Miracle-dz/UniFAR-FTUs huggingface dataset:
An Integrated Kinematic Modeling and Experimental Approach for an Active Endoscope

Continuum robots are a type of robotic device that are characterized by their flexibility and dexterity, thus making them ideal for an active endoscope. Instead of articulated joints they have flexible backbones that can be manipulated remotely, usually through tendons secured onto structures attached to the backbone. This structure makes them lightweight and ideal to be miniaturized for endoscopic applications. However, their flexibility poses technical challenges in the modeling and control of these devices, especially when closed-loop control is needed, as is the case in medical applications. There are two main approaches in the modeling of continuum robots, the first is to theoretically model the behavior of the backbone and the interaction with the tendons, while the second is to collect experimental observations and retrospectively apply a model that can approximate their apparent behavior. Both approaches are affected by the complexity of continuum robots through either model accuracy/computational time (theoretical method) or missing complex system interactions and lacking expandability (experimental method). In this work, theoretical and experimental descriptions of an endoscopic continuum robot are merged. A simplified yet representative mathematical model of a continuum robot is developed, in which the backbone model is based on Cosserat rod theory and is coupled to the tendon tensions. A robust numerical technique is formulated that has low computational costs. A bespoke experimental facility with precise automated motion of the backbone via the precise control of tendon tension, leads to a robust and detailed description of the system behavior provided through a contactless sensor. The resulting facility achieves a real-world mean positioning error of 3.95% of the backbone length for the examined range of tendon tensions which performs favourably to existing approaches. Moreover, it incorporates hysteresis behavior that could not be predicted by the theoretical modeling alone, reinforcing the benefits of the hybrid approach. The proposed workflow is theoretically grounded and experimentally validated allowing precise prediction of the continuum robot behavior, adhering to realistic observations. Based on this accurate estimation and the fact it is geometrically agnostic enables the proposed model to be scaled for various robotic endoscopes.
Increased Ascorbate Biosynthesis Does Not Improve Nitrogen Fixation Nor Alleviate the Effect of Drought Stress in Nodulated Medicago truncatula Plants

Legume plants are able to establish nitrogen-fixing symbiotic relations with Rhizobium bacteria. This symbiosis is, however, affected by a number of abiotic constraints, particularly drought. One of the consequences of drought stress is the overproduction of reactive oxygen (ROS) and nitrogen species (RNS), leading to cellular damage and, ultimately, cell death. Ascorbic acid (AsA), also known as vitamin C, is one of the antioxidant compounds that plants synthesize to counteract this oxidative damage. One promising strategy for the improvement of plant growth and symbiotic performance under drought stress is the overproduction of AsA via the overexpression of enzymes in the Smirnoff-Wheeler biosynthesis pathway. In the current work, we generated Medicago truncatula plants with increased AsA biosynthesis by overexpressing MtVTC2, a gene coding for GDP-L-galactose phosphorylase. We characterized the growth and physiological responses of symbiotic plants both under well-watered conditions and during a progressive water deficit. Results show that increased AsA availability did not provide an advantage in terms of plant growth or symbiotic performance either under well-watered conditions or in response to drought.
Examining Factors Associated With Farmers' Climate-Adaptive and Maladaptive Actions in the U.S. Midwest

