Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Testing of a Small-Scale Truss Beam That Adapts to Loads Through Large Shape Changes

Adaptive structures have the ability to modify their shape and internal forces through sensing and actuation in order to maintain optimal performance under changing actions. Previous studies have shown that substantial whole-life energy savings with respect to traditional passive designs can be achieved through well-conceived adaptive design strategies. The whole-life energy comprises an embodied part in the material and an operational part for structural adaptation. Structural adaptation through controlled large shape changes allows a significant stress redistribution so that the design is not governed by extreme loads with long return periods. This way, material utilization is maximized and embodied energy is reduced. A design process based on shape optimization has been formulated to obtain shapes that are optimal for each load case. A geometrically non-linear force method is employed to control the structure into required shapes. This paper presents the experimental testing of a small-scale prototype adaptive structure produced by this design process. The structure is a simply supported planar truss. Shape adaptation is achieved through controlled length changes of turnbuckles that strategically replace some of the structural elements. The stress is monitored by strain sensors fitted on some of the truss elements. The nodal coordinates are monitored by an optical tracking system. Numerical predictions and measurements have a minimum Pearson correlation of 0.86 which indicates good accordance. Although scaling effects have to be further investigated, experimental testing on a small-scale prototype has been useful to assess the feasibility of the design and control methods outlined in this work. Results show that stress homogenization through controlled large shape changes is feasible.
MADS-Box Genes Are Key Components of Genetic Regulatory Networks Involved in Abiotic Stress and Plastic Developmental Responses in Plants

Plants, as sessile organisms, adapt to different stressful conditions, such as drought, salinity, extreme temperatures, and nutrient deficiency, via plastic developmental and growth responses. Depending on the intensity and the developmental phase in which it is imposed, a stress condition may lead to a broad range of responses at the morphological, physiological, biochemical, and molecular levels. Transcription factors are key components of regulatory networks that integrate environmental cues and concert responses at the cellular level, including those that imply a stressful condition. Despite the fact that several studies have started to identify various members of the MADS-box gene family as important molecular components involved in different types of stress responses, we still lack an integrated view of their role in these processes. In this review, we analyze the function and regulation of MADS-box gene family members in response to drought, salt, cold, heat, and oxidative stress conditions in different developmental processes of several plants. In addition, we suggest that MADS-box genes are key components of gene regulatory networks involved in plant responses to stress and plant developmental plasticity in response to seasonal changes in environmental conditions.
Autoantibodies in Morphea: An Update

Skin autoimmune conditions belong to a larger group of connective tissue diseases and primarily affect the skin, but might also involve underlying tissues, such as fat tissue, muscle, and bone. Autoimmune antibodies (autoantibodies) play a role in autoimmune skin diseases, such as localized scleroderma also termed morphea, and systemic scleroderma, also called systemic sclerosis (SSc). The detailed studies on the biological role of autoantibodies in autoimmune skin diseases are limited. This results in a few available tools for effective diagnosis and management of autoimmune skin diseases. This review aims to provide an update on the detection and most recent research on autoantibodies in morphea. Several recent studies have indicated the association of autoantibody profiles with disease subtypes, damage extent, and relapse potential, opening up exciting new possibilities for personalized disease management. We discuss the role of existing autoantibody tests in morphea management and the most recent studies on morphea pathogenesis. We also provide an update on novel autoantibody biomarkers for the diagnosis and study of morphea.
Fruit Salad in the Lab: Comparing Botanical Species to Help Deciphering Fruit Primary Metabolism

Although fleshy fruit species are economically important worldwide and crucial for human nutrition, the regulation of their fruit metabolism remains to be described finely. Fruit species differ in the origin of the tissue constituting the flesh, duration of fruit development, coordination of ripening changes (climacteric vs. non-climacteric type) and biochemical composition at ripeness is linked to sweetness and acidity. The main constituents of mature fruit result from different strategies of carbon transport and metabolism. Thus, the timing and nature of phloem loading and unloading can largely differ from one species to another. Furthermore, accumulations and transformations of major soluble sugars, organic acids, amino acids, starch and cell walls are very variable among fruit species. Comparing fruit species therefore appears as a valuable way to get a better understanding of metabolism. On the one hand, the comparison of results of studies about species of different botanical families allows pointing the drivers of sugar or organic acid accumulation but this kind of comparison is often hampered by heterogeneous analysis approaches applied in each study and incomplete dataset. On the other hand, cross-species studies remain rare but have brought new insights into key aspects of primary metabolism regulation. In addition, new tools for multi-species comparisons are currently emerging, including meta-analyses or re-use of shared metabolic or genomic data, and comparative metabolic flux or process-based modeling. All these approaches contribute to the identification of the metabolic factors that influence fruit growth and quality, in order to adjust their levels with breeding or cultural practices, with respect to improving fruit traits.
Effects of Hardwood Biochar on Methane Production, Fermentation Characteristics, and the Rumen Microbiota Using Rumen Simulation

