Miracle-dz/UniFAR-FTUs huggingface dataset:
Best early-onset Parkinson dementia predictor using ensemble learning among Parkinson's symptoms, rapid eye movement sleep disorder, and neuropsychological profile

BACKGROUND Despite the frequent progression from Parkinson’s disease (PD) to Parkinson’s disease dementia (PDD), the basis to diagnose early-onset Parkinson dementia (EOPD) in the early stage is still insufficient. AIM To explore the prediction accuracy of sociodemographic factors, Parkinson's motor symptoms, Parkinson’s non-motor symptoms, and rapid eye movement sleep disorder for diagnosing EOPD using PD multicenter registry data. METHODS This study analyzed 342 Parkinson patients (66 EOPD patients and 276 PD patients with normal cognition), younger than 65 years. An EOPD prediction model was developed using a random forest algorithm and the accuracy of the developed model was compared with the naive Bayesian model and discriminant analysis. RESULTS The overall accuracy of the random forest was 89.5%, and was higher than that of discriminant analysis (78.3%) and that of the naive Bayesian model (85.8%). In the random forest model, the Korean Mini Mental State Examination (K-MMSE) score, Korean Montreal Cognitive Assessment (K-MoCA), sum of boxes in Clinical Dementia Rating (CDR), global score of CDR, motor score of Untitled Parkinson’s Disease Rating (UPDRS), and Korean Instrumental Activities of Daily Living (K-IADL) score were confirmed as the major variables with high weight for EOPD prediction. Among them, the K-MMSE score was the most important factor in the final model. CONCLUSION It was found that Parkinson-related motor symptoms (e.g., motor score of UPDRS) and instrumental daily performance (e.g., K-IADL score) in addition to cognitive screening indicators (e.g., K-MMSE score and K-MoCA score) were predictors with high accuracy in EOPD prediction.
Understanding the Use of Electronic Means to Seek Personal Health Information Among Adults in the United States

In this study, we explored who is most likely to use electronic means to seek health information and why; our research was guided by the Health Belief Model (HBM). We used the National Cancer Institute’s Health Information Trends Survey (HINTS) dataset for 2017 and 2018 (n=6,697). We found that 67.5% of US adults used electronic means to seek health information and that females (52.4%), non-Hispanic whites (63.8%), those with at least some college education (76.5%), and those with a household income of at least $50,000 per year (58.3%) were most likely to do so. Respondents reporting depression were 42% more likely to use electronic means to seek health information, suggesting that stigma about mental health may direct people with depression to seek online information to avoid face-to-face communication. Using a tablet to track progress on a health-related goal [odds ratio (OR)=2.38, p<0.0001], and using a tablet to make a decision about treating an illness (OR=6.00, p<0.0001) were highly associated with seeking electronic health information. As the internet remains largely unregulated, this suggests that health systems link their patients to trustworthy resources for preventive and treatment-related information, since many already engage in internet-guided health information-seeking.
Nonalcoholic fatty liver disease in lean subjects: Prognosis, outcomes and management

Nonalcoholic fatty liver disease (NAFLD) accounts for most cases of chronic liver disease worldwide, with an estimated global prevalence of approximately 25% and ranges from simple steatosis to nonalcoholic steatohepatitis and cirrhosis. NAFLD is strongly connected to metabolic syndrome, and for many years, fatty liver was considered to be an exclusive feature of obese patients. However, recent studies have highlighted the presence of NAFLD in non-obese subjects, with or without increased visceral fat or even in lean subjects without increased waist circumference. “Lean NAFLD” is a relatively new concept and there is significant scientific interest in understanding the differences in pathophysiology, prognosis and management compared with NAFLD in overweight/obese patients. In the present editorial, we discuss the clinical and metabolic profiles and outcomes of lean NAFLD compared with both obese NAFLD and lean healthy individuals from Asian and Western countries. Moreover, we shed light to the challenging topic of management of NAFLD in lean subjects since there are no specific guidelines for this population. Finally, we discuss open questions and issues to be addressed in the future in order to categorize NAFLD patients into lean and non-lean cohorts.
Extraction and Identification of Vitamin E from Pithecellobium Jiringan Seeds Using Supercritical Carbon Dioxide