The U.S. Midwest is a major producer of grain, meat, dairy, eggs, and other major agricultural commodities. It has also been increasingly impacted by climate change-related extreme weather over the last decade as droughts, extreme rains, floods, and, most recently, a severe derecho have damaged crops, livestock, and livelihoods. Climate and agricultural scientists and other stakeholders are concerned that without major shifts away from degrading practices toward regenerative systems, long-term sustainability will be compromised. We used cumulative logistic regression to analyze data from a 2020 survey of 1,059 Iowa farmers to examine (1) how farmers are adapting to increasingly variable and extreme weather-related to climate change and (2) whether selected factors were associated with different kinds of adaptive (e.g., increased use of cover crops) or potentially maladaptive (e.g., increased use of pesticides) actions. Our results found that many farmers have been taking adaptive and maladaptive actions. Stewardship ethics, attitudes toward adaptive action, and integration in conservation-related networks were consistent, positive predictors of increases in adaptive practices. On the other hand, faith in crop insurance as a coping strategy, farm scale, and other factors were associated with some maladaptive actions, with several positive predictors of adaptation also being positive predictors of maladaptation, use of pesticides and drainage in particular. This research contributes to the growing literature on climate risk management and adaptation in agricultural landscapes by providing empirical evidence of the factors related to farmers' adaptive and maladaptive actions.
Metals in the drinking water of First Nations across Canada

The First Nations Food, Nutrition and Environment Study (FNFNES), a community-based participatory research project, measured the levels of metals of health concern and the levels of metals that have operational guidance (OG) and aesthetic objectives (AO) in drinking water of First Nations (FN) south of the 60th parallel. Both stagnant (first draw) and flushed tap water samples were collected from participating households in 91 FN representing 11 ecozones. The concentrations of metals were quantified and compared to Health Canada’s Guidelines for Canadian Drinking Water Quality (GCDWQ). In total, 1516 FN households participated in this study component. Exceedances of the 2019 GCDWQ for the health-based maximum acceptable concentration (MAC) were found for five metals: lead 8.4% of households (first draw), manganese 4.0%, uranium 1.6%, aluminum 1.3%, and copper 0.2% (flushed). Flushing taps resulted in a decrease to 0.7% of households exceeding the lead MAC. Exceedances of the current OG for aluminum were found in 14.2% and the exceedances of the proposed OG were found in 18.1% of households (flushed). Exceedances of the AO (flushed) were as follows: manganese 12.8%, sodium 5.1%, iron 3.5%, and copper 0.4%. Results of the study were compared to provincial surveys where the data were available. Taste and colour were reported to be the main reasons for limiting the use of tap water. Overall, the quality of drinking water with respect to trace metals of human health concern is satisfactory. However, elevated lead levels were found in some FN communities. Until appropriate action can take place, it was recommended to flush the water before use to reduce the lead levels.
Levels of metals and persistent organic pollutants in traditional foods consumed by First Nations living on-reserve in Canada

First Nations may have a higher risk of contaminant exposure from the consumption of traditional foods. The objective of this study was to measure concentrations of metals and organochlorines in traditional foods commonly consumed by First Nations in Canada and estimate the risk from dietary exposure. Data were collected from the participatory First Nations Food, Nutrition and Environment Study (2008–2018). Traditional food samples were collected by community members and concentrations of metals and organochlorines were measured. The population-weighted mean daily contaminant intake from traditional food items was estimated. Hazard quotients (HQs) were calculated by dividing contaminant intake with the toxicological reference values (TRVs). A total of 2061 food samples (different parts and organs) from 221 species were collected. The highest concentrations of cadmium were found in the kidneys of land mammals: moose kidney was the most significant contributor to intake. The meat of land mammals and birds had the highest lead concentrations and were the most significant contributors to intake. Arsenic was highest in seaweed, and prawn was the most significant contributor. Mercury and methyl mercury were highest in harp seal meat, with walleye/pickerel contributing most to intake. Harp seal meat also had the highest p,p′-DDE and PCB concentrations, and ooligan grease and salmon were the most significant contributors to intake. The percentage of adults eating traditional food who exceeded the TRVs was 1.9% for cadmium, 3.7% for lead, 13.6% for arsenic, 0.7% for mercury, and 0% for p,p′-DDE and PCBs. All median HQs, and most 95th percentile HQs, were less than 1. These results can be used as a baseline of contaminant levels and exposure in traditional foods for future monitoring programs and to support risk assessment programs.
Hot-melt extrusion as processing technique for multiparticulate dosage forms containing lipophilic and hydrophilic polymers

