Expanding the scope of "trans-humanism": situating within the framework of life and death education - the importance of a "trans-mystical mindset".

Life and death education, as noted from the literatures, has been studied and researched extensively in China, Malaysia, and Taiwan. Our own research undertakings over the past several years, situated in different sociocultural settings have delved into aspects of life and death that could help advance theoretical understanding of the subject matters (e.g., does the meaning of "effective life functioning" connote differing interpretations for different cultural groups?). Situating within the framework of life and death education, we expand the study of trans-humanism by introducing an extended prefix or nomenclature known as "trans-mystical". Specifically, our philosophized concept of trans-mysticism considers a related concept, which we term as a "trans-mystical mindset". A trans-mystical mindset, differing from an ordinary mindset, from our philosophical rationalization, is defined as "a person's higher-order state of consciousness, espousing her perception, judgment, belief, and attempted interpretation of life and death phenomena that are mystifying and fall outside the ordinary boundaries of human psyche." Our focus of inquiry, as reported in the present article, seeks to advance our proposition: that a trans-mystical mindset, unlike an ordinary mindset, may help a person to rationalize, appreciate, and understand metaphysical contexts, mystical experiences, and the like. This focus, interestingly, serves to highlight an important discourse - namely, that there is a dichotomy in theoretical lenses (i.e., objective reality vs. individual subjectivity) that a person may use to rationalize the significance or non-significance of universal contexts, events, phenomena, etc. (e.g., a person's experience of "premonition"). As such, then, there is an important question that we seek to consider: whether philosophization, or the use of philosophical psychology, would yield perceived "scientific evidence" to support or to reject the study of metaphysicism, mysticism, and the like? For example, does our philosophization of an "equivalency" between a person's trans-mystical mindset and her experience of self-transcendence help to normalize and/or to scientize the subject matters of metaphysicism, mysticism, etc.?

A perspective on automated rapid eye movement sleep assessment.

Rapid eye movement sleep is associated with distinct changes in various biomedical signals that can be easily captured during sleep, lending themselves to automated sleep staging using machine learning systems. Here, we provide a perspective on the critical characteristics of biomedical signals associated with rapid eye movement sleep and how they can be exploited for automated sleep assessment. We summarise key historical developments in automated sleep staging systems, having now achieved classification accuracy on par with human expert scorers and their role in the clinical setting. We also discuss rapid eye movement sleep assessment with consumer sleep trackers and its potential for unprecedented sleep assessment on a global scale. We conclude by providing a future outlook of computerised rapid eye movement sleep assessment and the role AI systems may play.

Multiple Complicated Concurrent Hernias in a Single Patient: A Case Report.

Multiple complicated concurrent hernias with obturator hernia and paraesophageal hernia unusually occur in clinical settings. The obturator hernias belong to a rare pelvic hernia that accounts for a minority of all abdominal hernias. Besides, paraesophageal hernias occur commonly in elderly female patients. Clinical manifestations of these hernias are usually unspecific and the diagnosis is based on computed tomography (CT). In this paper, we presented a case of multiple complicated hernias in an 81-year-old woman. She was admitted to our hospital due to intestinal obstruction that was caused by a simultaneous obturator and paraesophageal hernia. She was successfully treated by laparoscopic hernia repair. Postoperative progression was favorable. She was then discharged from the hospital after four hospital days.

Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease.

Alzheimer's Disease (AD) is the leading cause of dementia. It results in cortical thickness changes and is associated with a decline in cognition and behaviour. Such decline affects multiple important day-to-day functions, including memory, language, orientation, judgment and problem-solving. Recent research has made important progress in identifying brain regions associated with single outcomes, such as individual AD status and general cognitive decline. The complex projection from multiple brain areas to multiple AD outcomes, however, remains poorly understood. This makes the assessment and especially the prediction of multiple AD outcomes - each of which may unveil an integral yet different aspect of the disease - challenging, particularly when some are not strongly correlated. Here, uniting residual learning, partial least squares (PLS), and predictive modelling, we develop an explainable, generalisable, and reproducible method called the Residual Partial Least Squares Learning (the re-PLS Learning) to (1) chart the pathways between large-scale multivariate brain cortical thickness data (inputs) and multivariate disease and behaviour data (outcomes); (2) simultaneously predict multiple, non-pairwise-correlated outcomes; (3) control for confounding variables (e.g., age and gender) affecting both inputs and outcomes and the pathways in-between; (4) perform longitudinal AD disease status classification and disease severity prediction. We evaluate the performance of the proposed method against a variety of alternatives on data from AD patients, subjects with mild cognitive impairment (MCI), and cognitively normal individuals (n=1,196) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results unveil pockets of brain areas in the temporal, frontal, sensorimotor, and cingulate areas whose cortical thickness may be respectively associated with declines in different cognitive and behavioural subdomains in AD. Finally, we characterise re-PLS' geometric interpretation and mathematical support for delivering meaningful neurobiological insights and provide an open software package (re-PLS) available at https://github.com/thanhvd18/rePLS.

