Miracle-dz/UniFAR-FTUs huggingface dataset:
Debugging and consolidating multiple synthetic chromosomes reveals combinatorial genetic interactions

The Sc2.0 project is building a eukaryotic synthetic genome from scratch, incorporating thousands of designer features. A major milestone has been achieved with the assembly of all individual Sc2.0 chromosomes. Here, we describe the consolidation of multiple synthetic chromosomes using endoreduplication intercross to generate a strain with 6.5 synthetic chromosomes. Genome-wide chromosome conformation capture and long-read direct RNA sequencing were performed on this strain to evaluate the effects of designer modifications, such as loxPsym site insertion, tRNA relocation, and intron deletion, on 3D chromosome organization and transcript isoform profiles. To precisely map “bugs”, we developed a method, CRISPR Directed Biallelic URA3-assisted Genome Scan, or “CRISPR D-BUGS”, exploiting directed mitotic recombination in heterozygous diploids. Using this method, we first fine-mapped a synII defect resulting from two loxPsym sites in the 3’ UTR of SHM1. This approach was also used to map a combinatorial bug associated with synIII and synX, revealing a highly unexpected genetic interaction that links transcriptional regulation, inositol metabolism and tRNASerCGA abundance. “Starvation” for tRNASerCGA leads to insufficient levels of the key positive inositol biosynthesis regulator, Swi3, which contains tandem UCG codons. Finally, to expedite consolidation, we employed a new method, chromosome swapping, to incorporate the largest chromosome (synIV), thereby consolidating more than half of the Sc2.0 genome in a single strain.
A spiking neural network model for the control of oscillatory phase differences between multiple brain areas

The causal mechanisms which underlie rapid, transient, and dynamic coordination of large-scale functional brain networks remain unknown. Neuronal oscillations have been hypothesized to play a critical role influencing long-range communication between brain areas, with oscillatory phase differences influencing the effective connectivity between areas (Fries, 2005). However, it remains unclear how networks of spiking neurons could causally and mechanistically control the relative phase difference between cortical regions. Here we describe a simple model of spiking neurons that can dynamically control the relative phase differences between multiple brain areas. The model applies to any inhibition-based rhythm, and has four essential elements: a) tonic excitation of local recurrent networks of excitatory and inhibitory cells produces oscillations (Börgers et al., 2012); b) winner-take-all (WTA) dynamics permit only a small fraction of cells to fire on any given cycle (de Almeida et al., 2009); c) di-synaptic feed-forward inhibition leads to oscillatory phase resetting in a target area (Traub et al., 1996); and d) the range of conduction delays for inter-regional axonal projections span the duration of one oscillatory cycle (Swadlow, 1998). The key assumption of our model is to link WTA dynamics to conduction delays, such that only axons with a narrow range of delays are active at one time and are selected via local activity. Phase resetting in the target area converts this conduction delay into a specific phase difference. Thus, the model converts local differences in cellular excitation into inter-areal phase differences via WTA dynamics and feed-forward inhibition, where excitatory cells act as phase difference effectors or detectors. Including several WTA modules within a given area allows it to serve as a coordinating hub, controlling the relative phase differences of multiple distributed brain regions. Finally, we show how this model can help explains recent empirical results showing that individual neurons are sensitive to the relative phase difference between areas (Canolty et al., 2010).
Mechanisms of human dynamic object recognition revealed by sequential deep neural networks

Humans recognize objects in a dynamically changing world integrating evidence across a vast variety of timescales. This ability is showcased by performance on rapid serial visual presentation (RSVP) tasks in which observers succeed at recognizing objects in rapid sequences of natural scenes, at up to 13 ms/image. To date, the computational mechanisms governing such dynamic object recognition remain poorly understood. Here, we developed deep learning models for dynamic recognition and compared different computational mechanisms, contrasting feedforward and recurrent, single and sequential image processing as well as different forms of adaptation. We found that only models that integrate images sequentially via lateral recurrence mirrored human performance (N=36) across different image durations (13-80 ms/image). Augmenting this model with a power-law adaptation mechanism was essential for predicting human trial-by-trial reports across image durations. These findings shed new insights into the mechanisms rendering object recognition so fast and effective in a dynamic visual world.
Environmental filtering and habitat (mis)matching of riverine invertebrate metacommunities

