Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing WSN-based IoT Systems using MDE Techniques and Petri-net Models

There are various computation components, operating systems, and ﬁrmware used in the development of the Internet of Things (IoT). This variety increases the structural complexity and development cost and eﬀort of the IoT systems. Besides, analyzing and troubleshooting these systems are time-consuming, costly, and cumbersome. To address these problems, this study aims to provide a higher level of abstraction for analyzing and developing IoT systems using Model-driven Engineering techniques and Petri-net models. To this end, a Domain-speciﬁc modeling Language (DSML), called DSML4Contiki, was presented in our previous study for the development of Wireless Sensor Systems (WSN) based IoT systems. The current study extends DSML4Contiki by providing an automated mechanism to analyze the IoT system at the early design phase, resulting in a reduction of the number of errors in the sys-tem and iterations in the development process. This is achieved using model transformation rules to transform the domain models at a high level to both the target platform artifacts as well as Petri-net models. By applying k-boundedness property checking on the Petri-net models, diﬀerent analyses (such as power consumption, bottlenecks, and ﬁrst crashing node) are realized for WSN based IoT systems. To evaluate the proposed approach, the engineering of a smart ﬁre detection system is considered as a case study.
The use of resuscitative endovascular balloon occlusion of the aorta (REBOA) for non‐traumatic cardiac arrest: A review

Resuscitative endovascular balloon occlusion of the aorta (REBOA) has been proposed as a novel approach to managing non‐traumatic cardiac arrest (NTCA). During cardiac arrest, cardiac output ceases and perfusion of vital organs is compromised. Traditional advanced cardiac life support (ACLS) measures and cardiopulmonary resuscitation are often unable to achieve return of spontaneous circulation (ROSC). During insertion of REBOA a balloon‐tipped catheter is placed into the femoral artery and advanced in a retrograde manner into the aorta while the patient is undergoing cardiopulmonary resuscitation (CPR). The balloon is then inflated to fully occlude the aorta. The literature surrounding the use of aortic occlusion in non‐traumatic cardiac arrest is limited to animal studies, case reports and one recent non‐controlled feasibility trial. In both human and animal studies, preliminary data show that REBOA may improve coronary and cerebral perfusion pressures and key physiologic parameters during cardiac arrest resuscitation, and animal data have demonstrated improved rates of ROSC. Multiple questions remain before REBOA can be considered as an adjunct to ACLS. If demonstrated to be effective clinically, REBOA represents a potentially cost‐effective and generalizable intervention that may improve quality of life for patients with non‐traumatic cardiac arrest.
Multiple Inertial Measurement Unit Combination and Location for Center of Pressure Prediction in Gait

Center of pressure (COP) during a gait cycle indicates crucial information with regard to fall risk such as balance capacity. The drawbacks of conventional research instruments include inconvenient use during activities of daily living and expensive costs. The present study illustrates the promising fall-relevant information predicted by acceleration and angular velocity data from different placement sensors with machine learning techniques. This approach is inspired by the emerging machine learning technique, specifically the long short-term memory (LSTM), which is often used in time series data and aims to decrease the burden of the user while using the novel wearable technology. The Jaccard similarity coefficient, which implies the consistency of profile alignment between prediction and real situation, achieved 94% accuracy in the walking direction. Furthermore, the number of sensors used and the placement influenced the feasibility of an application. The outcome revealed that the accuracy could exceed 90% with only one sensor placed on the foot in the walking direction, and the toe would be the best location for sensor placement. To examine the performance of machine learning, the current study employed two parameters from different perspectives. One is a commonly used parameter, which represented the error, and the other investigated the similarity between the prediction and ground truth. From a similarity perspective, the parameter can be used as a metric to assess the consistency of profile alignment.
Losing Ourselves: Active Inference, Depersonalization, and Meditation

