Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-uniform in vivo Expansion of Epstein-Barr Virus-Specific T-Cells Following Donor Lymphocyte Infusion for Post-transplant Lymphoproliferative Disease

Epstein-Barr virus (EBV)-associated post-transplant lymphoproliferative disease (PTLD) is a life-threatening complication of T-lymphocyte deplete allogeneic hematopoietic stem cell transplantation (allo-HSCT). For patients with PTLD refractory to Rituximab, donor lymphocyte infusion (DLI) is established as a successful option for salvage therapy. However, although in vivo lymphocyte expansion has been correlated with good clinical outcome following DLI, the specificity and functional characteristics of EBV-specific T-cell responses remain poorly characterized. Here we describe two patients with Rituximab-refractory PTLD complicating T-cell deplete allo-HSCT, both of whom were successfully rescued with 1 × 106/Kg unselected stem cell donor-derived DLI. Prospective analyses revealed that complete clinical and radiological responses were associated with in vivo expansion of T and NK cells. Furthermore, EBV MHC tetramer, and interferon gamma analyses revealed a marked increase in EBV-specific T-cell frequency from 4 weeks after DLI. Reactivity was demonstrated against a range of EBV latent and lytic antigens, including those detected in tumor biopsy material. The immunodominant EBV-specific T cell response expanding in vivo following infusion matched the dominant response present in the DLI preparations prior to administration. Furthermore, differences in the repertoire of subdominant antigen-specific T-cells were also detected, suggesting that antigen-encounter in vivo can shape the immune response. These results demonstrate the value of prospectively studying in vivo T-cell responses, by facilitating the identification of important specificities required for clinical efficacy. Applying this approach on a larger scale promises to yield data which may be essential for the optimization of future adoptive immunotherapeutic strategies for PTLD.
The Stain of the Original Salt: Red Heats on Chrome Tanned Leathers and Purple Spots on Ancient Parchments Are Two Sides of the Same Ecological Coin

Animal hides are one of man’s earliest and mostly used materials; many rawhide products, primarily leather, have for centuries been used for several purposes. The peculiar mechanical properties of leather depend on the hide composition, a dense collagen feltwork. Unfortunately, due to their proteic composition, rawhides may undergo microbial attack and biodeterioration. Over centuries, different processes and treatments (brining, vegetal or chrome tanning, tawing, etc.) were set up to face the biological attack and modify/stabilise the hide’s mechanical properties. Nevertheless, even present-day rawhides are subjected to biological colonisation, and traces of this colonisation are clearly shown in Chrome(III) tanned leathers (in the wet blue stage), with obvious economic damages. The colonisation traces on tanned leathers consist of isolated or coalescent red patches, known as red heat deterioration. Parchments are rawhide products, too; they derive from another manufacturing procedure. Even parchments undergo microbial attack; the parchment biodeterioration seems comparable to leather red heat deterioration and is known as purple spots. Recently, an ecological succession model explained the process of historical parchment purple spot deterioration; the haloarchaea Halobacterium salinarum is the pioneer organism triggering this attack. The marine salt used to prevent rawhide rotting is the carrier of haloarchaea colonisers (Migliore et al., 2019). The aim of this study was to investigate the dynamics of biodeterioration on Chrome(III) tanned leathers and its effects on the stability/integrity of collagen structure. To this end, standard cultivation methods were integrated with three updated technologies, Next-Generation Sequencing (NGS), Raman spectroscopy, and Light Transmitted Analysis (LTA). A bioinformatic comparison between chrome tanned leather vs. historical parchment colonisers was performed to evaluate if leather and parchment share common culprits; furthermore, the effect of the biodeterioration on the physical properties of the hide product was evaluated.
Thymus Imaging Detection and Size Is Inversely Associated With Metabolic Syndrome and Frailty in People With HIV

Abstract Background People with HIV (PWH) may experience accentuating aging in relation to immuno-activation. Little is known regarding thymus (THY) involution in this process. We sought to investigate the relationship between THY imaging detection/size and clinically relevant aging outcomes such as metabolic syndrome (MetS), multimorbidity (MM), and frailty in PWH. Methods This was a cross-sectional observational study including 665 HIV patients (81% males; median age, 53 years) attending Modena HIV Metabolic Clinic from 2014 to 2017. They underwent thoracic computed tomography scan as part of the medical assessment for cardiovascular disease, in which THY detection and size were reported using a semiquantitative score. Outcome measures were MetS, MM, and frailty. Results THY was detected in 27.0% of subjects; 71.1% showed THY size of grade 1–2, and 28.9% exhibited grade ≥3. Covariates that inversely correlated with THY detection were age, male gender, body mass index (BMI), and HIV duration. Covariates that inversely correlated with MetS were age, HIV duration, BMI, and THY grade 1–2. Covariates that inversely correlated with MM were age, HIV duration, and CD4 nadir. Covariates that inversely correlated with frailty were age, HIV duration, CD4 nadir, BMI, and THY detection. Conclusions THY is inversely associated with MetS and frailty in PWH.
How much does a zero-permission Android app know about us?