Aim Metacommunities are assembled through a combination of local and regional processes, with the relative importance of the drivers of assembly depending on ecological context. Global change can alter community assembly at both local and regional levels, potentially shifting communities into disequilibrium with their local environmental conditions. In this study, we evaluated the spatial variation in environmental filtering and habitat matching of 1078 riverine macroinvertebrate communities distributed across nine ecoregions within the conterminous United States. Location Conterminous United States. Taxon Freshwater macroinvertebrates. Methods We first quantified spatial patterns in environmental filtering, habitat matching, and functional trait diversity. We then used boosted regression trees to identify (1) functional trait predictors of environmental filtering and habitat matching and (2) environmental, landscape, and network variables that predict functional trait abundances. Results Our results demonstrated that environmental filtering but not habitat matching varied strongly by ecoregion. We also found that functional trait diversity varied by ecoregion, but not as strongly as the signatures of environmental filtering. We did not identify consistent functional trait predictors for both environmental filtering and habitat matching, with trait predictors instead varying by individual traits, trait categories, and ecoregions. Notwithstanding inconsistent trait predictors, environmental filtering was primarily influenced by habitat preference traits while habitat matching was primarily influenced by both habitat preference and dispersal traits. Predictors of functional traits also varied by trait category and ecoregion, with habitat preference and dispersal traits primarily influenced by network variables. Main conclusions Our study demonstrates the contingent patterns and drivers of environmental filtering and habitat matching on a macroecological scale. We aim for this work to provide the foundation on which trait-environment relationships can be further quantified and causal explanations established in the context of community disequilibrium and applied to conservation and management of freshwater systems.
Investigating tri-trophic interactions using bio-energetic demographic models

A central debate in ecology has been the long running discussion on the role of apex predators in affecting the abundance and dynamics of their prey. In terrestrial systems, research has primarily relied on correlational approaches, due to the challenge of implementing robust experiments with replication and appropriate controls. A consequence of this is that we largely suffer from a lack of mechanistic understanding of the population dynamics of interacting species that can be surprisingly complex. Mechanistic models offer an opportunity to examine the causes and consequences of some of this complexity. We present a bioenergetic mechanistic model of a tri-trophic system where the primary vegetation resource follows a seasonal growth function, and the herbivore and carnivore species are modelled using two integral projection models (IPMs) with body mass as the phenotypic trait. Within each IPM, the demographic functions are structured according to bioenergetic principles, describing how animals acquire and transform resources into body mass, energy reserves and breeding potential. We parameterise this model to reproduce the population dynamics of grass, elk and wolves in northern Yellowstone (USA), and investigate the impact of wolf reintroduction on the system. Our model generated predictions that closely matched the observed population sizes of elk and wolf in Yellowstone prior to and post wolf reintroduction. The introduction of wolves into our basal grass-elk bioenergetic model resulted in a population of 99 wolves, and a reduction in elk numbers by 61% (from 14,948 to 5,823) at equilibrium. In turn, vegetation biomass increased by approximately 25% in the growing season and more than 3-fold in the non-growing season. The addition of wolves to the model caused the elk population to shift from being food-limited to being predator-limited, and had a stabilising effect on elk numbers across different years. Wolf predation also led to a shift in the phenotypic composition of the elk population, via a small increase in elk average body mass. Our model represents a novel approach to the study of predator-prey interactions. Explicitly considering and linking bioenergetics, population demography and body mass phenotypes can provide novel insights into the mechanisms behind complex ecosystem processes.
Preclinical evaluation of CDK4 phosphorylation predicts high sensitivity of malignant pleural mesotheliomas to CDK4/6 inhibition