Disruptions in the ordinary sense of selfhood underpin both pathological and “enlightened” states of consciousness. People suffering from depersonalization can experience the loss of a sense of self as devastating, often accompanied by intense feelings of alienation, fear, and hopelessness. However, for meditative contemplatives from various traditions, “selfless” experiences are highly sought after, being associated with enduring peace and joy. Little is understood about how these contrasting dysphoric and euphoric experiences should be conceptualized. In this paper, we propose a unified account of these selfless experiences within the active inference framework. Building on our recent active inference research, we propose an account of the experiences of selfhood as emerging from a temporally deep generative model. We go on to develop a view of the self as playing a central role in structuring ordinary experience by “tuning” agents to the counterfactually rich possibilities for action. Finally, we explore how depersonalization may result from an inferred loss of allostatic control and contrast this phenomenology with selfless experiences reported by meditation practitioners. We will show how, by beginning with a conception of self-modeling within an active inference framework, we have available to us a new way of conceptualizing the striking experiential similarities and important differences between these selfless experiences within a unifying theoretical framework. We will explore the implications for understanding and treating dissociative disorders, as well as elucidate both the therapeutic potential, and possible dangers, of meditation.
In vitro Label Free Raman Microspectroscopic Analysis to Monitor the Uptake, Fate and Impacts of Nanoparticle Based Materials

The continued emergence of nanoscale materials for nanoparticle-based therapy, sensing and imaging, as well as their more general adoption in a broad range of industrial applications, has placed increasing demands on the ability to assess their interactions and impacts at a cellular and subcellular level, both in terms of potentially beneficial and detrimental effects. Notably, however, many such materials have been shown to interfere with conventional in vitro cellular assays that record only a single colorimetric end-point, challenging the ability to rapidly screen cytological responses. As an alternative, Raman microspectroscopy can spatially profile the biochemical content of cells, and any changes to it as a result of exogenous agents, such as toxicants or therapeutic agents, in a label free manner. In the confocal mode, analysis can be performed at a subcellular level. The technique has been employed to confirm the cellular uptake and subcellular localization of polystyrene nanoparticles (PSNPs), graphene and molybdenum disulfide micro/nano plates (MoS2), based on their respective characteristic spectroscopic signatures. In the case of PSNPs it was further employed to identify their local subcellular environment in endosomes, lysosomes and endoplasmic reticulum, while for MoS2 particles, it was employed to monitor subcellular degradation as a function of time. For amine functionalized PSNPs, the potential of Raman microspectroscopy to quantitatively characterize the dose and time dependent toxic responses has been explored, in a number of cell lines. Comparing the responses to those of poly (amidoamine) nanoscale polymeric dendrimers, differentiation of apoptotic and necrotic pathways based on the cellular spectroscopic responses was demonstrated. Drawing in particular from the experience of the authors, this paper details the progress to date in the development of applications of Raman microspectroscopy for in vitro, label free analysis of the uptake, fate and impacts of nanoparticle based materials, in vitro, and the prospects for the development of a routine, label free high content spectroscopic analysis technique.
Metannot: A succinct data structure for compression of colors in dynamic de Bruijn graphs

Much of the DNA and RNA sequencing data available is in the form of high-throughput sequencing (HTS) reads and is currently unindexed by established sequence search databases. Recent succinct data structures for indexing both reference sequences and HTS data, along with associated metadata, have been based on either hashing or graph models, but many of these structures are static in nature, and thus, not well-suited as backends for dynamic databases. We propose a parallel construction method for and novel application of the wavelet trie as a dynamic data structure for compressing and indexing graph metadata. By developing an algorithm for merging wavelet tries, we are able to construct large tries in parallel by merging smaller tries constructed concurrently from batches of data. When compared against general compression algorithms and those developed specifically for graph colors (VARI and Rainbowfish), our method achieves compression ratios superior to gzip and VARI, converging to compression ratios of 6.5% to 2% on data sets constructed from over 600 virus genomes. While marginally worse than compression by bzip2 or Rainbowfish, this structure allows for both fast extension and query. We also found that additionally encoding graph topology metadata improved compression ratios, particularly on data sets consisting of several mutually-exclusive reference genomes. It was also observed that the compression ratio of wavelet tries grew sublinearly with the density of the annotation matrices. This work is a significant step towards implementing a dynamic data structure for indexing large annotated sequence data sets that supports fast query and update operations. At the time of writing, no established standard tool has filled this niche.
Crafting Our Own Biased Media Diets: The Effects of Confirmation, Source, and Negativity Bias on Selective Attendance to Online News

