Miracle-dz/UniFAR-FTUs huggingface dataset:
Potencial da inoculação de fungos entomopatogênicos no plantio da soja para o manejo de pragas

Potential of the inoculation of entomopathogenic fungi in soybean planting for pest management Recent studies have shown that entomopathogenic fungi may have beneficial effects on plants such as growth promoters, phytopathogen antagonists and herbivory protection agents. The potential of the association of entomopathogenic fungi with soybean cultivation has not been revealed. In this sense, the objective of this study was to determine the effects of entomopathogenic fungi on the development of important soybean pests inoculated during planting of this crop. The effects of three entomopathogenic fungi, Metarhizium sp. indet. 1 ESALQ-1638, Beauveria bassiana ESALQ-3399 and Isaria fumosorosea ESALQ-3422, in the development and feeding preference of caterpillars Chrysodeixis includens and Helicoverpa armigera, and whitefly Bemisia tabaci adults and nymphs, after inoculation in soybean plants BRS 232 in a greenhouse. Mortality and biological parameters of the pests were evaluated in individuals fed on leaf discs and in whole soybean plants that were inoculated with the fungi. In addition, we investigated the persistence of entomopathogenic fungi in soybean plants and substrate, and the nutritional status carried out through leaf analysis of plants. The mortalities observed in C. includens caterpillars that fed leaf discs of the plants inoculated with the entomopathogenic fungi were significantly higher (33% 65%) than in the control (12% 13%). Mortality results in the whole plant experiment were similar to leaf discs, but the differences were statistically significant in only one of the two experiments. The H. armigera mortalities in fungi-inoculated plants varied from 36% to 60% while in the control it was less than 16%, both with leaves and whole plants, but these differences were not statistically significant. In general, caterpillars that fed on fungus inoculated plants had lower leaf consumption and both caterpillars or pupae reached lower weights than those reared on uninoculated plants. The period of caterpillars to pupae was equal to or greater in treatments with fungi. In the multiple choice tests, C. includens and H. armigera larvae, in general, preferred to feed on leaves inoculated with the fungi, especially with B. bassiana. On the other hand, B. tabaci preferred the uninoculated plants, where larger numbers of nymphs were observed. The inoculation of the soybean plants with the fungi did not alter the nutritional content of the leaves in relation to the uninoculated plants. The fungi remained in high concentrations in the substrate for 45 days, and after 60 days the density of Metarhizium sp. indet. 1 ESALQ-1638 was superior to the other fungi. These results reveal that the entomopathogenic fungi present potential to be used via substrate inoculation during planting.
Development of a Model to Predict Intention Using Deep Learning

This paper presents a method to analyze discussions from social network by using deep learning. We have prepared a new dataset by collecting discussions from a social network and annotating remarks of the discussion. The annotation consists of two types of labels for each message: intention type and direction of intention. Using this dataset and pre-trained word embeddings we have evaluated two neural network structures. On the basis of evaluation, we chose a model to automatically predict intention types and direction of intention of an arbitrary message from any social network.
General Model of Human Motivation and Goal Ranking

In this article, we describe high-fidelity human behaviour emulation model capable of ranking and re-ranking goals during plan execution based on changing emotional modes of an agent. Our model assumes the agent is rational but its reasoning is bounded. The agent's reasoning process incorporates emotions and basic human needs to emulate changes in human behaviour under cognitive limitations. The majority of cognitive systems that incorporate emotions rely on reactive models that elicit predetermined responses to emotional modes. Our model demonstrates how human emotions change during the execution of a plan independent of specific events that may elicit such responses. The initial goals of the agent are grounded in basic human needs outlined by Maslow's Hierarchy. Once a plan is generated under the cognitive limitations of the agent and execution begins, goals are re-ranked based on an emotional re-evaluation of the plan's progress. The result is a high-fidelity, domain-independent, general theory of motivation based on human needs and emotions. We demonstrate the algorithm with a use-case from the social service domain by emulating the behaviour of homeless clients in response to an intervention program.
Food Intake Detection and Classification Using a Necklace-Type Piezoelectric Wearable Sensor System

