Miracle-dz/UniFAR-FTUs huggingface dataset:
Prologue

Readers who leaf through this journal's pages in early 2021 or connect to it online will, sadly, need no reminder of how COVID-19-the respiratory illness caused by a novel strain of coronavirus-spread illness, death, and fear around the world, culminating in the declaration of a pandemic by the World Health Organization on 11 March 2020 Just as spring daffodils and orange blossoms burst forth last spring in the Mediterranean region that is our shared literary-cultural patria, we found ourselves involuntarily immersed in lockdowns Last but not least, we gratefully acknowledge Editorial Board members for continuing to provide detailed and rigorous article evaluations within the requested time frame, authors at all career stages for their submissions of exciting new work, and book reviewers for keeping our readers abreast of current scholarship in comedia studies and beyond
Whole-genome sequencing analysis of multidrug-resistant Mycobacterium tuberculosis from Java, Indonesia.

Introduction. Multidrug-resistant tuberculosis (MDR-TB) is a major public health problem globally, including in Indonesia. Whole-genome sequencing (WGS) analysis has rarely been used for the study of TB and MDR-TB in Indonesia.Aim. We evaluated the use of WGS for drug-susceptibility testing (DST) and to investigate the population structure of drug-resistant Mycobacterium tuberculosis in Java, Indonesia.Methodology. Thirty suspected MDR-TB isolates were subjected to MGIT 960 system (MGIT)-based DST and to WGS. Phylogenetic analysis was done using the WGS data. Results obtained using MGIT-based DST and WGS-based DST were compared.Results. Agreement between WGS and MGIT was 93.33 % for rifampicin, 83.33 % for isoniazid and 76.67 % for streptomycin but only 63.33 % for ethambutol. Moderate WGS-MGIT agreement was found for second-line drugs including amikacin, kanamycin and fluoroquinolone (73.33-76.67 %). MDR-TB was more common in isolates of the East Asian Lineage (63.3%). No evidence of clonal transmission of DR-TB was found among members of the tested population.Conclusion. Our study demonstrated the applicability of WGS for DST and molecular epidemiology of DR-TB in Java, Indonesia. We found no transmission of DR-TB in Indonesia.
Mobile Robotic Telepresence Between Hospital and School: Lessons Learned

If a pupil falls seriously ill, it is not only a shock for the pupil himself or herself, but also for his or her family and classmates. The project "Virtual Classroom" of the Heilbronn University in cooperation with the foundation "Big Help for Little Heroes" therefore tries to maintain the daily school routine and thus the contact with friends as far as possible with the help of mobile telepresence robots in the classroom from home or from the hospital. Here there are both technical and human factors that contribute to success or failure. Based on a systematic literature research and practical experience, these factors are identified, discussed and weighted in this paper. If the mobile telepresence robot is used successfully, this pays off twice for the pupils affected: It has been shown that pupils can progress socially as well as in school, even if attendance at school is not possible for a long time.
CluHTM - Semantic Hierarchical Topic Modeling based on CluWords

Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration. Despite advantages over traditional topic modeling, HTM poses its own challenges, such as (1) topic incoherence, (2) unreasonable (hierarchical) structure, and (3) issues related to the definition of the “ideal” number of topics and depth of the hierarchy. In this paper, we advance the state-of-the-art on HTM by means of the design and evaluation of CluHTM, a novel non-probabilistic hierarchical matrix factorization aimed at solving the specific issues of HTM. CluHTM’s novel contributions include: (i) the exploration of richer text representation that encapsulates both, global (dataset level) and local semantic information – when combined, these pieces of information help to solve the topic incoherence problem as well as issues related to the unreasonable structure; (ii) the exploitation of a stability analysis metric for defining the number of topics and the “shape” the hierarchical structure. In our evaluation, considering twelve datasets and seven state-of-the-art baselines, CluHTM outperformed the baselines in the vast majority of the cases, with gains of around 500% over the strongest state-of-the-art baselines. We also provide qualitative and quantitative statistical analyses of why our solution works so well.
Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks

Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer stem cells (CSCs) are identified by specific cell markers. These CSCs were extensively characterized by the stem cell (SC)-like gene expression and proliferation mechanisms for the development of tumors. In contrast, the morphological characterization remains elusive. This study aims to investigate the segmentation of CSCs in phase contrast imaging using conditional generative adversarial networks (CGAN). Artificial intelligence (AI) was trained using fluorescence images of the Nanog-Green fluorescence protein, the expression of which was maintained in CSCs, and the phase contrast images. The AI model segmented the CSC region in the phase contrast image of the CSC cultures and tumor model. By selecting images for training, several values for measuring segmentation quality increased. Moreover, nucleus fluorescence overlaid-phase contrast was effective for increasing the values. We show the possibility of mapping CSC morphology to the condition of undifferentiation using deep-learning CGAN workflows.
The Liver Microbiome Is Implicated in Cancer Prognosis and Modulated by Alcohol and Hepatitis B

Hepatocellular carcinoma (HCC) is one of the deadliest cancers in the world. Previous studies have identified the importance of alcohol and hepatitis B (HBV) infection on HCC carcinogenesis, indicating synergy in the methods by which these etiologies advance cancer. However, the specific molecular mechanism behind alcohol and HBV-mediated carcinogenesis remains unknown. Because the microbiome is emerging as a potentially important regulator of cancer development, this study aims to classify the effects of HBV and alcohol on the intratumoral liver microbiome. RNA-sequencing data from The Cancer Genome Atlas (TCGA) were used to infer microbial abundance. This abundance was then correlated to clinical variables and to cancer and immune-associated gene expression, in order to determine how microbial abundance may contribute to differing cancer progression between etiologies. We discovered that the liver microbiome is likely oncogenic after exposure to alcohol or HBV, although these etiological factors could decrease the abundance of a few oncogenic microbes, which would lead to a tumor suppressive effect. In HBV-induced tumors, this tumor suppressive effect was inferred based on the downregulation of microbes that induce cancer and stem cell pathways. Alcohol-induced tumors were observed to have distinct microbial profiles from HBV-induced tumors, and different microbes are clinically relevant in each cohort, suggesting that the effects of the liver microbiome may be different in response to different etiological factors. Collectively, our data suggest that HBV and alcohol operate within a normally oncogenic microbiome to promote tumor development, but are also able to downregulate certain oncogenic microbes. Insight into why these microbes are downregulated following exposure to HBV or alcohol, and why the majority of oncogenic microbes are not downregulated, may be critical for understanding whether a pro-tumor liver microbiome could be suppressed or reversed to limit cancer progression.
Changes in maternal blood glucose and lipid concentrations during pregnancy differ by maternal body mass index and are related to birthweight: A prospective, longitudinal study of healthy pregnancies

Background Maternal obesity is increasing worldwide but the consequences for maternal physiology and fetal growth are not fully understood. Objective To study whether changes in glucose and lipid metabolism during pregnancy differ between women with normal weight and overweight/obesity, and investigate which of these metabolic factors are associated with birthweight. Design Prospective, longitudinal study. Setting Department of Obstetrics, Oslo University Hospital, Rikshospitalet. Population 1031 healthy pregnant women with singleton pregnancies. Methods Blood samples from early and late pregnancy were analyzed for fasting glucose, insulin and lipids (total cholesterol, HDL-cholesterol, LDL-cholesterol and triglycerides). Associations between metabolic factors and birthweight (z-scores) were explored by linear regression models. Main Outcome Measures: Group-dependent longitudinal changes in glucose and lipids and their association with birthweight (z-scores). Results Compared to women with normal weight (BMI < 25), women with overweight (BMI 25–29.9) and obesity (BMI > 30) had significantly higher fasting glucose (4.54, 4.68 and 4.84 mmol/l), insulin (23, 33 and 50 pmol/l), total cholesterol (4.85, 4.99 and 5.14 mmol/l), LDL-C (2.49, 2.66 and 2.88 mmol/l) and triglycerides (1.10, 1.28 and 1.57 mmol/l), but lower HDL-C (1.86, 1.75 and 1.55 mmol/l). BMI (B 0.05, 95% CI 0.03–0.06, p<0.001), gestational weight gain (GWG) (B 0.06, 0.05–0.08, p<0.001) and an increase in fasting glucose (B 0.30, 0.16–0.43, p<0.001) were positively associated with birthweight, whereas a decrease in HDL-C (B -0.72, -0.96- -0.53, p<0.001) had a negative association with birthweight. Conclusions Overweight/obesity was associated with an unfavorable metabolic profile in early pregnancy which was associated with increased birthweight. However, modifiable factors like gestational weight gain and an increase in fasting glucose were identified and can be targeted for interventions.
Automatic Poetry Generation from Prosaic Text

