Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments.

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders' positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

Cytopathology of glioneuronal and neuronal tumours with histological correlations.

Glioneuronal and neuronal tumours constitute a diverse group of tumours that feature neuronal differentiation. In mixed glioneuronal tumours, a glial component is present in addition to the neuronal component. With a few exceptions (eg diffuse leptomeningeal glioneuronal tumour) they are well-circumscribed and slow-growing tumours, which is why their prognosis is intrinsically favourable after gross total resection. Rendering an intraoperative diagnosis of glioneuronal/neuronal tumour is therefore important-neurosurgeons should remove them to prevent the persistence of clinical symptoms and/or recurrence. In this context, cytopathological examination can be especially useful for assessing cellular details when frozen section artefacts render poor-quality preparations, as is the case for this group of tumours, which are frequently mistaken for infiltrating gliomas (eg diffuse astrocytoma infiltrating grey matter, oligodendroglioma) on frozen section slides. The aim of this article is to review the cytomorphological features of glioneuronal and neuronal tumours according to the 2021 World Health Organization classification of central nervous system tumours, 5th edition. Additionally, since interpretation in intraoperative cytology relies on intuiting tissue patterns from cytology preparations, representative histological figures of all discussed entities have been included. Clues for specific diagnoses and the primary diagnostic problems encountered during intraoperative procedures are also discussed.

Rapid peat development beneath created, maturing mangrove forests: ecosystem changes across a 25-yr chronosequence.

Mangrove forests are among the world's most productive and carbon-rich ecosystems. Despite growing understanding of factors controlling mangrove forest soil carbon stocks, there is a need to advance understanding of the speed of peat development beneath maturing mangrove forests, especially in created and restored mangrove forests that are intended to compensate for ecosystem functions lost during mangrove forest conversion to other land uses. To better quantify the rate of soil organic matter development beneath created, maturing mangrove forests, we measured ecosystem changes across a 25-yr chronosequence. We compared ecosystem properties in created, maturing mangrove forests to adjacent natural mangrove forests. We also quantified site-specific changes that occurred between 2010 and 2016. Soil organic matter accumulated rapidly beneath maturing mangrove forests as sandy soils transitioned to organic-rich soils (peat). Within 25 yr, a 20-cm deep peat layer developed. The time required for created mangrove forests to reach equivalency with natural mangrove forests was estimated as (1) <15 yr for herbaceous and juvenile vegetation, (2) ~55 yr for adult trees, (3) ~25 yr for the upper soil layer (0-10 cm), and (4) ~45-80 yr for the lower soil layer (10-30 cm). For soil elevation change, the created mangrove forests were equivalent to or surpassed natural mangrove forests within the first 5 yr. A comparison to chronosequence studies from other ecosystems indicates that the rate of soil organic matter accumulation beneath maturing mangrove forests may be among the fastest globally. In most peatland ecosystems, soil organic matter formation occurs slowly (over centuries, millennia); however, these results show that mangrove peat formation can occur within decades. Peat development, primarily due to subsurface root accumulation, enables mangrove forests to sequester carbon, adjust their elevation relative to sea level, and adapt to changing conditions at the dynamic land-ocean interface. In the face of climate change and rising sea levels, coastal managers are increasingly concerned with the longevity and functionality of coastal restoration efforts. Our results advance understanding of the pace of ecosystem development in created, maturing mangrove forests, which can improve predictions of mangrove forest responses to global change and ecosystem restoration.

Implicit and Explicit Regularization for Optical Flow Estimation.

