Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low Excitation Working Frequency Capacitively Coupled Contactless Conductivity Detection (C4D) Sensor for Microfluidic Devices

In this work, a new capacitively coupled contactless conductivity detection (C4D) sensor for microfluidic devices is developed. By introducing an LC circuit, the working frequency of the new C4D sensor can be lowered by the adjustments of the inductor and the capacitance of the LC circuit. The limits of detection (LODs) of the new C4D sensor for conductivity/ion concentration measurement can be improved. Conductivity measurement experiments with KCl solutions were carried out in microfluidic devices (500 µm × 50 µm). The experimental results indicate that the developed C4D sensor can realize the conductivity measurement with low working frequency (less than 50 kHz). The LOD of the C4D sensor for conductivity measurement is estimated to be 2.2 µS/cm. Furthermore, to show the effectiveness of the new C4D sensor for the concentration measurement of other ions (solutions), SO42− and Li+ ion concentration measurement experiments were also carried out at a working frequency of 29.70 kHz. The experimental results show that at low concentrations, the input-output characteristics of the C4D sensor for SO42− and Li+ ion concentration measurement show good linearity with the LODs estimated to be 8.2 µM and 19.0 µM, respectively.
The SF12 Well in LoRaWAN: Problem and End-Device-Based Solutions

LoRaWAN has become a popular technology for the Internet of Things (IoT) device connectivity. One of the expected properties of LoRaWAN is high network scalability. However, LoRaWAN network performance may be compromised when even a relatively small number of devices use link-layer reliability. After failed frame delivery, such devices typically tend to reduce their physical layer bit rate by increasing their spreading factor (SF). This reaction increases channel utilization, which may further degrade network performance, even into congestion collapse. When this problem arises, all the devices performing reliable frame transmission end up using SF12 (i.e., the highest SF in LoRaWAN). In this paper, we identify and characterize the described network condition, which we call the SF12 Well, in a range of scenarios and by means of extensive simulations. The results show that by using alternative SF-management techniques it is possible to avoid the problem, while achieving a packet delivery ratio increase of up to a factor of 4.7.
VirIoT: A Cloud of Things That Offers IoT Infrastructures as a Service

Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT’s ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD.
How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing

The possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users’ devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users’ mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.
An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen! J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.
mTORC1 Crosstalk With Stress Granules in Aging and Age-Related Diseases

The mechanistic target of rapamycin complex 1 (mTORC1) kinase is a master regulator of metabolism and aging. A complex signaling network converges on mTORC1 and integrates growth factor, nutrient and stress signals. Aging is a dynamic process characterized by declining cellular survival, renewal, and fertility. Stressors elicited by aging hallmarks such as mitochondrial malfunction, loss of proteostasis, genomic instability and telomere shortening impinge on mTORC1 thereby contributing to age-related processes. Stress granules (SGs) constitute a cytoplasmic non-membranous compartment formed by RNA-protein aggregates, which control RNA metabolism, signaling, and survival under stress. Increasing evidence reveals complex crosstalk between the mTORC1 network and SGs. In this review, we cover stressors elicited by aging hallmarks that impinge on mTORC1 and SGs. We discuss their interplay, and we highlight possible links in the context of aging and age-related diseases.
Impact of Intravenous Alteplase Door-to-Needle Times on 2-Year Mortality in Patients With Acute Ischemic Stroke

Objective: We sought to determine whether administration of Intravenous Thrombolysis (IVT) to patients with Acute Ischemic Stroke (AIS) within 60 min from hospital arrival is associated with lower 2-year mortality. Methods: This retrospective study was conducted among patients receiving IVT in hospitals participating in the Georgia Coverdell Acute Stroke Registry (GCASR) from January 1, 2008 through June 30, 2018. Two-year mortality data was obtained by linking the 2008–2018 Georgia Discharge Data System data and the 2008–2020 Georgia death records. We analyzed the study population in two groups based on the time from hospital arrival to initiation of IVT expressed as Door to Needle time (DTN) in a dichotomized (DTN ≤ 60 vs. > 60 min) fashion. Results: The median age of patients was 68 years, 49.4% were females, and the median NIHSS was 9. DTN ≤60 min was associated with lower 30-day [odds ratio (OR), 0.62; 95% CI, 0.52–0.73; P < 0.0001], 1-year (OR, 0.71; 95% CI, 0.61–0.83; P < 0.0001) and 2-year (OR, 0.76; 95% CI, 0.65–0.88; P = 0.001) mortality as well as lower rates of sICH at 36 h (OR, 0.57; 95% CI, 0.43–0.75; P = 0.0001), higher rates of ambulation at discharge (OR, 1.38; 95% CI, 1.25–1.53; P < 0.0001) and discharge to home (OR, 1.36; 95% CI, 1.23–1.52; P < 0.0001). Conclusion: Faster DTN in patients with AIS was associated with lower 2-year mortality across all age, gender and race subgroups. These findings reinforce the need for intensifying quality improvement measures to reduce DTN in AIS patients.
Optimizing Cognitive Training for the Treatment of Cognitive Dysfunction in Parkinson’s Disease: Current Limitations and Future Directions

