Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital assemblages, information infrastructures, and mobile knowledge work

We theorize mobile knowledge workers’ uses of digital and material resources in support of their working practices. We do so to advance current conceptualizations of both “information infrastructures” and “digital assemblages” as elements of contemporary knowledge work. We focus on mobile knowledge workers as they are (increasingly) self-employed (e.g., as freelancers, entrepreneurs, temporary workers, and contractors), competing for work, and collaborating with others: one likely future of work that we can study empirically. To pursue their work, mobile knowledge workers draw together collections of commodity digital technologies or digital assemblages (e.g., laptops, phones, public WiFi, cloud storage, and apps), relying on a reservoir of knowledge about new and emergent means to navigate this professional terrain. We find that digital assemblages are created and repurposed by workers in their infrastructuring practices and in response to mobility demands and technological environments. In their constitution, they are generative to both collaborative and organizational goals. Building from this, we theorize that digital assemblages, as individuated forms of information infrastructure, sustain stability and internal cohesion even as they allow for openness and generativity.
Line Voltage Sensorless Control of Grid-Connected Inverters Using Multisampling

Multisampling control provides an attractive way to improve the dynamics and stability of high-power three-phase grid-connected inverters. Beyond the desired performance in current control, more information can be acquired through multisampling. However, such an assumption is rarely discussed in grid-connected inverters. In order to fill this gap, a line voltage sensorless control scheme is proposed for LCL-filtered inverters. In this article, a multisampling is performed on the inverter-side current, and the linear regression of the multisampled current during zero vectors of inverter-modulation voltage is employed to estimate the filter capacitor voltage. Consequently, the cost of line voltage sensors is reduced and the failure of line voltage sensors is prevented when using multisampling current control. Furthermore, to address start-up transients, the line voltage can still be estimated by temporarily locking the upper arms of the inverter, and dc-link voltage can be precharged to the target value at the same time. Experimental results validate the effectiveness of the method on a down-scale inverter.
Longitudinal to transverse metachronal wave transitions in an in-vitro model of ciliated bronchial epithelium

Myriads of cilia beat on ciliated epithelia, which are ubiquitous in life. When ciliary beats are synchronized, metachronal waves emerge, whose direction of propagation depends on the living system in an unexplained way. We show on a reconstructed human bronchial epithelium in-vitro that the direction of propagation is determined by the ability of mucus to be transported at the epithelial surface. Numerical simulations show that longitudinal waves maximise the transport of mucus while transverse waves, observed when the mucus is rigid and still, minimize the energy dissipated by the cilia.
Lightweight deep residual network for alzheimer's disease classification using sMRI slices

Alzheimer’s disease (AD) is a degenerative brain disease and the most common cause of dementia. In recent years, with the widespread application of artificial intelligence in the medical field, various deep learning-based methods have been applied for AD detection using sMRI images. Many of these networks achieved AD vs HC (Healthy Control) classification accuracy of up to 90%but with a large number of computational parameters and floating point operations (FLOPs). In this paper, we adopt a novel ghost module, which uses a series of cheap operations of linear transformation to generate more feature maps, embedded into our designed ResNet architecture for task of AD vs HC classification. According to experiments on the OASIS dataset, our lightweight network achieves an optimistic accuracy of 97.92%and its total parameters are dozens of times smaller than state-of-the-art deep learning networks. Our proposed AD classification network achieves better performance while the computational cost is reduced significantly.
Histamine Increases Th2 Cytokine-Induced CCL18 Expression in Human M2 Macrophages

The chemokine CCL18 is produced in cells of the myelomonocytic lineage and represents one of the most highly expressed chemokines in lesional skin and serum of atopic dermatitis patients. We investigated the role of histamine in CCL18 production in human monocyte-derived M2 macrophages differentiated in the presence of M-CSF and activated with IL-4, IL-13 or with IL-10. Since expression and regulation of histamine H1 receptor (H1R), H2R and H4R by IL-4 and IL-13 on human M2 macrophages were described, we analyzed expression of the histamine receptors in response to IL-10 stimulation by quantitative RT-PCR. IL-10 upregulated H2R and downregulated H4R mRNA expression by trend in M2 macrophages. IL-10, but in a more pronounced manner, IL-4 and IL-13, also upregulated CCL18. Histamine increased the cytokine-induced upregulation of CCL18 mRNA expression by stimulating the H2R. This effect was stronger in IL-10-stimulated M2 macrophages where the upregulation of CCL18 was confirmed at the protein level by ELISA using selective histamine receptor agonist and antagonists. The histamine-induced CCL18 upregulation in IL-10-activated M2 macrophages was almost similar in cells obtained from atopic dermatitis patients compared to cells from healthy control persons. In summary, our data stress a new function of histamine showing upregulation of the Th2 cells attracting chemokine CCL18 in human, activated M2 macrophages. This may have an impact on the course of atopic dermatitis and for the development of new therapeutic interventions.
Development of Usable Security Heuristics for Fintech

