Miracle-dz/UniFAR-FTUs huggingface dataset:
Habitat Preferences of the Tawny Owl (Strix aluco) in a Special Conservancy Area of Eastern Spain

Abstract. The Tawny Owl (Strix aluco) is the most abundant nocturnal raptor in Europe. It has been thoroughly studied in various regions, but its habitat preferences in Mediterranean environments remain poorly understood. With the aim to present novel information about this aspect of the ecology of the Tawny Owl, we established 115 survey stations in the Special Conservancy Area “Sierras de Talayuelas y Aliaguilla” (Castilla–La Mancha region, eastern Spain) and carried out nocturnal surveys by recording spontaneous calls and vocal responses to call playbacks. We then assessed environmental characteristics (vegetation types, soil type, altitude, potential competitors, and anthropic disturbance) in areas where owls were detected or not detected during the breeding season. Overall, we detected 60 responding owls at 49 survey stations during breeding season in the study area (i.e., density 1.22 owls/km2). We found that Tawny Owls preferred lower altitudes and patchy heterogeneous areas. Owls seemed to avoid natural grasslands and areas characterized by limestone soils and associated vegetation, and preferred areas characterized by clay soils and associated vegetation. Interestingly, we did not detect owls close to wind farms, which seem to create a buffer effect on owls’ occurrence. The noise generated by the turbines might be a limiting factor that could account for this avoidance. Our multivariate results showed that Tawny Owls preferred heterogeneous patchy habitat but avoided non-irrigated arable land. Tawny Owls inhabit Mediterranean landscapes where conditions are favorable, but human activities such as wind farms may limit their distribution. Additional research is needed to determine the drivers of this avoidance and whether Tawny Owls also avoid wind farms in other regions of their range.
Relevance of STK11 Mutations Regarding Immune Cell Infiltration, Drug Sensitivity, and Cellular Processes in Lung Adenocarcinoma

Serine/threonine kinase 11 (STK11) is one member of the serine/threonine kinase family, which is involved in regulating cell polarity, apoptosis, and DNA damage repair. In lung adenocarcinoma (LUAD), it can play as one tumor suppressor and always be mutated. In this study, we aimed to assess the relevance of STK11 mutations in LUAD, in which we also studied the correlation among immune cell infiltration, drug sensitivity, and cellular processes. By performing the bioinformatics analysis of the Cancer Genome Atlas (TCGA) about LUAD patients, we found that the mutation efficiency of STK11 mutations is about 19%. Additionally, the differentially expressed gene analysis showed that there were 746 differentially expressed genes (DEGs) between LUAD patients with and without STK11 mutations. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis showed that the DEGs were enriched in various tumorigenesis signaling pathways and metabolic processes. Among these DEGs, the top ranking 21 genes were found that they were more frequently mutated in the STK11 mutation group than in the wild-type group (p-value<0.01). Finally, the LUAD patients with STK11 mutations suffered the worse immune cell infiltration levels than the LUAD patients with wild-type. The STK11 gene copy number was correlated with immune cell infiltration. Aiming to develop the therapeutic drugs, we performed Genomics of Drug Sensitivity in Cancer (GDSC) data to identify the potential therapeutic candidate and the results showed that Nutlin-3a(-) may be a sensitive drug for LUAD cases harboring STK11 mutations. The specific genes and pathways shown to be associated with LUAD cases involving STK11 mutations may serve as targets for individualized LUAD treatment.
Hospital Admissions of Australian Coastal Raptors Show Fishing Equipment Entanglement is an Important Threat

