Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanistic basis for ubiquitin modulation of a protein energy landscape

Significance Fluctuations on a protein’s energy landscape encode the mechanistic basis for vital biological processes not always evident from static structures alone. Ubiquitination, a key posttranslational modification, can affect a protein’s energy landscape with consequences for proteasomal degradation, but the molecular mechanisms driving ubiquitin-induced energetic changes remain elusive. Here, we find that distinct thermodynamic mechanisms can produce the same outcome of ubiquitin-induced destabilization at sensitive sites. At a nonsensitive site, we observe formation of a substrate–ubiquitin interaction that may serve to protect against destabilization. This work will enable development of predictive models of the effect of ubiquitin at any given site on a protein with implications for understanding and engineering regulated ubiquitin signaling and protein quality control in vivo. Ubiquitin is a common posttranslational modification canonically associated with targeting proteins to the 26S proteasome for degradation and also plays a role in numerous other nondegradative cellular processes. Ubiquitination at certain sites destabilizes the substrate protein, with consequences for proteasomal processing, while ubiquitination at other sites has little energetic effect. How this site specificity—and, by extension, the myriad effects of ubiquitination on substrate proteins—arises remains unknown. Here, we systematically characterize the atomic-level effects of ubiquitination at various sites on a model protein, barstar, using a combination of NMR, hydrogen–deuterium exchange mass spectrometry, and molecular dynamics simulation. We find that, regardless of the site of modification, ubiquitination does not induce large structural rearrangements in the substrate. Destabilizing modifications, however, increase fluctuations from the native state resulting in exposure of the substrate’s C terminus. Both of the sites occur in regions of barstar with relatively high conformational flexibility. Nevertheless, destabilization appears to occur through different thermodynamic mechanisms, involving a reduction in entropy in one case and a loss in enthalpy in another. By contrast, ubiquitination at a nondestabilizing site protects the substrate C terminus through intermittent formation of a structural motif with the last three residues of ubiquitin. Thus, the biophysical effects of ubiquitination at a given site depend greatly on local context. Taken together, our results reveal how a single posttranslational modification can generate a broad array of distinct effects, providing a framework to guide the design of proteins and therapeutics with desired degradation and quality control properties.
Multiple Temporal and Semantic Processes During Verbal Fluency Tasks in English-Russian Bilinguals

Category fluency test (CFT) performance is sensitive to cognitive processes of executive control and memory storage and access, and widely used to measure cognitive performance especially in early Alzheimer’s Disease. Analytical variables have included the number of items named, and various methods to identify and quantify clusters of semantically related words and cluster switches. Also encoded in the response sequence are temporal patterns as shown by “bursts” of responses and pauses between items, that have not been received attention in determining cluster characteristics. We studied a group of 51 adult Russian-English bilinguals and compared CFT responses based on two clustering methodologies: the semantic-based method (SEM) and a novel method based on the time interval between words (TEMP) with 8 different intercall time thresholds from 0.25 sec-15 sec. Each participant performed the task in both languages. Total number of words and cluster count was greater in Russian than English for both scoring methods, but cluster size did not differ between languages. We also studied stochastic modeling characteristics based on detrending of the “exponential exhaustion” effect seen with CFT, with most notable that total recall capacity (N∞) was greater in Russian than English (P<.05). Multiple demographic variables, and recent and lifetime usage of each language, affected both cognitive performance as measured by the Montreal Cognitive Assessment (MOCA; given in English only). Differential performance is driven by differences in demographics, more words stored in memory, and semantic and timing recall strategies.
Bridges, not walls: the hippocampus builds narrative memories across distant events

Recent studies have suggested that activity in the hippocampus is increased during divisions between events in memory, or "event boundaries. " Although hippocampal activity at event boundaries may play a role in segregating adjacent events, it might also serve a counterintuitive purpose: to bridge the divide between distant events that form a larger narrative. We used functional neuroimaging to investigate whether patterns of hippocampal activity at event boundaries support the integration of distant events in memory, when these events can form a larger narrative. During encoding of fictional stories, boundary-evoked hippocampal activity patterns were more similar between distant events that formed one coherent narrative, than between distant events from unrelated narratives. Twenty-four hours later, detailed recall of coherent narrative events was supported by the reinstatement of boundary-evoked hippocampal activity from encoding. These findings reveal a key function of the hippocampus: the dynamic integration of events into a narrative structure for memory.
Investigating the Conditions for Psychological Momentum in the Field: Evidence From Men’s Professional Tennis

