Miracle-dz/UniFAR-FTUs huggingface dataset:
Pure biochemicals and nanomaterials as next generation biostimulants for sustainable agriculture under abiotic stress – recent advances and future scope

ABSTRACT Sustainable agriculture faces major challenges under abiotic stress conditions owing to extensive application of chemical fertilizers which pollute water, soil and atmosphere. Biostimulants (BSs), comprising of naturally derived complex mixtures of uncharacterized biomolecules, pure biochemicals and nanomaterials, enhance nutrient use efficiency (NUE) and trigger crop’s natural defense mechanisms. While it is difficult to specify the metabolic effects of uncharacterized natural mixtures (seaweed extract, protein hydrolyzates, etc.), exogenous application of pure biochemicals and nanomaterials offers an edge as BSs since their physiological roles and mechanisms of action are decipherable. Foliar application or seed treatment of some amino acids, polyamines and biopolymers (chitosan, lipochitin oligosaccharides and thuricin 17) enable plants to overcome drought and salinity stress via activation of mechanisms for reactive oxygen species (ROS) scavenging, osmolyte regulation and chlorophyll accumulation. Interaction of nitric oxide (NO) with some vitamins and melatonin exhibits potential significance as BSs for mitigating stress by ROS scavenging and maintenance of intracellular ionic balance and membrane integrity. Near future is likely to see wide applications of nanoparticles (NPs) and nanomaterials (NMs) as BSs in view of their biphasic mode of action (bio-physical activation of membrane receptors followed by gradual release of BS into the plant cells).
Burden of Disease in the Real-Life Setting of Patients with Atopic Dermatitis: Italian Data From the MEASURE-AD Study

Introduction Atopic dermatitis (AD) is a chronic inflammatory skin disease that negatively impacts the quality of life and work productivity of patients. Objectives We sought to evaluate the real-world burden of AD patients in Italy. Methods This sub-analysis of the MEASURE-AD multicountry study conducted between December 2019–2020 included patients diagnosed with moderate-to-severe AD eligible for or receiving systemic therapy in the previous 6 months. During a single visit, physician and patient-reported questionnaires were used. Results A total of 118 adult patients were enrolled and 57.6% (N = 68) of patients had moderate-to-severe AD at the time of enrolment according to the Eczema Area and Severity Index. Sleep disorders interfered with daily function in the previous week in 58.5% (N = 69) of patients, pruritus was severe in 50% (N = 59) and 42.4% (N = 50) reported a flare lasting >7 days in the previous 6 months. According to the Dermatology Quality of Life Index, 37.3% (N = 44) of patients reported a severe impact of AD and approximately 10% had clinical depression/anxiety. Current drug therapy was considered inadequate in controlling AD in 26.3% (N=31) of patients. Work activity impairment was 38.6±31.7% and monthly AD-related expenses were 148.6±134.6 Euros per patient. Conclusions This real-life study documents a high burden of disease in patients with moderate-severe AD in Italy.
ASPECTOS ECOLÓGICOS DE LAS AVES MIGRATORIAS NEÁRTICAS EN EL CAMPUS DE LA UNIVERSIDAD DEL VALLE

Due to the destruction and fragmentation of pristine habitats, urban sprawl and lack of basic information, it is necessary to research on community ecology of birds. Urban arboreal areas represent permanent arboreal habitats that support,and maintain conservation of local and continental avifauna. The studies on Nearctic migratory birds in Colombia are focused on the determination of richness but there is a lack on ecological details about their migration dynamics. The objective of this study was to determine ecological aspects of Neartic birds in an urban habitat such as arrival and departure dates, abundance, sex ratio, variation in fat deposits and interactions with residents. Between September 2011 and May 2012, bird watching and capture tours were carried out using mist nets on the Universidad del Valle campus at 990 meters above sea level. The first and last entries were done on September 20, 2011 and May 5, 2012, respectively. A total of 13 migratory bird species were recorded from which Setophaga petechia and Piranga rubra displayed the highest relative importance index (IIR = 2.386 and1.136, respectively). Differences in sex and species patterns were observed on the arrival and departure toward wintering areas. No correlation was found between weight and length of stay of birds, and the variation of energy reserves through the season is described. The study demonstrates importance of the arboreal sites, within urban areas, as temporary harbor for migratory birds.
Dynamic action of an intrinsically disordered protein in DNA compaction that induces mycobacterial dormancy