Preparation of bio-based porous material with high oil adsorption capacity from bio-polyurethane and sugarcane bagasse.

This work presents the fabrication of bio-based porous material for highly efficient removing of oil from oil/water system. The sunflower oil-based polyol was synthesized and then used to replace the petro-polyol in the simultaneous preparation of a sugarcane bagasse-polyurethane composite (SC-PU composite) by inserting sugarcane fiber filler into the PU matrix. The bio-polyol was obtained from sunflower oil with a hydroxyl number of 182 mg KOH g-1, and functionality of 3.5 OH groups per mol. The bio-polyol and the newly designed bio-based SC-PU composite were characterized by NMR, FT-IR and SEM analysis. The effect of several parameters such as bio-polyol/petro-polyol ratio, dosage of adding sugarcane fiber and size of filler particles on oil adsorption capacity of a new sorbent material were also investigated. Oil sorption capacity of the newly designed sorbent was relatively high, up to 15.2 g g-1 when 20% sugarcane bagasse with a particle size of 1 mm was added into the bio-polyurethane matrix. This is nearly four times higher than that of neat PU foam without the biomass filler and lignocellulosic materials. This finding demonstrated the importance of selecting the right components to fabricate a cost-effective, highly renewable and biodegradable sorbent with high oil-water separation efficiency, reducing the use of chemicals from fossil sources.

CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities.

Sleep abnormalities can have severe health consequences. Automated sleep staging, i.e. labelling the sequence of sleep stages from the patient's physiological recordings, could simplify the diagnostic process. Previous work on automated sleep staging has achieved great results, mainly relying on the EEG signal. However, often multiple sources of information are available beyond EEG. This can be particularly beneficial when the EEG recordings are noisy or even missing completely. In this paper, we propose CoRe-Sleep, a Coordinated Representation multimodal fusion network that is particularly focused on improving the robustness of signal analysis on imperfect data. We demonstrate how appropriately handling multimodal information can be the key to achieving such robustness. CoRe-Sleep tolerates noisy or missing modalities segments, allowing training on incomplete data. Additionally, it shows state-of-the-art performance when testing on both multimodal and unimodal data using a single model on SHHS-1, the largest publicly available study that includes sleep stage labels. The results indicate that training the model on multimodal data does positively influence performance when tested on unimodal data. This work aims at bridging the gap between automated analysis tools and their clinical utility.

The roles, challenges, and merits of the p value.

Since the 18th century, the p value has been an important part of hypothesis-based scientific investigation. As statistical and data science engines accelerate, questions emerge: to what extent are scientific discoveries based on p values reliable and reproducible? Should one adjust the significance level or find alternatives for the p value? Inspired by these questions and everlasting attempts to address them, here, we provide a systematic examination of the p value from its roles and merits to its misuses and misinterpretations. For the latter, we summarize modest recommendations to handle them. In parallel, we present the Bayesian alternatives for seeking evidence and discuss the pooling of p values from multiple studies and datasets. Overall, we argue that the p value and hypothesis testing form a useful probabilistic decision-making mechanism, facilitating causal inference, feature selection, and predictive modeling, but that the interpretation of the p value must be contextual, considering the scientific question, experimental design, and statistical principles.

L-SeqSleepNet: Whole-cycle Long Sequence Modeling for Automatic Sleep Staging.

Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We thus introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, which takes into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single EEG channel input. Our analyses also show that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages. Moreover the network becomes much more robust, meaning that for all subjects where the baseline method had exceptionally poor performance, their performance are improved significantly. Finally, the computation time only grows at a sub-linear rate when the sequence length increases.

Advancing the study of life and death education: theoretical framework and research inquiries for further development.