SUMMARY Automatic monitoring of food intake in free living condi- tions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor in- gestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classiﬁcation. Unlike the previous state- of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for opti- mal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classiﬁcation. Experimental results show the validity of the proposed algorithm for food intake detection and food classiﬁcation in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classiﬁcation over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.
Singularly Perturbed Forward-Backward Stochastic Differential Equations: Application to the Optimal Control of Bilinear Systems

We study linear-quadratic stochastic optimal control problems with bilinear state dependence for which the underlying stochastic differential equation (SDE) consists of slow and fast degrees of freedom. We show that, in the same way in which the underlying dynamics can be well approximated by a reduced order effective dynamics in the time scale limit (using classical homogenziation results), the associated optimal expected cost converges in the time scale limit to an effective optimal cost. This entails that we can well approximate the stochastic optimal control for the whole system by the reduced order stochastic optimal control, which is clearly easier to solve because of lower dimensionality. The approach uses an equivalent formulation of the Hamilton-Jacobi-Bellman (HJB) equation, in terms of forward-backward SDEs (FBSDEs). We exploit the efficient solvability of FBSDEs via a least squares Monte Carlo algorithm and show its applicability by a suitable numerical example.
Periodontal reconstructive surgery of deep intraosseous defects using an apical approach. Non-incised papillae surgical approach (NIPSA): A retrospective cohort study.

BACKGROUND The objective of this study was to compare a minimally-invasive surgical technique (MIST) and a non-incised papilla surgical approach (NIPSA) in periodontal reconstructive surgery of deep intraosseous defects. METHODS Data on 30 patients with a deep intraosseous defect treated with MIST (n = 15) or NIPSA (n = 15) were analyzed retrospectively. All patients met the same inclusion criteria and were treated following the same protocol, except for the surgical management of soft tissue (MIST versus NIPSA). Clinical parameters at baseline and at 1-year post-surgery, early healing at 1 week, and postoperative pain were assessed. RESULTS NIPSA and MIST resulted in significant clinical attachment gain (CAG) (P < 0.001) and probing depth reduction (PDr) (P < 0.001) at 1-year post-surgery. However, NIPSA resulted in significantly lower recession of the tip of the interdental papilla compared with MIST (P < 0.001). Smoking negatively influenced early healing in both techniques (P < 0.05). CONCLUSIONS NIPSA and MIST both resulted in significant improvements in clinical parameters. NIPSA showed significant soft tissue preservation. NIPSA may represent a promising papillae preservation technique in the treatment of intraosseous periodontal defects.
An In Vitro Model of Charcot-Marie-Tooth Disease Type 4B2 Provides Insight Into the Roles of MTMR13 and MTMR2 in Schwann Cell Myelination

Charcot-Marie-Tooth Disorder Type 4B (CMT4B) is a demyelinating peripheral neuropathy caused by mutations in myotubularin-related (MTMR) proteins 2, 13, or 5 (CMT4B1/2/3), which regulate phosphoinositide turnover and endosomal trafficking. Although mouse models of CMT4B2 exist, an in vitro model would make possible pharmacological and reverse genetic experiments needed to clarify the role of MTMR13 in myelination. We have generated such a model using Schwann cell-dorsal root ganglion (SC-DRG) explants from Mtmr13−/− mice. Myelin sheaths in mutant cultures contain outfoldings highly reminiscent of those observed in the nerves of Mtmr13−/− mice and CMT4B2 patients. Mtmr13−/− SC-DRG explants also contain reduced Mtmr2, further supporting a role of Mtmr13 in stabilizing Mtmr2. Elevated PI(3,5)P2 has been implicated as a cause of myelin outfoldings in Mtmr2−/− models. In contrast, the role of elevated PI3P or PI(3,5)P2 in promoting outfoldings in Mtmr13−/− models is unclear. We found that over-expression of MTMR2 in Mtmr13−/− SC-DRGs moderately reduced the prevalence of myelin outfoldings. Thus, a manipulation predicted to lower PI3P and PI(3,5)P2 partially suppressed the phenotype caused by Mtmr13 deficiency. We also explored the relationship between CMT4B2-like myelin outfoldings and kinases that produce PI3P and PI(3,5)P2 by analyzing nerve pathology in mice lacking both Mtmr13 and one of two specific PI 3-kinases. Intriguingly, the loss of vacuolar protein sorting 34 or PI3K-C2β in Mtmr13−/− mice had no impact on the prevalence of myelin outfoldings. In aggregate, our findings suggest that the MTMR13 scaffold protein likely has critical functions other than stabilizing MTMR2 to achieve an adequate level of PI 3-phosphatase activity.
Losing Connection: Modal Logic of Definable Link Deletion

