Miracle-dz/UniFAR-FTUs huggingface dataset:
Reproductive life history may modulate selection on the germline mutation rate

A long lifespan is a potential liability from the point of view of mutation accumulation, allowing more time for cancer-causing mutations to accumulate in somatic tissues. In practice, however, long-lived organisms do not seem to suffer more cancer mortality than short-lived organisms, a pattern known as “Peto’s Paradox. ” One suggested resolution to Peto’s Paradox is that long lifespans may facilitate the evolution of better somatic DNA repair. This comes into some conflict with the predictions of the drift-barrier hypothesis of mutation rate evolution, which infers that germline DNA repair efficacy should be best in short-lived organisms with high genetic diversity and efficient natural selection. Here, we show that this conflict can be resolved by modeling the tendency of long generation times to increase the strength of selection against mutator alleles that affect clocklike mutational processes. By considering the empirical joint distribution of generation time and effective population size, we show that stronger selection against mutator alleles in long-lived organisms is likely to be partially but not completely offset by their tendency to have low effective population sizes. This model resolves a conflict between the predictions of theories of germline and somatic mutation rate evolution, leading to the prediction that both germline and somatic DNA repair should be most efficient in long-lived organisms despite their lower genetic diversity. Significance Statement All cells accumulate mutations due to DNA damage and replication errors. Mutations affecting the germline can harm future offspring, while mutations in other tissues can threaten longevity. Previous theories have predicted that somatic DNA repair should be most effective in long-lived organisms, but that germline DNA repair should instead be most effective in organisms with high genetic diversity that tend to be short-lived. We model how reproductive age should affect germline mutation rate evolution and show that this may reverse the prediction that high-diversity species should have more efficient selection for effective germline DNA repair. By demonstrating how population size and lifespan likely shape selection on mutagenesis, we are able to harmonize predictions about germline and somatic mutation rate evolution.
Axon guidance genes are regulated by TDP-43 and RGNEF through the rate of long-intron processing

Rho guanine nucleotide exchange factor (RGNEF) is a guanine nucleotide exchange factor (GEF) mainly involved in regulating the activity of Rho-family GTPases. Previous work has shown that RGNEF inclusions in the spinal motor neurons of ALS patients co-localise with TDP-43, the major RNA binding protein aggregating in the brain and spinal cord of ALS patients. To further characterise their relationship, we have compared the transcriptomic profiles of neuronal-like cells depleted of TDP-43 and RGNEF and show that these two factors predominantly act in an antagonistic manner when regulating the expression of axon guidance genes. From a mechanistic point of view, our experiments show that the effect of these factors on the processivity of long introns can explain their mode of action. Our findings highlight that neurodegenerative processes at the RNA level can often represent the result of combinatorial interactions between different RNA binding factors, leading to a better understanding of pathogenic mechanisms occurring in patients where more than one specific protein may be aggregating in their neurons.
FITM2 deficiency results in ER lipid accumulation, ER stress, reduced apolipoprotein B lipidation, and VLDL triglyceride secretion in vitro and in mouse liver