In this paper, two novel and practical regularizing methods are proposed to improve existing neural network architectures for monocular optical flow estimation. The proposed methods aim to alleviate deficiencies of current methods, such as flow leakage across objects and motion consistency within rigid objects, by exploiting contextual information. More specifically, the first regularization method utilizes semantic information during the training process to explicitly regularize the produced optical flow field. The novelty of this method lies in the use of semantic segmentation masks to teach the network to implicitly identify the semantic edges of an object and better reason on the local motion flow. A novel loss function is introduced that takes into account the objects' boundaries as derived from the semantic segmentation mask to selectively penalize motion inconsistency within an object. The method is architecture agnostic and can be integrated into any neural network without modifying or adding complexity at inference. The second regularization method adds spatial awareness to the input data of the network in order to improve training stability and efficiency. The coordinates of each pixel are used as an additional feature, breaking the invariance properties of the neural network architecture. The additional features are shown to implicitly regularize the optical flow estimation enforcing a consistent flow, while improving both the performance and the convergence time. Finally, the combination of both regularization methods further improves the performance of existing cutting edge architectures in a complementary way, both quantitatively and qualitatively, on popular flow estimation benchmark datasets.

The impact of depressive mood and cognitive impairment on quality of life of the elderly.

BACKGROUND: The quality of life (QOL) of the elderly can be influenced by numerous factors. We assessed QOL, cognitive functions, depression and clinical data in elderly aged 65 and over with the aim of analysing factors affecting their QOL. METHODS: Semi-structured interviews were conducted with elderly over the age of 65, and their QOL, cognitive functions and depressive symptoms were assessed by validated clinical tests and screening tools. RESULTS: The correlation between QOL scales and cognitive tests was not significant. In contrast, the results of depression scales showed significant negative correlation with the scores of the QOL scales. A better QOL was determined by lower age, lack of depressive symptoms, and higher scores in the QOL-AD (Alzheimer's disease) scale. Depressive mood has much more negative impact on the QOL of the elderly than cognitive impairment. CONCLUSIONS: Our results demonstrated a close correlation between QOL and depressive mood in the elderly. The early detection and effective management of affective and cognitive symptoms in the elderly can not only restore mental health but may also improve their QOL.

Three-D Wide Faces (3DWF): Facial Landmark Detection and 3D Reconstruction over a New RGB⁻D Multi-Camera Dataset.

Latest advances of deep learning paradigm and 3D imaging systems have raised the necessity for more complete datasets that allow exploitation of facial features such as pose, gender or age. In our work, we propose a new facial dataset collected with an innovative RGB⁻D multi-camera setup whose optimization is presented and validated. 3DWF includes 3D raw and registered data collection for 92 persons from low-cost RGB⁻D sensing devices to commercial scanners with great accuracy. 3DWF provides a complete dataset with relevant and accurate visual information for different tasks related to facial properties such as face tracking or 3D face reconstruction by means of annotated density normalized 2K clouds and RGB⁻D streams. In addition, we validate the reliability of our proposal by an original data augmentation method from a massive set of face meshes for facial landmark detection in 2D domain, and by head pose classification through common Machine Learning techniques directed towards proving alignment of collected data.

Automatic Change Detection System over Unmanned Aerial Vehicle Video Sequences Based on Convolutional Neural Networks.

In recent years, the use of unmanned aerial vehicles (UAVs) for surveillance tasks has increased considerably. This technology provides a versatile and innovative approach to the field. However, the automation of tasks such as object recognition or change detection usually requires image processing techniques. In this paper we present a system for change detection in video sequences acquired by moving cameras. It is based on the combination of image alignment techniques with a deep learning model based on convolutional neural networks (CNNs). This approach covers two important topics. Firstly, the capability of our system to be adaptable to variations in the UAV flight. In particular, the difference of height between flights, and a slight modification of the camera's position or movement of the UAV because of natural conditions such as the effect of wind. These modifications can be produced by multiple factors, such as weather conditions, security requirements or human errors. Secondly, the precision of our model to detect changes in diverse environments, which has been compared with state-of-the-art methods in change detection. This has been measured using the Change Detection 2014 dataset, which provides a selection of labelled images from different scenarios for training change detection algorithms. We have used images from dynamic background, intermittent object motion and bad weather sections. These sections have been selected to test our algorithm's robustness to changes in the background, as in real flight conditions. Our system provides a precise solution for these scenarios, as the mean F-measure score from the image analysis surpasses 97%, and a significant precision in the intermittent object motion category, where the score is above 99%.

