Search

There are several methods to improve cancer patient survival rates by inducing hyperthermia in tumor tissues, which involves raising their temperature above 41{\deg}C. These methods utilize different energy sources to deliver heat to the target region, including light, microwaves or radiofrequency electromagnetic fields. We have developed a new, magnetically responsive nanocarrier, consisting of liposomes loaded with magnetic nanoparticles and cis-diamminedichloroplatinum (II) (CDDP), commonly known as Cisplatin. The resulting magnetoliposome (ML) is rapidly internalized by lung and pancreas tumor cell lines, stored in intracellular vesicles, and capable of inducing hyperthermia under magnetic fields. The ML has no significant toxicity both in vitro and in vivo and, most importantly, enhances cell death by apoptosis after magnetic hyperthermia. Remarkably, mice bearing induced lung tumors, treated with CDDP-loaded nanocarriers and subjected to an applied electromagnetic field, showed an improved survival rate over those treated with either soluble CDDP or hyperthermia alone. Therefore, our approach of magnetic hyperthermia plus CDDP-ML significantly enhances in vitro cell death and in vivo survival of treated animals., Comment: 35 pages, 7 figures

We present Crab X-ray polarization measurements using IXPE data with a total exposure of 300ks, three times more than the initial 2022 discovery paper. Polarization is detected in three times more pulsar phase bins, revealing an S-shaped $+40^\circ$ polarization angle sweep in the main pulse and ${>}1\sigma$ departures from the OPTIMA optical polarization in both pulses, suggesting different radiation mechanisms or sites for the polarized emission at the two wavebands. Our polarization map of the inner nebula reveals a toroidal magnetic field, as seen in prior IXPE analyses. Along the southern jet, the magnetic field orientation relative to the jet axis changes from perpendicular to parallel and the polarization degree decreases by ${\sim}6\%$. These observations may be explained by kink instabilities along the jet or a collision with a dense, jet-deflecting medium at the tip. Using spectropolarimetric analysis, we find asymmetric polarization in the four quadrants of the inner nebula, as expected for a toroidal field geometry, and a spatial correlation between polarization degree and photon index., Comment: 14 pages, 7 figures, 3 tables. Accepted by The Astrophysical Journal

The importance of addressing fairness and bias in artificial intelligence (AI) systems cannot be over-emphasized. Mainstream media has been awashed with news of incidents around stereotypes and other types of bias in many of these systems in recent years. In this survey, we fill a gap with regards to the relatively minimal study of fairness and bias in Large Multimodal Models (LMMs) compared to Large Language Models (LLMs), providing 50 examples of datasets and models related to both types of AI along with the challenges of bias affecting them. We discuss the less-mentioned category of mitigating bias, preprocessing (with particular attention on the first part of it, which we call preuse). The method is less-mentioned compared to the two well-known ones in the literature: intrinsic and extrinsic mitigation methods. We critically discuss the various ways researchers are addressing these challenges. Our method involved two slightly different search queries on two reputable search engines, Google Scholar and Web of Science (WoS), which revealed that for the queries 'Fairness and bias in Large Multimodal Models' and 'Fairness and bias in Large Language Models', 33,400 and 538,000 links are the initial results, respectively, for Scholar while 4 and 50 links are the initial results, respectively, for WoS. For reproducibility and verification, we provide links to the search results and the citations to all the final reviewed papers. We believe this work contributes to filling this gap and providing insight to researchers and other stakeholders on ways to address the challenges of fairness and bias in multimodal and language AI., Comment: 12 pages

