Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Advances in Treatment of Meningiomas: Prognostic and Therapeutic Implications

Simple Summary Meningiomas are considered benign lesions and are frequently linked to a lower quality of life. Total surgical resection is the gold standard treatment. Combination treatments that target several molecular targets are becoming available and show great potential as adjuvant treatment alternatives. New classifications of these malignancies and novel therapeutic strategies are now possible thanks to recent developments in genetics, epigenetics, and, specifically, in the identification of specific genetic alterations. Although the outcomes to this point have not been very encouraging, different molecular-focused therapies have undoubtedly attracted a great deal of attention. Recent research has shown that microRNAs may have a role in the biology of meningiomas, allowing them to be used in meningioma treatment plans in the future. Abstract Meningiomas are the most frequent histotypes of tumors of the central nervous system. Their incidence is approximately 35% of all primary brain tumors. Although they have the status of benign lesions, meningiomas are often associated with a decreased quality of life due to focal neurological deficits that may be related. The optimal treatment is total resection. Histological grading is the most important prognostic factor. Recently, molecular alterations have been identified that are specifically related to particular phenotypes and, probably, are also responsible for grading, site, and prognostic trend. Meningiomas recur in 10–25% of cases. In these cases, and in patients with atypical or anaplastic meningiomas, the methods of approach are relatively insufficient. To date, data on the molecular biology, genetics, and epigenetics of meningiomas are insufficient. To achieve an optimal treatment strategy, it is necessary to identify the mechanisms that regulate tumor formation and progression. Combination therapies affecting multiple molecular targets are currently opening up and have significant promise as adjuvant therapeutic options. We review the most recent literature to identify studies investigating recent therapeutic treatments recently used for meningiomas.
SERS-Based Optical Nanobiosensors for the Detection of Alzheimer’s Disease

Alzheimer’s disease (AD) is a leading cause of dementia, impacting millions worldwide. However, its complex neuropathologic features and heterogeneous pathophysiology present significant challenges for diagnosis and treatment. To address the urgent need for early AD diagnosis, this review focuses on surface-enhanced Raman scattering (SERS)-based biosensors, leveraging the excellent optical properties of nanomaterials to enhance detection performance. These highly sensitive and noninvasive biosensors offer opportunities for biomarker-driven clinical diagnostics and precision medicine. The review highlights various types of SERS-based biosensors targeting AD biomarkers, discussing their potential applications and contributions to AD diagnosis. Specific details about nanomaterials and targeted AD biomarkers are provided. Furthermore, the future research directions and challenges for improving AD marker detection using SERS sensors are outlined.
Modeling of Three-Axis Hall Effect Sensor Based on CMOS Process

This work developed a three-axis Hall angle sensor suitable for the standard CMOS process, which senses the tilt angle and the absolute strength of a random external magnetic field. Several cross-shaped Hall devices were first designed and fabricated using a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, and the measurement results have been used to build scientific and reasonable foundations for modeling a three-axis Hall using COMSOL. Inside the three-axis Hall sensor, cross-shaped Hall devices detect the magnetic field in the vertical direction (<inline-formula> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula>-axis), whereas four-Folded three-contact vertical Hall devices (4-Folded 3CVHDs) detect the magnetic field in the two horizontal directions (x- and <inline-formula> <tex-math notation="LaTeX">${y}$ </tex-math></inline-formula>-axis). The developed model has been used to investigate the important factors influencing the accuracy of the three-axis Hall angle sensor including the process misalignment and variation of the n-well thickness due to the bias current. Unlike the traditional three-axis angle sensor based on the integrated magnetic concentrator (IMC), this work can directly combine the entire sensor interface on the chip, thereby significantly reducing manufacturing costs and packaging difficulties. Besides, four complementary Hall devices are used to eliminate the offset voltage when measuring the magnetic field in a similar direction. Finally, a simulated accuracy of 0.3° and 0.22° is achieved for the angles of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, respectively, with an external magnetic field.
Mitigating Dataset Bias in Image Captioning Through Clip Confounder-Free Captioning Network

