Miracle-dz/UniFAR-FTUs huggingface dataset:
An Updated Algorithm Integrated With Patient Data for the Differentiation of Atypical Nevi From Early Melanomas: the idScore 2021

Introduction It is well known that multiple patient-related risk factors contribute to the development of cutaneous melanoma, including demographic, phenotypic and anamnestic factors. Objectives We aimed to investigate which MM risk factors were relevant to be incorporated in a risk scoring-classifier based clinico-dermoscopic algorithm. Methods This retrospective study was performed on a monocentric dataset of 374 atypical melanocytic skin lesions sharing equivocal dermoscopic features, excised in the suspicion of malignancy. Dermoscopic standardized images of 258 atypical nevi (aN) and 116 early melanomas (eMM) were collected along with objective lesional data (i.e., maximum diameter, specific body site and body area) and 7 dermoscopic data. All cases were combined with a series of 10 MM risk factors, including demographic (2), phenotypic (5) and anamnestic (3) ones. Results The proposed iDScore 2021 algorithm is composed by 9 variables (age, skin phototype I/II, personal/familiar history of MM, maximum diameter, location on the lower extremities (thighs/legs/ankles/back of the feet) and 4 dermoscopic features (irregular dots and globules, irregular streaks, blue gray peppering, blue white veil). The algorithm assigned to each lesion a score from 0 to 18, reached an area under the ROC curve of 92% and, with a score threshold ≥ 6, a sensitivity (SE) of 98.2% and a specificity (SP) of 50.4%, surpassing the experts in SE (+13%) and SP (+9%). Conclusions An integrated checklist combining multiple anamnestic data with selected relevant dermoscopic features can be useful in the differential diagnosis and management of eMM and aN exhibiting with equivocal features.
The Impact of Interface Design Element Features on Task Performance in Older Adults: Evidence from Eye-Tracking and EEG Signals

It is crucial that the interface design of mobile apps be age-appropriate at this stage of global aging, as the new epidemic has resulted in a higher sense of isolation among older persons. In this study, four typical senior social service mobile applications were chosen to give older persons the ability to complete user login duties. The participants were 16 older adults (7 men and 9 women) aged 55 to 76. Both objective and subjective data, including task completion time, gaze length, pupil diameter changes, EEG wave amplitude changes, and subjective sensations of older persons, were gathered using a combination of eye-movement and EEG signal approaches. The program was created to investigate the effects of interface design aspects on older people’s task performance, including interface layout, interface color, information density, icon size and position, etc. The study’s findings revealed that when the user task completion time and average fixation duration were shorter, the line of sight was more equally distributed, the visual focus was closer to the login button, and the average EEG amplitude of the user changed more, the older adults performed better. The palace layout had a more positive effect on job completion among older individuals when it came to interface layout. In terms of interface color, colored (contrasting) colors should serve to highlight the interface’s essential information points while they can be removed. In terms of interface information density, a low-density level interface design can simplify and lower the cognitive load of task execution for older people. The first level of icons in the interface and their position in the visual center of the interface is the best interface design for older persons in terms of icon size and position. The results of this study have theoretical ramifications for a thorough understanding of the factors influencing older people’s task performance, practical ramifications for the design of older people-centered interfaces, and they contribute to our understanding of the characteristics of older people’s interface interaction behavior.
Design of functional biomaterials as substrates for corneal endothelium tissue engineering

Abstract Corneal endothelium defects are one of the leading causes of blindness worldwide. The actual treatment is transplantation, which requires the use of human cadaveric donors, but it faces several problems, such as global shortage of donors. Therefore, new alternatives are being developed and, among them, cell therapy has gained interest in the last years due to its promising results in tissue regeneration. Nevertheless, the direct administration of cells may sometimes have limited success due to the immune response, hence requiring the combination with extracellular mimicking materials. In this review, we present different methods to obtain corneal endothelial cells from diverse cell sources such as pluripotent or multipotent stem cells. Moreover, we discuss different substrates in order to allow a correct implantation as a cell sheet and to promote an enhanced cell behavior. For this reason, natural or synthetic matrixes that mimic the native environment have been developed. These matrixes have been optimized in terms of their physicochemical properties, such as stiffness, topography, composition and transparency. To further enhance the matrixes properties, these can be tuned by incorporating certain molecules that can be delivered in a sustained manner in order to enhance biological behavior. Finally, we elucidate future directions for corneal endothelial regeneration, such as 3D printing, in order to obtain patient-specific substrates.
SARS-CoV-2-Specific T Cell Immunity in HIV-Associated Kaposi Sarcoma Patients in Zambia

