Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of the COVID‐19 pandemic on inflammatory bowel disease: The role of emotional stress and social isolation

Abstract Inflammatory bowel disease (IBD) is a chronic health condition exacerbated by negative emotional stress experiences. In the current study, we examined whether the outbreak of the COVID‐19 pandemic coincided with an increase in stress experiences and accordingly an aggravation of disease activity in IBD patients. Sixty‐three IBD patients (30 Crohn's disease or CD, 33 ulcerative colitis) completed an online survey during the COVID‐19‐related lockdown, assessing clinical disease activity, disease‐related quality of life, presence of functional gastrointestinal symptoms, social isolation and stress experiences. Scores were then compared to pre‐lockdown baseline screening. The pandemic yielded a significant baseline‐to‐lockdown increase in emotional stress and social isolation. Stress increments, particularly those occasioned by interpersonal tension and excessive interpersonal proximity, were associated with a worsening of functional gastrointestinal symptoms. Exacerbations of loneliness coincided with an escalation of CD activity, functional gastrointestinal symptoms and a decline in subjective health. Lastly, COVID‐19 anxiety was significantly related to CD symptom severity and social dysfunction. The findings show that shifts in IBD expression are closely linked to changes in emotional stress experiences and interpersonal relatedness. As such, they contribute to a better understanding of inter‐individual differences in IBD progression and provide leads for therapeutic interventions.
The Type I interferon antiviral gene program is impaired by lockdown and preserved by caregiving

Significance “Shelter in place” (SIP) orders have been deployed to slow the spread of SARS-CoV-2, but they induce social isolation that may paradoxically weaken antiviral immunity. We examined the impact of 2-wk SIP on immune cell population dynamics and gene regulation in 21 adult rhesus macaques, finding 30 to 50% declines in circulating immune cells, decreases in antiviral gene expression, and increased inflammatory cells in blood and inflammatory gene expression in lymph nodes. Declines in antiviral gene expression (but not circulating immune cells) were blocked by the presence of a novel juvenile partner during SIP, suggesting a potential strategy for maintaining antiviral immunity during SIP by enhancing prosocial engagement. Previous research has linked perceived social isolation (loneliness) to reduced antiviral immunity, but the immunologic effects of the objective social isolation imposed by pandemic “shelter in place” (SIP) policies is unknown. We assessed the immunologic impact of SIP by relocating 21 adult male rhesus macaques from 2,000-m2 field cage communities of 70 to 132 other macaques to 2 wk of individual housing in indoor shelters. SIP was associated with 30% to 50% reductions in all circulating immune cell populations (lymphocytes, monocytes, and granulocytes), down-regulation of Type I interferon (IFN) antiviral gene expression, and a relative up-regulation of CD16− classical monocytes. These effects emerged within the first 48 h of SIP, persisted for at least 2 wk, and abated within 4 wk of return to social housing. A subsequent round of SIP in the presence of a novel juvenile macaque showed comparable reductions in circulating immune cell populations but reversal of Type I IFN reductions and classical monocyte increases observed during individual SIP. Analyses of lymph node tissues showed parallel up-regulation of Type I IFN genes and enhanced control of viral gene expression during juvenile-partnered SIP compared to isolated SIP. These results identify a significant adverse effect of SIP social isolation on antiviral immune regulation in both circulating immune cells and lymphoid tissues, and they suggest a potential behavioral strategy for ameliorating gene regulatory impacts (but not immune cell declines) by promoting prosocial engagement during SIP.
Deliberation, Dissent, and Distrust: Understanding Distinct Drivers of Coronavirus Disease 2019 Vaccine Hesitancy in the United States

