Miracle-dz/UniFAR-FTUs huggingface dataset:
Food resources drive rodent population demography mediated by seasonality and inter-specific competition

As fast reproducing species, rodents present proximate numerical responses to resource availability that have been assessed by experimental manipulation of food, with contrasting results. Other intrinsic and extrinsic factors, such as climate severity, species life cycles, and sympatry of potential competitors in the community, may interplay to modulate such responses, but their effects have rarely been evaluated ensemble. We applied a niche-based approach to experimentally determine the effect of bottom-up (food availability) and top-down (climate severity) extrinsic factors, as well as intrinsic seasonal cycles, on rodent demography, also in presence of sympatry between species in the community. To this end, we live-trapped rodents at two latitudinal extremes of the boreal-temperate gradient (Italian Alps and Norway) deploying control/treatment designs of food manipulation. We applied a multistate open robust design model to estimate population patterns and survival rate. Yellow-necked and wood mouse (Apodemus spp.) were sympatric with bank vole in Italy, while the latter was the only species trapped in Norway. At northern latitudes, where harsher climatic conditions occurred, vole survival was principally regulated by intrinsic seasonal cycles, with a positive effect of food also on population abundance. At southern latitudes, mice and voles exhibited asynchronous population patterns across seasons, with survival depending from seasonal cycles. When concentrated ad libitum food was experimentally provided, though, population size and survival of voles strongly decreased, while mice abundance benefited from food supplementation. Our results evidence that rodent demography is regulated by a combination of top-down (climate severity) and bottom-up (food availability) extrinsic factors, together with intrinsic seasonal ones. Moreover, we showed that the seasonal niche partitioning of mice and voles could be disrupted by availability of abundant resources that favour the demography of the more opportunistic Apodemus spp. at the expense of Myodes glareolus, suggesting competitive mechanisms. We conclude putting our results in the context of climate change, where shifts in vegetation productivity may affect the diversity of the rodent community via demographic effects.
Piezo buffers mechanical stress via modulation of intracellular Ca2+ handling in the Drosophila heart

Throughout its lifetime the heart is buffeted continuously by dynamic mechanical forces resulting from contraction of the heart muscle itself and fluctuations in haemodynamic load and pressure. These forces are in flux on a beat-by-beat basis, resulting from changes in posture, physical activity or emotional state, and over longer timescales due to altered physiology (e.g. pregnancy) or as a consequence of ageing or disease (e.g. hypertension). It has been known for over a century of the heart’s ability to sense differences in haemodynamic load and adjust contractile force accordingly1-4. These adaptive behaviours are important for cardiovascular homeostasis, but the mechanism(s) underpinning them are incompletely understood. Here we present evidence that the mechanically-activated ion channel, Piezo, is an important component of the Drosophila heart’s ability to adapt to mechanical force. We find Piezo is a sarcoplasmic reticulum (SR)-resident channel and is part of a mechanism that regulates Ca2+ handling in cardiomyocytes in response to mechanical stress. Our data support a simple model in which Drosophila Piezo transduces mechanical force such as stretch into a Ca2+ signal, originating from the SR, that modulates cardiomyocyte contraction. We show that Piezo mutant hearts fail to buffer mechanical stress, have altered Ca2+ handling, become prone to arrhythmias and undergo pathological remodelling.
Using Mobile Data for Understanding Population Movement and Disease Transmission during Covid-19 Outbreak in the Nordics

This study investigates the use of mobile data to understand patterns of population movements and disease transmission during the Covid-19 outbreak. It also focuses on understanding the implications of using this data for individual privacy. Using a mixed methods approach, we present 10 rich qualitative interviews and 412 survey responses from participants across the Nordics. Our novel results show that the use of mobile data can be characterized by two main categories: validation data and complementary data. We also identify five implications for practice: sharing resources and expertise between health agencies and telecom companies;extended collaboration with multiple network operators;cross-disciplinary collaboration among multiple parties;developing data and privacy guidelines;and developing novel methods and tools to address the trade-off between maintaining individual privacy and obtaining detailed information from mobile data. These implications may inform immediate and future actions to prepare for, mitigate, and control the spread of infectious diseases using mobile data. They also show privacy-driven limitations of mobile data in terms of data accuracy, richness, and scope. © 2022 IEEE Computer Society. All rights reserved.
Reverse Requirement Engineering

