Miracle-dz/UniFAR-FTUs huggingface dataset:
Crude honey bee venom and Bacillus thuringiensis L. Cry1c toxin in controlling wax moth Achroia grisella F. (Lepidoptera: Pyralidae)

Achroia grisella, is a noxious pest of honey bee hives. In this study the toxicity of honey bee venom and bacterial Cry1C toxin of Bacillus thuringiensis on A. grisella were assessed. In addition, the synergistic interaction between BT Cry1c and crude honey bee venom was determined. The combination of HBV and Cry1C increased the activity for both toxins. Therefore, further analysis was carried out to assess the synergistic interaction of each compound assay separately and in mixture. The results showed LC50 value of 1.105 μg/μl for HBV and LC50 value of 0.201 μg/μl for Cry1C. The combination bioassay result showed an LC50 value of 0.549 μg/μl. The combined calculated LC50 is containing lower concentration of the component toxins, this result proved that the toxicity was enhanced due to the combination. A chi square value of 39.93and relative synergistic factor ratio of 1.14 confirmed that a synergic interaction was achieved in combination.
The psychophysics of visual search with heterogeneous distractors

Visual search is one of the most ecologically important perceptual task domains. One research tradition has studied visual search using simple, parametric stimuli and a signal detection theory or Bayesian modeling framework. However, this tradition has mostly focused on homogeneous distractors (identical to each other), which are not very realistic. In a different tradition, Duncan and Humphreys (1989) conducted a landmark study on visual search with heterogeneous distractors. However, they used complex stimuli, making modeling and dissociation of component processes difficult. Here, we attempt to unify these research traditions by systematically examining visual search with heterogeneous distractors using simple, parametric stimuli and Bayesian modeling. Our experiment varied multiple factors that could influence performance: set size, task (N-AFC localization vs detection), whether the target was revealed before or after the search array (perception versus memory), and stimulus spacing. We found that performance robustly decreased with increasing set size. When examining within-trial summary statistics, we found that the minimum target-to-distractor feature difference was a stronger predictor of behavior than the mean target-to-distractor difference and than distractor variance. To obtain process-level understanding, we formulated a Bayesian optimal-observer model. This model accounted for all summary statistics, including when fitted jointly to localization and detection. We replicated these results in a separate experiment with reduced stimulus spacing. Together, our results represent a critique of Duncan and Humphrey’s descriptive approach, bring visual search with heterogeneous distractors firmly within the reach of quantitative process models, and affirm the “unreasonable effectiveness” of Bayesian models in explaining visual search.
A Crank-Nicolson Approximation for the time Fractional Burgers Equation

Abstract In the present manuscript, Crank Nicolson finite difference method is going to be applied to get the approximate solutions for the fractional Burgers equation. The fractional derivative used in this equation is going to be taken into consideration in the Caputo sense. The L1 type discretization formula is going to be applied to this equation. For checking the efficiency of proposed methods, the error norms L2 and L∞ have at the same time been calculated. Those newly got solutions using the presented method illustrate the easy usage and efficiency of the approach presented in this manuscript.
Smart Sensing for Container Trucks

Container logistics is a typical logistics model with complex business processes. In recent years, operation optimization in logistics is identified as significant in cutting labor costs and boosting productivity. To implement operation optimization in container logistics, the accurate load measuring and operational situation information are the prerequisites. However, the current labor-driven measurement and situation recognition suffer from inevitable delays, errors, and expenses. To tackle these challenges, the authors propose a smart sensing system that intelligently achieves the container load measurement and operational situation recognition. In this paper, the authors present a novel approach for load measurement and the design of an edge-computing-powered controller. The kNN (k-Nearest Neigh-bor) has been introduced to develop an Operational Situation Recognition (OSR) model. The authors realize and implement the smart sensing system in the production environment. Rigorous analysis and prototype evaluations demonstrate the effectiveness of the proposed system.
EGFR transactivates RON to drive oncogenic crosstalk

SUMMARY Crosstalk between disparate membrane receptors is thought to drive oncogenic signaling and allow for therapeutic resistance. EGFR and RON are members of two unique receptor tyrosine kinase (RTK) subfamilies that engage in crosstalk through unknown mechanisms. We combined high resolution imaging with biochemical studies and structural mutants to understand how EGFR and RON communicate. We found that EGF stimulation results in EGFR-dependent RON phosphorylation. Crosstalk is unidirectional, since MSP stimulation of RON does not trigger EGFR phosphorylation. Two-color single particle tracking captured the formation of complexes between RON and EGFR, supporting a role for direct interactions in propagating crosstalk. We further show that RON is a substrate for EGFR kinase, and transactivation of RON requires the formation of a signaling competent EGFR dimer. These results identify critical structural features of EGFR/RON crosstalk and provide new mechanistic insights into therapeutic resistance.
Location, location, location: Feeding site affects aphid performance by altering access and quality of nutrients

