Miracle-dz/UniFAR-FTUs huggingface dataset:
Gain of 1q confers an MDM4-driven growth advantage to undifferentiated and differentiating hESC while altering their differentiation capacity

Gains of 1q are a highly recurrent chromosomal abnormality in human pluripotent stem cells. In this work, we show that gains of 1q impact the differentiation capacity to derivates of the three germ layers, leading to miss-specification to cranial placode and non-neural ectoderm during neuroectoderm differentiation and by poorer expression of lineage specific markers in hepatoblasts and cardiac progenitors. Competition assays show that the cells retain their selective advantage during differentiation, which is mediated by a higher expression of MDM4, a gene located in the common region of gain. MDM4 drives the winner phenotype of the mutant cells in both the undifferentiated and differentiating state by reducing the cells’ sensitivity to DNA-damage through decreased p53-mediated apoptosis. Finally, we find that cell density in culture plays a key role in promoting the competitive advantage of the cells by increasing DNA damage.
Phylogenetics, Epidemiology and Temporal Patterns of Dengue Virus type 1 in Araraquara, São Paulo State

Dengue virus is a prominent arbovirus with global spread, causing approximately 390 million infections each year. In Brazil, yearly epidemics follow a well-documented pattern of serotype replacement every three to four years on average. Araraquara, located in the state of São Paulo, has faced significant impacts from DENV epidemics since the emergence of DENV-1 in 2010. The municipality then transitioned from low to moderate endemicity in less than 10 years. Yet, there remains an insufficient understanding of the virus circulation dynamics, particularly concerning DENV-1, in the region, as well as the genetic characteristics of the virus. To address this, we generated 37 complete or partial DENV-1 genomes sampled from 2015 to 2022 in Araraquara. Then, using also Brazilian and worldwide DENV-1 sequences we reconstructed the evolutionary history of DENV-1 in Araraquara and estimated the time to the most recent common ancestor (tMRCA) for the serotype 1, for the genotype V and its main lineages. Within the last ten years, there were at least three introductions of genotype V in Araraquara, distributed in the two main lineages (LIa and LIb, and LII). The tMRCA for the first sampled lineage (2015/2016 epidemics) was approximately 15 years ago (in 2008). Crucially, our analysis challenges existing assumptions regarding emergence time of the DENV-1 genotypes, suggesting that genotype V might have diverged more recently than previously described. The presence of the two lineages of genotype V in the municipality might have contributed to the extended persistence of DENV-1 in the region.
Higher-order combinatorial chromatin perturbations by engineered CRISPR-Cas12a for functional genomics

Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting 1-3 genomic sites per cell. To develop a tool for higher-order (>3) combinatorial targeting of genomic sites with CRISPRi in functional genomics screens, we engineered an Acidaminococcus Cas12a variant -- referred to as multiplexed transcriptional interference AsCas12a (multiAsCas12a). multiAsCas12a incorporates a key mutation, R1226A, motivated by the hypothesis of nicking-induced stabilization of the ribonucleoprotein:DNA complex for improving CRISPRi activity. multiAsCas12a significantly outperforms prior state-of-the-art Cas12a variants in combinatorial CRISPRi targeting using high-order multiplexed arrays of lentivirally transduced CRISPR RNAs (crRNA), including in high-throughput pooled screens using 6-plex crRNA array libraries. Using multiAsCas12a CRISPRi, we discover new enhancer elements and dissect the combinatorial function of cis-regulatory elements. These results instantiate a group testing framework for efficiently surveying potentially numerous combinations of chromatin perturbations for biological discovery and engineering.
Genetic variation exists within Zea mays to influence unsustainable nitrogen cycling microbiome function

Overuse of synthetic nitrogen fertilizers in agroecosystems causes environmental pollution and human harm at a global level. Nitrogenous fertilizers provide a short-lived benefit to crops in the agroecosystem, but stimulate microbially-mediated nitrification and denitrification, processes that result in N pollution, greenhouse gas (GHG) production, and reduced soil fertility. Recent advances in plant microbiome science suggest that plants can modulate the composition and activity of rhizosphere microbial communities. These rhizosphere communities act as an extended phenotype, primed by genetic variation in the plant host. Genetic variation in traits (e.g., plant secondary metabolites, root architecture, immune system, etc.) act as mechanistic selective agents on the composition of the microbiome. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the extended phenotype of rhizosphere soil microbiome composition and function. Specifically, we determined whether plants’ influence on soil nitrogen cycling activities was altered by plant genetics and thereby allowing it to be incorporated into breeding practices. To capture an extensive amount of genetic diversity within maize we sampled the rhizosphere microbiome of a germplasm chronosequence that included ex-PVP inbreds, hybrids, and teosinte (Z. mays ssp. mexicana and Z. mays ssp. parviglumis). We observed that potential N cycling processes were influenced by plant genetics. Teosinte and some hybrid genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbreds stimulated/did not inhibit these undesirable N-cycling activities. These potential differences translated to functional differences in N2O production, with teosinte plots producing less GHG than maize plots. Furthermore, within these Zea cultivars we found that plant genetics explained a significant amount of variation in the microbiome, particularly among different nitrification and denitrification functional genes within the community. We found that potential nitrification, potential incomplete denitrification, and overall denitrification rates, but not abundance of N-cycling genes of rhizosphere soils were influenced by growth stage and plant genetics. Taken together, these results suggest that crop selection can lead to changes in root phenotypes that could suppress unsustainable N-cycling processes. Reintroducing stress-adapted and “wild” root characteristics into modern germplasm may be a way to manipulate soil microbiomes at both a composition and functional level to improve sustainability.
Using Canonical Codes to Efficiently Solve the Benzenoid Generation Problem with Constraint Programming