Cognitive dysfunction, primarily involving impairments in executive function, visuospatial function and memory, is one of the most common non-motor symptoms of Parkinson’s disease (PD). Currently, the only pharmacological treatments available for the treatment of cognitive dysfunction in PD provide variable benefit, making the search for potential non-pharmacological therapies to improve cognitive function of significant interest. One such therapeutic strategy may be cognitive training (CT), which involves the repetition of standardized tasks with the aim of improving specific aspects of cognition. Several studies have examined the effects of CT in individuals with PD and have shown benefits in a variety of cognitive domains, but the widespread use of CT in these individuals may be limited by motor impairments and other concerns in study design. Here, we discuss the current state of the literature on the use of CT for PD and propose recommendations for future implementation. We also explore the potential use of more recent integrative, adaptive and assistive technologies, such as virtual reality, which may optimize the delivery of CT in PD.
Biological Characterization and Evolutionary Dynamics of Pigeon Paramyxovirus Type 1 in China

Pigeon paramyxovirus type 1 (PPMV-1) is considered as an antigenic variant of Newcastle disease virus (NDV) which has an obvious host preference for pigeons and has caused significant economic losses to the global poultry industry. The evolutionary dynamics of PPMV-1 in China, however, are poorly understood. In this study, we characterized seven PPMV-1 isolates from diseased pigeons collected in Jiangsu, Anhui, and Henan provinces during 2020. Phylogenetic analysis revealed that seven isolates belonged to sub-genotype VI.2. 1.1. 2.2. Biological characterization indicated that seven isolates were mesogenic based on the mean death time (69.6–91.2 h) and intracerebral pathogenicity index (1.19–1.40) and had similar growth kinetics in chicken embryos and CEFs. Furthermore, the four representative viruses (AH/01/20/Pi, JS/06/20/Pi, HN/01/20/Pi, and HN/02/20/Pi) could result in marked cytopathic effects (CPE) in CEFs and induced syncytium formation in Vero cells. Our Bayesian phylogenetic analysis showed that PPMV-1 might first emerge in East China in 1974 and East China had the highest genotypic diversity of PPMV-1. Besides, phylogeographic analysis indicated that East China and South China were probably the major epicenters of dissemination of PPMV-1 in China. Selection pressure analysis and amino acid substitutions analysis revealed that the viral replication complex (NP, P, and L proteins) was likely related with the host preference of PPMV-1. Collectively, this study uncovered the epidemiology and evolutionary dynamics of PPMV-1 circulating in China, emphasizing the importance of strengthening the monitoring of PPMV-1 in East China and South China and providing significant clues for further studies on the molecular mechanism underlying host preference of PPMV-1.
Basal and Calcium-Stimulated Procalcitonin for the Diagnosis of Medullary Thyroid Cancers: Lights and Shadows

