Enhancing antioxidant properties of CeO2 nanoparticles with Nd3+ doping: structural, biological, and machine learning insights.

The antioxidant capabilities of nanoparticles are contingent upon various factors, including their shape, size, and chemical composition. Herein, novel Nd-doped CeO2 nanoparticles were synthesized and the neodymium content was varied to investigate the synergistic impact on the antioxidant properties of CeO2 nanoparticles. Incorporating Nd3+ induced changes in lattice parameters and significantly altered the morphology from nanoparticles to nanorods. The biological activity of Nd-doped CeO2 was examined against pathogenic bacterial strains, breast cancer cell lines, and antioxidant models. The antibacterial and anticancer activities of nanoparticles were not observed, which could be associated with the Ce3+/Ce4+ ratio. Notably, the incorporation of neodymium improved the antioxidant capacity of CeO2. Machine learning techniques were employed to forecast the antioxidant activity to enhance understanding and predictive capabilities. Among these models, the random forest model exhibited the highest accuracy at 96.35%, establishing it as a robust computational tool for elucidating the biological behavior of Nd-doped CeO2 nanoparticles. This study presents the first exploration of the influence of Nd3+ on the structural, optical, and biological attributes of CeO2, contributing valuable insights and extending the application of machine learning in predicting the therapeutic efficacy of inorganic nanomaterials.

Perspectives of People At-Risk on Parkinson's Prevention Research.

 The movement toward prevention trials in people at-risk for Parkinson's disease (PD) is rapidly becoming a reality. The authors of this article include a genetically at-risk advocate with the LRRK2 G2019 S variant and two patients with rapid eye movement sleep behavior disorder (RBD), one of whom has now been diagnosed with PD. These authors participated as speakers, panelists, and moderators in the "Planning for Prevention of Parkinson's: A Trial Design Forum" hosted by Massachusetts General Hospital in 2021 and 2022. Other authors include a young onset person with Parkinson's (PwP) and retired family physician, an expert in patient engagement in Parkinson's, and early career and veteran movement disorders clinician researchers. Several themes emerged from the at-risk participant voice concerning the importance of early intervention, the legitimacy of their input in decision-making, and the desire for transparent communication and feedback throughout the entire research study process. Challenges and opportunities in the current environment include lack of awareness among primary care physicians and general neurologists about PD risk, legal and psychological implications of risk disclosure, limited return of individual research study results, and undefined engagement and integration of individuals at-risk into the broader Parkinson's community. Incorporating the perspectives of individuals at-risk as well as those living with PD at this early stage of prevention trial development is crucial to success.

Social Signal Processing in Affective Virtual Reality: Human-Shaped Agents Increase Electrodermal Activity in an Elicited Negative Environment.

Prior research on affect elicitation indicates that stimuli with social content (pictures or videos) are more arousing than nonsocial stimuli. In particular, they elicit stronger physiological arousal as measured by electrodermal activity (EDA; i.e., social EDA effect). However, it is unclear how this effect applies to virtual reality (VR), which enables an enhanced sense of presence (SoP) and ecological validity. The study here approached this question from a social-emotional VR framework. A sample of N = 72 participants (55 percent women) experienced a set of six virtual environments (VEs) in the form of emotional parks specifically designed to elicit positive, negative, or neutral affectivity. Half of these VEs included human-shaped agents (social context) and the other half omitted these agents (nonsocial context). The results supported the social EDA effect, which in addition was amplified by the reported SoP. Importantly, the VE featuring a social negative content qualified this observed social EDA effect. The finding is discussed in the light of a negativity bias reported in affect literature, through which negative stimuli typically mobilize attention and bodily activation as a mechanism linked to stress responses. The study's implications extend to the use of VR in both research and practical applications, emphasizing the role of social content in influencing affective and physiological responses.

Pressure-dependent flow enhancement in carbon nanotubes.

It is a known and experimentally verified fact that the flow of pressure-driven nanoconfined fluids cannot be accurately described by the Navier-Stokes (NS) equations with non-slip boundary conditions, and the measured volumetric flow rates are much higher than those predicted by macroscopical continuum models. In particular, the flow enhancement factors (the ratio between the flow rates directly measured by experiments or simulations and those predicted by the non-slip NS equation) reported by previous studies have more than five orders of magnitude differences. We showcased an anomalous phenomenon in which the flow enhancement exhibits a non-monotonic correlation with fluid pressure within the carbon nanotube with a diameter of 2 nm. Molecular dynamics simulations indicate that the inconsistency of flow behaviors is attributed to the phase transition of nanoconfined fluid induced by fluid pressures. The nanomechanical mechanisms are contributed by complex hydrogen-bonding interactions and regulated water orientations. This study suggests a method for explaining the inconsistency of flow enhancements by considering the pressure-dependent molecular structures.

