Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data

Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. To date, only one method—CDROM—has been developed with this goal in mind. In particular, CDROM employs gene expression distances as proxies for functional divergence, and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the underlying parameters of duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built upon a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents the best available method for classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby also highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.
Reduced horseshoe crab abundance and feeding activity beneath intertidal oyster aquaculture structures in the Delaware Bay

Around the world, tidal flats play a unique ecological role in estuaries and are a primary feeding habitat for shorebirds and other benthic feeding organisms. Development and economic use of tidal flats can exclude species that depend on this habitat and disrupt ecological processes. In this study we examine patterns of abundance and feeding activity of American horseshoe crabs among oyster aquaculture structures on tidal flats that are adjacent to one of the most important horseshoe crab spawning sites in the world. We used custom-designed traps to sample horseshoe crab abundance beneath rack and bag aquaculture structures and adjacent areas without structures. In addition, we developed predictive spatial models representing three hypotheses regarding the movement of horseshoe crabs through aquaculture structures when transiting to and from spawning beaches. We tested the predictive power of each model using data from traps and found the strongest support for an avoidance model, where on average, horseshoe crabs are avoiding arrays of aquaculture structures when moving across inundated tidal flats. The best-supported spatial model also indicates that patterns of structure avoidance by horseshoe crabs can potentially affect abundance on spawning beaches, particularly with larger gear arrays that are closer to shore. We found additional support for aquaculture structure avoidance by examining an independent data set of horseshoe crab feeding pits on the tidal flats. Patterns of feeding pit density mirrored our trapping results, with fewer pits beneath and among aquaculture structures when compared to adjacent control areas. Horseshoe crabs are important constituents of the benthic food web and their displacement by aquaculture may translate to significant disruptions to the ecological function of tidal flats. This impact can be limited through deliberative spatial planning that seeks to balance ecological and economic management objectives.
Dry Edible Bean Class and Cultivar Response to Dimethenamid and Metolachlor

Abstract Dry edible bean class and cultivar response to dimethenamid and metolachlor was investigated in the greenhouse and field. Kidney and cranberry cultivars, as well as a small red cultivar, were not injured by dimethenamid applied PRE at 2,100 g ai/ha in the greenhouse, whereas pinto bean tolerance varied and navy and black bean cultivars were injured by this rate. Injury to navy bean was greater in the greenhouse when dimethenamid and metolachlor were placed in the zone above and including the seed, compared with placement in the seed, root, or root plus seed zone. In an application timing field study, dimethenamid at 1,300 g/ha applied at the crook or unifoliate growth stage caused injury to navy bean, delayed maturity, and reduced seed yield. Metolachlor at 1,400 g ai/ha delayed maturity when applied at the unifoliate growth stage but did not reduce seed yield. Dimethenamid or metolachlor PRE, at 1,300 or 2,800 g ai/ha, respectively, injured navy and black bean cultivars, but seed yield was not reduced in a cultivar tolerance field study. In a planting date study, dimethenamid PRE at 2,300 g/ha reduced leaf area and delayed maturity compared with the nontreated control when pooled over five planting dates and cultivars in each of 2 yr. Metolachlor PRE at 2,800 g/ha reduced leaf area in 1 yr and delayed maturity in both years when pooled over planting dates and cultivars. If weed control and herbicide costs are comparable, metolachlor at a standard use rate is a safer choice than dimethenamid for use in navy and black bean production. Nomenclature: Dimethenamid; metolachlor; dry bean, Phaseolus vulgaris L. ‘Avanti’, ‘Schooner’, and ‘Vista’ navy bean, ‘Midnight’ and ‘T-39’ black bean, kidney bean, ‘Rufus’ small red bean, cranberry bean, pinto bean
Influence of Forest Vegetation Structure on Avian Community Composition in West-Central Idaho