Abstract Background Despite the availability of safe and efficacious coronavirus disease 2019 vaccines, a significant proportion of the American public remains unvaccinated and does not appear to be immediately interested in receiving the vaccine. Methods In this study, we analyzed data from the US Census Bureau’s Household Pulse Survey, a biweekly cross-sectional survey of US households. We estimated the prevalence of vaccine hesitancy across states and nationally and assessed the predictors of vaccine hesitancy and vaccine rejection. In addition, we examined the underlying reasons for vaccine hesitancy, grouped into thematic categories. Results A total of 459 235 participants were surveyed from 6 January to 29 March 2021. While vaccine uptake increased from 7.7% to 47%, vaccine hesitancy rates remained relatively fixed: overall, 10.2% reported that they would probably not get a vaccine and 8.2% that they would definitely not get a vaccine. Income, education, and state political leaning strongly predicted vaccine hesitancy. However, while both female sex and black race were factors predicting hesitancy, among those who were hesitant, these same characteristics predicted vaccine reluctance rather than rejection. Those who expressed reluctance invoked mostly “deliberative” reasons, while those who rejected the vaccine were also likely to invoke reasons of “dissent” or “distrust. ” Conclusions Vaccine hesitancy comprises a sizable proportion of the population and is large enough to threaten achieving herd immunity. Distinct subgroups of hesitancy have distinctive sociodemographic associations as well as cognitive and affective predilections. Segmented public health solutions are needed to target interventions and optimize vaccine uptake.
Dispersal in a house sparrow metapopulation: An integrative case study of genetic assignment calibrated with ecological data and pedigree information

Dispersal has a crucial role determining ecoevolutionary dynamics through both gene flow and population size regulation. However, to study dispersal and its consequences, one must distinguish immigrants from residents. Dispersers can be identified using telemetry, capture‐mark‐recapture (CMR) methods, or genetic assignment methods. All of these methods have disadvantages, such as high costs and substantial field efforts needed for telemetry and CMR surveys, and adequate genetic distance required in genetic assignment. In this study, we used genome‐wide 200K Single Nucleotide Polymorphism data and two different genetic assignment approaches (GSI_SIM, Bayesian framework; BONE, network‐based estimation) to identify the dispersers in a house sparrow (Passer domesticus) metapopulation sampled over 16 years. Our results showed higher assignment accuracy with BONE. Hence, we proceeded to diagnose potential sources of errors in the assignment results from the BONE method due to variation in levels of interpopulation genetic differentiation, intrapopulation genetic variation and sample size. We show that assignment accuracy is high even at low levels of genetic differentiation and that it increases with the proportion of a population that has been sampled. Finally, we highlight that dispersal studies integrating both ecological and genetic data provide robust assessments of the dispersal patterns in natural populations.
Landscape structure shapes the diversity of tree seedlings at multiple spatial scales in a fragmented tropical rainforest

The maintenance of seedling diversity of animal-dispersed tree species is fundamental for the structure and function of forest patches in fragmented tropical rainforests. Nonetheless, the effects of landscape structure at different spatial scales on α- and β-diversity of tree seedling communities are recently explored. Using a multi-scale approach, we assessed the relative effect of landscape composition and configuration on α- and β-diversity of animal-dispersed seedlings within 16 forest patches in the Lacandona rainforest, Mexico. We assessed these effects at 13 spatial scales (from 300 to 1500 m radius, at 100 m intervals) for three metrics of effective number of species considering α- and β-diversity. We found that α-diversity was largely affected by landscape composition and β-diversity by landscape configuration. On the one hand, the amount of secondary forest influenced α-diversity. Additionally, species richness increased in landscapes with highly aggregated forest patches. On the other hand, β-diversity was affected positively by forest fragmentation and negatively by the edge contrast of forest patches with the surrounding matrix. Our findings indicate that landscape configuration is a strong driver of seedling diversity in highly deforested rainforests. Promoting forest patches and secondary forests through payment for ecosystem services’ programs, favoring matrix quality within land-sharing schemes of smallholder agriculture and secondary forest management, and identifying restoration opportunities for assisted or unassisted natural regeneration are urgently needed for conservation of seedling diversity in human-modified tropical landscapes.
EFFECT OF SALINITY STRESS ON VICIA FABA PRODUCTIVITY WITH RESPECT TO ASCORBIC ACID TREATMENT