Abstract Mycobacteria are the major human pathogens with the capacity to become dormant persisters. Mycobacterial DNA-binding protein 1 (MDP1), an abundant histone-like protein in dormant mycobacteria, induces dormancy phenotypes, e.g. chromosome compaction and growth suppression. For these functions, the polycationic intrinsically disordered region (IDR) is essential. However, the disordered property of IDR stands in the way of clarifying the molecular mechanism. Here we clarified the molecular and structural mechanism of DNA compaction by MDP1. Using high-speed atomic force microscopy, we observed that monomeric MDP1 bundles two adjacent DNA duplexes side-by-side via IDR. Combined with coarse-grained molecular dynamics simulation, we revealed the novel dynamic DNA cross-linking model of MDP1 in which a stretched IDR cross-links two DNA duplexes like double-sided tape. IDR is able to hijack HU function, resulting in the induction of strong mycobacterial growth arrest. This IDR-mediated reversible DNA cross-linking is a reasonable model for MDP1 suppression of the genomic function in the resuscitable non-replicating dormant mycobacteria.
Somatosensory omissions reveal action‐related predictive processing

Abstract The intricate relation between action and somatosensory perception has been studied extensively in the past decades. Generally, a forward model is thought to predict the somatosensory consequences of an action. These models propose that when an action is reliably coupled to a tactile stimulus, unexpected absence of the stimulus should elicit prediction error. Although such omission responses have been demonstrated in the auditory modality, it remains unknown whether this mechanism generalizes across modalities. This study therefore aimed to record action‐induced somatosensory omission responses using EEG in humans. Self‐paced button presses were coupled to somatosensory stimuli in 88% of trials, allowing a prediction, or in 50% of trials, not allowing a prediction. In the 88% condition, stimulus omission resulted in a neural response consisting of multiple components, as revealed by temporal principal component analysis. The oN1 response suggests similar sensory sources as stimulus‐evoked activity, but an origin outside primary cortex. Subsequent oN2 and oP3 responses, as previously observed in the auditory domain, likely reflect modality‐unspecific higher order processes. Together, findings straightforwardly demonstrate somatosensory predictions during action and provide evidence for a partially amodal mechanism of prediction error generation.
Computational Pipeline for Analysis of Biomedical Networks with BioNAR

In a living cell, proteins interact to assemble both transient and constant molecular complexes, which transfer signals/information around internal pathways. Modern proteomic techniques can identify the constituent components of these complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. The Bioconductor package BioNAR combines a selection of existing R protocols for network analysis with newly designed original methodological features to support step‐by‐step analysis of biological/biomedical . Critically, BioNAR supports a pipeline approach whereby many networks and iterative analyses can be performed. Here we present a network analysis pipeline that starts from initiating a network model from a list of components/proteins and their interactions through to identifying its functional components based solely on network topology. We demonstrate that BioNAR can help users achieve a number of network analysis goals that are difficult to achieve anywhere else. This includes how users can choose the optimal clustering algorithm from a range of options based on independent annotation enrichment, and predict a protein's influence within and across multiple subcomplexes in the network and estimate the co‐occurrence or linkage between metadata at the network level (e.g., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms). The package is freely available in Bioconductor release 3.17: https://bioconductor.org/packages/3.17/bioc/html/BioNAR.html. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
Towards standardized human platelet lysate production in Europe: An initiative of the European Blood Alliance

Human platelet lysate (hPL) is a supplement for cell culture media that can be derived from platelet concentrates. As not‐for‐profit blood establishments, we endorse the evolution of maximally exploiting the potential of donated blood and its derived components, including platelets. The decision to use platelet concentrates to supply hPL as a cell culture supplement should align with the principles and values that blood establishments hold towards the use of donated blood components in transfusion. As a consequence, questions on ethics, practical standardization of hPL production and logistics as well as on assuring hPL quality and safety need careful consideration. We therefore propose an opinion on some of these matters based on available literature and on discussions within the proceedings of the Working Group on Innovation and New Products of the European Blood Alliance. In addition, we propose collaboration among European blood establishments to streamline efforts of hPL supply to maximize the potential of hPL and its application in the wider field of medicine.
EFFECT OF CLIMATE CHANGE ON PRODUCTION OF DARJEELING TEA: A CASE STUDY IN DARJEELING TEA RESEARCH and DEVELOPMENT CENTRE, TEA BOARD, KURSEONG

A study was conducted at Darjeeling Tea Research and Development Centre (DTR &DC), Kurseong to see the effect of climate change on production of tea. Results demonstrate that productivity of green leaf in 2012 has reduced by 41.97 % and 30.90 % as compared with 1993 and 2002, respectively. Highest productivity of 1974.44 kg green leaf ha -1 was obtained during 1994, thereafter productivity was declined continuously. Coefficient of determinants indicated that highest variation of (81.9 %) green leaf yield was due to relative humidity, followed by total annual rainfall (61.4%) and minimum temperature (13.6 %). Stronger positive and significant correlations were found between green leaf yield and relative humidity (r= 0.905) and total rainfall (r=0.78), whereas average maximum temperature showed negative correlation (r=-0.287) with green leaf yield.
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis

Simulation is a powerful tool to easily generate annotated data, and a highly desirable feature, especially in those domains where learning models need large training datasets. Machine learning and deep learning solutions, have proven to be extremely data-hungry and sometimes, the available real-world data are not sufficient to effectively model the given task. Despite the initial skepticism of a portion of the scientific community, the potential of simulation has been largely confirmed in many application areas, and the recent developments in terms of rendering and virtualization engines, have shown a good ability also in representing complex scenes. This includes environmental factors, such as weather conditions and surface reflectance, as well as human-related events, like human actions and behaviors. We present a human crowd simulator, called UniCrowd, and its associated validation pipeline. We show how the simulator can generate annotated data, suitable for computer vision tasks, in particular for detection and segmentation, as well as the related applications, as crowd counting, human pose estimation, trajectory analysis and prediction, and anomaly detection.
Synthetic Data Generation Techniques for Developing AI-based Speech Assessments for Parkinson's Disease (A Comparative Study)

Changes in speech and language are among the first signs of Parkinson's disease (PD). Thus, clinicians have tried to identify individuals with PD from their voices for years. Doctors can leverage AI-based speech assessments to spot PD thanks to advancements in artificial intelligence (AI). Such AI systems can be developed using machine learning classifiers that have been trained using individuals' voices. Although several studies have shown reasonable results in developing such AI systems, these systems would need more data samples to achieve promising performance. This paper explores using deep learning-based data generation techniques on the accuracy of machine learning classifiers that are the core of such systems.
DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors

Point clouds have shown significant potential in various domains, including Simultaneous Localization and Mapping (SLAM). However, existing approaches either rely on dense point clouds to achieve high localization accuracy or use generalized descriptors to reduce map size. Unfortunately, these two aspects seem to conflict with each other. To address this limitation, we propose an unified architecture, DeepPointMap, achieving excellent preference on both aspects. We utilize neural network to extract highly representative and sparse neural descriptors from point clouds, enabling memory-efficient map representation and accurate multi-scale localization tasks (e.g., odometry and loop-closure). Moreover, we showcase the versatility of our framework by extending it to more challenging multi-agent collaborative SLAM. The promising results obtained in these scenarios further emphasize the effectiveness and potential of our approach.
UCCA: A Verified Architecture for Compartmentalization of Untrusted Code Sections in Resource-Constrained Devices

Micro-controller units (MCUs) implement the de facto interface between the physical and digital worlds. As a consequence, they appear in a variety of sensing/actuation applications, from smart personal spaces to complex industrial control systems and safety-critical medical equipment. While many of these devices perform safety- and time-critical tasks, they often lack support for security features compatible with their importance to overall system functions. This lack of architectural support leaves them vulnerable to run-time attacks that can remotely alter their intended behavior, with potentially catastrophic consequences. In particular, we note that MCU software often includes untrusted third-party libraries (some of them closed-source) that are blindly used within MCU programs, without proper isolation from the rest of the system. In turn, a single vulnerability (or intentional backdoor) in one such third-party software can often compromise the entire MCU software state. In this paper, we tackle this problem by proposing, demonstrating security, and formally verifying the implementation of UCCA: an Untrusted Code Compartment Architecture. UCCA provides flexible hardware-enforced isolation of untrusted code sections (e.g., third-party software modules) in resource-constrained and time-critical MCUs. To demonstrate UCCA's practicality, we implement an open-source version of the design on a real resource-constrained MCU: the well-known TI MSP430. Our evaluation shows that UCCA incurs little overhead and is affordable even to lowest-end MCUs, requiring significantly less overhead and assumptions than prior related work.
Subcomplexes on filtered manifolds

In this paper, we present a general construction of complexes on filtered manifolds. In the particular case of regular subRiemannian manifolds, we show that this yields the so-called Rumin complex when the manifold is also equipped with a compatible Riemannian metric. Another instance is set on a nilpotent Lie group equipped with a left invariant filtration; our scheme then computes the Lie algebra cohomology.
Searching for the Highest-z Dual AGN in the Deepest Chandra Surveys