Investments in cybersecurity over the years have led to the availability of strong technical countermeasures and innovations that are being increasingly leveraged to strengthen the security posture of financial services systems. The effort to improve the security posture of the human element of financial services systems has not matched the effort in developing technical countermeasures, thereby undoing the gains of the later. One area where such problem exist is in Fintech where emphasis is placed on developing innovative and secured technical financial models aimed at making financial services more accessible through the mobile phone. These Fintech solutions however have shortcomings in securing the human element. This study seeks to address this problem through the development of heuristics that can be applied in the evaluation or design of Usable Security in Fintech. This study developed twelve (12) initial Usable Security heuristics which were validated through expert review. The heuristics were developed through an iterative approach that comprises a survey of Fintech users, semi-structured interviews of Fintech solution providers and thematic analysis of relevant literature. The findings of the study show that application of the developed heuristic provides for Usable Security.
Unsupervised Grammatical Pattern Discovery from Arabic Extra Large Corpora

: Many methods have been applied to automatic construction or expansion of lexical semantic resources. Most follow the distributional hypothesis applied to lexical context of words, eliminating grammatical context (stop-words). This paper will show that the grammatical context can yield information about semantic properties of words, if the corpus be large enough. In order to do this, we present an unsupervised pattern-based model building semantic word categories from large corpora, devised for resource-poor languages. We divide the vocabulary between high-frequency and lower frequency items, and explore the patterns formed by high-frequency items in the neighborhood of lower frequency words. Word categories are then created by clustering. This is done on a very large Arabic corpus, and, for comparison, on a large English corpus; results are evaluated with direct and indirect evaluation methods. We compare the results with state-of-the-art lexical models for performance and for computation time.
Prediction Sentiment Polarity using Past Textual Content and CNN-LSTM Neural Networks

: Sentiment analysis in social networks plays an important role in different areas, and one of its main tasks is to determine the polarity of sentiments about many things. In this paper, our goal is to create a supervised machine learning model for predicting the polarity of users’ sentiments, based solely on their textual history, about a predeﬁned topic. The proposed approach is based on neural network architectures: the long short term memory (LSTM) and the convolutional neural networks (CNN). To experiment our system, we have purposely created a collection from SemEval-2017 data. The results revealed that our approach outperforms the comparison approach.
An Integrated Model to Improve Medication Reconciliation in Oncology: Prospective Interventional Study

Background Accurate medication reconciliation reduces the risk of drug incompatibilities and adverse events that can occur during transitions in care. Community pharmacies (CPs) are a crucial part of the health care system and could be involved in collecting essential information on conventional and supplementary drugs used at home. Objective The aim of this paper was to establish an alliance between our cancer institute, Istituto Romagnolo per lo Studio dei Tumori (IRST), and CPs, the latter entrusted with the completion of a pharmacological recognition survey. We also aimed to integrate the national information technology (IT) platform of CPs with the electronic medical records of IRST. Methods Cancer patients undergoing antiblastic treatments were invited to select a CP taking part in the study and to complete the pharmacological recognition step. The information collected by the pharmacist was sent to the electronic medical records of IRST through the new IT platform, after which the oncologist performed the reconciliation process. Results A total of 66 CPs completed surveys for 134 patients. An average of 5.9 drugs per patient was used at home, with 12 or more used in the most advanced age groups. Moreover, 60% (80/134) of the patients used nonconventional products or critical foods. Some potential interactions between nonconventional medications and cancer treatments were reported. Conclusions In the PROF-1 (Progetto di Rete in Oncologia con le Farmacie di comunità della Romagna) study, an alliance was created between our cancer center and CPs to improve medication reconciliation, and a new integrated IT platform was validated. Trial Registration ClinicalTrials.gov NCT04796142; https://clinicaltrials.gov/ct2/show/NCT04796142
An Impact Analysis of Artificial Light at Night (ALAN) on Bats. A Case Study of the Historic Monument and Natura 2000 Wisłoujście Fortress in Gdansk, Poland

