Miracle-dz/UniFAR-FTUs huggingface dataset:
Tax Burden, Institutional Environment and Foreign Direct Investment Flow: From the Perspective of Asymmetric International Tax Competition

Abstract The global economic uncertainty is mounting. Governments need to respond with supporting measures for long-term external environment changes as they lower tax burden to attract working capital. Based on the asymmetric tax competition theory, this paper constructs a theoretical model of tax burden, institutional transaction costs and FDI flow. It is found that one country’s strength of institutional environment makes its equilibrium tax rate higher than that of another within certain limits of market size. Based on the data of 199 countries and regions from 2005 to 2018, this paper conducts an empirical analysis, proving that favorable institutional environment narrows the negative impact of tax burden on FDI flow. Moreover, it is showed that in small-market, low-income countries and regions, tax burden level has a larger negative impact on foreign direct investment (FDI) when institutional environment produces no positive impact; in large-market, high-income countries, the negative impact of tax burden is relatively weak but the institutional environment shows largely positive impact. This paper contributes some policy recommendations on how to make use of and improve institutional environment to meet challenges and impacts of the international economic climate.
Spanish Adaptation of the Self- and Other-Interest Inventory in Academic Settings

Abstract In the study of human motivations, self-interest is often seen as a determining factor and opposed to other-interest. Recently a new conceptualization has been proposed in which both interests can occur at the same time. In order to measure these constructs, the Self-and Other-Interest Inventory (SOII; Gerbasi & Prentice, 2013) was created, which has one version for adults and one for students. Due to the absence of similar measurement instruments in Spain, the aim of this work is to adapt the SOII to Spanish university students. Several studies were conducted. First, the construct was analyzed through rational-analytical procedures. Second, the items were translated following an iterative forward-translation design. Finally, evidence of validity was obtained through analytical procedures. Specifically, two pilot studies were carried out in which two independent samples of Spanish students participated (N1 = 119; N2 = 165). In both studies descriptive analyzes of the items were performed, reliability was estimated and the factor structure of the SOII was explored from an exploratory factor analysis. The results showed adequate reliability and a two-factor solution consistent with the original.
Ecological interactions shape the evolution of flower color in communities across a temperate biodiversity hotspot

Processes driving the divergence of floral traits may be integral to the extraordinary richness of flowering plants and the assembly of diverse plant communities. Several models of pollinator‐mediated floral evolution have been proposed; floral divergence may (i) be directly involved in driving speciation or may occur after speciation driven by (ii) drift or local adaptation in allopatry or (iii) negative interactions between species in sympatry. Here, we generate predictions for patterns of trait divergence and community assembly expected under these three models, and test these predictions in Hakea (Proteaceae), a diverse genus in the Southwest Australian biodiversity hotspot. We quantified functional richness for two key floral traits (pistil length and flower color), as well as phylogenetic distances between species, across ecological communities, and compared these to patterns generated from null models of community assembly. We also estimated the statistical relationship between rates of trait evolution and lineage diversification across the phylogeny. Patterns of community assembly suggest that flower color, but not floral phenology or morphology, or phylogenetic relatedness, is more divergent in communities than expected. Rates of lineage diversification and flower color evolution were negatively correlated across the phylogeny and rates of flower colour evolution were positively related to branching times. These results support a role for diversity‐dependent species interactions driving floral divergence during the Hakea radiation, contributing to the development of the extraordinary species richness of southwest Australia.
The continuing promise of the radial access for coronary interventions

• The radial artery access for coronary angiography and percutaneous coronary intervention is associated with reduced bleeding, vascular complications, and all-cause mortality compared with the femoral artery access. • The radial artery access is not associated with a higher risk of stroke or myocardial infarction. • The consistent use of the radial approach and intravascular imaging-guidance for femoral access, when needed, are likely to continue to reduce the risk of complications in the cardiac catheterization laboratory.
Photoreceptor Compartment-Specific TULP1 Interactomes

Photoreceptors are highly compartmentalized cells with large amounts of proteins synthesized in the inner segment (IS) and transported to the outer segment (OS) and synaptic terminal. Tulp1 is a photoreceptor-specific protein localized to the IS and synapse. In the absence of Tulp1, several OS-specific proteins are mislocalized and synaptic vesicle recycling is impaired. To better understand the involvement of Tulp1 in protein trafficking, our approach was to physically isolate Tulp1-containing photoreceptor compartments by serial tangential sectioning of retinas and to identify compartment-specific Tulp1 binding partners by immunoprecipitation followed by liquid chromatography tandem mass spectrometry. Our results indicate that Tulp1 has two distinct interactomes. We report the identification of: 1) an IS-specific interaction between Tulp1 and the motor protein Kinesin family member 3a (Kif3a), 2) a synaptic-specific interaction between Tulp1 and the scaffold protein Ribeye, and 3) an interaction between Tulp1 and the cytoskeletal protein Microtubule-associated protein 1B (MAP1B) in both compartments. Immunolocalization studies in the wild-type retina indicate that Tulp1 and its binding partners co-localize to their respective compartments. Our observations are compatible with Tulp1 functioning in protein trafficking in multiple photoreceptor compartments, likely as an adapter molecule linking vesicles to molecular motors and the cytoskeletal scaffold.
Confusions regarding stochas�c ﬂuctua�ons and accumulators in spontaneous movements