Relative importance of landscape versus local wetland characteristics for estimating wetland denitrification potential.

Wetlands can be significant sinks for Nr, via denitrification. There is a lack of understanding about factors controlling denitrification. Research suggests that hydrology, geomorphology, and nitrogen loading are dominant controls. We compared site-scale characteristics with denitrification enzyme activity (DEA) in wetlands along gradients of drainage basin land use to explore the relative importance of landscape and site-scale factors for determining denitrification potential. DEA rates ranged between 0.01-1.69 (µg N gdw-1 hr-1), with most sites falling at the lower end. Sites with higher DEA rates had higher percentages of soil carbon and nitrogen, concentrations of soil extractable NO3 and percent loss on ignition. Sites with upstream agricultural activity had higher DEA rates than more natural sites, but there existed a wide range of DEA rates along both agricultural and urban land gradients. When multiple site and landscape-scale explanatory factors were compared to DEA rates, two site and one landscape scale characteristic (Soil NO3, Soil Percent N, and Percent Agriculture) had significant (p<0.001, cum. r2 = 0.77) correlations with DEA rates. Our results suggest that DEA is controlled mainly by local-scale site characteristics with more work needed to determine the interdependencies and relative importance among these and potentially related landscape-scale factors.

Clinical performance of AI-integrated risk assessment pooling reveals cost savings even at high prevalence of COVID-19.

Individual testing of samples is time- and cost-intensive, particularly during an ongoing pandemic. Better practical alternatives to individual testing can significantly decrease the burden of disease on the healthcare system. Herein, we presented the clinical validation of Segtnan™ on 3929 patients. Segtnan™ is available as a mobile application entailing an AI-integrated personalized risk assessment approach with a novel data-driven equation for pooling of biological samples. The AI was selected from a comparison between 15 machine learning classifiers (highest accuracy = 80.14%) and a feed-forward neural network with an accuracy of 81.38% in predicting the rRT-PCR test results based on a designed survey with minimal clinical questions. Furthermore, we derived a novel pool-size equation from the pooling data of 54 published original studies. The results demonstrated testing capacity increase of 750%, 60%, and 5% at prevalence rates of 0.05%, 22%, and 50%, respectively. Compared to Dorfman's method, our novel equation saved more tests significantly at high prevalence, i.e., 28% (p = 0.006), 40% (p = 0.00001), and 66% (p = 0.02). Lastly, we illustrated the feasibility of the Segtnan™ usage in clinically complex settings like emergency and psychiatric departments.

Use of semi-synthetic data for catheter segmentation improvement.

In the era of data-driven machine learning algorithms, data is the new oil. For the most optimal results, datasets should be large, heterogeneous and, crucially, correctly labeled. However, data collection and labeling are time-consuming and labor-intensive processes. In the field of medical device segmentation, present during minimally invasive surgery, this leads to a lack of informative data. Motivated by this drawback, we developed an algorithm generating semi-synthetic images based on real ones. The concept of this algorithm is to place a randomly shaped catheter in an empty heart cavity, where the shape of the catheter is generated by forward kinematics of continuum robots. Having implemented the proposed algorithm, we generated new images of heart cavities with various artificial catheters. We compared the results of deep neural networks trained purely on real datasets with respect to networks trained on both real and semi-synthetic datasets, highlighting that semi-synthetic data improves catheter segmentation accuracy. A modified U-Net trained on combined datasets performed the segmentation with a Dice similarity coefficient of 92.6 ± 2.2%, while the same model trained only on real images achieved a Dice similarity coefficient of 86.5 ± 3.6%. Therefore, using semi-synthetic data allows for the decrease of accuracy spread, improves model generalization, reduces subjectivity, shortens the labeling routine, increases the number of samples, and improves the heterogeneity.