The artificial light at night (ALAN) present in many cities and towns has a negative impact on numerous organisms that live alongside humans, including bats. Therefore, we investigated if the artificial illumination of the historic Wisłoujście Fortress in Gdańsk, Poland (part of the Natura 2000 network), during nighttime events, which included an outdoor electronic dance music (EDM) festival, might be responsible for increased light pollution and the decline in recent years of the pond bat (Myotis dasycneme). An assessment of light pollution levels was made using the methods of geographical information system (GIS) and free-of-charge satellite remote sensing (SRS) technology. Moreover, this paper reviewed the most important approaches for environmental protection of bats in the context of ecological light pollution, including International, European, and Polish regulatory frameworks. The analysis of this interdisciplinary study confirmed the complexity of the problem and highlighted, too, the need for better control of artificial illumination in such sensitive areas. It also revealed that SRS was not the best light pollution assessment method for this particular case study due to several reasons listed in this paper. As a result, the authors’ proposal for improvements also involved practical recommendations for devising suitable strategies for lighting research and practice in the Natura 2000 Wisłoujście Fortress site located adjacent to urban areas to reduce the potential negative impact of ALAN on bats and their natural habitats.
Design of Password Guessing Prevention Protocol for Levelled-Security System

Most applications use the password for authentication of the legitimate user. These applications maintain a database of username and corresponding password. In databases, passwords are stored in the form of hash values which are irreversible. A potential hacker may use brute force attack or dictionary attack for guessing the password. In brute force attack; hacker tries all possible combinations of passwords to gain unauthorized access to user's account. In a dictionary attack, the hacker uses dictionary file containing possible passwords and tries every password from that file. It is noted that, even though password with certain patterns is accepted as strong by existing systems, they are vulnerable to dictionary attack. The proposed system allows the user to choose a password which is not present in the dictionary. Also, all possible alterations of passwords are matched against the supplied dictionary. Password containing personal information is not accepted by the system. The present study proposes a security method based on login attempts and respective security levels for the password. The concept of security levels is introduced which aims at increasing the strength of the password on detection of malicious activity such as invalid login attempts, login attempts performed for the same user account from two different locations in little time difference which are far away from each other, etc. On detection of any suspicious activity, the security level of the password gets upgraded to the higher level which increases length and strength of the password by making it more complex and hard to crack.
Synthesizing the effects of individual‐level variation on coexistence

Intraspecific trait variation (ITV) is a widespread feature of life, but it is an open question how ITV affects between-species coexistence. Recent theoretical studies have produced contradictory results, with ITV promoting coexistence in some models and undermining coexistence in others. Here we review recent work and propose a new conceptual framework to explain how ITV affects coexistence between two species. We propose that all traits belong to one of two categories: niche traits and hierarchical traits. Niche traits determine an individual’s location on a niche axis or trade-off axis, such that changing an individual’s trait makes it perform better in some circumstances and worse in others. Hierarchical traits represent cases where conspecifics with different traits have the same niche, but one performs better under all circumstances, such that there are winners and losers. Our framework makes predictions for how intraspecific variation in each type of trait affects coexistence by altering stabilizing mechanisms and fitness differences. For example, ITV in niche traits generally weakens the stabilizing mechanism, except when it generates a generalist–specialist trade-off. On the other hand, hierarchical traits tend to impact competitors differently, such that ITV in one species will strengthen the stabilizing mechanism while ITV in the other species will weaken the mechanism. We re-examine 10 studies on ITV and coexistence, along with four novel models, and show that our framework can explain why ITV promotes coexistence in some models and undermines coexistence in others. Overall, our framework reconciles what were previously considered to be contrasting results and provides both theoretical and empirical directions to study the effect of ITVon species coexistence.
Triple-negative breast cancer in children: a case report

Background Triple-negative breast cancer (TNBC) is often considered a breast tumor with a poor prognosis that lacks or decreases the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2). It tends to occur in middle-aged and older women, and cases of TNBC in children are rarely reported. Methods We report a case of basal-like TNBC in a child at our hospital and review the related literature. Results Based on the patient's condition we performed a local mass excision, and despite the postoperative pathological diagnosis of TNBC, this case of basic TNBC had a good prognosis and was very different from conventional basic TNBC in many ways. Conclusions Although secretory breast cancer is a subtype of basal-like TNBC, it has its peculiarities and is completely different from basal-like TNBC. There is a need to raise awareness of the disease, select individualized treatment options, and prevent medical errors.
Mismatch of Visual-Vestibular Information in Virtual Reality: Is Motion Sickness Part of the Brains Attempt to Reduce the Prediction Error?

