Miracle-dz/UniFAR-FTUs huggingface dataset:
Substructural Connectivity Fingerprint and Extreme Entropy Machines—A New Method of Compound Representation and Analysis

Key-based substructural fingerprints are an important element of computer-aided drug design techniques. The usefulness of the fingerprints in filtering compound databases is invaluable, as they allow for the quick rejection of molecules with a low probability of being active. However, this method is flawed, as it does not consider the connections between substructures. After changing the connections between particular chemical moieties, the fingerprint representation of the compound remains the same, which leads to difficulties in distinguishing between active and inactive compounds. In this study, we present a new method of compound representation—substructural connectivity fingerprints (SCFP), providing information not only about the presence of particular substructures in the molecule but also additional data on substructure connections. Such representation was analyzed by the recently developed methodology—extreme entropy machines (EEM). The SCFP can be a valuable addition to virtual screening tools, as it represents compound structure with greater detail and more specificity, allowing for more accurate classification.
Learning to Promote Saliency Detectors

The categories and appearance of salient objects vary from image to image, therefore, saliency detection is an image-specific task. Due to lack of large-scale saliency training data, using deep neural networks (DNNs) with pretraining is difficult to precisely capture the image-specific saliency cues. To solve this issue, we formulate a zero-shot learning problem to promote existing saliency detectors. Concretely, a DNN is trained as an embedding function to map pixels and the attributes of the salient/background regions of an image into the same metric space, in which an image-specific classifier is learned to classify the pixels. Since the image-specific task is performed by the classifier, the DNN embedding effectively plays the role of a general feature extractor. Compared with transferring the learning to a new recognition task using limited data, this formulation makes the DNN learn more effectively from small data. Extensive experiments on five data sets show that our method significantly improves accuracy of existing methods and compares favorably against state-of-the-art approaches.
Lack of deliberation drives honesty among men but not women

Previous experiments have explored the effect of gender and cognitive reflection on dishonesty. However, to the best of our knowledge, no studies have investigated potential interactions between these two factors. Here we report a large online experiment (N = 766) where subjects first have a chance to lie for their benefit and then take a Cognitive Reflection Test (CRT). We find a significant interaction between gender and CRT score such that lack of deliberation promotes honesty for men but not for women. Additional analyses highlight that this effect is not driven by intuitive men but, at least partly, by men whose answers are neither intuitive nor deliberative, who happen to be particularly honest in our deception game.
Ocean warming has a greater effect than acidification on the early life history development and swimming performance of a large circumglobal pelagic fish

Ocean warming and acidification are serious threats to marine life; however, their individual and combined effects on large pelagic and predatory fishes are poorly understood. We determined the effects of projected future temperature and carbon dioxide (CO2) levels on survival, growth, morphological development and swimming performance on the early life stages of a large circumglobal pelagic fish, the yellowtail kingfish Seriola lalandi. Eggs, larvae and juveniles were reared in cross‐factored treatments of temperature (21 and 25°C) and pCO2 (500 and 985 μatm) from fertilisation to 25 days post hatching (dph). Temperature had the greatest effect on survival, growth and development. Survivorship was lower, but growth and morphological development were faster at 25°C, with surviving fish larger and more developed at 1, 11 and 21 dph. Elevated pCO2 affected size at 1 dph, but not at 11 or 21 dph, and did not affect survival or morphological development. Elevated temperature and pCO2 had opposing effects on swimming performance at 21 dph. Critical swimming speed (Ucrit) was increased by elevated temperature but reduced by elevated pCO2. Additionally, elevated temperature increased the proportion of individuals that responded to a startle stimulus, reduced latency to respond and increased maximum escape speed, potentially due to the more advanced developmental stage of juveniles at 25°C. By contrast, elevated pCO2 reduced the distance moved and average speed in response to a startle stimulus. Our results show that higher temperature is likely to be the primary driver of global change impacts on kingfish early life history; however, elevated pCO2 could affect critical aspects of swimming performance in this pelagic species. Our findings will help parameterise and structure fisheries population dynamics models and improve projections of impacts to large pelagic fishes under climate change scenarios to better inform adaptation and mitigation responses.
SO2 absorption in EmimCl-TEG deep eutectic solvents.

