Miracle-dz/UniFAR-FTUs huggingface dataset:
Scale-Up Technologies for the Manufacture of Adherent Cells

Great importance is being given to the impact our food supply chain and consumers' food habits are having on the environment, human health, and animal welfare. One of the latest developments aiming at positively changing the food ecosystem is represented by cultured meat. This form of cellular agriculture has the objective to generate slaughter-free meat products starting from the cultivation of few cells harvested from the animal tissue of interest. As a consequence, a large number of cells has to be generated at a reasonable cost. Just to give an idea of the scale, there were billions of cells just in a bite of the first cultured-meat burger. Thus, one of the major challenges faced by the scientists involved in this new ambitious and fascinating field, is how to efficiently scale-up cell manufacture. Considering the great potential presented by cultured meat, audiences from different backgrounds are very interested in this topic and eager to be informed of the challenges and possible solutions in this area. In light of this, we will provide an overview of the main existing bioprocessing technologies used to scale-up adherent cells at a small and large scale. Thus, giving a brief technical description of these bioprocesses, with the main associated advantages and disadvantages. Moreover, we will introduce an alternative solution we believe has the potential to revolutionize the way adherent cells are grown, helping cultured meat become a reality.
Comparative analysis of student performance in collaborative problem solving: What does it tell us?

Collaboration is a complex skill, comprised of multiple subskills, that is of growing interest to policy makers, educators and researchers. Several definitions and frameworks have been described in the literature to support assessment of collaboration; however, the inherent structure of the construct still needs better definition. In 2015, the Organisation for Economic Cooperation and Development, in their Programme for International Student Assessment assessed 15-year-old students’ collaborative problem solving achievement, with the use of computer-simulated agents, aiming to address the lack of internationally comparable data in this field. This paper explores what the data from this assessment tell us about the skill, and how these data compare with data from two other assessments of collaboration. Analyses enable comment on the extent to which the three assessments are measuring the same construct, and the extent to which the construct can be covered using computer-based assessments. These investigations generate better understanding of this complex and innovative domain.
Selective Bacterial Colonization of the Murine Larynx in a Gnotobiotic Model

The larynx is a mucosal organ situated between the respiratory and gastrointestinal tracts. Little is known about microbial contributions to laryngeal epithelial health and pathogenesis. Developing a gnotobiotic laryngeal model will introduce new avenues for targeted explorations of microbes in laryngeal mucosal biology, allowing for enhanced understanding of host–microbe interaction in the upper airway. In this study, we first assessed the potential of using gut microbiota as a source to establish laryngeal microbiota in germ-free mice. Results demonstrated the selective nature of the upper airway and provided evidence that gut bacteria can assemble into communities that resemble the commensal resident bacteria occurring in the larynx of conventionally-raised animals phylogenetically and functionally. Then, we confirmed the reproducibility of laryngeal colonization through comparison of laryngeal microbiota in the larynx along with neighboring regions (base of tongue, esophagus, and trachea) between conventionally-raised and germ-free mice that conventionalized with cecal microbiota. Despite taxonomic differences, the established laryngeal microbiota from cecal content exhibited similarity to commensal resident microbiota in diversity within/between communities and predicted metagenomic functions. Our data also suggests little difference in bacterial distribution across the larynx and its surrounding regions and that cell motility and the ability to degrade xenobiotics is critical for bacteria colonizing upper airway. Successful colonization of laryngeal and oropharyngeal regions with gut microbiota in our study will greatly facilitate the investigation of potential localized inflammatory responses within host tissues that contribute to the disorders of essential laryngeal functions. Utilizing said gnotobiotic model to conduct future studies will allow for novel insights into direct microbial contributions to laryngeal epithelial health and pathogenesis.
Neurophysiological Differences Between Women With Fibromyalgia and Healthy Controls During Dual Task: A Pilot Study