A field experiment was carried out to study the response of presoaked faba bean seeds (Vicia faba cv. Misr 2) in freshly prepared ascorbic acid (50 ppm) or distilled water (control) for 4 hrs at natural environmental conditions, to salinity stress. NaCl treatments caused a reduction in all productivity and yield parameters as compared with control, particularly at high NaCl level (150 mM), whereas the number of pods per plant, fresh weight of pods per plant, number of seeds per pod and fresh weight of seeds per pod were reduced. In the mean time, ascorbic acid had no effect on the yield parameters. Salinity stress increased the protein content of seeds to some extent. Ascorbic acid treatment to salinized plants remarkably increased the protein content. Also, seed alkaloids were increased with salinity stress, but the response was more pronounced at 100 mM NaCl whether alone or combined with ascorbic acid. The results revealed that protein pattern showed considerable effects of salinity stress on the protein bands. Application of ascorbic acid induced the synthesis and increased the intensity of the original protein bands and caused the appearance of additional new bands.
Post-Discharge Symptoms among Hospitalized COVID-19 Patients in Nigeria: A Single-Center Study

ABSTRACT. There is a paucity of studies on post-acute COVID-19 syndrome (PCS) among hospitalized COVID-19 survivors from Nigeria. We describe the frequency, types, and duration of post-discharge symptoms suggestive of PCS among previously hospitalized COVID-19 patients in a treatment center in Nigeria. We conducted a retrospective review of admission and post-discharge follow-up medical records of COVID-19 survivors admitted between April and December 2020. A standardized checklist was used to document post-discharge symptoms. PCS was defined as persisting or new post-discharge symptoms lasting at least 3 weeks after initial COVID-19 symptoms. The relationship between study variables and development of PCS was ascertained by univariate analysis. Thirty of 51 previously hospitalized COVID-19 patients (median age, 46 years; male, 66.7%) were studied. Seventeen (56.7%) of the 30 patients developed features suggestive of PCS. Approximately three post-discharge symptoms were reported per patient over a follow-up period of ranging from 3 weeks to 9 months after initial COVID-19 symptoms. Cough, fatigue, and dyspnea were the most common post-discharge symptoms reported. A few patients had symptoms suggestive of thrombosis and COVID-19 reinfection. Among all study variables, baseline COVID-19 severity was the only significant variable associated with the development of PCS. PCS is common in our setting and is characterized by multisystemic signs and symptoms that require vigilance by clinicians for appropriate diagnosis and treatment. Long-term multicenter prospective studies are needed to characterize fully the burden of PCS among COVID-19 survivors in Nigeria.
Effective Transfer Learning Algorithm in Spiking Neural Networks

As the third generation of neural networks, spiking neural networks (SNNs) have gained much attention recently because of their high energy efficiency on neuromorphic hardware. However, training deep SNNs requires many labeled data that are expensive to obtain in real-world applications, as traditional artificial neural networks (ANNs). In order to address this issue, transfer learning has been proposed and widely used in traditional ANNs, but it has limited use in SNNs. In this article, we propose an effective transfer learning framework for deep SNNs based on the domain in-variance representation. Specifically, we analyze the rationality of centered kernel alignment (CKA) as a domain distance measurement relative to maximum mean discrepancy (MMD) in deep SNNs. In addition, we study the feature transferability across different layers by testing on the Office-31, Office-Caltech-10, and PACS datasets. The experimental results demonstrate the transferability of SNNs and show the effectiveness of the proposed transfer learning framework by using CKA in SNNs.
Harmonic Amplitude Summation for Frequency-tagging Analysis