Biochar is a novel carbonized feed additive sourced from pyrolyzed biomass. This compound is known to adsorb gasses and carbon, participate in biological redox reactions and provide habitat biofilms for desirable microbiota proliferation. Therefore, biochar holds potential to modify rumen fermentation characteristics and reduce enteric CH4 emissions. The objective of this study was to investigate the effect of hardwood biochar supplementation on fermentation parameters, methane (CH4) production and the ruminal archaeal, bacterial, and fungal microbiota using the in vitro RUSITEC (rumen simulation technique) system. Treatments consisted of a control diet (oaten pasture: maize silage: concentrate, 35:35:30 w/w) and hardwood biochar included at 400 or 800 mg per day (3.6 and 7.2% of substrate DM, respectively), over a 15-day period. Biochar supplementation had no effect (P ≥ 0.37) on pH, effluent (mL/d), total gas (mL/d), dry matter (DM) digestibility or CH4 production (mg/d). The addition of 800 mg biochar per day had the tendency (P = 0.10) to lower the % of CH4 released in fermentation compared to 400 mg/d biochar treatment. However, no effect (P ≥ 0.44) was seen on total VFA, acetate, propionate, butyric, branched-chain VFA, valerate and caproate production and the ratio of acetate to propionate. No effect (P > 0.05) was observed on bacterial, archaeal or fungal community structure. However, biochar supplementation at 800 mg/d decreased the abundance of one Methanomethylophilaceae OTU (19.8-fold, P = 0.046) and one Lactobacillus spp. OTU (31.7-fold, P < 0.01), in comparison to control treatments. Two fungal OTUs classified as Vishniacozyma victoriae (5.4 × 107 increase) and Sporobolomyces ruberrimus (5.4 × 107-fold increase) were more abundant in the 800 mg/d biochar samples. In conclusion, hardwood biochar had no effects on ruminal fermentation characteristics and may potentially lower the concentration of enteric CH4 when included at higher dosages by manipulating ruminal microbiota abundances.
The Coordination of Ethylene and Other Hormones in Primary Root Development

The primary root is the basic component of root systems, initiates during embryogenesis and develops shortly after germination, and plays a key role in early seedling growth and survival. The phytohormone ethylene shows significant inhibition of the growth of primary roots. Recent findings have revealed that the inhibition of ethylene in primary root elongation is mediated via interactions with phytohormones, such as auxin, abscisic acid, gibberellin, cytokinins, jasmonic acid, and brassinosteroids. Considering that Arabidopsis and rice are the model plants of dicots and monocots, as well as the fact that hormonal crosstalk in primary root growth has been extensively investigated in Arabidopsis and rice, a better understanding of the mechanisms in Arabidopsis and rice will increase potential applications in other species. Therefore, we focus our interest on the emerging studies in the research of ethylene and hormone crosstalk in primary root development in Arabidopsis and rice.
Modified 2-bit level Pipelined 2-Lookahead Add-Compare Select Unit for High Speed and Low-Complexity Viterbi Decoder

The convolutinal codes are widely used in many communication systems due to their excellent error control performance. In this paper, the modified 2-bit level pipelined 2-lookahead add-compare select (ACS) unit structure is proposed. The ACS unit has the main bottleneck on the decoding speed of a Viterbi decoder. With the proposed shared maximun selection module, it is possible to decrease the critical path and the complexity of the ACS unit. The complexity of the proposed ACS unit is significantly less compared with a conventional MSB-first ACS unit design.
History of Envenoming Therapy and Current Perspectives