Huntington's disease (HD) is an inherited neurodegenerative disorder caused by an expanded CAG repeat in the coding sequence of the huntingtin protein. Initially, it predominantly affects medium-sized spiny neurons (MSSNs) of the corpus striatum. No effective treatment is available, thus urging the identification of potential therapeutic targets. While evidence of mitochondrial structural alterations in HD exists, previous studies mainly employed 2D approaches and were performed outside the strictly native brain context. In this study, we adopted a novel multiscale approach to conduct a comprehensive 3D in situ structural analysis of mitochondrial disturbances in a mouse model of HD. We investigated MSSNs within brain tissue under optimal structural conditions utilizing state-of-the-art 3D imaging technologies, specifically FIB/SEM for the complete imaging of neuronal somas and Electron Tomography for detailed morphological examination and image processing-based quantitative analysis. Our findings suggest a disruption of the mitochondrial network towards fragmentation in HD. The network of interlaced, slim, and long mitochondria observed in healthy conditions transforms into isolated, swollen, and short entities, with internal cristae disorganization, cavities, and abnormally large matrix granules., Comment: 31 pages 5 figures

The integration of magnetic nanoparticles within fibrillar structures represents an interesting avenue for the remotely controlled release of therapeutic agents. This work presents a novel drug release platform based on electrospun magnetic fibers (EMFs) combining drugs, magnetic nanoparticles (MNPs) and mesoporous silica nanoparticles (MSNs) for controlled drug delivery via alternating magnetic fields (AMF). The platform was demonstrated to be versatile and effective for hydrophilic ketorolac (KET) and hydrophobic curcumin (CUR) encapsulation and the major response observed for AMF-triggered release was reached using drug-loaded MSNs within the fibers, providing fine control over drug release patterns. The EMFs exhibited excellent inductive heating capabilities, showing a temperature increase up to 8 {\deg}C within a 5 min AMF pulse. The system is shown to be promising for applications like transdermal pain management, oncological drug delivery, tissue engineering, and wound healing, enabling precise control over drug release in both spatial and temporal dimensions. The findings of this study offer valuable insights into the development of the next generation of smart drug delivery systems, based in multifunctional materials that can be remotely regulated and potentially revolutionize the field of nanomedicine.

Gender-neutral pronouns are increasingly being introduced across Western languages. Recent evaluations have however demonstrated that English NLP systems are unable to correctly process gender-neutral pronouns, with the risk of erasing and misgendering non-binary individuals. This paper examines a Dutch coreference resolution system's performance on gender-neutral pronouns, specifically hen and die. In Dutch, these pronouns were only introduced in 2016, compared to the longstanding existence of singular they in English. We additionally compare two debiasing techniques for coreference resolution systems in non-binary contexts: Counterfactual Data Augmentation (CDA) and delexicalisation. Moreover, because pronoun performance can be hard to interpret from a general evaluation metric like LEA, we introduce an innovative evaluation metric, the pronoun score, which directly represents the portion of correctly processed pronouns. Our results reveal diminished performance on gender-neutral pronouns compared to gendered counterparts. Nevertheless, although delexicalisation fails to yield improvements, CDA substantially reduces the performance gap between gendered and gender-neutral pronouns. We further show that CDA remains effective in low-resource settings, in which a limited set of debiasing documents is used. This efficacy extends to previously unseen neopronouns, which are currently infrequently used but may gain popularity in the future, underscoring the viability of effective debiasing with minimal resources and low computational costs., Comment: 22 pages, 2 figures. Accepted at the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24)

We introduce new large labeled datasets on bias in 3 languages and show in experiments that bias exists in all 10 datasets of 5 languages evaluated, including benchmark datasets on the English GLUE/SuperGLUE leaderboards. The 3 new languages give a total of almost 6 million labeled samples and we benchmark on these datasets using SotA multilingual pretrained models: mT5 and mBERT. The challenge of social bias, based on prejudice, is ubiquitous, as recent events with AI and large language models (LLMs) have shown. Motivated by this challenge, we set out to estimate bias in multiple datasets. We compare some recent bias metrics and use bipol, which has explainability in the metric. We also confirm the unverified assumption that bias exists in toxic comments by randomly sampling 200 samples from a toxic dataset population using the confidence level of 95% and error margin of 7%. Thirty gold samples were randomly distributed in the 200 samples to secure the quality of the annotation. Our findings confirm that many of the datasets have male bias (prejudice against women), besides other types of bias. We publicly release our new datasets, lexica, models, and codes., Comment: Presented at ICNLSP

