Miracle-dz/UniFAR-FTUs huggingface dataset:
The “Droplet”: A new personal device to enable fog computing

Fog computing was introduced to complement the cloud computing paradigm in the era of Internet of Things. The main objective of fog computing is to push the processing and storage of the data closer to the edge of the network. This computation paradigm is proposed to reduce the latency of data processing, decrease the data flow from the different sensors to the cloud by local processing, and to prevent single points of failure. Although the fog computing paradigm benefits are clear, there is no clear adoption model that will help in spreading this computation paradigm through the networks. In this paper, we propose a new personal device, namely the “Droplet”. The adoption model for the Droplet make it suitable to be an enabler to the fog computing paradigm. The Droplet is a fixed, always connected, edge device that is owned and managed by the user while allowing other players to use it to realize the benefits of the fog computing. The Droplet is inspired by the wide spread and decrease in cost of single board computers. We discuss some potential applications of the Droplet that makes it appealing to be spread by the users. A prototype for the Droplet was built, and some of the potential applications were developed to run on the Droplet to prove the concept of the new personal device.
Renal function and acid–base regulation in two Amazonian erythrinid fishes: Hoplias malabaricus, a water breather, and Hoplerythrinus unitaeniatus, a facultative air breather

The function of the kidney in ion, water, and acid excretion was investigated in two erythrinid fishes, the water-breathing Hoplias malabaricus and the facultative air-breathing Hoplerythrinus unitaeniatus. Chronic catheterization of the urinary papilla and the dorsal aorta provided information on the urinary parameters and blood acid–base status. By monitoring total flow of urine, pH, and concentrations of Na+, Cl−, ammonia, titratable acidity, and lactate, the total renal flux of water, various ions, and total acid was computed. The kidneys of both species were found capable of acidifying urine, creating gradients of up to 620:1 for H+ ion, and contributing substantially to steady-state acid excretion. There was no significant increase in lactate or total acid efflux from urine during postoperative (metabolic) acidosis. Respiratory (hypercapnic) acidosis caused a compensatory increase in blood HCO3−, and an increase in branchial Na+ uptake (presumably by Na–H exchange), but no change in ammonia excretio.
Hiding behaviour in fiddler crabs: how long should prey hide in response to a potential predator?

Abstract Do predator–prey ‘waiting games’ where prey hide from potential predators have inherently unstable evolutionary outcomes, making it impossible to generate quantitative predictions about hiding times? Fiddler crabs, Uca lactea perplexa, respond to potential predators by retreating into their burrows. Time inside the burrow during unprovoked retreats during normal activity provides a ‘null model’ to test whether sex, tidal cycle and body size affect hiding time from potential predators. Using experimentally created predator-like stimuli we found that males hid for significantly longer than females, and larger crabs of both sexes also hid for longer. This differs from burrow use during unprovoked retreats, suggesting hiding time varies depending on the potential risk of predation on re-emergence. If risk prior to hiding predicts risk on emergence, the closer the proximity of a predator-like stimulus when first encountered the longer crabs should hide. We confirmed this experimentally (stimuli at 0.5 versus 2.5 m). Finally, we tested whether males hide for longer when a predator-like stimulus approaches them directly rather than tangentially. None of three pairwise comparisons was statistically significant, but crabs hid less as the angle of approach became more tangential. These results suggest prey can use stimuli prior to hiding to predict predation risk on re-emergence, but studies on predators are required to test this claim. Finally, theoretical models must explain why hiding time has a lognormal distribution and low variance such that a predator can predict when most prey will re-emerge. For example, 95% of crabs re-emerged within 2.3 min of hiding. Copyright 2003 Published by Elsevier Science Ltd on behalf of The Association for the Study of Animal Behaviour.
Agile Robotic Fliers: A Morphing-Based Approach

Abstract The aerial robot presented here for the first time was based on a quadrotor structure, which is capable of unique morphing performances based on an actuated elastic mechanism. Like birds, which are able to negotiate narrow apertures despite their relatively large wingspan, our Quad-Morphing robot was able to pass through a narrow gap at a high forward speed of 2.5 m.s− 1 by swiftly folding up the structure supporting its propellers. A control strategy was developed to deal with the loss of controllability on the roll axis resulting from the folding process, while keeping the robot stable until it has crossed the gap. In addition, a complete recovery procedure was also implemented to stabilize the robot after the unfolding process. A new metric was also used to quantify the gain in terms of the gap-crossing ability in comparison with that observed with classical quadrotors with rigid bodies. The performances of these morphing robots are presented, and experiments performed with a real flying robot passing through a small aperture by reducing its wingspan by 48% are described and discussed.
Inflammatory bowel disease therapy: blockade of cytokines and cytokine signaling pathways