The dataset bias has been identified as a major challenge in image captioning. When the image captioning model predicts a word, it should consider the visual evidence associated with the word, but the model tends to use contextual evidence from the dataset bias and results in biased captions, especially when the dataset is biased toward some specific situations. To solve this problem, we approach from the causal inference perspective and design a causal graph. Based on the causal graph, we propose a novel method named C2Cap which is CLIP confounder-free captioning network. We use the global visual confounder to control the confounding factors in the image and train the model to produce debiased captions. We validate our proposed method on MSCOCO benchmark and demonstrate the effectiveness of our method. https://github.com/yeonju7kim/C2Cap
Nano-Biotechnology for Bacteria Identification and Potent Anti-bacterial Properties: A Review of Current State of the Art

Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed.
VECTOR FIELDS ON SPHERES

This paper presents a solution to the problem of finding the maximum number of linearly independent vector fields that can be placed on a sphere. To produce the correct upper bound, we make use of K-theory. After briefly recapitulating the basics of K-theory, we introduce Adams operations and compute the K-theory of the complex and real projective spaces. We then define the characteristic class ρk and develop some of its properties. Next, we recast the question of the upper bound into a question about fiber homotopy equivalent bundles over RPn, whose resolution reduces to a calculation in Ktheory. Finally, we give the purely algebraic proof that this upper bound is realized.
Seasonal variation in the xylem sap composition of six Australian trees and shrubs

Abstract In recent years, xylem sap composition has been shown to affect xylem hydraulics. However, information on how much xylem sap composition can vary across seasons and specifically under drought stress is still limited. We measured xylem sap chemical composition ([Ca2+], [K+], [Na+], electrical conductivity EC and pH) and surface tension (γ) of six Australian angiosperm trees and shrubs over 1 year, which comprised of exceptional dry and wet periods. Percentage losses of hydraulic conductivity and predawn leaf water potential were also monitored. In all species, measured parameters changed considerably over the annual time course. Ions and pH tended to decrease during winter months whereas γ showed a slight increase. No clear correlation was found between sap and hydraulic parameters, except for pH that was higher when plants suffered higher drought stress levels. Results indicate xylem sap composition to be complex and dynamic, where most variation in its composition seems to be dictated by season, even under severe dry conditions. However, pH might play a role as signals of drought stress.
Laboratory landquakes : Insights from experiments into 1 the high-frequency seismic signal generated by 2 geophysical granular flows

19 Geophysical granular flows exert basal forces that generate seismic signals, which can 20 be used to better monitor and model these severe natural hazards. A number of empir21 ical relations and existing models link these signals’ high-frequency components to a va22 riety of flow properties, many of which are inaccessible by other analyses. However, the 23 range of validity of the empirical relations remains unclear and the models lack valida24 tion, owing to the difficulty of adequately controlling and instrumenting field-scale flows. 25 Here, we present laboratory experiments investigating the normal forces exerted on a basal 26 plate by dense and partially dense flows of spherical glass particles. We measured the 27 power spectra of these forces and inferred predictions for these power spectra from the 28 models proposed by Kean et al. (2015), Lai et al. (2018), Farin et al. (2019), and Bachelet 29 (2018), using Hertz theory to extend Farin et al. (2019)’s models to higher frequencies. 30 The comparison of these predictions to our observations shows those of Farin et al. (2019)’s 31 ‘thin-flow’ model to be the most accurate, so we examine explanations for this accuracy 32 and discuss its implications for geophysical flows’ seismic signals. We also consider the 33 normalisation, by the mean force exerted by each flow, of the force’s mean squared fluc34 tuations, showing that this ratio varies by four orders of magnitude over our experiments, 35 but is determined by a bulk inertial number of the flow. 36 Plain Language Summary 37 Landslides, like earthquakes, generate seismic signals: vibrations of the earth that 38 can be detected a long way away. Analysis of the most rapid vibrations could provide 39 information about how large a landslide is or how damaging it will be, helping emergency 40 services respond. But full-size landslides are complex and difficult to study, so the gen41 eration of these vibrations is not yet sufficiently well understood for this information to 42 be reliable. Therefore, in the place of full-size landslides, we studied simplified, small43 scale versions in the laboratory, testing previous authors’ predictions for the seismic sig44 nals they generate. We find that one set of predictions was particularly accurate and show 45 that the corresponding predictions for full-size landslides are consistent with previous 46 observations. This implies that a landslide’s seismic signal can be used to calculate its 47 size, its speed, and the typical size of particles within it. 48 –2– ESSOAr | https://doi.org/10.1002/essoar.10504150. 1 | CC_BY_4. 0 | First posted online: Wed, 2 Sep 2020 07:14:41 | This content has not been peer reviewed. manuscript submitted to JGR: Earth Surface
Deep learning for Chilean native flora classification: a comparative analysis