Ferrite nanoparticles serve as potent heterogeneous Fenton-like catalysts, producing reactive oxygen species (ROS) for decomposing organic pollutants. We investigated the impact of temperature and copper content on the catalytic activity of nanoparticles with different oxidation states of iron. Via solvothermal synthesis, we fabricated copper-doped magnetite (Cu$_x$Fe$_{3-x}$O$_4$) with a Fe$^{2+}$/Fe ratio ~0.33 for the undoped system. Using a microwave-assisted method, we produced copper-doped oxidized ferrites, yielding a Fe$^{2+}$/Fe ratio of ~0.11 for the undoped nanoparticles. The ROS generated by the catalyst were identified and quantified by electron paramagnetic resonance, while optical spectroscopy allowed us to evaluate its effectiveness for the degradation of a model organic dye. At room temperature, the magnetite nanoparticles exhibited the most $\cdot$OH radical production and achieved almost 90% dye discoloration in 2 hours. This efficiency decreased with increasing Cu concentration, concurrently with a decrease in $\cdot$OH generation. Conversely, above room temperature, Cu-doped nanoparticles significantly enhance the dye degradation, reaching 100% discoloration at 90$^\circ$C. This enhancement is accompanied by a systematic increase in the kinetic constants, obtained from reaction equations, with Cu doping. This study highlights the superior stability and high-temperature catalytic advantages of copper ferrite holding promise for enhancing the performance of nanocatalysts for decomposing organic contaminants., Comment: 40 pages, 11 figures

This article focuses on the prediction of the vibration frequency response of handheld probes. A novel approach that involves machine learning and readily available data from probes was explored. Vibration probes are efficient and affordable devices that provide information about testing airborne sound insulation in building acoustics. However, fixing a probe to a vibrating surface downshifts sensor resonances and underestimates levels. Therefore, the calibration response of the sensor included in a probe differs from the frequency response of that same probe. Simulation techniques of complex mechanical systems may describe this issue, but they include hardly obtainable parameters, ultimately restricting the model. Thus, this study discusses an alternative method, which comprises different parts. Firstly, the vibration frequency responses of 85 probes were measured and labelled according to six features. Then, Linear Regression, Decision Tree Regression and Artificial Neural Networks algorithms were analysed. It was revealed that decision tree regression is the more appropriate technique for this data. The best decision tree models, in terms of scores and model structure, were fine-tuned. Eventually, the final suggested model employs only four out of the six original features. A trade-off solution that involved a simple structure, an interpretable model and accurate predictions was accomplished. It showed a maximum average deviation from test measurements ranging from 0.6 dB in low-frequency to 3 dB in high-frequency while remaining at a low computational load. This research developed an original and reliable prediction tool that provides the vibration frequency response of handheld probes.

Magnetic nanoparticles (MNPs) are the foundation of several new strategies for neural repair and neurological therapies. The fact that a remote force can act on MNPs at the cytoplasmic space constitutes the essence of many new neurotherapeutic concepts. MNPs with a predesigned physicochemical characteristic can interact with external magnetic fields to apply mechanical forces in definite areas of the cell to modulate cellular behaviour. Magnetic actuation to direct the outgrowth on neurons after nerve injury has already demonstrated the therapeutic potential for neural repair. When these magnetic cores are functionalized with molecules such as nerve growth factors or neuroprotective molecules, multifunctional devices can be developed. This chapter will review some of these new nanotechnology-based solutions for neurological diseases, specifically those based on the use of engineered MNPs used for neuroprotection and neuroregeneration. These include the use of MNPs as magnetic actuators to guide neural cells, modulate intracellular transport and stimulate axonal growth after nerve injury., Comment: 35 pages; 6 figures; Chapter 22 of Magnetic Nanoparticles: From Fabrication to Clinical Applications