Background Procalcitonin (proCt) was recently proposed as an alternative or in addition to calcitonin (Ct) in medullary thyroid cancer (MTC) diagnostics. Methods Serum basal Ct (bCt) and proCt (bproCt) levels were measured before surgery from a consecutive series of patients with (n=43) and without (n=75) MTC, retrospectively collected in Padua. Serum bproCt, bCt and stimulated proCt and Ct (sproCt and sCt) were measured in another consecutive series of 33 patients seen at three tertiary-level institutions undergoing a calcium stimulation test prior to surgery, 20 of them with a final diagnosis of MTC, and 13 with non-MTC nodular disease. Results Median bproCt levels were higher in MTC than in non-MTC. A positive correlation was found for bproCt with bCt (P<0.01, R2 = 0.75), and with tumor size (P<0.01, R2 = 0.39). The cut-off for bproCt differentiating between MTC and non-MTC patients was >0.07 ng/ml (sensitivity: 85.7%, specificity: 98.9%, positive predictive value [PPV]: 98.2%, negative predictive value [NPV]: 90.6%, P<0.01). While bproCt was >0.07 ng/ml in 38/39 (97.4%) patients with MTC >10 mm, it was above said cut-off only in 15/23 (65.2%) patients with tumors ≤10 mm. A sproCt >0.19 ng/ml was able to identify MTC [sensitivity: 90.0%, specificity:100.0%, PPV: 100.0%, NPV: 86.7% (P<0.01)]. Conclusions Our data suggest that bproCt can be a good adjunct to Ct for MTC diagnostic purposes. In consideration of its high specificity, it can be used in combination with Ct in MTC diagnostics, particularly in the case of mildly elevated basal Ct levels.
Prognostic Factors and Surgery for Breast Cancer Patients With Locoregional Recurrence: An Analysis of 5,202 Consecutive Patients

Purpose With the application of “less extensive surgery” in breast cancer treatment, the pattern of locoregional recurrence (LRR) has significantly changed. This study aims to evaluate the risk and prognostic factors of LRR in a recent large breast cancer cohort. Methods Consecutive early breast cancer patients who received surgery from January 2009 to March 2018 in Shanghai Ruijin Hospital were retrospectively analyzed. LRR was defined as recurrence at the ipsilateral breast (IBTR), chest wall, or regional lymph nodes and without concurrent distant metastasis (DM). Patients’ characteristics and survival were compared among these groups. Results Among 5,202 patients included, 87 (1.7%) and 265 (5.1%) experienced LRR and DM as first event after a median 47.0 (3.0–122.5) months’ follow-up. LRR was significantly associated with large tumor size and positive lymph node status (p < 0.05). Forty (46.0%) patients received further salvage surgery after LRR and had a significantly better 3-year post-recurrence overall survival than those who did not (94.7% vs. 60.7%, p = 0.012). Multivariate analysis showed that salvage surgery for LRR was independently associated with better survival (HR = 0.12, 95% CI 0.02–0.93, p = 0.043) along with estrogen receptor (ER) positivity (HR = 0.33, 95% CI 0.12–0.91, p = 0.033). Conclusion LRR rate was relatively low in recent era of breast cancer treatment. Tumor size and lymph node status were associated with risk of LRR, and salvage surgery for selected LRR patients achieved an excellent outcome.
Pathological Skin Picking: Phenomenology and Associations With Emotions, Self-Esteem, Body Image, and Subjective Physical Well-Being

Pathological skin picking (PSP) refers to the repetitive manipulation of the skin causing wounds, scars, emotional distress, and social impairment. Skin picking disorder was first recognized as a distinct disorder in the DSM-5 and is still understudied in terms of phenomenology, etiology, and associated consequences. However, the body-related pathology suggests that the relation to the own body might be a crucial factor in PSP. Previous studies provided first insights that affected individuals show a more negative body image and lower self-esteem than healthy individuals. The present study sought to investigate skin picking phenomenology, related emotions as well as associations with self-esteem, body image, and subjective physical well-being. The study was conducted as an open online study, and recruitment was generally targeted to individuals of full age and in addition specifically to individuals with PSP. A total of 363 individuals (82.9% female; age: M = 32.6, SD = 13.92) participated and answered various self-report measures. Nearly half of the sample exceeded the cutoff score for pathological skin picking (N = 163; 44.9%). The results suggest that boredom, bodily tension and strong negative feelings often precede PSP episodes. During the episode most individuals seem to experience a loss of control, trance and positive feelings. After the PSP episode, shame, guilt, anger and anger toward themselves are most prominent. As expected, skin picking severity was positively associated with body image disturbances and negatively with self-esteem, and subjective physical well-being. When controlling for depressive symptoms, all associations were reduced, but those with body image disturbances (r = 0.44; p < 0.001), self-esteem (r = −0.27; p < 0.001), subjective physical well-being (r = −0.22; p < 0.001), and peace of mind (r = 0.30; p < 0.01) remained significant. Moreover, greater skin damage due to skin picking was moderately associated with higher body image disturbances. The results indicate that PSP severity is associated with a negative body image and low self-esteem, and suggest that it may be warranted to consider these aspects in the development of future interventions for PSP. However, relations with body image and self-esteem should be examined in longitudinal studies investigating causal relationships between body image, self-esteem and skin picking. Moreover, PSP phenomenology and the role of specific emotions should be investigated in more detail.
Mechanisms of Proteinuria in HIV

