Miracle-dz/UniFAR-FTUs huggingface dataset:
Strain development of Gluconobacter oxydans: Complementation of non-functional metabolic pathways and increase of carbon flux

The obligatory aerobic acetic acid bacterium Gluconobacter oxydans rapidly oxidizes diverse carbon sources regioand stereoselectively in the periplasm. The products mainly remain in the medium without being further oxidized. These features are favorable for oxidative biotransformations, e. g. in vitamin C production, but they are unfavorable for biomass formation, as only a small fraction of the carbon sources enter the cytoplasm. Cytoplasmic carbon metabolism is characterized by an interrupted glycolysis and an incomplete tricarboxylic acid (TCA) cycle due to the lack of phosphofructokinase, succinylCoA synthetase, and succinate dehydrogenase. Excess pyruvate is converted to acetate via pyruvate decarboxylase and NADP-dependent acetaldehyde dehydrogenase. In order to increase the cell yield on glucose, we sequentially replaced (i) gdhS encoding the cytoplasmic NADP-dependent glucose dehydrogenase by the Acetobacter pasteurianus sdhCDABE genes for succinate dehydrogenase and the flavinylation factor SdhE (strain IK001), (ii) pdc encoding pyruvate decarboxylase by a second ndh gene encoding a type II NADH dehydrogenase (strain IK002.1), and (iii) gdhM encoding the membrane-bound PQQdependent glucose dehydrogenase by sucCD from Gluconacetobacter diazotrophicus encoding succinyl-CoA synthetase (strain IK003.1). Analysis under controlled cultivation conditions in bioreactors revealed that formation of gluconate, 2-ketogluconate, and acetate was eliminated for IK003.1. The strain excreted some 5-ketogluconate and pyruvate instead of acetate. CO2 formation was more than doubled compared to the reference strain. Growth of IK003.1 was retarded, but the cell yield was improved by 60 %, allowing a significant reduction of the glucose costs for biomass formation. Furthermore, IK003.1 serves as suitable host for further metabolic engineering addressing bottlenecks in pyruvate and acetylCoA oxidation that were identified by enzyme activity measurements. 3.2 Increased cell yield of G. oxydans on glucose
Pathways to Consumers’ Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

A basic aim of marketing research is to predict consumers’ preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects’ individual future preferences over the products and the commercials’ population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials’ effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials’ population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects’ preferences and marketing campaigns’ success.
Basal Cell Carcinoma Risk and TP53 Arg72Pro Polymorphism: An Algerian Population Study

Background: Basal cell carcinoma (BCC) is the commonest skin cancer in human. It is characterized by a strictly local malignity withafrequenttendencytorelapse. BCCdevelopmentresultsfromtheinteractionbetweenenvironmentalfactorsandgeneticalter-ations, including mutations inthe TP53 gene involvedin itsprogression and relapse. TP53 gene isnamed “guardianof the genome”, as it plays major roles in genomic stability. In addition to mutations, several polymorphisms had been detected in the wild-type TP53 . The polymorphic variant is usually associated to BCC diseases at codon 72 of TP53 (Arg72Pro). Objectives: In the present study, we undertook a case control study to explore a possible association between TP53 Arg72Pro polymorphism and the predisposition to BCC in Northwest Algerian population. Methods: TP53 Arg72Pro polymorphism was investigated by PCR/RFLP then conﬁrmed by DNA sequencing of 61 controls versus 50 BCC cases. Results: This study allows us to characterize BCC subgroups regarding age, tumor location, and relapse. No correlation was found betweenanyof thesecriteriaandeachof thetwovariantsof TP53 Arg72Pro. Noassociationwasfoundbetween TP53 Arg72Provariants anddevelopingBCCeither(BCCgroup: Pro=54%,versusControlgroup: Pro=53%,P> 0.05,OR1.52(0.89-2.60)). Finally,asexpected, sun-exposure was conﬁrmed as a risk factor for BCC. Conclusions: Thisstudysupportsthatanalyzing TP53 Arg72Propolymorphismisof nointerestforidentifyinghigh-risksubjectsfor BCC in the Algerian population. Further studies are needed to explain the role of this polymorphism in genetic predisposition to BCC in some other populations.
Accumulation and seasonal variation of toxic and trace elements in tissues of Cyprinus carpio from semi-intensive aquaculture ponds