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. Here, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of such features and classifiers is unfeasible. Perhaps more sophisticated integration of multimodal information may lead to a higher performance in this diagnostic task., Comment: arXiv admin note: text overlap with arXiv:2206.08122

The partition function of 3-dimensional quantum gravity has been argued to be 1-loop exact. Here, we verify the vanishing of higher-orders in perturbation theory by explicit computation in the second-order, metric formulation at 3-loops. The number of 1-particle irreducible Feynman diagrams involving both gravitons and ghosts turns out to be 17. Using dimensional regularization, we solve all the diagrams. At 2-loops, we find that all such diagrams vanish separately after regularization. At 3-loops, in contrast, a series of remarkable cancellations between different diagrams takes place, with 9 diagrams beautifully conspiring to yield a vanishing result. Our techniques are suitable to be applied to higher loops as well as to similar computations in higher dimensions., Comment: 6 pages, 8 figures, v3 comments and references added

The control of the magnetization inversion dynamics is one of the main challenges driving the design of new nanostructured magnetic materials for magnetoelectronic applications. Nanoparticles with onion-like architecture offer a unique opportunity to expand the possibilities allowing to combine different phases at the nanoscale and also modulate the coupling between magnetic phases by introducing spacers in the same structure. Here we report the fabrication, by a three-step high temperature decomposition method, of Fe3O4/MgO/CoFe2O4 onio-like nanoparticles and their detailed structural analysis, elemental compositional maps and magnetic response. The core/shell/shell nanoparticles present epitaxial growth and cubic shape with overall size of (29+-6) nm. These nanoparticles are formed by cubic iron oxide core of (22+-4) nm covered by two shells, the inner of magnesium oxide and the outer of cobalt ferrite of ~1 and ~2.5 nm of thickness, respectively. The magnetization measurements show a single reversion magnetization curve and the enhancement of the coercivity field, from HC~608 Oe for the Fe3O4/MgO to HC~5890 Oe to the Fe3O4/MgO/CoFe2O4 nanoparticles at T=5 K, ascribed to the coupling between both ferrimagnetic phases with a coupling constant of =2 erg/cm2. The system also exhibits exchange bias effect, where the exchange bias field increases up to HEB~2850 Oe at 5 K accompanied with the broadening of the magnetization loop of HC~6650 Oe. This exchange bias effect originates from the freezing of the surface spins below the freezing temperature TF=32 K that pinned the magnetic moment of the cobalt ferrite shell., Comment: 39 pages, 8 figures

Low-Gain Avalanche Diode (LGAD) sensor is one of candidate sensors for the tracker at future hadron collider experiments. To use this sensor as a tracking detector, AC-LGAD sensor is being developed which has both timing and spatial resolutions. In high luminosity environments, good timing resolution (typically 30 ps) together with $\mathcal{O}$(10) ${\mathrm{\mu}}$m spatial resolution helps to reduce pileup effect and reconstruct tracks correctly. By optimizing fabrication parameters, 80 ${\mathrm{\mu}}$m pitch strip and 100 ${\mathrm{\mu}}$m pitch pixel sensors are successfully produced. The signal height (MPV) was 39.26$\pm$0.08 mV and 128.9$\pm$3.3 mV, respectively, for penetrating $\beta$ particles. The observed 60% of signal reduction of the strip sensor is explained by a larger inter-electrode capacitance compared with the pixel sensor. In this paper, we present the performance of fine electrode pitch AC-LGAD sensors including the pulse height and cross-talk of pixel and strip type sensors evaluated using a $\beta$-ray source and the detection efficiency measured in an 800 MeV electron beam., Comment: 8 pages, 6 figures, Submitted to Japan Conference Proceedings