Deep eutectic solvents (DESs) based on 1-ethyl-3-methylimidazolium chloride (EmimCl) and triethylene glycol (TEG) with different molar ratios (from 6 : 1 to 1 : 1) were prepared. FTIR and theoretical calculation indicated that the C2-H on the imidazolium ring form hydrogen bonds with the hydroxyl group rather than the ether O atom of the TEG. The EmimCl-TEG DESs can efficiently capture SO2; in particular, EmimCl-TEG (6 : 1) can capture 0.54 g SO2 per gram of solvent at 0.10 atm and 20 °C, the highest absorption amount for DESs under the same conditions. Theoretical calculation showed that the high SO2 absorption capacity was mainly due to the strong charge-transfer interaction between SO2 and the anion Cl-. Moreover, SO2 desorption in the DESs can be controlled by tuning the interaction between EmimCl and TEG, and the DESs can be cycled many times.
Robust Rotor-Current Sensorless Control of Doubly Fed Induction Generators

This letter presents a robust rotor-current sensorless control (RCSC) scheme for decoupling control of active and reactive powers of a doubly fed induction generator (DFIG). The RCSC scheme does not involve power control loops and rotor-current measurements, which simplifies the implementation and improves the sensor fault tolerance of the system. Besides, RCSC provides fully decoupled power control, and it is robust to model uncertainties. Case studies are undertaken on a DFIG system to verify the control performance of the scheme.
Well-Annotated microRNAomes Do Not Evidence Pervasive miRNA Loss

Abstract microRNAs are conserved noncoding regulatory factors implicated in diverse physiological and developmental processes in multicellular organisms, as causal macroevolutionary agents and for phylogeny inference. However, the conservation and phylogenetic utility of microRNAs has been questioned on evidence of pervasive loss. Here, we show that apparent widespread losses are, largely, an artefact of poorly sampled and annotated microRNAomes. Using a curated data set of animal microRNAomes, we reject the view that miRNA families are never lost, but they are rarely lost (92% are never lost). A small number of families account for a majority of losses (1.7% of families account for >45% losses), and losses are associated with lineages exhibiting phenotypic simplification. Phylogenetic analyses based on the presence/absence of microRNA families among animal lineages, and based on microRNA sequences among Osteichthyes, demonstrate the power of these small data sets in phylogenetic inference. Perceptions of widespread evolutionary loss of microRNA families are due to the uncritical use of public archives corrupted by spurious microRNA annotations, and failure to discriminate false absences that occur because of incomplete microRNAome annotation.
Slug 2 Slug : A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation

Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.
Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport

Persistence diagrams (PDs) are now routinely used to summarize the underlying topology of complex data. Despite several appealing properties, incorporating PDs in learning pipelines can be challenging because their natural geometry is not Hilbertian. Indeed, this was recently exemplified in a string of papers which show that the simple task of averaging a few PDs can be computationally prohibitive. We propose in this article a tractable framework to carry out standard tasks on PDs at scale, notably evaluating distances, estimating barycenters and performing clustering. This framework builds upon a reformulation of PD metrics as optimal transport (OT) problems. Doing so, we can exploit recent computational advances: the OT problem on a planar grid, when regularized with entropy, is convex can be solved in linear time using the Sinkhorn algorithm and convolutions. This results in scalable computations that can stream on GPUs. We demonstrate the efficiency of our approach by carrying out clustering with diagrams metrics on several thousands of PDs, a scale never seen before in the literature.
Retrofit of a Hot Rolling Mill Plant With Three-Level Active Front End Drives

This paper analyzes the advantages of a selective retrofit of the drives in a hot rolling mill plant. More specifically, three cycloconverter-based drives of the finishing rolling mill in the plant are proposed to be replaced by back-to-back converters, thus enabling an efficient management of the active and reactive power flows, and improving the control of harmonic emissions. Three-level converters are utilized due to the high rated power and voltage of the roller motors. The retrofit is aimed to achieve the control of reactive power flows, active power losses, and voltage stability at the coupling point, as well as the reduction of harmonics, the possibility of regenerative braking, and the decrease of unscheduled shutdowns in the event of passive filtering trips. The study is based on real plant measurements, simulation tests, and results from an experimental platform built at scale.
Iron supplementation has minor effects on gut microbiota composition in overweight and obese women in early pregnancy

Abstract Fe is an essential nutrient for many bacteria, and Fe supplementation has been reported to affect the composition of the gut microbiota in both Fe-deficient and Fe-replete individuals outside pregnancy. This study examined whether the dose of Fe in pregnancy multivitamin supplements affects the overall composition of the gut microbiota in overweight and obese pregnant women in early pregnancy. Women participating in the SPRING study with a faecal sample obtained at 16 weeks’ gestation were included in this substudy. For each subject, the brand of multivitamin used was recorded. Faecal microbiome composition was assessed by 16S rRNA sequencing and analysed with the QIIME software suite. Dietary intake of Fe was assessed using a FFQ at 16 weeks’ gestation. Women were grouped as receiving low (<60 mg/d, n 94) or high (≥60 mg/d; n 65) Fe supplementation. The median supplementary Fe intake in the low group was 10 (interquartile range (IQR) 5–10) v. 60 (IQR 60–60) mg/d in the high group (P<0·001). Dietary Fe intake did not differ between the groups (10·0 (IQR 7·4–13·3) v. 9·8 (IQR 8·2–13·2) mg/d). Fe supplementation did not significantly affect the composition of the faecal microbiome at any taxonomic level. Network analysis showed that the gut microbiota in the low Fe supplementation group had a higher predominance of SCFA producers. Pregnancy multivitamin Fe content has a minor effect on the overall composition of the gut microbiota of overweight and obese pregnant women at 16 weeks’ gestation.
The Influence of Shape on the Output Potential of ZnO Nanostructures: Sensitivity to Parallel versus Perpendicular Forces

