Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural Analysis of Plasmodium falciparum Hexokinase Provides Novel Information about Catalysis Due to a Plasmodium-Specific Insertion

The protozoan parasite Plasmodium falciparum is the causative pathogen of the most severe form of malaria, for which novel strategies for treatment are urgently required. The primary energy supply for intraerythrocytic stages of Plasmodium is the production of ATP via glycolysis. Due to the parasite’s strong dependence on this pathway and the significant structural differences of its glycolytic enzymes compared to its human counterpart, glycolysis is considered a potential drug target. In this study, we provide the first three-dimensional protein structure of P. falciparum hexokinase (PfHK) containing novel information about the mechanisms of PfHK. We identified for the first time a Plasmodium-specific insertion that lines the active site. Moreover, we propose that this insertion plays a role in ATP binding. Residues of the insertion further seem to affect the tetrameric interface and therefore suggest a special way of communication among the different monomers. In addition, we confirmed that PfHK is targeted and affected by oxidative posttranslational modifications (oxPTMs). Both S-glutathionylation and S-nitrosation revealed an inhibitory effect on the enzymatic activity of PfHK.
Paléoenvironnement et chronologie des sites du domaine atlantique franc̜ais d'âge Pléistocène moyen et supérieur d'après l'étude des rongeurs

Dans divers sites paleontologiques d'Aquitaine et de Poitou-Charentes on etudie par traitement statistique des populations de rongeurs (en particulier le genre Arvicola) au Pleistocene ce qui permet de caracteriser le paleoenvironnement et les facteurs climatiques de l'epoque.
Dynamic Ensemble Algorithm Post-Selection Using Hardness-Aware Oracle

Dynamic Ensemble Selection (DES) algorithms have obtained better performance in many tasks compared to monolithic classifiers and static ensembles. However, it is reasonable to assume that no DES algorithm is the optimal solution in different scenarios since diversity plays an important role. Thus, this paper addresses this research gap by proposing a novel approach called Hardness-aware Oracle with Dynamic Ensemble Selection (HaO-DES) that operates as a post-selection strategy, evaluating and selecting the best DES techniques per instance. Each DES technique ensemble is evaluated using a new measure called Hardness-aware Oracle (HaO). HaO extends the traditional Oracle concept by assessing a DES technique based on how the classifiers in the selected ensemble work together, contrasting with the individual classifier evaluation in the traditional assessment. We performed experiments over 30 databases, using three base classifiers (Perceptron, Logistic Regression, and Naive Bayes) in homogeneous and heterogenous pools’ configurations, to assess HaO-DES with four DES approaches (KNORA-U, KNOP, DES-P, and META-DES). We use three performance metrics to evaluate the experiments: accuracy, F-score, and Matthews Correlation Coefficient (MCC). The results show that our approach outperforms or obtains similar results against the four individual DES approaches, mainly when considering heterogeneous pool settings. We also demonstrated the HaO-DES efficiency in choosing suitable DES techniques in different situations.
Controlling Multi-Class Human Vocalization Generation via a Simple Segment-based Labeling Scheme

As prompt-based generative models have received much attention, many studies have proposed a similar model for sound generation. While prompt-based generative models have an intuitive interface for non-professional users to experiment with, they lack the ability to control the generated sounds via a more direct means. In this work, we investigated the use of a simple segment-based labeling scheme for human vocalization generation, which is a specific subset of sound generation. By conditioning the generative models on the label sequence which marks the vocalization class of the segment, the generated sound can be controlled in a more detailed manner while maintaining a simple and intuitive input interface. Our experiments showed that simply switching the label scheme from global to segment-based does not degrade the quality of the generated samples in any way and provides a new method of controlling the generation process.
Automatic Neighborhood Search Clustering Algorithm Based on Feature Weighted Density

— The failure of traditional clustering methods on high-dimensional data has been a thorny problem. Therefore, we propose a simple but effective mean shift feature weighted deformation method (WDNS) to calculate the density value of high-dimensional data points by learning the weights of the features. The neighborhood search is then carried out using the density center in the decision diagram as the starting point, and the points of the same cluster are merged to finally complete the clustering. The experimental results show that the algorithm has higher clustering accuracy than the six existing clustering algorithms. In addition, it has the outstanding feature of automatic parameter setting, which is not available in its peers. In summary, this work can improve the state-of-the-art of clustering algorithms.
Do Different Learning Style Inventories Report Similar Findings Among Pilots?