Objectives Triglyceride (TG) association with apolipoprotein B100 (apoB100) serves to form very low density lipoproteins (VLDL) in the liver. The repertoire of factors that facilitate this association is incompletely defined. FITM2, an integral endoplasmic reticulum (ER) protein, was originally discovered as a factor participating in cytoplasmic lipid droplets (LDs) in tissues that do not form VLDL. We hypothesized that in the liver, in addition to promoting cytosolic LD formation, FITM2 would also transfer TG from its site of synthesis in the ER membrane to nascent VLDL particles within the ER lumen. Methods Experiments were conducted using a rat hepatic cell line (McArdle-RH7777, or McA cells), an established model of mammalian lipoprotein metabolism, and mice. FITM2 expression was reduced using siRNA in cells and by liver specific cre-recombinase mediated deletion of the Fitm2 gene in mice. Effects of FITM2 deficiency on VLDL assembly and secretion in vitro and in vivo were measured by multiple methods, including density gradient ultracentrifugation, chromatography, mass spectrometry, simulated Raman spectroscopy (SRS) microscopy, sub-cellular fractionation, immunoprecipitation, immunofluorescence, and electron microscopy. Main findings 1) FITM2-deficient hepatic cells in vitro and in vivo secrete TG-depleted VLDL particles, but the number of particles is unchanged compared to controls; 2) FITM2 deficiency in mice on a high fat diet (HFD) results in decreased plasma TG levels. The number of apoB100-containing lipoproteins remains similar, but shift from VLDL to LDL density; 3) Both in vitro and in vivo, when TG synthesis is stimulated and FITM2 is deficient, TG accumulates in the ER, and despite its availability this pool is unable to fully lipidate apoB100 particles; 4) FITM2 deficiency disrupts ER morphology and results in ER stress. Principal conclusions The results suggest that FITM2 contributes to VLDL lipidation, especially when newly synthesized hepatic TG is in abundance. In addition to its fundamental importance in VLDL assembly, the results also suggest that under dysmetabolic conditions, FITM2 may be a limiting factor that ultimately contributes to non-alcoholic fatty liver disease (NAFLD) and steatohepatitis (NASH).
A comprehensive overview and benchmarking analysis of fast algorithms for genome-wide association studies

Genome-wide association studies (GWAS) are a ubiquitous tool for identifying genetic variants associated with complex traits in structured populations. During the past 15 years, many fast GWAS algorithms based on a state-of-the-art model, namely the linear mixed model, have been published to cope with the rapidly growing data size. In this study, we provide a comprehensive overview and benchmarking analysis of 33 commonly used GWAS algorithms. Key mathematical techniques implemented in different algorithms were summarized. Empirical data analysis with 12 selected algorithms showed differences regarding the identification of quantitative trait loci (QTL) in several plant species. The performance of these algorithms evaluated in 10,800 simulated data sets with distinct population size, heritability and genetic architecture revealed the impact of these parameters on the power of QTL identification and false positive rate. Based on these results, a general guide on the choice of algorithms for the research community is proposed.
Multiple modes of DNA compaction by protamine

In sperm cells, protamine replaces histones to compact DNA 10-20 times more than in somatic cells. To characterize the extreme compaction, we employed confocal microscopy and optical tweezers to determine the conformations and stability of protamine-bound λ-DNA. Confocal images show increasing compaction of λ-DNA at increasing protamine concentration. In the presence of protamine, single λ-DNA molecules form bends and loops that unravel at 10-40 pN forces as well as coils that shorten the contour length by up to 40% and withstand forces strong enough (∼55 pN) for strand separation. Strand separation nucleates coils, indicating protamine insertion into DNA bases. Protamine may participate in both local and higher-order chromatin organization, leading to extreme compaction and global transcription silencing. One-Sentence Summary Protamine compacts sperm DNA in multiple modes, producing bends and loops but also coils that may block transcription.
A FIRST NATION MODEL OF MISHKAUZIWIN (STRENGTH; RESILIENCE): THE REVITALIZATION OF FAMILY/CLAN IDENTITY IN AN ANISHNAABE FAMILY OF NORTHWESTERN ONTARIO