With the consistent shrinking of devices, micro-systems are, nowadays, widely used in areas such as biomedics, electronics, automobiles, and measurement devices. As devices shrunk, so too did their energy consumptions, opening the way for the use of nanogenerators (NGs) as power sources. In particular, to harvest energy from an object’s motion (mechanical vibrations, torsional forces, or pressure), present NGs are mainly composed of piezoelectric materials in which, upon an applied compressive or strain force, an electrical field is produced that can be used to power a device. The focus of this work is to simulate the piezoelectric effect in different ZnO nanostructures to optimize the output potential generated by a nanodevice. In these simulations, cylindrical nanowires, nanomushrooms, and nanotrees were created, and the influence of the nanostructures’ shape on the output potential was studied as a function of applied parallel and perpendicular forces. The obtained results demonstrated that the output potential is linearly proportional to the applied force and that perpendicular forces are more efficient in all structures. However, nanotrees were found to have an increased sensitivity to parallel applied forces, which resulted in a large enhancement of the output efficiency. These results could then open a new path to increase the efficiency of piezoelectric nanogenerators.
Career and Technical Education, Inclusion, and Postsecondary Outcomes for Students With Learning Disabilities

We used longitudinal data from Washington State to investigate the relationships among career and technical education (CTE) enrollment, inclusion in general education, and high school and postsecondary outcomes for students with learning disabilities. We replicated earlier findings that students with learning disabilities who were enrolled in a “concentration” of CTE courses had higher rates of employment after graduation than observably similar students with learning disabilities who were enrolled in fewer CTE courses. We also found that students with learning disabilities who spent more time in general education classrooms in high school had higher rates of on-time graduation, college attendance, and employment than observably similar students with learning disabilities who spent less time in general education classrooms in these grades.
Enhancement-Mode Ga2O3 Vertical Transistors With Breakdown Voltage >1 kV

High-voltage vertical Ga<sub>2</sub>O<sub>3</sub> MISFETs are developed employing halide vapor phase epitaxial (HVPE) layers on bulk Ga<sub>2</sub>O<sub>3</sub> (001) substrates. The low charge concentration of ~10<sup>16</sup> cm<sup>−3</sup> in the n-drift region allows three terminal breakdown voltages to reach up to 1057 V without field plates. The devices operate in the enhancement mode (E-mode) with a threshold voltage of ~1.2–2.2 V, a current ON/OFF ratio of ~10<sup>8</sup>, an ON resistance of ~13–18 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega {}\cdot \text{cm}^{2}$ </tex-math></inline-formula>, and an output current of >300 A/cm<sup>2</sup>. This is the first report of high-voltage vertical Ga<sub>2</sub>O<sub>3</sub> transistors with E-mode operation, a significant milestone toward realizing Ga<sub>2</sub>O<sub>3</sub> based power electronics.
The identification of a novel antibody for CD133 using human antibody phage display

The transmembrane glycoprotein CD133 is believed to be a marker of adult prostate stem cells and cancer stem/initiating cells. Investigating the role of CD133 in the normal biology of the prostate and in cancer is complicated by the lack of a sensitive and accurate antibody for its detection. Here, we describe the characterization of a unique antibody identified using human antibody phage display that can recognize CD133 in both formalin‐fixed tissues and cell lines.
Elucidating the Role of the Maternal Embryonic Leucine Zipper Kinase in Adrenocortical Carcinoma