Abstract Locally derived information is needed if management guidelines intended to improve wildlife habitat are to be relevant, reliable, and applicable. Within the Intermountain West, few studies have examined habitat relationships of forest songbirds in mixed-conifer forests. During 1996–1997, we studied breeding bird communities in a mixed-conifer forest in west-central Idaho, USA, to describe how relative avian abundance was related to forest structure. Our study objectives were to 1) describe bird use of 3 distinct forest age-classes, 2) identify groups of species that expressed similar relationships with forest structural attributes, and 3) model species-specific bird–habitat relationships. We sampled 46 study sites (forest stands) classified into 1 of 3 common “vegetation growth stages” (seral stages): 1) shrub/seedling/sapling (n = 15 stands), 2) small tree (n = 16 stands), and 3) old growth (n = 15 stands). In each stand we surveyed breeding birds using fixed-radius point counts and measured forest structure at each point-count location. Of 30 species examined, the relative abundance of 10 differed among vegetation growth stages. Differences in stand structure were reflected by several strong gradients of vegetation structure and composition (e.g., canopy cover, diameter of large trees, snag density, and stump density) across vegetation growth stages. We used cluster analysis to establish novel information about how birds responded to these gradients and identified 3 distinct bird assemblages: forest generalists, early seral specialists, and mid- to late-seral specialists. Habitat models derived from multiple linear regression revealed that canopy cover was the best predictor of variation in abundance for numerous bird species. We conclude that changes in the amount of overstory cover will have the greatest influence on birds. Where canopy cover is reduced below, or increased above, a threshold level of approximately 25–40% cover, we predict that occurrence or density of some breeding birds will change. Our habitat models suggest that Townsend's warbler (Dendroica townsendi), red-breasted nuthatch (Sitta canadensis), and brown creeper (Certhia americana) would respond negatively to reductions in canopy cover; chipping sparrow (Spizella passerina), dusky flycatcher (Empidonax oberholseri), and warbling vireo (Vireo gilvus) would respond positively. This type of information will contribute to regional planning processes by allowing species “trade-offs” to be assessed under different forest management scenarios.
Identification of sex differences in tumor-specific T cell infiltration in bladder tumor-bearing mice treated with BCG immunotherapy

Bladder cancer is the fourth most common cancer for men. However, women are often diagnosed with later stage disease and have poorer outcomes. Whether immune-based sex differences contribute to this discrepancy is unclear. In addition, models to investigate tumor-specific immunity in bladder cancer, in the context of tumor development or response to therapy, are lacking. To address this specific unmet need, we incorporated a commonly used model antigen, ovalbumin, into two well-established models of bladder cancer; the orthotopic MB49 cell line model and the carcinogenic BBN bladder cancer model. We tested the utility of these models to investigate tumor-specific immunity in the context of immunotherapy in both sexes. We found that BCG vaccination, prior to weekly BCG instillation does not impart an immune-specific benefit to tumor-bearing mice in the context of multiple BCG instillations. Furthermore, tumors developed in the testes in male mice, precluding the use of the MB49 model to directly investigate sex-based immune differences. In the BBN model, we observed that more tumor antigen-specific CD8+ T cells infiltrated male bladders compared to female bladders in the context of BCG immunotherapy and that these cells had the highest levels of the exhaustion marker PD-1. We propose our modified BBN model will contribute to our understanding of how tumor-specific immunity arises in bladder cancer. Additionally, the BBN bladder cancer model may help to uncover sex differences in tumor-specific immunity, which would provide valuable information for the development of new treatments or combination therapies for bladder cancer in women and men.
Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim, confusingly, that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that the demographic models employed in such analyses are generally necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require fitting a very large number of free parameters.
Nonselective cation permeation in an AMPA-type glutamate receptor

