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.

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.

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.

Translational opportunities and challenges of invasive electrodes for neural interfaces.

Invasive brain-machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.

Targeting ATP12A, a Nongastric Proton Pump α Subunit, for Idiopathic Pulmonary Fibrosis Treatment.

Idiopathic pulmonary fibrosis (IPF) is a pathological condition of unknown etiology that results from injury to the lung and an ensuing fibrotic response that leads to the thickening of the alveolar walls and obliteration of the alveolar space. The pathogenesis is not clear, and there are currently no effective therapies for IPF. Small airway disease and mucus accumulation are prominent features in IPF lungs, similar to cystic fibrosis lung disease. The ATP12A gene encodes the α-subunit of the nongastric H+, K+-ATPase, which functions to acidify the airway surface fluid and impairs mucociliary transport function in patients with cystic fibrosis. It is hypothesized that the ATP12A protein may play a role in the pathogenesis of IPF. The authors' studies demonstrate that ATP12A protein is overexpressed in distal small airways from the lungs of patients with IPF compared with normal human lungs. In addition, overexpression of the ATP12A protein in mouse lungs worsened bleomycin induced experimental pulmonary fibrosis. This was prevented by a potassium competitive proton pump blocker, vonoprazan. These data support the concept that the ATP12A protein plays an important role in the pathogenesis of lung fibrosis. Inhibition of the ATP12A protein has potential as a novel therapeutic strategy in IPF treatment.

Nicotinamide riboside and pterostilbene reduces markers of hepatic inflammation in NAFLD: A double-blind, placebo-controlled clinical trial.

BACKGROUND AND AIMS: The prevalence of NAFLD is increasing globally and on a path to becoming the most frequent cause of chronic liver disease. Strategies for the prevention and treatment of NAFLD are urgently needed. APPROACH AND RESULTS: A 6-month prospective, randomized, double-blind, placebo-controlled clinical trial was conducted to assess the efficacy of daily NRPT (commercially known as Basis, a combination of nicotinamide riboside and pterostilbene) supplementation in 111 adults with NAFLD. The study consisted of three arms: placebo, recommended daily dose of NRPT (NRPT 1×), and a double dose of NRPT (NRPT 2×). NRPT appeared safe and well tolerated. At the end of the study, no significant change was seen in the primary endpoint of hepatic fat fraction with respect to placebo. However, among prespecified secondary outcomes, a time-dependent decrease in the circulating levels of the liver enzymes alanine aminotransferase (ALT) and gamma-glutamyltransferase (GGT) was observed in the NRPT 1× group, and this decrease was significant with respect to placebo. Furthermore, a significant decrease in the circulating levels of the toxic lipid ceramide 14:0 was also observed in the NRPT 1× group versus placebo, and this decrease was associated with a decrease in ALT in individuals of this group. A dose-dependent effect was not observed with respect to ALT, GGT, or ceramide 14:0 in the NRPT 2× group. CONCLUSIONS: This study demonstrates that NRPT at the recommended dose is safe and may hold promise in lowering markers of hepatic inflammation in patients with NAFLD.

Bowel habits, faecal microbiota and faecal bile acid composition of healthy adults consuming fruit pomace fibres: two-arm, randomised, double-blinded, placebo-controlled trials.

Dietary fibre modulates gastrointestinal (GI) health and function, providing laxation, shifting microbiota, and altering bile acid (BA) metabolism. Fruit juice production removes the polyphenol- and fibre-rich pomace fraction. The effects of orange and apple pomaces on GI outcomes were investigated in healthy, free-living adults. Healthy adults were enrolled in two double-blinded, crossover trials, being randomised by baseline bowel movement (BM) frequency. In the first trial, subjects (n 91) received orange juice (OJ, 0 g fibre/d) or OJ + orange pomace (OJ + P, 10 g fibre/d) for 4 weeks, separated by a 3-week washout. Similarly, in the second trial, subjects (n 90) received apple juice (AJ, 0 g fibre/d) or AJ + apple pomace (AJ + P, 10 g fibre/d). Bowel habit diaries, GI tolerance surveys and 3-d diet records were collected throughout. Fresh faecal samples were collected from a participant subset for microbiota and BA analyses in each study. Neither pomace interventions influenced BM frequency. At Week 4, OJ + P tended to increase (P = 0·066) GI symptom occurrence compared with OJ, while AJ + P tended (P = 0·089) to increase flatulence compared with AJ. Faecalibacterium (P = 0·038) and Negativibacillus (P = 0·043) were differentially abundant between pre- and post-interventions in the apple trial but were no longer significant after false discovery rate (FDR) correction. Baseline fibre intake was independently associated with several microbial genera in both trials. Orange or apple pomace supplementation was insufficient to elicit changes in bowel habits, microbiota diversity or BA of free-living adults with healthy baseline BM. Future studies should consider baseline BM frequency and habitual fibre intake.

