Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of an Accessible Ocean-Acidification Simulator to Investigate Physiological Responses of the Green Crab, Carcinus Maenas, to Acidified Conditions

The European green crab Carcinus maenas L, is a major invasive species in North America as well as many other regions around the world, including South Africa, Australia, South America, and Asia. The species poses a significant threat to the diverse ecosystems and the aquaculture industries on the East coast of the United States, with the state of Maine particularly at risk. The shellfish industry is a significant part of Maine’s economy, and is threatened by the foraging behavior of green crabs toward small bivalves (Beal 2015). Climate change likely plays a large role in the rapid population growth of C. maenas over the last 5-10 years by opening up marginal habitats for the crabs to occupy (Beal 2015). Steroid activity is highly dependent on environmental conditions, and changes in temperature have been linked to the ecological success of C. maenas. This project will focus on developing an accessible low-cost ocean-acidification simulator at the University of Maine Aquaculture Research Center to be used to explore physiological responses of C. maenas to acidified conditions and quantify estradiol levels in the animals using an Enzyme-Linked Immunosorbent Assay (ELISA). Animal trials conducted with the system will provide information on parameters related to the effect of acidic stress on the endocrine system of green crabs. Additionally, the outlines for the construction of the simulator can be used as a model for students for an inexpensive holding facility to test effects of acidification. Data obtained from the Accessible LowCost Ocean Acidification Simulation Tool (ALCOAST) can also be used by policy makers to evaluate whether climate change can provide information on physiological interactions between C. maenas and its environment. This study stands to fill a significant gap in knowledge that is relevant not only to Maine’s economy and management of invasive species, but also to studies of how invasive species react to climate change.
Milk provisioning in oviparous caecilian amphibians

Among vertebrates, the yolk is commonly the only form of nutritional investment offered by the female to the embryo. Some species, however, have developed parental care behaviors associated with specialized food provisioning essential for offspring survival, such as the production of lipidic-rich parental milk in mammals. Here, we show that females of the egg-laying caecilian amphibian Siphonops annulatus provide similarly lipid-rich milk to altricial hatchlings during parental care. We observed that for 2 months, S. annulatus babies ingested milk released through the maternal vent seemingly in response to tactile and acoustic stimulation by the babies. The milk, composed mainly of lipids and carbohydrates, originates from the maternal oviduct epithelium’s hypertrophied glands. Our data suggest lactation in this oviparous nonmammalian species and expand the knowledge of parental care and communication in caecilians. Editor’s summary The feeding of offspring with lipid-rich milk was long believed to be unique to mammals. However, we now know that such “milk” feeding is actually present across several taxa, including spiders and amphibians. Caecilians are an amphibian group known to feed their offspring from their bodies. Some live-bearing species feed their offspring with oviduct cells internally, and other egg layers provide food through their skin cells. Mailho-Fontana et al. describe an egg-laying caecilian species that produces an oviductal milk and feeds it to its offspring through its vent, likely in response to stimulation from the offspring (see the Perspective by Wake). —Sacha Vignieri During parental care, female oviparous Siphonops annulatus provide oviductal lipid-rich milk to hatchlings through the maternal vent.
Retooling Career Systems to Fight Workplace Bias: Evidence from U.S. Corporations

Abstract The civil rights movement spurred U.S. companies and universities to implement antidiscrimination programs. Beginning in the early 1960s, employers adopted anti bias training as their first line of defense against bigotry. Even then, there was substantial evidence that this approach was unlikely to lessen bias. In this essay, we discuss social science research on the effects of antibias training, as well as research on systemic approaches to reducing institutional discrimination based on insights from contact theory. As sociologist Samuel Stouffer and psychologist Gordon Allport, the progenitors of contact theory, might have predicted by the end of World War II, we find that interventions to change career systems to maximize intergroup contact can promote workplace equity.
“When the Cruiser Lights Come On”: Using the Science of Bias & Culture to Combat Racial Disparities in Policing

Abstract In this essay, we highlight the interplay between individuals' psychological processes and sociocultural systems in producing and maintaining racial bias. We use a conceptual tool we call the culture cycle to map these dynamics, and illustrate them with research and in-depth examples from our work reducing racial disparities in routine policing in Oakland, California. We feature the most common police encounter – the vehicle stop – and highlight evidence-based interventions we developed both to reduce the frequency of vehicle stops and mitigate racial disparities in stops. Throughout, we draw on our expertise in the social psychology of bias, culture, and inequality, as well as our experiences building research-driven partnerships with public- and private-sector leaders, to inform organizational and societal change.
Implicit Bias versus Intentional Belief: When Morally Elevated Leadership Drives Transformational Change