Experiments on choice-predicve brain signals have played an important role in the debate on free will. In a seminal study, Benjamin Libet and colleagues found that a negave-going EEG signal, the readiness poten�al (RP), can be observed over motor-related brain regions already a few hundred ms before a par�cipant makes a conscious decision to move. If the onset of the readiness poten�al is taken as an indicator of the “brain’s decision to move” this could mean that this decision to move is made early, by unconscious brain ac�vity, rather than later, at the �me when the subject believes to be deciding. However, an alterna�ve interpreta�on has recently been discussed, the stochas�c decision model (SDM), that takes its inspira�on from models of perceptual decision making. It suggests that the RP originates from an accumula�on of stochas�c internal ﬂuctua�ons. In this view the decision happens only at a much later stage when an accumulated noise signal reaches a threshold. Here we address a number of confusions regarding both the evidence for the stochas�c decision model as well as its interpreta�on. We will show: (a) that the evidence for the role of stochas�c ﬂuctua�ons is highly indirect; (b) that there is li�le direct support for the SDM from animal studies; (c) that determinis�c (non-random) processes can explain the data in a similar way; (d) that the rela�ve components of the model have been mischaracterized leading to an over-emphasis on the role of random ﬂuctua�ons and an under-emphasis of determinis�c aspects of the model; (e) that there is confusion regarding the role of “urgency” and “evidence” in the SDM and its link to perceptual decision making; (f) that the ques�on whether the decision happens early or late depends on the nature of the noise ﬂuctua�ons, speciﬁcally, whether they reﬂect “absolute” or “epistemic” randomness; (g) ﬁnally, that the model does not explain the temporal rela�onship between conscious decision and neural decision. Our aim is not to rehabilitate the role of RPs in the
Core and indicative bacterial and fungal taxa define characteristic soil communities of arable land, grassland, and forest

Soil microbial diversity has major influences on ecosystem functions and services. However, due to its complexity and uneven distribution of abundant and rare taxa, quantification of soil microbial diversity remains challenging and thereby impeding its integration into long-term monitoring programs. Using metabarcoding, we analyzed soil bacterial and fungal communities over five years at thirty long-term soil monitoring sites from the three land-use types, arable land, permanent grassland, and forest. Unlike soil microbial biomass and alpha-diversity, microbial community compositions and structures were site- and land-use-specific with CAP reclassification success rates of 100%. The temporally stable site core communities included 38.5% of bacterial and 33.1% of fungal OTUs covering 95.9% and 93.2% of relative abundances. We characterized bacterial and fungal core communities and their land-use associations at the family-level. In general, fungal families revealed stronger land-use type associations as compared to bacteria. This is likely due to a stronger vegetation effect on fungal core taxa, while bacterial core taxa were stronger related to soil properties. The assessment of core communities can be used to form cultivation-independent reference lists of microbial taxa, which may facilitate the development of microbial indicators for soil quality and the use of soil microbiota for long-term soil biomonitoring.
Multifunctionality of belowground food webs: resource, size and spatial energy channels

The belowground compartment of terrestrial ecosystems drives nutrient cycling, the decomposition and stabilisation of organic matter, and supports aboveground life. Belowground consumers create complex food webs that regulate functioning, ensure stability and support biodiversity both below and above ground. However, existing soil food-web reconstructions do not match recently accumulated empirical evidence and there is no comprehensive reproducible approach that accounts for the complex resource, size and spatial structure of food webs in soil. Here I build on generic food-web organization principles and use multifunctional classification of soil protists, invertebrates and vertebrates, to reconstruct ‘multichannel’ food-web across size classes of soil-associated consumers. This reconstruction is based on overlying feeding preference, prey protection, size spectrum and spatial distribution matrices combined with biomasses of trophic guilds to infer weighted trophic interactions. I then use food-web reconstruction, together with assimilation efficiencies, to calculate energy fluxes assuming a steady-state energetic system. Based on energy fluxes, I describe a number of indicators, related to stability, biodiversity and multiple ecosystem-level functions such as herbivory, top-down control, translocation and transformation of organic matter. I illustrate the approach with an empirical example, comparing it with traditional resource-focused soil food-web reconstruction. The multichannel reconstruction can be used to assess trophic multifunctionality (analogous to ecosystem multifunctionality), i.e. simultaneous support of multiple trophic functions by the food-web, and compare it across communities and ecosystems spanning beyond the soil. With further validation and parametrization, my multichannel reconstruction approach provides an effective tool for understanding and analysing soil food webs. I believe that having this tool will inspire more people to comprehensively describe soil communities and belowground-aboveground interactions. Such studies will provide informative indicators for including consumers as active agents in biogeochemical models, not only locally but also on regional and global scales.
Habitat percolation transition undermines sustainability in social-ecological agricultural systems