Malignant pleural mesothelioma (MPM) is an aggressive cancer with limited therapeutic options. In this study, we evaluated the impact of CDK4/6 inhibition by palbociclib in a panel of 28 MPM cell lines, including 19 patient-derived cell lines, using a variety of approaches including RNA-sequencing. Palbociclib used alone sufficed to strongly and durably inhibit the proliferation of 23 MPM cell lines, indicating a unique sensitivity of MPM to CDK4/6 inhibition. Importantly, insensitivity to palbociclib was mostly explained by the lack of active T172-phosphorylated CDK4. This was associated with the high p16INK4A (CDKN2A) levels that accompany RB1 defects or inactivation, and also (unexpectedly) cyclin E1 over-expression in the presence of wild-type RB1. Prolonged treatment with palbociclib irreversibly inhibited proliferation despite re-induction of cell cycle genes upon drug washout. A senescence-associated secretory phenotype including various potentially immunogenic components was also irreversibly induced. Phosphorylated CDK4 was detected in 80% of 47 MPM tumors indicating their intrinsic sensitivity to CDK4/6 inhibitors. The absence of this phosphorylation in some highly proliferative MPM tumors was linked to partial deletions of RB1, leading to very high p16 (CDKN2A) expression. Our study strongly supports the clinical evaluation of CDK4/6 inhibitory drugs for MPM treatment, in monotherapy or combination therapy.
Neuro-cognitive models of single-trial EEG measures describe latent effects of spatial attention during perceptual decision making

Visual perceptual decision-making involves multiple components including visual encoding, attention, accumulation of evidence, and motor execution. Recent research suggests that EEG oscillations can identify the time of encoding and the onset of evidence accumulation during perceptual decision-making. Although scientists show that spatial attention improves participant performance in decision making, little is know about how spatial attention influences the individual cognitive components that gives rise to that improvement in performance. We found evidence in this work that both visual encoding time (VET) before evidence accumulation and other non-decision time process after or during evidence accumulation are influenced by spatial top-down attention, but not evidence accumulation itself. Specifically we used an open-source data set in which participants were informed about the location of a target stimulus in the visual field on some trials during a face-car perceptual decision-making task. Fitting neural drift-diffusion models to response time, accuracy, and single-trial N200 latencies (~ 125 to 225 ms post-stimulus) of EEG allowed us to separate the processes of visual encoding and the decision process from other non-decision time processes such as motor execution. These models were fit in a single step in a hierarchical Bayesian framework. Model selection criteria and comparison to model simulations show that spatial attention manipulates both VET and other non-decision time process. We discuss why spatial attention may affect other non-evidence accumulation processes, such as motor execution time (MET), and why this may seem unexpected given the literature. We make recommendations for future work on this topic.
An intrinsically disordered transcription activation domain alters the DNA binding affinity and specificity of NFκB p50/RelA

Many transcription factors contain intrinsically disordered transcription activation domains (TADs), which mediate interactions with co-activators to activate transcription. Historically, DNA-binding domains and TADs have been considered as modular units, but recent studies have shown that TADs can influence DNA binding. We biophysically characterized the NFκB p50/RelA heterodimer including the RelA TAD and investigated the TAD’s influence on NFκB-DNA interactions. In solution the RelA TAD is disordered but compact, with helical tendency in two regions that interact with co-activators. The presence of the TAD increased the stoichiometry of NFκB-DNA complexes containing promoter DNA sequences with tandem κB recognition motifs by promoting the binding of NFκB dimers in excess of the number of κB sites. We measured the binding affinity of p50/RelA for DNA containing tandem κB sites and single κB sites. While the presence of the TAD enhanced the binding affinity of p50/RelA for all κB sequences tested, it increased the affinity for non-specific DNA sequences by over 10-fold, leading to an overall decrease in specificity for κB DNA sequences. Our results reveal a novel function of the RelA TAD in promoting binding to non-consensus DNA previously observed by in vivo studies of NFκB-DNA binding in response to strong inflammatory signals.
The effects of environmental heterogeneity within a city on the evolution of clines