This thesis uses a family-directed approach to study the topic of resilience through an extended Anishnaabe family’s perceptions of their mishkauziwin [strength; resilience] as they strive to revitalize their family/clan identity. The interdisciplinary/qualitative/indigenist study braids together complementary ways of research from indigenous knowledges and the Western academy in a participatory framework. The perceptions of participants in studies on resilience are often sidelined, as are aspects of Aboriginal realities often missing from mainstream views of resilience in Aboriginal contexts. The perceptions of research participants in this study are the core-organizing feature of the research as they look at their relationships, traditional practices, and indigenous knowledge. The study contributes significantly to Anishnaabe communities and individuals as well as expands the body of knowledge on the topic of resilience in general. The study examines systemic issues impacting the lived experiences of participants, reviews the topic of historical trauma as it relates to the family participants, inquires into cultural identity issues, and works to comprehend the ecological view in Aboriginal contexts. The themes, patterns and metaphors of resilience from an Anishnaabe family’s perceptions serve to clarify significant findings about mishkauziwin, illustrating the process of restoration of family/clan identity as that of a strength based process which carries with it significant protective factors. The work together reaffirmed the value of a knowledge study emerging from the ground up as contrasted with top down approaches.
How many bison originally populated western rangelands

In 1871, Colonel R. 1. Dodge traveled along the Arkansas River through an immense herd of American bison (Bison bison). Dodge estimated that the herd was at least 25 miles across and after consulting with hunters and other travelers, concluded that it must have been at least 50 miles long. From such descriptions, historians, anthropologists, and zoologists have tried to estimate the original or pre-settlement population size for bison. We will never have an answer that would meet the standards of modern wildlife research, which demand random or systematic sampling and replication. Nor is it realistic to presume that the preslaughter population was stable. Indeed, given pressures from subsistence hunting, the robe trade, passing wagon trains, livestock (along with their infectious diseases), altered fire regimes, and the encroaching railroads, it seems reasonable to infer that the population of bison was far from stable. Rather than trying to arrive at a single magic number, it might be more constructive to review the means through which various estimates were obtained. This review should impress upon readers the need for caution in accepting historical estimates. It also may provide insight into how modern landscape-level simulations might eventually replace the historical estimates with more realistic ranges of possible population sizes. Three approaches have traditionally been used: direct observations, estimates of numbers killed, and estimates based on carrying capacity.
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions

Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new drugs continues, the potential for unknown adverse effects resulting from DDIs becomes a growing concern. Traditional computational methods for DDI prediction may fail to capture interactions for new drugs due to the lack of knowledge. In this paper, we introduce a new problem setup as zero-shot DDI prediction that deals with the case of new drugs. Leveraging textual information from online databases like DrugBank and PubChem, we propose an innovative approach TextDDI with a language model-based DDI predictor and a reinforcement learning~(RL)-based information selector, enabling the selection of concise and pertinent text for accurate DDI prediction on new drugs. Empirical results show the benefits of the proposed approach on several settings including zero-shot and few-shot DDI prediction, and the selected texts are semantically relevant. Our code and data are available at \url{https://github.com/zhufq00/DDIs-Prediction}.
Impact of bentonite mining on selecting arthropods.

Arthropods were sampled in pitfall traps for 2 yr on bentonite mine spoils and adjacent, unmined big sagebrush (Artemisia tridentat grasslands in southeastern Montana to assess the impacts of bentonite mining on selected arthropods, evaluate the success of early reclamation efforts in restoring arthropods to mined sites, and to identify limiting factors for colonization of spoils by arthropods. The most significant impacts on selected arthropods were on old, unreclaimed bentonite mine spoils, where after nearly 30 yr, numbers of 7 arthropod groups remained lower than on unmined sagebrush grasslands. Spoils covered with topsoil had higher captures of ground beetles (Carabidae) and crickets (Gryllidae) than unreclaimed spoils. And spoils covered with topsoil and seeded supported captures of most arachnids, Coleoptera, Hemiptern, and Formicidae similar to those on unmined sagebrush grasslands. Vegetative parameters measured in this study accounted for a portion of the variability in arthropod captures; however, microarthropod populations, arthropod vagility, and soil water contents may influence repopulation of mine spoils by some arthropods.
A Fine-Grained Taxonomy of Replies to Hate Speech

