Miracle-dz/UniFAR-FTUs huggingface dataset:
Influência do metabolismo de carbono e do nível de oxigênio sobre a localização da permease de lactose em Kluyveromyces lactis

In this work we analyzed the influence of the carbon source, oxygen level and snf1 mutation on the subcelular localization of Lac12GFP permease in the yeast Kluyveromyces lactis. Initially we showed that K. lactis has higher affinity for lactose than for glucose. Our data suggest that the KlSnf1p dependent subcelular localization of Lac12GFP permease was not affected by glucose. Furthermore, we observed that the subcelular localization of this permease was similar when K. lactis was grown on galactose and glucose, but different on lactose. The mRNA levels of the LAC12GFP gene expressed from KlSnf1p independent promoter were similar between the wild type and Klsnf1 mutant, indicating that KlSnf1p markedly affects the LAC12GFP gene expression only at post- translational level. Analysis of the subcelular localization of lactose permease in Klsnf1 mutant showed that the permease is found intracellularly at the vacuole membrane, suggesting that Klsnf1p may be involved with the lactose permase translocation into vacuole. In hypoxic condition, more lactose permease was localized at plasma membrane up to 3 hours of growth, indicating that the oxygen availability influences the localization of Lac12GFPp. Furthermore, we performed a screening (multicopy suppression) to identify negative regulators of lactose uptake in K. lactis. The multicopy suppression was used to select transformants able to suppress lactose toxicity, because lactose is toxic for Kllac4 mutant grown on minimal medium containing lactose plus glycerol as a carbon source. Among the 68 plasmids rescued from WBS1 (lac4 mutant) transformed with a Kep6-based genomic plasmid library (multicopy suppression), nine were bigger than Kep6 (empty vector). Nevertheless, our data showed that the suppression was due to a spontaneous mutation. In these mutants the lactose uptake was not impaired. %%%%Neste trabalho foi analisada a influencia da fonte de carbono e do nivel de oxigenio sobre a localizacao da permease Lac12GFPp na levedura Kluyveromyces lactis. Inicialmente, foi mostrado que K. lactis apresenta afinidade maior para lactose do que para glicose. Os dados indicaram que a localizacao da permease Lac12GFPp dependente da KlSnf1p nao foi afetada pela presenca de glicose. A localizacao dessa permease foi similar quando K. lactis foi cultivada em galactose e glicose, mas diferente quando cultivada em lactose. Os niveis de mRNA do gene LAC12GFP expressos a partir do promotor ADH1, que nao esta sob a influencia da KlSnf1p, foram similares entre a linhagem selvagem e o mutante Klsnf1. Esse resultado mostra que a influencia da KlSnf1p sobre a expressao do gene LAC12GFP e exercida marcantemente somente em nivel pos-traducional. A analise da localizacao subcelular da permease de lactose no mutante Klsnf1 evidenciou que a permease e encontrada na membrana do vacuolo, sugerindo que Klsnf1p pode estar envolvida com a translocacao da permease para dentro do vacuolo. Em condicoes hipoxicas, ate 3 h de crescimento, foi observado aumento do sinal da permease de lactose na membrana…
Evaluating the Level of the Household Water Service Provided by a Private Water Enterprise in Ghana

Innovative service delivery models are attempting to more consistently provide clean water to communities in developing countries. It is imperative that these approaches be evaluated for their performance in these contexts while understating potential consequences. A private service delivery model in Ghana utilizing solar-powered water treatment, circuit rider principles, pre-paid metering, and a district-wide approach was assessed for three years. A quasi-experimental design used key informant surveys, household surveys, and water quality testing to investigate the service received by households under various management schemes. Service indicators were compared using logistic regression analysis. Private customers were shown to have significantly improved quality, annual reliability, and satisfaction ratings (p < 0.05) compared with control households, while maintaining the quantity of water collected. However, private customers were more reliant upon multiple water sources to meet domestic needs and suffered from lower affordability scores. About 38% of households used private water services, with no significant relationship with socioeconomic class. It is important for policy-makers and implementers to understand that some people will be unwilling or unable to take advantage of this model, and a transition from free improved sources to paid piped schemes will likely require a period of supporting both systems in order to reach everyone.
Traditional Plant Foods of Canadian Indigenous Peoples: Nutrition, Botany and Use

Traditional Plant Foods of Canadian Indigenous Peoples: Nutrition, Botany, and Use. Harriet V. Kuhnlein and Nancy J. Turner. Volume 8 in the Food and Nutrition in History and Anthropology Series, edited by Solomon Katz. New York and Philadelphia: Gordon and Breach Science Publishers, 1991. Pp. xi, 620. 48 figures, 3 maps. $88.00 (hardbound), $38.00 for individuals who are members of Science and Arts Society. ISBN 2-88124-465-3.
Contract work at older ages