ABSTRACT Audiences’ online information acquisition has raised questions about the nature of selective exposure in today’s high-choice and fragmented news environment. To offer an overview of the relative contribution of several key drivers of selective exposure to political news, we assess the guiding influence of (1) confirmation bias, (2) source bias, and (3) negativity bias. The findings of an experiment in two countries (UK and US, N = 858), demonstrate that confirmation bias has the most profound effect on selective exposure into news on immigration and the privatization of health-care systems, in conjunction with comparable and significant effects of source and negativity biases. The studied moderating role of preexisting levels of involvement and skepticism provides additional insights into news selection mechanisms. We conclude that today’s online media diets are guided by different biases, which may fragment audiences based on their news preferences and issue positions.
Origin of the Time Lag Phenomenon and the Global Signal in Resting-State fMRI

The global mean signal of resting-state fMRI (rs-fMRI) shows a characteristic spatiotemporal pattern that is closely related to the pattern of vascular perfusion. Although being increasingly adopted in the mapping of the flow of neural activity, the mechanism that gives rise to the BOLD signal time lag remains controversial. In the present study, we compared the time lag of the global mean signal with those of the local network components obtained by applying temporal independent component analysis to the resting-state fMRI data, as well as by using simultaneous wide-field visual stimulation, and demonstrated that the time lag patterns are highly similar across all types of data. These results suggest that the time lag of the rs-fMRI signal reflects the local variance of the hemodynamic responses rather than the arrival or transit time of the stimulus, whether the trigger is neuronal or non-neuronal in origin as long as it is mediated by local hemodynamic responses. Examinations of the internal carotid artery signal further confirmed that the arterial signal is tightly inversely coupled with the global mean signal in accordance with previous studies, presumably reflecting the blood flow or blood pressure changes that are occurring almost simultaneously in the internal carotid artery and the cerebral pial/capillary arteries, within the low-frequency component in human rs-fMRI.
A Deep Learning Model to Predict the Response to Neoadjuvant Chemoradiotherapy by the Pretreatment Apparent Diffusion Coefficient Images of Locally Advanced Rectal Cancer

Background and Purpose Pretreatment prediction of the response to neoadjuvant chemoradiotherapy (NCRT) helps to determine the subsequent plans for the patients with locally advanced rectal cancer (LARC). If the good responders (GR) and non-good responders (non-GR) can be accurately predicted, they can choose to intensify the neoadjuvant chemoradiotherapy to decrease the risk of tumor progression during NCRT and increase the chance of organ preservation. Compared with radiomics methods, deep learning (DL) may adaptively extract features from the images without the need of feature definition. However, DL suffers from limited training samples and signal discrepancy among different scanners. This study aims to construct a DL model to predict GRs by training apparent diffusion coefficient (ADC) images from different scanners. Methods The study retrospectively recruited 700 participants, chronologically divided into a training group (n = 500) and a test group (n = 200). Deep convolutional neural networks were constructed to classify GRs and non-GRs. The networks were designed with a max-pooling layer parallelized by a center-cropping layer to extract features from both the macro and micro scale. ADC images and T2-weighted images were collected at 1.5 Tesla and 3.0 Tesla. The networks were trained by the image patches delineated by radiologists in ADC images and T2-weighted images, respectively. Pathological results were used as the ground truth. The deep learning models were evaluated on the test group and compared with the prediction by mean ADC value. Results Area under curve (AUC) of receiver operating characteristic (ROC) is 0.851 (95% CI: 0.789–0.914) for DL model with ADC images (DL_ADC), significantly larger (P = 0.018, Z = 2.367) than that of mean ADC with AUC = 0.723 (95% CI: 0.637–0.809). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of DL_ADC model are 94.3%, 68.3%, 87.4% and 83.7%, respectively. The DL model with T2-weighted images (DL_T2) produces an AUC of 0.721 (95% CI: 0.640–0.802), significantly (P = 0.000, Z = 3.554) lower than that of DL_ADC model. Conclusion Deep learning model reveals the potential of pretreatment apparent diffusion coefficient images for the prediction of good responders to neoadjuvant chemoradiotherapy.
Older Adults Encode Task-Irrelevant Stimuli, but Can This Side-Effect be Useful to Them?

