Miracle-dz/UniFAR-FTUs huggingface dataset:
REDUCING OXIDATIVE STRESS ON ZYGOTIC EMBRYOS OF WALNUT (JUGLANS REGIA L.) UNDER IN VITRO CONDITIONS BY THEIR PRETREATMENT WITH ASCORBIC ACID

Persian walnut (Juglans regia L.) is an autochthonous species of Albania and considered endangered (EN) based on IUCN criteria. It is important to find and develop successful protocols for in vitro propagation. A major problem in this regard is the oxidative stress that explant undergo in nutrient media after their isolation. The purpose of this study is to find a suitable method for preventing polyphenolics exudation by pre-treatment of zygotic embryos with different concentrations of ascorbic acid and different exposing time in this solution. It was observed that browning rate, is a parameter highly affected by the concentration of ascorbic acid solution and exposing time. Explants from Control group show a high rate of browning and this parameter is significantly different from all the other treatments tested. Exposing time does not have any significant effect when using 150 mg/l ascorbic acid. Furthermore, treating with 200 mg/l ascorbic acid for 10 min. does not have any significant effect with none of the treatments using 150 mg/l ascorbic acid. It was observed from the results, the groups that showed high values of browning rates, had lower values of proliferation rates. It was evident that treating with 200 mg/l ascorbic acid for 20 min, gave a lower proliferation rate (82.2 %) in comparison with treatment using 200 mg/l ascorbic acid for 15 min (88.8). This may be related to the side effects that occur when the treatment lasts for a longer time.
DataHub – A Collaborative Dataset Management Platform

The rise of the Internet, smart phones, and wireless sensors has produced a huge diversity of datasets about all aspects of our lives, from our social interactions to our personal preferences to our vital signs and medical records. Increasingly, researchers and “data science” teams want to collect and collaborate on these datasets. Consider a few examples: • a geneticist who wants to share and collaborate on genome data with other research groups; • an athlete who wants to quickly study and visualize his performance data; • a member of a web advertising team who wants to distill insights from unstructured ad-click data; • a university scheduling office that wants to share room reservations data with campus; • a journalist who wants to examine public data related to terrorist strikes in Afghanistan; • a city that wants to publish transit ridership data; • a small-shop owner who wants to study their sales data in the context of public economic data Each of these is an example of a data “enthusiast” or a data sub-community with a need to share, exchange, collaborate on, distill, and/or visualize datasets. Our thesis is that these (and many more similar) communities are under-served by the current data management and collaboration technologies available. Although there is a wealth of “data science” research addressing stand-alone data analysis issues or building integrated tools for analysis, the dataset management, collaboration, and sharing aspects of these tools are poor, requiring data scientists to use ad-hoc mechanisms to record and reason about datasets. Furthermore, although there is a vast array of “big data” research, the emphasis is on performance, and not on simplifying and automating many of the fundamental bookkeeping and management procedures necessary to facilitate seamless data science. To this end, we propose DataHub, a hosted, collaborative platform for preparing, storing, and analyzing datasets. DataHub’s goal is to provide a common substrate for members of data science teams to collaborate, analyze, and reason about datasets. Our goal is not, in fact, to replace existing data science tools: rather, we effectively support existing data science tools through much-needed basic infrastructure and management capabilities. DataHub stands apart from previous systems in two main ways:
Dispositional Scepticism , Attitudes to Science , and Belief in the Paranormal

An online survey was undertaken to examine the relationship between the intensity of beliefs in paranormal phenomena and two facets of a scientific worldview, namely, an appreciation of the values of science and a disposition to presumptive scepticism. A sample of 202 British residents participated in the survey. The findings indicate that paranormal believers have both a relatively low regard for the values of science and weak dispositional scepticism. These findings are discussed in terms of the worldview hypothesis of paranormal belief.
An Event-related Potential Study of Second Language Semantic and Syntactic Processing: Evidence From the Declarative/Procedural Model