Abstract The twenty-first century is witnessing rapid and deep change in the global economy. These changes require innovation-driven solutions and motivated, skilled workforces. The talents of every person will be required to support performance in every domain, and deliberate actions must be taken to address impediments to full engagement. Even with clear government policy and significant investments in encouraging representation and inclusion of diversity of race, sexual orientation, gender identity, and ability, progress continues to lag. This essay captures promising practices and recommendations for structural or systemic change punctuated with stories of leadership driven by the belief that implementing strategies to disrupt the effects of implicit bias are important to develop diverse, fully engaged populations.
Graph perceiver network for lung tumor and bronchial premalignant lesion stratification from histopathology

Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. In this context, we present a novel computational approach, the Graph Perceiver Network (GRAPE-Net), leveraging hematoxylin and eosin (H&E) stained whole slide images (WSIs) to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. GRAPE-Net outperforms existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma (LUAD), and non-tumor (normal) lung tissue on The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps. The network was further tested using endobronchial biopsies from two data cohorts, containing normal to carcinoma in situ histology, and it demonstrated a unique capability to differentiate carcinoma in situ lung squamous PMLs based on their progression status to invasive carcinoma. The network may have utility in stratifying PMLs for chemoprevention trials or more aggressive follow-up.
Revised Mechanism of Hydroxyurea Induced Cell Cycle Arrest and an Improved Alternative

Replication stress describes various types of endogenous and exogenous challenges to DNA replication in S-phase. Stress during this critical process results in helicase-polymerase decoupling at replication forks, triggering the S-phase checkpoint, which orchestrates global replication fork stalling and delayed entry into G2. The replication stressor most often used to induce the checkpoint response is hydroxyurea (HU), a chemotherapeutic agent. The primary mechanism of S-phase checkpoint activation by HU has thus far been considered to be a reduction of dNTP synthesis by inhibition of ribonucleotide reductase (RNR), leading to helicase-polymerase decoupling and subsequent activation of the checkpoint, mediated by the replisome associated effector kinase Mrc1. In contrast, we observe that HU causes cell cycle arrest in budding yeast independent of both the Mrc1-mediated replication checkpoint response and the Psk1-Mrc1 oxidative signaling pathway. We demonstrate a direct relationship between HU incubation and reactive oxygen species (ROS) production in yeast nuclei. We further observe that ROS strongly inhibits the in vitro polymerase activity of replicative polymerases (Pols), Pol α, Pol δ, and Pol ε, causing polymerase complex dissociation and subsequent loss of DNA substrate binding, likely through oxidation of their integral iron sulfur Fe-S clusters. Finally, we present “RNR-deg,” a genetically engineered alternative to HU in yeast with greatly increased specificity of RNR inhibition, allowing researchers to achieve fast, nontoxic, and more readily reversible checkpoint activation compared to HU, avoiding harmful ROS generation and associated downstream cellular effects that may confound interpretation of results.
Prediction of Ca2+ binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures

Predicting the precise locations of metal binding sites within metalloproteins is a crucial challenge in biophysics. A fast, accurate, and interpretable computational prediction method can complement the experimental studies. With this endeavor, In the current work, we have developed a method to predict the location of Ca2+ ions in calcium-binding proteins using a physics-based method with an all-atom description of the proteins, which is substantially faster than the molecular dynamics simulation-based methods with accuracy as good as data-driven approaches. Our methodology uses the three-dimensional reference interaction site model (3D-RISM), a statistical mechanical theory, to calculate Ca2+ ion density around protein structures, and the locations of the Ca2+ ions are obtained from the density. We have taken previously used datasets to assess the efficacy of our method as compared to previous works. Our accuracy is found to be 88%, comparable with the FEATURE program, one of the well-known data-driven methods. Moreover, our method being physical, the reasons for failures can be ascertained in most cases. We have thoroughly examined the failed cases using different structural and crystallographic measures, such as B-factor, R-factor, electron density map, and geometry at the binding site. It has been found that X-ray structures have issues in many of the failed cases, such as geometric irregularities and dubious assignment of ion positions. Our algorithm, along with the checks for structural accuracy, is a major step in predicting calcium ion positions in metalloproteins.
The Role of Inhibition in fMRI Resting-State Negative Correlations