Abstrakt: Software ist mehr als nur Code. Laut Harlan Mills besteht Software aus verschiedenen Elementtypen wie Source-Code-Dateien, Maschinencode-Komponenten, Steuerungsprozeduren, Testfällen und Dokumenten [Mills80]. Die Dokumente, e.g. Anforderungsdokumente, Entwurfsdokumente, Benutzungsdokumente, usw. , gehören ebenso zum Softwareprodukt wie der Code selbst. Vor allem spielen die Anforderungsdokumente eine wichtige Rolle. Bei der Evolution des Softwareproduktes müssten sie neben dem Code fortgeschrieben werden. Sonst driften Code und Dokumentation immer weiter auseinander. Die Dokumentation wird immer chaotischer und schwieriger zu verstehen. Wie beim Code wird sie sanierungsreif.
Attachment Theory as a Framework to Understand Relationships with Social Chatbots: A Case Study of Replika

With increasing adoption of AI social chatbots, especially during the pandemic-related lockdowns, when people lack social companionship, there emerges a need for in-depth understanding and theorizing of relationship formation with digital conversational agents. Following the grounded theory approach, we analyzed in-depth interview transcripts obtained from 14 existing users of AI companion chatbot Replika. The emerging themes were interpreted through the lens of the attachment theory. Our results show that under conditions of distress and lack of human companionship, individuals can develop an attachment to social chatbots if they perceive the chatbots ’ responses to offer emotional support, encouragement, and psychological security. These findings suggest that social chatbots can be used for mental health and therapeutic purposes but have the potential to cause addiction and harm real-life intimate relationships.
Industrial Cyber Vulnerabilities: Lessons from Stuxnet and the Internet of Things

Cyber breaches continue at an alarming pace with new vulnerability warnings an almost daily occurrence. Discovery of the industrial virus Stuxnet during 2010 introduced a global threat of malware focused toward disruption of industrial control devices. By the year 2020, it is estimated that over 30 billion Internet of Things (IoT) devices will exist. The IoT global market spend is estimated to grow from $591.7 billion in 2014 to $1.3 trillion in 2019 with a compound annual growth rate of 17%. The installed base of IoT endpoints will grow from 9.7 billion in 2014 to more than 25.6 billion in 2019. With this tremendous growth in both data and devices, a security nightmare appears more reasonable than not. The proliferation of novel consumer devices and increased Internet-dependent business and government data systems introduces vulnerabilities of unprecedented magnitude. This paper adds to our understanding of the development of cyber vulnerabilities resulting directly from: (1) the Stuxnet code and its progeny, and (2) widespread malware exposure associated with the IoT.
Damage and discontinuity detection in planar composites using millimeter wave permittivity measurement

A free space measurement of permittivity within the millimeter wave band is employed to detect and locate discontinuities within fiberglass composite samples. This technique offers a noncontacting and nondestructive technique of locating a variety of discontinuities. The millimetric wave experimental technique is described and its theoretical basis is supplied. Experiments have been conducted on 12 samples of composite materials, some of which were nominally ideal control samples while others had damage induced within them deliberately. The discontinuity categories were low level impact damage, incomplete cure and vacuum release during cure. Ultrasonic C-scan imaging was used to confirm the presence or absence of discontinuities in each sample. The permittivity of a large planar sample was mapped and found to be reasonably consistent. The measured permittivity data are then studied to determine the feasibility of identifying the discontinuities. The results demonstrate the ability to resolve discontinuities from nominally ideal material and to identify the nature of the discontinuity. The experimental technique can also detect low level impact damage with a superior sensitivity to that of the ultrasonic imaging technique. The spatial resolution of the millimetric technique is around 40 mm (1.6 in.) or better. This paper also discusses the potential of millimeter wave imaging techniques as a nondestructive test tool for the rapid scanning of structures for discontinuity detection.
A Deep Learning–Based Approach for Moving Vehicle Counting and Short-Term Traffic Prediction From Video Images