Android devices contain a vast amount of personal data of their owners. These data are stored on the device and are protected by the Android permission scheme. Android apps can obtain access to specific data items by requesting the appropriate permissions from the user. However, in Android, the access to certain assets is granted by default to the installed apps. For example, any Android app has the right to get the device's network operator, which may be used to infer information about user's country and nationality. Similarly, any app has access to the clipboard which may occasionally contain very sensitive information, like a password. Consequently, a honest but curious Android app may leverage the implicit access rights to accumulate such unguarded information pieces and gradually build a detailed profile of the user. The device owner has no immediate way to control this flow of information and, even worse, may not even be aware that this type personal data flow can take place. In this work, we examine the issue of default access rights of Android apps and discuss the potential threat against user privacy. We assess the user awareness and present a prototype zero-permission app that collects user data.
Case Study: Analysis and Mitigation of a Novel Sandbox-Evasion Technique

Malware is one of the most popular cyber-attack methods in the digital world. According to the independent test company AV-TEST, 350,000 new malware samples are created every day. To analyze all samples by hand to discover whether they are malware does not scale, so antivirus companies automate the process e.g., using sandboxes where samples can be run, observed, and classified. Malware authors are aware of this fact, and try to evade detection. In this paper we describe one of such evasion technique: unprecedented, we discovered it while analyzing a ransomware sample. Analyzed in a Cuckoo Sandbox, the sample was able to avoid triggering malware indicators, thus scoring significantly below the minimum severity level. Here, we discuss what strategy the sample follows to evade the analysis, proposing practical defense methods to nullify, in our turn, the sample's furtive strategy.
Supporting Family Caregivers With Technology for Dementia Home Care: A Randomized Controlled Trial

Abstract Background and Objectives The number of persons living with dementia (PLWD) in the United States will reach 16 million by 2050. Behavioral and psychological symptoms of dementia challenge family caregivers and contribute to negative caregiver outcomes such as burden and depression. Available technology can support the delivery of effective interventions to families providing dementia care at home. The Supporting Family Caregivers with Technology for Dementia Home Care (FamTechCare) randomized controlled trial evaluated the effects of a telehealth intervention on caregiver outcomes. Research Design and Methods The FamTechCare intervention provides tailored dementia-care strategies to in-home caregivers based on video recordings caregivers submit of challenging care situations. An expert team reviews the videos and provides individualized interventions weekly for the experimental group. In the telephone-support attention control group, caregivers receive feedback from an interventionist via the telephone based on caregiver retrospective recall of care challenges. Effects of the intervention on caregiver outcomes, including burden, depression, sleep disturbance, competence, desire to institutionalize the PLWD, and caregiver reaction to behavioral symptoms were evaluated by fitting linear mixed regression models to changes in the outcomes measured at 1 and 3 months. Results FamTechCare caregivers (n = 42) had greater reductions in depression (p = .012) and gains in competence (p = .033) after 3 months compared to the attention control group (n = 41). Living in rural areas was associated with a reduction in depression for FamTechCare caregivers (p = .002). Higher level of education was associated with greater improvements or lesser declines in burden, competence, and reaction to behavioral symptoms for both the FamTechCare and attention control caregivers. Discussion and Implications This research demonstrated benefits of using available technology to link families to dementia care experts using video-recording technology. It provides a foundation for future research testing telehealth interventions, tailored based on rich contextual data to support families, including those in rural or remote locations.
Is Your Game Generator Working? Evaluating Gemini, an Intentional Generator

Determining whether a game generator is working properly is challenging, since it entails conducting potentially many evaluations of generated games and synthesizing these into a net evaluation of the system. The problem is compounded when the generator has a human-centered goal: for example, that the generated games should be interpreted as having certain mechanics or as being about particular ideas. In this paper, we examine the Gemini game generator and develop an evaluation instrument that tests the interpretability of its generated games’ mechanics and higher-order proceduralist arguments. In the process we build empirical evidence for the claim that some amount of non-systems-based framing is required in order for arguments made by procedural rhetorics to be sensible to players. The tools we have assembled for this evaluation can be applied to game generators more broadly; game generators should be allowed to invent games which go beyond merely formally “good” or subjectively “fun. ”
Knowledge-Powered Inference of Crowd Behaviors in Semantically Rich Environments