Abstract In the approach of frequency tagging, stimuli that are presented periodically generate periodic responses of the brain. Following a transformation into the frequency domain, the brain's response is often evident at the frequency of stimulation, F, and its higher harmonics (2F, 3F, etc.). This approach is increasingly used in neuroscience, as it affords objective measures to characterize brain function. However, whether these specific harmonic frequency responses should be combined for analysis—and if so, how—remains an outstanding issue. In most studies, higher harmonic responses have not been described or were described only individually; in other studies, harmonics have been combined with various approaches, for example, averaging and root-mean-square summation. A rationale for these approaches in the context of frequency-based analysis principles and an understanding of how they relate to the brain's response amplitudes in the time domain have been missing. Here, with these elements addressed, the summation of (baseline-corrected) harmonic amplitude is recommended.
Effects of Dopamine D1-Like Receptor Ligands on Food-Cocaine Choice in Socially Housed Male Cynomolgus Monkeys

Although dopamine plays a prominent role in mediating cocaine’s abuse-related effects, the specific roles of dopamine receptor subtypes are not fully understood. Whereas the effects of drugs acting at dopamine D2-like receptors (D2Rs) have been characterized, less is known about dopamine D1-like receptors (D1Rs). The present experiments examined the effects of drugs with varying intrinsic efficacy at D1R on the relative reinforcing strength of cocaine in male cynomolgus monkeys. Use of socially housed monkeys permitted the assessment of whether social status influenced the behavioral effects of D1R-acting drugs. The high-efficacy D1R agonist SKF 81297, low-efficacy D1R agonist SKF 38393, and D1R antagonist SCH 23390 were administered acutely to monkeys self-administering cocaine under a food-cocaine choice procedure in which a cocaine-choice dose-effect curve was determined daily. To assess selectivity of behavioral effects on cocaine choice, effects of doses that did not disrupt responding (indicated by a ≥35% decrease in total reinforcers delivered) were analyzed. Neither SKF 81297 nor SCH 23390 affected cocaine choice in dominant or subordinate monkeys. However, the low-efficacy agonist SKF 38393 selectively decreased cocaine choice; this effect was larger and only reached statistical significance in subordinate monkeys. Increasing the time between D1-acting drug administration and the cocaine choice session did not affect these results. The results indicate that, like D2R-acting drugs, the behavioral effects of D1R-acting drugs on cocaine choice can vary according to intrinsic efficacy and social status. Moreover, they demonstrate that D1R-acting drugs affect behavior under a narrower range of conditions than D2R-acting drugs. SIGNIFICANCE STATEMENT Cocaine use disorder represents an insidious public health concern with no Food and Drug Administration–approved medications. Although dopamine receptors have been strongly implicated in mediating the abuse-related effects of cocaine, the roles of dopamine receptor subtypes are incompletely understood. The present study in nonhuman primates found that cocaine choice was decreased only by a low-efficacy D1R agonist, and that this effect depended on the social status of the monkey.
Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory

Abstract What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.
Metabolic Adaptations to Marine Environments: Molecular Diversity and Evolution of Ovothiol Biosynthesis in Bacteria

Abstract Ovothiols are sulfur-containing amino acids synthesized by marine invertebrates, protozoans, and bacteria. They act as pleiotropic molecules in signaling and protection against oxidative stress. The discovery of ovothiol biosynthetic enzymes, sulfoxide synthase OvoA and β-lyase OvoB, paves the way for a systematic investigation of ovothiol distribution and molecular diversification in nature. In this work, we conducted genomic and metagenomics data mining to investigate the distribution and diversification of ovothiol biosynthetic enzymes in Bacteria. We identified the bacteria endowed with this secondary metabolic pathway, described their taxonomy, habitat and biotic interactions in order to provide insight into their adaptation to specific environments. We report that OvoA and OvoB are mostly encountered in marine aerobic Proteobacteria, some of them establishing symbiotic or parasitic relationships with other organisms. We identified a horizontal gene transfer event of OvoB from Bacteroidetes living in symbiosis with Hydrozoa. Our search within the Ocean Gene Atlas revealed the occurrence of ovothiol biosynthetic genes in Proteobacteria living in a wide range of pelagic and highly oxygenated environments. Finally, we tracked the evolutionary history of ovothiol biosynthesis from marine bacteria to unicellular eukaryotes and metazoans. Our analysis provides new conceptual elements to unravel the evolutionary and ecological significance of ovothiol biosynthesis.
Ab initio prediction of crystalline phases and electronic properties of alloys and other compounds