The objective of this doctoral thesis was to develop by means of a simple, flexible and continuous procesesing technique (i.e. hot-melt extrusion) a multiparticulate dosage form with zero-order sustained-release properties. This study demonstrated that the in vitro and in vivo drug release kinetics could be varied by selecting a formulation with an appropriate ratio of lipophilic and hydrophilic polymers, and by changing the particle size and/or molecular weight of the hydrophilic polymer (i.e. xanthan gum and polyethylene oxide).
Helicase functional dynamics from low-resolution experimental data and simulation

The biological function of large macromolecular assemblies depends on their structure and their dynamics over a broad range of timescales; for this reason its investigation poses significant challenges to conventional experimental techniques. A promising experimental technique is hydrogen-deuterium exchange detected by mass spectrometry (HDX-MS). I begin by presenting a new computational method for quantitative interpretation of deuterium exchange kinetics. The method is tested on a hexameric viral helicase φ12 P4 that pumps RNA into a virus capsid at the expense of ATP hydrolysis. Molecular dynamics simulations predict accurately the exchange kinetics of most peptide fragments and provide a residue-level interpretation of the low-resolution experimental results. This approach is also a powerful tool to probe mechanisms that cannot be observed by X-ray crystallography, or that occur over timescales longer than those that can be realistically simulated, such as the opening of the hexameric ring. Once validated, the method is applied on a homologous system, the packaging motor φ8 P4, for which RNA loading and translocation mechanisms remain elusive. Quantitative interpretation of HDX-MS data, as well as Forster resonance energy transfer (FRET) and computational observations, suggest that the C-terminal domain of the motor plays a crucial role. A new translocation model of φ8 P4 is proposed, for which the affinity between the motor and RNA is modulated by the C-termini. In the final result chapter, the amount of the structural information carried by HDX-MS data is quantitatively analysed. The impact of the averaging of the exchange over peptide fragments on the information content is investigated. The complementarity of data obtained from HDX-MS and data obtained from other techniques (such as NMR, FRET or SAXS) is also examined.
Preparation of polyetheretherketone composites with nanohydroxyapatite rods and carbon nanofibers having high strength, good biocompatibility and excellent thermal stability

In recent years, research in the development of polymeric materials for orthopedic implants has become ever more important because the global demand of biocompatible implants has been steadily increasing. Bioinert polyetheretherketone (PEEK) is typically reinforced with bioactive hydroxyapatite microparticles. However, the tensile strength of conventional PEEK/hydroxyapatite microcomposites falls sharply with increasing filler loading. To address low strength and high filler loading issues, nanohydroxyapatite rods (nHA) and carbon nanofibers (CNF) were employed to reinforce PEEK. In this study, molded-grade PEEK pellets, nHA and CNF fillers were melt-mixed and injection molded to form PEEK/nHA and hybrid PEEK/nHA–CNF nanocomposites. The tensile and thermal properties, as well as the bioactivity and biocompatibility of such nanocomposites, were investigated. Tensile test results showed that elastic modulus of PEEK/nHA nanocomposites increases with increasing nHA content. The PEEK/9.3 vol% nHA nanocomposite exhibited higher tensile strength than that of a conventional HAPEX microcomposite. Thermogravimetric measurements indicated that the nHA addition improves the thermal stability of PEEK. Thus, the PEEK/9.3 vol% nHA nanocomposite that had good mechanical, thermal and biological performances was an attractive biomaterial for use in maxillofacial surgery. Furthermore, the tensile property of the PEEK/15 vol% nHA–1.9 vol% nHA nanocomposite compared favorably with that of human cortical bones. The results of biomineralization, alkaline phosphatase (ALP), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and 2-(4-iodophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfophenyl)-2H-tetrazolium (WST-1) assays also showed that the PEEK/15 vol% nHA and PEEK/15 vol% nHA–1.9 vol% CNF nanocomposites exhibited excellent bioactivity and biocompatibility. The ALP assay showed good activity of osteoblast cells on the composite specimens with high nHA content. Moreover, CNF addition further increased the ALP activity of PEEK/15 vol% nHA nanocomposites. The PEEK/15 vol% nHA–1.9 vol% CNF composite, with enhanced tensile strength and excellent biocompatibility, shows large potential for load-bearing implant applications.
The Effect of Perceptions and Attitudes Regarding COVID-19 on Somatization Symptoms and Empathy Skills