Abstract. Coastal raptors in urban landscapes face multiple threats. Australia's coastal raptors (Osprey [Pandion haliaetus cristatus], White-bellied Sea-Eagle [Haliaeetus leucogaster], Brahminy Kite [Haliastur indus], and Whistling Kite [Haliastur sphenurus]) are all found in the urban region of South East Queensland/northern New South Wales, and have varying sensitivities to urbanization. To assess some of the effects of urbanization on these species on Australia's mid-east coast, we reviewed Currumbin Wildlife Hospital Foundation admissions data from July 1998 to February 2020. Overall, more birds were admitted because of anthropogenic causes than natural causes, and the hospital had high treatment success, with most patients surviving their injuries to be released to the wild. The most significant identifiable impact was fishing equipment entanglement, accounting for 21% of raptors for which we could determine the cause of admission, followed by bird attack (20%), and vehicle strike (19%). To our knowledge, this is the first assessment to show fishing activities to be a significant cause of injury for coastal raptors. Understanding the causes of injury and mortality to these birds, including especially anthropogenic causes, is critically important for species management and conservation. As recreational fishing is a popular practice in this region, it is important to continue to provide education about the threats fishing can cause, and disposal bins for fishing tackle in popular fishing areas to reduce the number of coastal raptors affected.
Tencent Neural Machine Translation Systems for the WMT20 News Translation Task

This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks. We participate in the shared news translation task on English \leftrightarrow Chinese and English \rightarrow German language pairs. Our systems are built on deep Transformer and several data augmentation methods. We propose a boosted in-domain finetuning method to improve single models. Ensemble is used to combine single models and we propose an iterative transductive ensemble method which can further improve the translation performance based on the ensemble results. We achieve a BLEU score of 36.8 and the highest chrF score of 0.648 on Chinese \rightarrow English task.
The DeepMind Chinese–English Document Translation System at WMT2020

This paper describes the DeepMind submission to the Chinese\rightarrowEnglish constrained data track of the WMT2020 Shared Task on News Translation. The submission employs a noisy channel factorization as the backbone of a document translation system. This approach allows the flexible combination of a number of independent component models which are further augmented with back-translation, distillation, fine-tuning with in-domain data, Monte-Carlo Tree Search decoding, and improved uncertainty estimation. In order to address persistent issues with the premature truncation of long sequences we included specialized length models and sentence segmentation techniques. Our final system provides a 9.9 BLEU points improvement over a baseline Transformer on our test set (newstest 2019).
The NiuTrans Machine Translation Systems for WMT21

This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks. We made submissions to 9 language directions, including English2Chinese, Japanese, Russian, Icelandic and English2Hausa tasks. Our primary systems are built on several effective variants of Transformer, e.g., Transformer-DLCL, ODE-Transformer. We also utilize back-translation, knowledge distillation, post-ensemble, and iterative fine-tuning techniques to enhance the model performance further.
Huawei’s Submissions to the WMT20 Biomedical Translation Task

This paper describes Huawei’s submissions to the WMT20 biomedical translation shared task. Apart from experimenting with finetuning on domain-specific bitexts, we explore effects of in-domain dictionaries on enhancing cross-domain neural machine translation performance. We utilize a transfer learning strategy through pre-trained machine translation models and extensive scope of engineering endeavors. Four of our ten submissions achieve state-of-the-art performance according to the official automatic evaluation results, namely translation directions on English<->French, English->German and English->Italian.
Exploring Antiparasitic Molecule Sources from Timber by-Product Industries—Leishmanicidal and Trypanocidal Compounds from Clathrotropis brunnea Amshoff