Countering rather than censoring hate speech has emerged as a promising strategy to address hatred. There are many types of counterspeech in user-generated content: addressing the hateful content or its author, generic requests, well-reasoned counter arguments, insults, etc. The effectiveness of counterspeech, which we deﬁne as subsequent incivility, depends on these types. In this paper, we present a theoretically grounded taxonomy of replies to hate speech and a new corpus. We work with real, user-generated hate speech and all the replies it elicits rather than replies generated by a third party. Our analyses provide insights into the content real users reply with as well as which replies are empirically most effective. We also experiment with models to characterize the replies to hate speech, thereby opening the door to estimating whether a reply to hate speech will result in further incivility.
The Hubble Deep Hydrogen Alpha (HDHα) Project. I. Catalog of Emission-line Galaxies

We present the first results of the Hubble Deep Hydrogen Alpha (HDHα) project, which analyzes the space-borne deep Hα narrowband imaging data in the GOODS-S region. The HDHα data comprises 72 orbits’ images taken with the Hubble Space Telescope (HST) Advanced Camera for Surveys/Wide Field Channel F658N filter. The exposure time varies across a total area of ∼76.1 arcmin2, adding up to a total exposure time of 195.7 ks, among which 68.8 ks are spent in the deepest region. These images are aligned, reprojected, and combined to have the same pixel grid as the Hubble Legacy Fields. The scientific goals of the HDHα include establishing a sample of emission-line galaxies (ELGs) including [O iii] emitters at z ∼ 0.3, [O ii] emitters at z ∼ 0.8, and Lyα emitters (LAEs) at z ∼ 4.4, studying the line morphology of ELGs with high resolution imaging data, and statistically analyzing the line luminosity functions and line equivalent-width distributions of ELGs selected with HST. Furthermore, the HDHα project enhances the legacy value of the GOODS-S field by contributing the first HST-based narrowband image to the existing data sets, which includes the HST broadband data and other ancillary data from X-ray to radio taken by other facilities. In this paper, we describe the data reduction process of the HDHα, select ELGs based on HST's F658N and broadband data, validate the redshifts of the selected candidates by crossmatching with the public spectroscopic catalogs in the GOODS-S, and present a final catalog of the confirmed [O iii] emitters at z ∼ 0.3, [O ii] emitters at z ∼ 0.8, and LAEs at z ∼ 4.4.
Stationary measures of continuous time Markov chains with applications to stochastic reaction networks

We study continuous-time Markov chains on the non-negative integers under mild regularity conditions (in particular, the set of jump vectors is finite and both forward and backward jumps are possible). Based on the so-called flux balance equation, we derive an iterative formula for calculating stationary measures. Specifically, a stationary measure $\pi(x)$ evaluated at $x\in\mathbb{N}_0$ is represented as a linear combination of a few generating terms, similarly to the characterization of a stationary measure of a birth-death process, where there is only one generating term, $\pi(0)$. The coefficients of the linear combination are recursively determined in terms of the transition rates of the Markov chain. For the class of Markov chains we consider, there is always at least one stationary measure (up to a scaling constant). We give various results pertaining to uniqueness and non-uniqueness of stationary measures, and show that the dimension of the linear space of signed invariant measures is equal to the number of generating terms. A minimization problem is constructed in order to compute stationary measures numerically. Moreover, a heuristic linear approximation scheme is suggested for the same purpose by first approximating the generating terms. The correctness of the linear approximation scheme is justified in some special cases. Furthermore, a decomposition of the state space into different types of states (open and closed irreducible classes, and trapping, escaping and neutral states) is presented. Applications to stochastic reaction networks are well illustrated.
A Climate‐Based Approach to the Restoration of Fire‐Dependent Ecosystems

Recurrent fires are integral to the function of many ecosystems worldwide. The management of fire‐frequented ecosystems requires the application of fire at the appropriate frequency and seasonality, but establishing the natural fire regime for an ecosystem can be problematic. Historical records of fires are often not available, and surrogates for past fires may not exist. We suggest that the relationship between climate and fire can provide an alternative means for inferring past fire regimes in some ecosystems.
THE EFFECT OF PRECIPITATION FORECASTS AND MONITORING ON HOURLY HYDROLOGICAL STATISTICAL PREDICTION MODELS