We examine how interruptions and personal and contextual factors affect the manifestation of psychological momentum (PM). Using men’s singles tennis point-by-point data from the two Grand Slam tournaments, Wimbledon and Roland Garros, between 2009 and 2014 (N=29,934), we employ realized break points as the potential triggers of PM and rest periods between two sequential games as the exogenous task interruptions. Controlling for player ability and the state of the match, we find that players are more likely to win the next game after realizing a break point only if there is no rest period between games. Thus, our results suggest that interruptions terminate the momentum effect. Further-more, we find that the effect of PM increases for players with a lesser relative ability and at a later stage within a match, showing the importance of personal and contextual factors for PM.
Making the most of potential: potential games and genotypic convergence

We consider genotypic convergence of populations and show that under fixed fitness asexual and haploid sexual populations attain monomorphic convergence (even under genetic linkage between loci) to basins of attraction with locally exponential convergence rates; the same convergence obtains in single locus diploid sexual reproduction but to polymorphic populations. Furthermore, we show that there is a unified underlying theory underlying these convergences: all of them can be interpreted as instantiations of players in a potential game implementing a multiplicative weights updating algorithm to converge to equilibrium, making use of the Baum–Eagon Theorem. To analyse varying environments, we introduce the concept of ‘virtual convergence’, under which, even if fixation is not attained, the population nevertheless achieves the fitness growth rate it would have had under convergence to an optimal genotype. Virtual convergence is attained by asexual, haploid sexual, and multi-locus diploid reproducing populations, even if environments vary arbitrarily. We also study conditions for true monomorphic convergence in asexually reproducing populations in varying environments.
A new and improved genome sequence of Cannabis sativa

Cannabis is a diploid species (2n = 20), the estimated haploid genome sizes of the female and male plants using flow cytometry are 818 and 843 Mb respectively. Although the genome of Cannabis has been sequenced (from hemp, wild and high-THC strains), all assemblies have significant gaps. In addition, there are inconsistencies in the chromosome numbering which limits their use. A new comprehensive draft genome sequence assembly (~900 Mb) has been generated from the medicinal cannabis strain Cannbio-2, that produces a balanced ratio of cannabidiol and delta-9-tetrahydrocannabinol using long-read sequencing. The assembly was subsequently analysed for completeness by ordering the contigs into chromosome-scale pseudomolecules using a reference genome assembly approach, annotated and compared to other existing reference genome assemblies. The Cannbio-2 genome sequence assembly was found to be the most complete genome sequence available based on nucleotides assembled and BUSCO evaluation in Cannabis sativa with a comprehensive genome annotation. The new draft genome sequence is an advancement in Cannabis genomics permitting pan-genome analysis, genomic selection as well as genome editing.
Integrating continuous hypermutation with high‐throughput screening for optimization of cis,cis‐muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection, and amplification. While powerful, systematic searches through large sequence-spaces is a labor-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generates enzyme variants using the recently developed orthogonal replication system, OrthoRep, and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a ratelimiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers >13-fold compared to reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study, is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.
CDK5 Inhibitor Seliciclib Promotes Osteoblastic Differentiation of MSCs and Suppresses the Migration of MG-63 Osteosarcoma Cells

CDK5 belongs to the cycling dependent kinase family, which is multifunctional and plays an important role in neural differentiation. However, the role of CDK5 in osteoblastic differentiation remains unclear. The present study investigated functions and molecular mechanism of CDK5 in osteoblastic differentiation. It was found that, the addition of CDK5 inhibitor Seliciclib promoted the expression of Runx2, ALP, OCN and OPN of MSCs and the mineralization of MC-3T3E1 cells. Seliciclib enhanced the development of F-actin, nuclear localization of β-catenin and YAP, as well as the expression of RMRP RNA. When F-actin was suppressed by Blebbistatin, the nuclear localization of YAP and β-catenin, and expression of RMRP RNA as well as Runx2 and ALP were decreased. These indicate Seliciclib promotes osteoblastic differentiation mainly by F-actin. Moreover, Seliciclib also suppressed the migration of MG-63, suggesting a potential application for Seliciclib in bone defect repair and inhibition of the migratiion of osteosarcoma cells.
Allelic diversity of the PRDM9 coding minisatellite in minke whales

