Miracle-dz/UniFAR-FTUs huggingface dataset:
Canopy Ant Assemblage (Hymenoptera: Formicidae) in Two Vegetation Formations in the Northern Brazilian Pantanal

The landscape of the northern Pantanal region is a mosaic of fields and forests, distributed according to topography and hydrology of this floodplain, resulting in a particular pattern of vegetation distribution. Among the forest formations, mixedspecies and monodominant landscape units can be found which are associated with floodable or non-floodable habitats. Our study tested the hypothesis that forest formations with greater tree richness and which are non floodable (cordilheiras) maintain distinct richness and composition in canopy ant assemblages in relation to the seasonally floodable monodominant forests (cambarazais). Sampling was performed in 10 sample areas (five cambarazais and five cordilheiras) by means of canopy insecticide fogging during the dry and high water seasons of the Pantanal’s hydrological cycle. The canopy ant assemblages revealed 105 species belonging to 30 genera and nine subfamilies. Myrmicinae (41 spp.), Formicinae (20 spp.) and Pseudomyrmecinae (17 spp.) predominated. Our results revealed that the composition of canopy ant assemblages varied between cambarazal and cordilheira forests, as well as between the dry and high water periods. Nevertheless, the richness was homogeneous between these forests and in the dry and high water periods. These results show the specificity of each forest, as well as its structure, in maintaining distinct compositions in ant assemblages in canopies in the Pantanal of Mato Grosso.
Gene Regulatory Cross Networks: Inferring Gene Level Cell-to-Cell Communications of Immune Cells

Background Gene level cell-to-cell communications are crucial part of biology as they may be potential targets of drugs and vaccines against a disease condition of interest. Yet, there are only few studies that propose algorithms on this particularly important research field. Results In this study, we first overview the current literature and define two general terms for the types of approaches in general for gene level cell-to-cell communications: Gene Regulatory Cross Networks (GRCN) and Gene Co-Expression Cross Networks (GCCN). We then propose two algorithms for each type, named as GRCNone and GCCNone. We applied them to reveal communications among 8 different immune cell types and evaluate their performances mainly via membrane protein database. Also, we show the biological relevance of the predicted cross-networks with pathway enrichment analysis. We then provide an approach that prioritize the targets by ranking them before experimental validations. Conclusions We establish two main approaches and propose algorithms for genome-wide scale gene level cell-to-cell communications between any two different cell-types. This study aims accelerating this relatively new avenue of research in cross-networks and points out the gap of it with the well-established single cell type gene networks. The proposed algorithms have the potential to reveal gene level interactions between normal and disease cell types. For instance, they might reveal the interaction of genes between tumor and normal cells, which are the potential drug-targets and thus can help finding new cures that might prevent the prevailing of tumor cells.
Evaluation of genetic diversity in drought tolerant and sensitive varieties of wheat using ISSR markers

Drought stress is one of the major constraints that greatly limit the productivity of wheat worldwide. Deciphering the molecular basis of drought tolerance can help in combating drought stress. Therefore, the present study was undertaken to determine genetic diversity among six drought tolerant and six drought sensitive varieties of wheat. Out of 15 ISSR primers used, 14 primers gave reproducible banding pattern. These 14 primers generated 191 amplified products out of which 93 were polymorphic. Overall size of PCR amplified products ranged between 400 bp and 4000 bp. Based on similarity matrix data, the value of similarity coefficient ranged from 0.70 to 0.94. Cluster analysis discriminated the wheat varieties into two major clusters, one consisted of drought tolerant varieties and the other had all drought sensitive varieties. The present study showed that ISSR markers provide a valuable tool to study genetic variability in wheat varieties.
Confidence in predicted position error explains saccadic decisions during pursuit

A fundamental problem in motor control is the coordination of complementary movement types to achieve a common goal. As a common example, humans view moving objects through coordinated pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and velocity errors and the timing of saccades can be highly variable. The observed distributions of saccade occurrence and trigger time remain poorly understood and this decision process remains imprecisely quantified. Here we propose a predictive, probabilistic model explaining the decision to trigger saccades during pursuit to foveate moving targets. In this model, expected position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed threshold. The model qualitatively explains behavioural observations on the probability of occurrence and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions about the influence of sensory uncertainty and target motion parameters on saccade decisions. We suggest that this predictive, confidence-based decision making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements. New & Noteworthy This is the first stochastic dynamical systems model of pursuit-saccade coordination accounting for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a threshold. This model explains saccade probability and trigger time distributions across target trajectories and makes novel predictions about the influence of sensory uncertainty in saccade decisions during pursuit.
Precise Noninvasive Estimation of Glucose Using UWB Microwave With Improved Neural Networks and Hybrid Optimization