Interactive authoring of collaborative, context-dependent virtual agent behaviors can be challenging. Current approaches often rely heavily on users’ input, leading to cumbersome behavior authoring experiences and biased results, which do not reflect realistic space-people interactions in virtual settings. To address these issues, we generate an ontology graph from commonsense knowledge corpus and use it to automatically infer behavior distributions that determine agents’ context-dependent interactions with the built environment. By means of a natural-language interface, users can interactively refine a building’s design by adding semantic labels to spaces and populating rooms with equipment following suggestions that the system provides based on commonsense knowledge. Based on the chosen setup, an authoring system automatically populates the environment and allocates agents to specific behaviors while satisfying a behavior distribution inferred from the ontology graph. This approach holds promise to help architects, engineers, and game designers interactively author plausible agent behaviors that reveal the mutual interactions between people and the spaces they inhabit.
Approaching the Automation of Cyber Security Testing of Connected Vehicles

The advancing digitalization of vehicles and automotive systems bears many advantages for creating and enhancing comfort and safety-related systems ranging from drive-by-wire, inclusion of advanced displays, entertainment systems up to sophisticated driving assistance and autonomous driving. It, however, also contains the inherent risk of being used for purposes that are not intended for, raging from small non-authorized customizations to the possibility of full-scale cyberattacks that affect several vehicles to whole fleets and vital systems such as steering and engine control. To prevent such conditions and mitigate cybersecurity risks from affecting the safety of road traffic, testing cybersecurity must be adopted into automotive testing at a large scale. Currently, the manual penetration testing processes cannot uphold the increasing demand due to time and cost to test complex systems. We propose an approach for an architecture that (semi-)automates automotive cybersecurity test, allowing for more economic testing and therefore keeping up to the rising demand induced by new vehicle functions as well as the development towards connected and autonomous vehicles.
Adaptive Game Soundtrack Generation Based on Music Transcription

This research aims to bring new prospects for adaptive game soundtrack generation, which theoretically can increase the involvement of players into the gaming process. To investigate the problem of adaptive soundtrack generation, human-centered evaluation has to be done. Furthermore, an important part of this research is a collaborative soundtrack generation with the participation of composers and artificial intelligence, which is able to decrease expenses of game developing companies. For soundtrack customization, this work suggests a joint generation of music by human and artificial intelligence and its further intuitive modification during the game based on in-game actions.
The role of ambient temperature and body mass on body temperature, standard metabolic rate and evaporative water loss in southern African anurans of different habitat specialisation

Temperature and water availability are two of the most important variables affecting all aspects of an anuran’s key physiological processes such as body temperature (Tb), evaporative water loss (EWL) and standard metabolic rate (SMR). Since anurans display pronounced sexual dimorphism, evidence suggests that these processes are further influenced by other factors such as vapour pressure deficit (VPD), sex and body mass (Mb). However, a limited number of studies have tested the generality of these results across a wide range of ecologically relevant ambient temperatures (Ta), while taking habitat use into account. Thus, the aim of this study was to investigate the role of Ta on Tb, whole-animal EWL and whole-animal SMR in three wild caught African anuran species with different ecological specialisations: the principally aquatic African clawed frog (Xenopus laevis), stream-breeding common river frog (Amietia delalandii), and the largely terrestrial raucous toad (Sclerophrys capensis). Experiments were conducted at a range of test temperatures (5–35 °C, at 5 °C increments). We found that VPD better predicted rates of EWL than Ta in two of the three species considered. Moreover, we found that Tb, whole-animal EWL and whole-animal SMR increased with increasing Ta, while Tb increased with increasing Mb in A. delalandii and S. capensis but not in X. laevis. Whole-animal SMR increased with increasing Mb in S. capensis only. We did not find any significant effect of VPD, Mb or sex on whole-animal EWL within species. Lastly, Mb did not influence Tb, whole-animal SMR and EWL in the principally aquatic X. laevis. These results suggest that Mb may not have the same effect on key physiological variables, and that the influence of Mb may also depend on the species ecological specialisation. Thus, the generality of Mb as an important factor should be taken in the context of both physiology and species habitat specialisation.
1986. Impact of Two-Step Testing on the Diagnosis and Management of Clostridium difficile in a Multi-Hospital Healthcare System