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.

Dietary Supplements for Weight Management: A Narrative Review of Safety and Metabolic Health Benefits.

Dietary supplements for weight management include myriad ingredients with thermogenic, lipotropic, satiety, and other metabolic effects. Recently, the safety of this product category has been questioned. In this review, we summarize the safety evidence as well as relevant clinical findings on weight management and metabolic effects of six representative dietary supplement ingredients: caffeine, green tea extract (GTE), green coffee bean extract (GCBE), choline, glucomannan, and capsaicinoids and capsinoids. Of these, caffeine, GTE (specifically epigallocatechin gallate [EGCG]), and choline have recommended intake limits, which appear not to be exceeded when used according to manufacturers' instructions. Serious adverse events from supplements with these ingredients are rare and typically involve unusually high intakes. As with any dietary component, the potential for gastrointestinal intolerance, as well as possible interactions with concomitant medications/supplements exist, and the health status of the consumer should be considered when consuming these components. Most of the ingredients reviewed also improved markers of metabolic health, such as glucose, lipids, and blood pressure, although the data are limited for some. In summary, weight management supplements containing caffeine, GTE, GCBE, choline, glucomannan, and capsaicinoids and capsinoids are generally safe when taken as directed and demonstrate metabolic health benefits for overweight and obese people.

Thermometric lateral flow immunoassay with colored latex beads as reporters for COVID-19 testing.

Temperature sensing is a promising method of enhancing the detection sensitivity of lateral flow immunoassay (LFIA) for point-of-care testing. A temperature increase of more than 100 °C can be readily achieved by photoexcitation of reporters like gold nanoparticles (GNPs) or colored latex beads (CLBs) on LFIA strips with a laser power below 100 mW. Despite its promise, processes involved in the photothermal detection have not yet been well-characterized. Here, we provide a fundamental understanding of this thermometric assay using non-fluorescent CLBs as the reporters deposited on nitrocellulose membrane. From a measurement for the dependence of temperature rises on the number density of membrane-bound CLBs, we found a 1.3-fold (and 3.2-fold) enhancement of the light absorption by red (and black) latex beads at 520 nm. The enhancement was attributed to the multiple scattering of light in this highly porous medium, a mechanism that could make a significant impact on the sensitivity improvement of LFIA. The limit of detection was measured to be 1 × 105 particles/mm2. In line with previous studies using GNPs as the reporters, the CLB-based thermometric assay provides a 10× higher sensitivity than color visualization. We demonstrated a practical use of this thermometric immunoassay with rapid antigen tests for COVID-19.

SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification.

BACKGROUND: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. METHODS: Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the model's decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection. RESULTS: Making sense of the transformer's self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts. CONCLUSION: Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes. SIGNIFICANCE: Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings.

The Effect of Multi-Vitamin/Multi-Mineral Supplementation on Nutritional Status in Older Adults Receiving Drug Therapies: A Double-Blind, Placebo-Controlled Trial.

Polypharmacy regimens may increase the susceptibility of older adults to micronutrient inadequacy and deficiency via impairment of nutritional status. We hypothesized that a multi-vitamin-mineral supplement (MVMS) could improve nutritional status in older adults prescribed diuretics, metformin, and/or proton pump inhibitors (PPIs). We conducted a randomized, double-blind, placebo controlled, parallel clinical trial in which eligible subjects were instructed to consume either a MVMS or placebo for 16 wk. Fasting blood was collected at baseline, 8, and 16 wk and the status of selected vitamins and minerals determined. Thirty-five and 19 men and women aged 45-75 yrs in the in MVMS and placebo arms, respectively, completed the trial. The mean total number of medications among the three drug classes taken by participants did not differ between two groups. The status of vitamins B1, B12, C and folate and calcium, copper, magnesium and zinc at baseline were within normal ranges. The MVMS group had a greater change in nutrient status after 16 wk compared to the placebo group for serum folate (7.5 vs. -1.6 ng/mL, p < 0.0001), vitamin B12 (159.2 vs. -33.9 pg/mL, p = 0.007), and plasma vitamin C (0.2 vs. 0.0 mg/dL, p = 0.004). Other measured vitamins and minerals were not significantly changed during the intervention. In conclusion, the status of vitamins B12, C and folate improved with MVMS but remained within normal ranges in older adults taking diuretics, metformin, and/or PPIs.

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging.