Pithecellobium Jiringan (P. Jiringan) is traditionally known as natural herb consists of several medicinal compounds (vitamin E). Supercritical carbon dioxide extraction (SC-CO 2 ) has been proven as potential method to extract interest compound from herbs. By altering pressure and temperature, the specific compound can be extracted. In this study, the SC-CO 2 operating conditions are pressure (20.68 MPa to 55.16 MPa) and temperature (40°C to 80°C) in one hour extraction regime was used to extract vitamin E from P. jiringan . The quantification of vitamin E was analysed with Gas Chromatography Time of Flight Mass Spectrometry (GC-TOF-MS). The responses are overall oil yield and vitamin E yield. The overall oil yield was obtained at the highest condition of 55.16 MPa and 80°C with asymptotic yield of 8.06%. In contrast, the highest amount of vitamin E obtained is 0.0458mg/g sample (80.14 ppm) at the lowest extraction condition of 20.68 MPa and 40ᵒC.
CLOCS: Contrastive Learning of Cardiac Signals

The healthcare industry generates troves of unlabelled physiological data. This data can be exploited via contrastive learning, a self-supervised pre-training mechanism that encourages representations of instances to be similar to one another. We propose a family of contrastive learning methods, CLOCS, that encourages representations across time, leads, and patients to be similar to one another. We show that CLOCS consistently outperforms the state-of-the-art approach, SimCLR, on both linear evaluation and fine-tuning downstream tasks. We also show that CLOCS achieves strong generalization performance with only 25% of labelled training data. Furthermore, our training procedure naturally generates patient-specific representations that can be used to quantify patient-similarity.
Cotranscriptional Kinetic Folding of RNA Secondary Structures Including Pseudoknots

Computational prediction of ribonucleic acid (RNA) structures is an important problem in computational structural biology. Studies of RNA structure formation often assume that the process starts from a fully synthesized sequence. Experimental evidence, however, has shown that RNA folds concurrently with its elongation. We investigate RNA secondary structure formation, including pseudoknots, that takes into account the cotranscriptional effects. We propose a single-nucleotide resolution kinetic model of the folding process of RNA molecules, where the polymerase-driven elongation of an RNA strand by a new nucleotide is included as a primitive operation, together with a stochastic simulation method that implements this folding concurrently with the transcriptional synthesis. Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of conformations that are favored in actual biological systems. Our new computational tool can thus provide quantitative predictions and offer useful insights into the kinetics of RNA folding.
Connecting Context-specific Adaptation in Humans to Meta-learning.

Cognitive control, the ability of a system to adapt to the demands of a task, is an integral part of cognition. A widely accepted fact about cognitive control is that it is context-sensitive: Adults and children alike infer information about a task's demands from contextual cues and use these inferences to learn from ambiguous cues. However, the precise way in which people use contextual cues to guide adaptation to a new task remains poorly understood. This work connects the context-sensitive nature of cognitive control to a method for meta-learning with context-conditioned adaptation. We begin by identifying an essential difference between human learning and current approaches to meta-learning: In contrast to humans, existing meta-learning algorithms do not make use of task-specific contextual cues but instead rely exclusively on online feedback in the form of task-specific labels or rewards. To remedy this, we introduce a framework for using contextual information about a task to guide the initialization of task-specific models before adaptation to online feedback. We show how context-conditioned meta-learning can capture human behavior in a cognitive task and how it can be scaled to improve the speed of learning in various settings, including few-shot classification and low-sample reinforcement learning. Our work demonstrates that guiding meta-learning with task information can capture complex, human-like behavior, thereby deepening our understanding of cognitive control.
Dynamic social learning in temporally and spatially variable environments