The limited availability of information on Chilean native flora has resulted in a lack of knowledge among the general public, and the classification of these plants poses challenges without extensive expertise. This study evaluates the performance of several Deep Learning (DL) models, namely InceptionV3, VGG19, ResNet152, and MobileNetV2, in classifying images representing Chilean native flora. The models are pre-trained on Imagenet. A dataset containing 500 images for each of the 10 classes of native flowers in Chile was curated, resulting in a total of 5000 images. The DL models were applied to this dataset, and their performance was compared based on accuracy and other relevant metrics. The findings highlight the potential of DL models to accurately classify images of Chilean native flora. The results contribute to enhancing the understanding of these plant species and fostering awareness among the general public. Further improvements and applications of DL in ecology and biodiversity research are discussed.
ARRIGO, FRANCESCA; HIGHAM, DESMOND J.; NOFERINI, VANNI A theory for backtrack-downweighted walks

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Role of institution quality in the impact of government expenditure on economic development: a case study in Vietnamese provinces

ABSTRACT To examine the impact of fiscal policy through public spending at provincial level in Vietnam, this study uses the combination among different regression methods for panel data of 63 Vietnamese provinces during the period 2010–2020. The Feasible General Least Squares (FGLS) estimator and S-GMM estimator shows that there exists a positive effect of government expenditure on provincial economic growth. Moreover, the contribution of investment expense to supporting economic activity is expected more effective than that of current expenditure in the context of Vietnamese provinces. With techniques for analysing the marginal effects of interactive variables in research model, the role of local institutional quality is confirmed to improve the positive impact of government expenditure on provincial economic growth. In general, some policy implications are suggested based on the results to help local government stimulate the economic development in Vietnam.
The War in Ukraine and the EU’s Geopolitical Role in Spanish Media Discourses

The EU’s ability to protect common interests and effectively address the challenges faced by its members relating to external threats is one of the most debated questions in the European landscape. Understandably, the war in Ukraine has had a major impact on discourses regarding the EU Common Security and Defense Policy, granting them more space and thus visibility in the media and public debates. Our study examines Spanish media discourses about the EU’s geopolitical role and, more specifically, to what extent such discourses foster or hamper European integration processes. To collect data and carry out this study, we selected six media outlets based on their ownership, ideological stance, consumption frequency, and impact on public opinion. Our sample includes 540 news items, collected between July 2021 and March 2022. Our discourse analysis benefits from, inter alia, a Foucauldian framework that focuses on the sayable, conservation, memory, reactivation, and appropriation. In addition, we also identify communicative strategies that are employed to promote different discourses, as well as possible policy alternatives, concerning the EU’s geopolitical role and future prospects.
Ellagic acid for oral mucositis: investigation of the therapeutic effect in rats

Abstract The aim of this study was to evaluate the effect of ellagic acid on 5-fluorouracil-induced oral mucositis in rats. Twenty-four 6-month-old male Wistar albino rats were used. The rats were divided into three groups: Experimental Group I, Experimental Group II and Control Group. A superficial scratch was made on the mucosa of the right cheek pouch on the 3rd day and the 5th day. Oral mucositis protocol was applied to the Control Group. Ellagic acid was administered by gavage to the Experimental Group I from the 1st day to the 5th day along with the 5-FU procedure, and to the Experimental Group II from the 5th day to the 10th day. On the 11th day, all rats were sacrificed. The effect of ellagic acid on the healing of oral mucositis was evaluated histopathologically and immunohistochemically. TNF-α expression was observed at various intensities in the infected incision sites of the Experimental Group I sample, especially in the interstitial area in the lamina propria and mostly in the cytoplasm of the inflammatory cells within the vessel. Similar to the Experimental Group I, TNF-α expression of varying intensity occurred in the inflammatory cells in the Experimental Group II, partly in the epithelium and mostly in the inflammatory cells in the lamina propria. Additional research is needed to elucidate the pathogenic inflammatory mechanisms involved in the mucositis site and the prophylactic and therapeutic roles of antioxidants in the healing of oral mucositis by ellagic acid.
Local-Global Contrast for Learning Voice-Face Representations