Life and death education, also known as life education and death education, is an interesting subject that may coincide with the subject of lifespan development. In brief, from our theoretical perspective, which guides our teaching and curriculum development, life education considers personal understanding of life functioning on a daily basis, whereas death education explores matters that are related to death and dying. For example, how can a social worker utilize his life knowledge, or life wisdom, to assist a relative to understand the intricate nature of death? In a similar vein, how can a senior citizen use her personal experience of Buddhist meditation practice to overcome a minor Covid setback? Central to our teaching practice is the premise of 'active transformation' (i.e., transforming life knowledge into positive practice) and the premise of 'theoretical infusion' (e.g., the infusion of a distinctive epistemological belief in the teaching of life) that would, in turn, help to enhance and facilitate deep, meaningful understanding of life and death. The purpose of the present article is for us to discuss a proposition of a theoretical-conceptual model, which depicts the 'unification' or integration of three major viewpoints of life and death: the social viewpoint, the philosophical viewpoint, and the psychological viewpoint. We theorize that unification of the three theoretical viewpoints may help provide grounding for effective teaching and holistic understanding of the subject contents of life and death. Such discourse, importantly, may also assist to advance the scope and complexity of the lifespan development subject. Finally, in addition to our theoretical-conceptual model of life and death, we propose three major research inquiries for development: the meaning of situated mindset, the underlying nature of spiritual transcendence, and proposition of appropriate methodological accounts for usage. Overall, then, we purport that our conceptual analysis and discussion overview, based on philosophical reflection, may serve to stimulate interest, intellectual curiosity, scholarly dialog, etc.

Spin-orbit interaction in nanofiber-based Brillouin scattering.

Angular momentum is an important physical property that plays a key role in light-matter interactions, such as spin-orbit interaction. Here, we investigate theoretically and experimentally the spin-orbit interaction between a circularly polarized optical (spin) and a transverse vortex acoustic wave (orbital) using Brillouin backscattering in a silica optical nanofiber. We specifically explore the state of polarization of Brillouin backscattering induced by the TR21 torso-radial vortex acoustic mode that carries an orbital angular momentum. Using a full-vectorial theoretical model, we predict and observe two operating regimes for which the backscattered Brillouin signal is either depolarized or circularly polarized, depending on the input pump polarization. We demonstrate that when the pump is circularly polarized and thus carries a spin angular momentum, the backscattered signal undergoes a handedness reversal of circular polarization due to opto-acoustic spin-orbit interaction and the conservation of overall angular momentum.

Improving electrochemical performance of hybrid electrode materials by a composite of nanocellulose, reduced oxide graphene and polyaniline.

Novel ternary composites of polyaniline (PANI), reduced graphene oxide (RGO), and cellulose nanofibers (CNFs) are prepared by a chemical method for hybrid supercapacitors. CNFs were extracted from sugarcane bagasse waste in sugar production, by physicochemical processes. The composites were investigated as electrode-active materials for hybrid supercapacitors. The obtained results revealed that the presence of RGO and CNFs in the composites led to enhanced electrochemical performances, such as capacitance, rate capability, and long-term cyclability of the composite. The optimal composite of CNFs/RGO/PANI with a weight ratio of 4/16/80 can deliver the highest specific capacitance at 566.2 F g-1 under an applied current of 1 A g-1. After 1000 cycles of repetitive charge and discharge, the optimal composite retains 85.4% of its initial capacitance, whereas the PANI electrode obtained only 36.7% under the same conditions. Moreover, the supercapacitive performance is also strongly dependent on the component of the ternary composites. Overall, the composite is a promising material for hybrid supercapacitors; and the CNF component is a renewable material and a product of waste materials.

Advancing the study of levels of best practice pre-service teacher education students from Spain: Associations with both positive and negative achievement-related experiences.

The study of optimal best practice, coinciding with a person's 'motivational mindset', is an interesting research inquiry for development. Optimal best practice, in brief, relates to the maximization of a person's state of functioning (e.g., cognitive functioning). Moreover, the nature of optimal best practice is positive and motivational, helping individuals to flourish in different courses of action (e.g., academic performance at school). Several research undertakings, non-experimental in design, have provided clear and consistent evidence to substantiate the existing viewpoints and perspectives of optimal best practice. Our proposed investigation, which involved physical education pre-service teacher students from Spain (N = 681), explored one notable focus of inquiry-namely, the formation of optimal best practice and its predictive and explanatory account on future adaptive outcomes. As such, using Likert-scale measures and path analysis techniques, we were able to identify two associative patterns: achievement of optimal best practice is positively accounted for by academic self-concept, optimism, and current best practice and, in contrast, negatively accounted for by pessimism; and that optimal best practice could act as a determinant of academic engagement for effective learning. Such associations are significant, providing relevant information for different teaching and research purposes.

