Miracle-dz/UniFAR-FTUs huggingface dataset:
ASIAN CULTIVATED RICE DOMESTICATION SUPPRESSES THE EXPRESSION OF ABIOTIC STRESS- AND REACTIVE OXYGEN SPECIES SCAVENGING-RELATED GENES IN ROOTS

Asian cultivated rice (Oryza sativa L), the important cereal crop, has low resistance to numerous biotic and abiotic stresses compared to its ancestral wild rice (Oryza rufipogon). Although genetic studies have shown that the susceptibility of cultivated rice towards various environmental stresses is due to its narrow genetic diversity caused by domestication, whereas wild rice possesses tremendous gene pools; yet very little is known about the domestication-induced transcriptional changes in abiotic stress-related genes and/or pathways in cultivated rice. Thus, to investigate these changes, we retrieved the root transcriptome data sets of cultivated and wild rice from the GenBank of National Center for Biotechnology information. Next, we performed MapMan-based analysis of the root transcriptome data sets of cultivated and wild rice. Cellular response overview from MapMan analysis showed that key genes related to abiotic stress categories; heat stress and drought/salt stress were significantly suppressed in cultivated rice relative to wild rice. In addition, the expression level of ascorbateand dismutaserelated transcripts, which also function in abiotic stress tolerance through involvement in the detoxification of reactive oxygen species (ROS), were also down-regulated in cultivated rice compared with wild rice. In conclusion, transcriptomic-based survey of cultivated rice and wild rice reflects that domestication has significantly changed (reduced) the transcriptional level of abiotic stress and ROS-scavenging related transcripts in cultivated rice. These findings further explain the susceptibility of cultivated rice towards abiotic stresses. Thus, re-introduction of the identified desirable genes of wild rice through conventional breeding and genetic engineering may improve the abiotic stress resistance mechanisms of modern rice cultivars.
AngularQA: Protein Model Quality Assessment with LSTM Networks

Abstract Quality Assessment (QA) plays an important role in protein structure prediction. Traditional multimodel QA method usually suffer from searching databases or comparing with other models for making predictions, which usually fail when the poor quality models dominate the model pool. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure parsed from protein structure at each time-step without using any database, which is different than all existed QA methods. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA
Two Dimensional Speckle Tracking Echocardiography Assessment of Left Ventricular Remodeling in Patients after Myocardial Infarction

Background: Adverse left ventricular remodelling (LVR), defined as progressive ventricular dilatation, distortion of chamber shape, myocardial hypertrophy, and deteriorating function, which if uninterrupted leads to congestive heart failure (CHF) and a poor clinical outcome, begins in some patients with acute myocardial infarction (AMI) even after successful percutaneous coronary intervention (PCI). Aims: This study aims at evaluating the value of speckle tracking echocardiography in predicting LVR after successful PCI in AMI patients. Materials and Methods: Eighty-four acute myocardial infarction patients. A thorough physical examination, electrocardiography (ECG) and a complete echocardiographic assessment, including speckle tracking study, was performed two days after PCI and then a follow up echocardiography with speckle tracking study was done two months afterwards. Patients were then divided into two groups based on the presence of remodelling (R+, R-). RESULTS: at baseline study global longitudinal strain (GLS) (-11.14±0.5 VS -16.78±0.4, P<0.0001), longitudinal strain rate (LSr) (-1.01±0.05 VS -1.07±0.04, P<0.0001), culprit longitudinal strain (CulLS) (-9.74±0.59 VS -15.68±0.49, P<0.0001), culprit longitudinal strain rate (CulLSr) (-0.95±0.05 VS -1.02±0.04, P<0.0001) were all lower in group R+ than in R-. In the follow up study, all strain parameters studied were significantly lower in the R+ group than R- group. The most sensitive and specific parameters were the GLS and CulLS (sensitivities of 91.7% and 95.8% respectively) and (specificities of 95% and 96.7% respectively). CONCLUSION: Our findings show that impaired indices LV deformation detected two days after successful PCI for AMI may provide predictive value in detecting LV remodelling.
Altered gut microbiota by voluntary exercise in donor mice may contribute eo high physical activity in recipient mice

Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso. Altered gut microbiota by voluntary exercise in donor mice may contribute eo high physical activity in recipient mice Autor(es): Yano, Hiromi; Oyanagi, Eri; Uchida, Masataka; Kremenik, Michael J. Publicado por: Imprensa da Universidade de Coimbra URL persistente: URI:http://hdl.handle. net/10316.2/44157 DOI: DOI:https://doi.org/10.14195/2182-7087_ex2018_94
High‐Elevation Monsoon Precipitation Processes in the Central Andes of Peru

Measurements at the high‐elevation Lamar Observatory in the Mantaro Valley (MV) in the Central Andes of Peru demonstrate a diurnal cycle of precipitation characterized by convective rainfall during the afternoon and nighttime stratiform rainfall with embedded convection. Wet season data (2016–2018) reveal long‐duration (6–12 hr) shallow precipitating systems (LDPS) that produced about 17% of monsoon rainfall in 2016 and 2018 associated with El Niño and La Niña, respectively. The LPDS fraction of monsoon rainfall doubles to 35% with weekly recurrence in 2017 under El Niño Costero (coastal) conditions. LDPS occur under favorable moisture conditions dictated by the South America (SA) Low‐Level Jet (SALLJ) and Cold Air Intrusions (CAIs). Backward trajectory analysis shows that precipitable water sustains >80% of seasonal precipitation and ties the LPDS to particular moisture source regions in the eastern Andes foothills 1–2 days in advance, enhanced by increased moisture supply in the midtroposphere. Higher frequency of CAIs and enhanced midlevel moisture convergence along CAI fronts explain the increased LDPS frequency during the 2017 El Niño Costero. These findings highlight the functional role of the Andes morphology in organizing moisture supply to high‐elevation precipitation systems on the orographic envelope. Analysis of the Global Precipitation Measurement (GPM) mission satellite‐based radar observations points to challenges to precipitation detection and estimation in this region as the GPM clutter‐free height (~1–2 km AGL) exceeds the depth of shallow precipitation systems in the MV.
Low Latency Speech Recognition Using End-to-End Prefetching

Latency is a crucial metric for streaming speech recognition systems. In this paper, we reduce latency by fetching responses early based on the partial recognition results and refer to it as prefetching . Speciﬁcally, prefetching works by submitting partial recognition results for subsequent processing such as obtaining assistant server responses or second-pass rescoring before the recognition result is ﬁnalized. If the partial result matches the ﬁnal recognition result, the early fetched response can be delivered to the user instantly. This effectively speeds up the system by saving the execution latency that typically happens after recognition is completed. Prefetching can be triggered multiple times for a single query, but this leads to multiple rounds of downstream processing and increases the computation costs. It is hence desirable to fetch the result sooner but meanwhile limiting the number of prefetches. To achieve the best trade-off between latency and computation cost, we investigated a series of prefetching decision models including decoder silence based prefetching, acoustic silence based prefetching and end-to-end prefetching. In this paper, we demonstrate the proposed prefetching mechanism reduced latency by ∼ 200 ms for a system that consists of a streaming ﬁrst pass model using recurrent neural network transducer and a non-streaming second pass rescoring model using Listen, Attend and Spell. We observe that the end-to-end prefetching provides the best trade-off between cost and latency and is 120 ms faster compared to silence based prefetching at a ﬁxed prefetch rate.
Are Germans Better Haters Than Danes? Language-Specific Implicit Prosodies of Types of Hate Speech and How They Relate to Perceived Severity and Societal Rules