This study investigated the gender and generational learning preferences of pilots and non-pilots and the gender and generational differences among the pilots surveyed. The Felder and Soloman Index of Learning Styles questionnaire measured individual learning styles on four continuums: Active-Reflective, Sensing-Intuitive, Visual-Verbal, and Sequential-Global. Survey data indicate a statistically significant difference in learning styles of non-pilots and pilots, males and females, and different generations of pilots. Among all participants, pilots scored higher than non-pilots on the Sensing and Visual scales, and males scored higher on the Visual aspect of that scale. Generation variation occurred between Generation X and Y, where Generation Y favored the Sensing learning style more than Generation X. Among pilots, males scored higher than females on the Visual preference, and Generation Y and Z preferred the Sensing learning style. Generation Z favored the Sequential learning style more than Generation X. Curriculum design, instructional methodologies, and technologies selected to deliver course content should focus on active, sensing, visual, and sequential learning styles while balancing the other styles in the design to produce learners who can thrive in any educational setting.
The Astronomical Society of Southern Africa

a) Communication and Editorial Board Report: Case Rijsdijk b) Deep Sky Section: Douglas Bullis c) Observing and Outreach: Kos Coronaios d) SA Astronomy History Section: Chris de Coning e) Shallow Sky: Clyde Foster f) Double Stars: Dave Blane g) Imaging / Astrophotography: Martin Heigan h) Citizen Science: Allen Versfeld i) Instrumentation: Chris Stewart j) Photometry, Spectroscopy: Percy Jacobs k) Cosmology and Astrophysics: Bruce Dickson l) ASSA Scholarship Convener report: Dr Claire Flanagan m) Membership Report: Wilmi Nel n) Financial Report: Adv AJ Nel
Intelligent Control of Multilegged Robot Smooth Motion: A Review

Motion control is crucial for multilegged robot locomotion and task completion. This study aims to address the fundamental challenges of inadequate foot tracking and weak leg compliance control in multilegged robot motions. The first section summarizes and discusses the feasibility and operability of smooth motion for multilegged robots based on the necessary conditions and application value of walking over complicated and unstructured terrain. The second to fourth sections present a qualitative research method of literature review to study the effects of several factors on the smooth motion of multilegged robots, including foot force perception and motion planning, active compliance control, gait stability, and Deep Reinforcement Learning (DRL) control. We conducted a case analysis of multilegged robots using traditional Nonlinear Model Predictive Control (NMPC), biomimetic Central Pattern Generator (CPG), and DRL control in the latest international open-source projects to help readers further follow the latest open-source algorithms for legged robot motion control. In addition, we analyze the challenges these influencing factors face in optimization and provide suggestions for effectively promoting the realization of review objectives. In the fifth section, we discuss the challenges of springy gait and intelligent switching control in solving the problems of poor foot tracking and weak leg compliance. We explore and forecast the potential application of the Probability Density Function (PDF) control of the foot force in the smooth motion of multilegged robots. In the sixth section, we systematically discuss, analyze, and summarize the existing problems in this research field. We also highlight the shortcomings of existing research methods, look forward to future research areas, and highlight the obstacles that need to be overcome to promote future research in this field.
Associations between Menopausal Hormone Therapy and Colorectal, Lung, or Melanoma Cancer Recurrence and Mortality: A Narrative Review

