Miracle-dz/UniFAR-FTUs huggingface dataset:
Red light imaging for programmed cell death visualization and quantification in plant–pathogen interactions

Studies on plant-pathogen interactions often involve monitoring disease symptoms or responses of the host plant to pathogen-derived immunogenic patterns, either visually or by staining the plant tissue. Both these methods have limitations with respect to resolution, reproducibility and the ability to quantify the results. In this study we show that red light detection in a multi-purpose fluorescence imaging system that is probably available in many labs can be used to visualize plant tissue undergoing cell death. Red light emission is the result of chlorophyll fluorescence upon thylakoid membrane disassembly during the development of a programmed cell death process. The activation of programmed cell death can occur either during a hypersensitive response to a biotrophic pathogen or an apoptotic cell death triggered by a necrotrophic pathogen. Quantifying the intensity of the red light signal enables to evaluate the magnitude of programmed cell death and provides a non-invasive readout of the plant immune response in a faster and safer manner as compared to chemical staining methodologies previously developed. This application can be implemented to screen for differences in symptom severity in plant-pathogen interactions, and to visualize and quantify in a sensitive and objective manner the intensity of a plant response upon perception of a given immunological pattern. We illustrate the utility and versatility of the method using diverse immunogenic patterns and pathogens.
Ever-increasing viral diversity associated with the red imported fire ant Solenopsis invicta (Formicidae: Hymenoptera)

Background Advances in sequencing and analysis tools have facilitated discovery of many new viruses from invertebrates, including ants. Solenopsis invicta is an invasive ant that has quickly spread worldwide causing significant ecological and economic impacts. Its virome has begun to be characterized pertaining to potential use of viruses as natural enemies. Although the S. invicta virome is the best characterized among ants, most studies have been performed in its native range, with less information from invaded areas. Methods Using a metatranscriptome approach, we further identified and molecularly characterized virus sequences associated with S. invicta , in two introduced areas, U.S and Taiwan. The data set used here was obtained from different stages (larvae, pupa, and adults) of S. invicta life cycle. Publicly available RNA sequences from GenBank’s Sequence Read Archive were downloaded and de novo assembled using CLC Genomics Workbench 20.0. 1. Contigs were compared against the non-redundant protein sequences and those showing similarity to viral sequences were further analyzed. Results We characterized five putative new viruses associated with S. invicta transcriptomes. Sequence comparisons revealed extensive divergence across ORFs and genomic regions with most of them sharing less than 40% amino acid identity with those closest homologous sequences previously characterized. The first negative-sense single-stranded RNA virus genomic sequences included in the orders Bunyavirales and Mononegavirales are reported. In addition, two positive single-strand virus genome sequences and one single strand DNA virus genome sequence were also identified. While the presence of a putative tenuivirus associated with S. invicta was previously suggested to be a contamination, here we characterized and present strong evidence that Solenopsis invicta virus 14 (SINV-14) is a tenui-like virus that has a long-term association with the ant. Furthermore, based on virus sequence abundance compared to housekeeping genes, phylogenetic relationships, and completeness of viral coding sequences, our results suggest that four of five virus sequences reported, those being SINV-14, SINV-15, SINV-16 and SINV-17, may be associated to viruses actively replicating in the ant S. invicta . Conclusions The present study expands our knowledge about viral diversity associated with S. invicta in introduced areas with potential to be used as biological control agents, which will require further biological characterization.
Receptor tyrosine kinases activate heterotrimeric G proteins via phosphorylation within the interdomain cleft of Gαi

Significance Growth factors and heterotrimeric G proteins are two of the most widely studied signaling pathways in eukaryotes; their cross-talk shapes some of the most fundamental cellular responses in both health and disease. Although mechanisms by which G protein pathways transactivate growth factor receptor tyrosine kinases (RTKs) have been well-defined, how the reverse may happen is less understood. This study defines the key steps and cellular consequences of a fundamental mechanism of signal cross-talk that enables RTKs to transactivate heterotrimeric G protein, Gαi. Mutations found in tumors shed light on how derailing this mechanism impacts tumor cell behavior. Thus, findings not only show how cells integrate extracellular signals via pathway cross-talk, but also demonstrate the relevance of this pathway in cancers. The molecular mechanisms by which receptor tyrosine kinases (RTKs) and heterotrimeric G proteins, two major signaling hubs in eukaryotes, independently relay signals across the plasma membrane have been extensively characterized. How these hubs cross-talk has been a long-standing question, but answers remain elusive. Using linear ion-trap mass spectrometry in combination with biochemical, cellular, and computational approaches, we unravel a mechanism of activation of heterotrimeric G proteins by RTKs and chart the key steps that mediate such activation. Upon growth factor stimulation, the guanine-nucleotide exchange modulator dissociates Gαi•βγ trimers, scaffolds monomeric Gαi with RTKs, and facilitates the phosphorylation on two tyrosines located within the interdomain cleft of Gαi. Phosphorylation triggers the activation of Gαi and inhibits second messengers (cAMP). Tumor-associated mutants reveal how constitutive activation of this pathway impacts cell’s decision to “go” vs. “grow. ” These insights define a tyrosine-based G protein signaling paradigm and reveal its importance in eukaryotes.
Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.
Maximizing the Correlation: Extending Grothendieck's Inequality to Large Domains

