Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of histo‐blood group antigens and predisposition to gastrointestinal diseases

Infectious gastroenteritis is a common illness afflicting people worldwide. The two most common etiological agents of viral gastroenteritis, rotavirus and norovirus are known to recognize histo‐blood group antigens (HBGAs) as attachment receptors. ABO, Lewis, and secretor HBGAs are distributed abundantly on mucosal epithelia, red blood cell membranes, and also secreted in biological fluids, such as saliva, intestinal content, milk, and blood. HBGAs are fucosylated glycans that have been implicated in the attachment of some enteric pathogens such as bacteria, parasites, and viruses. Single nucleotide polymorphisms in the genes encoding ABO (H), fucosyltransferase gene FUT2 (Secretor/Se), FUT3 (Lewis/Le) have been associated with changes in enzyme expression and HBGAs production. The highly polymorphic HBGAs among different populations and races influence genotype‐specific susceptibility or resistance to enteric pathogens and its epidemiology, and vaccination seroconversion. Therefore, there is an urgent need to conduct population‐based investigations to understand predisposition to enteric infections and gastrointestinal diseases. This review focuses on the relationship between HBGAs and predisposition to common human gastrointestinal illnesses caused by viral, bacterial, and parasitic agents.
Quantum modulation against electromagnetic interference

Periodic signals in electrical and electronic equipment can cause interference in nearby devices. Randomized modulation of those signals spreads their energy through the frequency spectrum and can help to mitigate electromagnetic interference problems. The inherently random nature of quantum phenomena makes them a good control signal. I present a quantum modulation method based on the random statistics of quantum light. The paper describes pulse width modulation schemes where a Poissonian light source acts as a random control that spreads the energy of the potential interfering signals. I give an example application for switching-mode power supplies and comment the further possibilities of the method.
The “Whack-a-Mole” Challenge of Hypnosis Research: A Commentary Regarding “Guidelines for the Assessment of Efficacy of Clinical Hypnosis Applications”

ABSTRACT In this short commentary, the author acknowledges the merits of trying to establish treatment guidelines for the use of hypnosis in treatment and applauds the efforts and intentions of the Task Force for Establishing Efficacy Standards for Clinical Hypnosis. He identifies a few of the complex issues in trying to promote guidelines for conducting research and clinical practice in the domain of hypnosis; these include the difficulties in defining hypnosis and hypnotically-based interventions, the divergent ways hypnosis is applied in actual practice by clinicians who rely on their own understandings and biases in designing and delivering hypnosis, and the inevitable variations in skill level across practitioners. To their credit, the Task Force has considered these and other practical issues in their approach to formulating guidelines.
Lipid Readjustment in Yarrowia lipolytica Odd-Chain Fatty Acids Producing Strains

Yarrowia lipolytica is a promising oleaginous yeast for producing unusual lipids, such as odd-chain fatty acids (OCFA). Their diverse applications and low natural production make OCFA particularly interesting. In recent studies, inhibiting the catabolic pathway of precursor, boosting precursor pools, and optimizing substrate combination greatly improved the production of OCFA in Y. lipolytica. We explored the lipid readjustment of OCFA in engineered Y. lipolytica strains. NPLC-Corona-CAD® evidenced a time-dependent overproduction of free fatty acids, diglycerides, and phosphatidylcholine (PC) in obese LP compared to obese L. Phosphatidylethanolamine (PE) and phosphatidylinositol, largely overproduced in obese LP at 72 h compared to obese L, vanished at 216 h. The fatty acyls (FAs) composition of glycero- and glycerophospholipids was determined by NPLC-APPI+-HRMS from in-source generated monoacylglycerol-like fragment ions. C18:1 and C17:1 were predominant acylglycerols in obese L and obese LP, respectively. Phosphatidic acid, PE, and PC exhibited similar FAs composition but differed in their molecular species distributions. Cardiolipin (CL) is known to contain mostly C18:2 FAs corresponding to the composition in obese L, 50% of C18:2, and 35% of C18:1. In obese LP, both FAs dropped to drop to 20%, and C17:1 were predominant, reaching 55%. We hypothesize that CL-modified composition in obese LPs may alter mitochondrial function and limit lipid production.
Intravital calcium imaging in myeloid leukocytes identifies calcium frequency spectra as indicators of functional states