Steady increases in human population size and resource consumption are driving rampant agricultural expansion and intensification. Habitat loss caused by agriculture puts the integrity of ecosystems at risk, and threatens the persistence of human societies that rely on ecosystem services. We develop a spatially explicit model describing the coupled dynamics of an agricultural landscape and human population size to study the effect of different land-use management strategies, defined by agricultural clustering and intensification, on the sustainability of the social-ecological system. We show how agricultural expansion can cause natural habitat to undergo a percolation transition leading to abrupt habitat fragmentation that feedbacks on human’s decision making, aggravating landscape degradation. We found that agricultural intensification to spare land from conversion is a successful strategy only in highly natural landscapes, and that clustering agricultural land is the most effective measure to preserve large connected natural fragments, avoid severe fragmentation, and thus, enhance sustainability.
Hypoxia is a dominant remodeler of the CD8+ T cell surface proteome relative to activation and regulatory T cell-mediated suppression

Immunosuppressive factors in the tumor microenvironment (TME) impair T cell function and limit the anti-tumor immune response. T cell surface receptors that influence interactions and function in the TME are already proven targets for cancer immunotherapy. However, surface proteome remodeling of primary human T cells in response to suppressive forces in the TME has never been characterized systematically. Using a reductionist cell culture approach with primary human T cells and SILAC-based quantitative cell surface capture glycoproteomics, we examined how two immunosuppressive TME factors, regulatory T cells (Tregs) and hypoxia, globally affect the activated CD8+ surface proteome (surfaceome). Surprisingly, the CD8+/Treg co-culture only modestly affected the CD8+ surfaceome, but did reverse several activation-induced surfaceomic changes. In contrast, hypoxia dramatically altered the CD8+ surfaceome in a manner consistent with both metabolic reprogramming and induction of an immunosuppressed state. The CD4+ T cell surfaceome similarly responded to hypoxia, revealing a novel hypoxia-induced surface receptor program. Our findings are consistent with the premise that hypoxic environments create a metabolic challenge for T cell activation, which may underlie the difficulty encountered in treating solid tumors with immunotherapies. Together, the data presented here provide insight into how suppressive TME factors remodel the T cell surfaceome and represent a valuable resource to inform future therapeutic efforts to enhance T cell function in the TME.
Natural variation in the irld gene family affects insulin/IGF signaling and starvation resistance

: Starvation resistance is a fundamental, disease-relevant trait, but the genetic basis of its natural variation is unknown. We developed a synthetic population-sequencing approach to measure starvation resistance for many wild C. elegans strains simultaneously. We identified three quantitative trait loci with variants in 16 insulin/EGF receptor-like domain ( irld ) family members. We show that four irld genes affect starvation resistance by regulating insulin/IGF signaling. We propose that IRLD proteins bind insulin-like peptides to modify signaling in the sensory nervous system thereby affecting organismal physiology. This work demonstrates efficacy of using population sequencing to dissect a complex trait, identifies irld genes that regulate insulin/IGF signaling, and shows that an expanded gene family modifies a deeply conserved signaling pathway to affect a fitness-proximal trait. data from one biological replicate from RAD-seq that had data at time points over the course of starvation, including days 1, 7, 14, 21, and 24, was used. The frequency of each strain at each time point was divided by its frequency on day 1. These values were log2 transformed, so positive values indicate an increase in frequency over time and negative values indicate a decrease in frequency over time. A linear regression was then fit to each with a y-intercept of 0 through the data points over time. The slope of the line was calculated as the trait value for each strain. RAD-seq and MIP-seq data were filtered to include only the 34 strains that were present in both analyses. The values were plotted against each other and a linear regression was fit through these points to determine their correlation (R 2 = 0.24, p = 0.002). for RNA precipitation. RNA was eluted in nuclease-free water and incubated at 55ºC for approximately four minutes to resuspend the pellet. 150 ng of total RNA was used for each library preparation. NEBNext Poly(A) mRNA Isolation Module (New England Biolabs# E7490) and NEBNext Ultra II RNA Library Prep kit (New England Biolabs# E7770) were used to perform poly-A selection and prepare libraries for sequencing, respectively. The final libraries were enriched with 11 cycles of PCR. Libraries were then sequenced using Illumina NovaSeq 6000 to obtain 50 bp paired-end reads.
AGNOSTOS-DB: a resource to unlock the uncharted regions of the coding sequence space