Background Women with FM have a reduced ability to perform two simultaneous tasks. However, the impact of dual task (DT) on the neurophysiological response of women with FM has not been studied. Objective To explore both the neurophysiological response and physical performance of women with FM and healthy controls while performing a DT (motor–cognitive). Design Cross-sectional study. Methods A total of 17 women with FM and 19 age- and sex-matched healthy controls (1:1 ratio) were recruited. The electroencephalographic (EEG) activity was recorded while participants performed two simultaneous tasks: a motor (30 seconds arm-curl test) and a cognitive (remembering three unrelated words). Theta (4–7 Hz), alpha (8–12 Hz), and beta (13–30) frequency bands were analyzed by using EEGLAB. Results Significant differences were obtained in the healthy control group between single task (ST) and DT in the theta, alpha, and beta frequency bands (p-value < 0.05). Neurophysiological differences between ST and DT were not found in women with FM. In addition, between-group differences were found in the alpha and beta frequency bands between healthy and FM groups, with lower values of beta and alpha in the FM group. Therefore, significant group∗condition interactions were detected in the alpha and beta frequency bands. Regarding physical condition performance, between groups, analyses showed that women with FM obtained significantly worse results in the arm curl test than healthy controls, in both ST and DT. Conclusion Women with FM showed the same electrical brain activity pattern during ST and DT conditions, whereas healthy controls seem to adapt their brain activity to task commitment. This is the first study that investigates the neurophysiological response of women with FM while simultaneously performing a motor and a cognitive task.
Psychological Health Issues Subsequent to SARS-Cov 2 Restrictive Measures: The Role of Parental Bonding and Attachment Style

Background: The novel coronavirus 2019 (COVID-19) has caused severe panic among people worldwide. In Italy, a nationwide state of alert was declared on January 31st, leading to the confinement of the entire population from March 11 to May 18, 2020. Isolation and quarantine measures cause psychological problems, especially for individuals who are recognized as being vulnerable. Parental bonding and attachment styles play a role in the programming of the stress response system. Here, we hypothesize that the response to restricted social contact and mobility due to the pandemic has detrimental effects on mental-psychological health and that this relationship is, at least in part, modulated by parental bonding and attachment relationships that are experienced at an early age. Methods: A sample of 68 volunteer University students was screened for psychopathological symptoms (SCL-90-R and STAI-Y), stress perception (PSS), attachment style (RQ), and parental care and overcontrol (PBI) 6 months before the confinement. In the same subjects, psychopathological symptoms and stress perception were measured again during confinement. Results: Overall, psychological health and stress management deteriorated across the entire sample during confinement. Specifically, a significant increase in phobic anxiety, depression, psychological distress, and perceived stress was observed. Notably, parental bonding and attachment styles modulated the psychological status during the lockdown. Individuals with secure attachment and high levels of parental care (high care) showed increased levels of state anxiety and perceived stress in phase 2, compared with phase 1. In contrast, individuals with insecure attachment and low levels of parental care (low care) already showed a high rate of state anxiety and perceived stress in phase 1 that did not increase further during phase 2. Conclusion: The general deterioration of psychological health in the entire sample demonstrates the pervasiveness of this stressor, a decline that is partially modulated by attachment style and parental bonding. These results implicated disparate sensitivities to environmental changes in the high- and low care groups during the lockdown, the former of which shows the greatest flexibility in the response to environment, suggesting adequate and functional response to stress in high care individuals, which is not observable in the low care group.
Efficacy, Treatment Characteristics, and Biopsychological Mechanisms of Music-Listening Interventions in Reducing Pain (MINTREP): Study Protocol of a Three-Armed Pilot Randomized Controlled Trial

Background: Pain can severely compromise a person's overall health and well-being. Music-listening interventions have been shown to alleviate perceived pain and to modulate the body's stress-sensitive systems. Despite the growing evidence of pain- and stress-reducing effects of music-listening interventions from experimental and clinical research, current findings on music-induced analgesia are inconclusive regarding the role of specific treatment characteristics and the biopsychological mechanisms underlying these effects. Objective: The overall aim of this pilot randomized controlled trial is to test and compare the differential effects of frequency-modulated and unmodulated music (both researcher-selected) on experimentally induced perception of acute pain and to test the efficacy of the interventions in reducing biological and subjective stress levels. Moreover, these two interventions will be compared to a third condition, in which participants listen to self-selected unmodulated music. Methods and Analysis: A total of 90 healthy participants will be randomly allocated to one of the three music-listening intervention groups. Each intervention encompasses 10 sessions of music listening in our laboratory. Frequency-modulation will involve stepwise filtering of frequencies in the audible range of 50–4,000 Hz. Acute pain will be induced via the cold pressor test. Primary (i.e., pain tolerance, perceived pain intensity) and secondary (i.e., heart rate variability, electrodermal activity, hair cortisol, subjective stress) outcomes will be measured at baseline, post, and follow-up. In addition, intermittent measurements as well as a follow-up assessment and a range of tertiary measures (e.g., music-induced emotions) are included. Discussion: This is the first study to systematically test and compare the effects of music frequencies along with the control over music selection, both of which qualify as central treatment characteristics of music-listening interventions. Results will be highly informative for the design of subsequent large-scale clinical trials and provide valuable conclusions for the implementation of music-listening interventions for the reduction of perceived pain. Clinical Trial Registration: Clinical Trials Database of the U.S. National Library of Medicine: Identifier NCT02991014.
Distribution of Sisyridae and Freshwater Sponges in the upper-Susquehanna Watershed, Otsego County, New York with a New Locality for Climacia areolaris (Hagen)