We present an analysis searching for dual AGN among 62 high-redshift ($2.5<z<3.5$) X-ray sources selected from publicly available deep Chandra fields. We aim to quantify the frequency of dual AGN in the high-redshift Universe, which holds implications for black hole merger timescales and low-frequency gravitational wave detection rates. We analyze each X-ray source using BAYMAX, an analysis tool that calculates the Bayes factor for whether a given archival Chandra AGN is more likely a single or dual point source. We find no strong evidence for dual AGN in any individual source in our sample. We then increase our sensitivity to search for dual AGN across the sample by comparing our measured distribution of Bayes factors to that expected from a sample composed entirely of single point sources, and again find no evidence for dual AGN in the observed sample distribution. Although our analysis utilizes one of the largest Chandra catalogs of high-$z$ X-ray point sources available to study, the findings remain limited by the modest number of sources observed at the highest spatial resolution with Chandra and the typical count rates of the detected sources. Our non-detection allows us to place an upper-limit on the X-ray dual AGN fraction between $2.5<z<3.5$ of 4.8\%. Expanding substantially on these results at X-ray wavelengths will require future surveys spanning larger sky areas and extending to fainter fluxes than has been possible with Chandra. We illustrate the potential of the AXIS mission concept in this regard.
Coverage Explorer: Coverage-guided Test Generation for Cyber Physical Systems

Given the safety-critical functions of autonomous cyber-physical systems (CPS) across diverse domains, testing these systems is essential. While conventional software and hardware testing methodologies offer partial insights, they frequently do not provide adequate coverage in a CPS. In this study, we introduce a testing framework designed to systematically formulate test cases, effectively exploring the state space of CPS. This framework introduces a coverage-centric sampling technique, coupled with a cluster-based methodology for training a surrogate model. The framework then uses model predictive control within the surrogate model to generates test cases tailored to CPS specifications. To evaluate the efficacy of the framework, we applied it on several benchmarks, spanning from a kinematic car to systems like an unmanned aircraft collision avoidance system (ACAS XU) and automatic transmission system. Comparative analyses were conducted against alternative test generation strategies, including randomized testing, as well as falsification using S-TaLiRo.
Predicting the morphology of multiphase biomolecular condensates from protein interaction networks

Phase-separated biomolecular condensates containing proteins and RNAs can assemble into higher-order structures by forming thermodynamically stable interfaces between immiscible phases. Using a minimal model of a protein/RNA interaction network, we demonstrate how a"shared"protein species that partitions into both phases of a multiphase condensate can function as a tunable surfactant that modulates the interfacial properties. We use Monte Carlo simulations and free-energy calculations to identify conditions under which a low concentration of this shared species is sufficient to trigger a wetting transition. We also describe a numerical approach based on classical density functional theory to predict concentration profiles and surface tensions directly from the model protein/RNA interaction network. Finally, we show that the wetting phase diagrams that emerge from our calculations can be understood in terms of a simple model of selective adsorption to a fluctuating interface. Our work shows how a low-concentration protein species might function as a biological switch for regulating multiphase condensate morphologies.
Generator Born from Classifier

In this paper, we make a bold attempt toward an ambitious task: given a pre-trained classifier, we aim to reconstruct an image generator, without relying on any data samples. From a black-box perspective, this challenge seems intractable, since it inevitably involves identifying the inverse function for a classifier, which is, by nature, an information extraction process. As such, we resort to leveraging the knowledge encapsulated within the parameters of the neural network. Grounded on the theory of Maximum-Margin Bias of gradient descent, we propose a novel learning paradigm, in which the generator is trained to ensure that the convergence conditions of the network parameters are satisfied over the generated distribution of the samples. Empirical validation from various image generation tasks substantiates the efficacy of our strategy.
Zero Trust for Cyber Resilience

The increased connectivity and potential insider threats make traditional network defense vulnerable. Instead of assuming that everything behind the security perimeter is safe, the zero-trust security model verifies every incoming request before granting access. This chapter draws attention to the cyber resilience within the zero-trust model. We introduce the evolution from traditional perimeter-based security to zero trust and discuss their difference. Two key elements of the zero-trust engine are trust evaluation (TE) and policy engine (PE). We introduce the design of the two components and discuss how their interplay would contribute to cyber resilience. Dynamic game theory and learning are applied as quantitative approaches to achieve automated zero-trust cyber resilience. Several case studies and implementations are introduced to illustrate the benefits of such a security model.
Geometric Data-Driven Dimensionality Reduction in MPC with Guarantees

We consider the problem of reducing the dimension of the discrete-time optimal control problem that is solved repeatedly online in model predictive control. We show that a reduced-order scheme, which solves the optimization problem in a low-dimensional subspace, inherits the stability and recursive feasibility properties from the original formulation. We introduce a necessary and sufficient condition for initial feasibility and incorporate that in the subspace design. Finally, we use concepts of optimization over Riemannian manifolds to compute a subspace that provides optimal representations for a set of pre-defined high-dimensional optimizers under the initial admissibility constraint.
Rare Galaxy Classes Identified In Foundation Model Representations

We identify rare and visually distinctive galaxy populations by searching for structure within the learned representations of pretrained models. We show that these representations arrange galaxies by appearance in patterns beyond those needed to predict the pretraining labels. We design a clustering approach to isolate specific local patterns, revealing groups of galaxies with rare and scientifically-interesting morphologies.