Adrenocortical carcinoma (ACC) is an aggressive cancer with a 5-year survival rate <35%. Mortality remains high due to lack of targeted therapies. Using bioinformatic analyses, we identified maternal embryonic leucine zipper kinase (MELK) as 4.1-fold overexpressed in ACC compared with normal adrenal samples. High MELK expression in human tumors correlated with shorter survival and with increased expression of genes involved in cell division and growth. We investigated the functional effects of MELK inhibition using newly developed ACC cell lines with variable MELK expression, CU-ACC1 and CU-ACC2, compared with H295R cells. In vitro treatment with the MELK inhibitor, OTSSP167, resulted in a dose-dependent decrease in rates of cell proliferation, colony formation, and cell survival, with relative sensitivity of each ACC cell line based upon the level of MELK overexpression. To confirm a MELK-specific antitumorigenic effect, MELK was inhibited in H295R cells via multiple short hairpin RNAs. MELK silencing resulted in 1.9-fold decrease in proliferation, and 3- to 10-fold decrease in colony formation in soft agar and clonogenicity assays, respectively. In addition, although MELK silencing had no effect on survival in normoxia, exposure to a hypoxia resulted in a sixfold and eightfold increase in apoptosis as assessed by caspase-3 activation and TUNEL, respectively. Together these data suggest that MELK is a modulator of tumor cell growth and survival in a hypoxic microenvironment in adrenal cancer cells and support future investigation of its role as a therapeutic kinase target in patients with ACC.
Tubular Micro/Nanomotors: Propulsion Mechanisms, Fabrication Techniques and Applications

Micro/nanomotors are self-propelled machines that can convert various energy sources into autonomous movement. With the great advances of nanotechnology, Micro/Nanomotors of various geometries have been designed and fabricated over the past few decades. Among them, the tubular Micro/Nanomotors have a unique morphology of hollow structures, which enable them to possess a strong driving force and easy surface functionalization. They are promising for environmental and biomedical applications, ranging from water remediation, sensing to active drug delivery and precise surgery. This article gives a comprehensive and clear review of tubular Micro/Nanomotors, including propulsion mechanisms, fabrication techniques and applications. In the end, we also put forward some realistic problems and speculate about corresponding methods to improve existing tubular Micro/Nanomotors.
Extraction de connaissances sur les défaillances de compteurs d'essieux

Cet article propose une methode d'analyse pour des enregistrements operationnels d'un ensemble de compteurs d'essieux, qui constituent un element central a l'infrastructure ferroviaire. Notre objectif est de fournir une facon efficace d'extraire automatiquement des elements de connaissance concernant les defaillances de ces systemes. Puisque les donnees fournies ne contiennent pas de verite de terrain sur les causes de defaillances, les informations et leurs causes doivent etre extraites des relations sous-tendant les evenements enregistres. Apres une phase de pretraitement, les evenements sont groupes en fonction des relations qui ont ete mises en lumiere entre eux. Ces regroupements peuvent ensuite etre utilises pour creer des classes d'evenements en utilisant un systeme de classification adapte. Au dela de cette application specifique, cette approche est une facon nouvelle d'aborder les problemes d'analyse de fiabilite.
Computational Creativity in Slovenia

Computational Creativity is a field of Articial Intelligence that addresses processes that would be deemed creative if performed by a human. The field has been very active since 1999, and is now an established research field with its own International Conference on Computational Creativity (ICCC) conference series founded in 2010. This paper brifley surveys the field of Computational Creativity (CC) that is based on the analysis of ICCC conference papers, followed by a more detailed presentation of projects and selected contributions of Slovenian researchers to the field.
Structured Bayesian Gaussian process latent variable model

We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on the model covariance matrices for computational tractability. Inference is made tractable through a collapsed variational bound with similar computational complexity to that of the traditional Bayesian GP-LVM. Inference over partially-observed test cases is achieved by optimizing a "partially-collapsed" bound. Modeling high-dimensional time series systems is enabled through use of a dynamical GP latent variable prior. Examples imputing missing data on images and super-resolution imputation of missing video frames demonstrate the model.
Participatory Design in Gerontechnology: A Systematic Literature Review

Purpose of the Study Participatory design (PD) is widely used within gerontechnology but there is no common understanding about which methods are used for what purposes. This review aims to examine what different forms of PD exist in the field of gerontechnology and how these can be categorized. Design and Methods We conducted a systematic literature review covering several databases. The search strategy was based on 3 elements: (1) participatory methods and approaches with (2) older persons aiming at developing (3) technology for older people. Results Our final review included 26 studies representing a variety of technologies designed/developed and methods/instruments applied. According to the technologies, the publications reviewed can be categorized in 3 groups: Studies that (1) use already existing technology with the aim to find new ways of use; (2) aim at creating new devices; (3) test and/or modify prototypes. The implementation of PD depends on the questions: Why a participatory approach is applied, who is involved as future user(s), when those future users are involved, and how they are incorporated into the innovation process. Implications There are multiple ways, methods, and instruments to integrate users into the innovation process. Which methods should be applied, depends on the context. However, most studies do not evaluate if participatory approaches will lead to a better acceptance and/or use of the co-developed products. Therefore, participatory design should follow a comprehensive strategy, starting with the users' needs and ending with an evaluation if the applied methods have led to better results.