We consider a natural variant of Sabotage Game. Different to the action of Blocker in a sabotage game, links are cut in a local, definable and uniform way, where local is used as usual; definable means that links are cut based on some considerations on the properties of the nodes in the graph; and uniform shows that it is permitted to cut more links than one in each round. As what we will see, this variant can model some interesting scenarios in everyday life. After this, a suitable modal logic is presented to characterize the game. First, we provide some preliminaries for it, including syntax, semantics and some validities. Next, we describe the standard translation and its correctness. Then, an appropriate notion of bisimulation is proposed for the logic introduced. Afterwards, we show a characterization theorem for the logic as a fragment of first-order logic that is invariant for the bisimulation, and investigate its expressive power. Further more, we prove that the logic is also a fragment of some hybrid logics, and provide some considerations on the recursion axioms. Finally, we show that the logic lacks the tree model property and the finite model property,and that the satisfiability problem for it is undecidable.
Association between religiosity and happiness in patients with chronic kidney disease on hemodialysis

Abstract Objectives: Religiosity/spirituality (R/S) seems to be a relevant factor in chronic diseases adaptation, but there is a lack of studies involving chronic kidney disease (CKD). This study aimed to investigate the association between R/S and happiness among CKD patients on hemodialysis and whether Sense of Coherence (SC) mediates this possible association. Methods: This was a cross-sectional study in two renal replacement therapy centers in Brazil, involving 161 adults on hemodialysis. Linear regressions were performed to evaluate the association between R/S (predicting variable measured with Duke Religious Index - DUREL) and happiness (outcome variable), adjusted for sociodemographic, clinical, and some laboratory variables. Later, SC was added to the model to test the possible mediating effect. Results: Most patients (91.20%) reported some religious affiliation. Private Religiosity (PR) (β = 0.53; 95% CI = 0.01 a 1.06), Intrinsic Religiosity (IR) (β = 0.48; 95% CI = 0.18 a 0.79), and SC (β = 0.11; 95% CI = -0.09 a 0.15) correlated with higher levels of happiness, controlling for clinical and sociodemographic variables. When SC was included in the model, IR (β = 0.34; 95% CI = 0.07 to 0.60) and SC (β = 0.11; 95% CI = 0.08 to 0.14) remained significantly. No clinical or sociodemographic variable correlated with happiness. Conclusions: Patients on hemodialysis showed high levels of R/S, which correlated with higher happiness levels. Clinical and sociodemographic variables were not correlated with patients' happiness. Psychosocial variables such as R/S and SC are potential key targets for interventions to promote better survival quality among CKD patients.
Robust Subspace Clustering by Cauchy Loss Function