The so-called “electric sector” of Brazil, namely the set comprising electricity companies (state-owned and private) plus regulatory agency has been implementing automated telemetric systems for hydrological monitoring in the country’s main energy-producing watersheds. This is motivated both by the electricity regulatory agency (ANEEL) requirements and by the belief that better (meaning higher frequency and telemetric) monitoring will lead to more safety and better reservoir operation with regard to multiple use criteria (mainly energy-generation and floodcontrol). Once an automated monitoring system is in place, it is desirable to assess its effectiveness in providing reliable hydrological forecasts. Here, we will try to study the case of the Iguacu River Monitoring System upstream of Foz do Areia Reservoir. For that purpose, we use relatively well-known statistical forecasting tools: ARIMA models and Kalman Filter models. In both cases we compare the use of conventionally operated stream gages (with data reported by radio or telephone two times a day) to the now more readily available hourly telemetric data. We also analyze the impact of precipitation forecasts (simulated from historical records with different degrees of skill) on the quality of the streamflow forecasts. It is expected that objective ways of assessing the benefits of enhanced monitoring will play an important role in defining criteria for network project and cost-benefit analysis in some of the Country’s key water resource sectors.
Preliminary assessment of aridity conditions in the Iberian Peninsula

Aridity is one of the key elements characterizing the climate of a region, having a severe impact on human activities. Aiming at assessing aridity conditions in the Iberian Peninsula, the spatial distribution of the UNEP aridity index is analyzed during the period 1901−2012. Gridded precipitation and potential evapotranspiration datasets are used on a monthly basis. Results show that the southern half of Iberia is particularly vulnerable to water stress and hence to desertification processes. In particular, the UNEP aridity index reveals an increase and northward extension of the semi-arid regime in the Iberian Peninsula between 1901 and 2012. More than 50% of the north and western territory have experienced humid/sub-humid conditions, while the other regions underwent semi-arid settings. Results also reveal that climate was subjected to spatial and temporal variabilities with an overall statistically significant (at a 95% confidence level) trend to aridification in the south-easternmost and central regio.
Investigation of South African estuarine microbial species and genome diversity

A study of the microbial diversity in sediments of the Great Berg River estuary is carried out using modern molecular phylogenetic methods. The aim of the study is to determine the effect of (pollution by) the effluents of the fish industry on the composition of the microbial community in the sediments. The diversity in microbial groups of sediment samples that received wastewater from the local fishing industry is investigated by a PCR-DGGE (polymerase chain reaction-denaturing gradient gel electrophoresis) approach and compared to an unaffected site. DGGE is used for the separation of 16S rDNA amplified from metagenomic DNA, which is expected to provide qualitative information on sediment microbial community composition. The DGGE method is also applied to monitor changes of the microbial community at different depths in the estuarine sediment. Two primer sets is used in this study, one specific for 16S rDNA from the domain Bacteria and the other for DNA from the domain Archaea, which allowed the depth profiles for these groups of organisms to be compared. The DGGE profiles representing the bacteria revealed a decrease in diversity with depth at the downstream site of the wastewater outlet. In contrast, the archaeal diversity increases with depth. In addition to the DGGE analyses, 16S rDNA clone libraries were constructed from both sampling sites. A total of fifty one clones were sequenced. The phylogenetic analysis of the sequences revealed that the class Anaerolineae (phylum Chloroflexi) was only present at the downstream site. Two bacterial groups the δ-Proteobacteria and Anaerolineae (30% respectively) were the most dominant groups at the downstream site. There were slightly higher percentages of the δ-Proteobacteria observed from the downstream site (30%) compared to the upstream site (28%). Certain bacterial groups such as Sphingobacteria, Acidobacteria, Actinobacteria, Deinococci, and Plactomycete were only found in the upstream site.