The Benzenoid Generation Problem (BGP) aims at generating all benzenoid molecules that satisfy some given properties. This problem has important applications in chemistry, and Carissan et al (2021) have shown us that Constraint Programming (CP) is well suited for modelling this problem because properties defined by chemists are easy to express by means of constraints. Benzenoids are described by hexagon graphs and a key point for an efficient enumeration of these graphs is to be invariant to rotations and symmetries. In this paper, we introduce canonical codes that uniquely characterise hexagon graphs while being invariant to rotations and symmetries. We show that these codes may be defined by means of constraints. We also introduce a global constraint for ensuring that codes are canonical, and a global constraint for ensuring that a pattern is included in a code. We experimentally compare our new CP model with the CP-based approach of Carissan et al (2021), and we show that it has better scale-up properties.
Correctness under Weak Memory Consistency Specifying and Verifying Concurrent Libraries under Declarative Consistency Models

Concurrent libraries are the building blocks for concurrency. They encompass a range of abstractions (e.g. locks, exchangers, stacks, queues, sets) built in a layered fashion: more advanced libraries are built out of simpler ones. While there has been a lot of work on verifying such libraries in a sequentially consistent (SC) environment, little is known about how to specify and verify them under weak memory consistency (WMC). We propose a general declarative framework that allows us to specify concurrent libraries declaratively, and to verify library implementations against their specifications compositionally. Our framework is sufficient to encode standard models such as SC, (R)C11 and TSO. Additionally, we specify several concurrent libraries, including mutual exclusion locks, reader-writer locks, exchangers, queues, stacks and sets. We then use our framework to verify multiple weakly consistent implementations of locks, exchangers, queues and stacks.
SAT-Based Learning of Compact Binary Decision Diagrams for Classification

Decision trees are a popular classification model in machine learning due to their interpretability and performance. However, the number of splits in decision trees grow exponentially with their depth which can incur a higher computational cost, increase data fragmentation, hinder interpretability, and restrict their applicability to memory-constrained hardware. In constrast, binary decision diagrams (BDD) utilize the same split across each level, leading to a linear number of splits in total. Recent work has considered optimal binary decision diagrams (BDD) as compact and accurate classification models, but has only focused on binary datasets and has not explicitly optimized the compactness of the resulting diagrams. In this work, we present a SAT-based encoding for a multi-terminal variant of BDDs (MTBDDs) that incorporates a state-of-the-art direct encoding of numerical features. We then develop and evaluate different approaches to explicitly optimize the compactness of the diagrams. In one family of approaches, we learn a tree BDD first and model the size of the diagram the tree will be reduced to as a secondary objective, in a one-stage or two-stage optimization scheme. Alternatively, we directly learn diagrams that support multi-dimensional splits for improved expressiveness. Our experiments show that direct encoding of numerical features leads to better performance. Furthermore, we show that exact optimization of size leads to more compact solutions while maintaining higher accuracy. Finally, our experiments show that multi-dimensional splits are a viable approach to achieving higher expressiveness with a lower computational cost.
Optimizing redundancy design for chip-multiprocessors for flexible utility functions*