[Greater occipital nerve block in the treatment of headaches. Review of evidence]. / Bloqueo anestésico del nervio occipital mayor en el tratamiento de las cefaleas. Revisión de la evidencia.

INTRODUCTION: Peripheral nerve blocks have been a common treatment for multiple headaches. By far, the greater occipital nerve block is the most used and with the stronger body of evidence in routine clinical practice. METHODS: We searched Pubmed Meta-Analysis/Systematic Review, in the last 10 years. Of these results, meta-analyses, and in the absence of these systematic reviews, assessing Greater Occipital Nerve Block in headache has been selected for review. RESULTS: We identified 95 studies in Pubmed, 13 that met the inclusion criteria. CONCLUSION: Greater occipital block is an effective and safe technique, easy to perform and which has shown its usefulness in migraine, cluster headache, cervicogenic headache and Post-dural puncture headache. However, more studies are needed to clarify its long-term efficacy, its place in clinical treatment, the possible difference between different anaesthetics, the most convenient dosage and the role of concomitant use of corticosteroids.

Sickle cell disease landscape and challenges in the EU: the ERN-EuroBloodNet perspective.

Sickle cell disease is a hereditary multiorgan disease that is considered rare in the EU. In 2017, the Rare Diseases Plan was implemented within the EU and 24 European Reference Networks (ERNs) were created, including the ERN on Rare Haematological Diseases (ERN-EuroBloodNet), dedicated to rare haematological diseases. This EU initiative has made it possible to accentuate existing collaborations and create new ones. The project also made it possible to list all the needs of people with rare haematological diseases not yet covered health-care providers in the EU to allow optimised care of individuals with rare pathologies, including sickle cell disease. This Viewpoint is the result of joint work within 12 EU member states (ie, Belgium, Cyprus, Denmark, France, Germany, Greece, Ireland, Italy, Portugal, Spain, Sweden, and The Netherlands), all members of the ERN-EuroBloodNet. We describe the role of the ERN-EuroBloodNet to improve the overall approach to and the management of individuals with sickle cell disease in the EU through specific on the pooling of expertise, knowledge, and best practices; the development of training and education programmes; the strategy for systematic gathering and standardisation of clinical data; and its reuse in clinical research. Epidemiology and research strategies from ongoing implementation of the Rare Anaemia Disorders European Epidemiological Platform is depicted.

The relapsed acute lymphoblastic leukemia network (ReALLNet): a multidisciplinary project from the spanish society of pediatric hematology and oncology (SEHOP).

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, with survival rates exceeding 85%. However, 15% of patients will relapse; consequently, their survival rates decrease to below 50%. Therefore, several research and innovation studies are focusing on pediatric relapsed or refractory ALL (R/R ALL). Driven by this context and following the European strategic plan to implement precision medicine equitably, the Relapsed ALL Network (ReALLNet) was launched under the umbrella of SEHOP in 2021, aiming to connect bedside patient care with expert groups in R/R ALL in an interdisciplinary and multicentric network. To achieve this objective, a board consisting of experts in diagnosis, management, preclinical research, and clinical trials has been established. The requirements of treatment centers have been evaluated, and the available oncogenomic and functional study resources have been assessed and organized. A shipping platform has been developed to process samples requiring study derivation, and an integrated diagnostic committee has been established to report results. These biological data, as well as patient outcomes, are collected in a national registry. Additionally, samples from all patients are stored in a biobank. This comprehensive repository of data and samples is expected to foster an environment where preclinical researchers and data scientists can seek to meet the complex needs of this challenging population. This proof of concept aims to demonstrate that a network-based organization, such as that embodied by ReALLNet, provides the ideal niche for the equitable and efficient implementation of "what's next" in the management of children with R/R ALL.

Cytologic Features of Ventricular Tumors of the Central Nervous System: A Review with Emphasis on Diff-Quik Stained Smears.