Electrochemical Analysis of Curcumin in Real Samples Using Intelligent Materials.

Curcumin is a compound of great importance in the food industry due to its biological and pharmacological properties, which include being an antioxidant, anti-inflammatory, antibacterial, antiviral, and anticarcinogenic. This paper proposes the synthesis of an electrochemical sensor based on molecularly imprinted polymers (MIPs) and MWCNT by drop casting deposited on a glassy carbon electrode (GCE) for the selective quantification of curcumin in food samples. The synthesized compounds are characterized by Fourier transform infrared (IR), Brunauer-Emmett-Teller (BET), and electrochemical techniques such as cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The optimal conditions for further experiments were determined by selecting these parameters. We examined three food products, commercial capsules, turmeric rhizomes, and commercial turmeric powder, employing both electrochemical and HPLC methods for the analysis. The electrochemical method revealed a limit of detection (LOD) value of 0.1365 µmol L-1, compared with the HPLC analysis, which gave a value of 3.55 µmol L-1. Furthermore, the MIP material demonstrated superior selectivity for the analyte compared to potential interferents. The recovery percentage, determined using the HPLC method, fell within the range of 87.5% to 102.6.

Estimation of the ice melting point in molecular dynamics simulations based on the finite-size effects.

Predicting the ice melting point using molecular dynamics (MD) simulations is nontrivial due to uncertainty associated with the stochastic nature of the simulation and effect of finite domain sizes on the simulated ice-water phase transition. We developed a method based on the percolation theory to make use of the finite size effects to allow determination of a unique critical phase transition temperature as the melting point. The method involves construction of melting/freezing probability curves from multiple simulations with varying temperatures for different domain sizes. While the domain sizes affect the apparent melting/freezing probability and hence generate different curves with a wider probability distribution for a smaller size, the intersection of these curves is unique and locates the melting point. Based on MD simulations using the Tip4p/Ice water model, we tested and demonstrated the effectiveness of this method in locating the critical ice-water phase transition at a melting temperature of 268.78 K. Our analysis also showed that the apparent melting probability at this critical point is ∼0.69, not 0.5 assumed in the ad hoc method used previously. Our method, making no assumption about the system size, may provide a generic framework for analyzing phase transitions influenced by the finite size effects in MD simulations.

Optimizing filters of activated carbons obtained from biomass residues for ethylene removal in agro-food industry devices.

A series of adsorbents (activated carbons, ACs) were synthesized by physical and chemical activation of olive stones (OS) and their textural and chemical characteristics determined by complementary techniques such as N2 and CO2 physisorption, pH of the point zero of charge (pHPZC), HRSEM or XPS. Samples with a wide range of physicochemical properties were obtained by fitting the activation procedure. The performance of these adsorbents in filters working under dynamic conditions was studied by determining the corresponding breakthrough curves for the ethylene removal. The physicochemical transformations of OS during activation were related with the adsorptive performance of derivative ACs. Results were compared to those obtained using commercial carbons, in particular ACs, carbon black or carbon fibers, in order to identify the properties of these materials on influencing the adsorptive performance. In general, ACs from OS perform better than the commercial samples, being also easily regenerated and properly used during consecutive adsorption cycles. CO2-activation showed to be the best synthesis option, leading to granular ACs with a suitable microporosity and surface chemistry. These results could favour the integration of this type of inexpensive materials on devices for the preservation of climacteric fruits, in a clear example of circular economy by reusing the agricultural residues.

Recent technologies for glyphosate removal from aqueous environment: A critical review.