Correlation Clustering is an elegant model where given a graph with edges labeled + or − , the goal is to produce a clustering that agrees the most with the labels: + edges should reside within clusters and − edges should cross between clusters. In this work we study the MaxCorr objective, aiming to ﬁnd a clustering that maximizes the diﬀerence between edges classiﬁed correctly and incorrectly. We focus on the case of bipartite graphs and present an improved approximation of 0 . 254, improving upon the known approximation of 0 . 219 given by Charikar and Wirth [FOCS‘2004] and going beyond the 0 . 2296 barrier imposed by their technique. Our algorithm is inspired by Krivine’s method for bounding Grothendieck’s constant, and we extend this method to allow for more than two clusters in the output. Moreover, our algorithm leads to two additional results: (1) the ﬁrst known approximation guarantees for MaxCorr where the output is constrained to have a bounded number of clusters; and (2) a natural extension of Grothendieck’s inequality to large domains.
PowerPool: Multi-source Ambient Energy harvesting

Energy has become the bottleneck issue restricting the development of IoT. Ambient energy harvesting is a promising mechanism to resolve the contradiction between continuous perception and energy costs of IoT devices. Multi-source Ambient energy harvesting could further increase the availability and stability of energy supply. However, when multiple energy sources work together, it is a challenge to address issues as current backflow and low efficiency due to the difference in energy level.We establish the energy pool model to achieve multi-source energy harvesting fusion. We build an unified access interface for different energy sources, where MOSTEF is used to replace the diode for achieving unidirectional conduction and preventing current backflow. We implement the prototype and evaluate it with different energy sources such as wind, light, radio frequency and piezoelectric energy. The experiment results suggest that energy transmission loss of the interface is less than one percent of total energy.
Chernoff Bound for High-Dimensional Expanders

We generalize the expander Chernoff bound to high-dimensional expanders. The expander Chernoff bound is an essential property of expanders, first proved by Gillman [Gillman, 1993]. Given a graph G and a function f on the vertices, it states that the probability of f’s mean sampled via a random walk on G to deviate from its actual mean, has a bound that depends on the spectral gap of the walk and decreases exponentially as the walk’s length increases. We are interested in obtaining an analog Chernoff bound for high order walks on high-dimensional expanders. A naive generalization of the expander Chernoff bound from expander graphs to high-dimensional expanders gives a very poor bound due to obstructions that occur in high-dimensional expanders and are not present in (one-dimensional) expander graphs. Because of these obstructions, the spectral gap of high-order random walks is inherently small. A natural question that arises is how to get a meaningful Chernoff bound for high-dimensional expanders. In this paper, we manage to get a strong Chernoff bound for high-dimensional expanders by looking beyond the spectral gap. First, we prove an expander Chernoff bound that depends on a notion that we call the "shrinkage of a function" instead of the spectral gap. In one-dimensional expanders, the shrinkage of any function with zero-mean is bounded by λ(M). Therefore, the spectral gap is just the one-dimensional manifestation of the shrinkage. Next, we show that in good high-dimensional expanders, the shrinkage of functions that "do not come from below" is good. A function does not come from below if from any local point of view (called "link") its mean is zero. Finally, we prove a high-dimensional Chernoff bound that captures the expansion of the complex. When the function on the faces has a small variance and does not "come from below", our bound is better than the naive high-dimensional expander Chernoff bound.
Unpredictable hummingbirds: Flight path entropy is constrained by speed and wing loading

Unpredictable movement can provide an advantage when animals avoid predators and other threats. Previous studies have examined how varying environments can elicit unpredictable movement, but the intrinsic causes of complex, unpredictable behavior are not yet known. We addressed this question by analyzing >200 hours of flight performed by hummingbirds, a group of aerial specialists noted for their extreme agility and escape performance. We used information theory to calculate unpredictability based on the positional entropy of short flight sequences during 30-min and 2-hour trials. We show that a bird’s entropy is repeatable, with stable differences among individuals that are negatively correlated with wing loading: birds with lower wing loading are less predictable. Unpredictability is also positively correlated with a bird’s overall acceleration and rotational performance, and yet we find that moment-to-moment changes in acceleration and rotational velocities do not directly influence entropy. This indicates that biomechanical performance must share an underlying basis with a bird’s ability to combine maneuvers into unpredictable sequences. Contrary to expectations, hummingbirds achieve their highest entropy at relatively slow speeds, pointing to a fundamental trade-off whereby individuals must choose to be either fast or unpredictable.
Ca 2+ release via IP 3 Rs increases RyR mediated Ca 2+ spark frequency in ventricular cardiomyocytes without altering spark amplitude and duration