We present a study on the magnetic behavior of dextran-coated magnetite nanoparticles (DM NPs) with sizes between 3 and 19 nm, synthesized by hydrothermal-assisted co-precipitation method. The decrease of saturation magnetization ($M_s$) with decreasing particle size has been modeled by assuming the existence of a spin-disordered layer at the particle surface, which is magnetically dead. Based on this core-shell model and taking into account the weight contribution of the non-magnetic coating layer (dextran) to the whole magnetization, the dead layer thickness ($t$) and saturation magnetization $M_s$ of the magnetic cores in our samples were estimated to be $t = 6.8~\mathrm{\AA}$ and $M_s = 98.8~\mathrm{emu/g}$, respectively. The data of $M_s$ were analyzed using a law of approach to saturation, indicating an increase in effective magnetic anisotropy ($K_{eff}$) with decreasing particle size as expected from the increased surface/volume ratio in small MNPs. The obtained $K_{eff}$ values were successfully modeled by including an extra contribution of dipolar interactions due to the formation of chain-like clusters of MNPs. The surface magnetic anisotropy ($K_s$) was estimated to be about $K_s = 1.04\times10^5~\mathrm{J/m^3}$. Our method provides a simple and accurate way to obtain the $M_s$ core values in surface-disordered MNPs, a relevant parameter required for magnetic modeling in many applications., Comment: 16 pages, 1 table, 3 figures

This work reports on the synthesis and characterization of a new type of electrospun magnetic nanofibers (MNFs), and their application for degradation of organic pollutants using remote magnetic inductive heating. We describe a simple protocol combining a fast (app. 5 min) synthesis of MnFe\textsubscript{2}O\textsubscript{4} magnetic nanoparticles (MNPs) by sonochemical route, optimized for inductive heating, with their subsequent incorporation in electrospun MNFs composed of polyacrylonitrile (PAN) nanofibers. The resulting multifunctional MNFs (average diameter $\phi = 760 \pm 150$ nm) contain up to $\approx 30%$wt. of the MNPs. The composite showed superhydrophobic behaviour ($\theta_c = 165^\circ$) and a band gap value of 1.75 eV. We found that the presence of MNPs embedded into the polymeric nanofibers modify the exothermic and the glass transitions temperatures compared with pure PAN nanofibers, suggesting a strong attachment between MNPs and polymeric chains. The MNFs could be remotely activated by alternating magnetic fields (AMF, $f = 200-800$ kHz, $H_0 = 10-36$ kA/m) for accelerating the catalytic reactions of the organic dye methylene blue (MB). A remarkable stability of the MNFs against degradation under extreme pH conditions ($380%$ in the presence of hydrogen peroxide under AMFs, attributed to Fe\textsuperscript{2+}/\textsuperscript{3+} and Mn\textsuperscript{2+}/\textsuperscript{3+}/\textsuperscript{4+} active centers on the surface of the MNP/MNFs observed from XPS data. The capacity of these materials for magnetic remote activation appeals catalytic applications under conditions of darkness or restrained access, where no photocatalytic reactions can be achieved., Comment: 31 pages, 16 figures

Forest fires may cause considerable damages both in ecosystems and lives. This proposal describes the application of Internet of Things and wireless sensor networks jointly with multi-hop routing through a real time and dynamic monitoring system for forest fire prevention. It is based on gathering and analyzing information related to meteorological conditions, concentrations of polluting gases and oxygen level around particular interesting forest areas. Unusual measurements of these environmental variables may help to prevent wildfire incidents and make their detection more efficient. A forest fire risk controller based on fuzzy logic has been implemented in order to activate environmental risk alerts through a Web service and a mobile application. For this purpose, security mechanisms have been proposed for ensuring integrity and confidentiality in the transmission of measured environmental information. Lamport's signature and a block cipher algorithm are used to achieve this objective.