Abstract Background Distinguishing active C. difficile infection (CDI) from asymptomatic colonization remains a significant challenge. A multi-step testing algorithm can improve the diagnostic accuracy of CDI and associated antibacterial prescribing. This study evaluated the impact of two-step testing on CDI rates and management in a multi-hospital community health system. Methods Two-step C. difficile testing (PCR for initial screening followed by EIA for toxin detection) was implemented in 6 acute care community hospitals in April 2018. EIA testing was automatically performed on all stool samples with a positive C. difficile PCR result. Prior to implementation, PCR alone was used to identify CDI. Messaging attached to the PCR laboratory report alerted prescribers of discrepant results (PCR +/EIA -). Anti-C. difficile therapy was at the discretion of the prescriber. We performed a retrospective cohort analysis over a 2-year period to evaluate the effect of two-step testing on system-wide hospital-onset CDI (HO-CDI) per 10,000 patient-days (PD) and anti-CDI antimicrobial use (AU) in days of therapy (DOT) per 1,000 PD. Segmented negative binomial regression with hospital clustering was used to estimate predicted HO-CDI rate for the baseline period between April 1, 2017 through March 31, 2018 and the post-intervention between May 1, 2018 through March 31, 2019. The implementation date at all sites in April 2018 was unknown; therefore, this month was removed from the analysis. Anti-CDI agents included fidaxomicin, metronidazole, and oral vancomycin, but may have included non-CDI indications for metronidazole. Results A total of 115 HO-CDI cases were identified; 91 (79%) before and 24 (21%) after. Prior to implementation of two-step testing, CDI rates declined at 4% per month (P = NS). The rate immediately dropped by 36% (P = 0.004) after two-step testing was implemented, but the trend did not significantly change (P = 0.52, Figure 1). Community-onset CDI rates also decreased during this time period. Combined facility-wide anti-CDI agent use was 824.87 before and 838.21 DOT/1,000 PD after and did not significantly change. Conclusion Use of a two-step approach for CDI testing reduced HO-CDI rates, but did not have a significant impact on anti-CDI antibiotic use in a multi-hospital community health system. Disclosures All authors: No reported disclosures.
Achieving Consistency of Software Updates against Strong Attackers

Update systems regularly distribute updates for installed software to end users. Problems arise when the update system is misused and malicious updates are sent to a small set of users only. Such situations can occur if the software supplier has been successfully attacked or is coerced by government agencies to distribute handcrafted updates containing promiscuous functionality like backdoors. In this paper, we define a set of general security requirements for update systems that encompass protection against malicious updates. We then introduce the design of an update system that satisfies the requirements and present an implementation as an extension to the advanced package tool (APT) for the Debian OS. We evaluate the strengths and weaknesses of the system and discuss its large-scale applicability with respect to security and performance overhead.
Multiagent Narrative Experience Management as Story Graph Pruning

In interactive narratives, experience management is used to control the world and the nonplayer characters (NPCs) a player interacts with, encouraging particular types of stories or discouraging others. The space of all stories in a narrative can be understood as a story graph, with story states as nodes and actions in the story as directed edges. In this article, we present experience management as a graph pruning problem. Starting with the full story graph, edges representing NPC actions may be pruned until there is at most one action per NPC per state. With the full graph available, the choice of what to prune may consider all possible futures, and we can ensure that undesirable stories are not reachable. By never pruning player actions, we ensure the player may make any choice and still be accommodated in the story. When this method was used to manage the story of an adventure game, players found our technique generally produced higher agency and more-believable NPC behaviors than a control. Finally, we discuss scaling the results of this method for practical use.
Sources of bias and variation in diagnostic accuracy studies

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Embedded Deep Inference in Practice: Case for Model Partitioning

With increased focus on in situ analytics, artificial intelligence (AI) algorithms are getting deployed on embedded devices at the network edge. Growing popularity of Deep Learning (DL) and inference largely due to minimization of feature engineering, availability of pre-trained models and fine-tunable datasets especially in image and video analytics, have made these de-facto standard. However, the embedded systems employing these models are often resource constrained and fail to handle scenarios where arrival rate and input data volume increase over a given time period. This has a direct effect on the storage and network usage of such devices, rendering the traditional strategies of input buffering and network offloading ineffective. This paper investigates the use of dynamic layer-wise partitioning and partial execution of DL inference phase to enable inelastic embedded systems to support varying sensing rates and large data volume. The proposed partial execution scheme and partitioning algorithm perform better than standard frame-wise inference methods, when evaluated using workloads of few popular CNNs used in standard object detection models.