There is increasing evidence that environmental change associated with urbanization can drive rapid adaptation. However, most studies of urban adaptation have focused on coarse urban vs. rural comparisons or sampled along a single urban-rural environmental gradient, thereby ignoring the role that within-city environmental heterogeneity might play in adaptation to urban environments. In this study, we examined fine-scale variation in the presence of HCN—a potent anti-herbivore defense—and its two underlying genes (Ac and Li) between park green spaces and surrounding suburban habitats for five city parks in the Greater Toronto Area. We show that fine-scale urbanization has driven the formation of micro-clines in HCN on a scale of < 2 km, though the presence and strength of micro-clines varied across parks. Interestingly, these micro-clines were driven by lower HCN frequencies inside park green spaces, and are therefore in the opposite direction to that predicted based on previously described patterns of HCN frequency change along urban-rural gradients. Synthesis: These results suggest larger scale, adaptive urban-rural clines occur across a complex matrix of environmental heterogeneity within cities that drives fine-scale adaptive microclines of varying strengths and directions.
Exploring the mobilome and resistome of Enterococcus faecium in a One Health context across two continents

Enterococcus faecium is a ubiquitous opportunistic pathogen that is exhibiting increasing levels of antimicrobial resistance (AMR). Many of the genes that confer resistance and pathogenic functions are localized on mobile genetic elements (MGEs), which facilitate their transfer between lineages. Here, features including resistance determinants, virulence factors, and MGEs were profiled in a set of 1273 E. faecium genomes from two disparate geographic locations (in the UK and Canada) from a range of agricultural, clinical, and associated habitats. Neither lineages of E. faecium nor MGEs are constrained by geographic proximity, but our results show evidence of a strong association of many profiled genes and MGEs with habitat. Many features were associated with a group of clinical and municipal wastewater genomes that are likely forming a new human-associated ecotype. The evolutionary dynamics of E. faecium make it a highly versatile emerging pathogen, and its ability to acquire, transmit, and lose features presents a high risk for the emergence of new pathogenic variants and novel resistance combinations. This study provides a workflow for MGE-centric surveillance of AMR in Enterococcus that can be adapted to other pathogens.
Survey on Testing of Autonomous Driving Systems

—Before autonomous driving vehicles are commercialized, they need to undergo a series of rigorous tests. This paper first proposes the general process of autonomous driving system testing, and then summarizes the research progress of autonomous driving vehicle testing from the model, simulation, network, and vehicle levels, and analyzes the characteristics of various testing technologies. Finally, this paper gives suggestions on the development of autonomous driving testing technology.
Measuring the Effectiveness of the Chinese Innovation System: A Global Value Chain Approach

This paper study argues that some disruptive new trends are surfacing with the emergence of new technologies, which makes an efficient and effective RD in fact, since 2009, the efficiency has accelerated. However, it still lags behind the major global players. This means China still has some room to improve in order to transform its increasing inputs in R&D and technology innovation into commensurate outputs. This situation calls for further improvements in its innovation ecosystem.
A novel reduced-complexity approach for modelling ebullition in peatlands