Introduction: This study examined the effect of proficiency level on the second Language (L2) syntactic and semantic processing by addressing the role of procedural and declarative memory systems in light of the Declarative/Procedural (DP) model. The primary purpose was to determine to what extent proficiency accounts for native-like language processing in L2 in adult bilinguals who learned English (L2) after the age of 15 under explicit instruction. Methods: Using a mixed-method design and an oddball violation paradigm, we examined the functional neural correlates of syntactic and semantic processing in two groups of Persian-English bilinguals (L1=Persian, L2=English; N=10 high-proficient, N=10 pre-intermediate levels; Gender= Female; mean age=25.50 years, SD = 5.09 years, age range = 19–35 years of age) across 6 different conditions. They included a visual stimulus task of 240 English sentences with three different experimental conditions (violated regular past forms or phrase structure rules or final-word semantic violation) and three control conditions (sets of correct sentences for each experimental condition). Both groups started learning English late (age of onset=15+) and under an explicit learning context. To evaluate the effect of L2 proficiency, Event-related potentials (ERPs) to target words in each condition were elicited across the N400 time window (300–500 ms) and the P600 time window (500–700 ms). Results: Results showed different cortical responses in the two groups. Upon processing the violated forms, high-proficient subjects showed more native-like patterns of scalp activity in both lexical-semantic and syntactic processing. In contrast, less proficient learners have shown delayed onsets and or peaks of components, reduced amplitudes, or absent components in some regions. For instance, the difference in N400 amplitude for the incorrect regular past conditions was observed only in the pre-intermediate (PI) subjects in the O1 channel. This finding is compatible with the DP Model in that at lower levels of L2 proficiency, the participants show N400s or N400-like posterior negativities instead of Anterior Negativities (ANs). This finding shows the initial reliance on the declarative memory system for syntactic processing at lower levels of L2. Conclusion: Our findings suggest that attained proficiency was a more determinant factor in the L1-like cortical representation of L2 than the age of acquisition and or the type of instruction/context. Several brain areas, similar to those observed for L1, were activated during L2 syntactic processing in high-proficient subjects addressing their reliance on the procedural memory system for syntactic processing to gain more proficiency. For instance, our results showed a significant difference in N400 amplitude for the incorrect regular past conditions in O1 for the PI subjects, which shows the initial reliance on the declarative memory system for syntactic processing at lower levels of L2.
Recent Progress on Hole‐Transporting Materials for Emerging Organometal Halide Perovskite Solar Cells

In less than three years, the photovoltaic community has witnessed a rapid emergence of a new class of solid‐state heterojunction solar cells based on solution‐processable organometal halide perovskite absorbers. The energy conversion efficiency of solid‐state perovskite solar cells (PSCs) has been quickly increased to a certified value of 20.1% by the end of 2014 because of their unique characteristics, such as a broad spectral absorption range, large absorption coefficient, high charge carrier mobility and diffusion length. Here, the focus is specifically on recent developments of hole‐transporting materials (HTMs) in PSCs, which are essential components for achieving high solar cell efficiencies. Some fundamentals with regard to PSCs are first presented, including the history of PSCs, device architectures and general operational principles of PSCs as well as various techniques developed for the fabrications of uniform and dense perovskite complexes. A broad range of the state‐of‐the‐art HTMs being used in PSCs are then discussed in detail. Finally, an outlook on the design of more efficient HTMs for highly efficient PSCs is addressed.
Family Spillover Effects of Marginal Diagnoses: The Case of Adhd

The health care system commonly relies on information about family medical history in the allocation of screenings and in diagnostic processes. At the same time, an emerging literature documents that treatment for “marginally diagnosed” patients often has minimal impacts. This paper shows that reliance on information about relatives' health can perpetuate marginal diagnoses across family members, thereby raising caseloads and health care costs, but without improving patient well-being. We study Attention Deficit Hyperactivity Disorder (ADHD), the most common childhood mental health condition, and document that the younger siblings and cousins of marginally diagnosed children are also more likely to be diagnosed with and treated for ADHD. Moreover, we find that the younger relatives of marginally diagnosed children have no better adult human capital and economic outcomes than the younger relatives of those who are less likely to be diagnosed. Our analysis points to a simple adjustment to physician protocol that can mitigate these marginal diagnosis spillovers. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber. org.
10 Future Applications of the TIME