The growing demand for food has led to an increase in the use of herbicides and pesticides over the years. One of the most widely used herbicides is glyphosate (GLY). It has been used extensively since 1974 for weed control and is currently classified by the World Health Organization (WHO) as a Group 2A substance, probably carcinogenic to humans. The industry and academia have some disagreements regarding GLY toxicity in humans and its effects on the environment. Even though this herbicide is not mentioned in the WHO water guidelines, some countries have decided to set maximum acceptable concentrations in tap water, while others have decided to ban its use in crop production completely. Researchers around the world have employed different technologies to remove or degrade GLY, mostly at the laboratory scale. Water treatment plants combine different technologies to remove it alongside other water pollutants, in some cases achieving acceptable removal efficiencies. Certainly, there are many challenges in upscaling purification technologies due to the costs and lack of factual information about their adverse effects. This review presents different technologies that have been used to remove GLY from water since 2012 to date, its detection and removal methods, challenges, and future perspectives.

Efectos de un Programa de Ejercicios y Educación en Dolor mediante Tele-rehabilitación en la Calidad de Vida de Mujeres Jóvenes con Dismenorrea Primaria / Effects of an Exercise and Pain Education Program via Telerehabilitation on the Quality of Life of Young Women with Primary Dysmenorrhea

Introducción. La dismenorrea primaria afecta la calidad de vida de mujeres jóvenes. Este estudio investiga los efectos de un protocolo de ejercicios y educación en dolor mediante telerehabilitación. Objetivo. Determinar los efectos de un protocolo de ejercicios y educación en dolor y su impacto en la calidad de vida mediante tele-rehabilitación en mujeres jóvenes con dismenorrea primaria. Métodos. Se realizó un estudio experimental pre y post test con un grupo control, utilizando tele-rehabilitación. Participaron 21 mujeres jóvenes con dismenorrea primaria, asignadas aleatoriamente a dos grupos: el grupo experimental (10 mujeres) y el grupo control (11 mujeres). Se utilizaron cuestionarios específicos de calidad de vida relacionada con la menstruación y dolor (cuestionario breve de McGill) para evaluar los efectos del programa de ejercicios y educación en dolor. Resultados. En el grupo control, no se observaron diferencias significativas en las puntuaciones de calidad de vida relacionada con la menstruación ni en la intensidad del dolor antes y después de la intervención (p>0,05). En contraste, el grupo experimental experimentó mejoras significativas en ambas áreas después de la intervención (todos los p<0,001). Conclusiones. Este estudio demuestra que un protocolo de ejercicios y educación en dolor realizado mediante telerehabilitación tiene un impacto positivo y significativo en la calidad de vida y la intensidad del dolor percibido en mujeres jóvenes con dismenorrea primaria. Estos resultados sugieren que la telerehabilitación puede ser una estrategia efectiva para abordar este problema de salud en esta población.

Background. Primary dysmenorrhea impacts the quality of life of young women. This study investigates the effects of an exercise and pain education protocol through telerehabilitation. Objective: To determine the effects of an exercise and pain education protocol and its impact on the quality of life through telerehabilitation in young women with primary dysmenorrhea. Methods: A pre and post-test experimental study with a control group was conducted using telerehabilitation. Twenty-one young women with primary dysmenorrhea participated, randomly assigned to two groups: the experimental group (10 women) and the control group (11 women). Specific questionnaires related to menstruation-related quality of life and pain (McGill short-form questionnaire) were used to assess the effects of the exercise and pain education program. Results: In the control group, no significant differences were observed in menstruation-related quality of life scores or pain intensity before and after the intervention (p>0.05). In contrast, the experimental group experienced significant improvements in both areas after the intervention (all p<0.001). Conclusion: This study demonstrates that an exercise and pain education protocol conducted through telerehabilitation has a positive and significant impact on the perceived quality of life and pain intensity in young women with primary dysmenorrhea. These results suggest that telerehabilitation can be an effective strategy for addressing this health issue in this population.

Data-Driven Personalized Care in Lung Cancer: Scoping Review and Clinical Recommendations on Performance Status and Activity Level of Patients With Lung Cancer Using Wearable Devices.