Subspace clustering is a problem of exploring the low-dimensional subspaces of high-dimensional data. State-of-the-art approaches are designed by following the model of spectral clustering-based method. These methods pay much attention to learn the representation matrix to construct a suitable similarity matrix and overlook the influence of the noise term on subspace clustering. However, the real data are always contaminated by the noise and the noise usually has a complicated statistical distribution. To alleviate this problem, in this paper, we propose a subspace clustering method based on Cauchy loss function (CLF). Particularly, it uses CLF to penalize the noise term for suppressing the large noise mixed in the real data. This is due to that the CLF’s influence function has an upper bound that can alleviate the influence of a single sample, especially the sample with a large noise, on estimating the residuals. Furthermore, we theoretically prove the grouping effect of our proposed method, which means that highly correlated data can be grouped together. Finally, experimental results on five real data sets reveal that our proposed method outperforms several representative clustering methods.
Effect of metformin exposure on growth and photosynthetic performance in the unicellular freshwater chlorophyte, Chlorella vulgaris

Many pharmaceuticals have negative effects on biota when released into the environment. For example, recent work has shown that the commonly prescribed antidiabetic drug, metformin (N,N-dimethylbiguanide), has endocrine disrupting effects on fish. However, effects of metformin on aquatic primary producers are poorly known. We exposed cultured isolates of a freshwater chlorophyte, Chlorella vulgaris, to a range of metformin concentrations (0–767.9 mg L-1) to test the hypothesis that exposure negatively affects photosynthesis and growth. A cessation of growth, increase in non-photochemical quenching (NPQ, NPQmax), and reduced electron transport rate (ETR) were observed 24 h after exposure to a metformin concentration of 767.8 mg L-1 (4.6 mM). By 48 h, photosynthetic efficiency of photosystem II (Fv/Fm), α, the initial slope of the ETR-irradiance curve, and Ek (minimum irradiance required to saturate photosynthesis) were reduced. At a lower concentration (76.8 mg L-1), negative effects on photosynthesis (increase in NPQ, decrease in ETR) were delayed, occurring between 72 and 96 h. No negative effects on photosynthesis were observed at an exposure concentration of 1.5 mg L-1. It is likely that metformin impairs photosynthesis either through downstream effects from inhibition of complex I of the electron transport chain or via activation of the enzyme, SnRK1 (sucrose non-fermenting-related kinase 1), which acts as a cellular energy regulator in plants and algae and is an ortholog of the mammalian target of metformin, AMPK (5' adenosine monophosphate-activated protein kinase).
A Review Paper on Pothole Detection Methods

The growth of a country depends on the transportation services for traveling safely. Many distresses of asphalt pavements are responsible for the unsafe pavement surface. Potholes are the main reason for the distress of pavement. For detecting and repairing asphalt pavement having potholes, many methods are proposed in the literature for detecting potholes. There are many reasons for the distress of pavement surfaces like heavyweights of vehicles, an unspecified amount of materials, and environmental changes. In this paper, different methods are surveyed like vibration-based method, 3D Reconstruction, and vision-based method for detecting potholes. The vibration-based methods use an accelerometer, 3D laser method uses laser sensor, and vision-based method uses different image processing techniques for detecting potholes. 3D Reconstruction includes 3D laser method, Stereo Vision method, and Kinect Sensor method. The Vision-based method includes a 2D image-based method and Video-based method. In this review paper, the advantages and disadvantages of various methods are also discussed. KeywordsPotholes, Vibration-based method, Vision-based method, Stereo vision method, Image processing.
Grammar Scaling: Leveraging FrameNet Data to Increase Embodied Construction Grammar Coverage

Construction grammars are commonly written by hand. This is not only time and resource intensive, but also limits the potential for applications that need to extract accurate and deep semantics from real-world language. In the present paper we explore novel ways to tap into existing resources to expand Embodied Construction Grammar (ECG) semi-automatically from FrameNet, an approach motivated by the shared theoretical underpinnings of ECG and FrameNet. We show how FrameNet data can be readily translated into ECG constructions and schemas , and discuss ways to identify the kinds of general patterns that are crucial to construction grammar approaches. The results achieved thus far indicate how a data-driven approach to construction grammar development is not only desirable but feasible.
Modelling the Meaning of Argument Constructions with Distributional Semantics