Cultural evolution is partly driven by the strategies individuals use to learn behaviour from others. Previous experiments on strategic learning let groups of participants engage in repeated rounds of a learning task and analysed how choices are affected by individual payoffs and the choices of group members. While groups in such experiments are fixed, natural populations are dynamic, characterized by overlapping generations, frequent migrations and different levels of experience. We present a preregistered laboratory experiment with 237 mostly German participants including migration, differences in expertise and both spatial and temporal variation in optimal behaviour. We used simulation and multi-level computational learning models including time-varying parameters to investigate adaptive time dynamics in learning. Confirming theoretical predictions, individuals relied more on (conformist) social learning after spatial compared with temporal changes. After both types of change, they biased decisions towards more experienced group members. While rates of social learning rapidly declined in rounds following migration, individuals remained conformist to group-typical behaviour. These learning dynamics can be explained as adaptive responses to different informational environments. Summarizing, we provide empirical insights and introduce modelling tools that hopefully can be applied to dynamic social learning in other systems.
Experimental evolution of post-ingestive nutritional compensation in response to a nutrient-poor diet

The geometric framework of nutrition predicts that populations restricted to a single imbalanced diet should evolve post-ingestive nutritional compensation mechanisms bringing the blend of assimilated nutrients closer to physiological optimum. The evolution of such nutritional compensation is thought to be mainly driven by the ratios of major nutrients rather than overall nutritional content of the diet. We report experimental evolution of divergence in post-ingestive nutritional compensation in populations of Drosophila melanogaster adapted to diets that contained identical imbalanced nutrient ratios but differed in total nutrient concentration. Larvae from ‘Selected’ populations maintained for over 200 generations on a nutrient-poor diet with a 1 : 13.5 protein : carbohydrate ratio showed enhanced assimilation of nitrogen from yeasts and reduced assimilation of carbon from sucrose than ‘Control’ populations evolved on a diet with the same nutrient ratio but fourfold greater nutrient concentration. Compared to the Controls, the Selected larvae also accumulated less triglycerides relative to protein. This implies that the Selected populations evolved a higher assimilation rate of amino acids from the poor imbalanced diet and a lower assimilation of carbohydrates than Controls. Thus, the evolution of nutritional compensation may be driven by changes in total nutrient abundance, even if the ratios of different nutrients remain unchanged.
Physiological Profiling of Embryos and Dormant Seeds in Two Arabidopsis Accessions Reveals a Metabolic Switch in Carbon Reserve Accumulation

In flowering plants, sugars act as carbon sources providing energy for developing embryos and seeds. Although most studies focus on carbon metabolism in whole seeds, knowledge about how particular sugars contribute to the developmental transitions during embryogenesis is scarce. To develop a quantitative understanding of how carbon composition changes during embryo development, and to determine how sugar status contributes to final seed or embryo size, we performed metabolic profiling of hand-dissected embryos at late torpedo and mature stages, and dormant seeds, in two Arabidopsis thaliana accessions with medium [Columbia-0 (Col-0)] and large [Burren-0 (Bur-0)] seed sizes, respectively. Our results show that, in both accessions, metabolite profiles of embryos largely differ from those of dormant seeds. We found that developmental transitions from torpedo to mature embryos, and further to dormant seeds, are associated with major metabolic switches in carbon reserve accumulation. While glucose, sucrose, and starch predominantly accumulated during seed dormancy, fructose levels were strongly elevated in mature embryos. Interestingly, Bur-0 seeds contain larger mature embryos than Col-0 seeds. Fructose and starch were accumulated to significantly higher levels in mature Bur-0 than Col-0 embryos, suggesting that they contribute to the enlarged mature Bur-0 embryos. Furthermore, we found that Bur-0 embryos accumulated a higher level of sucrose compared to hexose sugars and that changes in sucrose metabolism are mediated by sucrose synthase (SUS), with SUS genes acting non-redundantly, and in a tissue-specific manner to utilize sucrose during late embryogenesis.
The Suppression of Th1 Response by Inducing TGF-β1 From Regulatory T Cells in Bovine Mycoplasmosis