Significance AMPA-type glutamate receptors (AMPARs) make the final step in a relay of excitability from one brain cell to another. The receptor contains an integral ion channel, which, when opened by neurotransmitter, permits sodium and other cations to cross the cell membrane. We investigated permeation of sodium, potassium, and cesium at the atomistic level using a computational molecular dynamics approach and obtained ion transit rates similar to those in wet experiments over tens of microseconds of simulations. We determined that the region selecting between cations is the simplest of any channel of this type. Distinct from ion channels that select single ion species, sodium and potassium remain partly hydrated and have only one major binding site in the channel. Fast excitatory synaptic transmission in the central nervous system relies on the AMPA-type glutamate receptor (AMPAR). This receptor incorporates a nonselective cation channel, which is opened by the binding of glutamate. Although the open pore structure has recently became available from cryo-electron microscopy (Cryo-EM), the molecular mechanisms governing cation permeability in AMPA receptors are not understood. Here, we combined microsecond molecular dynamic (MD) simulations on a putative open-state structure of GluA2 with electrophysiology on cloned channels to elucidate ion permeation mechanisms. Na+, K+, and Cs+ permeated at physiological rates, consistent with a structure that represents a true open state. A single major ion binding site for Na+ and K+ in the pore represents the simplest selectivity filter (SF) structure for any tetrameric cation channel of known structure. The minimal SF comprised only Q586 and Q587, and other residues on the cytoplasmic side formed a water-filled cavity with a cone shape that lacked major interactions with ions. We observed that Cl− readily enters the upper pore, explaining anion permeation in the RNA-edited (Q586R) form of GluA2. A permissive architecture of the SF accommodated different alkali metals in distinct solvation states to allow rapid, nonselective cation permeation and copermeation by water. Simulations suggested Cs+ uses two equally populated ion binding sites in the filter, and we confirmed with electrophysiology of GluA2 that Cs+ is slightly more permeant than Na+, consistent with serial binding sites preferentially driving selectivity.
U2AF1 is a haplo-essential gene required for cancer cell survival

Somatic mutations in the spliceosome gene U2AF1 are common in patients with myelodysplastic syndromes. U2AF1 mutations that code for the most common amino acid substitutions are always heterozygous, and the retained wild-type allele is expressed, suggesting that mutant hematopoietic cells may require the residual wild-type allele to be viable and cause disease. We show that hematopoiesis and RNA splicing in U2af1 heterozygous knock-out mice was similar to control mice, but that deletion of the wild-type allele in U2AF1(S34F) heterozygous mutant expressing hematopoietic cells (i.e., hemizygous mutant) was lethal. These results confirm that U2AF1 mutant hematopoietic cells are dependent on the expression of wild-type U2AF1 for survival in vivo and that U2AF1 is a haplo-essential cancer gene. Mutant U2AF1 (S34F) expressing cells were also more sensitive to reduced, but not absent, expression of wild-type U2AF1 than non-mutant cells. Furthermore, mice transplanted with leukemia cells expressing mutant U2AF1 had significantly reduced tumor burden and improved survival after the wild-type U2af1 allele was deleted compared to when it was not deleted. These results suggest that selectively targeting the wild-type U2AF1 allele in heterozygous mutant cells could induce cancer cell death and be a therapeutic strategy for patients harboring U2AF1 mutations.
Convergence of Modality Invariance and Attention Selectivity in the Cortical Semantic Circuit

The human linguistic system is characterized by modality invariance and attention selectivity. Previous studies have examined these properties independently and reported perisylvian region involvement for both; however, their relationship and the linguistic information they harbor remain unknown. Participants were assessed by functional MRI, while spoken narratives (auditory) and written texts (visual) were presented, either separately or simultaneously. Participants were asked to attend to one stimulus when both were presented. We extracted phonemic and semantic features from these auditory and visual modalities, to train multiple, voxel-wise encoding models. Cross-modal examinations of the trained models revealed that perisylvian regions were associated with modality-invariant semantic representations. Attentional modulation was quantified by examining the modeling performance for attended and unattended conditions. We have determined that perisylvian regions exhibited attention selectivity. Both modality invariance and attention selectivity are both prominent in models that use semantic but not phonemic features. Modality invariance was significantly correlated with attention selectivity in some brain regions; however, we also identified cortical regions associated with only modality invariance or only attention selectivity. Thus, paying selective attention to a specific sensory input modality may regulate the semantic information that is partly processed in brain networks that are shared across modalities.
fruitless tunes functional flexibility of courtship circuitry during development