Resting-state brain activity, as observed via functional magnetic resonance imaging (fMRI), displays non-random fluctuations whose covariance structure (aka functional connectivity; FC) is commonly parsed into spatial patterns of positive and negative correlations (PCs and NCs). Mapping NC patterns for certain key seed regions has shown considerable promise in recent years as a tool for enhancing neuro-navigated targeting and clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) therapies in psychiatry. These successes bring to the fore several major outstanding questions around the neurophysiological origins of fMRI NCs, the answers to which should prove useful for future therapeutic protocol development. In this work, we studied candidate mechanisms for the emergence of fMRI NCs using connectome-based computational modeling. Simulations of fMRI data under manipulation of inhibitory parameters WI and λ, representing local and network-mediated inhibition respectively, were explored, focusing on the impact of inhibition levels on the emergence of NCs. Despite the considerable difference in time scales between GABAergic neuronal inhibition (tens of ms) and fMRI FC (dozens of seconds), a clear relationship was observed, whereby the greater levels of overall inhibition led to significantly greater magnitude and spatial extent of NCs. We show that this effect is due to a leftward shift in the FC correlation distribution, leading to a reduction in the number of PCs and a concomitant increase in the number of NCs. Relatedly, we observed that those connections available for recruitment as NCs were precisely those with the weakest corresponding structural connectivity. Relative to nominally default values for the models used, greater levels of inhibition also improved, quantitatively and qualitatively, single-subject fits of simulated to empirical FC matrices. Our results provide new insights into how individual variability in anatomical connectivity strengths and neuronal inhibition levels may determine individualized expression of NCs in fMRI data. These, in turn, may offer new directions for optimization and personalization of rTMS therapies and other clinical applications of fMRI NC patterns. Highlights The reduced Wong-Wang (RWW) neural mass model was used to understand and study the physiological origin of negative correlations in rs-fMRI. The simulated data can partially capture the dynamics of the empirical rs-fMRI data. Increasing the level of inhibition leads to an increase in the number of negative correlations. The levels of excitation and inhibition have differential effects on model stability and negative correlations. Insights from this work can contribute towards fine-tuning model parameters and in the future, aim to further validate findings using empirical data from clinical studies, and explore alternate parameters influencing negative correlations in rs-fMRI FC.
Examination of the role of mutualism in immune evasion

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumor requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumor implemented as a 2D-hexagonal lattice model suggests tumor survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumors with equivalent growth rates and no mutualism are more likely to be eliminated than tumors with mutualism. Tumors with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumors, mutualism is critical for early tumorigenesis.
Inflammation promotes aging-associated oncogenesis in the lung

Background Lung cancer is the leading cause of cancer death in the world. While cigarette smoking is the major preventable factor for cancers in general and lung cancer in particular, old age is also a major risk factor. Aging-related chronic, low-level inflammation, termed inflammaging, has been widely documented; however, it remains unclear how inflammaging contributes to increased lung cancer incidence. Aim To establish connections between aging-associated changes in the lungs and cancer risk. Methods We analyzed public databases of gene expression for normal and cancerous human lungs and used mouse models to understand which changes were dependent on inflammation, as well as to assess the impact on oncogenesis. Results Analyses of GTEx and TCGA databases comparing gene expression profiles from normal lungs, lung adenocarcinoma, lung squamous cell carcinoma of subjects across age groups revealed upregulated pathways such as inflammatory response, TNFA signaling via NFκB, and interferon-gamma response. Similar pathways were identified comparing the gene expression profiles of young and old mouse lungs. Transgenic expression of alpha 1 antitrypsin (AAT) partially reverses increases in markers of aging-associated inflammation and immune deregulation. Using an orthotopic model of lung cancer using cells derived from EML4-ALK fusion-induced adenomas, we demonstrated an increased tumor outgrowth in lungs of old mice while NLRP3 knockout in old mice decreased tumor volumes, suggesting that inflammation contributes to increased lung cancer development in aging organisms. Conclusions These studies reveal how expression of an anti-inflammatory mediator (AAT) can reduce some but not all aging-associated changes in mRNA and protein expression in the lungs. We further show that aging is associated with increased tumor outgrowth in the lungs, which may relate to an increased inflammatory microenvironment.
Beyond power limits: the kinetic energy capacity of skeletal muscle