Feeding location on a plant can affect aphid reproduction and survival, however little is known about factors that influence aphid performance on specific plant parts. We determined performance and feeding behavior of soybean aphid (Aphis glycines) on stem, adaxial (upper) and abaxial (lower) leaf surfaces during early vegetative growth of soybean plants and analyzed the associated phloem sap composition. Stems harbored greater aphid populations and aphids had shorter development time on stems compared to adaxial and abaxial leaf surfaces. While aphids feeding on the stem took the longest time to begin probing, potentially due to higher density and length of trichomes, this did not impact aphid population growth. Once aphids began probing, the sieve elements were more conducive to feeding as evidenced by less salivation from the stem as compared to either leaf surface. Moreover, vascular sap-enriched exudates from stems had higher sugars and amino acids, which supported higher aphid populations in artificial diet feeding assays. The high quality of stems as a food source may in part explain the shorter development time and overall greater population of aphids observed on stems. In summary, our findings suggest that the choice of feeding location and performance of aphids on a specific plant is driven largely by accessibility to and the quality of nutrients rather than morphological factors.
PTEN Inhibition Ameliorates Muscle Degeneration and Improves Muscle Function in a Mouse Model of Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy (DMD) is caused by mutation of the muscle membrane protein dystrophin and characterized by severe degeneration of myofibers, progressive muscle wasting and loss of mobility, ultimately cardiorespiratory failure and premature death. Here we report that skeletal muscle-specific knockout (KO) of Phosphatase and tensin homolog (Pten) gene in an animal model of DMD (mdx mice) alleviates myofiber degeneration and restores muscle function without increasing tumor incidences. Specifically, Pten KO normalizes myofiber size and prevents muscular atrophy, and improves grip strength and exercise performance of mdx mice. Pten KO also reduces fibrosis and inflammation; and ameliorates muscle pathology in mdx mice. Moreover, we found that Pten KO upregulates extracellular matrix and basement membrane components positively correlated to wound healing, but suppresses negative regulators of wound healing and lipid biosynthesis; and restores the integrity of muscle basement membrane in mdx mice. Importantly, pharmacological inhibition of PTEN similarly ameliorates muscle pathology and improves muscle integrity and function in mdx mice. Our finding provides evidence that PTEN inhibition may represent a potential therapeutic strategy to restore muscle function in DMD.
Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier

We present a real-time stamp classifier of astronomical events for the Automatic Learning for the Rapid Classification of Events broker, ALeRCE. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the science, reference, and difference images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids, and bogus classes, with high accuracy (∼94%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates to report to the Transient Name Server database. From 2019 June 26 to 2021 February 28, we have reported 6846 SN candidates to date (11.8 candidates per day on average), of which 971 have been confirmed spectroscopically. Our ability to report objects using only a single detection means that 70% of the reported SNe occurred within one day after the first detection. ALeRCE has only reported candidates not otherwise detected or selected by other groups, therefore adding new early transients to the bulk of objects available for early follow-up. Our work represents an important milestone toward rapid alert classifications with the next generation of large etendue telescopes, such as the Vera C. Rubin Observatory.
How to Cut a Ball Without Separating: Improved Approximations for Length Bounded Cut

The Minimum Length Bounded Cut problem is a natural variant of Minimum Cut : given a graph, terminal nodes s, t and a parameter L , ﬁnd a minimum cardinality set of nodes (other than s, t ) whose removal ensures that the distance from s to t is greater than L . We focus on the approximability of the problem for bounded values of the parameter L . The problem is solvable in polynomial time for L ≤ 4 and NP-hard for L ≥ 5. The best known algorithms have approximation factor (cid:100) ( L − 1) / 2 (cid:101) . It is NP-hard to approximate the problem within a factor of 1 . 1715 and Unique Games hard to approximate it within Ω( L ), for any L ≥ 5. Moreover, for L = 5 the problem is 4 / 3 − ε Unique Games hard for any ε > 0. Our ﬁrst result matches the hardness for L = 5 with a 4 / 3-approximation algorithm for this case, improving over the previous 2-approximation. For 6-bounded cuts we give a 7 / 4-approximation, improving over the previous best 3-approximation. More generally, we achieve approximation ratios that always outperform the previous (cid:100) ( L − 1) / 2 (cid:101) guarantee for any (ﬁxed) value of L , while for large values of L , we achieve a signiﬁcantly better ((11 / 25) L + O (1))- approximation. Allouralgorithms apply in the weighted setting, in both directed and undirected graphs, as well as for edge-cuts, which easily reduce to the node-cut variant. Moreover, by rounding the natural linear programming relaxation, our algorithms also bound the corresponding bounded-length ﬂow-cut gaps.
Topology in cyber research

