A Differentially Expressed Gene from a High Oil Producer Cultivar of Castor Bean (<i>Ricinus communis</i>) Is Involved in the Biosynthesis of Ricinoleic Acid

Ricinus communis or castor bean is a non-edible oilseed plant widely cultivated worldwide for the high content of castor oil in its seeds and the different uses the oil has in the industry. An increase in its oil content and production efficiency is difficult, making understanding the molecular mechanisms underlying the synthesis of oils in the seed necessary. Here, a combined analysis of protein-protein interaction networks was performed using public data on differential gene expression in castor bean seeds at different stages of development. From this analysis, four key enzymes were selected and analyzed in the polyunsaturated fatty acids pathways, whose gene expression was subsequently quantified during the development of the seeds in a Colombian cultivar that produces high amounts of oils and contrasted with a lower producing cultivar. The gene coding FAH12 was differentially expressed in the early stages of seed development in the high oil-producing cultivar and has differences in amino acids A242V and Q319H. The analysis presents this gene as one of those responsible for early ricinoleic acid synthesis, making it a candidate for use in crop genetic improvement programs to increase the oil content in castor bean.

Fitting of Analytic Surfaces to Noisy Point Clouds

Fitting C2-continuous or superior surfaces to a set S of points sampled on a 2-manifold is central to reverse engineering, computer aided geometric modeling, entertaining, modeling of art heritage, etc. This article addresses the fitting of analytic (ellipsoid, cones, cylinders) surfaces in general position in . Currently, the state of the art presents limitations in 1) automatically finding an initial guess for the analytic surface F sought, and 2) economically estimating the geometric distance between a point of S and the analytic surface F. These issues are central in estimating an analytic surface which minimizes its accumulated distances to the point set. In response to this situation, this article presents and tests novel user-independent strategies for addressing aspects 1) and 2) above, for cylinders, cones and ellipsoids. A conjecture for the calculation of the distance point-ellipsoid is also proposed. Our strategies produce good initial guesses for F and fast fitting error estimation for F, leading to an agile and robust optimization algorithm. Ongoing work addresses the fitting of free-form parametric surfaces to S.

Analysis of the role of diffraction in topographic site effects using boundary element techniques

The role played by the diffraction field on the problem of seismic site effects is studied. For that purpose we solve and analyze simple scattering problems under P and SV in-plane wave assumptions, using two well known direct boundary-element-based numerical methods. After establishing the difference between scattered and diffracted motions, and introducing the concept of artificious and physically based incoming fields, we obtain the amplitude of the Fourier spectra for the diffracted part of the response： this is achieved after establishing the connection between the spatial distribution of the transfer function over the studied simple topographies and the diffracted field. From the numerical simulations it is observed that this diffracted part of the response is responsible for the amplification of the surface ground motions due to the geometric effect. Furthermore, it is also found that the diffraction field sets in a fingerprint of the topographic effect in the total ground motions. These conclusions are further supported by observations in the time-domain in terms of snapshots of the propagation patterns over the complete computational model. In this sense the geometric singularities are clearly identified as sources of diffraction and for the considered range of dimensionless frequencies it is evident that larger amplifications are obtained for the geometries containing a larger number of diffraction sources thus resulting in a stronger topographic effect. The need for closed-form solutions of canonical problems to construct a robust analysis method based on the diffraction field is identified.

A novel simple procedure to consider seismic soil structure interaction effects in 2D models

A method is proposed to estimate the seismic soil-structure-interaction （SSI） effects for use in engineering practice. It is applicable to 2D structures subjected to vertically incident shear waves supported by homogenous half-spaces. The method is attractive since it keeps the simplicity of the spectral approach, overcomes some of the difficulties and inaccuracies of existing classical techniques and yet it considers a physically consistent excitation. This level of simplicity is achieved through a response spectra modification factor that can be applied to the free-field 5%-damped response spectra to yield design spectral ordinates that take into account the scattered motions introduced by the interaction effects. The modification factor is representative of the Transfer Function （TF） between the structural relative displacements and the free- field motion, which is described in terms of its maximum amplitude and associated frequency. Expressions to compute the modification factor by practicing engineers are proposed based upon a parametric study using 576 cases representative of actual structures. The method is tested in 10 cases spanning a wide range of common fundamental vibration periods.