A precise noninvasive estimation method for blood glucose concentration is proposed using an improved neural network model and hybrid optimization (INNHO) method. This method consists of a modified back propagation neural network model and a hybrid least squares-random sample consensus algorithm for accuracy optimization. The S-parameter and frequency data measured by ultrawide band (UWB) microwave are employed as the input data for glucose concentration estimation. Before model training, a preprocess technique is put forward to select the data range and generate relative S-parameters from a reference baseline. In this study, a three-layer earlobe model is built to theoretically analyze the wave propagation and elucidate the relationship between the S-parameters and the glucose concentration. Based on the model, the sensitivity and uncertainty of the system are investigated and the suitable frequency range is determined. During the measurement, the S-parameters of UWB signals in the 0.2–4 GHz range are measured and the concentration of glucose water solutions from 20 to 500 mg/dL is investigated. The results show that using the proposed preprocessing method and neural networks, the estimation accuracy can be significantly improved. Then, the hybrid optimization algorithm is able to further enhance the estimation accuracy. The final estimations for solutions of unknown concentrations agree well with the true concentration values. The relative error ranges from 0.31% to 4.64% and the root-mean-square error of the final results is 5.5237 mg/dL, demonstrating that the proposed method is effective and precise for glucose concentration estimation.
LEARNING, STRATEGIZING, AND ORGANIZATIONAL ROUTINES IN THE PRODUCT DEVELOPMENT PROCESS FROM A DYNAMIC CAPABILITY PERSPECTIVE

ABSTRACT Purpose: To understand how learning, the constituent elements of strategizing and organizational routines act in the process of product development, from the perspective of dynamic capabilities (DC), in a company of the food sector. Originality/value: To the extent that the learning-practice-routine trinomial is addressed, the results of this research present new and enlightening insights for the understanding of how dynamic capabilities are developed. Additionally, by considering the role of practitioners in the constitution of routines and by identifying how they relate to practices and praxis, evidence advance knowledge on the subject. Design/methodology/approach: Qualitative approach through the selection of a company that has worked overtime in the development of new products. The data were collected through non-participant observation, documentary analysis, and narrative interview. Data analysis occurred through narrative analysis. Findings: Through the appropriation of practices and the institutionalization of praxis, the strategic routines are enacted by practitioners, who accumulate and generate learning capable of developing DC. Thus, the learning-practice-routine trinomial allows to reveal how the DC are developed. In addition, the important role that practitioners play in this process was evidenced in the sense that, without them, routines are not enacted and, consequently, there is no development of DC.
Libraries of Approximate Circuits: Automated Design and Application in CNN Accelerators

Libraries of approximate circuits are composed of fully characterized digital circuits that can be used as building blocks of energy-efficient implementations of hardware accelerators. They can be employed not only to speed up the accelerator development but also to analyze how an accelerator responds to introducing various approximate operations. In this paper, we present a methodology that automatically builds comprehensive libraries of approximate circuits with desired properties. Target approximate circuits are generated using Cartesian genetic programming. In addition to extending the EvoApprox8b library that contains common approximate arithmetic circuits, we show how to generate more specific approximate circuits; in particular, MxN-bit approximate multipliers that exhibit promising results when deployed in convolutional neural networks. By means of the evolved approximate multipliers, we perform a detailed error resilience analysis of five different ResNet networks. We identify the convolutional layers that are good candidates for adopting the approximate multipliers and suggest particular approximate multipliers whose application can lead to the best trade-offs between the classification accuracy and energy requirements. Experiments are reported for CIFAR-10 and CIFAR-100 data sets.
Active Transport of Membrane Components by Self-Organization of the Min Proteins

Heterogeneous distribution of components in the biological membrane is critical in the process of cell polarization. However, little is known about the mechanisms that can generate and maintain the heterogeneous distribution of the membrane components. Here we report that the propagating wave patterns of the bacterial Min proteins can impose corresponding steric pressure on the membrane to establish a directional accumulation of the membrane components, resulting in segregation of the components in the membrane. The diffusivity, influenced by the membrane anchor of the component, and the repulsed ability, influenced by the steric property of the soluble region of the component and molecular crowding, determine the differential spatial distribution of the component in the membrane. Thus, transportation of the membrane components by the Min proteins follows a simple physical principle, which resembles a linear peristaltic pumping process, to selectively segregate and maintain heterogeneous distribution of materials in the membrane.
Incorporating Insect and Wind Disturbances in a Natural Disturbance-Based Management Framework for the Boreal Forest