In the last few years, a number of successful approaches have emerged that are able to adequately model various aspects of natural language. In particular, language models based on neural networks have improved the state of the art with regard to predictive language modeling, while topic models are successful at capturing clear-cut, semantic dimensions. In this paper, we will explore how these approaches can be adapted and combined to model the linguistic and literary aspects needed for poetry generation. The system is exclusively trained on standard, non-poetic text, and its output is constrained in order to confer a poetic character to the generated verse. The framework is applied to the generation of poems in both English and French, and is equally evaluated for both languages. Even though it only uses standard, non-poetic text as input, the system yields state of the art results for poetry generation.
A Two-Step Approach for Implicit Event Argument Detection

In this work, we explore the implicit event argument detection task, which studies event arguments beyond sentence boundaries. The addition of cross-sentence argument candidates imposes great challenges for modeling. To reduce the number of candidates, we adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset (Ebner et al., 2020), our model achieves overall better performance than a strong sequence labeling baseline. We further provide detailed error analysis, presenting where the model mainly makes errors and indicating directions for future improvements. It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task.
The Genera Aspidobracon and Philomacroploea (Hymenoptera: Braconidae: Braconinae) in China, with Descriptions of Two New Species

Abstract The species of the genera Aspidobracon van Achterberg, 1984 and Philomacroploea Cameron, 1905 in China are revised. Five species are recognized, and two new species, Aspidobracon flavithorax sp. nov. and Aspidobracon longyanensis sp. nov., are described and illustrated. The genus Philomacroploea Cameron and the species Aspidobracon hesperivorus van Achterberg and Philomacroploea basimacula Cameron are reported from China for the first time. A key to the species of the genus Aspidobracon van Achterberg is provided.
Molecular analysis of wild Solanum clones in search for late blight resistance genes.

Breeding for durable resistance to potato late blight (LB) heavily relies on expanding the resource of resistance genes (R genes). We developed clonal collections of wild Solanum species to study genetically identical plants with independent molecular and phytopathological methods. DNA markers for R genes were employed to screen about 300 Solanum accessions representing over twenty species from seven series of section Petota. By collating the distribution of the structural homologues of R genes with the indices of resistance to simple and complex races of Phytophthora infestans, we have searched for new alleles of the R genes already characterized in other Solanum species. Specific markers have been developed to elucidate genome constitution of Solanum species employed in breeding for LB resistance.
Systems biology of responses to simultaneous copper and iron deficiency in Arabidopsis.

Plant responses to coincident nutrient deficiencies cannot be predicted from the responses to individual deficiencies. Although copper (Cu) and iron (Fe) are essential micronutrients for plant growth that are often and concurrently limited in soils, the combinatorial response to Cu-Fe deficiency remains elusive. In this work, we characterised the responses of Arabidopsis thaliana plants deprived of Cu, Fe or both (-Cu-Fe) at the level of plant development, mineral composition, and reconfiguration of transcriptomes, proteomes and metabolomes. Compared to single deficiencies, simultaneous -Cu-Fe leads to a distinct pattern in leaf physiology and microelement concentration characterised by lowered protein content and enhanced manganese and zinc levels. Conditional networking analysis of molecular changes indicates that biological processes also display different co-expression patterns among single and double deficiencies. Indeed, the interaction between Cu and Fe deficiencies causes distinct expression profiles for 15% of all biomolecules, leading to specific enhancement of general stress responses and protein homeostasis mechanisms, while severely arresting photosynthesis. Accordingly, central carbon metabolites, in particular photosynthates, decrease especially under -Cu-Fe conditions, whereas the pool of free amino acids increases. Further meta-analysis of transcriptomes and proteomes corroborated that protein biosynthesis and folding capacity were readjusted during the combinatorial response and unveiled important rearrangements in the metabolism of organic acids. Consequently, our results demonstrate that the response to -Cu-Fe imposes a distinct reconfiguration of large sets of molecules, not triggered by single deficiencies, resulting into a switch from autotrophy to heterotrophy and involving organic acids like fumaric acid as central mediators of the response.
Suppressive Effects of Low Seed-Soaking Temperatures on Germination of Long-Term-Stored Rice Seeds