The development of TIME electrodes was driven by the goal to investigate the treatment of phantom limb pain in upper limb amputees by selective intraneural stimulation. Can the experience with the approach be transferred in other applications? What lessons have been learned with the approach of intraneural thin-film-based stimulation electrodes embedded in polymer substrates with respect to other implantation sites, stimulation paradigms, and even other designs? This chapter will discuss the most obvious next applications and speculate about transfer in other treatment and rehabilitation scenarios in which flexible but mechanically robust nerve interfaces could be advantageous. Electrical stimulation to deliver sensory feedback (Pasluosta, 2017) is not only limited to patients with limb loss after amputation. Evoked somatosensory potentials (Granata, 2018) of the phantom hand cannot only be used to diminish phantom limb pain (Stieglitz, 2012) but also to deliver signals for sensory feedback in the control of artificial hand prostheses (Micera, 2016). While the benefit of information about grasp force and object compliance during grasping is beyond discussion and was achieved in a subchronic pilot study (Raspopovic, 2014), improvement of proprioception (D’Anna) and
Near‐band‐gap photoluminescence from pseudomorphic Si1−xGex single layers on silicon

The systematic study of band‐edge luminescence in pseudomorphic Si/Si1−xGex/Si double‐heterostructure layers is reported for a wide composition range, 0.12<x<0.24, for the first time. An analytical expression for the exciton energy gap at 4.2 K valid for x<0.24 is derived from the no‐phonon line energies: ESX(x) = 1.155−0.874x+0.376x2 eV. Addition of an expression for the exciton binding energy provides an approximation for the energy difference between the alloy valence band and the lowest conduction‐band edge at low temperature. An exciton upshift of 16.9 meV due to quantum confinement is observed in a 6.3‐nm Si0.83Ge0. 17 alloy well. This is consistent with either type‐I or type‐II band alignment for which the conduction‐band offset has a magnitude ‖ΔEc‖ ≤ 10 meV. The excitonic hole is closely confined in the alloy but the spectra suggest that the electron density in the silicon barriers is increased for the thin layer.
Electrochemical Studies on Glucose Oxidation in an Enzymatic Fuel Cell with Enzyme Immobilized on to Reduced Graphene Oxide Surface

The present study reports fabrication and characterization of glucose oxidase (GOx) immobilized reduced graphene oxide (rGO), gold nanoparticles (AuNp) and rGO/AuNp composite electrodes. The study further evaluated their electrochemical behaviors using cyclic voltammetry (CV) at different scan rates (10–150 mV/s) and different glucose concentrations (0.1–0.9 M) in phosphate buffer solution (PBS) at pH 7.4. Electron transfer rate constants (ks) for glucose oxidation onto these enzyme immobilized electrodes have been calculated from the plot of Ep vs. log ν ( peak potential vs. log of scan rate) which showed faster electron transfer rate onto rGO/AuNp composite surface compared to AuNp or rGO surfaces alone. A laboratory model enzymatic fuel cell (EFC) was constructed by coupling the anode with a potassium ferrocyanide-ferricyanide cathode under argon atmosphere. From the discharge studies, the rGO/AuNp/GOx/C-felt electrode was found to be better than AuNp/GOx/C-felt or rGO/GOx/C-felt electrodes. This better performance in rGO/AuNp/GOx/C-felt electrode has been related to the enhanced attachment of GOx enzyme onto AuNp surface through “S” atoms of sulfur containing amino acids and better electronic conductivity of rGO surface. Hence, the current study concluded that the rGO/AuNp/GOx/C-felt electrode is a promising electrode for glucose oxidation in EFCs.
In situ monitoring of the biofilm formation of Pseudomonas putida on hematite using flow-cell ATR-FTIR spectroscopy to investigate the formation of inner-sphere bonds between the bacteria and the mineral