Proteinuria is common in the setting of HIV infection, and may reflect comorbid kidney disease, treatment-related nephrotoxicity, and HIV-related glomerular diseases. The mechanisms of podocyte and tubulointerstial injury in HIV-associated nephropathy (HIVAN) have been the subject of intense investigation over the past four decades. The pathologic contributions of viral gene expression, dysregulated innate immune signaling, and ancestry-driven genetic risk modifiers have been explored in sophisticated cellular and whole animal models of disease. These studies provide evidence that injury-induced podocyte dedifferentiation, hyperplasia, cytoskeletal dysregulation, and apoptosis may cause the loss of glomerular filtration barrier integrity and slit diaphragm performance that facilitates proteinuria and tuft collapse in HIVAN. Although the incidence of HIVAN has declined with the introduction of antiretroviral therapy, the collapsing FSGS lesion has been observed in the context of other viral infections and chronic autoimmune disorders, and with the use of interferon-based therapies in genetically susceptible populations. This highlights the fact that the lesion is not specific to HIVAN and that the role of the immune system in aggravating podocyte injury warrants further exploration. This review will summarize our progress in characterizing the molecular mechanisms of podocyte dysfunction in HIVAN and other forms of HIV-associated kidney disease.
Efficient Self-Supervised Metric Information Retrieval: A Bibliography Based Method Applied to COVID Literature

The literature on coronaviruses counts more than 300,000 publications. Finding relevant papers concerning arbitrary queries is essential to discovery helpful knowledge. Current best information retrieval (IR) use deep learning approaches and need supervised training sets with labeled data, namely to know a priori the queries and their corresponding relevant papers. Creating such labeled datasets is time-expensive and requires prominent experts’ efforts, resources insufficiently available under a pandemic time pressure. We present a new self-supervised solution, called SUBLIMER, that does not require labels to learn to search on corpora of scientific papers for most relevant against arbitrary queries. SUBLIMER is a novel efficient IR engine trained on the unsupervised COVID-19 Open Research Dataset (CORD19), using deep metric learning. The core point of our self-supervised approach is that it uses no labels, but exploits the bibliography citations from papers to create a latent space where their spatial proximity is a metric of semantic similarity; for this reason, it can also be applied to other domains of papers corpora. SUBLIMER, despite is self-supervised, outperforms the Precision@5 (P@5) and Bpref of the state-of-the-art competitors on CORD19, which, differently from our approach, require both labeled datasets and a number of trainable parameters that is an order of magnitude higher than our.
Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

Compound extremes pose immense challenges and hazards to communities, and this is particularly true for compound hydrometeorological extremes associated with deadly floods, surges, droughts, and heat waves. To mitigate and better adapt to compound hydrometeorological extremes, we need to better understand the state of knowledge of such extremes. Here we review the current advances in understanding compound hydrometeorological extremes: compound heat wave and drought (hot-dry), compound heat stress and extreme precipitation (hot-wet), cold-wet, cold-dry and compound flooding. We focus on the drivers of these extremes and methods used to investigate and quantify their associated risk. Overall, hot-dry compound extremes are tied to subtropical highs, blocking highs, atmospheric stagnation events, and planetary wave patterns, which are modulated by atmosphere-land feedbacks. Compared with hot-dry compound extremes, hot-wet events are less examined in the literature with most works focusing on case studies. The cold-wet compound events are commonly associated with snowfall and cold frontal systems. Although cold-dry events have been found to decrease, their underlying mechanisms require further investigation. Compound flooding encompasses storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding. Overall, there is a growing risk of compound flooding in the future due to changes in sea level rise, storm intensity, storm precipitation, and land-use-land-cover change. To understand processes and interactions underlying compound extremes, numerical models have been used to complement statistical modeling of the dependence between the components of compound extremes. While global climate models can simulate certain types of compound extremes, high-resolution regional models coupled with land and hydrological models are required to simulate the variability of compound extremes and to project changes in the risk of such extremes. In terms of statistical modeling of compound extremes, previous studies have used empirical approach, event coincidence analysis, multivariate distribution, the indicator approach, quantile regression and the Markov Chain method to understand the dependence, greatly advancing the state of science of compound extremes. Overall, the selection of methods depends on the type of compound extremes of interests and relevant variables.