadpole species. We then identified caudal posterior tuberculum dopamine neurons as more active during begging behavior and sensitive to caregiver olfactory cues. Projections of these dopaminergic neurons to the spinal accessory motor nucleus are required for begging displays. These findings support the idea that dopamine regulates olfactory-guided parental recognition in young and opens many avenues for studying how new communication behaviors can evolve from ancestral motor circuits.
Protein-intrinsic properties and context-dependent effects regulate pioneer-factor binding and function

Chromatin is a barrier to the binding of many transcription factors. By contrast, pioneer factors access nucleosomal targets and promote chromatin opening. Despite binding to target motifs in closed chromatin, many pioneer factors display cell-type specific binding and activity. The mechanisms governing pioneer-factor occupancy and the relationship between chromatin occupancy and opening remain unclear. We studied three Drosophila transcription factors with distinct DNA-binding domains and biological functions: Zelda, Grainy head, and Twist. We demonstrated that the level of chromatin occupancy is a key determinant of pioneering activity. Multiple factors regulate occupancy, including motif content, local chromatin, and protein concentration. Regions outside the DNA-binding domain are required for binding and chromatin opening. Our results show that pioneering activity is not a binary feature intrinsic to a protein but occurs on a spectrum and is regulated by a variety of protein-intrinsic and cell-type-specific features.
Deep Learning Based Emotion Classification through EEG Spectrogram Images 211-215

Emotion modeling for social robotics has the great potential to improve the life quality for the elderly and individuals with disabilities by making communication, care, and interactions more effective. It can help individuals with communication difficulties express their emotions. It can also be used to monitor the emotional well-being of elderly persons living alone and alert caregivers or family members if there are signs of distress. More broadly, emotion modeling is necessary to design robots closer and closer to human beings that can naturally interact with them by understanding their behavior and reactions. Here, we propose a deep learning technique for emotion classification using electroencephalogram (EEG) signals. We aim to recognize valence, arousal, dominance, and likability. Our technique uses the spectrogram from each of the 32 electrodes applied in the skull area. Then, we employ a Resnet101 convolutional neural network to learn a model capable of predicting several emotions. We built and tested our model on the DEAP dataset.