The intelligent transportation system (ITS) is one of the effective solutions to the problem of urban traffic congestion, and it is also one of the important topics of smart city construction. One particular application is the traffic monitoring and flow prediction. However, there are still challenges regarding both aspects. On the one hand, the current traffic monitoring relies heavily on the single object detection method that cannot achieve accurate statistics of moving target counting and, meanwhile, has limited speed advantage; on the other hand, the existing traffic flow prediction models rarely consider different weather conditions. Therefore, the present article attempts to propose a packaged solution, which combines a new target tracking and moving vehicle counting method and an improved long short-term memory (LSTM) network for traffic flow forecast with weather conditions. More specifically, the DCN V2 convolution kernel and MultiNetV3 framework are used to replace YOLOv4’s conventional convolution kernel and backbone network to realize multi-target tracking and counting, respectively. Subsequently, combined with the temporal characteristics of historical traffic flow, this article introduces weather conditions into the LSTM network and realizes the short-term prediction of traffic flow at the road junction level. This study carries out a series of experiments using the real traffic video data with a 2-month time span at a popular road junction in the downtown of Shenzhen, China. The results suggest that the proposed algorithms outperform the previous methods in terms of the 10% higher accuracy of target detection tracking and about a half reduction of traffic prediction error, when considering weather conditions.
Parallelization of Neural Processing on Neuromorphic Hardware

Learning and development in real brains typically happens over long timescales, making long-term exploration of these features a significant research challenge. One way to address this problem is to use computational models to explore the brain, with Spiking Neural Networks a popular choice to capture neuron and synapse dynamics. However, researchers require simulation tools and platforms to execute simulations in real- or sub-realtime, to enable exploration of features such as long-term learning and neural pathologies over meaningful periods. This article presents novel multicore processing strategies on the SpiNNaker Neuromorphic hardware, addressing parallelization of Spiking Neural Network operations through allocation of dedicated computational units to specific tasks (such as neural and synaptic processing) to optimize performance. The work advances previous real-time simulations of a cortical microcircuit model, parameterizing load balancing between computational units in order to explore trade-offs between computational complexity and speed, to provide the best fit for a given application. By exploiting the flexibility of the SpiNNaker Neuromorphic platform, up to 9× throughput of neural operations is demonstrated when running biologically representative Spiking Neural Networks.
Perfusate Composition and Duration of Ex-Vivo Normothermic Perfusion in Kidney Transplantation: A Systematic Review

Graphical Abstract Ex-vivo normothermic perfusion (EVNP) is an emerging strategy in kidney preservation that enables resuscitation and viability assessment under pseudo-physiological conditions prior to transplantation. The optimal perfusate composition and duration, however, remain undefined. A systematic literature search (Embase; Medline; Scopus; and BIOSIS Previews) was conducted. We identified 1,811 unique articles dating from January 1956 to July 2021, from which 24 studies were deemed eligible for qualitative analysis. The perfusate commonly used in clinical practice consisted of leukocyte-depleted, packed red blood cells suspended in Ringer’s lactate solution with Mannitol, dexamethasone, heparin, sodium bicarbonate and a specific nutrient solution supplemented with insulin, glucose, multivitamins and vasodilators. There is increasing support in preclinical studies for non-blood cell-based perfusates, including Steen solution, synthetic haem-based oxygen carriers and acellular perfusates with supraphysiological carbogen mixtures that support adequate oxygenation whilst also enabling gradual rewarming. Extended durations of perfusion (up to 24 h) were also feasible in animal models. Direct comparison between studies was not possible due to study heterogeneity. Current evidence demonstrates safety with the aforementioned widely used protocol, however, extracellular base solutions with adequate oxygenation, supplemented with nutrient and metabolic substrates, show promise by providing a suitable environment for prolonged preservation and resuscitation. Systematic Review Registration: https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42021231381, identifier PROSPERO 2021 CRD42021231381
Systemic Analysis on the Features of Immune Microenvironment Related to Prognostic Signature in Head and Neck Squamous Cell Carcinoma