Hate speech, both written and spoken, is a growing source of concern as it often discriminates societal minorities for their national origin, sexual orientation, gender or disabilities. Despite its destructive power, hardly anything is known about whether there are cross-linguistic mechanisms and acousticphonetic characteristics of hate speech. For this reason, our experiment analyzes the implicit prosodies that are caused by written Twitter and Facebook hate-speech items and made phonetically "tangible" through a special, introspective readingaloud task. We compare the elicited (implicit) prosodies of Danish and German speakers with respect to f0, intensity, HNR, and the Hammarberg index. While we found no evidence for a consistent hate-speech-specific prosody either within or between the two languages, our results show clear prosodic differences associated with types of hate speech and their targeted minority groups. Moreover, language-specific differences suggest that – compared to Danish – German hate speech sounds more expressive and hateful. Results are discussed regarding their implications for the perceived severity and the automatic flagging and deletion of hate-speech posts in social media.
A synthesis of high purity single-walled carbon nanotubes from small diameters of cobalt nanoparticles by using oxygen-assisted chemical vapor deposition process

A successful combination of "oxygen-assisted chemical vapor deposition (CVD) process" and Co catalyst nanoparticles to grow highly pure single walled carbon nanotubes (SWNTs) was demonstrated. Recently, it was reported that addition of small amounts of oxygen during CVD process dramatically increased the purity and yield of carbon nanotubes. However, this strategy could not be applied for discrete Fe nanoparticle catalysts from which appropriate yields of SWNTs could be grown directly on solid substrates, and fabricated into field effect transistors (FETs) quite efficiently. The main reason for this failure is due to the carbothermal reduction which results in SiO 2 nanotrench formation. We found that the oxygen-assisted CVD process could be successfully applied for the growth of highly pure SWNTs by switching the catalyst from Fe to Co nanoparticles. The topological morphologies and p-type transistor electrical transport properties of the grown SWNTs were examined by using atomic force microscope (AFM), Raman, and from FET devices fabricated by photolithography.
Measuring Unique Changes: How do Distinct Changes Affect the Size and Lifetime of Pull Requests?

Size metrics are commonly cited features in studies that analyze influencing factors on pull request lifetime. These metrics are also important for integrators, as based on them, they may prefer to prioritize pull requests easy to assess. However, code changes that form pull requests may not be unique, and repetitive changes may represent less complexity than expected by considering size metrics like lines of code or source files. The goal of this study is to analyze the influence of unique changes over pull requests relative to its size and lifetime. We collected data from 83,000+ pull requests of 26 projects hosted on GitHub. Also, we proposed a metric called unique changes rate to measure the proportion of unique changes over the total changes made by a pull request. We conducted experiments with Random Forest regression models and association rules to examine the influence of unique changes rate. Results show that unique changes have more influence over the lifetime of large pull requests, is determined mainly by the number of source files, and low levels of unique changes rate affect more the relationship between pull request size and lifetime than high levels. We conclude that unique changes can figure as an interesting feature in the context of the pull request lifetime. Results indicate that unique changes may increase or decrease the influence of pull request size on its lifetime. Our work has implications for researchers and core team members in software projects since unique changes represent helpful information.
Contemporary Polish Language Model (Version 2) Using Big Data and Sub-Word Approach

Language and vocabulary continue to evolve in this era of big data, making language modelling an important language processing task that benefits from the enormous data in different languages provided by web-based corpora. In this paper, we present a set of 6-gram language models based on a big-data training of the contemporary Polish language, using the Common Crawl corpus (a compilation of over 3.25 billion webpages) and other resources. The corpus is provided in different combinations of POS-tagged, grammatical groups-tagged, and sub-word-divided versions of raw corpora and trained models. The dictionary of contemporary Polish was updated and presented, and we used the KENLM toolkit to train big-data language models in ARPA format. Additionally, we have provided pre-trained vector models. The language model was trained, and the advances in BLEU score were obtained in MT systems along with the perplexity values, utilizing our models. The superiority of our model over Google’s WEB1T n-gram counts and the first version of our model was demonstrated through experiments, and the results illustrated that it guarantees improved quality in perplexity and machine translation. Our models can be applied in several natural language processing tasks and several scientific interdisciplinary fields.
Towards Automatic Assessment of Voice Disorders: A Clinical Approach