PURPOSE: Performance status (PS) is a crucial assessment for research and clinical practice in lung cancer (LC), including its usage for the assessment of the suitability and toxicity of treatment or eligibility for clinical trials of patients with LC. These PS assessments are subjective and lead to substantial discrepancies between observers. To improve the objectivity of PS assessments, Electronic Activity Monitoring devices (EAMs) are increasingly used in oncology, but how these devices are used for PS assessments in LC is an issue that remains unclear. The goal of this study is to address the challenges and opportunities of the use of digital tools to support PS assessments in patients with LC. METHODS: The literature review followed PRISMA-ScR methodology. Searches were performed in the ScienceDirect, PsycInfo, ACM, IEEE Xplore, and PubMed databases. Furthermore, a panel discussion was performed to address the clinical use cases. RESULTS: Thirty-two publications were found. Most of the studies used wrist accelerometry-based wearables (59%) and monitored sleep activity (SA; 28%) or physical activity (PA; 72%). Critical findings include positive usefulness of the use of wearables to categorize moderate-to-vigorous/light PA, which was associated with better sleep and health. In addition, steps and time awake immobile were found to be associated with risk of hospitalization and survival. Use cases identified included the health assessment of patients and clinical research. CONCLUSION: There are positive experiences in the use of EAM to complement PS assessment in LC. However, there is a need for adapting thresholds to the particularities of patients with LC, for example, differentiating moderate-to-vigorous and light. Moreover, developing methodologies combining PS assessments and the use of EAM adapted to clinical and research practice is needed.

A Spatial-Spectral Classification Method Based on Deep Learning for Controlling Pelagic Fish Landings in Chile.

Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red-Green-Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.

Digital Classification of Chilean Pelagic Species in Fishing Landing Lines.

Fishing landings in Chile are inspected to control fisheries that are subject to catch quotas. The control process is not easy since the volumes extracted are large and the numbers of landings and artisan shipowners are high. Moreover, the number of inspectors is limited, and a non-automated method is utilized that normally requires months of training. In this work, we propose, design, and implement an automated fish landing control system. The system consists of a custom gate with a camera array and controlled illumination that performs automatic video acquisition once the fish landing starts. The imagery is sent to the cloud in real time and processed by a custom-designed detection algorithm based on deep convolutional networks. The detection algorithm identifies and classifies different pelagic species in real time, and it has been tuned to identify the specific species found in landings of two fishing industries in the Biobío region in Chile. A web-based industrial software was also developed to display a list of fish detections, record relevant statistical summaries, and create landing reports in a user interface. All the records are stored in the cloud for future analyses and possible Chilean government audits. The system can automatically, remotely, and continuously identify and classify the following species: anchovy, jack mackerel, jumbo squid, mackerel, sardine, and snoek, considerably outperforming the current manual procedure.

Synthesis, Characterization, and Evaluation of a Novel Molecularly Imprinted Polymer (MIP) for Selective Quantification of Curcumin in Real Food Sample by UV-Vis Spectrophotometry.

Curcumin is the main colorant of the curcuma longa plant, a food with many benefits for human health. This work aims to synthesize a novel molecularly imprinted polymer (MIP) for the selective detection of curcumin in real samples obtained from the local market of Peru. MIPs were synthesized via bulk polymerization using curcumin, acrylamide, ethylene glycol dimethacrylate, ABCV, and acetonitrile. FTIR spectra showed equal spectra for MIP and NIP. N2 physisorption analysis presented a higher value BET surface for the MIP (28.5 m2 g-1) compared to the NIP (18.5 m2 g-1). The adsorption capacity of the MIP was evaluated using UV-vis spectrophotometry in the band around 430 nm. The adsorption kinetics found were of pseudo-second-order and a Qe value of 16.2 mg g-1. Furthermore, the adsorption process resembles the Freundlich adsorption model with a heterogeneity factor of less than 1 (0.61) and Kf greater for MIP (1.97). The selectivity test indicated that MIP is more selective for curcumin (Q = 13.20 mg g-1) than against interferents (Q = 2.19 mg g-1). The specific selectivity factor (S) obtained for the interferents was greater than 1 which indicates a good selectivity. Finally, the application of MIP in real samples using UV-vis spectrophotometry yielded a recovery value greater than 70%.

Effects of Heat Treatment on the Physicochemical Properties and Electrochemical Behavior of Biochars for Electrocatalyst Support Applications.