The PRDM9 protein controls the reshuffling of parental genomes in most metazoans. During meiosis the PRDM9 protein recognizes and binds specific target motifs via its zinc-finger array, which is encoded by a hypervariable minisatellite. To date, PRDM9 diversity has been little studied outside humans, wild mice and some domesticated species where evolutionary constraints may have been relaxed. Here we explore the structure and variability of PRDM9 in samples of the minke whales from the Atlantic, Pacific and Southern Oceans. We show that minke whales possess all the features characteristic of organisms with PRDM9-directed recombination initiation, including complete KRAB, SSXRD and SET domains and a rapidly evolving array of C2H2-type-Zincfingers (ZnF). We uncovered eighteen novel PRDM9 variants and evidence of rapid evolution, particularly of DNA-recognizing positions that evolve under positive selection. At different geographical scales, we observed extensive PRDM9 diversity in Antarctic minke whales (Balaenoptera bonarensis), that conversely lack observable population differentiation in mitochondrial DNA and microsatellites. In contrast, a single PRDM9 variant is shared between all Common Minke whales and even across subspecies boundaries of North Atlantic (B. a. acutorostrata) and North Pacific (B. a. scammoni) minke whale, which do show clear population differentiation. PRDM9 variation of whales predicts distinct recombination initiation landscapes genome wide, which has possible consequences for speciation.
Individual variation in tolerance of human activity by urban Dark-eyed Juncos (Junco hyemalis)

ABSTRACT An important goal of urban ecology is determining what differentiates urban-tolerant populations of birds from their non-urban ancestors and urban-intolerant species. One key to urban success may be reacting appropriately to human activity, and the degree to which birds view humans as threats can be quantified by their escape behavior. Understanding individual-level plasticity, however, requires the tracking of known individuals. We compared flight-initiation distances (FID) and distances fled (DF) from approaches by a human between an urban and a non-urban population of individually marked Dark-eyed Juncos (Junco hyemalis) in Southern California. The urban population is more tolerant to people as evidenced by attenuated FIDs and DFs relative to non-urban birds. Although individual urban birds either habituated or sensitized to repeated approaches, there was no significant pattern at the population level. Overall, the behavioral patterns exhibited by this urban population of juncos is more supportive of in situ evolution of tolerance than either being a biased sample from an ancestral non-urban population or intrinsic behavioral plasticity that produces a uniform adjustment to urban life.
Plastic-Degrading Potential across the Global Microbiome Correlates with Recent Pollution Trends

Utilization of synthetic biology approaches to enhance current plastic degradation processes is of crucial importance, as natural plastic degradation processes are very slow. For instance, the predicted lifetime of a polyethylene terephthalate (PET) bottle under ambient conditions ranges from 16 to 48 years. ABSTRACT Biodegradation is a plausible route toward sustainable management of the millions of tons of plastic waste that have accumulated in terrestrial and marine environments. However, the global diversity of plastic-degrading enzymes remains poorly understood. Taking advantage of global environmental DNA sampling projects, here we constructed hidden Markov models from experimentally verified enzymes and mined ocean and soil metagenomes to assess the global potential of microorganisms to degrade plastics. By controlling for false positives using gut microbiome data, we compiled a catalogue of over 30,000 nonredundant enzyme homologues with the potential to degrade 10 different plastic types. While differences between the ocean and soil microbiomes likely reflect the base compositions of these environments, we find that ocean enzyme abundance increases with depth as a response to plastic pollution and not merely taxonomic composition. By obtaining further pollution measurements, we observed that the abundance of the uncovered enzymes in both ocean and soil habitats significantly correlates with marine and country-specific plastic pollution trends. Our study thus uncovers the earth microbiome's potential to degrade plastics, providing evidence of a measurable effect of plastic pollution on the global microbial ecology as well as a useful resource for further applied research. IMPORTANCE Utilization of synthetic biology approaches to enhance current plastic degradation processes is of crucial importance, as natural plastic degradation processes are very slow. For instance, the predicted lifetime of a polyethylene terephthalate (PET) bottle under ambient conditions ranges from 16 to 48 years. Moreover, although there is still unexplored diversity in microbial communities, synergistic degradation of plastics by microorganisms holds great potential to revolutionize the management of global plastic waste. To this end, the methods and data on novel plastic-degrading enzymes presented here can help researchers by (i) providing further information about the taxonomic diversity of such enzymes as well as understanding of the mechanisms and steps involved in the biological breakdown of plastics, (ii) pointing toward the areas with increased availability of novel enzymes, and (iii) giving a basis for further application in industrial plastic waste biodegradation. Importantly, our findings provide evidence of a measurable effect of plastic pollution on the global microbial ecology.
WASSKIL: An Oversampling Method for Fault Detection of Industrial Plants