Purpose of review Treatment of inflammatory bowel disease (IBD) patients can vary depending on the degree of response, lack of response or intolerance to conventional or biological agents aimed at blocking various cytokines or integrins. Recent therapies targeting several cytokines were reviewed to evaluate efficacy in IBD patients. Recent findings Ustekinumab is an interleukin inhibitor which blocks the p40 subunit of IL-12 and IL-23 axis and is already approved for the treatment of Crohn's disease patients, specially those who had inadequate response or intolerance to conventional treatment with anti-TNF-&agr; agents. Several treatments have been developed that are focused on the blockade of specific cytokines such as IL-6, IL-12, IL-13, IL-17, IL-23 and a chemokine named IFN-&ggr;-inducible protein-10 as well as some oral small-molecule inhibitors of intracellular cytoplasmic tyrosine kinases like tofacitinib, filgotinib and upadacitinib. Summary Several biologics blocking different and specific cytokines and oral small molecule agents have been and are being evaluated in IBD patients. A comprehensive understanding of the underlying immunological mechanisms will allow to develop effective and safe agents that inhibit one or more cytokines to improve the outcome in patients with IBD.
A comparison of linear and branched wormlike micelles using large amplitude oscillatory shear and orthogonal superposition rheology

In this study, we present a comparative investigation of linear and branched wormlike micelles using two nonlinear rheological tools: orthogonal superposition rheology and large amplitude oscillatory shear (LAOS) rheology. The surfactants were a series of mixtures of octyl trimethyl ammonium bromide (C8TAB) and sodium oleate (NaOA). A transition from linear to branched wormlike micelles was obtained by either varying the relative ratio of NaOA to C8TAB at a fixed total surfactant concentration or by fixing the ratio of NaOA to C8TAB and varying the total surfactant concentration. Orthogonal superposition rheology imposes a small amplitude oscillatory shear strain over an orthogonally imposed shear flow to probe the effect of shear on the storage and loss modulus of the fluid. For both the linear and branched wormlike micelle solutions, the plateau modulus, the relaxation times, and the dynamic viscosity were all found to be sensitive to the strength of the orthogonally imposed shear rate. However, the non.
High-performance two-step lagrange interpolation technique for 4K UHD applications

In this paper, we present an advanced interpolation algorithm to up-scale high definition (HD) and full-HD streams to 4K ultra-HD (UHD) videos. The proposed method basically adopts the 4-point-based Lagrange interpolation. When the interpolated pixel value using Lagrange interpolation is invalid, the order Lagrange polynomial is reduced for selecting more reliable results. By removing the unwanted overshooting or undershooting values in the high-order interpolation results, as a result, the proposed algorithm achieves improved visual quality.
Geographical context of floral evolution: towards an improved research programme in floral diversification

The diversification of animal-pollinated angiosperms is related to divergence in floral characteristics promoted by adaptations to different pollinators. According to prevailing evolutionary theory, this macroevolutionary pattern results from adaptive local or regional differentiation of pollination-related features in response to spatial divergence in pollinators. This crucial process links the microand macroevolution of floral adaptation, yet it has received much less attention than either floral diversification of species in a phylogenetic context, or pollinator-mediated phenotypic selection on pollination-related traits within populations. This chapter includes two components. We first use a literature survey to demonstrate that the study of plant–pollinator interaction in a geographical context is a relatively neglected element of research on floral diversification. In addition, the few studies that explicitly assess intraspecific variation in pollinators and pollination-related traits generally do not provide unequivocal evidence for a causal role of divergent selection from pollinators in intraspecific differentiation in floral traits. We then describe an analysis of regional variation in pollinators and corolla traits (upper lip and corolla tube length) of Lavandula latifolia, a Mediterranean evergreen shrub, which illustrates a five-step protocol for identifying geographical differentiation in floral traits driven by spatially variable selection from pollinators. Corolla traits, pollinator composition, and phenotypic selection on the upper corolla lip all vary geographically, and the morphological and pollination-related selection clines are closely congruent. Our results for this species implicate adaptive intraspecific floral differentiation in response to a cline in pollinator-mediated selection on pollination success, although confirmation of this conclusion awaits experiments to determine the genetic basis of floral variation.
Sampling Error Does Not Invalidate the Yule-Coalescent Model for Species Delimitation. A Response to Lohse (2009)

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Automated brain data segmentation and pattern recognition using ANN .