Through bioguided in vitro assays, the leishmanicidal and trypanocidal effects of an ethanol extract, seven fractions, and two pure substances obtained from Clathrotropis brunnea Amshoff sawdust were established. The effectiveness of the two metabolites was confirmed in a hamster model of cutaneous Leishmaniasis by Leishmania braziliensis and in Balb/c mice infected by Trypanosoma cruzi. In vitro, 3,5-dimethoxystilbene was the most active against L. braziliensis amastigotes, with a median lethal concentration (LC50) of 4.18 μg/ml (17.40 μM) and a selectivity index of 3.55, but showed moderate activity for T. cruzi, with a median effective concentration (EC50) value of 27.7 μg/ml (115.36 μM). Flavanone pinostrobin, meanwhile, showed high activity against L. braziliensis, with an EC50 of 13.61 μg/ml (50.39 μM), as well as for T. cruzi, with an EC50 of 18.2 μg/ml (67.38 μM). The animal model assay of cutaneous Leishmaniasis showed that 50% of the hamsters treated with pinostrobin were definitively cured the cutaneous ulcer, and 40% showed an improvement, with a reduction in the size of the of 84–87%. Moreover, Balb/c mice experimentally infected with T. cruzi and treated for 25 days with pinostrobin experienced a reduction in their parasitemia by 71%. These results demonstrate the high potential of C. brunnea Amshoff against cutaneous Leishmaniasis and American trypanosomiasis and indicate the pharmacological potential of waste from the wood industry, which has tons of potentially useful chemicals for the development of new medicines.
Noising Scheme for Data Augmentation in Automatic Post-Editing

This paper describes POSTECH’s submission to WMT20 for the shared task on Automatic Post-Editing (APE). Our focus is on increasing the quantity of available APE data to overcome the shortage of human-crafted training data. In our experiment, we implemented a noising module that simulates four types of post-editing errors, and we introduced this module into a Transformer-based multi-source APE model. Our noising module implants errors into texts on the target side of parallel corpora during the training phase to make synthetic MT outputs, increasing the entire number of training samples. We also generated additional training data using the parallel corpora and NMT model that were released for the Quality Estimation task, and we used these data to train our APE model. Experimental results on the WMT20 English-German APE data set show improvements over the baseline in terms of both the TER and BLEU scores: our primary submission achieved an improvement of -3.15 TER and +4.01 BLEU, and our contrastive submission achieved an improvement of -3.34 TER and +4.30 BLEU.
PROMT Systems for WMT 2020 Shared News Translation Task

This paper describes the PROMT submissions for the WMT 2020 Shared News Translation Task. This year we participated in four language pairs and six directions: English-Russian, Russian-English, English-German, German-English, Polish-English and Czech-English. All our submissions are MarianNMT-based neural systems. We use more data compared to last year and update our back-translations with better models from the previous year. We show competitive results in terms of BLEU in most directions.
NLPRL System for Very Low Resource Supervised Machine Translation

This paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task. For our experiments, we use a byte-level version of BPE, which requires a base vocabulary of size 256 only. BPE based models are a kind of sub-word models. Such models try to address the Out of Vocabulary (OOV) word problem by performing word segmentation so that segments correspond to morphological units. They are also reported to work across different languages, especially similar languages due to their sub-word nature. Based on BLEU cased score, our NLPRL systems ranked ninth for HSB to GER and tenth in GER to HSB translation scenario.
Sorption of Methane, Nitrogen, Oxygen, and Argon in ZSM-5 with different SiO2/Al2O3 Ratios: Grand Canonical Monte Carlo Simulation and Volumetric Measurements

Sorption of CH4, N2, O2, and Ar in ZSM-5 with different SiO2/Al2O3 ratios was studied by volumetric measurements and grand canonical Monte Carlo simulation. Sorption capacity for all the gases studied was observed to decrease with an increase in the SiO2/Al2O3 ratio of ZSM-5. The sorption selectivity of CH4 over N2 increases, whereas sorption selectivities of N2 over O2 and Ar decrease with an increase in the SiO2/Al2O3 ratio of ZSM-5. ZSM-5 with SiO2/Al2O3 of 900 showed higher selectivity for CH4 over N2, whereas the SiO2/Al2O3 ratio of 25 showed higher N2 selectivity over O2 and Ar. The heats of adsorption for CH4 and N2 decrease and that for O2 and Ar remain unaffected with an increase in SiO2/Al2O3 ratio of ZSM-5. The values of Henry’s constant were also calculated for CH4, N2, O2, and Ar for adsorption in ZSM-5 having varied SiO2/Al2O3 ratio. The experimental results were compared with theoretically calculated data using grand canonical Monte Carlo (GCMC) simulation. Simulation studies expectedly sho.
THE SEARCH FOR AN ASSOCIATION OF HLA ALLELES AND COVID-19 RELATED MORTALITY IN THE RUSSIAN POPULATION