We give an idiosyncratic overview of applications of topology to cyber research, spanning the analysis of variables/assignments and control flow in computer programs, a brief sketch of topological data analysis in one dimension, and the use of sheaves to analyze wireless networks. The text is from a chapter in the forthcoming book Mathematics in Cyber Research, to be published by Taylor and Francis.
Pollen-feeding Heliconius butterflies host distinctive adult-stage microbiomes

Evolutionary transitions in animal diets often coincide with shifts in the microbiome, but the degree to which diet-microbiome feedbacks vary across host taxa and development is unresolved. We examined these potential feedbacks from the perspective of Lepidoptera (butterflies and moths), a diverse clade in which little is known about adult-stage microbial associations. With the exception of Heliconius butterflies, most lepidopteran adults are short-lived and either feed on simple substrates, like nectar, or do not feed at all. Heliconius consume pollen as adults, which provides amino acids and allows the butterflies to have an extended lifespan. Using 16S rRNA gene sequencing of 214 field-collected individuals, we found that adult passion-vine butterfly microbiomes exhibited a strong signal of host phylogeny, with a clear distinction between Heliconius and non-pollen-feeding relatives. This pattern was largely driven by differing relative abundances of bacterial phylotypes shared among host taxa, as opposed to the presence or absence of host-specific phylotypes. Using shotgun metagenomic sequencing, we also discovered trypanosomatids and microsporidia to be prevalent in butterfly guts, suggesting potential interactions with co-localized gut bacteria. Overall, we show that a major transition in adult-stage lepidopteran diet and life history coincides with a shift in microbiomes, and our work provides a foundation for future tests of microbiome function in adult butterflies.
Single position substitution of hairpin pyrrole-imidazole polyamides imparts distinct DNA-binding profiles across the human genome

Regulating desired loci in the genome with sequence-specific DNA-binding molecules is a major goal for the development of precision medicine. Pyrrole–imidazole (Py–Im) polyamides are synthetic molecules that can be rationally designed to target specific DNA sequences to both disrupt and recruit transcriptional machinery. While in vitro binding has been extensively studied, in vivo effects are often difficult to predict using current models of DNA binding. Determining the impact of genomic architecture and the local chromatin landscape on polyamide-DNA sequence specificity remains an unresolved question that impedes their effective deployment in vivo. In this report we identified polyamide–DNA interaction sites across the entire genome, by covalently crosslinking and capturing these events in the nuclei of human LNCaP cells. This method, termed COSMIC-seq, confirms the ability of hairpin-polyamides, with similar architectures but differing at a single ring position, to retain in vitro specificities and display distinct genome-wide binding profiles. These results underpin the development of Py-Im polyamides as DNA-targeting molecules that mediate their regulatory or remedial functions at desired genomic loci.
Probabilistic thermodynamic analysis of metabolic networks

Random sampling of metabolic fluxes can provide an unbiased description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism’s potential or actual metabolic operations. We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of E. coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E. coli ‘s metabolic capabilities. C++ source code with MATLAB interface available at https://gitlab.com/csb.ethz/pta.
Genotypic diversity and dynamic nomenclature of Parechovirus A

Human parechoviruses (PeV-A) can cause severe sepsis and neurological syndromes in neonates and children and are currently classified into 19 genotypes based on genetic divergence in the VP1 gene. However, the genotyping system has notable limitations including an arbitrary distance threshold and reliance on insufficiently robust phylogenetic reconstruction approaches leading to inconsistent genotype definitions. In order to improve the genotyping system, we investigated the molecular epidemiology of human parechoviruses, including the evolutionary history of the different PeV-A lineages as far as is possible. We found that PeV-A lineages suffer from severe substitution saturation in the VP1 gene which limit the inference of deep evolutionary timescales among the extant PeV-A and suggest that the degree of evolutionary divergence among current PeV-A lineages has been substantially underestimated, further confounding the current genotyping system. We propose an alternative nomenclature system based on robust, amino-acid level phylogenetic reconstruction and clustering with the PhyCLIP algorithm which delineates highly divergent currently designated genotypes more informatively. We also describe a dynamic nomenclature framework that combines PhyCLIP’s progressive clustering with phylogenetic placement for genotype assignment.
Scanned optogenetic control of mammalian somatosensory input to map input-specific behavioral outputs