In this project we implement an artificial neural network (ANN) algorithm to perform the segmentation of brain MRI data. The multispectral characteristics of MR images with different modalities such as T1, T2 and PD are exploited to segment different brain tissues. The ANN algorithm used in this implementation is the Learning Vector Quantization (LVQ) network. The images required for training and test are obtained from a simulated brain database integrated in the McConell Brain Imaging Center (McBIC) of McGill University’s Montreal Neurological Institute. The results of the segmentation algorithms are qualitatively compared to the phantom images to mask each tissue. Our results suggest excellent brain tissue segmentation. We plan to exploit our results in formulating biologically plausible models for automated tumor detection.
An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories.
General Introduction to Spin States

Spin is a fundamental property of all elements and molecules, which originates from their unpaired electrons. Spin states have a major role in defining the structure, reactivity, magnetic and spectroscopic properties of a molecule. Furthermore it is possible that more than one spin state is energetically accessible for a given molecule. In such cases, the molecule can accumulate multiple spectroscopic, magnetic and reactivity patterns arising from the different accessible spin states. The ground spin state of most organic molecules is a singlet, that is, they have a closed-shell electronic structure, and other states are energetically not accessible under standard conditions. Important exceptions are carbenes, which can exist as singlet and triplet spin states, and the molecule of dioxygen, whose triplet nature poses kinetic barriers to its thermodynamically favorable reaction with organic matter. The situation is completely reversed when transition metals are present, which makes that different spin states are accessible for the majority of transition metal complexes. This primarily results from the particular nature of d-orbitals of the metals (see Figure 1.1) that are close in energy and which can be occupied in different ways depending on the metal oxidation state, its ligands and its coordination geometry (see Figure 1.1). This picture can be further complicated when ligands are not redox innocent and can have a spin that can also engage in ferro or anti-ferromagnetic interactions with the spin of the metal center. Spin states play an important role [1, 2] in metalloenzymatic reactions (e.g. cytochrome P450cam), in metal-oxo complexes, in spin-crossover compounds and even in catalysis processes mediated by organometallic compounds where different reactions take place via different spin states [3, 4]. However, computational studies have shown that a correct description of the spin state is not trivial [1, 5, 6], and a combination of different density functionals (DFT) and/or ab initio methods may be needed. Experimental studies on
A novel CLCN5 pathogenic mutation supports Dent disease with normal endosomal acidification

Dent disease is an X‐linked recessive renal tubular disorder characterized by low‐molecular‐weight proteinuria, hypercalciuria, nephrolithiasis, nephrocalcinosis, and progressive renal failure. Inactivating mutations of CLCN5, the gene encoding the 2Cl−/H+ exchanger ClC‐5, have been reported in patients with Dent disease 1. In vivo studies in mice harboring an artificial mutation in the “gating glutamate” of ClC‐5 (c.632A > C, p.Glu211Ala) and mathematical modeling suggest that endosomal chloride concentration could be an important parameter in endocytosis, rather than acidification as earlier hypothesized. Here, we described a novel pathogenic mutation affecting the “gating glutamate” of ClC‐5 (c.632A>G, p.Glu211Gly) and investigated its molecular consequences. In HEK293T cells, the p.Glu211Gly ClC‐5 mutant displayed unaltered N‐glycosylation and normal plasma membrane and early endosomes localizations. In Xenopus laevis oocytes and HEK293T cells, we found that contrasting with wild‐type ClC‐5, the mutation abolished the outward rectification, the sensitivity to extracellular H+ and converted ClC‐5 into a Cl− channel. Investigation of endosomal acidification in HEK293T cells using the pH‐sensitive pHluorin2 probe showed that the luminal pH of cells expressing a wild‐type or p.Glu211Gly ClC‐5 was not significantly different. Our study further confirms that impaired acidification of endosomes is not the only parameter leading to defective endocytosis in Dent disease 1.
Pathway‐structured predictive modeling for multi‐level drug response in multiple myeloma