BACKGROUND: Post-disaster somatization symptoms are common. But post- disaster somatization symptoms are one of the psychiatric symptoms that are usually ignored. This study evaluates the relationship between somatization disorder and empathy during the novel coronavirus disease (COVID-19) pandemic. METHOD: The study has a descriptive cross-sectional design. The study sample consisted of 1029 subjects, of whom 658 were healthcare workers and 371 non-healthcare workers. The study used the measurement tools of Somatization Scale, Empathy Quotient Scale, Perception of COVID-19 (sub-dimension; dangerousness, contagiousness), and Avoidance Attitudes from COVID-19 (sub- dimension; behavioral avoidance, cognitive avoidance). RESULTS: There was a significant difference between the groups in terms of marital status (p
Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Abstract Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models.
In situ forming gelatin/graphene oxide hydrogels for facilitated C2C12 myoblast differentiation

ABSTRACT Recently, numerous studies have focused on the development of scaffolds for skeletal tissue engineering and regeneration with various structures. Among various structures, in situ forming hydrogels have attracted considerable attention because they can provide 3D microenvironments for cells, and their stiffness and elasticity can be easily controlled by physical or chemical means. Over the last decade, graphene oxide (GO) has been widely explored as a potential candidate for biomaterials because of its excellent physicochemical properties and outstanding biocompatibility. In this study, horseradish peroxide-reactive gelatin polymer (GH) hydrogels incorporated with GO were prepared and their physicochemical and biomechanical properties were characterized by scanning electron microscopy, Raman and Fourier transform-infrared spectroscopy, ther-mogravimetric analysis, and rheological study. The cellular behaviors of the C2C12 myoblasts within the GO-incorporated GH (GH/GO) hydrogels were examined by a cell counting kit-8 assay and immunocytochemistry. GO was uniformly distributed inside the GH hydrogels without affecting their physicochemical and biomechanical properties. GH/GO hydrogels facilitated the myogenic differentiation of C2C12 cells without hindering their proliferation. These results suggest that GH/GO hydrogels can be exploited to craft a range of strategies for the development of promising scaffolds to accelerate skeletal tissue regeneration because of their potential to stimulate myogenesis.
Role of Edge Oxygen Atoms on the Adhesive Interaction between Carbon Fiber and Epoxy Resin

A mechanism of the adhesion between carbon fiber and epoxy resin is studied by using density functional theory (DFT) calculations. Surface structures of carbon fiber were modeled by the armchair-edge structure of graphite functionalized with OH and COOH groups. DFT calculations were performed to construct two realistic models of adhesion interface consisting of the functionalized carbon surface and a fragment of epoxy resin. Adhesive properties of the model interfaces were evaluated based on the binding energy (Eb) between the carbon surface and the resin as well as the maximum adhesive force (Fmax) acting at the interface. Calculated values of Eb are 13.8 kcal/mol for the OH-functionalized surface and 19.1 kcal/mol for the COOH-functionalized surface. The binding energy per hydrogen bond is calculated to be 6.9 kcal/mol (OH model; two H-bonds) and 6.3 kcal/mol (COOH model; three H-bonds), both of which are virtually similar and reasonable for the bond energy of a typical OH···O hydrogen bond. Analysis of.
2D DDoA-based Self-Positioning for Mobile Devices