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) virus is the cause of coronavirus disease 2019 (COVID-19). It has caused millions of infections and deaths globally over a 2-year period. Some populations including those living with HIV and/or cancer are reported to be at a higher risk of infection and severe disease. HIV infection leads to a depletion of CD4+ T cells which impairs cell-mediated immunity and increases the risk of malignancies such as Kaposi sarcoma (KS) and viral infections such as SARS-CoV-2. However, several other factors including level of immunosuppression and chemotherapy may also affect the immune response against SARS-CoV-2. In this study, we investigated factors affecting SARS-CoV-2-specific T cell immunity towards the spike, nucleoprotein, membrane protein, and other open reading frame proteins in individuals with HIV-associated KS. The KS patients were SARS-CoV-2 seropositive with detectable T cell responses, but had no history of symptomatic SARS-CoV-2 infection. We observed that the T cell responses increase from baseline levels during follow-up, with responses towards the NMO peptide pool being statistically significant. Low CD4 counts below 200 cells/μl were associated with lower SARS-CoV-2-specific T cell responses. Cancer chemotherapy and KS T staging did not have a significant effect on the T cell responses.
Benefits of Carotenoid Astaxanthin: A Review

Astaxanthin is a high value keto-carotenoid pigment renowned for its commercial application in various industries such as aquaculture, food, cosmetic, nutraceutical and pharmaceutical sectors. It is commonly employed in salmonid and crustacean aquaculture to give the pink hue that these species are known for. Scientific literature reviews have persistently demonstrated the instrumental role of astaxanthin in targeting several animal health conditions. Most importantly, the profound effect on pigmentation, where astaxanthin is frequently utilized as an additive in formulating diets to boost and improve the coloration of many aquaculture farmed species, subsequently product quality, consumers’ acceptance and market demand are increasing, and revenue generated. Moreover, the wide range of other physiological benefits of astaxanthin includes various improvements in survival, growth performance, reproductive capacity, stress tolerance, and disease resistance as well. Also, astaxanthin has some other applications like, it is an anticancerous agent, it can prevent diabetes and cardiovascular diseases and enhances nutritional qualities. Astaxanthin products are used for commercial applications in the dosage forms as tablets, capsules, syrups, oils, soft gels, creams, biomass and granulated powders. Astaxanthin patent applications are available in food, feed and nutraceutical applications. This manuscript basically reviews the current available evidence regarding biological sources of astaxanthin, extraction procedures, stability, biological activity, health benefits, and commercial uses.
MOOC-Rec: Instructional Video Clip Recommendation for MOOC Forum Questions

In this work, we address the information overload issue that learners in Massive Open Online Courses (MOOCs) face when attempting to close their knowledge gaps via the use of MOOC discussion forums. To this end, we investigate the recommendation of one-minute-resolution video clips given the textual similarity between the clips’ transcripts and MOOC discussion forum entries. We first create a large-scale dataset from Khan Academy video transcripts and their forum discussions. We then investigate the effectiveness of applying pre-trained transformers-based neural retrieval models to rank video clips in response to a forum discussion. The retrieval models are trained with supervised learning and distant supervision to effectively leverage the unlabeled data— which accounts for more than 80% of all available data. Our experimental results demonstrate that the proposed method is effective for this task, by outperforming a standard base-line by 0.208 on the absolute change in terms of precision.
Using community-based problems to increase motivation in a data science virtual internship

Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced dataset and authentic problems (Phase 2) in the context of an 8-week virtual internship. Retrospective survey questions indicated interns experienced increased motivation in Phase 2. Additionally, analysis of intern discourse using Linguistic Inquiry and Word Count (LIWC) indicated a significant difference in linguistic measures between the two phases. Phase 1 had significantly greater measures of pronouns with a small-medium effect size, 2 nd person words with a medium-large effect size, positive emotion with a medium effect size, inter-rogations with a medium-large effect size, question marks with a medium-large effect size, risk with a medium-large effect size, and causal words with a medium effect size. These results in conjunction with a retrospective survey suggest that phase 1 had more questions asked, more causal relationships defined, and included linguistic features of success and failure. Results from Phase 2 indicated that community-sourced data and problems may increase motivation for learning data science.
Item Response Theory-Based Gaming Detection