Natural disturbances are fundamental to forest ecosystem dynamics and have been used for two decades to improve forest management, notably in the boreal forest. Initially based on fire regimes, there is now a need to extend the concept to include other types of disturbances as they can greatly contribute to forest dynamics in some regions of the boreal zone. Here we review the main descriptors—that is, the severity, specificity, spatial and temporal descriptors and legacies, of windthrow and spruce bud worm outbreak disturbance regimes in boreal forests—in order to facilitate incorporating them into a natural disturbance-based forest management framework. We also describe the biological legacies that are generated by these disturbances. Temporal and spatial descriptors characterising both disturbance types are generally variable in time and space. This makes them difficult to reproduce in an ecosystem management framework. However, severity and specificity descriptors may provide a template upon which policies for maintaining post harvesting and salvage logging biological legacies can be based. In a context in which management mainly targets mature and old-growth stages, integrating insect and wind disturbances in a management framework is an important goal, as these disturbances contribute to creating heterogeneity in mature and old-growth forest characteristics.
Efficient workflow for optimizing intelligent well completion using production parameters in real-time

Intelligent Well Completion (IWC) has been successfully deployed over the years to improve reservoir management, with better results in heterogeneous reservoirs. This work proposes an efficient workflow to identify well candidates for Inflow Control Valves (ICV) application and production optimization using parameters in real time. The methodology searches for potential reservoir layer grouping in the producer well, in order to control zonal flow, without expending too much computational time in valve positioning. ICV control strategy uses real-time production guide rates generated by the simulator, reducing optimization parameters. The proposed workflow is applied to a synthetic reservoir model, with properties similar to the Brazilian pre-salt area. This novelty strategy for ICV modelling and control presented a significant reduction in optimization parameters. Results show that Net Present Value (NPV) – and IWC economic gain – are highly dependent on the economic scenario. Nevertheless, the methodology has potential for application in more complex simulations, with greater number of wells or optimization parameters, like multi-position or continuously variable position ICV.
Electrokinetic stabilisation of soft clay

The application of chemical ground improvement using the electrokinetic stabilisation(EKS)method has the potential to overcome problems soft highly compressibility soil. This technique has the potential to enhance the strength and reduce compressibility of a wide range of soils. The aim of this study was to evaluate the use of EKS as an effective method to strengthen soft clay soils. A detailed laboratory programme of work was conducted using the initial base model developed by Liaki (2006). This study was conducted in two stages using laboratory scale models, using an inactive kaolinite clay. The test model using reusable Electrokinetic Geosythentics (EKG) developed at the Newcastle University to apply a constant voltage gradient of 50 V/m across a soil sample approximately 400 mm. The first stage involved testing of a ‘pure’ system with distilled water as the main pore electrolyte fluid supplied under zero hydraulic gradient conditions for periods of 3, 7 and 14 days. The second stage repeated test using calcium chloride and distilled water (CaCl-DW), DW and sodium silicate (DW-NaSiO) and CaCl-NaSiO, at the anode and cathode, respectively. Throughout both physical and chemical characteristics were measured. This enabled assessment of the design and configuration that could be used in the field together with a number of key limitations. The data presented herein enables a fuller understanding of the mechanisms contributing to the improvements achieved and how effective monitoring through the use of relatively simple test, e.g. pH and Atterberg Limits, can be achieved. Specifically cation exchange was considered to be the main mechanism causing a significant increase in shear strength observed for the CaCl-DW system with the increase seen to be more uniform across the soil sample for a period up to 14 days. This was supported by results of Atterberg limits,
I know that I don’t know: Structural and functional connectivity underlying meta-ignorance in pre-schoolers

Research Highlights [1] Children develop the ability to report that they do not know something at around five years of age. [2] Children who could correctly report their own ignorance in a partial-knowledge task showed thicker cortices within medial orbitofrontal cortex. [3] This region was functionally connected to parts of the default-mode network. [4] The default-mode network might support the development of correct metacognitive monitoring. Abstract Metacognition plays a pivotal role in human development. The ability to realize that we do not know something, or meta-ignorance, emerges after approximately five years of age. We aimed at identifying the brain systems that underlie the developmental emergence of this ability in a preschool sample. Twenty-four children aged between five and six years answered questions under three conditions of a meta-ignorance task twice. In the critical partial knowledge condition, an experimenter first showed two toys to a child, then announced that she would place one of them in a box behind a screen, out of sight from the child. The experimenter then asked the child whether or not she knew which toy was in the box. Children who answered correctly both times to the metacognitive question in the partial knowledge condition (n=9) showed greater cortical thickness in a cluster within left medial orbitofrontal cortex than children who did not (n=15). Further, seed-based functional connectivity analyses of the brain during resting state revealed that this region is functionally connected to the medial orbitofrontal gyrus, posterior cingulate gyrus and precuneus, and mid- and inferior temporal gyri. This finding suggests that the default mode network, critically through its prefrontal regions, supports introspective processing. It leads to the emergence of metacognitive monitoring allowing children to explicitly report their own ignorance.
The ecology of Black Guillemot Cepphus grylle grylle chicks in the Baltic Sea region: insights into their diet, survival, nest predation and moment of fledging