The assessment of leukocyte activation in vivo is mainly based on surrogate parameters, such as cell shape changes and migration patterns. Consequently, additional parameters are required to dissect the complex spatiotemporal activation of leukocytes during inflammation. Here, we showed that intravital microscopy of myeloid leukocyte Ca2+ signals with Ca2+ reporter mouse strains combined with bioinformatic signal analysis provided a tool to assess their activation in vivo. We demonstrated by two-photon microscopy that tissue-resident macrophages reacted to sterile inflammation in the cremaster muscle with Ca2+ transients in a distinct spatiotemporal pattern. Moreover, through high-resolution, intravital spinning disk confocal microscopy, we identified the intracellular Ca2+ signaling patterns of neutrophils during the migration cascade in vivo. These patterns were modulated by the Ca2+ channel Orai1 and Gαi-coupled GPCRs, whose effects were evident through analysis of the range of frequencies of the Ca2+ signal (frequency spectra), which provided insights into the complex patterns of leukocyte Ca2+ oscillations. Together, these findings establish Ca2+ frequency spectra as an additional dimension to assess leukocyte activation and migration during inflammation in vivo. Description Decoding intracellular calcium signals in immune cells in live mice reveals their functional status during inflammation. Decoding Ca2+ signals The immune responses to infection and sterile inflammation are complex, involving the coordinated recruitment and activation of multiple cell types. To better understand the activation states of immune cells in the context of inflammation, Mehari et al. used various in vivo imaging techniques with transgenic Ca2+ reporter mice to measure the intensity and frequency of intracellular Ca2+ signals in macrophages and neutrophils as they responded to a laser-induced tissue injury. Analysis of these data revealed the kinetics of recruitment of tissue-resident macrophages and identified distinct patterns of intracellular Ca2+ signals in neutrophils that characterized the different stages of their recruitment from the blood to sites of injury. These findings suggest that in vivo monitoring of Ca2+ signals provides insights into the functional states of immune cells.
Association between trends of mortality and incidence, survival and stage at diagnosis for six digestive and respiratory cancers in United States (2009–2013)

Background A decrease in cancer mortality has been reported in the USA, possibly due to decreased incidence, downstaging and improved survival. The aim of the present study is to estimate the contribution of these factors on the trend in cancer mortality. Methods Data on incidence, mortality, stage at diagnosis, and overall and stage-specific survival for six common digestive and respiratory cancers (esophagus, stomach, colorectal, liver, pancreas and lung) during 2009–2013 in the USA from the surveillance, epidemiology and end results (SEER) program, was analyzed using generalized linear models separately among men and women. Results Our study showed a decrease in mortality for esophageal (−0.09/100 000/year and −0.03/100 000/year), stomach (−0.11/100 000/year and −0.05/100 000/year), colorectal (−0.45/100 000/year and −0.29/100 000/year) and lung cancer (−1.89/100 000/year in men and −0.78/100 000/year in women) in men and women, respectively: for all of them, except lung cancer in women, there was a decrease in the incidence of comparable or greater magnitude; stage distribution and survival also contributed to the decrease in mortality for lung and colorectal cancer. Mortality from pancreatic cancer was stable: an increase in incidence was counterbalanced by an improvement in survival. Mortality from liver cancer increased, driven by an increase in mortality that was not offset by favorable trends in stage distribution and survival. Conclusions Trends in mortality were primarily affected by changes in incidence; an increase in the proportion of local stage at diagnosis and improved survival, although evident for some cancers, played a lesser role in mortality trends.
A Novel CaMKII Inhibitory Peptide Blocks Relapse to Morphine Seeking by Influencing Synaptic Plasticity in the Nucleus Accumbens Shell

Drugs of abuse cause enduring functional disorders in the brain reward circuits, leading to cravings and compulsive behavior. Although people may rehabilitate by detoxification, there is a high risk of relapse. Therefore, it is crucial to illuminate the mechanisms of relapse and explore the therapeutic strategies for prevention. In this research, by using an animal model of morphine self-administration in rats and a whole-cell patch–clamp in brain slices, we found changes in synaptic plasticity in the nucleus accumbens (NAc) shell were involved in the relapse to morphine-seeking behavior. Compared to the controls, the amplitude of long-term depression (LTD) induced in the medium spiny neurons increased after morphine self-administration was established, recovered after the behavior was extinguished, and increased again during the relapse induced by morphine priming. Intravenous injection of MA, a new peptide obtained by modifying Ca2+/calmodulin-dependent protein kinase II (CaMKII) inhibitor “myr-AIP”, decreased CaMKII activity in the NAc shell and blocked the reinstatement of morphine-seeking behavior without influence on the locomotor activity. Moreover, LTD was absent in the NAc shell of the MA-pretreated rats, whereas it was robust in the saline controls in which morphine-seeking behavior was reinstated. These results indicate that CaMKII regulates morphine-seeking behavior through its involvement in the change of synaptic plasticity in the NAc shell during the relapse, and MA may be of great value in the clinical treatment of relapse to opioid seeking.
Cancer treatment-related cardiac dysfunction

Objectives This paper describes cardiotoxicity and cancer treatment-related cardiac dysfunction (CTRCD). Long-term sequelae of treatment are important, and changing, and may manifest when a patient is under the care of a supportive care service. Methods Key messages for supportive and palliative care clinicians are outlined to facilitate identification and management of CTRCD. Results Not all cardiotoxicity is alike. Types of cardiotoxicity, cardiac complications of immunotherapy, the challenge of autonomic nervous system dysfunction in cancer and management of cardiotoxicity are highlighted. Conclusions The key strategies are early detection of cardiotoxicity, monitoring for development of CTRCD during treatment and surveillance in survivorship.
Is Attention All That NeRF Needs?