Calcium plays critical roles in cardiac cells, coupling electrical excitation to mechanical contraction with each heartbeat, while simultaneously mediating biochemical signals that regulate cell growth. While ryanodine receptors (RyRs) are fundamental to generation of elementary calcium release events (sparks) and global calcium elevations that underlie excitation-contraction coupling (ECC), calcium release via inositol 1,4,5-trisphosphate receptors (IP 3 Rs) is also reported in cardiomyocytes. IP 3 R calcium release modifies ECC as well as contributing to downstream regulation of hypertrophic gene expression. Recent studies suggest that proximal localisation of IP 3 Rs with RyRs contributes to their ability to modify Ca 2+ handling during ECC. Here we aim to determine the mechanism by which IP 3 Rs modify Ca 2+ handling in cardiomyocytes. We develop a mathematical model incorporating the stochastic behaviour of receptor opening that allows for the parametric tuning of the system to reveal the impact of IP 3 Rs on spark activation. By testing multiple spark initiation mechanisms, we find that Ca 2+ release via IP 3 Rs result in increased propensity for spark initiation within the cardiac dyad. Our simulations suggest that opening of
Different mutant RUNX1 oncoproteins program alternate haematopoietic differentiation trajectories

Using integrated genome-wide and phenotypic methods this study investigates four different mutant RUNX1 oncoproteins and reveals how they differentially contribute to aberrant haematopoiesis. Mutations of the haematopoietic master regulator RUNX1 are associated with acute myeloid leukaemia, familial platelet disorder and other haematological malignancies whose phenotypes and prognoses depend upon the class of the RUNX1 mutation. The biochemical behaviour of these oncoproteins and their ability to cause unique diseases has been well studied, but the genomic basis of their differential action is unknown. To address this question we compared integrated phenotypic, transcriptomic, and genomic data from cells expressing four types of RUNX1 oncoproteins in an inducible fashion during blood development from embryonic stem cells. We show that each class of mutant RUNX1 deregulates endogenous RUNX1 function by a different mechanism, leading to specific alterations in developmentally controlled transcription factor binding and chromatin programming. The result is distinct perturbations in the trajectories of gene regulatory network changes underlying blood cell development which are consistent with the nature of the final disease phenotype. The development of novel treatments for RUNX1-driven diseases will therefore require individual consideration.
Modelling Consciousness within Mental Monism: An Automata-Theoretic Approach

Models of consciousness are usually developed within physical monist or dualistic frameworks, in which the structure and dynamics of the mind are derived from the workings of the physical brain. Little attention has been given to modelling consciousness within a mental monist framework, deriving the structure and dynamics of the mental world from primitive mental constituents only—with no neural substrate. Mental monism is gaining attention as a candidate solution to Chalmers’ Hard Problem on philosophical grounds, and it is therefore timely to examine possible formal models of consciousness within it. Here, I argue that the austere ontology of mental monism places certain constraints on possible models of consciousness, and propose a minimal set of hypotheses that a model of consciousness (within mental monism) should respect. From those hypotheses, it would be possible to construct many formal models that permit universal computation in the mental world, through cellular automata. We need further hypotheses to define transition rules for particular models, and I propose a transition rule with the unusual property of deep copying in the time dimension.
Nestmate recognition in social insects and the role of hydrocarbons

One of the conditions favoring the evolution and maintenance of sociality is the ability to discriminate between kin and non-kin, because it allows altruistic acts to be directed to recipients of high relatedness (Hamilton, 1987). Nestmate recognition is the process whereby social insects recognize individuals belonging to their own colony or an alien colony, and accordingly allow or prohibit entry to their nest. Nestmate recognition often results in the discrimination of kin from non-kin, but in social insects there is a distinction to be made between nestmate and kin recognition. Where kin recognition is the assessment of the degree of relatedness towards another individual, nestmate recognition is the binary recognition of group membership (Arnold et al. , 1996; Lenoir et al. , 1999). In ants, wasps and termites, the blend of hydrocarbons present on the cuticle appears to comprise the essential compounds that serve as nestmate recognition cues (Howard and Blomquist, 2005), although in honeybees fatty acids and esters may also play an important role (Breed, 1998a; but see Dani et al. , 2005; Ch? ine et al. , 2005). At the end of the 1990s, the notion that cuticular hydrocarbons act as recognition cues was supported mainly by correlative evidence (Singer, 1998; Vander Meer and Morel, 1998; Lenoir et al. , 1999), but significant progress has been made over the last decade.
Antagonism of STAT3 signalling by Ebola virus