Abstract The headwaters of the Susquehanna River are contained within the Otsego Lake catchment in central New York and are home to members of the aquatic neuropteran family Sisyridae, which are predators/parasites of freshwater sponges as larvae. This study used cytochrome oxidase I (COI) barcoding to identify the neuropteran taxa present on freshwater sponges and both spicule morphology and COI to identify sponge species in the Otsego Lake watershed. Both Climacia areolaris (Hagen, 1861) and Sisyra vicaria (Walker, 1853) were identified from a tributary to Otsego Lake and within the main branch of the Susquehanna River, while multiple sponge species were identified from the watershed. These are the first records of sisyrids from the Otsego Lake watershed and Climacia areolaris from Otsego County, New York.
Rule-based Reordering Constraints for Phrase-based SMT

Translation results suffer when a standard phrase-based statistical machine translation system is used for translating long sentences. The translation output will not preserve the same word order as the source, especially between a language pair that has different syntactic structures. When a sentence is long, it should be partitioned into several clauses, and the word reordering during the translation should be done within clauses, not between clauses. In this paper, we propose splitting the long sentences using linguistic information, and translating the sentences with the split boundaries. In other words, we constrain the word reordering so that it cannot cross the boundaries. We propose two types of constraints: split condition and block condition. By doing so, word order can be pre-served and translation quality improved. Our experiments on the patent translation between Japanese and English are able to achieve better translations measured by BLEU score, NIST score and word error rate (WER).
SMT at the International Maritime Organization experiences with combining in-house corpus with more general corpus

This paper presents a machine translation tool – based on Moses – developed for the International Maritime Organization (IMO) for the automatic translation of documents from Spanish, French, Russian and Arabic to/from English. The main challenge lies in the insufficient size of inhouse corpora (especially for Russian and Arabic). The United Nations (UN) granted IMO the right to use UN resources and we describe experiments and results we obtained with different translation model combination techniques. While BLEU results remain inconclusive for combinations, we also analyze user preferences for certain models (when choosing betweeen IMO only or combined with UN). The combined models are perceived by translators as being much better for general texts while IMO only models seem better for technical texts.
Unsupervised training of maximum-entropy models for lexical selection i in rule-based machine translation

This article presents a method of training maximum-entropy models to perform lexical selection in a rule-based machine translation system. The training method described is unsupervised; that is, it does not require any annotated corpus. The method uses source-language monolingual corpora, the machine translation (MT) system in which the models are integrated, and a statistical target-language model. Using the MT system, the sentences in the sourcelanguage corpus are translated in all possible ways according to the different translation equivalents in the bilingual dictionary of the system. These translations are then scored on the target-language model and the scores are normalised to provide fractional counts for training source-language maximum-entropy lexical-selection models. We show that these models can perform equally well, or better, than using the target-language model directly for lexical selection, at a substantially reduced computational cost.
Fast Distributed kNN Graph Construction Using Auto-tuned Locality-sensitive Hashing

The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. Approximate solutions have been described in the literature using diverse techniques, among which Locality-sensitive Hashing (LSH) is a promising alternative that still has unsolved problems. We present Variable Resolution Locality-sensitive Hashing, an algorithm that addresses these problems to obtain an approximate kNN graph at a significantly reduced computational cost. Its usability is greatly enhanced by its capacity to automatically find adequate hyperparameter values, a common hindrance to LSH-based methods. Moreover, we provide an implementation in the distributed computing framework Apache Spark that takes advantage of the structure of the algorithm to efficiently distribute the computational load across multiple machines, enabling practitioners to apply this solution to very large datasets. Experimental results show that our method offers significant improvements over the state-of-the-art in the field and shows very good scalability as more machines are added to the computation.
QTLeap: quality translation with deep language engineering approaches