Methane (CH4) is a greenhouse gas with a global warming potential much greater than carbon dioxide, and one of the major sources of naturally occurring CH4 are peatlands. Large amounts of CH4 can be transported from peat to the atmosphere through bubbles (ebullition). The exact controls of ebullition remain uncertain, but evidence suggests that physical processes related to gas transport and storage within the peat structure is important. Although these processes are key to replicating ebullition, no computer models of ebullition contain a detailed spatial representation of the peat structure. This thesis investigated the role of peat structure on CH4 ebullition using computer and physical models. A computer model of ebullition was developed and tested against physical models of ebullition. The computer model contained a spatial representation of peat and rules to transport gas through the peat structure. Physical models consisted of i. ) air injected into a simple porous medium, and ii. ) a separate physical model with peat. Following a pattern oriented modelling approach, gas storage, bubble size, and bubble release (ebullition) were three patterns used to test the computer model. Overall the computer model was able to replicate the patterns generated from the physical models and this demonstrated that the computer model was useful for modelling CH4 ebullition from peat. The results generated with the computer model confirmed our hypothesis: peat structure and subsequent gas storage were found to be important controls on ebullition timing location and quantity. From these results, we were able to make recommendations on sampling CH4 ebullition from peat in the field.
Association between physical activity and life satisfaction among adults with multimorbidity in Canada

Maintaining life satisfaction may aid in multimorbidity resilience. As the prevalence of multimorbidity continues to rise in Canada, understanding modifiable factors that can influence life satisfaction among people with multimorbidity is warranted. This study aimed to examine the relationship between physical activity and life satisfaction among adults affected by multimorbidity. Cross-sectional data from the 2015–2018 cycles of the Canadian Community Health Survey were used; 22,851 respondents with multimorbidity aged 20 years and older were included. Multiple linear regression models were used to investigate the relationship between physical activity (sedentary, somewhat active, moderately active, active) and life satisfaction for the whole population and for those having specific types of chronic conditions, controlling for self-perceived health status and sociodemographic factors. Respondents affected by multimorbidity who were somewhat active (β = 0.20, 95% CI: 0.08, 0.32), moderately active (β = 0.28, 95% CI: 0.13, 0.44), and active (β = 0.29, 95% CI: 0.17, 0.41) were more satisfied with life than respondents who had a sedentary lifestyle. The relationship between physical activity and life satisfaction was also found to be statistically significant in sub-populations of respondents affected by cancer, diabetes, chronic respiratory diseases, and mental health disorders but not cardiovascular diseases. Physical activity may contribute to better life satisfaction among adults with multimorbidity. As multimorbidity increases in Canada, further investigation on the relationship between physical activity and life satisfaction is warranted to help improve interventions to cope with the effects of multimorbidity.
Construction of a Novel Degradation Model of Bacillus thuringiensis Protein in Soil and Its Application in Estimation of the Degradation Dynamics of Bt-Cry1Ah Protein

Bacillus thuringiensis (Bt) protein expressed by genetically modified (GM) crops is released into the soil ecosystem, where it accumulates for a long time; therefore, degradation of Bt protein has gained increased attention for environmental risk assessments. A first-order kinetic model (Y = ae−b*X) is usually used to evaluate the degradation of Bt proteins, including Bt-Cry1Ab and Bt-Cry1Ac; this has some limitations regarding the precise fitting and explanation of the influence of various factors on Bt protein degradation in the later stage. Therefore, to amend these limitations, we report a new degradation model Y = Y0 + ae−b*X. The effects of soil temperature, water content, soil types, and soil sterilization on the degradation of Bt-Cry1Ah protein in soil were estimated in a 96d long laboratory study using a GM maize leaf–soil mixture. The results showed that the Bt-Cry1Ah protein degraded rapidly in the early stage and then slowly in the middle and late stages. Temperature was identified as the key factor affecting the degradation of Cry1Ah protein—a relatively higher temperature favored the degradation. The degradation rate of Cry1Ah protein was the fastest when the water content was 33 and 20% in the early and later stages, respectively. The soil types had a significant effect on the degradation of Cry1Ah protein. Moreover, soil sterilization slowed down the rate of protein degradation in both the early and later stages. In conclusion, the model Y = Y0 + ae−b*X established in this study provided a more robust model for exploring and simulating the degradation of Bt protein in soil growing GM crops and overcame the shortcomings of the Y = ae−b*X model. The findings of this study enriched the understanding of Bt protein degradation in soil ecosystems. They would be helpful for evaluating the environmental safety of GM crops.