Diabetes Type 2: Poincaré Data Preprocessing for Quantum Machine Learning

Quantum Machine Learning(QML)techniques have been recently attracting massive interest.However reported applications usually employ synthetic or well-known datasets.One of these techniques based on using a hybrid approach combining quantum and classic devices is the Variational Quantum Classifier(VQC),which development seems promising.Albeit being largely studied,VQC implementations for“real-world”datasets are still challenging on Noisy Intermediate Scale Quantum devices(NISQ).In this paper we propose a preprocessing pipeline based on Stokes parameters for data mapping.This pipeline enhances the prediction rates when applying VQC techniques,improving the feasibility of solving classification problems using NISQ devices.By including feature selection techniques and geometrical transformations,enhanced quantum state preparation is achieved.Also,a representation based on the Stokes parameters in the PoincaréSphere is possible for visualizing the data.Our results show that by using the proposed techniques we improve the classification score for the incidence of acute comorbid diseases in Type 2 Diabetes Mellitus patients.We used the implemented version of VQC available on IBM’s framework Qiskit,and obtained with two and three qubits an accuracy of 70%and 72%respectively.

Two-tiered reconstruction of Late Pleistocene to Holocene changes in the freezing level height in the largest glacierized areas of the Colombian Andes

One way of deducing vertical shifts in the altitudinal distribution of Colombian high-altitude páramo environments is by inferring fluctuations in the height of the local freezing level.In our research,we are implementing two complementary approaches to reconstruct Late Pleistocene to Holocene changes in the freezing level height(FLH)in two of the most extensively glacier-covered areas of the northern Andes.We combined remote sensing and field-based geomorphological mapping with time-series reconstruction of changes in the altitude of the 0°C isotherm.Changes in the FLH were based on alreadypublished~30 kyr paleo-reconstructions of sea surface temperatures(SSTs)of the eastern tropical Pacific and the western tropical Atlantic,as well as on reconstructed long-term sea level changes and empirical orthogonal functions of present-day(historical)Indo-Pacific and tropical Atlantic SST anomalies.We also analyzed the probability distribution of air-sea temperature differences and the spatial distribution of grid points with SSTs above the minimum threshold necessary to initiate deep convection.We considered available historical nearsurface and free air temperature data of ERA-Interim reanalysis products,General Circulation Model(GCM)simulations,weather stations,and(deployed by our group)digital sensors,to assess the normal Environmental Lapse Rates(ELRs)at the regional to local scale.The combined maps of glacial landforms and our reconstructed FLHs provided us with a wellfounded inference of potential past glacier advances,narrowing down the coarse resolution of ice margins suggested by previous research efforts.The extent of the areas with temperatures below the freezing point suggested here for the summits of our main study site exceeds in magnitude the corresponding glacier icecaps and front advances proposed by previous studies.Conversely,our average lowest altitudes of the FLH for our comparative site are consistently above the main glacier-front advances previously suggested.Our results indicate that,compared to the maximum upward changes that likely took place over the past ca.20,000 years in our two areas of interest,the observed(present-day)upward shifts of the FLH have occurred at a rate that significantly surpasses our inferred rates.Our study helps fill the gaps in understanding past climatic changes and present trends in the region of interest and provides some insights into analyzing the signals of natural and anthropogenic climate change.

Exploiting Deep Learning Techniques for Colon Polyp Segmentation

As colon cancer is among the top causes of death, there is a growinginterest in developing improved techniques for the early detection of colonpolyps. Given the close relation between colon polyps and colon cancer,their detection helps avoid cancer cases. The increment in the availability ofcolorectal screening tests and the number of colonoscopies have increasedthe burden on the medical personnel. In this article, the application of deeplearning techniques for the detection and segmentation of colon polyps incolonoscopies is presented. Four techniques were implemented and evaluated:Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC).These were trained and tested using CVC-Colon database, ETIS-LARIBPolyp, and a proprietary dataset. Three experiments were conducted to assessthe techniques performance: (1) Training and testing using each databaseindependently, (2) Mergingd the databases and testing on each database independently using a merged test set, and (3) Training on each dataset and testingon the merged test set. In our experiments, PANet architecture has the bestperformance in Polyp detection, and HTC was the most accurate to segmentthem. This approach allows us to employ Deep Learning techniques to assisthealthcare professionals in the medical diagnosis for colon cancer. It is anticipated that this approach can be part of a framework for a semi-automatedpolyp detection in colonoscopies.