The incremental advancement of research on Machine Translation (MT) has been obtained by encompassing increasingly sophisticated statistical approaches and fine-grained linguistic features that add to the surface level alignment on which these approaches are ultimately anchored. The goal of this project is to contribute for the advancement of quality MT by pursuing an approach that further relies on semantics and opens the way to higher quality translation. We build on the complementarity of the two pillars of language technology — symbolic and probabilistic — and seek to advance their hybridization. We explore combinations of them that amplify their strengths and mitigate their drawbacks, along the development of three MT pilot systems that progressively seek to integrate deep language engineering approaches. The construction of deep treebanks has progressed to be delivering now the first significant Parallel DeepBanks, where pairs of synonymous sentences from different languages are annotated with their fully-fledged grammatical representations, up to the level of their semantic representation. The construction of Linked Open Data and other semantic resources, in turn, has progressed now to support impactful application of lexical semantic processing that handles and resolves referential and conceptual ambiguity. These cutting edge advances permit for the cross-lingual alignment supporting translation to be established at the level of deeper semantic representation. The deeper the level the less languagespecific differences remain among source and target sentences and new chances of success become available for the statistically based transduction.
Narrative Productions of Meanings: Exploring the Work of Stories in Social Life

knowledge of one’s likely future life span, as well as less rational feelings about what one’s demise may mean for loved ones, and folk (mis)perceptions regarding how exactly one’s future symptoms will play out, for how long, and to what end. Practice-oriented readers might also hunger for clear recommendations for policy and practice, guided by Livne’s deep knowledge of end-of-life and palliative care. Livne has deliberately chosen not to get into the weeds of such recommendations, preferring ‘‘the comfort of the ivory tower . free from the heavy responsibility that the clinicians carried’’ (p. 271). This active choice to ‘‘not propose applicable professional or policy reforms’’ may be a wise decision, helping Livne’s work to live on long after particular policy debates have died down and keeping it above the fray of politically motivated (although factually hollow) debates like the ‘‘death panel’’ rhetoric of the early 2010s. These quibbles are minor, however. This powerful work is poised to be a future classic in economic sociology, medical sociology, and health policy. Livne provides a fascinating glimpse into the challenges that nearly every American will face, as they and their loved ones grapple with end-of-life decision-making. The book’s important conclusions extend beyond the case of hospice and palliative care; they reveal how economies can emerge in morally contested areas and the role that stakeholders can play in this emergence.
Dependency-based Reordering Model for Constituent Pairs in Hierarchical SMT

We propose a novel dependency-based re-ordering model for hierarchical SMT that predicts the translation order of two types of pairs of constituents of the source tree: head-dependent and dependent-dependent. Our model uses the dependency structure of the source sentence to capture the medium-and long-distance reorder-ings between these pairs of constituents. We describe our reordering model in detail and then apply it to a language pair in which the languages involved follow different word order patterns, English (SVO) and Farsi (free word order being SOV the most frequent pattern). Our model out-performs a baseline (standard hierarchical SMT) by 0.78 BLEU points absolute, statistically signiﬁcant at p = 0 . 01 .
Use of Linguistic Resources like Translation Memories in Machine Translation Systems

The context and general technical strategy for MAHT and MT at ERLI is presented. After presenting the commercial context of translation, we outline the architecture of AlethTR, ERLI’s assisted-translation platform. Special attention is paid to the question of linguistic resources : general lexicon, terminology, translation memory. We consider briefly the integration of translation memory with MT and its extension to the sub-sentence level.
A Novel Centrality for Finding Key Persons in a Social Network by the Bi-Directional Influence Map

Symmetry is one of the important properties of Social networks to indicate the co-existence relationship between two persons, e.g., friendship or kinship. Centrality is an index to measure the importance of vertices/persons within a social network. Many kinds of centrality indices have been proposed to find prominent vertices, such as the eigenvector centrality and PageRank algorithm. PageRank-based algorithms are the most popular approaches to handle this task, since they are more suitable for directed networks, which are common situations in social media. However, the realistic problem in social networks is that the process to find true important persons is very complicated, since we should consider both how the influence of a vertex affects others and how many others follow a given vertex. However, past PageRank-based algorithms can only reflect the importance on the one side and ignore the influence on the other side. In addition, past algorithms only view the transition from one status to the next status as a linear process without considering more complicated situations. In this paper, we develop a novel centrality to find key persons within a social network by a proposed synthesized index which accounts for both the inflow and outflow matrices of a vertex. Besides, we propose different transition functions to represent the relationship from status to status. The empirical studies compare the proposed algorithms with the conventional algorithms and show the differences and flexibility of the proposed algorithm.