Browsing by Subject "12 Producción y consumo responsables"

Malaria is an infectious disease caused by Plasmodium spp. parasites, with widespread drug resistance to most antimalarial drugs. We report the development of two 3D-QSAR models based on comparative molecular field analysis (CoMFA), comparative molecular similarity index analysis (CoMSIA), and a 2D-QSAR model, using a database of 349 compounds with activity against the P. falciparum 3D7 strain. The models were validated internally and externally, complying with all metrics (q2 > 0.5, r2test > 0.6, r2m > 0.5, etc.). The final models have shown the following statistical values: r2test CoMFA = 0.878, r2test CoMSIA = 0.876, and r2test 2D-QSAR = 0.845. The models were experimentally tested through the synthesis and biological evaluation of ten quinoline derivatives against P. falciparum 3D7. The CoMSIA and 2D-QSAR models outperformed CoMFA in terms of better predictive capacity (MAE = 0.7006, 0.4849, and 1.2803, respectively). The physicochemical and pharmacokinetic properties of three selected quinoline derivatives were similar to chloroquine. Finally, the compounds showed low cytotoxicity (IC50 > 100 µM) on human HepG2 cells. These results suggest that the QSAR models accurately predict the toxicological profile, correlating well with experimental in vivo data.

Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a ‘cyborg walk’ method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this ‘cyborg walk’ experiment and the seminal cyborg metaphor proposed by Donna Haraway.

The Gaussian copula is a powerful tool that has been widely used to model spatial and/or temporal correlated data with arbitrary marginal distributions. However, this kind of model can potentially be too restrictive since it expresses a reflection symmetric dependence. In this paper, we propose a new spatial copula model that makes it possible to obtain random fields with arbitrary marginal distributions with a type of dependence that can be reflection symmetric or not. Particularly, we propose a new random field with uniform marginal distributions that can be viewed as a spatial generalization of the classical Clayton copula model. It is obtained through a power transformation of a specific instance of a beta random field which in turn is obtained using a transformation of two independent Gamma random fields. For the proposed random field, we study the second-order properties and we provide analytic expressions for the bivariate distribution and its correlation. Finally, in the reflection symmetric case, we study the associated geometrical properties. As an application of the proposed model we focus on spatial modeling of data with bounded support. Specifically, we focus on spatial regression models with marginal distribution of the beta type. In a simulation study, we investigate the use of the weighted pairwise composite likelihood method for the estimation of this model. Finally, the effectiveness of our methodology is illustrated by analyzing point-referenced vegetation index data using the Gaussian copula as benchmark. Our developments have been implemented in an open-source package for the R statistical environment.

Estimation of power consumption in digital circuits is performed at gate-level simulation. Its accuracy depends on the models of gate delays that capture the effects of spurious signal transitions, called “glitches”. Electronic Design Automation (EDA) software considers inertial gate delays and represses a glitch in the cell’s output if its width is below a threshold. Selecting threshold values for the inertial glitch classification and filtering is crucial for precise power estimations. In this direction, we explore the effectiveness of automatically adjusting such thresholds on a cell-specific basis according to the local cell’s information. We used a commercial industry-standard gate-level power estimation tool and a 32 nm CMOS standard cell library. Via power measurements in circuit simulations, we created customized lookup tables for each library cell employed in the benchmark circuits. We compared the proposed approach’s performance with other methods for glitch threshold definition. Our method demonstrated good power estimation accuracy while presenting the lowest mean absolute error among all the cells of the circuits under test and the smallest standard deviation. The latter suggests that the proposed method achieves better cell-specific accuracy, which is expected to allow for more precise circuit-level power estimations in complex circuits with a large number of combinational cells.

The Actinomycetota (formerly Actinobacteria) genus Clavibacter includes phytopathogens with devasting effects in several crops. Clavibacter michiganensis, the causal agent of tomato bacterial canker, is the most notorious species of the genus. Yet, its origin and natural reservoirs remain elusive, and its populations show pathogenicity profiles with unpredictable plant disease outcomes. Here, we generate and analyse a decade-long genomic dataset of Clavibacter from wild and commercial tomato cultivars, providing evolutionary insights that directed phenotypic characterization. Our phylogeny situates the last common ancestor of C. michiganensis next to Clavibacter isolates from grasses rather than to the sole strain we could isolate from wild tomatoes. Pathogenicity profiling of C. michiganensis isolates, together with C. phaseoli and C. californiensis as sister taxa and the wild tomato strain, was found to be congruent with the proposed phylogenetic relationships. We then identified gene enrichment after the evolutionary event, leading to the appearance of the C. michiganesis clade, including known pathogenicity factors but also hitherto unnoticed genes with the ability to encode adaptive traits for a pathogenic lifestyle. The holistic perspective provided by our evolutionary analyses hints towards a host shift event as the origin of C. michiganensis as a tomato pathogen and the existence of pathogenic genes that remain to be characterized.

Refractory high-entropy alloys (RHEAs) are promising candidates for those applications requiring of strong materials at high temperatures with elevated thermal stability and excellent oxidation, irradiation, and corrosion resistance. Particularly, RHEAs synthesized using mechanical alloying (MA) followed by spark plasma sintering (SPS) has proven to be a successful path to produce stronger alloys than those produced by casting techniques. This superior behavior, at both room and high temperature, can be attributed to the microstructural features resultant from this powder metallurgy route, that include the presence of homogeneously distributed nonmetallic particles, fine- and ultrafine-grained microstructures, and higher content of interstitial solutes. Nevertheless, the powder metallurgy fabrication relies over a complex balance of several operational variables, and the process is no exempt of certain challenges, such as contamination or the presence of pores in the bulk parts. This review aims to cover all the peculiarities of the MA + SPS route, the resultant microstructures, their mechanical properties, and the strengthening and deformation mechanisms behind their superior performance, as well as a brief description of their oxidation resistance.

Temporal (race) computing schemes rely on temporal memories, where information is represented with the timing of signal edges. Standard digital circuit techniques can be used to capture the relative timing characteristics of signal edges. However, the properties of emerging device technologies could be particularly exploited for more efficient circuit implementations. Specifically, the collective dynamics of networks of memristive devices could be leveraged to facilitate time-domain computations in emerging memristive memories. To this end, this work studies the star interconnect configuration of bipolar memristive devices. Through circuit simulations using a behavioral model of voltage-controlled bipolar memristive devices, we demonstrated the suitability of such circuits in two different contexts, namely sensing and “rank-order” coding. We particularly analyzed the conditions that the employed memristive devices should meet to guarantee the expected operation of the circuit and the possible effects of device variability in the storage and the reproduction of the information in arriving signal edges. The simulation results in LTSpice validate the correct operation and confirm the promising application prospects of such simple circuit structures, which, we show, natively exist in the crossbar geometry. Therefore, the star interconnect configuration could be considered for temporal computations inside resistive memory (ReRAM) arrays.