Gaming the system, a behavior in which learners exploit a system’s properties to make progress while avoiding learning, has frequently been shown to be associated with lower learning. However, when we applied a previously validated gaming detector across conditions in experiments with an algebra tutor, the detected gaming was not associated with learning, challenging its construct validity. Our iterative exploratory data analysis suggested that some contextual factors that varied across and within conditions might contribute to this lack of association. We present a latent variable model, item response theory-based gaming detection (IRT-GD), that accounts for contextual factors and estimates latent gaming tendencies as the degree of deviation from normative behaviors across contexts. Item response theory models, widely used in knowledge assessment, account for item difficulty in estimating latent student abilities: students are estimated as having higher ability when they can get harder items correct than when they only get easier items correct. Similarly, IRT-GD accounts for contextual factors in estimating latent gaming tendencies: students are estimated as having a higher gaming tendency when they game in less commonly gamed contexts than when they only game in more commonly gamed contexts. IRT-GD outperformed the original detector on three datasets in terms of the association with learning. IRT-GD also more accurately revealed intervention effects on gaming and revealed a correlation between gaming and perceived competence in math. Our approach is not only useful for others wanting to apply a gaming assessment in their context but is also generally applicable in creating robust behavioral measures.
Metallic ordered double-perovskite Sr2CrReO6 with maximal Curie temperature of 635 K

Electric, thermal, optical, and magnetic properties have been investigated for ferromagnetic (ferrimagnetic) ordered double perovskites, A2CrReO6 (A=Sr and Ca). Sr2CrReO6 is found to be a metallic ferromagnet with a high Curie temperature (TC=635 K), possibly the highest-TC half metal among the perovskite family. By contrast, Ca2CrReO6 is a ferromagnetic Mott insulator with TC of 360 K.
High-Frequency Radiowave Probing of the High-Latitude Ionosphere,

Abstract : During the past several years, a program of high-frequency radiowave studies of the high latitude ionosphere has been developed. Studies are now being conducted on the formation and motion of high-latitude ionospheric electron density irregularities, using a sophisticated high-frequency radar system installed at Goose Bay, Labrador. The radar antenna is also being used to receive signals from a beacon transmitter located at Thule, Greenland. This information is providing a better understanding of the spatial and temporal variability of high-latitude propagation channels and their relationship to disturbances in the magnetosphere ionosphere system.
Non-parametric Estimation approach in statistical investigation of nuclear spectra

In this paper, Kernel Density Estimation (KDE) as a non-parametric estimation method is used to investigate statistical properties of nuclear spectra. The deviation to regular or chaotic dynamics, is exhibited by closer distances to Poisson or Wigner limits respectively which evaluated by Kullback-Leibler Divergence (KLD) measure. Spectral statistics of different sequences prepared by nuclei corresponds to three dynamical symmetry limits of Interaction Boson Model(IBM), oblate and prolate nuclei and also the pairing effect on nuclear level statistics are analyzed (with pure experimental data). KD-based estimated density function, confirm previous predictions with minimum uncertainty (evaluated with Integrate Absolute Error (IAE)) in compare to Maximum Likelihood (ML)-based method. Also, the increasing of regularity degrees of spectra due to pairing effect is reveal.
On modification of the q-L-series and its applications

We slightly modify the definitions of q-Hurwitz ζ-functions and q-L-series constructed by J. Satoh. By using these modified functions, we give some relations for the ordinary Dirichlet L-series. Especially we give an elementary proof of Katsurada’s formula on the values of Dirichlet L-series at positive integers.
Institutional trust in times of Corona

During the corona pandemic, governments of all countries appealed strongly to the trust of their populations by implementing drastic social and economic measures to prevent the spread of the virus. This study seeks to understand mechanisms that influence the level of institutional trust at the time of the corona pandemic. We are specifically interested in how three explanatory factors (socioeconomic status, experienced economic insecurity and dissatisfaction with the implemented corona policies) can, in mutual association, explain differences in institutional trust. This study is based on data from a large-scale panel survey on the social impact of COVID-19, carried out by Kieskompas research agency (N=22,696). Using a serial mediation analysis, we show that SES has both a direct and indirect effect on the level of institutional trust. People with higher SES experience less economic insecurity and have less dissatisfaction with the corona policies and, partly as a result of this, stronger institutional trust. It is also true that economic insecurity increases dissatisfaction with the corona policies and, partly as a result of this, weakens the level of trust.
Bibliometric Patterns and Concept Evolution Trajectories in research publications in Future Generation Computer Systems

