Transfer Learning for Multi-language Twitter Election Classification

Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure

Learning to Learn from Weak Supervision by Full Supervision

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.Comment: Accepted at NIPS Workshop on Meta-Learning (MetaLearn 2017), Long Beach, CA, US

Comparative Genomics Analysis of a New Exiguobacterium Strain from Salar de Huasco Reveals a Repertoire of Stress-Related Genes and Arsenic Resistance

Indexación: Web of Science; Scopus.The Atacama Desert hosts diverse ecosystems including salt flats and shallow Andean lakes. Several heavy metals are found in the Atacama Desert, and microorganisms growing in this environment show varying levels of resistance/tolerance to copper, tellurium, and arsenic, among others. Herein, we report the genome sequence and comparative genomic analysis of a new Exiguobacterium strain, sp. SH31, isolated from an altiplanic shallow athalassohaline lake. Exiguobacterium sp. SH31 belongs to the phylogenetic Group II and its closest relative is Exiguobacterium sp. S17, isolated from the Argentinian Altiplano (95% average nucleotide identity). Strain SH31 encodes a wide repertoire of proteins required for cadmium, copper, mercury, tellurium, chromium, and arsenic resistance. Of the 34 Exiguobacterium genomes that were inspected, only isolates SH31 and S17 encode the arsenic efflux pump Acr3. Strain SH31 was able to grow in up to 10 mM arsenite and 100 mM arsenate, indicating that it is arsenic resistant. Further, expression of the ars operon and acr3 was strongly induced in response to both toxics, suggesting that the arsenic efflux pump Acr3 mediates arsenic resistance in Exiguobacterium sp. SH31.http://journal.frontiersin.org/article/10.3389/fmicb.2017.00456/ful

Identification of a 48kDa form of unconventional myosin 1c in blood serum of patients with autoimmune diseases

AbstractWe searched for protein markers present in blood serum of multiple sclerosis (MS), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) patients in comparison to healthy human individuals. We used precipitation/extraction methods and MALDI TOF/TOF mass spectrometry, and identified a protein with Mr ~46kDa as a fragment of human unconventional myosin IC isoform b (Myo1C). Western blotting with specific anti-human Myo1C antibodies confirmed the identity. Screening of blood serum samples from different autoimmune patients for the presence of Myo1c revealed its high level in MS and RA patients, relatively low level in SLE patients, and undetected in healthy donors. These data are suggesting that the level of p46 Myo1C in blood serum is a potential marker for testing of autoimmune diseases

Determinants of copper resistance in Acidithiobacillus ferrivorans ACH isolated from the Chilean altiplano

Indexación; Scopus.The use of microorganisms in mining processes is a technology widely employed around the world. Leaching bacteria are characterized by having resistance mechanisms for several metals found in their acidic environments, some of which have been partially described in the Acidithiobacillus genus (mainly on ferrooxidans species). However, the response to copper has not been studied in the psychrotolerant Acidithiobacillus ferrivorans strains. Therefore, we propose to elucidate the response mechanisms of A. ferrivorans ACH to high copper concentrations (0–800 mM), describing its genetic repertoire and transcriptional regulation. Our results show that A. ferrivorans ACH can grow in up to 400 mM of copper. Moreover, we found the presence of several copper-related makers, belonging to cop and cus systems, as well as rusticyanins and periplasmatic acop protein in the genome. Interestingly, the ACH strain is the only one in which we find three copies of copB and copZ genes. Moreover, transcriptional expression showed an up-regulation response (acop, copZ, cusA, rusA, and rusB) to high copper concentrations. Finally, our results support the important role of these genes in A. ferrivorans copper stress resistance, promoting the use of the ACH strain in industrial leaching under low temperatures, which could decrease the activation times of oxidation processes and the energy costs. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.https://www.mdpi.com/2073-4425/11/8/84

Genomic Variation and Arsenic Tolerance Emerged as Niche Specific Adaptations by Different Exiguobacterium Strains Isolated From the Extreme Salar de Huasco Environment in Chilean – Altiplano

Indexación: Scopus.Polyextremophilic bacteria can thrive in environments with multiple stressors such as the Salar de Huasco (SH). Microbial communities in SH are exposed to low atmospheric pressure, high UV radiation, wide temperature ranges, salinity gradient and the presence of toxic compounds such as arsenic (As). In this work we focus on arsenic stress as one of the main adverse factors in SH and bacteria that belong to the Exiguobacterium genus due to their plasticity and ubiquity. Therefore, our aim was to shed light on the effect of niche conditions pressure (particularly arsenic), on the adaptation and divergence (at genotypic and phenotypic levels) of Exiguobacterium strains from five different SH sites. Also, to capture greater diversity in this genus, we use as outgroup five As(III) sensitive strains isolated from Easter Island (Chile) and The Great Salt Lake (United States). For this, samples were obtained from five different SH sites under an arsenic gradient (9 to 321 mg/kg: sediment) and isolated and sequenced the genomes of 14 Exiguobacterium strains, which had different arsenic tolerance levels. Then, we used comparative genomic analysis to assess the genomic divergence of these strains and their association with phenotypic differences such as arsenic tolerance levels and the ability to resist poly-stress. Phylogenetic analysis showed that SH strains share a common ancestor. Consequently, populations were separated and structured in different SH microenvironments, giving rise to multiple coexisting lineages. Hence, this genotypic variability is also evidenced by the COG (Clusters of Orthologous Groups) composition and the size of their accessory genomes. Interestingly, these observations correlate with physiological traits such as growth patterns, gene expression, and enzyme activity related to arsenic response and/or tolerance. Therefore, Exiguobacterium strains from SH are adapted to physiologically overcome the contrasting environmental conditions, like the arsenic present in their habitat. © Copyright © 2020 Castro-Severyn, Pardo-Esté, Mendez, Morales, Marquez, Molina, Remonsellez, Castro-Nallar and Saavedra.https://www.frontiersin.org/articles/10.3389/fmicb.2020.01632/ful