Many viruses target signal transducers and activators of transcription (STAT) 1 and 2 to antagonise antiviral interferon (IFN) signalling, but targeting of signalling by other STATs/cytokines, including STAT3/interleukin (IL-) 6 that regulate processes important to Ebola virus (EBOV) haemorrhagic fever, is poorly defined. We report that EBOV potently inhibits STAT3 responses to IL-6 family cytokines, and that this is mediated by the IFN-antagonist VP24. Mechanistic analysis indicates that VP24 effects a unique strategy combining distinct karyopherin-dependent and karyopherin-independent mechanisms to antagonise STAT3-STAT1 heterodimers and STAT3 homodimers, respectively. This appears to reflect distinct mechanisms of nuclear trafficking of the STAT3 complexes, revealed for the first time by our analysis of VP24 function. These findings are consistent with major roles for global inhibition of STAT3 signalling in EBOV infection, and provide new insights into the molecular mechanisms of STAT3 nuclear trafficking, significant to pathogen-host interactions, cell physiology and pathologies such as cancer. Author summary Ebola virus (EBOV) continues to pose a significant risk to human health globally, causing ongoing disease outbreaks with case-fatality rates between 40 and 60%. Suppression of immune responses is a critical component of EBOV haemorrhagic fever, but understanding of EBOV impact on signalling by cytokines other than interferon is limited. We find that infectious EBOV inhibits interleukin-6 cytokine signalling via antagonism of STAT3. The antagonistic strategy uniquely combines two distinct mechanisms, which appear to reflect differing nuclear trafficking mechanisms of critical STAT3 complexes. This provides fundamental insights into the mechanisms of pathogenesis of a lethal virus, and biology of STAT3, a critical player in immunity, development, growth and cancer.
The chaperonin CCT8 controls proteostasis essential for T cell maturation, selection, and function

T cells rely for their development and function on the correct folding and turnover of proteins generated in response to a broad range of molecular cues. In the absence of the eukaryotic type II chaperonin complex, CCT, T cell activation induced changes in the proteome are compromised including the formation of nuclear actin filaments and the formation of a normal cell stress response. Consequently, thymocyte maturation and selection, and T cell homeostatic maintenance and receptor-mediated activation are severely impaired. In the absence of CCT-controlled protein folding, Th2 polarization diverges from normal differentiation with paradoxical continued IFN-γ expression. As a result, CCT-deficient T cells fail to generate an efficient immune protection against helminths as they are unable to sustain a coordinated recruitment of the innate and adaptive immune systems. These findings thus demonstrate that normal T cell biology is critically dependent on CCT-controlled proteostasis and that its absence is incompatible with protective immunity. Oftedal et al. generate mice lacking the chaperonin subunit CCT8 in T cells. They find that loss of CCT8 leads to reduced formation of nuclear actin filaments, changes in proteostasis, defective Th2 cell polarization and T cell metabolism and a failed antigenic response to intestinal helminths.
Phytochemical profiles of Iris unguicularis Poir. with antioxidant, antibacterial, and anti-Alzheimer activities

Abstract Phytochemical investigation of medicinally important plants has gained great attention, lately. This study aims to investigate the phytochemical constituents of the partitioned extracts (ethyl acetate (AI), dichloromethane (CI) and n-butanol (BI)) derived from the 70% ethanol extract of the rhizomes (RI) of Iris unguicularis Poir. an Algerian-Tunisian endemic plant and to evaluate their biological activities. Seventy constituents of these partitioned extracts were qualitatively characterized by liquid chromatography ion trap time of flight mass spectrometry (LC-MS-IT-TOF) technique. Moreover, quantitative screening of phenolic acids and flavonoids in the studied extracts was accomplished by a previously validated LC-MS/MS method. It was determined that, AI and CI, which were the richest extracts in terms of phytochemical content, showed high antioxidant and antibacterial activities in parallel with their identified constituents, while AI extract was rich in protocatechuic (1080.4 µg analyte/g), 4-OH-benzoic (2218.2 µg/g), syringic (1218.6 µg/g) and pcoumaric (1071.2 µg/g) acids, CI extract has been found to be rich in naringenin (1453.4 µg/g) and ferulic acid (1088.4 µg/g). These results revealed and confirmed the importance of the species and especially the genus rhizomes which have largely been used in folk medicine for the treatment of many diseases. However, all extracts of this endemic plant were inactive at all concentration against acetyl-and butyrylcholinesterase enzymes.