Visually induced motion sickness (VIMS) is a relevant limiting factor in the use of virtual reality (VR) devices. Understanding the origin of this problem might help to develop strategies to circumvent this limitation. Previous studies have attributed VIMS to a mismatch between visual, and vestibular information, causing ambiguity of the position of the body in relation to its surrounding. Studies using EEG have shown a shift of the power spectrum to lower frequencies while VIMS is experienced. However, little is known about the relationship between the intensity of the VIMS and the changes in these power spectra. Moreover, the effect of different varieties of VIMS on the causal relationship between brain areas is largely unknown. Here, we used EEG to study 14 healthy subjects in a VR environment who were exposed to increasing levels of mismatch between vestibular and visual information. The frequency power and the bivariate transfer entropy as a measure for the information transfer were calculated. We found a direct association between increasing mismatch levels and subjective VIMS. With increasing VIMS, the proportion of slow EEG waves (especially 1–10 Hz) increases, especially in temporo-occipital regions. Furthermore, we found a general decrease in the information flow in most brain areas but especially in brain areas involved in the processing of vestibular signals and the detection of self-motion. We hypothesize that the general shift of frequency power and the decrease in information flow while experiencing high intensity VIMS represent a brain state of a reduced ability to receive, transmit and process information. We further hypothesize that the mechanism of reduced information flow is a general reaction of the brain to an unresolvable mismatch of information. This reaction aims on transforming a currently unstable model with a high prediction error into a stable model in an environment of minimal contradictory information.
Sensitive detection of enrofloxacin using an electrochemiluminescence immunosensor based on gold-functionalized C60 and Au@BSA nanoflowers

A novel competitive electrochemiluminescence (ECL) immunosensor based on CdSe quantum dots was developed for the detection of enrofloxacin (Enro). Fullerene (C60) as a substrate is chemically surface modified using L-cysteine to obtain better water solubility and abundant amino and thiol groups that can load Au nanoparticles. The loading of Au can not only anchor the antigen, but also accelerate the electron transfer at the electrode surface. Since flower-like Au@BSA nanoparticles have abundant amino groups and a large specific surface area, they can act as a probe carrier. In other words, they can immobilize a large number of CdSe QDs via an amidation reaction to amplify the ECL signal. The Enro antigens as well as Enro analytes can connect with Enro antibodies that bind to Au@BSA through gold–nitrogen bonds. As the concentration of analytes increase, their ability to compete with antigens increases. So the number of antibodies bound to the antigens on the electrode decreases. This means that the number of CdSe QDs connected to the antibodies also decreases, resulting in a relatively low ECL response. The ECL intensity is linearly related to the Enro concentration in the range of 0.001–20 ng mL−1 with a detection limit of 0.33 pg mL−1.
Alternative Pathway Is Involved in Hydrogen Peroxide-Enhanced Cadmium Tolerance in Hulless Barley Roots

Hulless barley, grown in the Qinghai Tibet Plateau, has a wide range of environmental stress tolerance. Alternative pathway (AP) and hydrogen peroxide (H2O2) are involved in enhancing plant tolerance to environmental stresses. However, the relationship between H2O2 and AP in hulless barley tolerance to cadmium (Cd) stress remains unclear. In the study, the role and relationship of AP and H2O2 under Cd stress were investigated in hulless barley (Kunlun14) and common barley (Ganpi6). Results showed that the expression level of alternative oxidase (AOX) genes (mainly AOX1a), AP capacity (Valt), and AOX protein were clearly induced more in Kunlun14 than in Ganpi 6 under Cd stress; moreover, these parameters were further enhanced by applying H2O2. Malondialdehyde (MDA) content, electrolyte leakage (EL) and NAD(P)H to NAD(P) ratio also increased in Cd-treated roots, especially in Kunlun 14, which can be markedly alleviated by exogenous H2O2. However, this mitigating effect was aggravated by salicylhydroxamic acid (SHAM, an AOX inhibitor), suggesting AP contributes to the H2O2-enhanced Cd tolerance. Further study demonstrated that the effect of SHAM on the antioxidant enzymes and antioxidants was minimal. Taken together, hulless barley has higher tolerance to Cd than common barley; and in the process, AP exerts an indispensable function in the H2O2-enhanced Cd tolerance. AP is mainly responsible for the decrease of ROS levels by dissipating excess reducing equivalents.
Beyond the Scales: A physics-informed machine learning approach for more efficient modeling of SARS-CoV-2 spike glycoprotein

This paper presents a physics-informed machine learning approach to the derivation of a bottom-up coarse-grained model of the SARS-CoV-2 spike glycoprotein from all-atomic molecular dynamics simulations. The machine learning procedure employs a force-matching scheme in the optimization of interaction parameters, where the force-matching scheme is combined in methodology with the initialization of the interaction parameters by the traditional iterative Boltzmann inversion method. The force-matched machine learning procedure is constructed based on two physics-informed layers: one is the Harmonic layer consisting of bond, angle, and dihedral terms as bonded potentials; the other is the Lennard-Jones layer consisting of the non-bonded Lennard-Jones potential. Coarse-grained validation simulations are performed with the learned parameters to test the derived bottom-up coarse-grained model. The simulations are able to reach the microsecond time scale with stability. The physics-informed learning approach yields simulation speeds nearly 40,000 times faster than conventional all-atomic simulations while maintaining comparable simulation accuracy. Additionally, through examination of the non-bonded Lennard-Jones parameters and the radial distribution function analysis, the learning approach matches pairwise distances of the ground-truth data with greater accuracy than the conventional iterative approach method.