ABSTRACT Capsule: The diet of Black Guillemot Cepphus grylle grylle chicks in the Baltic Sea region was dominated by Viviparous Eelpout Zoarces viviparus. Risk of nest predation by avian and mammalian predators was perceived to be low, and hatching and fledging success were high. Aims: To gain insight into the ecology of nestling Black Guillemots in the Baltic Sea region to fill knowledge gaps and benefit its conservation. Methods: Two island groups in the Baltic Sea were visited several times during the breeding season of 2014 and 2015 to monitor nestling survival and fledging. In addition, camera traps were used in 2014 to monitor prey brought to chicks by adults and record possible nest predation events. Results: Hatching success was 0.89 and 0.73 in 2014 and 2015, respectively, and fledging success was very high (0.95 and 0.97). No incidences of avian or mammalian predation were observed. Chicks fledged at night between 32 and 38 days after hatching. Viviparous Eelpout made up 95% of the prey items brought to the chicks by adults. Conclusions: The hatching rate and fledging rate of the Black Guillemot was high in our study region. Juveniles seemed highly dependent on the availability of eelpout. Changes in the abundance of this species may therefore have negative effects on chick survival.
A Stochastic Approach to Finding Densest Temporal Subgraphs in Dynamic Graphs

One important problem that is insufficiently studied is finding densest lasting-subgraphs in large dynamic graphs, which considers the time duration of the subgraph pattern. We propose a framework called Expectation-Maximization with Utility functions (EMU), a novel stochastic approach that nontrivially extends the conventional EM approach. EMU has the flexibility of optimizing any user-defined utility functions. We validate our EMU approach by showing that it converges to the optimum—by proving that it is a specification of the general Minorization-Maximization (MM) framework with convergence guarantees. We devise EMU algorithms for the densest lasting subgraph problem, as well as several variants by varying the utility function. Using real-world data, we evaluate the effectiveness and efficiency of our techniques, and compare them with two prior approaches on dense subgraph detection.
Biogeochemical tools for characterizing organic carbon in inland aquatic ecosystems

Integration of inland waters into regional and global carbon (C) budgets requires a comprehensive understanding of factors regulating organic carbon (OC) delivery and in situ processing. This study reviews advances in optical, molecular, and isotopic approaches to resolve the sources, ages, and transformations of OC in aquatic systems. OC characterization using excitation emission matrix spectra, Fourier transform ion cyclotron mass spectrometry, and nuclear magnetic resonance provides detailed molecular level insight. Radiocarbon isotopic approaches and compound‐specific techniques resolve the input, metabolic fate, and turnover time of OC in ecosystems ranging in size from streams to the open ocean. Accumulating evidence suggests that aquatic OC is composed of diverse biogeochemical components. We conclude with enduring and emerging questions that underscore the role of inland systems in the global C cycle and propose unique combinations of approaches to better discern their role in the delivery and transformation of OC from soils to seas.
Nonionic Surfactants: Physical Chemistry

"Surface Monolayers, Hermann Lange and Peter Jeschke Adsorption and Wetting, Milan J. Schwuger and Wolfgang von Rybinski Micelle Formation in Aqueous Media, Kenjiro Meguro, Minour Ueno, and Kunio Esumi Micelle Formation and Catalysis in Nonaqueous Media, Kijiro Kon-no, Ayao Kitahara, and Omar A. El Seoud Thermodynamics of Micelle Formation, Denver G. Hall Solubilization, Raymond A. Mackay Phase Equilibria of Nonionic Surfactants and the Formation of Microemulsions, Johan SjOblom, Per Steinius, and Ingvar Danielsson Macroemulsions, Paul Becker and Martin J. Schick HLB of Nonionic Surfactants: PIT and EIP Methods, Leszek Marszall W/O/W-Type Multiple Emulsions, Sachio Matsumoto Effect of Nonionic Surfactants on the Stability of Dispersions, David B. Hough and Lurie Thompson Structure and Dynamics by Small-Angle Neutron Scattering, Linda J. Magid Detergency, Martin J. Schick Foaming, Martin J. Schick and Irving Schmolka Polymer-Surfactant Interactions, Shuji Saito Configuration and Hydrodynamic Properties of the Polyoxethylene Chain in Solution, Frederick E. Baily, Jr. and Joseph V. Koleske Structure and Dynamics by NMR and Other Methods, Anthony A. Robeiro and Edward A. Dennis Stability of the Polyoxethylene Chain, Max Donbrow "