We studied whether, due to deteriorating inhibitory functions, older people are more likely to process irrelevant stimuli; and if so, could they later use this information better than young adults. In the study phase of our experiment, a Posner-type gaze-cued version of a Simon task was performed in which we presented task-irrelevant cues, where faces or patches with either left- or right-looking dots for the pupil of the eye preceded the task to press a button congruent or incongruent with the presentation side of the target stimulus. In the follow-up test phase, participants completed an unexpected facial recognition test. In the study phase not only a decreased P1, but also an increased N170 amplitude of the event-related potentials (ERPs) were found in older, compared to younger adults, and also for faces compared to patches. Even though in the test phase both age-groups could recognize the faces better than statistically by chance, neither the older nor the younger participants could discriminate them effectively. The late positive component (LPC)—the ERP correlates of the old/new effect, being the higher amplitude for the earlier presented stimuli when compared with the unseen stimuli during the recognition test—was not evolved in the older group, while a reversed old/new effect was seen in younger participants: higher amplitude was found in New-Right and Old-Wrong conditions (for faces they did not recognize independent of seeing them before) compared to Old-Right and New-Wrong conditions (for faces they thought they recognized from the study phase). In conclusion, although older adults showed enhanced processing of task-irrelevant stimuli compared to younger adults, as indicated by the N170 amplitude, however, they were not able to utilize this information in a later task, as was suggested by the recognition rate and LPC amplitude results.
Predictors and Changes in Paternal Perinatal Depression Profiles—Insights From the DREAM Study

In contrast to the large body of research on maternal perinatal depression, paternal perinatal mental health has received little attention; and longitudinal studies on paternal perinatal depression, following (expectant) fathers over time, are exceedingly rare. This population-based study aimed to (1) estimate prevalence rates of perinatal depression symptoms among German (expectant) fathers, (2) identify differential profiles of perinatal depression in (expectant) fathers, (3) determine modifiable predictors of latent depression profiles, and (4) estimate how membership in subgroups changes during the perinatal period. Data were derived from the longitudinal cohort study DREAM (Dresden Study on Parenting, Work, and Mental Health), including 1,027 (expectant) fathers responding to the Edinburgh Postnatal Depression Scale (EPDS) during pregnancy and 8 weeks postpartum. Unobserved profiles of paternal perinatal depression and movement between profiles were investigated using latent transition analysis. A number of potential predictors with regard to lifestyle and current life situation were included as covariates. We found that rates of paternal depression symptoms decreased with 9% during pregnancy to 5% at 8 weeks postpartum. Further, four latent depression profiles emerged: most (expectant) fathers did not exhibit any depression symptoms (not depressed), whereas some reported mainly the absence of joy (anhedonic) and some experienced mainly self-blame and worries (anxious-worried). The depressive profile was characterized by endorsement to most symptoms of perinatal depression. Perceived social support and relationship satisfaction appeared to be protective against paternal depression symptoms. Differential transitioning or stability patterns in profile membership during the perinatal period were found, whereas the depressive profile showed to be the least stable. This prospective population-based cohort study is the first study to identify paternal perinatal depression profiles together with their predictors and changes during the perinatal period. Future research is warranted to examine whether the identified paternal depression profiles have differential outcomes, particularly in the context of person-centered prevention and intervention strategies. Further, longitudinal trajectories of paternal depression ought to be studied, taking into account additional measurement points as well as modifiable risk factors.
Limited effect of duration of CMV infection on adaptive immunity and frailty: insights from a 27‐year‐long longitudinal study