Background: Head and neck squamous cell carcinoma’s tumor immune microenvironment (TIME) plays an important role in tumorigenesis and progression, but its clinical significance remains unclear. Therefore, the TIME needs to be better understood in order to improve the response of diagnosis and therapy. Methods: The gene expression and clinical data of 569 HNSCC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Immune-related genes (IRGs) from the ImmPort database were used for immunotyping of HNSCC patients, and independent GEO datasets were used for subtype verification and comprehensive molecular identification. Results: The patients were divided into three subtypes (C1, C2, and C3) related to different gene expression profiles. The three subtypes showed widely different patterns in tumor genetic distortion, immune cell composition, cytokine profile, and so on, verifying that the immune-enhanced C2 subtype was associated with better prognosis. In addition, the stroma-deficient C1 subtype may be more efficient for the immune response than the C3 subtype. Furthermore, using WGCNA on the IRGs of those three subtypes, we found two C2-positive gene modules closely related to infection- and immune-associated pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, and the two modules had 22 common pathways. Conclusion: This study improves the power for prognosis prediction and develops new therapeutic strategies to stratify HNSCC patients into clinically significant groups through TIME-related prognostic signature.
Are No-Till Herbicide-Free Systems Possible? A Simulation Study

Conservation agriculture (CA) allows farmers to reduce costs and enhance soil health, but tends to increase weed infestation and associated crop yield loss, and/or herbicide use. We investigated how much tillage reduces weed infestation and yield loss, and which systems and weed species are the most affected by tillage suppression. We collected farming practices on 395 arable cropping systems mainly from France, and simulated them over 30 years and with 10 weather repetitions, using FlorSys. This process-based model simulates daily multi-species weed floras and crop canopies from cropping systems and pedoclimate over the years. Three series were simulated, (1) using the recorded systems, (2) eliminating tillage without any other changes, (3) eliminating herbicides without any other changes. Each series was run twice, once with a regional weed-flora pool to simulate weed dynamics and their impact on crop production and biodiversity, and once without weeds to predict potential crop yield. Among the recorded systems, herbicide treatment frequency index (HTFI) averaged over rotation increased when tillage frequency decreased. No recorded no-till system was herbicide-free. The untilled crops with the lowest HTFI (0.16) were unusual crops, i.e., relay grass-crops or multi-annual crops. Simulations of the recorded systems showed no correlation between tillage-frequency, and weed biomass or weed-caused yield loss. When tillage was deleted without any other change, yield loss almost doubled. We identified (1) which weed species and traits (e.g., spring annuals) increased after tillage suppression and in which cropping systems, (2) which recorded systems were robust to tillage suppression in terms of yield loss (with cover crops, summer crops, etc.), (3) which no-till systems (recorded or obtained after deleting tillage) limited yield loss (e.g., with frequent and/or efficient herbicides), (4) which management techniques were associated with a reduction in tillage, in herbicides, and in yield loss (long and diverse rotations, cover crops, etc.). No tested system achieved all three objectives simultaneously. The simulations indicated that two CA pillars (diverse crop rotations, cover cropping) were essential to manage weeds while reducing (or eliminating) both tillage and herbicide use. More no-till cropping systems must be investigated to determine whether sustainable no-till herbicide-free systems are possible.
Xylitol-Containing Chewing Gum Reduces Cariogenic and Periodontopathic Bacteria in Dental Plaque—Microbiome Investigation

