Miracle-dz/UniFAR-FTUs huggingface dataset:
25-Hydroxycholesterol Inhibits Kaposi’s Sarcoma Herpesvirus and Epstein-Barr Virus Infections and Activates Inflammatory Cytokine Responses

A cholesterol derivative, 25-hydroxycholesterol (25HC), has been demonstrated to inhibit infections from widely different bacteria and viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, its mechanism of activity is still not fully understood. ABSTRACT Oncogenic gammaherpesviruses express viral products during latent and lytic infection that block the innate immune response. Previously, we found that Kaposi’s sarcoma herpesvirus (KSHV/human herpesvirus-8) viral microRNAs (miRNAs) downregulate cholesterol biogenesis, and we hypothesized that this prevents the production of 25-hydroxycholesterol (25HC), a cholesterol derivative. 25HC blocks KSHV de novo infection of primary endothelial cells at a postentry step and decreases viral gene expression of LANA (latency-associated nuclear antigen) and RTA. Herein we expanded on this observation by determining transcriptomic changes associated with 25HC treatment of primary endothelial cells using RNA sequencing (RNA-Seq). We found that 25HC treatment inhibited KSHV gene expression and induced interferon-stimulated genes (ISGs) and several inflammatory cytokines (interleukin 8 [IL-8], IL-1α). Some 25HC-induced genes were partially responsible for the broadly antiviral effect of 25HC against several viruses. Additionally, we found that 25HC inhibited infection of primary B cells by a related oncogenic virus, Epstein-Barr virus (EBV/human herpesvirus-4) by suppressing key viral genes such as LMP-1 and inducing apoptosis. RNA-Seq analysis revealed that IL-1 and IL-8 pathways were induced by 25HC in both primary endothelial cells and B cells. We also found that the gene encoding cholesterol 25-hydroxylase (CH25H), which converts cholesterol to 25HC, can be induced by type I interferon (IFN) in human B cell-enriched peripheral blood mononuclear cells (PBMCs). We propose a model wherein viral miRNAs target the cholesterol pathway to prevent 25HC production and subsequent induction of antiviral ISGs. Together, these results answer some important questions about a widely acting antiviral (25HC), with implications for multiple viral and bacterial infections. IMPORTANCE A cholesterol derivative, 25-hydroxycholesterol (25HC), has been demonstrated to inhibit infections from widely different bacteria and viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, its mechanism of activity is still not fully understood. In this work, we look at gene expression changes in the host and virus after 25HC treatment to find clues about its antiviral activity. We likewise demonstrate that 25HC is also antiviral against EBV, a common cancer-causing virus. We compared our results with previous data from antiviral screening assays and found the same pathways resulting in antiviral activity. Together, these results bring us closer to understanding how a modified form of cholesterol works against several viruses.
Demonic Interventions

The proliferation of robots in performative arts has brought awareness to a new kind of performative intervention, which -- uncanny by its very origin -- revisits the idea of human animatedness, and, more importantly, enquires after the nature and role of objects that have already functioned mostly as props. Inspired by contemporary machine performances - from Bill Vorn’s human-robot spectacles, to Marco Donnarumma’s performative fusions with technological things, to Giles Walker’s Robotic Burlesque, to Oriza Hirata’s quiet robot theatre -- the article analyses robots’ stage appearances to interpret them in terms of ‘demonic interventions’. Following the historically grounded reputation of automata as demon-like creatures, the article outlines the role of robots in performative art, with a special focus on the posthuman climax in which the human element seems to be gradually possessed by the ‘machinic’ (the robotic, the technologically engendered). The article explores the motivations of our turn to ‘technological objects’ to see why, despite much fear and scepticism, we invite them to spaces - ontological and performative - so far reserved for humans.
The chiral anomalous Hall effect at high magnetic fields in Au-Fe alloys

ABSTRACT The Hall effect of the disordered and frustrated magnetic alloys Au-Fe 8 at% and Au-Fe 18 at% was studied in applied magnetic fields up to 14 T. The measurements were carried out in fixed temperatures spanning the spin glass and paramagnetic phases of Au-Fe 8 at% and in the reentrant, ferromagnetic and paramagnetic states in Au-Fe 18 at%. The experiments confirm the occurrence of a large contribution from spin chiralities to the anomalous Hall effect (AHE) in both systems, either in the spin glass and the canted ferromagnetic phases. The amplitude of the chirality-driven term is progressively attenuated as the field intensity is increased. This effect is consistent with the expected field-induced closing of the solid angle defining scalar spin chirality. Evidence for the occurrence of a chiral contribution to AHE was also detected in the paramagnetic phases of the two studied systems, showing that this term persists as long as the typical time scale for the spin dynamics is significantly larger than the transport relaxation time.
Practice report