Automatic detection and assessment of voice disorders is important in diagnosis and treatment planning of voice disorders. This work proposes an approach for automatic detection and assessment of voice disorders from a clinical perspective. To accomplish this, a multi-level classiﬁcation approach was explored in which four binary classiﬁers were used for the assessment of voice disorders. The binary classiﬁers were trained using support vector machines with excitation source features, vocal-tract system features, and state-of-art OpenSMILE features. In this study source features namely, glottal parameters obtained from glottal ﬂow waveform, perturbation measures obtained from epoch locations, and cepstral features obtained from linear prediction residual and zero frequency ﬁltered signal were explored. The present study used the Saarbucken voice disorders database to evaluate the performance of proposed approach. The OpenSMILE features namely ComParE and eGEMAPS feature sets shown better performance in terms of classiﬁcation accuracies of 82.8% and 76%, respectively for voice disorder detection. The combination of excitation source features with baseline feature sets further improved the performance of detection and assessment systems, that highlight the complimentary nature of exciting source features.
Detecting Domain-Specific Credibility and Expertise in Text and Speech

We investigate and explore the interplay of credibility and expertise level in text and speech. We collect a unique domain-speciﬁc multimodal dataset and analyze a set of acoustic-prosodic and linguistic features in both credible and less cred-ible speech by professionals of varying expertise levels. Our analyses shed light on potential indicators of domain-speciﬁc perceived credibility and expertise, as well as the interplay in-between. Moreover, we build multimodal and multi-task deep learning models that outperform human performance by 6 . 2% in credibility and 3 . 8% in expertise level, building upon state-of-the-art self-supervised pre-trained language models. To our knowledge, this is the ﬁrst multimodal multi-task study that analyzes and predicts domain-speciﬁc credibility and expertise level at the same time. 1
Lip Graph Assisted Audio-Visual Speech Recognition Using Bidirectional Synchronous Fusion

Current studies have shown that extracting representative visual features and efﬁciently fusing audio and visual modalities are vital for audio-visual speech recognition (AVSR), but these are still challenging. To this end, we propose a lip graph assisted AVSR method with bidirectional synchronous fusion. First, a hybrid visual stream combines the image branch and graph branch to capture discriminative visual features. Specially, the lip graph exploits the natural and dynamic connections between the lip key points to model the lip shape, and the temporal evolution of the lip graph is captured by the graph convolutional networks followed by bidirectional gated recurrent units. Second, the hybrid visual stream is combined with the audio stream by an attention-based bidirectional synchronous fusion which allows bidirectional information interaction to resolve the asynchrony between the two modalities during fusion. The experimental results on LRW-BBC dataset show that our method out-performs the end-to-end AVSR baseline method in both clean and noisy conditions.
MiR-100 is a predictor of endocrine responsiveness and prognosis in patients with operable luminal breast cancer