Each year, millions of humans fall victim to animal envenomings, which may either be deadly or cause permanent disability to the effected individuals. The Nobel Prize-winning discovery of serum therapy for the treatment of bacterial infections (tetanus and diphtheria) paved the way for the introduction of antivenom therapies for envenomings caused by venomous animals. These antivenoms are based on polyclonal antibodies derived from the plasma of hyperimmunized animals and remain the only specific treatment against animal envenomings. Following the initial development of serum therapy for snakebite envenoming by French scientists in 1894, other countries with high incidences of animal envenomings, including Brazil, Australia, South Africa, Costa Rica, and Mexico, started taking up antivenom production against local venomous animals over the course of the twentieth century. These undertakings revolutionized envenoming therapy and have saved innumerous patients worldwide during the last 100 years. This review describes in detail the above-mentioned historical events surrounding the discovery and the application of serum therapy for envenomings, as well as it provides an overview of important developments and scientific breakthroughs that were of importance for antibody-based therapies in general. This begins with discoveries concerning the characterization of antibodies, including the events leading up to the elucidation of the antibody structure. These discoveries further paved the way for other milestones in antibody-based therapies, such as the introduction of hybridoma technology in 1975. Hybridoma technology enabled the expression and isolation of monoclonal antibodies, which in turn formed the basis for the development of phage display technology and transgenic mice, which can be harnessed to directly obtain fully human monoclonal antibodies. These developments were driven by the ultimate goal of producing potent neutralizing monoclonal antibodies with optimal pharmacokinetic properties and low immunogenicity. This review then provides an outline of the most recent achievements in antivenom research, which include the application of new biotechnologies, the development of the first human monoclonal antibodies that can neutralize animal toxins, and efforts toward creating fully recombinant antivenoms. Lastly, future perspectives in the field of envenoming therapies are discussed, including rational engineering of antibody cross-reactivity and the use of oligoclonal antibody mixtures.
RNA Synthesis and Capping by Non-segmented Negative Strand RNA Viral Polymerases: Lessons From a Prototypic Virus

Non-segmented negative strand (NNS) RNA viruses belonging to the order Mononegavirales are highly diversified eukaryotic viruses including significant human pathogens, such as rabies, measles, Nipah, and Ebola. Elucidation of their unique strategies to replicate in eukaryotic cells is crucial to aid in developing anti-NNS RNA viral agents. Over the past 40 years, vesicular stomatitis virus (VSV), closely related to rabies virus, has served as a paradigm to study the fundamental molecular mechanisms of transcription and replication of NNS RNA viruses. These studies provided insights into how NNS RNA viruses synthesize 5′-capped mRNAs using their RNA-dependent RNA polymerase L proteins equipped with an unconventional mRNA capping enzyme, namely GDP polyribonucleotidyltransferase (PRNTase), domain. PRNTase or PRNTase-like domains are evolutionally conserved among L proteins of all known NNS RNA viruses and their related viruses belonging to Jingchuvirales, a newly established order, in the class Monjiviricetes, suggesting that they may have evolved from a common ancestor that acquired the unique capping system to replicate in a primitive eukaryotic host. This article reviews what has been learned from biochemical and structural studies on the VSV RNA biosynthesis machinery, and then focuses on recent advances in our understanding of regulatory and catalytic roles of the PRNTase domain in RNA synthesis and capping.
Pre-specified Anxiety Predicts Future Decision-Making Performances Under Different Temporally Constrained Conditions

In real-life circumstances, people occasionally require making forced decisions when encountering unpredictable events and situations that yield socially and privately unfavorable consequences. In order to prevent future negative consequences, it is beneficial to successfully predict future decision-making behaviors based on various types of information, including behavioral traits and/or psychological states. For this prospective purpose, the present study used the Iowa Gambling Task, which simulates multiple aspects of real-life decision-making processes, such as choice preference, selection and evaluation of output feedback, and investigated how anxiety profiles predict decision-making performances under conditions with different temporal pressures on task execution. To conduct a temporally causal analysis, we assessed the trait and state anxiety profiles of 33 young participants prior to the task and analyzed their subsequent decision-making performances. We separated two disadvantageous card decks with high rewards and losses into high- and middle-risk decks, and calculated local performance indexes for decision-making immediately after salient penalty events for the high-risk deck in addition to traditional global performance indexes concerning overall trial outcomes such as final winnings and net scores. For global decision-making, higher trait anxiety predicted more risky choices solely in the self-paced condition without temporal pressure. For local decision-making, state anxiety predicted risk-taking performances differently in the self- and forced-paced conditions. In the self-paced condition, higher state anxiety predicted higher risk-avoidance. In the forced-paced condition, higher state anxiety predicted more frequent choices of the middle-risk deck. These findings suggest not only that pre-specified anxiety profiles can effectively predict future decision-making behaviors under different temporal pressures, but also newly indicate that behavioral mechanisms for moderate risk-taking under an emergent condition should be focused on to effectively prevent future unfavorable consequences when actually encountering negative events.
Implementation of high speed hash function Keccak on GPU