We have been training spatial skills for Computing Science students over several years with positive results, both in terms of the students’ spatial skills and their CS outcomes. The delivery and structure of the training has been modified over time and carried out at several institutions, resulting in variations across each intervention. This article describes six distinct case studies of training deliveries, highlighting the main challenges faced and some important takeaways. Our goal is to provide useful guidance based on our varied experience for any practitioner considering the adoption of spatial skills training for their students
Tracking CS graduates and non-retained students to the following year's academic programs

W hat happens to students who depart from their computer science program but remain in academia? How many students go on to study at the next degree level? When they do, are they still studying CS? How many who don’t get a degree go to other computing programs, or other STEM programs? This paper addresses these questions through a study of nearly 400,000 students who were enrolled in U.S. computer science bachelor’s and associate’s programs in 2017-2018. The results will be of interest to computing educators and academic leaders, who can compare their local data with these national level data
A Cross-Cultural Genre Analysis of Firm-Generated Advertisements on Twitter and Sina Weibo

To investigate the generic features of firm-generated advertisements (FGAs) in cross-cultural contexts, this study analyzed 327 FGAs by Dell Technologies and the Lenovo Group on Twitter and Sina Weibo. Integrating affordances and multimodality into genre analysis, the study showed that the FGAs were characterized by (a) flexible move structure, (b) persuasive language, (c) visual illustration, and (d) hyperlinks, hashtagging (#), and mentioning (@) functions. The FGAs on Sina Weibo, compared with those on Twitter, tended to use more language play, emojis, and contextual product pictures and show more emphasis on the niche of products, incentives, and celebrity endorsement.
Plasto-Elastohydrodynamic Lubrication (PEHL) in Point Contacts

Elastohydrodynamic lubrication (EHL) is an important branch of the lubrication theory, describing lubrication mechanisms in nonconformal contacts widely found in many mechanical components such as various gears, rolling bearings, cams and followers, metal-rolling tools, traction drives, and continuous variable transmissions. These components. often transmit substantial power under heavy loading conditions. Also, the roughness of machined surfaces is usually of the same order of magnitude as, or greater than, the estimated average EHL film thickness. Consequently, most components operate in mixed lubrication regime with significant asperity contacts. Due to very high pressure concentrated in small areas, resulted from either heavy external loading or severe asperity contacts, or often a combination of both, subsurface stresses may exceed the material yield limit, causing considerable plastic deformation, which may not only permanently change the surface profiles and contact geometry but also alter material properties through work hardening as well. In the present study, a three-dimensional plasto-elastohydrodynamic lubrication (PEHL) model has been developed by taking into account plastic deformation and material work-hardening. The effects of surface/subsurface plastic deformation on lubricant film thickness, surface pressure distribution, and subsurface stress field have been investigated. This paper briefly describes the newly developed PEHL model and presents preliminary results and observed basic behavior of the PEHL in smooth-surface point contacts, in comparison with those from corresponding EHL solutions under the same conditions. The results indicate that plastic deformation may greatly affect contact and lubrication characteristics, resulting in significant reductions in lubricant film thickness, peak surface pressure and maximum subsurface stresses.
Phase-based treatment versus immediate trauma-focused treatment for post-traumatic stress disorder due to childhood abuse: randomised clinical trial

Background It is unclear whether people with post-traumatic stress disorder (PTSD) and symptoms of complex PTSD due to childhood abuse need a treatment approach different from approaches in the PTSD treatment guidelines. Aims To determine whether a phase-based approach is more effective than an immediate trauma-focused approach in people with childhood-trauma related PTSD (Netherlands Trial Registry no.: NTR5991). Method Adults with PTSD following childhood abuse were randomly assigned to either a phase-based treatment condition (8 sessions of Skills Training in Affect and Interpersonal Regulation (STAIR), followed by 16 sessions of eye-movement desensitisation and reprocessing (EMDR) therapy; n = 57) or an immediately trauma-focused treatment condition (16 sessions of EMDR therapy; n = 64). Participants were assessed for symptoms of PTSD and complex PTSD, and other forms of psychopathology before, during and after treatment and at 3- and 6-month follow-ups. Results Data were analysed with linear mixed models. No significant differences between the two treatments on any variable at post-treatment or follow-up were found. Post-treatment, 68.8% no longer met PTSD diagnostic criteria. Self-reported PTSD symptoms significantly decreased for both STAIR–EMDR therapy (d = 0.93) and EMDR therapy (d = 1.54) from pre- to post-treatment assessment, without significant difference between the two conditions. No differences in drop-out rates between the conditions were found (STAIR–EMDR 22.8% v. EMDR 17.2%). No study-related adverse events occurred. Conclusions This study provides compelling support for the use of EMDR therapy alone for the treatment of PTSD due to childhood abuse as opposed to needing any preparatory intervention.
Current-mode versus voltage-mode G/sub m/-C biquad filters: what the theory says