Regulatory T cells (Tregs) regulate immune responses and maintain host immune homeostasis. Tregs contribute to the disease progression of several chronic infections by oversuppressing immune responses via the secretion of immunosuppressive cytokines, such as transforming growth factor (TGF)-β and interleukin-10. In the present study, we examined the association of Tregs with Mycoplasma bovis infection, in which immunosuppression is frequently observed. Compared with uninfected cattle, the percentage of Tregs, CD4+CD25highFoxp3+ T cells, was increased in M. bovis-infected cattle. Additionally, the plasma of M. bovis-infected cattle contained the high concentrations of TGF-β1, and M. bovis infection induced TGF-β1 production from bovine immune cells in in vitro cultures. Finally, we analyzed the immunosuppressive effects of TGF-β1 on bovine immune cells. Treatment with TGF-β1 significantly decreased the expression of CD69, an activation marker, in T cells, and Th1 cytokine production in vitro. These results suggest that the increase in Tregs and TGF-β1 secretion could be one of the immunosuppressive mechanisms and that lead to increased susceptibility to other infections in terms of exacerbation of disease during M. bovis infection.
Spatial correlation between the LDOS modulation and electronic inhomogeneity in Bi2Sr2-xLaxCuO6+δ

Low-temperature scanning tunneling microscopy has been performed on underdoped Bi2Sr1.4La0. 6CuO6+δ (Tc ~ 29 K) at 4.2 K to observe the non-dispersive spatial modulation of local density of states (LDOS). The LDOS modulation is along the Cu-O-Cu directions and its period is about 4a0, though the period is slightly distributed over the surface. The correlation between the modulation period and the nanoscale electronic inhomogeneity, which is represented by the spatial variation of the low-energy integrated LDOS (ILDOS), was analyzed. The result exhibits clear tendency of that the period in the low-ILDOS region is slightly shorter than that in the high-ILDOS region.
LETTER TO THE EDITOR: All-optical digitizing of laser transmission through thin-film GaAs on glass

All-optical transmission switching is realized by solely interfacing laser emissions at 532 nm and 633 nm in thin-film GaAs on glass. Specifically, from the viewpoint of material requirements, the switching concept is extremely undemanding. The results reveal that the inherent application potential of absorbing materials in digital photonics is so far not fully comprehended and thin films may play a part in future all-optical concepts beyond expectations.
Foxp3-Positive Regulatory T Cells Contribute to Antifibrotic Effects in Renal Fibrosis via an Interleukin-18 Receptor Signaling Pathway

Renal interstitial fibrosis is a common lesion in the process of various progressive renal diseases. Interleukin (IL)-18 is a proinflammatory cytokine that plays an important role in the induction of Th1 responses and is associated with renal interstitial fibrosis, but the mechanism of fibrosis remains unclear. Here we used IL-18 receptor alpha knockout (IL-18Rα KO) mice to investigate the role of an IL-18Rα signaling pathway in renal fibrosis in a murine model of unilateral ureteral obstruction. IL-18 Rα KO mice showed decreased renal interstitial fibrosis and increased infiltration of CD4+ T cells and Foxp3+ regulatory T cells (Tregs) compared to wildtype (WT) mice. The expression of renal transforming growth factor beta 1 (TGF-β1, which is considered an important cytokine in renal interstitial fibrosis) was not significantly different between WT and IL-18Rα KO mice. The adoptive transfer of CD4+ T cells from the splenocytes of IL-18Rα KO mice to WT mice reduced renal interstitial fibrosis and increased the number of Foxp3+ Tregs in WT mice. These results demonstrated that Foxp3+ Tregs have a protective effect in renal interstitial fibrosis via an IL-18R signaling pathway.
Threats to Belonging—Stressful Life Events and Mental Health Symptoms in Aging Men—A Longitudinal Cohort Study