Towards single-photon Brillouin optical time domain reflectometry.

We investigate a novel distributed Brillouin optical time domain reflectometer (BOTDR) using standard telecommunication fibers based on single-photon avalanche diodes (SPADs) in gated mode, ν -BOTDR, with a range of 120 km and 10 m spatial resolution. We experimentally demonstrate the ability to perform a distributed temperature measurement, by detecting a hot spot at 100 km. Instead of using a frequency scan like conventional BOTDR, we use a frequency discriminator based on the slope of a fiber Bragg grating (FBG) to convert the count rate of the SPAD into a frequency shift. A procedure to take into account the FBG drift during the acquisition and perform sensitive and reliable distributed measurements is described. We also present the possibility to differentiate strain and temperature.

Optical nanofiber stretcher.

Piezoelectric stretching of optical fiber is a technique that enables the creation of optical delays of a few picoseconds; this is useful in a variety of applications in interferometry or optical cavities. Most commercial fiber stretchers involve lengths of fiber of a few tens of meters. Using a 120-mm-long optical micro-nanofiber, we can create a compact optical delay line that achieves tunable delays of up to 19 ps at telecommunication wavelengths. The high elasticity of silica and the micron-scale diameter allow this significant optical delay to be achieved with low tensile force while keeping the overall length short. We successfully report both static and dynamic operation of this novel, to the best of our knowledge, device. It could find application in interferometry and laser cavity stabilization, where short optical paths and strong resistance to the environment would be required.

Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones.

Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using smartphone sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.

Automatic sleep staging for the young and the old - Evaluating age bias in deep learning.

BACKGROUND: Various deep-learning systems have been proposed for automated sleep staging. Still, the significance of age-specific underrepresentation in training data and the resulting errors in clinically used sleep metrics are unknown. METHODS: We adopted XSleepNet2, a deep neural network for automated sleep staging, to train and test models using polysomnograms of 1232 children (7.0 ± 1.4 years) and 3757 adults (56.9 ± 19.4 years) and 2788 older adults (mean 80.7 ± 4.2 years). We developed four separate sleep stage classifiers using exclusively pediatric (P), adult (A), older adults (O) as well as PSG from mixed cohorts: pediatric, adult, and older adult (PAO). Results were compared against an alternative sleep stager (DeepSleepNet) for validation purposes. RESULTS: When pediatric PSG was classified by XSleepNet2 exclusively trained on pediatric PSG, the overall accuracy was 88.9%, dropping to 78.9% when subjected to a system trained exclusively on adult PSG. Errors performed by the system staging PSG of older people were comparably lower. However, all systems produced significant errors in clinical markers when considering individual PSG. Results obtained with DeepSleepNet showed similar patterns. CONCLUSION: Underrepresentation of age groups, in particular children, can significantly lower the performance of automatic deep-learning sleep stagers. In general, automated sleep stagers may behave unexpectedly, limiting clinical use. Future evaluation of automated systems must pay attention to PSG-level performance and overall accuracy.

Navigating rice seedling cold resilience: QTL mapping in two inbred line populations and the search for genes.

Due to global climate change resulting in extreme temperature fluctuations, it becomes increasingly necessary to explore the natural genetic variation in model crops such as rice to facilitate the breeding of climate-resilient cultivars. To uncover genomic regions in rice involved in managing cold stress tolerance responses and to identify associated cold tolerance genes, two inbred line populations developed from crosses between cold-tolerant and cold-sensitive parents were used for quantitative trait locus (QTL) mapping of two traits: degree of membrane damage after 1 week of cold exposure quantified as percent electrolyte leakage (EL) and percent low-temperature seedling survivability (LTSS) after 1 week of recovery growth. This revealed four EL QTL and 12 LTSS QTL, all overlapping with larger QTL regions previously uncovered by genome-wide association study (GWAS) mapping approaches. Within the QTL regions, 25 cold-tolerant candidate genes were identified based on genomic differences between the cold-tolerant and cold-sensitive parents. Of those genes, 20% coded for receptor-like kinases potentially involved in signal transduction of cold tolerance responses; 16% coded for transcription factors or factors potentially involved in regulating cold tolerance response effector genes; and 64% coded for protein chaperons or enzymes potentially serving as cold tolerance effector proteins. Most of the 25 genes were cold temperature regulated and had deleterious nucleotide variants in the cold-sensitive parent, which might contribute to its cold-sensitive phenotype.

Probing potential priming: Defining, quantifying, and testing the causal priming effect using the potential outcomes framework.