Purpose Overexpression of miR-100 in stem cells derived from basal-like breast cancers causes loss of stemness, induction of luminal breast cancer markers and response to endocrine therapy. We, therefore, explored miR-100 as a novel biomarker in patients with luminal breast cancer. Methods miR-100 expression was studied in 90 patients with oestrogen-receptor-positive/human-epidermal growth factor receptor 2-negative breast cancer enrolled in a prospective study of endocrine therapy given either preoperatively, or for the treatment of de novo metastatic disease. Response was defined as a Ki67 ≤2.7% after 21±3 days of treatment. The prognostic role of miR-100 expression was evaluated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) breast cancer datasets. Additionally, we explored the correlation between miR-100 and the expression its targets reported as being associated with endocrine resistance. Finally, we evaluated whether a signature based on miR-100 and its target genes could predict the luminal A molecular subtype. Results Baseline miR-100 was significantly anticorrelated with baseline and post-treatment Ki67 (p<0.001 and 0.004, respectively), and independently associated with response to treatment (OR 3.329, p=0.047). In the METABRIC dataset, high expression of miR-100 identified women with luminal A tumours treated with adjuvant endocrine therapy with improved overall survival (HR 0.55, p<0.001). miR-100 was negatively correlated with PLK1, FOXA1, mTOR and IGF1R expression, potentially explaining its prognostic effect. Finally, a miR-100-based signature developed in patients enrolled in the prospective study outperformed Ki67 alone in predicting the luminal A phenotype. Conclusions Our findings suggest that miR-100 should be further explored as a biomarker in patients with luminal breast cancer.
Speaker-Independent Mel-Cepstrum Estimation from Articulator Movements Using D-Vector Input

We describe a speaker-independent mel-cepstrum estimation system which accepts electromagnetic articulography (EMA) data as input. The system collects speaker information with d-vectors generated from the EMA data. We have also investigated the effect of speaker independence in the input vectors given to the mel-cepstrum estimator. This is accomplished by introducing a two-stage network, where the first stage is trained to output EMA sequences that are averaged across all speakers on a per-triphone basis (and so are speaker-independent) and the second receives these as input for mel-cepstrum estimation. Experimental results show that using the d-vectors can improve the performance of mel-cepstrum estimation by 0.19 dB with regard to mel-cepstrum distortion in the closed-speaker test set. Additionally, giving triphone-averaged EMA data to a mel-cepstrum estimator is shown to improve the performance by a further 0.16 dB, which indicates that the speaker-independent input has a positive effect on mel-cepstrum estimation.
Reanimation – praktische Umsetzung von Leitlinien und Standards in der Klinik

Abstract Out of hospital cardiac arrest (OHCA) requiring cardio-pulmonary resuscitation (CPR) remains a mojor public health concern. OHCA affects nearly 275 000 people in Europe annualy with more than 70 000 of those in Germany alone. It represents of the major causes of death in Europe. In 40 % of patients CPR is successful and leads to a return of spontaneous circulation (ROSC). Due to the relevance of OHCA and CPR detailed guidelines with sound scientific foundation exist. However, even after successful CPR, mortality and morbidity remain high due to the severity of underlying diseases and sequelae of OHCA and CPR itself. Thus, optimization of the initial CPR treatment with reducing overall no-flow time (time from collaps with cardiac arrest to start of CPR) and optimization and streamlining of treatment algorithms and quality in hospitals receiving patients after cardiac arrest have been a strong focus to improve overall survival. Current guidelines suggest creation of Cardiac Arrest Centers as specialized hospitals who focus on high quality of post-resuscitation care with standardized processes and interdisciplinary treatment of patients after OHCA to establish fast, secure and effective treatment that is widely available in all regions. This article will address the relevant items to be considered in daily practice of resuscitation and post-resuscitation care for cardiac arrest.
Listen, Watch and Understand at the Cocktail Party: Audio-Visual-Contextual Speech Separation

Solving the cocktail party problem with the multi-modal approach has become popular in recent years. Humans can focus on the speech that they are interested in for the multi-talker mixed speech, by hearing the mixed speech, watching the speaker, and understanding the context what the speaker is talking about. In this paper, we try to solve the speaker-independent speech separation problem with all three audio-visual-contextual modalities at the ﬁrst time, and those are hearing speech, watching speaker and understanding contextual language. Compared to the previous methods applying pure audio modal or audio-visual modals, a speciﬁc model is further designed to extract contextual language information for all target speakers directly from the speech mixture. Then these extracted contextual knowledge are further incorporated into the multi-modal based speech separation architecture with an appropriate attention mechanism. The experiments show that a signiﬁcant performance improvement can be observed with the newly proposed audio-visual-contextual speech separation.