Abstract In situ flow-cell ATR-FTIR using a hematite-coated germanium crystal was used to investigate the chemical interactions between Pseudomonas putida and hematite in real time, when compared with cells not attached to the mineral surface. ATR-FTIR spectra of bacteria growing on hematite showed a shift in the carboxylate signal when compared to the samples obtained from free cells, indicating a chemical interaction between the carboxylate groups and the Fe metal ions of the hematite surface. Small differences in the polysaccharide and phosphoryl regions of the IR spectra of bacteria attached to hematite were also observed. This work shows how the use of in-situ flow-cell experiments with a mineral-coated germanium crystal allows a better description of the bacterial interactions with minerals in real time, as an initial step to understand the fundamental mechanisms involved in the relationship between bacteria and mineral surfaces.
SN 2013ai: A Link between Hydrogen-rich and Hydrogen-poor Core-collapse Supernovae

We present a study of the optical and near-infrared (NIR) spectra of SN 2013ai along with its light curves. These data range from discovery until 380 days after explosion. SN 2013ai is a fast declining Type II supernova (SN II) with an unusually long rise time, 18.9 ± 2.7 days in the V-band, and a bright V-band peak absolute magnitude of −18.7 ± 0.06 mag. The spectra are dominated by hydrogen features in the optical and NIR. The spectral features of SN 2013ai are unique in their expansion velocities, which, when compared to large samples of SNe II, are more than 1,000 km s−1 faster at 50 days past explosion. In addition, the long rise time of the light curve more closely resembles SNe IIb rather than SNe II. If SN 2013ai is coeval with a nearby compact cluster, we infer a progenitor zero-age main-sequence mass of ∼17 M ⊙. After performing light-curve modeling, we find that SN 2013ai could be the result of the explosion of a star with little hydrogen mass, a large amount of synthesized 56Ni, 0.3–0.4 M ⊙, and an explosion energy of 2.5–3.0 × 1051 erg. The density structure and expansion velocities of SN 2013ai are similar to those of the prototypical SN IIb, SN 1993J. However, SN 2013ai shows no strong helium features in the optical, likely due to the presence of a dense core that prevents the majority of γ-rays from escaping to excite helium. Our analysis suggests that SN 2013ai could be a link between SNe II and stripped-envelope SNe.
Automating Gamification Personalization to the User and Beyond

Personalized gamification explores user models to tailor gamification designs to mitigate cases wherein the one-size-fits-all approach ineffectively improves learning outcomes. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several combinations to tailor. Consequently, tools for automating gamification personalization are needed. However, which of those characteristics are relevant and how to do such tailoring are open questions. Furthermore, the required automating tools are lacking. We tackled these problems in two steps. First, we conducted an exploratory study, collecting participants' opinions on the game elements they consider the most useful for different learning activity types (LAT) via survey. Then, we modeled opinions through Conditional Decision Trees to address the aforementioned tailoring process. Second, as a product of the first step, we implemented a recommender system that suggests personalized gamification designs (which game elements to use), addressing the problem of automating gamification personalization. Our findings present empirical evidence that LAT, geographic locations, and other user characteristics affect users' preferences, enable defining gamification designs tailored to user and contextual features simultaneously, and provide technological aid for those interested in designing personalized gamification. The main implications are that demographics, game-related characteristics, geographic location, and LAT to be done, as well as the interaction between different kinds of information (user and contextual characteristics), should be considered in defining gamification designs and that personalizing gamification designs can be improved with aid from our recommender system.
Intriguing magnetism of the topological kagome magnet TbMn_6Sn_6