BACKGROUND: Neoplasms from the ventricular system share a common location but have highly variable histogenesis. Many are slowly growing tumors that behave in a benign fashion. They can be classified as primary and secondary tumors. The most common primary tumors are ependymomas, subependymomas, subependymal giant cell astrocytomas, central neurocytomas, choroid plexus tumors, meningiomas, germinomas, pineal parenchymal tumors, papillary tumors of the pineal region, chordoid gliomas, rosette-forming glioneuronal tumors of the fourth ventricle, and craniopharyngiomas. Pilocytic astrocytomas, medulloblastomas, and atypical teratoid/rhabdoid tumors often show secondary involvement of the ventricular system. SUMMARY: Advances in neurosurgery have facilitated access to the ventricular system increasing the number of cases in which such tumors can be biopsied. In this context, cytology has been proven to be an extremely useful diagnostic tool during intraoperative pathologic consultations. Many ventricular tumors are infrequent, and the cytologic information available is limited. In this review, we describe the cytologic features of the uncommon ventricular tumors and report on unusual findings of the more common ones. For the cytologic evaluation of brain tumors, many neuropathologists prefer formalin fixation and hematoxylin and eosin staining. In this review, we highlight the cytologic findings as seen with Diff-Quik, a very popular staining method among cytopathologists. In fact, when pathologists are unfamiliar with cytology, it is common to request the assistance of cytopathologists during the evaluation of intraoperative procedures. Key Message: Ventricular tumors of the central nervous system comprise a group of heterogeneous tumors with very different cytologic features. The cytomorphology of these tumors, including rare entities, is often very characteristic, allowing a precise recognition during intraoperative pathologic consultations. Diff-Quik is a valuable staining method that can be used alone or as a complement to hematoxylin and eosin staining. Diff-Quik allows for clear visualization of the overall architecture, cytoplasmic details, and extracellular material.

On the influence of low-level visual features in film classification.

BACKGROUND: In this paper we present a model of parameters to aesthetically characterize films using a multi-disciplinary approach: by combining film theory, visual low-level video descriptors (modeled in order to supply aesthetic information) and classification techniques using machine and deep learning. METHODS: Four different tests have been developed, each for a different application, proving the model's usefulness. These applications are: aesthetic style clustering, prediction of production year, genre detection and influence on film popularity. RESULTS: The results are compared against high-level information to determine the accuracy of the model to classify films without knowing such information previously. The main difference with other film characterization approaches is that we are able to isolate the influence of high-level descriptors to really understand the relevance of low-level features and, accordingly propose a useful set of low-level visual descriptors for that purpose. This model has been tested with a representative number of films to prove that it can be used for different applications.

Disease of the holobiont, the example of multiple sclerosis. / El holobionte enfermo, el ejemplo de la esclerosis múltiple.

In recent years there has been a revolution regarding the role of the microbiota in different diseases, most of them within the spectrum of inflammatory and autoimmune diseases, associated with the development of metagenomics and the concept of holobiont, a large organism together with its microbiota. Specifically, in Multiple Sclerosis, multiple evidence points to the role of the microbiota in experimental autoimmune encephalomyelitis, animal model of the disease, and several articles have been published in recent years about differences in intestinal microbiota among patients with multiple sclerosis and control subjects. We review in this article the concept of holobiont and the gut microbiota functions, as well as the evidence accumulated about the role of the microbiota in experimental autoimmune encephalomyelitis and multiple sclerosis. Nowadays, there is a lot of evidence showing the role of the microbiota in the genesis, prevention and treatment of experimental autoimmune encephalomyelitis based mainly on three immunological pillars, the Th1-Th17 / Th2 balance, the Treg cells and the humoral immunity. It is also well documented that there are differences in the microbiota of patients with MS that are associated with a different expression of genes related to inflammation.

Posterior cingulate activation during moral dilemma in adolescents.