HLA genes play a pivotal role in an immune response via the presentation of pathogen peptides in a complex on the surface of cells of a host organism. Here, we studied the association of class I and class II genes with the severity of COVID-19 infection and HLA allele variants. We performed high-resolution sequencing of class I and class II HLA genes using the sample population of 147 patients who died of COVID-19 and statistically compared our results with the frequencies of the HLA genotypes in a control population of 270 samples. The obtained data demonstrated that 51:05 and 15:18 alleles from locus B* are statistically significantly associated with COVID-19 severity, while C*14:02 allele correlates with the probability of death from COVID-19 for patients without comorbidities.
Findings of the WMT 2020 Shared Task on Automatic Post-Editing

We present the results of the 6th round of the WMT task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a “black-box” machine translation system by learning from existing human corrections of different sentences. This year, the challenge consisted of fixing the errors present in English Wikipedia pages translated into German and Chinese by state-ofthe-art, not domain-adapted neural MT (NMT) systems unknown to participants. Six teams participated in the English-German task, submitting a total of 11 runs. Two teams participated in the English-Chinese task submitting 2 runs each. Due to i) the different source/domain of data compared to the past (Wikipedia vs Information Technology), ii) the different quality of the initial translations to be corrected and iii) the introduction of a new language pair (English-Chinese), this year’s results are not directly comparable with last year’s round. However, on both language directions, participants’ submissions show considerable improvements over the baseline results. On English-German, the top ranked system improves over the baseline by -11.35 TER and +16.68 BLEU points, while on EnglishChinese the improvements are respectively up to -12.13 TER and +14.57 BLEU points. Overall, coherent gains are also highlighted by the outcomes of human evaluation, which confirms the effectiveness of APE to improve MT quality, especially in the new generic domain selected for this year’s round.
A Study of Residual Adapters for Multi-Domain Neural Machine Translation

Domain adaptation is an old and vexing problem for machine translation systems. The most common approach and successful to supervised adaptation is to fine-tune a baseline system with in-domain parallel data. Standard fine-tuning however modifies all the network parameters, which makes this approach computationally costly and prone to overfitting. A recent, lightweight approach, instead augments a baseline model with supplementary (small) adapter layers, keeping the rest of the mode unchanged. This has the additional merit to leave the baseline model intact, and adaptable to multiple domains. In this paper, we conduct a thorough analysis of the adapter model in the context of a multidomain machine translation task. We contrast multiple implementations of this idea on two language pairs. Our main conclusions are that residual adapters provide a fast and cheap method for supervised multi-domain adaptation; our two variants prove as effective as the original adapter model, and open perspective to also make adapted models more robust to label domain errors.
FJWU participation for the WMT20 Biomedical Translation Task

This paper reports system descriptions for FJWU-NRPU team for participation in the WMT20 Biomedical shared translation task. We focused our submission on exploring the effects of adding in-domain corpora extracted from various out-of-domain sources. Systems were built for French to English using in-domain corpora through fine tuning and selective data training. We further explored BERT based models specifically with focus on effect of domain adaptive subword units.
Transformer-based Neural Machine Translation System for Hindi – Marathi: WMT20 Shared Task

This paper reports the results for the Machine Translation (MT) system submitted by the NLPRL team for the Hindi – Marathi Similar Translation Task at WMT 2020. We apply the Transformer-based Neural Machine Translation (NMT) approach on both translation directions for this language pair. The trained model is evaluated on the corpus provided by shared task organizers, using BLEU, RIBES, and TER scores. There were a total of 23 systems submitted for Marathi to Hindi and 21 systems submitted for Hindi to Marathi in the shared task. Out of these, our submission ranked 6th and 9th, respectively.
Findings of the First Shared Task on Lifelong Learning Machine Translation