: Future Generation Computer Systems (FGCS), published by Elsevier, is a prestigious peer reviewed journal that started in 1984. As on date of writing this article, the journal is in its 137 th volume. Owing to its publication quality and continued academic standards, it has been indexed by major academic databases such as Web of Science, Scopus, DOAJ etc. It is now ranked among the top journals in the area of Computer Science (General). Motivated by the long history, academic reputation and prestige of the journal, the present study attempts to do a bibliometric and text-based analysis of publications in the journal during the period of 1984-2020 (37 years). Bibliometric analysis helped to identify publishing and citation patterns, authorship and collaboration structure, funding patterns of the published research, open access and altmetric impact, gender distribution and SDG connections etc. The text-integrated path analysis helped in identifying the thematic structure and major concept evolution trajectories. The results of the analysis indicate that the FGCS journal is a high-quality research journal that has grown over time in terms of publications, citations and rankings. It publishes articles in specified thematic areas, with cloud, grid, IoT, Blockchain etc. found as the major themes addressed. Knowledge extension from cloud computing to multi-domain networks is witnessed and a divergence towards ‘service function chains’, ‘distributed machine learning’ and ‘edge computing systems’ is visible. The article thus presents very informative and useful insight about research publication trends in FGCS.
Encoder–Decoder Architecture for 3D Seismic Inversion

Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wave equation, as required by industry-standard tools such as Full Waveform Inversion (FWI). For example, in an area with surface dimensions of 4.5 km × 4.5 km, hundreds of seismic shot-gather cubes are required for 3D model reconstruction, leading to Terabytes of recorded data. This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder–decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed solution demonstrates that realistic 3D models can be reconstructed with a structural similarity index measure (SSIM) of 0.9143 (out of 1.0) in the presence of field noise at 10 dB signal-to-noise ratio.
Model selection with Gini indices under auto-calibration

The Gini index does not give a strictly consistent scoring rule in general. Therefore, maximizing the Gini index may lead to wrong decisions. The main issue is that the Gini index is a rank-based score that is not calibration-sensitive. We show that the Gini index allows for strictly consistent scoring if we restrict to the class of auto-calibrated regression models.
Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations

Enhanced sampling methods are indispensable in computational chemistry and physics, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space of dynamical systems due to the sampling problem. A class of such enhanced sampling methods works by identifying a few slow degrees of freedom, termed collective variables (CVs), and enhancing the sampling along these CVs. Selecting CVs to analyze and drive the sampling is not trivial and often relies on chemical intuition. Despite routinely circumventing this issue using manifold learning to estimate CVs directly from standard simulations, such methods cannot provide mappings to a low-dimensional manifold from enhanced sampling simulations, as the geometry and density of the learned manifold are biased. Here, we address this crucial issue and provide a general reweighting framework based on anisotropic diffusion maps for manifold learning that takes into account that the learning data set is sampled from a biased probability distribution. We consider manifold learning methods based on constructing a Markov chain describing transition probabilities between high-dimensional samples. We show that our framework reverts the biasing effect, yielding CVs that correctly describe the equilibrium density. This advancement enables the construction of low-dimensional CVs using manifold learning directly from the data generated by enhanced sampling simulations. We call our framework reweighted manifold learning. We show that it can be used in many manifold learning techniques on data from both standard and enhanced sampling simulations.
Leveraging Data from Nearby Stations to Improve Short-Term Wind Speed Forecasts

In this paper, we address the issue of short-term wind speed prediction at a given site. We show that, when one uses spatiotemporal information as provided by wind data of neighboring stations, one significantly improves the prediction quality. Our methodology does not focus on any peculiar forecasting model but rather considers a set of various prediction methods, from a very basic linear regression to different machine learning models. In each case, our approach consists in specifically and incrementally studying the benefits of using wind data of the surrounding stations. We show that, at all horizons ranging from 1 to 6 hours ahead, the relative gain on the RMSE of the predicted wind speed can increase up to 20 %. For all the considered forecasting methods, we show that such a gain is far better than the one obtained by considering other kind of information like local weather variables or seeking for an optimal deep learning model. Moreover we provide evidence that non-linear models, as neural networks or gradient boosting methods, significantly outperform linear regression. These conclusions are simply interpreted as resulting from the ability of a method to capture the transport of the information by the main flow in the upwind direction.