Abstract The share of workers who are self-employed rises markedly with age. Given policy concerns about inadequate retirement savings, especially among those with lower education, and the resulting interest in encouraging employment at older ages, it is important to understand the role that self-employment arrangements play in facilitating work among seniors. New data from a survey module fielded on a Gallup telephone survey distinguish independent contractor work from other self-employment and provide information on informal and online platform work. The Gallup data show that, especially after accounting for individuals who are miscoded as employees, self-employment is even more prevalent at older ages than suggested by existing data. Work as an independent contractor is the most common type of self-employment. Roughly one-quarter of independent contractors aged 50 and older work for a former employer. At older ages, self-employment generally – and work as an independent contractor specifically – is more common among the highly educated, accounting for much of the difference in employment rates across education groups. We provide suggestive evidence that differences in opportunities for independent contractor work play an important role in the lower employment rates of less-educated older adults.
Consumer Behavior for Milk and Dairy Products as Daily Consumption Products in Every Household—The Case of Kosovo

In our study, we used consumer behavior to study the buying and consuming patterns for milk and dairy products for households in Kosovo. The study uses data collected from consumers of milk and dairy products. Data obtained from the field were recorded and analyzed with the SPSS statistical program. The results of this study show that the date of production and the content of milk and dairy products are the most important factors that consumers consider when purchasing milk and dairy products. Moreover, supermarkets are the favorite place of supply for milk and dairy products for consumers. Consumers are most likely to buy milk and dairy products twice a week.
Effect of donepezil and memantine on improvement of cognitive function in patients with temporal lobe epilepsy

Background: Cognitive impairment is a common complication of patients with temporal lobe epilepsy (TLE). Therefore, the aim of this study was to compare the effects of donepezil and memantine on improving the cognitive function of patients with TLE. Materials and Methods: In a clinical trial study, 70 patients with TLE were divided into two groups of 35 each: 10 mg doses of donepezil (first group) and memantine (second group) were applied for 16 weeks. The level of cognitive function of patients in both groups before and after treatment was determined using Montreal Cognitive Assessment (MoCA) test. Results: The mean score of MoCA before and after intervention was 23.55 ± 3.67 and 26.09 ± 2.5, respectively, in the group treated with memantine, and the mean score of intervention was significantly improved (P < 0.001). In the group treated with donepezil, the score before and after the operation was 23.87 ± 3.18 and 24.35 ± 2.17, respectively, and no significant difference was observed in this group (P = 0.38). Conclusion: Hence, memantine was better than donepezil in the improvement of cognitive impairment in patients with TLE.
Establishment, agronomic and qualitative evaluation of collections of Coffea arabica and Coffea canephora cultivated varieties.

In the mid nineties it has been decided to bring most of the cultivated varieties under comparative growing and post harvest conditions in order to be able to evaluate these varieties on fully comparable bean samples. Many cultivated varieties from different sources have been transferred to the Nestle Research Center Tours (NRC-T) where they have been DNA fingerprinted before the selection of subsets well representative of the available genetic diversity. A selection of 30 C. arabica and 55 C. canephora accessions were in vitro propagated through micro-cuttings and then transplanted for multilocated and replicated field trials. The agronomic criteria have been regularly recorded during the development of the trees and the first significant crops have been comparatively processed and evaluated for biochemical, processing and sensory characteristics. The diversity observed for these traits were statistically analyzed with a special emphasis on the variety effect and its genetic origin. A very large diversity has been observed for all C. canephora recorded characteristics and a lower one, but still significant, for some of the Arabica characteristics. That leads to a variety ranking and to a better use of the existing diversity through the recommendation of varieties well adapted for both the growers and the processors according to the final consumer product.
Bank Financial Stability, Bank Valuation and International Oil Prices: Evidence From Listed Russian Public Banks

Using data on 17 listed public banks from Russia over the period 2008 to 2016, we analyze whether international oil prices affect the bank stability in an oil-dependent country. We posit that a decrease in international oil prices has a negative long-run macroeconomic impact for an oil-exporting country, which further deteriorates the bank financial stability. More specifically, a decrease in international oil prices leads for an oil-exporting country as Russia to a currency depreciation and to a deterioration of the fiscal stance. In addition, given the positive correlation of oil and stock prices documented by numerous previous studies, a decrease in international oil prices represents a negative signal for the stock markets investors, negatively affecting banks' share prices and thus, their capacity to generate sustainable earnings. In this context, the bank financial stability can be menaced. With a focus on public listed banks and using a Pool Mean Group (PMG) estimator, we show that an increase in international oil prices and in the price to book value ratio has a long-run positive effect on Russian public banks stability, and conversely. While positive oil-price shocks contribute to bank stability in the long run, an opposite effect is recorded for negative shocks. However, no significant impact is documented in the short run. Our findings are robust to different bank stability specifications, different samples and control variables.
A Batch Normalized Inference Network Keeps the KL Vanishing Away