Background Dental caries and periodontal disease remain the most prevalent oral health problems in the world. Chewing xylitol gum may help reduce the risk of caries and periodontitis for dental health benefits. However, little evidence has shown healthy food estimation by sequencing 16S rDNA in oral microbial communities. This study investigated the clinical effect of xylitol chewing gum on dental plaque accumulation and microbiota composition using the PacBio full-length sequencing platform in 24 young adults (N = 24). The participants were randomly assigned to xylitol chewing gum and control (no chewing gum) groups. Participants in the chewing gum group chewed ten pieces of gum (a total of 6.2 g xylitol/day). Dental plaque from all teeth was collected for weighing, measuring the pH value, and analysis of microbial communities at the beginning (baseline, M0) and end of the 2-week (effect, M1) study period. Results The results suggested a 20% reduction in dental plaque accumulation (p < 0.05) among participants chewing xylitol gum for 2 weeks, and the relative abundance of Firmicutes (a type of pathogenic bacteria associated with caries) decreased by 10.26% (p < 0.05) and that of Bacteroidetes and Actinobacteria (two types of pathogenic bacteria associated with periodontitis) decreased by 6.32% (p < 0.001) and 1.66% (p < 0.05), respectively. Moreover, the relative abundance of Fusobacteria was increased by 9.24% (p < 0.001), which has been proven to have a higher proportion in dental plaque of healthy adults. However, the dental plaque pH value stayed in a healthy range for the two groups. Conclusion In conclusion, chewing xylitol gum would benefit cariogenic and periodontal bacterial reduction in the oral cavity, which could help to prevent the diseases related to these bacteria.
Role and Diagnostic Performance of Host Epigenome in Respiratory Morbidity after RSV Infection: The EPIRESVi Study

Background Respiratory syncytial virus (RSV) infection has been associated with the subsequent development of recurrent wheezing and asthma, although the mechanisms involved are still unknown. We investigate the role of epigenetics in the respiratory morbidity after infection by comparing methylation patterns from children who develop recurrent wheezing (RW-RSV), subsequent asthma (AS-RVS), and those experiencing complete recovery (CR-RSV). Methods Prospective, observational study of infants aged < 2 years with RSV respiratory infection admitted to hospital and followed-up after discharge for at least three years. According to their clinical course, patients were categorized into subgroups: RW-RSV (n = 36), AS-RSV (n = 9), and CR-RSV (n = 32). The DNA genome-wide methylation pattern was analyzed in whole blood samples, collected during the acute phase of the infection, using the Illumina Infinium Methylation EPIC BeadChip (850K CpG sites). Differences in methylation were determined through a linear regression model adjusted for age, gender and cell composition. Results Patients who developed respiratory sequelae showed a statistically significant higher proportion of NK and CD8T cells (inferred through a deconvolution approach) than those with complete recovery. We identified 5,097 significant differentially methylated positions (DMPs) when comparing RW-RSV and AS-RVS together against CR-RSV. Methylation profiles affect several genes involved in airway inflammation processes. The most significant DMPs were found to be hypomethylated in cases and therefore generally leading to overexpression of affected genes. The lead CpG position (cg24509398) falls at the gene body of EYA3 (P-value = 2.77×10-10), a tyrosine phosphatase connected with pulmonary vascular remodeling, a key process in the asthma pathology. Logistic regression analysis resulted in a diagnostic epigenetic signature of 3-DMPs (involving genes ZNF2698, LOC102723354 and RPL15/NKIRAS1) that allows to efficiently differentiate sequelae cases from CR-RSV patients (AUC = 1.00). Enrichment pathway analysis reveals the role of the cell cycle checkpoint (FDR P-value = 4.71×10-2), DNA damage (FDP-value = 2.53×10-2), and DNA integrity checkpoint (FDR P-value = 2.56×10-2) in differentiating sequelae from CR-RSV patients. Conclusions Epigenetic mechanisms might play a fundamental role in the long-term sequelae after RSV infection, contributing to explain the different phenotypes observed.