Somatosensory stimuli guide and shape behavior, from immediate protective reflexes to longer-term learning and high-order processes related to pain and touch. However, somatosensory inputs are challenging to control in awake mammals due to the diversity and nature of contact stimuli. Application of cutaneous stimuli is currently limited to relatively imprecise methods as well as subjective behavioral measures. The strategy we present here overcomes these difficulties by achieving spatiotemporally precise, remote and dynamic optogenetic stimulation of skin by projecting light to a small defined area in freely-behaving mice. We mapped behavioral responses to specific nociceptive inputs and revealed a sparse code for stimulus intensity: using the first action potential, the number of activated nociceptors governs the timing and magnitude of rapid protective pain-related behavior. The strategy can be used to define specific behavioral repertoires, examine the timing and nature of reflexes, and dissect sensory, motor, cognitive and motivational processes guiding behavior.
Regulation of CYP94B1 by WRKY33 controls apoplastic barrier formation in the roots leading to salt tolerance

Salinity is an environmental stress that causes decline in crop yield. Avicennia officinalis and other mangroves have adaptations such as ultrafiltration at the roots aided by apoplastic cell-wall barriers to thrive in saline conditions. We studied a Cytochrome P450 gene, AoCYP94B1 from A. officinalis and its Arabidopsis ortholog AtCYP94B1 that are involved in apoplastic barrier formation, and are induced by 30 minutes of salt treatment in the roots. Heterologous expression of AoCYP94B1 in atcyp94b1 Arabidopsis mutant and wild-type rice conferred increased NaCl tolerance to seedlings by enhancing root suberin deposition. Histochemical staining and GC-MS/MS quantification of suberin precursors confirmed the role of CYP94B1 in suberin biosynthesis. Using chromatin immunoprecipitation, yeast one-hybrid and luciferase assays, we identified AtWRKY33 as the upstream regulator of AtCYP94B1 in Arabidopsis. In addition, atwrky33 mutants exhibited reduced suberin and salt sensitive phenotypes, which were rescued by expressing 35S::AtCYP94B1 in atwrky33 mutant. This further confirms that the regulation of AtCYP94B1 by AtWRKY33 is part of the salt tolerance mechanism, and our findings can help in generating salt tolerant crops. One sentence summary AtWRKY33 transcription factor regulates AtCYP94B1 to increase plant salt tolerance by enhanced suberin deposition in the endodermal cells of Arabidopsis roots
Decoding Analysis of Alpha Oscillation Networks on Maintaining Driver Alertness

The countermeasure of driver fatigue is valuable for reducing the risk of accidents caused by vigilance failure during prolonged driving. Listening to the radio (RADIO) has been proven to be a relatively effective “in-car” countermeasure. However, the connectivity analysis, which can be used to investigate its alerting effect, is subject to the issue of signal mixing. In this study, we propose a novel framework based on clustering and entropy to improve the performance of the connectivity analysis to reveal the effect of RADIO to maintain driver alertness. Regardless of reducing signal mixing, we introduce clustering algorithm to classify the functional connections with their nodes into different categories to mine the effective information of the alerting effect. Differential entropy (DE) is employed to measure the information content in different brain regions after clustering. Compared with the Louvain-based community detection method, the proposed method shows more superior ability to present RADIO effectin confused functional connection matrices. Our experimental results reveal that the active connection clusters distinguished by the proposed method gradually move from frontal region to parieto-occipital regionwith the progress of fatigue, consistent with the alpha energy changes in these two brain areas. The active class of the clusters in parieto-occipital region significantly decreases and the most active clusters remain in the frontal region when RADIO is taken. The estimation results of DE confirm the significant change (p < 0.05) of information content due to the cluster movements. Hence, preventing the movement of the active clusters from frontal region to parieto-occipital region may correlate with maintaining driver alertness. The revelation of alerting effect is helpful for the targeted upgrade of fatigue countermeasures.
Population-Based Metaheuristic Optimization Using Organized Social Wound Treatment

This paper introduces a new strategy by means of the social wound treatment for improving optimization in population-based metaheuristic methods. This strategy is inspired on the behavior observed in social animals when dealing with injuries at both social and individual levels. An implementation of this strategy is done in the so-called wound treatment optimization (WTO) algorithm. A benchmark on several test functions for optimization was conducted to compare the response between the simple particle swarm optimization (PSO) and the WTO. Also, an application of WTO in decentralized system of robots was developed aiming to learn suitable parameters in the navigation controller of robots. This is the first time that social wound treatment is considered in these optimization methods.