Abstract Abstract: We investigated the effects of soaking temperature and duration on the germinability of seeds of rice (Oryza sativa L., cv. Koganemochi, Gohyakumangoku, and Koshihikari) that had been stored for a long period. The germinability of the seeds soaked at 5ºC for 5 d was markedly lower than that of seeds soaked at 12ºC for 5 d. The germinability of the seeds soaked at 5ºC for 24 hr was not increased by subsequent soaking at 12ºC for 4 d. On the other hand, the germinability of the seeds soaked either at 12ºC for 24 hr or at 30ºC for 80 min was similar to that of seeds soaked at 12ºC for 5 d, even when followed by treatment at 5ºC. Thus, the soaking temperature during the first 24 hr was most important for the germination of rice seeds that had been stored for a long period. Western blotting analysis revealed characteristic expression patterns of α-amylase isoforms in cultivars correlating with the germinability after soaking at a low-temperature.
Theory of Mind Deficits and Neurophysiological Operations in Autism Spectrum Disorders: A Review

Theory of Mind (ToM) is a multifaceted skill set which encompasses a variety of cognitive and neurobiological aspects. ToM deficits have long been regarded as one of the most disabling features in individuals with Autism Spectrum Disorder. One of the theories that attempts to account for these impairments is that of “broken mirror neurons”. The aim of this review is to present the most recent available studies with respect to the connection between the function of mirror neurons in individuals with ASD and ToM-reflecting sensorimotor, social and attentional stimuli. The majority of these studies approach the theory of broken mirror neurons critically. Only studies from the last 15 years have been taken into consideration. Findings from electroencephalography (EEG) studies so far indicate that further research is necessary to shed more light on the mechanisms underlying the connection(s) between ToM and neurophysiological operations.
In Vitro Efficacy of Essential Oils from Melaleuca Alternifolia and Rosmarinus Officinalis, Manuka Honey-based Gel, and Propolis as Antibacterial Agents Against Canine Staphylococcus Pseudintermedius Strains

Essential oils (EOs) and honeybee products (e.g., honey and propolis) are natural mixtures of different volatile compounds that are frequently used in traditional medicine and for pathogen eradication. The aim of this study was to evaluate the antibacterial properties of tea tree (Melaleuca alternifolia) EO (TTEO), Rosmarinus officinalis EO (ROEO), manuka-based gel, and propolis against 23 strains of Staphylococcus pseudintermedius (SP) isolated from canine pyoderma. Antimicrobial resistance screening was assessed using a panel of nine antimicrobial agents coupled with a PCR approach. An aromatogram was done for both EOs, using the disk diffusion method. The minimum inhibitory concentration (MIC) was determined for all the compounds. Among the 23 SP strains, 14 (60.9%) were multidrug-resistant (MDR), 11 strains (47.8%) were methicillin-resistant (MRSP), and 9 (39.1%) were non-MDR. The mean diameter of the inhibition zone for Melaleuca and Rosmarinus were 24.5 ± 8.8 mm and 15.2 ± 8.9 mm, respectively, resulting as statistically different (p = 0.0006). MIC values of TTEO and ROEO were similar (7.6 ± 3.2% and 8.9 ± 2.1%, respectively) and no statistical significances were found. Honeybee products showed lower MIC compared to those of EOs, 0.22 ± 0.1% for Manuka and 0.8 ± 0.5% for propolis. These findings reveal a significant antibacterial effect for all the tested products.
Multimodal Emotion Evaluation: A Physiological Model for Cost-Effective Emotion Classification

Emotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples’ emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering

Visual question answering aims to answer the natural language question about a given image. Existing graph-based methods only focus on the relations between objects in an image and neglect the importance of the syntactic dependency relations between words in a question. To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph convolutional network (DC-GCN) for better combining visual and textual advantages. The DC-GCN model consists of three parts: an I-GCN module to capture the relations between objects in an image, a Q-GCN module to capture the syntactic dependency relations between words in a question, and an attention alignment module to align image representations and question representations. Experimental results show that our model achieves comparable performance with the state-of-the-art approaches.