Yield of chip-multiprocessors (CMP) can be improved by adding spares to the design. The optimal spare configuration has previously been derived for certain evaluation metrics, such as yield per area, performance-averaged yield, and so on. However, all previous approaches are limited to the scenario where only those chips which have the fullconfiguration (i.e., have n working processors, where n is the number of processors in the CMP's specifications) can be sold. In the meantime, yield problems have forced vendors of high-volume CMPs to sell chips with different numbers of working processors. The purpose of this paper is to extend our recent framework for the optimal redundancy design to systematically capture such flexibilities. We first define utility function for CMPs in terms of two functions: (i) the number-of-processors-binning (NPB) function, which captures the range of the number of (enabled) working processors over which a chip can be sold, and (ii) the value function, which captures how the value of a chip to the user depends upon the number of processors enabled in the chip. We use case studies to identify the relationships between utility functions and optimal spare configurations. Then we extend our branch and bound algorithm and incorporate a dynamic programming approach to develop the first framework for optimal and ϵ-optimal (i.e., within ϵ of optimal, typically at much lower area overhead) redundancy designs for different utility functions. We demonstrate that even in an era of high defect density which leads to extremely low yield, we are able to combine approaches in a way that provide figure of merit that is up to 83.8% of that of the ideal case, i.e., for a process with zero defect density.
RP-SYN : Synthesis of Random Pattern Testable Circuits with Test Point Insertion

An automated logic synthesis procedure, called RPSYN, is described for synthesizing random pattern testable circuits. RP-SYN takes as an input a two-level description of a circuit and a constraint on the minimum fault detection probability (threshold below which faults are considered randompattern-resistant), and generates a multilevel implementation which satisfies the constraint while minimizing the literal count. RP-SYN identifies random-pattern-resistant faults and eliminates them through testability-driven factoring combined with test point insertion. By moving the task of test point insertion from the back-end into the synthesis process, RP-SYN reduces design time and enables better optimization of the resulting implementation. Results are shown for benchmark circuits which indicate that RP-SYN can generally reduce the random pattern test length by at least an order of magnitude with only a small area overhead.
Role of Ethanol Plants in Dakotas’ Land Use Change: Analysis Using Remotely Sensed Data

North and South Dakota have experienced rapid land-use changes in the past decade. Recent studies have shown that these land-use changes are mainly characterized by conversions of grasslands to crop production, especially corn and soybeans. Approximately 271,000 hectares of grasslands were lost to corn and soy production in 2006-2011 period, almost seven times the losses in 1989-2003. The implications of these changing land-uses range from reduced biodiversity and loss of habitat for waterfowl species to low agricultural productivity on drought-sensitive marginal lands. While progress has been made in characterizing regional land-use changes, formal analyses establishing causal relationships at the local level are lacking. We construct a spatially delineated dataset for the Dakotas and utilize a Difference-in-Difference (DID) model in conjugation with Propensity Score Matching to estimate the impact of an ethanol plant on nearby corn-acres. We hold the advent of an ethanol plant to be a treatment that influences land-use on surrounding agricultural plots. In our preliminary work, based on the Parallel Paths assumption of the DID, we find that the effect of ethanol plants on corn production varies by plants and a single point estimate for all ethanol plants in a region, as usually provided in the literature, can be highly misleading. Surprisingly, we find both positive as well as negative effects of ethanol plants on corn-acres that may be statistically insignificant. Negative estimates are irreconcilable to the economic incentives due to these corn-based ethanol plants. We find intensified corn production and reduced soybeans due to the ethanol plants. Our analysis also reflects a difference in opportunity of converting from wheat to corn and from grass to corn. We use placebo tests and pre-treatment trends in corn acres to examine the Parallel Paths assumption that identifies the DID estimates. We find that this assumption fails and propose to carry out this analysis by incorporating differentiated trends into the DID framework through more flexible assumptions in future. An important contribution of this paper is that it presents a unique research design that uses quasi-experimental techniques to evaluate the impact of a change/policy upon availability of spatially delineated datasets. To this extent, our results are to be viewed as preliminary.
Simplifying Step-Wise Explanation Sequences

Explaining constraint programs is useful for debugging an unsatisfiable program, to understand why a given solution is optimal, or to understand how to find a unique solution. A recently proposed framework for explaining constraint programs works well to explain the unique solution to a problem step by step. It can also be used to step-wise explain why a model is unsatisfiable, but this may create redundant steps and introduce superfluous information into the explanation sequence. This paper proposes methods to simplify a (step-wise) explanation sequence, to generate simple steps that together form a short, interpretable sequence. We propose an algorithm to greedily construct an initial sequence and two filtering algorithms that eliminate redundant steps and unnecessarily complex parts of explanation sequences. Experiments on diverse benchmark instances show that our techniques can significantly simplify step-wise explanation sequences
With Applications

Recommender systems have been researched extensively over the past decades. Whereas several algo- rithms have been developed and deployed in various application domains, recent research effort s are increasingly oriented towards the user experience of recommender systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction. Then, we analyze existing interactive recommender systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.
Novel Methods for Multi-ancestry Polygenic Prediction and their Evaluations in 5.1 Million Individuals of Diverse Ancestry