Leveraging deep learning to explore the associations between voices and faces has attracted extensive research interest. Usually, this research is formalized by cross-modal verification, matching, and retrieval tasks. Most works rely on a single local or global optimization objective for learning, ignoring that testing scenarios may prefer different optimization objectives. For example, local objectives are more helpful for verification and matching tasks, while global objectives contribute more to retrieval. In this study, we proposed a learning framework based on local and global objectives to improve the generalizability of the learned representations. Firstly, we explored two ways of applying supervised contrastive loss (SCL) to learn voice-face representations. Secondly, we designed a contrastive-form global optimization objective, which shows better performance and training efficiency. Experiments on the VoxCeleb dataset demonstrate the effectiveness of our framework.
Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images

Background Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation. However, accurate segmentation of the left ventricular (LV) myocardium remains a challenge due to limited training data and lack of quality control. This study addresses these issues by leveraging generative adversarial networks (GAN)-generated virtual native enhancement (VNE) images to expand the training set and incorporating an automated quality control-driven (QCD) framework to improve segmentation reliability. Methods A dataset comprising 4,716 LGE images (from 1,363 patients with hypertrophic cardiomyopathy and myocardial infarction) was used for development. To generate additional clinically validated data, LGE data were augmented with a GAN-based generator to produce VNE images. LV was contoured on these images manually by clinical observers. To create diverse candidate segmentations, the QCD framework involved multiple U-Nets, which were combined using statistical rank filters. The framework predicted the Dice Similarity Coefficient (DSC) for each candidate segmentation, with the highest predicted DSC indicating the most accurate and reliable result. The performance of the QCD ensemble framework was evaluated on both LGE and VNE test datasets (309 LGE/VNE images from 103 patients), assessing segmentation accuracy (DSC) and quality prediction (mean absolute error (MAE) and binary classification accuracy). Results The QCD framework effectively and rapidly segmented the LV myocardium (<1 s per image) on both LGE and VNE images, demonstrating robust performance on both test datasets with similar mean DSC (LGE: 0.845±0.075; VNE: 0.845±0.071; p=ns). Incorporating GAN-generated VNE data into the training process consistently led to enhanced performance for both individual models and the overall framework. The quality control mechanism yielded a high performance (MAE=0.043, accuracy=0.951) emphasising the accuracy of the quality control-driven strategy in predicting segmentation quality in clinical settings. Overall, no statistical difference (p=ns) was found when comparing the LGE and VNE test sets across all experiments. Conclusions The QCD ensemble framework, leveraging GAN-generated VNE data and an automated quality control mechanism, significantly improved the accuracy and reliability of LGE segmentation, paving the way for enhanced and accountable diagnostic imaging in routine clinical use.
High Capacity Transmission Systems Using Homogeneous Multi-Core Fibers [Invited]

We discuss transmission using homogeneous, single-mode, multi-core fibers. We first describe the key fiber properties that can affect transmission, such as inter-core crosstalk and the variation of propagation delay between cores, and the consequences of these properties on spatial super-channel transmission. We then describe a series of experiments and demonstrations of system features including self-homodyne detection and joint spatial super-channel modulation before discussing their suitability to high-capacity transmission.
Chapter 7 ZnO for Probes in Diagnostics

Nanoparticles have revolutionized the field of diagnostics in recent years and ZnO nanoparticles (ZnO-NPs) have been one of the most commonly used ones. These easily synthesizable ZnO-NPs have a multitude of advantages over other metal-based nanoparticles owing to their biocompatibility, easy functionalization through their hydroxyl group-rich surface, and cost-effectiveness among several other benefits. Due to their inherent
A Content Analysis of the Metaverse Articles

— Metaverse, which was first defined as fictional about 20 years ago, refers to a virtual universe where people feel entirely mentally with engaged augmented virtual reality devices today. The first applications of metaverse were computer games consisting of virtual worlds. Gaming companies were racing to offer more unique experiences to their users. With social media giants and big technology companies announcing the metaverse as the future of the internet, it started to attract the attention of the wider masses. The concept of metaverse has been the subject of academic studies in many different fields, from literature to art, from music to education over the years. In this review article, a total of 40 journal articles containing the "metaverse" keyword in all fields in the Web of Science database were examined in terms of content and method. The outputs of this study provide a piece of brief information about the research area to both researchers and technology developers.