Motivation: Molecular analyses suggest that myeloma is composed of distinct sub‐types that have different molecular pathologies and various response rates to certain treatments. Drug responses in multiple myeloma (MM) are usually recorded as a multi‐level ordinal outcome. One of the goals of drug response studies is to predict which response category any patients belong to with high probability based on their clinical and molecular features. However, as most of genes have small effects, gene‐based models may provide limited predictive accuracy. In that case, methods for predicting multi‐level ordinal drug responses by incorporating biological pathways are desired but have not been developed yet. Results: We propose a pathway‐structured method for predicting multi‐level ordinal responses using a two‐stage approach. We first develop hierarchical ordinal logistic models and an efficient quasi‐Newton algorithm for jointly analyzing numerous correlated variables. Our two‐stage approach first obtains the linear predictor (called the pathway score) for each pathway by fitting all predictors within each pathway using the hierarchical ordinal logistic approach, and then combines the pathway scores as new predictors to build a predictive model. We applied the proposed method to two publicly available datasets for predicting multi‐level ordinal drug responses in MM using large‐scale gene expression data and pathway information. Our results show that our approach not only significantly improved the predictive performance compared with the corresponding gene‐based model but also allowed us to identify biologically relevant pathways. Availability and implementation: The proposed approach has been implemented in our R package BhGLM, which is freely available from the public GitHub repository https://github.com/abbyyan3/BhGLM.
Improved Non-Interactive Zero Knowledge with Applications to Post-Quantum Signatures

Recent work, including ZKBoo, ZKB++, and Ligero, has developed efficient non-interactive zero-knowledge proofs of knowledge (NIZKPoKs) for Boolean circuits based on symmetric-key primitives alone, using the "MPC-in-the-head" paradigm of Ishai et al. We show how to instantiate this paradigm with MPC protocols in the preprocessing model; once optimized, this results in an NIZKPoK with shorter proofs (and comparable computation) as in prior work for circuits containing roughly 300--100,000 AND~gates. In contrast to prior work, our NIZKPoK also supports witness-independent preprocessing, which allows the prover to shift most of its work to an offline phase before the witness is known. We use our NIZKPoK to construct a signature scheme based only on symmetric-key primitives (and hence with "post-quantum" security). The resulting scheme has shorter signatures than the scheme built using ZKB++ (and comparable signing/verification time), and is even competitive with hash-based signature schemes. To further highlight the flexibility and power of our NIZKPoK, we also use it to build efficient ring and group signatures based on symmetric-key primitives alone. To our knowledge, the resulting schemes are the most efficient constructions of these primitives that offer post-quantum security.
Leaf Anatomical Studies of Maize (Zea maysL.) in Relation to Tolerance and Susceptibility to Turcicum Leaf Blight Disease

Screening of a set of 12 different composite and commercial hybrid varieties/land races against Turcicum leaf blight disease showed differences in disease reaction. These varieties differed significantly and Percent Disease index (PDI) ranged from 10 to 62%. Among the different varieties, Allrounder, Prabal, DKC- 9081 showed significantly less (P=0.05) disease as compared to other varieties. Keeping this in mind, they were classified as Tolerant Type and Susceptible Type. The studies on leaf structural anatomy of the both the types reveled that difference in number of stomata per unit area, size of vascular bundle, lower and upper epidermis and number of trichomes per unit area. It may be concluded that the structural anatomy of maize leaf may play an important role in resistance and susceptibility to the Turcicum leaf blight disease.
Recent Trends in Plant Protein Complex Analysis in a Developmental Context

Because virtually all proteins interact with other proteins, studying protein–protein interactions (PPIs) is fundamental in understanding protein function. This is especially true when studying specific developmental processes, in which proteins often make developmental stage- or tissue specific interactions. However, studying these specific PPIs in planta can be challenging. One of the most widely adopted methods to study PPIs in planta is affinity purification coupled to mass spectrometry (AP/MS). Recent developments in the field of mass spectrometry have boosted applications of AP/MS in a developmental context. This review covers two main advancements in the field of affinity purification to study plant developmental processes: increasing the developmental resolution of the harvested tissues and moving from affinity purification to affinity enrichment. Furthermore, we discuss some new affinity purification approaches that have recently emerged and could have a profound impact on the future of protein interactome analysis in plants.
Vulnerability Detection in IoT Firmware: A Survey

With the development of Internet of Things(IoT), more and more smart devices are connected into the Internet. The security and privacy issues of IoT devices have received increasingly academic and industrial attentions. Vulnerability detection is the key technology to protect IoT devices from zero-day attacks. However, traditional methods and tools of vulnerability detection cannot be directly used in analyzing IoT firmware. This paper firstly reviews related works on vulnerability detection in IoT firmware, previous researches are classified into four types i.e. static analysis, symbolic execution, fuzzing on emulators and comprehensive testing. Then, this paper points out that the specificity of vulnerability detection in IoT firmware is to detect logical flaws in embedded binaries which are built on the MIPS architecture. Finally, this paper proposes a method based on fuzzing and static analysis to detect authentication bypass flaws in IoT embedded binary servers. The proposed method is proved to be effective by verifying known CVEs as well as discovering unknown ones.