We present Generalizable NeRF Transformer (GNT), a transformer-based architecture that reconstructs Neural Radiance Fields (NeRFs) and learns to renders novel views on the fly from source views. While prior works on NeRFs optimize a scene representation by inverting a handcrafted rendering equation, GNT achieves neural representation and rendering that generalizes across scenes using transformers at two stages. (1) The view transformer leverages multi-view geometry as an inductive bias for attention-based scene representation, and predicts coordinate-aligned features by aggregating information from epipolar lines on the neighboring views. (2) The ray transformer renders novel views using attention to decode the features from the view transformer along the sampled points during ray marching. Our experiments demonstrate that when optimized on a single scene, GNT can successfully reconstruct NeRF without an explicit rendering formula due to the learned ray renderer. When trained on multiple scenes, GNT consistently achieves state-of-the-art performance when transferring to unseen scenes and outperform all other methods by ~10% on average. Our analysis of the learned attention maps to infer depth and occlusion indicate that attention enables learning a physically-grounded rendering. Our results show the promise of transformers as a universal modeling tool for graphics. Please refer to our project page for video results: https://vita-group.github. io/GNT/.
Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms

Learning in high dimensional continuous tasks is challenging, mainly when the experience replay memory is very limited. We introduce a simple yet effective experience sharing mechanism for deterministic policies in continuous action domains for the future off-policy deep reinforcement learning applications in which the allocated memory for the experience replay buffer is limited. To overcome the extrapolation error induced by learning from other agents' experiences, we facilitate our algorithm with a novel off-policy correction technique without any action probability estimates. We test the effectiveness of our method in challenging OpenAI Gym continuous control tasks and conclude that it can achieve a safe experience sharing across multiple agents and exhibits a robust performance when the replay memory is strictly limited.
Shape of the outer stellar warp in the Large Magellanic Cloud disk

Warps are vertical distortions of the stellar or gaseous disks of galaxies. One of the proposed scenarios for the formation of warps involves tidal interactions among galaxies. A recent study identified a stellar warp in the outer regions of the south-western (SW) disk of the Large Magellanic Cloud (LMC) and suggested that it might have originated due to the tidal interaction between the LMC and the Small Magellanic Cloud (SMC). Due to the limited spatial coverage of the data, the authors could not investigate the counterpart of this warp in the north-eastern (NE) region, which is essential to understanding the global shape, nature, and origin of the outer LMC warp. In this work, we study the structure of the LMC disk using data on red clump stars from the Gaia Early Data Release 3 (EDR3), which cover the entire Magellanic system. We detected a warp in the NE outer LMC disk which is deviated from the disk plane in the same direction as that of the SW outer warp, but with a lower amplitude. This suggests that the outer LMC disk has an asymmetric stellar warp, which is likely to be a U-shaped warp. Our result provides an observational constraint to the theoretical models of the Magellanic system aimed at improving the understanding the LMC-SMC interaction history.
Visualizing Confidence Intervals for Critical Point Probabilities in 2D Scalar Field Ensembles

An important task in visualization is the extraction and highlighting of dominant features in data to support users in their analysis process. Topological methods are a well-known means of identifying such features in deterministic fields. However, many real-world phenom-ena studied today are the result of a chaotic system that cannot be fully described by a single simulation. Instead, the variability of such systems is usually captured with ensemble simulations that pro-duce a variety of possible outcomes of the simulated process. The topological analysis of such ensemble data sets and uncertain data, in general, is less well studied. In this work, we present an approach for the computation and visual representation of confidence intervals for the occurrence probabilities of critical points in ensemble data sets. We demonstrate the added value of our approach over existing methods for critical point prediction in uncertain data on a synthetic data set and show its applicability to a data set from climate research.
On Selecting and Conditioning in Multiple Testing and Selective Inference