Multi-antenna techniques enable Direction of Arrival (DoA) estimation and lead to highly accurate results. In DoA-based positioning, the angles values must be predefined in an interval of $2\pi$-length, and the prior knowledge of x-direction is required. However, this prerequisite is impractical for a self-positioning task at a mobile device because its orientation is unknown. In this paper, we propose an algorithm using the differences among the DoAs to overcome such a difficulty. The algorithm relies on the estimation of the Direction Difference of Arrival (DDoA), which is linked to the DoAs. In our procedure, the definition of DoA utilizes the atan2 function, which has the $2{\pi}$-long codomain to map the DoA. An iterative Maximum Likelihood (ML) estimator for position estimation is presented. Noisy measurements with values near the edges of this codomain can lead to drastic position estimation errors, making the convergence of iterative procedures more challenging. Therefore, a phase correction scheme is proposed to robustify the estimation considerably. Simulation results show substantial improvement in performances compared to the methods without correction.
Glyphosate and atrazine inhibit growth of Azospirillum brasilense, Bacillus subtilis, Bacillus thuringiensis, Chromobacterium subtsugae and Saccharopolyspora spinosa

Glyphosate and atrazine are two herbicides used worldwide to ensure high yields in different types of crops. Despite the importance of herbicides, their application may have negative effects on non-target organisms, including bacteria used in biological control and biological nitrogen fixation. Therefore, this research aimed to analyze the in vitro effect of glyphosate and atrazine on the growth of bacteria Azospirillum brasilense, Bacillus subtilis, Bacillus thuringiensis, Chromobacterium subtsugae, and Saccharopolyspora spinosa. The design used was completely randomized, and the doses of the herbicides evaluated were 1.0, 2.0, 3.0 and 4.0 L ha-1. The results showed that glyphosate and atrazine affected the development of the bacteria under study. Atrazine showed a lineal increasing effect between the doses used and inhibition of bacterial growth. Therefore, the dose of 4.0 L ha-1 of this herbicide was the one that showed the highest inhibition of bacteria, whereas glyphosate at a dose of 2.0 L ha-1 showed the highest inhibition of bacteria compared to doses of 1.0, 3.0 and 4.0 L ha-1.
Can Transformer Models Measure Coherence In Text: Re-Thinking the Shuffle Test

The Shuffle Test is the most common task to evaluate whether NLP models can measure coherence in text. Most recent work uses direct supervision on the task; we show that by simply finetuning a RoBERTa model, we can achieve a near perfect accuracy of 97.8%, a state-of-the-art. We argue that this outstanding performance is unlikely to lead to a good model of text coherence, and suggest that the Shuffle Test should be approached in a Zero-Shot setting: models should be evaluated without being trained on the task itself. We evaluate common models in this setting, such as Generative and Bi-directional Transformers, and find that larger architectures achieve high-performance out-of-the-box. Finally, we suggest the k-Block Shuffle Test, a modification of the original by increasing the size of blocks shuffled. Even though human reader performance remains high (around 95% accuracy), model performance drops from 94% to 78% as block size increases, creating a conceptually simple challenge to benchmark NLP models.
Cross-Flow Naphtha Reforming in Stacked-Bed Radial Reactors with Continuous Solid Circulation: Catalyst Deactivation and Solid Circulation between Reactors

A naphtha-reforming reactor system, consisting of four stacked-bed reactors with cross-flow of gas and circulation of catalyst, was simulated. Commercial and micropilot plant data were used to adjust the kinetics and deactivation constants, based on previous results obtained in fixed-bed operations with another catalyst. The simulation of radial commercial reactors was performed using mixing zones for the intervessel solid transfer and a second zone through which the solids move downward and within which reactions take place. The mass and energy differential equations were solved in the radial and axial direction point by point, using the Runge–Kutta–Fehlberg (RKF) numerical method. The effects of solid mixing on catalyst profiles of coke were determined in a cold model. Sequential deactivation and partial mixing tests were performed to verify their effects on catalyst activities. Spent catalysts were characterized and tested under different operating conditions. The feed and reformate were characterized .