Objective: to develop eligibility criteria for use in non-gynecological cancer patients. Methods: We searched all the articles published in peer-reviewed journals up to March 2021. We utilized the PICOS standards and the following selection criteria: menopausal women with a history of non-gynecological and non-breast cancer who underwent hormone replacement therapy (HRT) using various preparations (oestrogens alone or in combination with a progestogen, tibolone, or tissue selective oestrogen complex) and different routes of administration (including oral, transdermal, vaginal, or intra-nasal). We focused on randomized controlled trials as well as relevant extension studies or follow-up reports, specifically examining recurrence and mortality outcomes. Results: Women colorectal cancer survivors who use MHT have a lower risk of death from any cause than those survivors who do not use MHT. Women who are skin melanoma survivors using MHT have a longer survival rate than non-MHT survivors. There is no evidence that women lung cancer survivors who use MHT have a different survival rate than those who do not use MHT. Conclusions: MHT is safe for women who have a history of colorectal, lung, or skin melanoma cancers.
Speech-Based Classification of Defensive Communication: A Novel Dataset and Results

Defensive communication is known to have detrimental effects on the quality of social interactions. Hence, recognising and reducing defensive behaviour is crucial to improving professional and personal communication. We introduce DefComm-DB, a novel multimodal dataset comprising video recordings in which one of the following types of defensive communication is present: (i) verbally attacking the conversation partner, (ii) withdrawing from the communication, (iii) making oneself greater, and (iv) making oneself smaller. Sub-sequently, we present a machine learning approach for the automatic classification of DefComm-DB. In particular, we utilise wav2vec2, autoencoders, a pre-trained CNN and openSMILE for feature extraction from the audio modality. For the text stream, we apply ELECTRA and SBERT. On the unseen test set, our models achieve an Unweighted Average Recall of 49.4% and 52.2% for the audio and text modalities, respectively, showing the feasibility of the introduced challenge.
The Potent and Selective Histamine H3 Receptor Antagonist E169 Counteracts Cognitive Deficits and Mitigates Disturbances in the PI3K/AKT/GSK-3β Signaling Pathway in MK801-Induced Amnesia in Mice

The role of histamine H3 receptors (H3Rs) in memory and the prospective of H3R antagonists in pharmacological control of neurodegenerative disorders, e.g., Alzheimer’s disease (AD), is well-accepted. Therefore, the procognitive effects of acute systemic administration of H3R antagonist E169 (2.5–10 mg/kg, i.p.) on MK801-induced amnesia in C57BL/6J mice using the novel object recognition test (NORT) were evaluated. E169 (5 mg) provided a significant memory-improving effect on MK801-induced short- and long-term memory impairments in NORT. The E169 (5 mg)-provided effects were comparable to those observed with the reference phosphatidylinositol 3-kinase (PI3K) inhibitor LY294002 and were abrogated with the H3R agonist (R)-α-methylhistamine (RAMH). Additionally, our results demonstrate that E169 ameliorated MK801-induced memory deficits by antagonism of H3Rs and by modulation of the level of disturbance in the expression of PI3K, Akt, and GSK-3β proteins, signifying that E169 mitigated the Akt-mTOR signaling pathway in the hippocampus of tested mice. Moreover, the results observed revealed that E169 (2.5–10 mg/kg, i.p.) did not alter anxiety levels and locomotor activity of animals in open field tests, demonstrating that performances improved following acute systemic administration with E169 in NORT are unrelated to changes in emotional response or in spontaneous locomotor activity. In summary, these obtained results suggest the potential of H3R antagonists such as E169, with good in silico physicochemical properties and stable retained key interactions in docking studies at H3R, in simultaneously modulating disturbed brain neurotransmitters and the imbalanced Akt-mTOR signaling pathway related to neurodegenerative disorders, e.g., AD.
Influence of nutritional management prior to adaptation to a feedlot diet on ruminal microbiota of Nellore cattle