WAdS/WCFT correspondence is an interesting realization of non-AdS holography. It relates 3-dimensional Warped-Anti-de Sitter (WAdS$_3$) spaces to a special class of 2-dimensional quantum field theory with chiral scaling symmetry that acts only on right-moving modes. The latter are often called Warped Conformal Field Theories (WCFT$_2$), and their existence makes WAdS/WCFT particularly interesting as a tool to investigate a new type of 2-dimensional conformal structure. Besides, WAdS/WCFT is interesting because it enables to apply holographic techniques to the microstate counting problem of non-AdS, non-supersymmetric black holes. Asymptotically WAdS$_3$ black holes (WBH$_3$) appear as solutions of topologically massive theories, Chern-Simons theories, and many other models. Here, we explore WBH$_3\times \Sigma_{D-3}$ solutions of $D$-dimensional higher-curvature gravity, with $\Sigma_{D-3}$ being different internal manifolds, typically given by products of deformations of hyperbolic spaces, although we also consider warped products with time-dependent deformations. These geometries are solutions of the second order higher-curvature theory at special (critical) points of the parameter space, where the theory exhibits a sort of degeneracy. We argue that the dual (W)CFT at those points is actually trivial. In many respects, these critical points of WAdS$_3 \times \Sigma_{D-3}$ vacua are the squashed/stretched analogs of the AdS$_D$ Chern-Simons point of Lovelock gravity., Comment: 14 pages

Temporal sequences of satellite images constitute a highly valuable and abundant resource for analyzing regions of interest. However, the automatic acquisition of knowledge on a large scale is a challenging task due to different factors such as the lack of precise labeled data, the definition and variability of the terrain entities, or the inherent complexity of the images and their fusion. In this context, we present a fully unsupervised and general methodology to conduct spatio-temporal taxonomies of large regions from sequences of satellite images. Our approach relies on a combination of deep embeddings and time series clustering to capture the semantic properties of the ground and its evolution over time, providing a comprehensive understanding of the region of interest. The proposed method is enhanced by a novel procedure specifically devised to refine the embedding and exploit the underlying spatio-temporal patterns. We use this methodology to conduct an in-depth analysis of a 220 km$^2$ region in northern Spain in different settings. The results provide a broad and intuitive perspective of the land where large areas are connected in a compact and well-structured manner, mainly based on climatic, phytological, and hydrological factors.

This paper describes a decentralized low-cost system designed to reinforce personal security in big events in case of emergency. The proposal consists of using smart contracts supported by blockchain in the management of events. An alternative communication channel that does not require any cloud service is also provided with the aim of improving the coordination of emergency services. Peers may use this emergency support tool to interact with each other through a chat when additional support is required. Since information security is mandatory in this scenario, Identity-Based Signcryption schemes are here used in order to guarantee communication confidentiality, authenticity and integrity. Depending on the communication mode (peer-to-peer or broadcast), different signcryption methods are used. A first implementation of the proposal has produced promising results.

Nowadays, many technological advances applied to the Internet of Things (IoT) make the introduction of innovative sensors aimed to deploy efficient wireless sensor networks possible. In order to improve the environment and people's lives, real time analysis of certain environmental variables may favor the reduction of health risks related to the deterioration of air quality. To this respect, the proposed system implements a particular prototype of IoT device characterized by the assembly of ambient sensors capable of measuring pollutant gases, temperature and humidity. For this purpose, Raspberry Pi and Arduino platforms are used. Several security methods are introduced to ensure the integrity of air quality data by implementing Merkle Trees on each IoT node and on the Cloud server. Besides, the authenticity of IoT devices and the confidentiality of communications are guaranteed by implementing HTTPS requests. Finally, authentication tokens are used to identify system users, and different security rules are applied to manage database operations.

Currently, special attention is being paid to scenarios where the interconnection of devices with heterogeneous computational and communication capabilities it is required. It is essential to integrate security services during the stages of design and deployment of these networks since many of these scenarios provide critical services such as medical health, payment systems, military affairs, access control, e-banking, etc. This work analyses several cryptographic primitives related to entity authentication providing robust solutions according to device capabilities.