Objectives: Stressful life events, especially relationship events, are frequent in adult life. We investigated the impact of a variety of stressful life events on symptoms of depression, anxiety, and hostility. Methods: We analyzed data from a large prospective cohort study of men (n = 1,437) in the Boston area (assessed in 1985, 1988, and 1991). Main outcomes were measures of depression, anxiety and hostility symptoms. We used the Elders Life Stress Inventory (ELSI) to measure stressful life events in the past 12 months and examine their association with symptoms of depression, anxiety and hostility. First, we analyzed the association of stressful life events with symptom changes; second, we categorized stressful life events into finance/work, health, relationships, loss, living situations events; and third, we estimated the specific association between relationship events and depression, anxiety and hostility symptoms using multilevel models. Results: The most frequent stressful life events were health, relationship, and financial events. Depression, anxiety, and hostility symptoms were relatively stable among men who did not experience these life events. However, those who reported life events in the past 12 months had a greater increase in symptoms of depression (+0.05; 95% CI: 0.01 to 0.10) and of hostility (+0.05; 95% CI: 0.01 to 0.09) than those who did not. Additionally, we found a significant decrease in hostility (−0.05; 95% CI: −0.08 to −0.01) in those experiencing no life events. Conclusion: Relationship events were more important than any other type of events, and were significantly associated with increased depression and hostility in aging men. Although the effects were small, the results point to a need to understand better the impact of relationships on psychopathology in the aging population.
Impact of Covid-19 on Household Food Waste: The Case of Italy

Covid-19 has significantly affected people's food purchasing and consumption habits. Fears of disruptions in the food supply chain have caused an increase in the quantity and type of food bought by households. However, increases in food purchases could give rise to food waste with negative ramifications for the environment in terms of greenhouse emissions and groundwater pollution. To assess whether household food waste has changed during Covid-19 lockdown, we conducted a nationwide survey of household food purchasers in Italy. Although the amount of food purchases increased during the lockdown, our results show that food waste actually decreased as people mainly bought more non-perishable food. Interestingly, concerns about the impact that the pandemic could have on the waste management system and the desire not to add pressure to the waste management system are key drivers of decreased food waste in Italy during the pandemic. Our findings seem to suggest that Italian consumers are developing a new level of awareness about food waste with potential positive impacts on the environment in terms of reducing greenhouse gas emissions and groundwater pollution.
Simulation of rotary friction welding using a viscoelastic Maxwell model

A 3-D finite element model of rotary friction welding was developed using a viscoelastic Maxwell model. The thermal softening of the material was accounted for in the viscous part of the material model as well as in an additional friction model. The thermal and mechanical material data were adapted from existing data of tempering steel AISI 4140. The final model was used to predict rotary friction welding of shafts using three different parameter settings. The simulation results showed that the melt temperature was never exceeded at the faying surfaces, which is seen as a main characteristic of friction welding. Subsequent validation also demonstrated that the shortening of the specimen as well as the resulting flash geometry could be predicted successfully.
Not Part of the Temporal Lobe, but Still of Importance? Substantia Nigra and Subthalamic Nucleus in Epilepsy

The most researched brain region in epilepsy research is the temporal lobe, and more specifically, the hippocampus. However, numerous other brain regions play a pivotal role in seizure circuitry and secondary generalization of epileptic activity: The substantia nigra pars reticulata (SNr) and its direct input structure, the subthalamic nucleus (STN), are considered seizure gating nuclei. There is ample evidence that direct inhibition of the SNr is capable of suppressing various seizure types in experimental models. Similarly, inhibition via its monosynaptic glutamatergic input, the STN, can decrease seizure susceptibility as well. This review will focus on therapeutic interventions such as electrical stimulation and targeted drug delivery to SNr and STN in human patients and experimental animal models of epilepsy, highlighting the opportunities for overcoming pharmacoresistance in epilepsy by investigating these promising target structures.