Current computational models of argument constructions typically represent their semantic content with hand-made formal structures. Here we present a distributional model implementing the idea that the meaning of a construction is intimately related to the semantics of its typical verbs. First, we identify the typical verbs occurring with a given syntactic construction and build their distributional vectors. We then calculate the weighted centroid of these vectors in order to derive the distributional signature of a construction. In order to assess the goodness of our approach, we replicated the priming effect described by Johnson and Golberg (2013) as a function of the semantic distance between a construction and its prototypical verbs. Additional support for our view comes from a regression analysis showing that our distributional information can be used to model behavioral data collected with a crowdsourced elicitation experiment.
Inhibiting APOBEC3 Activity with Single-Stranded DNA Containing 2′-Deoxyzebularine Analogues

APOBEC3 enzymes form part of the innate immune system by deaminating cytosine to uracil in single-stranded DNA (ssDNA) and thereby preventing the spread of pathogenic genetic information. However, APOBEC mutagenesis is also exploited by viruses and cancer cells to increase rates of evolution, escape adaptive immune responses, and resist drugs. This raises the possibility of APOBEC3 inhibition as a strategy for augmenting existing antiviral and anticancer therapies. Here we show that, upon incorporation into short ssDNAs, the cytidine nucleoside analogue 2′-deoxyzebularine (dZ) becomes capable of inhibiting the catalytic activity of selected APOBEC variants derived from APOBEC3A, APOBEC3B, and APOBEC3G, supporting a mechanism in which ssDNA delivers dZ to the active site. Multiple experimental approaches, including isothermal titration calorimetry, fluorescence polarization, protein thermal shift, and nuclear magnetic resonance spectroscopy assays, demonstrate nanomolar dissociation constants and low micromolar inhibition constants. These dZ-containing ssDNAs constitute the first substrate-like APOBEC3 inhibitors and, together, comprise a platform for developing nucleic acid-based inhibitors with cellular activity.
Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma

Lung cancer is the second most common cancer in the United States and the leading cause of mortality in cancer patients. Biomarkers predicting survival of patients with lung cancer have a profound effect on patient prognosis and treatment. However, predictive biomarkers for survival and their relevance for lung cancer are not been well known yet. The objective of this study was to perform machine learning with data from The Cancer Genome Atlas of patients with lung adenocarcinoma (LUAD) to find survival-specific gene mutations that could be used as survival-predicting biomarkers. To identify survival-specific mutations according to various clinical factors, four feature selection methods (information gain, chi-squared test, minimum redundancy maximum relevance, and correlation) were used. Extracted survival-specific mutations of LUAD were applied individually or as a group for Kaplan-Meier survival analysis. Mutations in MMRN2 and GMPPA were significantly associated with patient mortality while those in ZNF560 and SETX were associated with patient survival. Mutations in DNAJC2 and MMRN2 showed significant negative association with overall survival while mutations in ZNF560 showed significant positive association with overall survival. Mutations in MMRN2 showed significant negative association with disease-free survival while mutations in DRD3 and ZNF560 showed positive associated with disease-free survival. Mutations in DRD3, SETX, and ZNF560 showed significant positive association with survival in patients with LUAD while the opposite was true for mutations in DNAJC2, GMPPA, and MMRN2. These gene mutations were also found in other cohorts of LUAD, lung squamous cell carcinoma, and small cell lung cancer. In LUAD of Pan-Lung Cancer cohort, mutations in GMPPA, DNAJC2, and MMRN2 showed significant negative associations with survival of patients while mutations in DRD3 and SETX showed significant positive association with survival. In this study, machine learning was conducted to obtain information necessary to discover specific gene mutations associated with the survival of patients with LUAD. Mutations in the above six genes could predict survival rate and disease-free survival rate in patients with LUAD. Thus, they are important biomarker candidates for prognosis.