Neuroimaging research examining correlates of adolescent behavioral maturation has focused largely on issues related to higher cognitive development. Currently few studies have explored neural correlates of emotional reactivity in adolescent groups. In this study, we sought to examine the nature of posterior cingulate activation during situations of moral dilemma in normal adolescents. We focused on this region because of emerging evidence that suggests its role in emotionally self-relevant mental processing. Ten healthy teenagers, aged from 14 to 16 years, underwent three fMRI sequences designed to examine (i) brain responses during moral dilemma; (ii) brain responses during passive viewing of the moral dilemma outcome; and (iii); "deactivation" during a simple cognitive task compared with resting-state activity. Our main finding was that during moral dilemma, all subjects showed significant activation of the posterior cingulate cortex, and more variable activation of the medial frontal cortex and angular gyrus. Interestingly, these findings were replicated in each subject using the passive viewing task, suggesting that the previous pattern was not specific to moral reasoning or decision making. Finally, six of the ten subjects showed deactivation of the same posterior cingulate region during the cognitive task, indicating some commonality of function between posterior cingulate activity during moral dilemmas and rest. We propose that these posterior cingulate changes may relate to basic neuronal activities associated with processing self-relevant emotional stimuli. Given the high single-subject reproducibility of posterior cingulate activations, our findings may contribute to further characterize adolescent emotional reactivity in developmental neuroimaging studies.

Cytologic features of pleomorphic xanthoastrocytoma, WHO grade II. A comparative study with glioblastoma.

Pleomorphic xanthoastrocytoma (PXA) is a WHO grade II astrocytic tumor of children and young adults. It is characterized by pleomorphic, atypical astrocytes. Atypia is so remarkable, that PXA can be easily misdiagnosed as malignant glioma. If confused with a high-grade glioma the neurosurgeon may not proceed with a complete resection. Therefore, a specific recognition during intraoperative consultation is particularly important. We describe four cases of PXA evaluated during intraoperative procedures. Findings were compared with those of 22 glioblastomas. PXA smears were moderately cellular and showed a variable population of pleomorphic cells and fibrillary fragments with vessels. Tumoral cells were of intermediate size with a less frequent population of large, atypical cells. Some showed bi/trinucleation with bizarre nuclei. In two cases, tumoral cells with microvacuolization resembling xanthic astrocytes were present. No necrosis, mitotic activity, phagocytic macrophages or apoptotic fragments were seen. Smears from glioblastoma were more cellular than those of PXA with numerous neoplastic cells, branching vessels and myxoid substance. Cellular atypia was evident and mitoses were seen in all cases. Most cases showed an abundant population of accompanying macrophages and cellular debris. Differences between PXA and glioblastoma were related to cell turnover rather than cytomorphologic features. Glioblastoma shows features of high cellular replication showing a dirty background with necrosis and phagocytic macrophages as well as mitotic figures and apoptosis. On the other hand, smears from PXA have a clean background with no necrosis, cellular fragments or relevant mitotic activity. Diagn. Cytopathol. 2017;45:339-344. © 2016 Wiley Periodicals, Inc.

Botulinum toxin in trigeminal neuralgia. / Toxina botulínica en la neuralgia del trigémino.

INTRODUCTION: Trigeminal neuralgia is one of the most disabling facial pain syndromes, with a significant impact on patients' quality of life. Pharmacotherapy is the first choice for treatment but cases of drug resistance often require new strategies, among which various interventional treatments have been used. In recent years a new therapeutic strategy consisting of botulinum toxin has emerged, with promising results. DEVELOPMENT: We reviewed clinical cases and case series, open-label studies and randomized clinical trials examining the use of botulinum toxin for drug-refractory trigeminal neuralgia published in the literature. CONCLUSIONS: The administration of botulinum toxin has proven to be a safe and effective therapeutic strategy in patients with drug-refractory idiopathic trigeminal neuralgia, but many questions remain unanswered as to the precise role of botulinum toxin in the treatment of this disease.