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. Learning from raw polysomnography signals and their derived time-frequency image representations has been prevalent. However, learning from multi-view inputs (e.g., both the raw signals and the time-frequency images) for sleep staging is difficult and not well understood. This work proposes a sequence-to-sequence sleep staging model, XSleepNet,1 that is capable of learning a joint representation from both raw signals and time-frequency images. Since different views may generalize or overfit at different rates, the proposed network is trained such that the learning pace on each view is adapted based on their generalization/overfitting behavior. In simple terms, the learning on a particular view is speeded up when it is generalizing well and slowed down when it is overfitting. View-specific generalization/overfitting measures are computed on-the-fly during the training course and used to derive weights to blend the gradients from different views. As a result, the network is able to retain the representation power of different views in the joint features which represent the underlying distribution better than those learned by each individual view alone. Furthermore, the XSleepNet architecture is principally designed to gain robustness to the amount of training data and to increase the complementarity between the input views. Experimental results on five databases of different sizes show that XSleepNet consistently outperforms the single-view baselines and the multi-view baseline with a simple fusion strategy. Finally, XSleepNet also outperforms prior sleep staging methods and improves previous state-of-the-art results on the experimental databases.

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis.

Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.

Effect of COVID-19 Pandemic-Induced Dietary and Lifestyle Changes and Their Associations with Perceived Health Status and Self-Reported Body Weight Changes in India: A Cross-Sectional Survey.

Home confinement during the COVID-19 pandemic is accompanied by dramatic changes in lifestyle and dietary behaviors that can significantly influence health. We conducted an online cross-sectional survey to assess COVID-19 pandemic-induced dietary and lifestyle changes and their association with perceived health status and self-reported body weight changes among 1000 Indian adults in early 2021. Positive improvements in dietary habits, e.g., eating more nutritious (85% of participants) and home-cooked food (89%) and an increase in overall nutrition intake (79%), were observed. Sixty-five percent of participants self-reported increased oat consumption to support immunity. There were some negative changes, e.g., more binge eating (69%), eating more in between meals (67%), and increasing meal portion size (72%). Two-thirds of participants reported no change in lifestyles, whereas 21 and 23% reported an increase, and 13 and 10% reported a decrease in physical activity and sleep, respectively. Overall, 64 and 65% of participants reported an improvement in perceived health and an increase in body weight during the COVID-19 period compared to pre-COVID-19, respectively. The top motivations for improving dietary habits included improving physical and mental health and building immunity. In conclusion, the overall perceived health was improved and there was an increase in self-reported body weight in most participants during COVID-19. Diet emerged as the most crucial determinant for these changes.

Infective thrombophlebitis after great saphenous vein cyanoacrylate embolization.

The use of cyanoacrylate embolization has increased in interest as a safe, effective, and minimally invasive method to treat symptomatic saphenous reflux. The procedure is generally well tolerated by patients, and complications such as phlebitis are minor and usually self-limiting. Postprocedural infections have been described but occur infrequently and usually in the early postoperative course. In the present case report, we have described a late-onset infective thrombophlebitis of the great saphenous vein after cyanoacrylate embolization, requiring surgical excision of the treated vein.

Resolution of Diplopia and Torsion Following Yttrium Aluminum Garnet (YAG) Laser Capsulotomy in a Patient With the Ocular Tilt Reaction.

The ocular tilt reaction is a rare neuro-ophthalmological phenomenon commonly occurring due to an injury to the vestibulo-ocular pathway, or a thalamic, brainstem, or cerebellar lesion. Most ocular tilt reactions are transient and demonstrate spontaneous recovery. This report documents the immediate resolution of diplopia and the patient's ocular tilt reaction following visual recovery from left yttrium aluminum garnet laser capsulotomy.

Relaxation of a dense ensemble of spins in diamond under a continuous microwave driving field.

Decoherence of Rabi oscillation in a two-level quantum system consists of two components, a simple exponential decay and a damped oscillation. In dense-ensemble spin systems like negatively charged nitrogen-vacancy (NV-) centers in diamond, fast quantum state decoherence often obscures clear observation of the Rabi nutation. On the other hand, the simple exponential decay (or baseline decay) of the oscillation in such spin systems can be readily detected but has not been thoroughly explored in the past. This study investigates in depth the baseline decay of dense spin ensembles in diamond under continuously driving microwave (MW). It is found that the baseline decay times of NV- spins decrease with the increasing MW field strength and the MW detuning dependence of the decay times shows a Lorentzian-like spectrum. The experimental findings are in good agreement with simulations based on the Bloch formalism for a simple two-level system in the low MW power region after taking into account the effect of inhomogeneous broadening. This combined investigation provides new insight into fundamental spin relaxation processes under continuous driving electromagnetic fields and paves ways to better understanding of this underexplored phenomena using single NV- centers, which have shown promising applications in quantum computing and quantum metrology.