Polygenic risk scores are becoming increasingly predictive of complex traits, but their suboptimal performance in non-European ancestry populations raises questions about their clinical applications and impact on health inequities. We develop CT-SLEB, a powerful and scalable method to calculate PRS based on ancestry-specific GWAS summary statistics from multi-ancestry training samples by integrating multiple techniques, including clumping and thresholding, empirical Bayes and super learning. We evaluate the performance of the proposed method and nine alternatives using large-scale simulated GWAS on ~19 million common variants and large datasets from 23andMe Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, which include up to 1.18 million individuals from four non-European populations across thirteen complex traits. Results show that the proposed method can substantially improve the performance of PRS in non-European populations relative to simple alternatives and has comparable or superior performance relative to a recent method that requires a higher order of computational time. Furthermore, our simulation studies provide novel insights to sample size requirements and the effect of SNP density on multi-ancestry risk prediction.
A Guide for the Perplexed

E F Schumacher asserts that it is the task of philosophy to provide a map of life and knowledge which exhibits the most important features of life in their proper prominence. The questions: How am I to conduct my life? What is the nature of art and nature? What is the meaning of religion? are restored to daylight on Schumacher's map of life by his maxim 'if in doubt show it prominently'. Science is therefore restored to its home territory and its growing imperialism over the fields is reserved.
Performance of grain legume crops in organic farms of Central Italy.

In the 2005-2006 growing season, eight varieties of faba bean, pea and lupin were compared in two organic farms, located in two regions of Central Italy (Tuscany and Marche), to evaluate their adaptation to local environment and agronomic performance in terms of grain yield and competitive ability against weeds. Pea showed a higher grain production than faba bean and lupin, which were negatively effected by the environmental conditions during winter 2005 and spring 2006. Time of seeding seems to be very important for the competitive ability against weed of the different varieties. In Tuscany the lupin Italian variety Multitalia, the only one Italian variety, obtained interesting performance in terms of grain yield and weed competition, although the spring seeding.
Combining lime and organic amendments based on titratable alkalinity for efficient amelioration of acidic soils

Abstract. Ameliorating soil acidity using a combination of lime and organic amendments (OAs) can be an alternative to lime alone, but determining the appropriate OA rates can be difficult. We developed a new method for calculating the combined application rate of lime and OAs (wheat straw, faba bean straw, blended poultry litter, biochar, and compost) that is based on the titratable alkalinity of OAs and the equilibrium lime buffer capacity (LBCeq) of acidic soils. The effect of calculated soil amendment rates on soil pH was validated at soil water contents of 60 %, 100 %, and 150 % of field capacity (FC). The soil used to develop and validate the method was a sandy loam with a soil pH in deionised water (pHW) of 4.84 and a soil pH in 0.01 M CaCl2 solution (pHCa) of 4.21. The LBCeq of the soil was 1657 mg CaCO3 kg−1 pH−1 (where “CaCO3 kg−1 pH−1” denotes the amount of lime required to raise the pH of 1 kg of soil by one unit). The titratable alkalinity of the OAs ranged from 11.7 cmol Heq+ kg−1 for wheat straw to 357 cmol Heq+ kg−1 for compost. At 60 % FC, faba bean and wheat straw amendment increased the soil pHW to 6.48 and 6.42, respectively, but lower pH values were reached in soil amended with less biodegradable or resistant OAs (ROAs) (i.e. blended poultry litter, biochar, and compost). At 150 % FC, the two straws increased the soil pHW to only 5.93 and 5.75, respectively, possibly due to slower decomposition under submerged conditions, resulting in limited alkalinity production, whereas amendment with ROAs produced pHW values close to 6.5. With an increasing lime-equivalent value (LEV) of the OA, from 5.8 g CaCO3 kg−1 (wheat straw) to 179 g CaCO3 kg−1 (compost), the lime requirement to reach pHW 6.5 in lime–OA combinations decreased from 2.72 to 0.09 g CaCO3 kg−1. The developed method was shown to be effective in determining the appropriate rates of OAs (with or without additional lime) for the management of acidic sandy loam soils in this study and highlights the importance of the soil water content with respect to its acid-neutralising effect.
Practical recommendations for measuring rates of visual field change in glaucoma

To date, there has been a lack of evidence-based guidance on the frequency of visual field examinations required to identify clinically meaningful rates of change in glaucoma. The objective of this perspective is to provide practical recommendations for this purpose. The primary emphasis is on the period of time and number of examinations required to measure various rates of change in mean deviation (MD) with adequate statistical power. Empirical data were used to obtain variability estimates of MD while statistical modelling techniques derived the required time periods to detect change with various degrees of visual field variability. We provide the frequency of examinations per year required to detect different amounts of change in 2, 3 and 5 years. For instance, three examinations per year are required to identify an overall change in MD of 4 dB over 2 years in a patient with average visual field variability. Recommendations on other issues such as examination type, strategy and quality are also made.