The aim of this study was to assess the accumulation and seasonal variations of 20 toxic and trace elements in the abdominal and dorsal muscle (DM), liver and gut contents (GCs) of Cyprinus carpio, and in the sediments and zooplankton from semi-intensive aquaculture ponds during a six-month production season (from May to October). Sediments showed the highest concentrations of the measured elements, followed by zooplankton, fish GCs and livers, while the lowest levels were present in fish muscles. Correspondence analysis revealed that the elemental composition of the fish GCs was mostly similar to that of the sediment and zooplankton. The element concentrations of carp liver and muscles were more similar to each other. Overall, the results indicate that fish liver was the main target organ accumulating Cu, Se and Zn, regardless of their initial amounts in the environment. A seasonal decrease of a range of elements in fish liver (Al, Co, Fe, Li, Mn, Ni, Pb and Sr) was evident towards the end of the investigation period. In addition, the fish DM showed a seasonal accumulation of Sr and Li. However, toxic elements i.e. As, B, Ba Hg and V did not show such a seasonal variation, and were evenly distributed between fish liver and muscles. The concentrations of some elements, such as As, in fish tissues were similar or even higher than in literature data from polluted sites, but none of them exceeded the maximum acceptable concentrations prescribed by Serbian legislation or by FAO or EU regulation.
Variation of Agronomic Traits of Ravenna Grass and Its Potential as a Biomass Crop

Ravenna grass (Tripidium ravennae) is a tall robust bunchgrass with potential as an energy crop. The aim was to investigate the variation of agronomic traits of Ravenna grass. Univariate analyses of traits were conducted on 95 plants from 2013 to 2017. The traits were: biomass yield per plant; C, N, and ash concentrations; leaf and culm sap sucrose concentrations; percentage seed set, and the number of caryopses per panicle. In 2013, the biomass yield averaged 0.21 ± 0.09 kg per plant (mean ± the standard deviation). In 2014 to 2017, the yield averaged from 3.9 ± 0.8 kg per plant to 7.5 ± 1.8 kg per plant. Carbon concentration was generally higher than other energy crops, while N and ash concentrations were generally lower. Leaf sap sucrose ranged from 24.4 ± 4.6 g kg−1 in 2016 to 41.6 ± 7.6 g kg−1 in 2013. Culm sap sucrose varied from approximately 1.6 to 2.1 times that of leaf sap depending upon the harvest year. The percentage seed set varied between years ranging from 37.2 ± 12.4% to 56.6 ± 9.8%, and the mean number of caryopses per panicle varied from 4,770 ± 2,000 to 11,470 ± 3,075.
Learnable Spectro-Temporal Receptive Fields for Robust Voice Type Discrimination

Voice Type Discrimination (VTD) refers to discrimination between regions in a recording where speech was produced by speakers that are physically within proximity of the recording device ("Live Speech") from speech and other types of audio that were played back such as traffic noise and television broadcasts ("Distractor Audio"). In this work, we propose a deep-learning-based VTD system that features an initial layer of learnable spectro-temporal receptive fields (STRFs). Our approach is also shown to provide very strong performance on a similar spoofing detection task in the ASVspoof 2019 challenge. We evaluate our approach on a new standardized VTD database that was collected to support research in this area. In particular, we study the effect of using learnable STRFs compared to static STRFs or unconstrained kernels. We also show that our system consistently improves a competitive baseline system across a wide range of signal-to-noise ratios on spoofing detection in the presence of VTD distractor noise.
Learning to solve TV regularised problems with unrolled algorithms

Total Variation (TV) is a popular regularization strategy that promotes piece-wise constant signals by constraining the l1-norm of the first order derivative of the estimated signal. The resulting optimization problem is usually solved using iterative algorithms such as proximal gradient descent, primal-dual algorithms or ADMM. However, such methods can require a very large number of iterations to converge to a suitable solution. In this paper, we accelerate such iterative algorithms by unfolding proximal gradient descent solvers in order to learn their parameters for 1D TV regularized problems. While this could be done using the synthesis formulation, we demonstrate that this leads to slower performances. The main difficulty in applying such methods in the analysis formulation lies in proposing a way to compute the derivatives through the proximal operator. As our main contribution, we develop and characterize two approaches to do so, describe their benefits and limitations, and discuss the regime where they can actually improve over iterative procedures. We validate those findings with experiments on synthetic and real data.
Cryopreservation and effect of lighting conditions and cytokinins on in vitro multiplication of Miconia ligustroides (DC.) Naudin