Cytomegalovirus infection is thought to affect the immune system and to impact general health during ageing. Higher CMV‐specific antibody levels in the elderly are generally assumed to reflect experienced viral reactivation during life. Furthermore, high levels of terminally differentiated and CMV‐specific T cells are hallmarks of CMV infection, which are thought to expand over time, a process also referred to as memory inflation.
Neuro-symbolic Computation for XAI: Towards a Unified Model

The idea of integrating symbolic and sub-symbolic approaches to make intelligent systems (IS) under-standable and explainable is at the core of new fields such as neuro-symbolic computing (NSC). This work lays under the umbrella of NSC, and aims at a twofold objective. First, we present a set of guidelines aimed at building explainable IS, which leverage on logic induction and constraints to integrate symbolic and sub-symbolic approaches. Then, we reify the proposed guidelines into a case study to show their effectiveness and potential, presenting a prototype built on the top of some NSC technologies.
Bacteriome-Associated Endosymbiotic Bacteria of Nosodendron Tree Sap Beetles (Coleoptera: Nosodendridae)

The family Nosodendridae is a small group of tree sap beetles with only 91 described species representing three genera from the world. In 1930s, bacteria-harboring symbiotic organs, called bacteriomes, were briefly described in a European species Nosodendron fasciculare. Since then, however, no studies have been conducted on the nosodendrid endosymbiosis for decades. Here we investigated the bacteriomes and the endosymbiotic bacteria of Nosodendron coenosum and Nosodendron asiaticum using molecular phylogenetic and histological approaches. In adults and larvae, a pair of slender bacteriomes were found along both sides of the midgut. The bacteriomes consisted of large bacteriocytes at the center and flat sheath cells on the surface. Fluorescence in situ hybridization detected preferential localization of the endosymbiotic bacteria in the cytoplasm of the bacteriocytes. In reproductive adult females, the endosymbiotic bacteria were also detected at the infection zone in the ovarioles and on the surface of growing oocytes, indicating vertical symbiont transmission via ovarial passage. Transmission electron microscopy unveiled bizarre structural features of the bacteriocytes, whose cytoplasm exhibited degenerate cytology with deformed endosymbiont cells. Molecular phylogenetic analysis revealed that the nosodendrid endosymbionts formed a distinct clade in the Bacteroidetes. The nosodendrid endosymbionts were the most closely related to the bacteriome endosymbionts of bostrichid powderpost beetles and also allied to the bacteriome endosymbionts of silvanid grain beetles, uncovering an unexpected endosymbiont relationship across the unrelated beetle families Nosodendridae, Bostrichidae and Silvanidae. Host-symbiont co-evolution and presumable biological roles of the endosymbiotic bacteria are discussed.
Information Technology for Extracting the Accurate, Compact and Interpretable Mamdani-type Rule Base

The paper dedicates to development of an information technology for the design of Mamdani-type fuzzy systems derived from experimental datasets. The proposed information technology provides synthesis of accurate, compact and interpretable fuzzy rule bases. A distinctive feature of the information technology is the following four-stage operation scheme: 1) generating a list of adequate candidate-rules; 2) selection of the rules; 3) reduction of the antecedents of the selected rules; 4) parametric tuning of a fuzzy rule base. The criteria of accuracy and interpretability are involved in the first and the fourth stages, and the criteria of accuracy and compactness are used in the second and the third stages. New models for interpretability protection of fuzzy rule bases during the tuning are also proposed. Examples of the application of this information technology for solving 7 identification problems with real experimental data are given.