Nowadays, a hash function is used for password management. The hash function is desired to possess the following three characteristics: Pre-Image Resistance, Second Pre-Image Resistance, and Collision Resistance. They are set on the assumption that it is computationally difficult to find the original message from a given hash value. However, the security level of the password management will be further reduced by implementing a high speed hash function on GPU. In this paper, the implementation of high speed hash function Keccak-512 using the integrated development environment CUDA for GPU is proposed. The following four techniques are used in order to speed up its implementation. The first one is reforming lookup tables from 2 dimensional arrays to 1 dimensional arrays at step rho and pi. The second is an investigation into the effect of using constant memory and shared memory for constant values. The third is the finding out the optimal configuration of blocks-threads, then evaluate the implementation according to the occupancy. And the last one is using CUDA streams with overlapping to hide the overhead of data transfer and GPU processing. As the result, the throughput of implemented Keccak on GeForce GTX 1080 achieved up to maximum 64.58 GB/s. It is about 14.0 times faster than the previous research result. In addition, the safety level of Keccak is also discussed at the point of Pre-Image Resistance especially. In order to implement a high speed hash function for password cracking, we developed a special program for passwords up to 71 characters. Moreover, the throughputs of 2 times as well as 3 times hash are also evaluated. It is proved that multiple times hash is possible to greatly improved the security level of Keccak with password management. The throughput of hashing password with a large number of iterations confirmed the effect was about 90%. That is the time required to hash one password for 1000 times was almost the same as the total time to sequentially hash 900 passwords.
A Searchable CP-ABE Privacy Preserving Scheme

The separation of users and data in the cloud storage system brings new security threats. The cloud storage scheme with single encryption mechanism has been unable to meet new demands. Aiming at the above problems, the PEKS was introduced into the CP-ABE scheme with multiple authorities to propose a searchable CP-ABE privacy-preserving scheme which supports the direct re-vocation of users. The access control of users is achieved by the central authority, which avoids the security risks caused by submitting the private keys and access structure to cloud server. The collusion problems are solved by using 1-out-of-n Obvious Transfer algorithm and associating the private key components with random identity token of users. The privacy of keywords is guaranteed though the new improved algorithm. Based on the DBDH assumption, the security of proposed scheme is proved in the random oracle model.
A Systematic Review of Online Bitcoin Visualizations

We contribute a systematic review of online visualizations of the Bitcoin blockchain. Bitcoin is currently the most active cryptocurrency with the largest market share among other cryptocurrencies. It has attracted a large user base and more and more businesses are beginning to accept Bitcoin as payment. While there are still relatively few visualization research papers on Bitcoin, a growing number of online tools visualize data about the Bitcoin blockchain. We provide a first systematic assessment of these online tools to inform future research efforts on making the Bitcoin blockchain more accessible.
Model-Agnostic Visual Explanation of Machine Learning Models Based on Heat Map

It is essential to assess the trustworthiness of the machine learning models when deploying them to real-world applications, such as healthcare and risk management, in which domain experts need to make critical decisions. We propose a visual analysis method for supporting domain experts to understand and improve a given machine learning model based on a model-agnostic interpretable explanation technique. Our visualization method provides a heat map matrix as an overview of the model explanation and helps efﬁcient feature engineering and data cleaning. We demonstrate our visualization method on a text classiﬁcation task
Analyse de signaux sociaux multimodaux: application à la synthèse d'attitudes sociales chez un agent conversationnel animé. (Multimodal analysis and recognition of social signals: application to social stance generation in virtual agents)

D URING an interaction, non-verbal behavior reflects the emotional state of the speaker, such as attitude or personality. Modulations in social signals tell about someone’s affective state like variations in head movements, facial expressions or prosody. Nowadays, machines can use embodied conversational agents to express the same kind of social cues. Thus, these agents can improve the quality of life in our modern societies if they provide natural interactions with users. Indeed, the virtual agent must express different attitudes according to its purpose, such as dominance for a tutor or kindness for a companion. Literature in sociology and psychology underlines the importance of the dynamic of social signals for the expression of different affective states. Thus, this thesis proposes models focused on temporality to express a desired affective phenomenon. They are designed to handle social signals that are automatically extracted from a corpus. The purpose of this analysis is the generation of embodied conversational agents expressing a specific stance. A survey of existing databases lead to the design of a corpus composed of presidential addresses. The high definition videos allow algorithms to automatically evaluate the social signals. After a corrective process of the extracted social signals, an agent clones the human’s behavior during the addresses. This provides an evaluation of the perception of attitudes with a human or a virtual agent as a protagonist. The SMART model use sequence mining to find temporal association rules in interaction data. It finds accurate temporal information in the use of social signals and links it with a social attitude. The structure of these rules allows an easy transposition of this information to synthesize the behavior of a virtual agent. Perceptual studies validate this approach. A second model, SSN, designed during an international collaboration, is based on deep learning and domain separation. It allows multi-task learning of several affective phenomena and proposes a method to analyse the dynamics of the signals used. These different contributions underline the importance of temporality for the synthesis of virtual agents to improve the expression of certain affective phenomena. Perspectives give recommendation to integrate this information into multimodal solutions.