As there is a growing interest in the design of so called "current-mode filters", this paper is concerned with an investigation into the general performance criteria and the speed/dynamic range limits of current-mode G/sub m/-C based filters, compared to conventional voltage-mode G/sub m/-C filters. It is shown here that, as far as speed, dynamic range, and power consumption are concerned and provided that both kinds of filter topology employ the same type of transconductors and capacitors, the result comes out in the favor of the use of the voltage-mode filter processing as a solution for realizing high performance G/sub m/-C filters.
Chlorine demand-based predictive modeling of THM formation in water distribution networks

The potential carcinogenicity of trihalomethanes (THMs) has led to increasingly stricter regulation of drinking water supplies. This has led to the need to manage better the chemical and microbiological risk balance in chlorinated supplies. The use of empirical equations to predict THM concentrations in water quality models is challenging and expensive due to the numerous temporally and spatially dependent uncertainties involved. In this paper, the benefits of a simple predictive method using a THM productivity parameter based on chlorine consumed by bulk free chlorine reactions are explored using extensive field data from a water distribution system in the Midlands region of the UK. It is concluded that the productivity parameter provides an appropriate, relatively robust, yet straightforward alternative to the use of an empirical equation based on regression analyses to predict THM concentrations in distribution, and that the method has the potential to help distribution system water quality model calibration.
Boosting Neural Machine Translation from Finnish to Northern Sámi with Rule-Based Backtranslation

We consider a low-resource translation task from Finnish into Northern Sámi. Collecting all available parallel data between the languages, we obtain around 30,000 sentence pairs. However, there exists a significantly larger monolingual Northern Sámi corpus, as well as a rule-based machine translation (RBMT) system between the languages. To make the best use of the monolingual data in a neural machine translation (NMT) system, we use the backtranslation approach to create synthetic parallel data from it using both NMT and RBMT systems. Evaluating the results on an in-domain test set and a small out-of-domain set, we find that the RBMT backtranslation outperforms NMT backtranslation clearly for the out-of-domain test set, but also slightly for the in-domain data, for which the NMT backtranslation model provided clearly better BLEU scores than the RBMT. In addition, combining both backtranslated data sets improves the RBMT approach only for the in-domain test set. This suggests that the RBMT system provides general-domain knowledge that cannot be found from the relative small parallel training data.
Achieved and tangible benefits: lessons learned from a landmark EDRMS implementation

Purpose – Entering the twenty‐first century, burgeoning information management needs in both the private and public sectors are pushing adoption of automated records management systems. Electronic Document and Records Management Systems (EDRMS) are evolving as the most likely solution. Despite this trend, relatively few published accounts of e‐records implementations exist that present achieved and measurable benefits. This paper seeks to discuss these issues.Design/methodology/approach – The authors highlight a set of workflow driven strategies that required considerable managerial and financial investment. Findings – The case study shows just how crucial it is for any organisation to bring with it the people and the processes involved in the creation, management and maintenance of records and information, if a centralised approach is to work over the longer term.Practical implications – The detailed nature of the investigation also serves to highlight a set of well‐documented IT strategies for records m.
Beyond Grammatical Error Correction: Improving L1-influenced research writing in English using pre-trained encoder-decoder models

In this paper, we present a new method for training a writing improvement model adapted to the writer’s ﬁrst language (L1) that goes beyond grammatical error correction (GEC). Without using annotated training data, we rely solely on pre-trained language models ﬁne-tuned with parallel corpora of reference translation aligned with machine translation. We evaluate our model with corpora of academic papers written in English by L1 Portuguese and L1 Spanish scholars and a reference corpus of expert academic English. We show that our model is able to address speciﬁc L1-inﬂuenced writing and more complex linguistic phenomena than existing methods, outper-forming what a state-of-the-art GEC system can achieve in this regard. Our code and data are open to other researchers 1 .
proScript: Partially Ordered Scripts Generation via Pre-trained Language Models