Magnetic topological phases of quantum matter are an emerging frontier in physics and material science. Along these lines, several kagome magnets have appeared as the most promising platforms. Here, we explore magnetic correlations in the transition-metal-based kagome magnet TbMn$_{6}$Sn$_{6}$ using muon spin rotation, combined with local field analysis and neutron diffraction. Our results show that the system exhibits an out-of-plane ferrimagnetic structure $P6/mm'm'$ (comprised by Tb and Mn moments) with slow magnetic fluctuations below $T_{\rm C2}$~=~320~K. These fluctuations exhibit a slowing down below $T_{\rm C1}^{*}$~${\simeq}$~120~K, and we see the formation of static patches with ideal out-of-plane order below $T_{\rm C1}$~${\simeq}$~20~K which grow in a volume with decreasing temperature. The appearance of the static patches has a similar onset to the interesting phenomenon such as spin-polarized Dirac dispersion with a large Chern gap and topological edge states. We further show that the temperature evolution of the anomalous Hall conductivity (AHC) is strongly influenced by the low temperature magnetic crossover. Our presented experimental results show that the onset of the topological electronic properties tied to the Dirac band is promoted only by true static out-of-plane ferrimagnetic order in TbMn$_{6}$Sn$_{6}$ and is washed out by the slow magnetic fluctuations above $T_{\rm C1}$~${\simeq}$~20~K. Remarkably, hydrostatic pressure of 2.1 GPa stabilises static out-of-plane topological ferrimagnetic ground state in the whole volume of the sample. Therefore the exciting perspective arises of a magnetic system in which the topological response can be controlled, and thus explored, over a wide range of parameters.
P-spline smoothed functional ICA of EEG data

We propose a novel functional data framework for artifact extraction and removal to estimate brain electrical activity sources from EEG signals. Our methodology is derived on the basis of event related potential (ERP) analysis, and motivated by mapping adverse artifactual events caused by body movements and physiological activity originated outside the brain. A functional independent component analysis (FICA) based on the use of fourth moments is conducted on the principal component expansion in terms of B-spline basis functions. We extend this model setup by introducing a discrete roughness penalty in the orthonormality constraint of the functional principal component decomposition to later compute estimates of FICA. Compared to other ICA algorithms, our method combines a regularization mechanism stemmed from the principal eigendirec-tions with a discrete penalization given by the 𝑑 -order diﬀerence operator. In this regard, it allows to naturally control high-frequency remnants of neural origin overlapping latent artifactual eigen-functions and thus to preserve this persistent activity at artifact extraction level. Furthermore, we introduce a new cross-validation method for the selection of the penalization parameter which uses shrinkage to asses the performance of the estimators for functional representations with larger basis dimension and excess of roughness. This method is used in combination with a kurtosis measure in order to provide the optimal number of independent components.The FICA model is illustrated at functional and longitudinal dimensions by an example on real EEG data where a subject willingly performs arm gestures and stereotyped physiological artifacts. Our method can be relevant in neurocognitive research and related ﬁelds, particularlly in situations where movement can bias the estimation of brain potentials.
Unsupervised Heart Abnormality Detection Based on Phonocardiogram Analysis with Beta Variational Auto-Encoders

Heart Sound (also known as phonocardiogram (PCG)) analysis, is a popular way that detects cardiovascular diseases (CVDs). Most PCG analysis uses supervised way, which demands both normal and abnormal samples. This paper proposes a method of unsupervised PCG analysis that uses beta variational auto-encoder (β – VAE) to model the normal PCG signals. The best performed model reaches an AUC (Area Under Curve) value of 0.91 in ROC (Receiver Operating Characteristic) test for PCG signals collected from the same source. Unlike majority of β – VAEs that are used as generative models, the best-performed β – VAE has a β value smaller than 1. This fact demonstrates that the resampling process helps the improvements on anomaly PCG detection through reconstruction loss worth a heavier weight. Further investigations suggest that anomaly score based on reconstruction loss may be better than anomaly scores based on latent vectors of samples in PCG analysis based on VAE systems.