The present work reports the synthesis and the physicochemical characterization of biochar from the organic wastes of nopal (Opuntia Leucotricha), coffee grounds (Coffea arabica) and Ataulfo mango seeds (Mangifera indica) as alternative electrocatalyst supports to Vulcan XC-72 carbon black. The biochars were prepared using pyrolysis from organic wastes collected at three temperatures, 600, 750 and 900 °C, under two atmospheres, N2 and H2. The synthesized biochars were characterized using Raman spectroscopy and scanning electron microscopy (SEM) to obtain insights into their chemical structure and morphological nature, respectively, as a function of temperature and pyrolysis atmosphere. A N2 adsorption/desorption technique, two-point conductivity measurements and cyclic voltammetry (CV) were conducted to evaluate the specific surface area (SSA), electrical conductivity and double-layer capacitance, respectively, of all the biochars to estimate their physical properties as a possible alternative carbon support. The results indicated that the mango biochar demonstrated the highest properties among all the biochars, such as an electrical conductivity of 8.3 S/cm-1 at 900 °C in N2, a specific surface area of 829 m2/g at 600 °C in H2 and a capacitance of ~300 mF/g at 900 °C in N2. The nopal and coffee biochars exhibited excellent specific surface areas, up to 767 m2/g at 600 °C in N2 and 699 m2/g at 750 °C in H2, respectively; nonetheless, their electrical conductivity and capacitance were limited. Therefore, the mango biochar at 900 °C in N2 was considered a suitable alternative carbon material for electrocatalyst support. Additionally, it was possible to determine that the electrical conductivity and capacitance increased as a function of the pyrolysis temperature, while the specific surface area decreased for some biochars as the pyrolysis temperature increased. Overall, it is possible to conclude that heat treatment at a high temperature of 900 °C enhanced the biochar properties toward electrocatalyst support applications.

Inkjet-printed h-BN memristors for hardware security.

Inkjet printing electronics is a growing market that reached 7.8 billion USD in 2020 and that is expected to grow to ∼23 billion USD by 2026, driven by applications like displays, photovoltaics, lighting, and radiofrequency identification. Incorporating two-dimensional (2D) materials into this technology could further enhance the properties of the existing devices and/or circuits, as well as enable the development of new concept applications. Along these lines, here we report an easy and cheap process to synthesize inks made of multilayer hexagonal boron nitride (h-BN)-an insulating 2D layered material-by the liquid-phase exfoliation method and use them to fabricate memristors. The devices exhibit multiple stochastic phenomena that are very attractive for use as entropy sources in electronic circuits for data encryption (physical unclonable functions [PUFs], true random number generators [TRNGs]), such as: (i) a very disperse initial resistance and dielectric breakdown voltage, (ii) volatile unipolar and non-volatile bipolar resistive switching (RS) with a high cycle-to-cycle variability of the state resistances, and (iii) random telegraph noise (RTN) current fluctuations. The clue for the observation of these stochastic phenomena resides on the unpredictable nature of the device structure derived from the inkjet printing process (i.e., thickness fluctuations, random flake orientations), which allows fabricating electronic devices with different electronic properties. The easy-to-make and cheap memristors here developed are ideal to encrypt the information produced by multiple types of objects and/or products, and the versatility of the inkjet printing method, which allows effortless deposition on any substrate, makes our devices especially attractive for flexible and wearable devices within the internet-of-things.

Copper concentrate dual-band joint classification using reflectance hyperspectral images in the VIS-NIR and SWIR bands.

A study on the classification of copper concentrates relevant to the copper refining industry is performed by means of reflectance hyperspectral images in the visible and near infrared (VIS-NIR) bands (400-1000 nm) and in the short-wave infrared (SWIR) (900-1700 nm) band. A total of 82 copper concentrate samples were press compacted into 13-mm-diameter pellets, and their mineralogical composition was characterized via quantitative evaluation of minerals and scanning electron microscopy. The most representative minerals contained in these pellets are bornite, chalcopyrite, covelline, enargite, and pyrite. Three databases (VIS-NIR, SWIR, and VIS-NIR-SWIR) containing a collection of average reflectance spectra computed from 9×9p i x e l neighborhoods in each pellet hyperspectral image are compiled to train the classification models. The classification models tested in this work are a linear discriminant classifier and two non-linear classifiers, a quadratic discriminant classifier, and a fine K-nearest neighbor classifier (FKNNC). The results obtained show that the joint use of VIS-NIR and SWIR bands allows for the accurate classification of similar copper concentrates that contain only minor differences in their mineralogical composition. Specifically, among the three tested classification models, the FKNNC performs the best in terms of overall classification accuracy, achieving 93.4% accuracy in the test set when only VIS-NIR data are used to construct the classification model, up to 80.5% using only SWIR data, and up to 97.6% using both VIS-NIR and SWIR bands together.