- The objective of this study was to evaluate the effect of either a limited forage intake or concentrate supplementation prior to the adaptation to high-concentrate diets on dry matter intake, ruminal pH, bacteria, and protozoa of Nellore cattle. The experiment was designed as a two 3×3 Latin square, and six cannulated Nellore steers were used. Each experimental period was composed by three feeding phases: pre-adaptation (14 days), adaptation (12 days), and finishing (seven days) diet, in a total of 33 days per period. The steers were assigned to one of three pre-adaptation dietary treatments: control (Tifton hay fed ad libitum + mineral supplement), restriction (Tifton hay fed at 1.4% of BW + mineral supplement), and concentrate (Tifton hay fed ad libitum + 0.5% of BW of a mix of concentrate feedstuffs and mineral supplement). The adaptation period consisted of two adaptation diets, which contained 72 and 79% concentrate for six days each. The finishing diet contained 86% concentrate. During the pre-adaptation phase, restricted cattle had higher pH than concentrate-fed cattle. There was a reduction in M. elsdenii relative population in cattle from either restriction or concentrate groups. During adaptation and finishing phases, cattle from concentrate group had smaller F. succinogenes populations compared with the control group. The previous nutritional backgrounds impact ruminal microbiota during adaptation and finishing phases without causing any negative effect on ruminal pH. Feeding concentrate prior to the adaptation positively impacted the transition to high-concentrate diets and promoted increased dry matter intake.
In Planta Study Localizes an Effector Candidate from Austropuccinia psidii Strain MF-1 to the Nucleus and Demonstrates In Vitro Cuticular Wax-Dependent Differential Expression

Austropuccinia psidii is a biotrophic fungus that causes myrtle rust. First described in Brazil, it has since spread to become a globally important pathogen that infects more than 480 myrtaceous species. One of the most important commercial crops affected by A. psidii is eucalypt, a widely grown forestry tree. The A. psidii–Eucalyptus spp. interaction is poorly understood, but pathogenesis is likely driven by pathogen-secreted effector molecules. Here, we identified and characterized a total of 255 virulence effector candidates using a genome assembly of A. psidii strain MF-1, which was recovered from Eucalyptus grandis in Brazil. We show that the expression of seven effector candidate genes is modulated by cell wax from leaves sourced from resistant and susceptible hosts. Two effector candidates with different subcellular localization predictions, and with specific gene expression profiles, were transiently expressed with GFP-fusions in Nicotiana benthamiana leaves. Interestingly, we observed the accumulation of an effector candidate, Ap28303, which was upregulated under cell wax from rust susceptible E. grandis and described as a peptidase inhibitor I9 domain-containing protein in the nucleus. This was in accordance with in silico analyses. Few studies have characterized nuclear effectors. Our findings open new perspectives on the study of A. psidii–Eucalyptus interactions by providing a potential entry point to understand how the pathogen manipulates its hosts in modulating physiology, structure, or function with effector proteins.
Inter-Twin Connectivity for Digital Twin Networks in Secure Contactless Delivery Service Scenarios

Along with the advent of COVID-19 epidemic, delivery services have become one of the most common services in our daily life. However, the current services merely assume that the contact for handing over delivery items is denied to avoid facing between carriers and receivers. This extreme concept might lead to the ruin of typical processes for delivery services, such as face-to-face authentication, item checking, and payment in person. So, it may cause insufficiency regarding both sides of the secure item delivery and customers’ personal security. Therefore, this paper comes up with a novel CONTACTFUL delivery service with the digital twin paradigm to resolve contactless problems. The proposed idea establishes two-tier digital twin architecture to arrange digital twins and physical twins of carriers and receivers. Then, inter-digital-twin interaction virtually supports on-site delivery service processes. The inter-twin interaction relies on highly accurate proximity detection for items, carriers, and receivers. So, the proposed scheme considers machine-learning technologies and filtering algorithms to achieve high precision based on mobile-based Bluetooth low energy (BLE) beaconing. This scheme is implemented as a proof-of-concept prototype, and various field data are exploited to build ML-filtering models including their ensemble one. On the steel door, the ensemble model shows an accuracy improvement of 65% compared to SVM, 14% to CNN, and 73% to MLP. On the wood door, the ensemble model shows an accuracy improvement of 21% compared to SVM, 9% to CNN, and 17% to MLP.
MAC-Based Stream-Aware Mechanism for IEEE 802.1Qbv Networks