Scripts - standardized event sequences describing typical everyday activities - have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information. However, to date they have proved hard to author or extract from text. In this work, we demonstrate for the first time that pre-trained neural language models (LMs) can be be finetuned to generate high-quality scripts, at varying levels of granularity, for a wide range of everyday scenarios (e.g., bake a cake). To do this, we collected a large (6.4k), crowdsourced partially ordered scripts (named proScript), which is substantially larger than prior datasets, and developed models that generate scripts with combining language generation and structure prediction. We define two complementary tasks: (i) edge prediction: given a scenario and unordered events, organize the events into a valid (possibly partial-order) script, and (ii) script generation: given only a scenario, generate events and organize them into a (possibly partial-order) script. Our experiments show that our models perform well (e.g., F1=75.7 in task (i)), illustrating a new approach to overcoming previous barriers to script collection. We also show that there is still significant room for improvement toward human level performance. Together, our tasks, dataset, and models offer a new research direction for learning script knowledge.
LAMAD: A Linguistic Attentional Model for Arabic Text Diacritization

In Arabic Language, diacritics are used to specify meanings as well as pronunciations. However, diacritics are often omitted from written texts, which increases the number of possible meanings and pronunciations. This leads to an ambiguous text and makes the computational process on undiacritized text more difﬁcult. In this paper, we propose a Linguistic Attentional Model for Arabic text Diacritization (LAMAD). In LAMAD, a new linguistic feature representation is presented, which uti-lizes both word and character contextual features. Then, a linguistic attention mechanism is proposed to capture the important linguistic features. In addition, we explore the impact of the linguistic features extracted from the text on Arabic text diacritization (ATD) by introducing them to the linguistic attention mechanism. The extensive experimental results on three datasets with different sizes illustrate that LAMAD outperforms the existing state-of-the-art models.
Don't Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting

User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e.g., green tea), identify the online users to whom the ad package should be targeted. A (ad package speciﬁc) user targeting model is typically trained using historical clickthrough data: positive instances correspond to users who have clicked on an ad in the package before, whereas negative instances correspond to users who have not clicked on any ads in the package that were displayed to them. Collecting a sufﬁcient amount of positive training data for training an accurate user targeting model, however, is by no means trivial. This paper proposes a novel method for automatic augmentation of the set of positive training instances. Experimental results on two datasets, including a real-world company dataset, demonstrate the effectiveness of our proposed method.
deepQuest-py: Large and Distilled Models for Quality Estimation

We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE). deepQuest-py provides access to (1) state-of-the-art models based on pre-trained Transformers for sentence-level and word-level QE; (2) light-weight and efficient sentence-level models implemented via knowledge distillation; and (3) a web interface for testing models and visualising their predictions. deepQuest-py is available at https://github.com/sheffieldnlp/deepQuest-py under a CC BY-NC-SA licence.
Obstacles Affect Perceptions of Egocentric Distances in Virtual Environments

Distance perception in humans can be affected by oculomotor and optical cues and a person’s action capability in a given environment, known as action-specific effects. For example, a previous study has demonstrated that egocentric distance estimation to a target is affected by the width of a transparent barrier placed in the intermediate space between a participant and a target. However, the characteristics of a barrier’s width that affect distance perception remain unknown. Therefore, we investigated whether visual and tactile inputs and actions related to a barrier affect distance estimation to a target behind the barrier. The results confirmed previous studies by demonstrating that visual and tactile presentations of the barrier’s width affected distance estimation to the target. However, this effect of the barrier’s width was not observed when the barrier was touchable but invisible nor when the barrier was visible but penetrable. These findings indicate the complexity of action-specific effects and the difficulty of identifying necessary information for inducing these effects.
Impacts of Landscape Evolution on Heterotrophic Carbon Loss in Intensively Managed Landscapes

Soil respiration that releases CO2 into the atmosphere roughly balances the net primary productivity and varies widely in space and time. However, predicting its spatial variability, particularly in intensively managed landscapes, is challenging due to a lack of understanding of the roles of soil organic carbon (SOC) redistribution resulting from accelerated soil erosion. Here we simulate the heterotrophic carbon loss (HCL)—defined as microbial decomposition of SOC—with soil transport, SOC surface redistribution, and biogeochemical transformation in an agricultural field. The results show that accelerated soil erosion extends the spatial variation of the HCL, and the mechanical-mixing due to tillage further accentuates the contrast. The peak values of HCL occur in areas where soil transport rates are relatively small. Moreover, HCL has a strong correlation with the SOC redistribution rate rather than the soil transport rate. This work characterizes the roles of soil and SOC transport in restructuring the spatial variability of HCL at high spatio-temporal resolution.