Class imbalance is a major issue when adopting machine learning algorithms to build a predictive model for fault detection of industrial plants in smart factories. In this paper, we propose a data oversampling method termed WASSKIL. WASSKIL is developed based on MAHAKIL that simulates the genetic breeding process, where Wasserstein distance is leveraged rather than Mahalanobis distance when partitioning two sets of data for oversampling. We evaluate the performance of WASSKIL over 5 industrial plants of PHM 2015 dataset, using raw features of sensors and statistical features of the dataset in time series. The results show that WASSKIL can outperform MAHAKIL under both raw features and statistical features. Consequently, our proposed oversampling method has the potential to tame class imbalance, which can be used for prognostics and health management in smart factories.
Revisiting the tax treatment of bidis in India

Background Bidi use remains an intractable public health problem for India. This is partly due to the informal nature of the bidi supply chain, including tax exemptions for small producers. The aim of this paper is to assess the impact of making all bidis subject to duty and Goods and Services Tax. Although this may require legislative changes and incur some extra administrative costs, the net benefits would include greater oversight of the supply chain as well as increased tax revenues and reduced consumption. Methods We use a form of gap analysis (the difference between duty paid and total bidi consumption) to estimate the number of tax-exempt bidis. We then use local evidence on the price elasticity of demand for bidis to assess the impact of eliminating these exemptions on the price and consumption of presently tax-exempt bidis. Findings Total bidi consumption is estimated at 400 billion sticks per annum, including 275 billion duty paid sticks and 125 billion duty exempt sticks. Removing the small producer exemptions would increase the price of currently exempt bidis by INR4.6/pack. Total bidi consumption would decrease by 6% and the number of smokers would decrease by 2.2 million adults. This would bring the rate of bidi smoking down from 7.7% to 7.5%, while generating INR14.8 billion in tax revenues. Conclusions Eliminating India’s tax exemptions for small bidis producers would make a significant contribution to tobacco control, both directly by reducing the number of smokers and indirectly by plugging a loophole in the supply chain.
A New Early Stage Diabetic Retinopathy Diagnosis Model Using Deep Convolutional Neural Networks and Principal Component Analysis

Diabetic retinopathy (DR) is a disease of the retina, which leads over time to vision problems such retinal detachment, vitreous hemorrhage, glaucoma, and in worse cases leads to blindness, which can initially be controlled by periodic DR-screening. Early diagnosis will lead to greater control of the disease, whereas performing retinal examinations on all diabetic patients is an unattainable need, as diabetes is a chronic disease and its global prevalence has been steadily increasing over the past few decades. According to recent World Health Organization statistics, about 422 million people worldwide have diabetes, the majority living in low-and middle-income countries. This paper proposes a new strategy that brings the strength of convolutional neural networks (CNNs) to the diagnosis of DR. Coupled with using principal component analysis (PCA) that performs dimension reduction to improve the diagnostic accuracy, the proposed model exploiting edge-preserving guided image filtering (E-GIF) that performs as a contrast enhancement mechanism, and in addition to smoothing low gradient areas, it also accentuates strong edges. Diabetic retinopathy causes progressive damage to the blood vessels in the retina to the extent that it leaves traces and lesions in the tissues of the retina. These lesions appear in the form of edges and when processing retinal images, we seek to accentuate these edges to enable better diagnosis of diabetic retinopathy symptoms. A new CNN architecture with residual connections is used, which performs very well in diagnosing DR. The proposed model is named with RUnet-PCA: Residual U-net Deep CNN with Principal Component Analysis. The well-known AlexNet, VggNet-s, VggNet-16, VggNet-19, GoogleNet, and ResNet models were adopted for comparison with the proposed model. Publicly available Kaggle dataset was employed for training exploring the DR diagnosis accuracy. Experimental results show that the proposed RUnet-PCA model achieved a diagnosis accuracy of 98.44% and it was extremely robust and promising in comparison to other diagnosis methods.