Having previously seen an item helps uncover the item another time, given a perceptual or cognitive cue. Oftentimes, however, it may be difficult to quantify or test the existence and size of a perceptual or cognitive effect, in general, and a priming effect, in particular. This is because to examine the existence of and quantify the effect, one needs to compare two outcomes: the outcome had one previously seen the item vs. the outcome had one not seen the item. But only one of the two outcomes is observable. Here, we argue that the potential outcomes framework is useful to define, quantify, and test the causal priming effect. To demonstrate its efficacy, we apply the framework to study the priming effect using data from a between-subjects study involving English word identification. In addition, we show that what has been used intuitively by experimentalists to assess the priming effect in the past has a sound mathematical foundation. Finally, we examine the links between the proposed method in studying priming and the multinomial processing tree (MPT) model, and how to extend the method to study experimental paradigms involving exclusion and inclusion instructional conditions.

Dry gel synthesis of hierarchical ZSM-5 zeolite using hydroxyl propyl methyl cellulose (HPMC) as a mesoporogen and its catalytic activity in alkylation reactions.

In this work, a "green" and facile method for synthesis of hierarchical ZSM-5 zeolite was presented by combination of a bio-mesoporogen and a dry gel conversion (DGC) process. ZSM-5 zeolite with high hierarchy factors and excellent mesoporosity was synthesized by adding hydroxypropylmethylcellulose (HPMC), which originated from cellulose biomass, to a zeolitic synthetic gel. The obtained zeolite samples were analyzed by X-ray diffraction (XRD), nitrogen adsorption-desorption (BET) and scanning electron microscopy (SEM) to determine their properties. The influence of crystallization time on the crystallinity of ZSM-5 zeolite was investigated. Moreover, the results showed that HPMC as a pore directing agent is an important factor for the formation of hierarchical zeolite with high mesoporosity. The as-prepared ZSM-5 sample with high pore volume, large surface area and abundant accessible acid sites, which seriously improves diffusion efficiency and catalytic activity, exhibited high catalytic performance in the benzylation reaction.

The prevalence of self-reported anxiety, depression, and associated factors among Hanoi Medical University's students during the first wave of COVID-19 pandemic.

BACKGROUND: Medical students are known to have higher levels of these issues than the general population but in Vietnam the effects of the pandemic on medical student mental health was not documented. OBJECTIVES: To estimate the prevalence and identify factors associated with self-reported anxiety disorder, depression, and perception of worsening mental health among Vietnamese medical students during the COVID-19 pandemic. METHOD: A cross-sectional study was conducted from April 7th to 29th, 2020. All students in Doctor of General Medicine, Doctor of Preventive Medicine, and Bachelor of Nursing tracks at Hanoi Medical University (3672 students) were invited to participate. Data were collected using an online questionnaire including demographic characteristics, Generalized Anxiety Disorder 7 items, Patient Health Questionnaire 9 items, Fear of COVID-19 scale, and question about worsening mental health status. Robust Poisson regression was used to assess the association between mental health status and associated factors. RESULTS: Among 1583 students (43.1% response rate), the prevalence of students screened positive for anxiety disorder was 7.3%(95%C.I.:6.0-8.7), depression was 14.5%(95%C.I.:12.8-16.3), and perceiving worsening mental health was 6.9%(95%C.I.:5.7-8.3). In multivariable regression models, significant factors associated with self-reported anxiety disorder included being male (PR = 1.99,95%C.I.:1.35-2.92), difficulty in paying for healthcare services (PR = 2.05,95%C.I.:1.39-3.01), and high level of fear of COVID-19 (Q3:PR = 2.36,95%C.I.:1.38-4.02 and Q4:PR = 4.75,95%C.I.:2.65-8.49). Significant factors associated with self-reported depression were difficulty in paying for healthcare services (PR = 1.78,95%C.I.:1.37-2.30), and high level of fear of COVID-19 (Q3:PR = 1.41,95%C.I.:1.02-1.95 and Q4:PR = 2.23,95%C.I.:1.51-3.29). Significant factors associated with perceived worsening mental health status included having clinical experience (PR = 1.83,95%C.I.:1.17-2.88) and having atypical symptoms of COVID-19 (PR = 1.96,95%C.I.:1.31-2.94). CONCLUSION: The prevalence of self-reported depression, anxiety disorder, and worsening mental health among Vietnamese students during the first wave of COVID-19 was lower than in medical students in other countries. Further investigation is needed to confirm this finding.