Variational Autoencoder (VAE) is widely used as a generative model to approximate a model’s posterior on latent variables by combining the amortized variational inference and deep neural networks. However, when paired with strong autoregressive decoders, VAE often converges to a degenerated local optimum known as “posterior collapse”. Previous approaches consider the Kullback–Leibler divergence (KL) individual for each datapoint. We propose to let the KL follow a distribution across the whole dataset, and analyze that it is sufficient to prevent posterior collapse by keeping the expectation of the KL’s distribution positive. Then we propose Batch Normalized-VAE (BN-VAE), a simple but effective approach to set a lower bound of the expectation by regularizing the distribution of the approximate posterior’s parameters. Without introducing any new model component or modifying the objective, our approach can avoid the posterior collapse effectively and efficiently. We further show that the proposed BN-VAE can be extended to conditional VAE (CVAE). Empirically, our approach surpasses strong autoregressive baselines on language modeling, text classification and dialogue generation, and rivals more complex approaches while keeping almost the same training time as VAE.
Four-quark exotic mesons

We review our investigations devoted to the analysis of the resonances $ Z_{c}(3900)$, $Z_{c}(4430)$, $Z_{c}(4100)$, $X(4140)$, $X(4274)$, $a_1(1420)$ , $Y(4660)$, $X(2100)$, $X(2239)$ and $Y(2175)$ discovered in various processes by Belle, BaBar, BESIII, D0, CDF, CMS, LHCb and COMPASS collaborations. These resonances are considered as serious candidates to four-quark (tetraquark) exotic mesons. We treat all of them as diquark-antidiquark states with relevant spin-parities, find their masses and couplings, as well as explore their dominant strong decay channels. Calculations are performed in the context of the QCD sum rule method. Thus, the spectroscopic parameters of the tetraquarks are evaluated using the two-point sum rules. For computations of the strong couplings $G_{TM_1M_2}$, corresponding to the vertices $TM_1M_2$ and necessary to find the partial widths of the strong decays $T \to M_1M_2$, we employ either the three-point or full/approximate versions of the QCD light-cone sum rules methods. Obtained results are compared with available experimental data, and with predictions of other theoretical studies.
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?

Social biases are encoded in word embeddings. This presents a unique opportunity to study society historically and at scale, and a unique danger when embeddings are used in downstream applications. Here, we investigate the extent to which publicly-available word embeddings accurately reflect beliefs about certain kinds of people as measured via traditional survey methods. We find that biases found in word embeddings do, on average, closely mirror survey data across seventeen dimensions of social meaning. However, we also find that biases in embeddings are much more reflective of survey data for some dimensions of meaning (e.g. gender) than others (e.g. race), and that we can be highly confident that embedding-based measures reflect survey data only for the most salient biases.
Practical Comparable Data Collection for Low-Resource Languages via Images

We propose a method of curating high-quality comparable training data for low-resource languages with monolingual annotators. Our method involves using a carefully selected set of images as a pivot between the source and target languages by getting captions for such images in both languages independently. Human evaluations on the English-Hindi comparable corpora created with our method show that 81.1% of the pairs are acceptable translations, and only 2.47% of the pairs are not translations at all. We further establish the potential of the dataset collected through our approach by experimenting on two downstream tasks - machine translation and dictionary extraction. All code and data are available at this https URL.
Scaling laws and dynamics of hashtags on Twitter

In this paper, we quantify the statistical properties and dynamics of the frequency of hashtag use on Twitter. Hashtags are special words used in social media to attract attention and to organize content. Looking at the collection of all hashtags used in a period of time, we identify the scaling laws underpinning the hashtag frequency distribution (Zipf's law), the number of unique hashtags as a function of sample size (Heaps' law), and the fluctuations around expected values (Taylor's law). While these scaling laws appear to be universal, in the sense that similar exponents are observed irrespective of when the sample is gathered, the volume and the nature of the hashtags depend strongly on time, with the appearance of bursts at the minute scale, fat-tailed noise, and long-range correlations. We quantify this dynamics by computing the Jensen-Shannon divergence between hashtag distributions obtained τ times apart and we find that the speed of change decays roughly as 1/τ. Our findings are based on the analysis of 3.5×109 hashtags used between 2015 and 2016.
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning

This paper proposes a Deep Reinforcement Learning (DRL) approach for solving knapsack problem. The proposed method consists of a state aggregation step based on tabular reinforcement learning to extract features and construct states. The state aggregation policy is applied to each problem instance of the knapsack problem, which is used with Advantage Actor Critic (A2C) algorithm to train a policy through which the items are sequentially selected at each time step. The method is a constructive solution approach and the process of selecting items is repeated until the final solution is obtained. The experiments show that our approach provides close to optimal solutions for all tested instances, outperforms the greedy algorithm, and is able to handle larger instances and more flexible than an existing DRL approach. In addition, the results demonstrate that the proposed model with the state aggregation strategy not only gives better solutions but also learns in less timesteps, than the one without state aggregation.
Critical edge behavior in the singularly perturbed Pollaczek–Jacobi type unitary ensemble

In this paper, we study the strong asymptotic for the orthogonal polynomials and universality associated with singularly perturbed Pollaczek-Jacobi type weight $$w_{p_J2}(x,t)=e^{-\frac{t}{x(1-x)}}x^\alpha(1-x)^\beta, $$ where $t \ge 0$, $\alpha >0$, $\beta >0$ and $x \in [0,1]. $ Our main results obtained here include two aspects: { I. Strong asymptotics:} We obtain the strong asymptotic expansions for the monic Pollaczek-Jacobi type orthogonal polynomials in different interval $(0,1)$ and outside of interval $\mathbb{C}\backslash (0,1)$, respectively; Due to the effect of $\frac{t}{x(1-x)}$ for varying $t$, different asymptotic behaviors at the hard edge $0$ and $1$ were found with different scaling schemes. Specifically, the uniform asymptotic behavior can be expressed as a Airy function in the neighborhood of point $1$ as $\zeta= 2n^2t \to \infty, n\to \infty$, while it is given by a Bessel function as $\zeta \to 0, n \to \infty$. { II. Universality:} We respectively calculate the limit of the eigenvalue correlation kernel in the bulk of the spectrum and at the both side of hard edge, which will involve a $\psi$-functions associated with a particular Painlev$\acute{e}$ \uppercase\expandafter{\romannumeral3} equation near $x=\pm 1$. Further, we also prove the $\psi$-funcation can be approximated by a Bessel kernel as $\zeta \to 0$ compared with a Airy kernel as $\zeta \to \infty$. Our analysis is based on the Deift-Zhou nonlinear steepest descent method for the Riemann-Hilbert problems.
The Gutenberg Dialogue Dataset

Large datasets are essential for neural modeling of many NLP tasks. Current publicly available open-domain dialogue datasets offer a trade-off between quality (e.g., DailyDialog) and size (e.g., Opensubtitles). We narrow this gap by building a high-quality dataset of 14.8M utterances in English, and smaller datasets in German, Dutch, Spanish, Portuguese, Italian, and Hungarian. We extract and process dialogues from public-domain books made available by Project Gutenberg. We describe our dialogue extraction pipeline, analyze the effects of the various heuristics used, and present an error analysis of extracted dialogues. Finally, we conduct experiments showing that better response quality can be achieved in zero-shot and finetuning settings by training on our data than on the larger but much noisier Opensubtitles dataset. Our open-source pipeline (https://github.com/ricsinaruto/gutenberg-dialog) can be extended to further languages with little additional effort. Researchers can also build their versions of existing datasets by adjusting various trade-off parameters.
Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data

The utility of aerial imagery (Satellite, Drones) has become an invaluable information source for cross-disciplinary applications, especially for crisis management. Most of the mapping and tracking efforts are manual which is resource-intensive and often lead to delivery delays. Deep Learning methods have boosted the capacity of relief efforts via recognition, detection, and are now being used for non-trivial applications. However the data commonly available is highly imbalanced (similar to other real-life applications) which severely hampers the neural network’s capabilities, this reduces robustness and trust. We give an overview on different kinds of techniques being used for handling such extreme settings and present solutions aimed at maximizing performance on minority classes using a diverse set of methods (ranging from architectural tuning to augmentation) which as a combination generalizes for all minority classes. We hope to amplify cross-disciplinary efforts by enhancing model reliability.
VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments

Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies. Recently, the end-to-end driving method has emerged, which performs well and generalizes to new environments by directly learning from expert-provided data. However, many existing methods on this topic neglect to check the confidence of the driving actions and the ability to recover from driving mistakes. In this paper, we develop an uncertainty-aware end-to-end trajectory generation method based on imitation learning. It can extract spatiotemporal features from the front-view camera images for scene understanding, and then generate collision-free trajectories several seconds into the future. The experimental results suggest that under various weather and lighting conditions, our network can reliably generate trajectories in different urban environments, such as turning at intersections and slowing down for collision avoidance. Furthermore, closed-loop driving tests suggest that the proposed method achieves better cross-scene/platform driving results than the state-of-the-art (SOTA) end-to-end control method, where our model can recover from off-center and off-orientation errors and capture 80% of dangerous cases with high uncertainty estimations.