We investigate a class of methods for selective inference that condition on a selection event. Such methods follow a two-stage process. First, a data-driven collection of hypotheses is chosen from some large universe of hypotheses. Subsequently, inference takes place within this data-driven collection, conditioned on the information that was used for the selection. Examples of such methods include basic data splitting, as well as modern data carving methods and post-selection inference methods for lasso coefficients based on the polyhedral lemma. In this paper, we adopt a holistic view on such methods, considering the selection, conditioning, and final error control steps together as a single method. From this perspective, we demonstrate that multiple testing methods defined directly on the full universe of hypotheses are always at least as powerful as selective inference methods based on selection and conditioning. This result holds true even when the universe is potentially infinite and only implicitly defined, such as in the case of data splitting. We give general theory and intuitions before investigating in detail several case studies where a shift to a non-selective or unconditional perspective can yield a power gain.
Adapting the Exploration–Exploitation Balance in Heterogeneous Swarms: Tracking Evasive Targets

Abstract There has been growing interest in the use of multi-robot systems in various tasks and scenarios. The main attractiveness of such systems is their flexibility, robustness, and scalability. An often overlooked yet promising feature is system modularity, which offers the possibility of harnessing agent specialization, while also enabling system-level upgrades. However, altering the agents’ capacities can change the exploration–exploitation balance required to maximize the system’s performance. Here, we study the effect of a swarm’s heterogeneity on its exploration–exploitation balance while tracking multiple fast-moving evasive targets under the cooperative multi-robot observation of multiple moving targets framework. To this end, we use a decentralized search and tracking strategy with adjustable levels of exploration and exploitation. By indirectly tuning the balance, we first confirm the presence of an optimal balance between these two key competing actions. Next, by substituting slower moving agents with faster ones, we show that the system exhibits a performance improvement without any modifications to the original strategy. In addition, owing to the additional amount of exploitation carried out by the faster agents, we demonstrate that a heterogeneous system’s performance can be further improved by reducing an agent’s level of connectivity, to favor the conduct of exploratory actions. Furthermore, in studying the influence of the density of swarming agents, we show that the addition of faster agents can counterbalance a reduction in the overall number of agents while maintaining the level of tracking performance. Finally, we explore the challenges of using differentiated strategies to take advantage of the heterogeneous nature of the swarm.
CONSTRUCTION OF MULTI-BUBBLE BLOW-UP SOLUTIONS TO THE L 2 -CRITICAL HALF-WAVE EQUATION

A bstract . In this paper, we consider the L 2 -critical half-wave equation in one space dimension. Given arbitrary K distinct points, we construct multi-bubble blow-up solutions with the mass concentrating at these points separately. This result exhibits an interesting multi-bubble structure in the nonlocal setting, where some new ingredients are required to overcome the di ﬃ culty arising from the strong interaction between the bubbles and the interplay between the nonlocal dispersion and a localization procedure.
Special generic maps into ${\mathbb{R}}^5$ on closed and simply-connected manifolds and information on the cohomology of the manifolds

Morse functions with exactly two singular points on spheres and canonical projections of spheres belong to the class of a certain good class of smooth maps: special generic maps. We mainly investigate information on cohomology of closed and simply-connected manifolds admitting such maps into the 5-dimensional Euclidean spaces by investigating the embedded curves and submanifolds and their preimages. Studies on homology groups for ones into the Euclidean spaces (whose dimensions are lower than 5 in most cases) have been pioneered by Saeki and Sakuma since 1990s and later by Nishioka and Wrazidlo since 2010s. Recently the author has started pioneering studies on the cohomology for cases where the dimensions of the Euclidean spaces may not be lower than 5. Our new cases are difficult due to the situation that the dimensions of manifolds we consider are higher. Previously, we have found several restrictions on the cohomology rings. We present new restrictions by new investigations.
Axionlike particle production at beam dump experiments with distinct nuclear excitation lines

Searches for axion-like particles (ALPs) are motivated by the strong CP problem in particle physics and by unexplained dark matter in astrophysics. In this letter, we discuss novel ALP searches using monoenergetic nuclear de-excitation photons from a beam dump, using IsoDAR as an example. We show that IsoDAR can set limits that close a gap in traditional QCD axion searches using the ALP-photon coupling, as well as provide sensitivity to large regions of new parameter space in models where ALPs couple to nucleons and electrons. We also show how isotope decay-at-rest experiments may be designed to improve potential ALP production and optimize detection sensitivity.
BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). However, during the COVID-19 pandemic, data collection and analysis could be more challenging than before. This paper explored machine learning algorithms that can run efficiently on personal computers for BCI classification tasks. Also, we investigated a way to conduct such BCI experiments remotely via Zoom. The results showed that Random Forest and RBF SVM performed well for EEG classification tasks. The remote experiment during the pandemic yielded several challenges, and we discussed the possible solutions;nevertheless, we developed a protocol that grants non-experts who are interested a guideline for such data collection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.