Miconia ligustroides is a species that is native to Brazil and has medicinal and ecological importance. However, the species shows a lack of uniformity and delay in ex vitro germination. Thus, this study aimed to establish in vitro propagation for the species and to develop a protocol for the cryopreservation of seeds. For in vitro germination, activated charcoal (0.0, 0.5, 1.0, and 2.0 g L -1 ) was tested in Murashige and Skoog (MS) culture medium. Lateral buds excised from the plants were germinated in vitro and were encapsulated in an alginate matrix supplemented with 6benzylaminopurine (BAP), kinetin, and thidiazuron (TDZ: 0.0, 2.0, 8.0, and 16.0 μM). Shoots derived from encapsulated units were inoculated in MS culture medium supplemented with different concentrations of BAP (0.0, 2.5, 5.0, and 10.0 μM) under white or Gro-Lux fluorescent lamps for multiplication. For cryopreservation, the toxicity of the cryoprotectant solution PVS2 (0, 15, 30, 60, 120, and 180 min) was evaluated before the seeds were immersed in liquid nitrogen. The MS culture medium supplemented with 1.0 g L -1 of activated charcoal yielded the highest percentage of germination (78%). The encapsulated units presented the largest percentages of regeneration (75%) with 8.0 μM BAP, which assisted in the formation of shoots that were 8.03 cm in length. For shoot production, the highest mean number (3.03 shoots) was obtained in the MS medium containing 5.41 μM BAP. When seeds were subjected to cryopreservation, the immersion time in the PVS2 did not affect the survival of the seeds, which was satisfactory (70%). The protocols developed are considered viable alternatives for use in the conservation of the species, production of seedlings for commercialization purposes, and use in programs of reintroduction in degraded environments.
05. Geographic variation in advertisement calls among populations of Dendropsophus cruzi (Anura: Hylidae)

Anuran advertisement calls receive much attention because of their importance in mating behaviour and the recognition of conspecifics. Herein, we studied geographic variation in the advertisement call of Dendropsophus cruzi from central Brazil, to test the hypothesis that between-population differences in call parameters can be explained by geographical distance. We recorded 129 individuals from 10 populations of D. cruzi in Goiás State, Brazil, measuring call parameters of five calls of each individual. We found that dominant frequency is useful to distinguish populations, and that geographic distance is not a predictor of overall call differences. However, clinal geographical patterns were found for call duration, dominant frequency, call rate and snout-vent length (SVL). We suggest that differences in calls among populations can be due to local selective pressures.
A proof of Ibukiyama's Shimura type conjecture on Siegel modular forms of half-integral weight and of degree 2

We prove Ibukiyama's Shimura type conjecture on Siegel modular forms of half-integral weight and of degree 2 by using Arthur's multiplicity formula on the split odd special orthogonal group of rank 2 and Gan-Ichino's multiplicity formula on the metaplectic group of rank 2. In the proof, the representation theory of the Jacobi groups also plays an important role.
Integrative cross tissue analysis of gene expression identifies novel type 2 diabetes genes

To understand the mechanistic underpinnings of type 2 diabetes (T2D) loci mapped through GWAS, we performed a tissue-specific gene association study in a cohort of over 100K individuals (ncases ≈ 26K,ncontrols ≈ 84K) across 44 human tissues using MetaXcan, a summary statistics extension of PrediXcan. We found that 90 genes significantly (FDR < 0.05) associated with T2D, of which 24 are previously reported T2D genes, 29 are novel in established T2D loci, and 37 are novel genes in novel loci. Of these, 13 reported genes, 15 novel genes in known loci, and 6 genes in novel loci replicated (FDRrep < 0.05) in an independent study (ncases ≈ 10K, ncontrols ≈ 62K). We also found enrichment of significant associations in expected tissues such as liver, pancreas, adipose, and muscle but also in tibial nerve, fibroblasts, and breast. Finally, we found that monogenic diabetes genes are enriched in T2D genes from our analysis suggesting that moderate alterations in monogenic (severe) diabetes genes may promote milder and later onset type 2 diabetes.
Didispeech: A Large Scale Mandarin Speech Corpus

This paper introduces a new open-sourced Mandarin speech corpus, called DiDiSpeech. It consists of about 800 hours of speech data at 48kHz sampling rate from 6000 speakers and the corresponding texts. All speech data in the corpus is recorded in quiet environment and is suitable for various speech processing tasks, such as voice conversion, multi-speaker text-to-speech and automatic speech recognition. We conduct experiments with multiple speech tasks and evaluate the performance, showing that it is promising to use the corpus for both academic research and practical application. The corpus is available at https://outreach.didichuxing. com/research/opendata/.
Logical Relations as Types: Proof-Relevant Parametricity for Program Modules

The theory of program modules is of interest to language designers not only for its practical importance to programming, but also because it lies at the nexus of three fundamental concerns in language design: the phase distinction , computational effects , and type abstraction . We contribute a fresh “synthetic” take on program modules that treats modules as the fundamental constructs, in which the usual suspects of prior module calculi (kinds, constructors, dynamic programs) are rendered as derived notions in terms of a modal type-theoretic account of the phase distinction. We simplify the account of type abstraction (embodied in the generativity of module functors) through a lax modality that encapsulates computational effects, placing projectibility of module expressions on a type-theoretic basis. Our main result is a (significant) proof-relevant and phase-sensitive generalization of the Reynolds abstraction theorem for a calculus of program modules, based on a new kind of logical relation called a parametricity structure . Parametricity structures generalize the proof-irrelevant relations of classical parametricity to proof- relevant families, where there may be non-trivial evidence witnessing the relatedness of two programs—simplifying the metatheory of strong sums over the collection of types, for although there can be no “relation classifying relations,” one easily accommodates a “family classifying small families. ” Using the insight that logical relations/parametricity is itself a form of phase distinction between the syntactic and the semantic, we contribute a new synthetic approach to phase separated parametricity based on the slogan logical relations as types , by iterating our modal account of the phase distinction. We axiomatize a dependent type theory of parametricity structures using two pairs of complementary modalities (syntactic, semantic) and (static, dynamic), substantiated using the topos theoretic Artin gluing construction. Then, to construct a simulation between two implementations of an abstract type, one simply programs a third implementation whose type component carries the representation invariant.
Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds

We propose a general scheme for solving convex and non-convex optimization problems on manifolds. The central idea is that, by adding a multiple of the squared retraction distance to the objective function in question, we "convexify" the objective function and solve a series of convex sub-problems in the optimization procedure. One of the key challenges for optimization on manifolds is the difficulty of verifying the complexity of the objective function, e.g., whether the objective function is convex or non-convex, and the degree of non-convexity. Our proposed algorithm adapts to the level of complexity in the objective function. We show that when the objective function is convex, the algorithm provably converges to the optimum and leads to accelerated convergence. When the objective function is non-convex, the algorithm will converge to a stationary point. Our proposed method unifies insights from Nesterov's original idea for accelerating gradient descent algorithms with recent developments in optimization algorithms in Euclidean space. We demonstrate the utility of our algorithms on several manifold optimization tasks such as estimating intrinsic and extrinsic Frechet means on spheres and low-rank matrix factorization with Grassmann manifolds applied to the Netflix rating data set.
Large-deviation results for triangular arrays of semiexponential random variables

Abstract Asymptotics deviation probabilities of the sum $S_n=X_1+\dots+X_n$ of independent and identically distributed real-valued random variables have been extensively investigated, in particular when $X_1$ is not exponentially integrable. For instance, Nagaev (1969a, 1969b) formulated exact asymptotics results for $\mathbb{P}(S_n>x_n)$ with $x_n\to \infty$ when $X_1$ has a semiexponential distribution. In the same setting, Brosset et al. (2020) derived deviation results at logarithmic scale with shorter proofs relying on classical tools of large-deviation theory and making the rate function at the transition explicit. In this paper we exhibit the same asymptotic behavior for triangular arrays of semiexponentially distributed random variables.
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accuracy in the low-data regime. Compared to classification, semantic segmentation tasks require much more intensive labeling costs. Thus, these tasks greatly benefit from data-efficient training methods. However, structured outputs in segmentation render particular difficulties (e.g., designing pseudo-labeling and augmentation) to apply existing SSL strategies. To address this problem, we present a simple and novel re-design of pseudo-labeling to generate well-calibrated structured pseudo labels for training with unlabeled or weakly-labeled data. Our proposed pseudo-labeling strategy is network structure agnostic to apply in a one-stage consistency training framework. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes. Extensive experiments have validated that pseudo labels generated from wisely fusing diverse sources and strong data augmentation are crucial to consistency training for segmentation. The source code is available at this https URL.
Finite‐difference‐based simulation and adjoint optimization of gas networks

The stable operation of gas networks is an important optimization target. While for this task commonly finite volume methods are used, we introduce a new finite difference approach. With a summation by part formulation for the spatial discretization, we get well‐defined fluxes between the pipes. This allows a simple and explicit formulation of the coupling conditions at the node. From that, we derive the adjoint equations for the network simply and transparently. The resulting direct and adjoint equations are numerically efficient and easy to implement.
Reflective Decoding: Unsupervised Paraphrasing and Abductive Reasoning

Pretrained Language Models (LMs) generate text with remarkable quality, novelty,and coherence. Yet applying LMs to the problems of paraphrasing and infilling currently requires direct supervision, since these tasks break the left-to-right generation setup of pretrained LMs. We present Reflective Decoding, a novel unsupervised approach to apply the capabilities of pretrained LMs to non-sequential tasks. Our approach is general and applicable to two distant tasks - paraphrasing and abductive reasoning. It requires no supervision or parallel corpora, only two pretrained language models: forward and backward. Reflective Decoding operates in two intuitive steps. In the contextualization step, we use LMs to generate many left and right contexts which collectively capture the meaning of the input sentence. Then, in the reflection step we decode in the semantic neighborhood of the input, conditioning on an ensemble of generated contexts with the reverse direction LM. We reflect through the generated contexts, effectively using them as an intermediate meaning representation to generate conditional output. Empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods.Reflective Decoding introduces the concept of using generated contexts to represent meaning, opening up new possibilities for unsupervised conditional text generation.