Genomes and metagenomes contain a considerable percentage of genes of unknown function, which are often excluded from downstream analyses limiting our understanding of the studied biological systems. To address this challenge, we developed AGNOSTOS, a combined database-computational workflow resource that unifies the known and unknown coding sequence space of genomes and metagenomes. Here, we present AGNOSTOS-DB, an extensive database of high-quality gene clusters enriched with functional, ecological and phylogenetic information. Moreover, AGNOSTOS allows integrating new data into existing AGNOSTOS-DBs, maximizing the information retrievable for the genes of unknown function. As a proof of concept, we provide a seed database that integrates the predicted genes from marine and human metagenomes, as well as from Bacteria, Archaea, Eukarya and giant viruses environmental and cultivar genomes. The seed database comprises 6,572,081 gene clusters connecting 342 million genes and represents a comprehensive and scalable resource for the inclusion and exploration of the unknown fraction of genomes and metagenomes.
Distinctive properties of cones in the retinal clock and the circadian system

Multiple circadian clocks dynamically regulate mammalian physiology. In retina, rhythmic gene expression serves to align vision and tissue homeostasis with daily light changes. Photic input is relayed to the brain to entrain the master circadian clock, the suprachiasmatic nucleus, which matches behaviour to environmental changes. Circadian organization of the mouse retina involves coordinated, layer-specific oscillators, but so far little is known about the cone photoreceptor clock and its role in the circadian system. Using the cone-only Nrl-/- mouse model we show that cones contain a functional self-sustained molecular clockwork. By bioluminescence-combined imaging, we also show that cones provide substantial input to the retinal clock network. Furthermore, we found that light entrainment and negative masking in cone-only mice are subtly altered and that constant light displayed profound effects on their central clock. Thus, our study demonstrates the contribution of cones to retinal circadian organisation and their role in finely tuning behaviour to environmental conditions.
NeTOIF: A Network-based Approach for Time-Series Omics Data Imputation and Forecasting

Motivation High-throughput studies of biological systems are rapidly generating a wealth of ‘omics’-scale data. Many of these studies are time-series collecting proteomics and genomics data capturing dynamic observations. While time-series omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values resulting in data shortage. Data missing and shortage are especially problematic for downstream applications such as omics data integration and computational analyses that need complete and sufficient data representations. Data imputation and forecasting methods have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. As a result, these techniques lack the ability to capture the time-ordered nature of data and cannot handle omics data containing missing values at multiple time points. Results We propose a network-based method for time-series omics data imputation and forecasting (NeTOIF) that handle omics data containing missing values at multiple time points. NeTOIF takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform time-series imputation and forecasting using a long short-term memory network. Evaluating NeTOIF with a proteomic and a genomic dataset demonstrated a distinct advantage of NeTOIF over existing data imputation and forecasting methods. The average mean square error of NeTOIF improved 11.3% for imputation and 6.4% for forcasting compared to the baseline methods. Contact smollah@wustl.edu
Number of Synergies Impacts Sensitivity of Gait to Weakness and Contracture

Muscle activity during gait can be described by a small set of synergies, weighted groups of muscles, that are often theorized to reflect underlying neural control. For people with neurologic injuries, like in cerebral palsy or stroke, even fewer (e.g., < 5) synergies are required to explain muscle activity during gait. This reduction in synergies is thought to reflect simplified control strategies and is associated with impairment severity and treatment outcomes. Individuals with neurologic injuries also develop secondary musculoskeletal impairments, like weakness or contracture, that can also impact gait. The combined impacts of simplified control and musculoskeletal impairments on gait remains unclear. In this study, we use a musculoskeletal model constrained to synergies to simulate unimpaired gait. We vary the number of synergies (3-5), while simulating muscle weakness and contracture to examine how altered control impacts sensitivity to muscle weakness and contracture. Our results highlight that reducing the number of synergies increases sensitivity to weakness and contracture. For example, simulations using five-synergy control tolerated 40% and 51% more knee extensor weakness than those using four- and three-synergy control, respectively. Furthermore, the model became increasingly sensitive to contracture and proximal muscle weakness, such as hamstring and hip flexor weakness, when constrained to four- and three-synergy control. However, the model’s sensitivity to weakness of the plantarflexors and smaller bi-articular muscles was not affected by the number of synergies. These findings provide insight into the interactions between altered control and musculoskeletal impairments, emphasizing the importance of incorporating both in future simulation studies.