Muscle is the universal agent of animal movement, and limits to muscle performance are therefore an integral aspect of animal behaviour, ecology, and evolution. A mechanical perspective on movement makes it amenable to analysis from first principles, and so brings the seeming certainty of simple physical “laws” to the challenging comparative study of complex biological systems. Early work in biomechanics considered muscle energy output to be limited by muscle work capacity, Wmax; triggered by seminal work in the late 1960s, it is now held broadly that a complete analysis of muscle energy output during impulsive contractions is to also consider muscle power capacity, for no unit of work can be delivered in arbitrarily brief time. This work adopts a critical stance towards this paradigmatic notion of a “power-limit”, and argues that the de facto alternative constraint to muscle energy output is imposed by a characteristic “kinetic energy capacity”, Emax, dictated by the maximum speed with which the actuating muscle can shorten. The two critical energies can now be directly compared, and define the “physiological similarity index”, Γ = Emax/Wmax. It is the explanatory power of this comparison that lends weight to a shift in perspective from muscle power to kinetic energy capacity, as is argued through a series of brief illustrative examples. Γ emerges as a key dimensionless number in musculoskeletal dynamics, and sparks novel hypotheses on functional adaptations in musculoskeletal “design” that depart from the eternal evolutionary null hypothesis of geometric similarity.
High-throughput genetics enables identification of nutrient utilization and accessory energy metabolism genes in a model methanogen

Archaea are widespread in the environment and play fundamental roles in diverse ecosystems; however, characterization of their unique biology requires advanced tools. This is particularly challenging when characterizing gene function. Here, we generate randomly-barcoded transposon libraries in the model methanogenic archaeon Methanococcus maripaludis and use high-throughput growth methods to conduct fitness assays (RB-TnSeq) across over 100 unique growth conditions. Using our approach, we identified new genes involved in nutrient utilization and response to oxidative stress. We identified novel genes for the usage of diverse nitrogen sources in M. maripaludis including a putative regulator of alanine deamination and molybdate transporters important for nitrogen fixation. Furthermore, leveraging the fitness data, we provide the first evidence that M. maripaludis can utilize the nitrogen sources L-glutamine, D-glucuronamide, and adenosine. Under autotrophic growth conditions, we identified a gene encoding a domain of unknown function (DUF166) that is important for fitness and hypothesize it has an accessory role in carbon dioxide assimilation. Finally, comparing fitness costs of oxygen versus sulfite stress, we identified a previously uncharacterized class of dissimilatory sulfite reductase-like proteins (Dsr-LP, group IIId) that is important during growth in the presence of sulfite. When overexpressed, Dsr-LP conferred sulfite resistance and enabled use of sulfite as the sole sulfur source. The high-throughput approach employed here allowed for generation of a large-scale dataset that can be used as a resource to further understand gene function and metabolism in the archaeal domain. Significance Statement Archaea are widespread in the environment, yet basic aspects of their biology remain underexplored. To address this, we apply randomly-barcoded transposon libraries (RB-TnSeq) to the model archaeon Methanococcus maripaludis. RB-TnSeq coupled with high-throughput growth assays across over 100 unique conditions identified roles for previously uncharacterized genes, including several encoding proteins with domains of unknown function (DUFs). We also expand on our understanding of carbon and nitrogen metabolism and characterize a group IIId dissimilatory sulfite reductase-like protein as a functional sulfite reductase. This dataset encompasses a wide range of additional conditions including stress, nitrogen fixation, amino acid supplementation, and autotrophy; thus, providing an extensive dataset for the archaeal community to mine for characterizing additional genes of unknown function.
Effective Reconstruction of Generic Genus 4 Curves from Their Theta Hyperplanes

Effective reconstruction formulas of a curve from its theta hyperplanes are known classically in genus 2 (where the theta hyperplanes are Weierstrass points), and 3 (where, for a generic curve, the theta hyperplanes are bitangents to a plane quartic). However, for higher genera, no formula or algorithm are known. In this paper we give an explicit (and simple) algorithm for computing a generic genus 4 curve from it's theta hyperplanes.
Manifold Learning Uncovers Nonlinear Interactions between the Adolescent Brain and the Social Environment in Predicting Mental Health Problems

Advanced statistical methods that capture the complex interplay between adolescents and their social environments are essential for improving our understanding of how differences in brain function contribute to mental health problems. To move the study of adolescent mental health beyond what we have achieved so far–a complex account of brain and environmental risk factors without understanding the neurobiological embedding of the social environment–we need to find ways of studying the complex, nonlinear relationships between brain function and adolescents’ experiences in the real-world. Manifold learning techniques can discover and highlight latent structure from high-dimensional, complex biomedical data, such as fMRI. Here, we develop a novel manifold learning technique, exogenous PHATE (EPHATE), to capture the interplay between brain function and adolescents’ social environments. By applying EPHATE, we demonstrate that harmonizing cutting-edge computational methods with longstanding developmental theory can advance efforts to detect and predict mental health problems during the transition to adolescence.