In this work we present an ab initio materials design study of several systems covering intermetallic and semiconducting alloys, transparent conductive oxides and molecular solids. We performed Minima Hopping calculations combined with Density Functional Theory that made possible to unveil several stable compounds in the phase diagrams of lithium-aluminium and sodium-gold binary alloys, as well as low-symmetry geometries of CuBO2, significantly lower in energy than the controversial delafossite structure reported as its ground state. We also found that the H3 molecule can be stabilized inside Cl cages at pressures of around 100 GPa. Additionally, we combined high-throughput techniques and global structure prediction methods to find nitride perovskites structures. In a different line, we studied the change in the absorption properties of the Cu(In,Ga)S2 chalcopyrite alloys as it was unexpectedly observed in experiment that with the change of the In/Ga ratio, the S K-absorption edge shifts, while the absorption edges of the other species is largely independent of the composition. In a more fundamental chapter, we propose a semi empirical exchange correlation functional optimized to yield accurate energies of formation of solids. The manuscript is organized as follows
The impact of automated hippocampal volumetry on diagnostic confidence in patients with suspected Alzheimer's disease: an EADC study

INTRODUCTION: Hippocampal volume is a core biomarker of Alzheimer's Disease (AD). However, its contribution over the standard diagnostic workup is unclear. METHODS: 356 patients, under clinical evaluation for cognitive impairment, with suspected AD and MMSE≥20, were recruited across 17 European memory clinics. After the traditional diagnostic workup, diagnostic confidence of AD pathology (DCAD) was estimated by the physicians in charge. The latter were provided with the results of automated hippocampal volumetry in standardized format and DCAD was reassessed. RESULTS: An increment of one interquartile range in hippocampal volume was associated with a mean change of DCAD of -8.0% (95% Credible Interval:[-11.5,-5.0]). Automated hippocampal volumetry showed a statistically significant impact on DCAD beyond the contributions of neuropsychology, FDG-PET/SPECT and CSF markers (-8.5, CrI:[-11.5,5.6]; -14.1, CrI:[-19.3,-8.8]; -10.6, CrI:[-14.6,-6.1] respectively). DISCUSSION: There is a measurable effect of hippocampal volume on DCAD even when used on top of the traditional diagnostic workup.
The Effect of Diet and Farm Management on N2O Emissions from Dairy Farms Estimated from Farm Data

The N2O emissions of 21 dairy farms in Germany were evaluated to determine the feasibility of an estimation of emissions from farm data and the effects of the farm management, along with possible mitigation strategies. Emissions due to the application of different fertilisers, manure storage and grazing were calculated based on equations from the IPCC (Intergovernmental Panel of Climate Change) and German emission inventory. The dependence of the N2O emissions on fertiliser type and quantity, cultivated crops and diet composition was assessed via correlation analysis and linear regression. The N2O emissions ranged between 0.11 and 0.29 kg CO2eq per kilogram energy-corrected milk, with on average 60% resulting from fertilisation and less than 30% from fertiliser storage and field applications. The total emissions had a high dependence on the diet composition; in particular, on the grass/maize ratio and the protein content of the animal diet, as well as from the manure management. A linear model for the prediction of the N2O emissions based on the diet composition and the fertilisation reached a predictive power of R2 = 0.89. As a possible mitigation strategy, the substitution of slurry for solid manure would reduce N2O emissions by 40%. Feeding cows maize-based diets instead of grass-based diets could reduce them by 14%.