Thanks to Time-Sensitive Networking (TSN) developed by the IEEE 802.1 TSN Task Group, best-effort streams and time-sensitive streams can be transmitted in the same network. Generally, the real-time performance of time-sensitive streams is guaranteed by strict constraints, especially in IEEE 802.1Qbv networks, where time-sensitive streams are transmitted according to pre-designed scheduling tables. This brings better real-time performance but also makes the system more vulnerable. When there is huge clock synchronization error or packet loss due to environment or equipment failure, streams are likely not to be transmitted as scheduled, resulting in increased latency and jitter. In this article, we propose a MAC-based stream-aware mechanism for the switch supporting Qbv functionality to reduce the jitter in case of packet loss. The results indicate that the jitter of streams can be improved to almost zero with this stream-aware mechanism.
Survey on GAN-based face hallucination with its model development

Face hallucination aims to produce a high-resolution face image from an input low-resolution face image, which is of great importance for many practical face applications, such as face recognition and face verification. Since the structure of the face image is complex and sensitive, obtaining a super-resolved face image is more difficult than generic image super-resolution. Recently, with great success in the high-level face recognition task, deep learning methods, especially generative adversarial networks (GANs), have also been applied to the low-level vision task – face hallucination. This work is to provide a model evolvement survey on GAN-based face hallucination. The principles of image resolution degradation and GAN-based learning are presented firstly. Then, a comprehensive review of the state-of-art GAN-based face hallucination methods is provided. Finally, the comparisons of these GAN-based face hallucination methods and the discussions of the related issues for future research direction are also provided.
Impact of the Precursor on the Physicochemical Properties and Photoactivity of TiO2 Nanoparticles Produced in Supercritical CO2

The synthesis of TiO2 nanoparticles (NPs) in supercritical media has been reported over the last two decades. However, very few studies have compared the physicochemical characteristics and photoactivity of the TiO2 powders produced from different precursors, and even fewer have investigated the effect of using different ratios of hydrolytic agent/precursor (HA/P) on the properties of the semiconductor. To bridge this knowledge gap, this research focuses on the synthesis and characterization of TiO2 NPs obtained in a supercritical CO2 medium from four different TiO2 precursors, namely diisopropoxytitanium bis (acetylacetonate) (TDB), titanium (IV) isopropoxide (TIP), titanium (IV) butoxide (TBO), and titanium (IV) 2-ethylhexyloxide (TEO). Further, the effect of various HA/P ratios (10, 20, 30, and 40 mol/mol) when using ethanol as a hydrolytic agent has also been analyzed. Results obtained have shown that the physicochemical properties of the catalysts are not significantly affected by these variables, although some differences do exist between the synthesized materials and their catalytic performances. Specifically, photocatalysts obtained from TIP and TEO at the higher HA/P ratios (HA/P = 30 and HA/P = 40) led to higher CO2 photoconversions (6.3–7 µmol·g−1·h−1, Apparent Quantum Efficiency < 0.1%), about three times higher than those attained with commercial TiO2 P-25. These results have been imputed to the fact that these catalysts exhibit appropriate values of crystal size, surface area, light absorption, and charge transfer properties.
Multi-modal Information Fusion for Action Unit Detection in the Wild

Action Unit (AU) detection is an important research branch in affective computing, which better understands human emotional intentions and responds more naturally to their needs and desires. In this paper, we present our latest progress techniques in the 5th Affective Behavior Analysis in-the-wild (ABAW) competition, including data balancing by marking, extracting features visual through models trained in face database and audio through deep networks and traditional methods, proposing model structures for mapping multimodal information to a unify multimodal vector space and fusing results from multiple models. These methods are effective on the official validation dataset of the Aff-Wild2. The final F1 in the 5th ABAW competition test dataset achieves 54.22%, 4.33% higher than the best results in the 3rd ABAW competition.