Bifurcations and Sequences of Elements in Non-Smooth Systems Cycles

This article describes the implementation of a novel method for detection and continuation of bifurcations in non-smooth complex dynamic systems. The method is an alternative to existing ones for the follow-up of associated phenomena, precisely in the circumstances in which the traditional ones have limitations (simultaneous impact, Filippov and first derivative discontinuities and multiple discontinuous boundaries). The topology of cycles in non-smooth systems is determined by a group of ordered segments and points of different regions and their boundaries. In this article, we compare the limit cycles of non-smooth systems against the sequences of elements, in order to find patterns. To achieve this goal, a method was used, which characterizes and records the elements comprising the cycles in the order that they appear during the integration process. The characterization discriminates: a) types of points and segments;b) direction of sliding segments;and c) regions or discontinuity boundaries to which each element belongs. When a change takes place in the value of a parameter of a system, our comparison method is an alternative to determine topological changes and hence bifurcations and associated phenomena. This comparison has been tested in systems with discontinuities of three types: 1) impact;2) Filippov and 3) first derivative discontinuities. By coding well-known cycles as sequences of elements, an initial comparison database was built. Our comparison method offers a convenient approach for large systems with more than two regions and more than two sliding segments.

City Development, Natural Resources and Human Impact: The Case of Medellin, Colombia

Medellin is a 3.5 M inhabitant city located in an Andean valley in northwestern Colombia. Its initial prosperity was due to agriculture and cattle-raising carried out in the valley itself and sold to the surrounding gold mining fields. The investment of these monies in coffee plantations and industry boosted the city development, accelerated urban growth, and since the middle of twentieth century, relegated food production to surrounding regions, which are also responsible for almost the totality of natural resource supply: water, electricity, food, building and industrial raw materials. Among the problems which will have to be solved in order to reach a sustainable development are relocation of population living in areas exposed to natural risks, improvement of road communications with surrounding regions and of internal public transportation and pollution control.

Study of the Effect of Cu2+in the Lattice Dynamics of Doped Magnetites Obtained by the Hydrothermal Synthesis Method

In this work, the effect of Cu2+ on the structural and magnetic properties of samples of magnetite is addressed. Samples of magnetite, both pure and Cu2+ doped, Fe3-xCuxO4, with x = 0, 5, 10 and 20 atm.% were synthesized hydrothermally. The two-lattice method was employed to measure the Mossbauer recoilless fraction of magnetite relative to hematite (fmag/fhem) of the samples, looking for evidence of substitution of Fe2+ by Cu2+. The relative recoilless fraction measurements were performed by taking room temperature Mossbauer spectra of mixtures of each sample with analytical grade hematite. The Mossbauer measurements were complemented with Atomic Absorption Spectroscopy (AAS) and Energy Dispersive X-ray Spectroscopy (EDS). The analyses by AAS and EDS showed that the copper concentration in the final products increases with increasing the content of Cu2+ in the starting solutions. The Mossbauer analyses showed a linear decrease trend of the relative Mossbauer recoilless fraction with increasing concentration of Cu2+in the samples, as well as a reduction in the hyperfine magnetic field, which was more significant in the octahedral sites than tetrahedral sites. The broadening of the Mossbauer spectral lines was more significant for the octahedral sub spectrum than for the tetrahedral sub spectrum. Our study points that Cu2+ occupies preferentially the octahedral sites, where it substitutes Fe2+ species, generating broadening in the lines of the octahedral sub spectrum and a reduction in the probability of having nuclear resonant absorption of Mossbauer gamma rays in the samples.

Smart dental materials for antimicrobial applications

Smart biomaterials can sense and react to physiological or external environmental stimuli(e.g.,mechanical,chemical,electrical,or magnetic signals).The last decades have seen exponential growth in the use and development of smart dental biomaterials for antimicrobial applications in dentistry.These biomaterial systems offer improved efficacy and controllable bio-functionalities to prevent infections and extend the longevity of dental devices.This review article presents the current state-of-the-art of design,evaluation,advantages,and limitations of bioactive and stimuli-responsive and autonomous dental materials for antimicrobial applications.First,the importance and classification of smart biomaterials are discussed.Second,the categories of bioresponsive antibacterial dental materials are systematically itemized based on different stimuli,including pH,enzymes,light,magnetic field,and vibrations.For each category,their antimicrobial mechanism,applications,and examples are discussed.Finally,we examined the limitations and obstacles required to develop clinically relevant applications of these appealing technologies.

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Malfunction or breakdown of certain mission critical systems(MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.