Drosophila male courtship is controlled by the male-specific products of the fruitless (fruM gene and its expressing neuronal circuitry. fruM is considered a master gene that controls all aspects of male courtship. By temporally and spatially manipulating fruM expression, we found that fruM is required during a critical developmental period for innate courtship towards females, and its function during adulthood is relatively trivial. By altering or eliminating fruM expression, we generated males that are innately heterosexual, homosexual, bisexual, or without innate courtship but could acquire such behavior in an experience-dependent manner. These findings show that fruM is not absolutely necessary for courtship but is critical during development to build a sex circuitry with reduced flexibility and enhanced efficiency and provide a new view about how fruM tunes functional flexibility of a sex circuitry.
Exercise mitigates sleep-loss-induced changes in glucose tolerance, mitochondrial function, sarcoplasmic protein synthesis, and circadian rhythms

Sleep loss has emerged as a risk factor for the development of impaired glucose tolerance. The mechanisms underpinning this observation are unknown; however, both mitochondrial dysfunction and circadian misalignment have been proposed. Given that exercise improves glucose tolerance, mitochondrial function, and alters circadian rhythms, we investigated whether exercise may counteract the effects induced by inadequate sleep. We report that sleeping 4 hours per night, for five nights, reduced glucose tolerance, with novel observations of associated reductions in mitochondrial function, sarcoplasmic protein synthesis, and measures of circadian rhythmicity; however, incorporating three sessions of high-intensity interval exercise (HIIE) during this period mitigates these effects. These data demonstrate, for the first time, a sleep loss-induced concomitant reduction in a range of physiological processes linked to metabolic function. These same effects are not observed when exercise is performed during a period of inadequate sleep, supporting the use of HIIE as an intervention to mitigate the detrimental physiological effects of sleep loss.
Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

Modern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.
Modeling the Covid-19 Pandemic Response of the US States

Background: The United States of America (USA) has been the country worst affected, in absolute terms, by the Covid-19 pandemic. The country comprises 50 states under a federal system. The impact of the pandemic has resulted in different responses at the state level, which are driven by differing intervention policies, demographics, connectedness and other factors. Understanding the dynamics of the Covid-19 pandemic at the state level is essential in predicting its future evolution. Objective: Our objective is to identify and characterize multiple waves of the pandemic by analyzing the reported infected population curve in each of the 50 US states. Based on the intensity of the waves, characterized by declining, stationary, or increasing strengths, each state's response can be inferred and quantified. Methods: We apply a recently proposed multiple-wave model to fit the infected population data for each state in USA, and use the proposed Pandemic Response Index to quantify their response to the Covid-19 pandemic. Results: We have analyzed reported infected cases from each one of the 50 USA states and the District of Columbia, based on the multiple-wave model, and present the relevant parameters. Multiple waves have been identified and this model is found to describe the data better. Each of the states can be classified into one of three distinct classes characterized by declining, increasing, or stationary strength of the waves following the initial one. The effectiveness of intervention measures can be inferred by the peak intensities of the waves, and states with similar population characteristics can be directly compared. We estimate how much lower the number of infections might have been, if early and strict intervention measures had been imposed to stop the disease spread at the first wave, as was the case for certain states. Based on our model's results, we compute the value of the Pandemic Response Index, a recently introduced metric for quantifying in an objective manner the response to the pandemic. Conclusions: Our results reveal a series of epidemic waves, characterizing USA's pandemic response at the state level, and also infer to what extent the imposition of early intervention measures could have had on the spread and impact of the disease. As of June 11, 2020, only 19 states and the District of Columbia (40% of the total) clearly exhibit declining trends in the numbers of reported infected cases, while 13 states exhibit stationary and 18 states increasing trends in the numbers of reported cases.