A lifelong learning system can adapt to new data without forgetting previously acquired knowledge. In this paper, we introduce the first benchmark for lifelong learning machine translation. For this purpose, we provide training, lifelong and test data sets for two language pairs: English-German and English-French. Additionally, we report the results of our baseline systems, which we make available to the public. The goal of this shared task is to encourage research on the emerging topic of lifelong learning machine translation.
Functional Imaging to Guide Network-Based TMS Treatments: Toward a Tailored Medicine Approach in Alzheimer’s Disease

A growing number of studies is using fMRI-based connectivity to guide transcranial magnetic stimulation (TMS) target identification in both normal and clinical populations. TMS has gained increasing attention as a potential therapeutic strategy also in Alzheimer’s disease (AD), but an endorsed target localization strategy in this population is still lacking. In this proof of concept study, we prove the feasibility of a tailored TMS targeting approach for AD, which stems from a network-based perspective. Based on functional imaging, the procedure allows to extract individual optimal targets meanwhile accounting for functional variability. Single-subject resting-state fMRI was used to extract individual target coordinates of two networks primarily affected in AD, the default mode and the fronto-parietal network. The localization of these targets was compared to that of traditional group-level approaches and tested against varying degrees of TMS focality. The distance between individual fMRI-derived coordinates and traditionally defined targets was significant for a supposed TMS focality of 12 mm and in some cases up to 20 mm. Comparison with anatomical labels confirmed a lack of 1:1 correspondence between anatomical and functional targets. The proposed network-based fMRI-guided TMS approach, while accounting for inter-individual functional variability, allows to target core AD networks, and might thus represent a step toward tailored TMS interventions for AD.
NRC Publications Archive Archives des publications du CNRC

We describe the National Research Council of Canada (NRC) submissions for the 2020 Inuktitut–English shared task on news translation at the Fifth Conference on Machine Translation (WMT20). Our submissions consist of ensembled domain-specific finetuned transformer models, trained using the Nunavut Hansard and news data and, in the case of Inuktitut–English, backtranslated news and parliamentary data. In this work we explore challenges related to the relatively small amount of parallel data, morphological complexity, and domain shifts.
Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation

In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Quality Estimation (QE). For word-level and sentence-level translation quality estimation, we fine-tune XLM-RoBERTa, the state-of-the-art cross-lingual language model, with a few additional parameters. Model training consists of two phases. We first pre-train our model on a huge artificially generated QE dataset, and then we fine-tune the model with a human-labeled dataset. When evaluated on the WMT 2020 English-German QE test set, our systems achieve the best result on the target-side of word-level QE and the second best results on the source-side of word-level QE and sentence-level QE among all submissions.
Prevalence of bacterial pathogens and potential role in COVID-19 severity in patients admitted to intensive care units in Brazil

Secondary bacterial and fungal infections are associated with respiratory viral infections and invasive mechanical ventilation. In Coronavirus disease 2019 (COVID-19), lung injury by SARS-CoV-2 and impaired immune response can provide a favorable environment for microorganism growth and colonization in hospitalized individuals. Recent studies suggest that secondary bacterial pneumonia is a risk factor associated with COVID-19. In Brazil, knowledge about microbiota present in COVID-19 patients is incipient. This work describes the microbiota of 21 COVID-19 patients admitted to intensive care units from two Brazilian centers. We identified respiratory, nosocomial and bacterial pathogens as prevalent microorganisms. Other bacterial opportunistic and commensal species are also represented. Virulence factors of these pathogenic species, metabolic pathways used to evade and modulate immunological processes and the interconnection between bacterial presence and virulence in COVID-19 progression are discussed.