Emerging Technologies in Edge Computing and Networking.

The global evolution of the Internet is experiencing a notable and inevitable change towards a convergent scenario known as the Internet of Things (IoT), where a large number of devices with heterogeneous characteristics and requirements have to be interconnected to serve different verticals, such as smart cities, intelligent transportation systems, smart grids, (ITS) or e-health [...].

Literature Review of Deep-Learning-Based Detection of Violence in Video.

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.

Clinical outcomes and complications of a new high-density polyethylene-based spherical integrated porous orbital implant.

PURPOSE: To describe our experience over 6 years using a new high-density polyethylene-based spherical integrated porous orbital implants (Oculfit). METHODS: This is an observational retrospective case series study analyzing all cases requiring Oculfit implants between February 2015 and September 2021. Clinical information regarding the population included, the characteristics of the implant, and the outcomes and complications during the follow-up were noted. The success of the implant was defined according to anatomical and functional parameters. RESULTS: The study analyzed 90 cases of anophthalmic patients. The main causes for enucleation or evisceration were ocular decompensations (36.7%) and neoplasms (27.8% uveal melanoma and 7.8% retinoblastoma). Anatomical success was identified in 63 (70.0%) cases, functional success in 79 (87.8%) and complete success (anatomical + functional) in 61 (67.7%) cases. Factors associated with the functional success were age and exposure of the primary orbital implant. Complications appeared in 11 (12.2%) cases, which were completely resolved without sequelae in 4 (4.4%). Orbital explant was required in 5 (5.6%) cases. CONCLUSION: In our experience, Oculfit can be considered a useful alternative among the currently available options for orbital implants and has a good efficacy/safety profile.

Controlled Covalent Functionalization of 2 H-MoS2 with Molecular or Polymeric Adlayers.

Most air-stable 2D materials are relatively inert, which makes their chemical modification difficult. In particular, in the case of MoS2 , the semiconducting 2 H-MoS2 is much less reactive than its metallic counterpart, 1T-MoS2 . As a consequence, there are hardly any reliable methods for the covalent modification of 2 H-MoS2 . An ideal method for the chemical functionalization of such materials should be both mild, not requiring the introduction of a large number of defects, and versatile, allowing for the decoration with as many different functional groups as possible. Herein, a comprehensive study on the covalent functionalization of 2 H-MoS2 with maleimides is presented. The use of a base (Et3 N) leads to the in situ formation of a succinimide polymer layer, covalently connected to MoS2 . In contrast, in the absence of base, functionalization stops at the molecular level. Moreover, the functionalization protocol is mild (occurs at room temperature), fast (nearly complete in 1 h), and very flexible (11 different solvents and 10 different maleimides tested). In practical terms, the procedures described here allow for the chemist to manipulate 2 H-MoS2 in a very flexible way, decorating it with polymers or molecules, and with a wide range of functional groups for subsequent modification. Conceptually, the spurious formation of an organic polymer might be general to other methods of functionalization of 2D materials, where a large excess of molecular reagents is typically used.

Pattern of Activated Pathways and Quality of Collateral Status in Patients with Symptomatic Internal Carotid Artery Occlusion.

BACKGROUND: Internal carotid artery occlusion (ICAO) is an important risk factor for stroke. Cerebral hemodynamics in patients with ICAO depends on the individual capacity to activate sufficient collateral pathways. Therefore, the assessment of intracranial collaterals is essential for the acute and long-term management of these patients and accurate estimation of further stroke risk. METHODS: Acute stroke patients with unilateral ICAO were prospectively enrolled. We assessed the following collaterals by transcranial color-coded sonography (TCCS): the anterior and posterior communicating artery (ACoA, PCoA), the ophthalmic artery (OA), and leptomeningeal collaterals of the posterior cerebral artery (LMC). We subdivided the flow pattern of the Doppler spectrum in the middle cerebral artery (MCA) into 3 categories: (1) good, (2) moderate, and (3) bad according to the hemodynamic effects on the ipsilateral MCA flow. Finally, we compared the individual TCCS results with the stroke pattern detected on CT or MRI scan. RESULTS: One hundred thirteen patients (age 66 ± 12 years; -female 24) were included. The collateral status was good, moderate, and bad in 59 (52%), 37 (33%), and 17 (15%) patients, respectively. The ACoA collateral was most frequently activated (81%), followed by the OA (63%), the PCoA (53%), and the LMC (22%). The quality of the collateral status was determined by the type (p = 0.0003) but not by the number (p = 0.19) of activated collateral pathways. Good collateral function was highly associated with primary collaterals (ACoA > PCoA). Best parameter for a good collateral status was an antegrade flow in the OA, indicating a high blood supply via the communicating arteries. CONCLUSIONS: TCCS allows the assessment of intracranial collaterals and their hemodynamic capacity. Prevalence of collateral sufficiency in ICAO seems to be higher than previously reported. ACoA cross flow is essential for the optimal hemodynamic compensation of ICAO. Antegrade OA flow indicates good collateral status.

Smart Buildings IoT Networks Accuracy Evolution Prediction to Improve Their Reliability Using a Lotka-Volterra Ecosystem Model.

Internet of Things (IoT) is the paradigm that has largely contributed to the development of smart buildings in our society. This technology makes it possible to monitor all aspects of the smart building and to improve its operation. One of the main challenges encountered by IoT networks is that the the data they collect may be unreliable since IoT devices can lose accuracy for several reasons (sensor wear, sensor aging, poorly constructed buildings, etc.). The aim of our work is to study the evolution of IoT networks over time in smart buildings. The hypothesis we have tested is that, by amplifying the Lotka-Volterra equations as a community of living organisms (an ecosystem model), the reliability of the system and its components can be predicted. This model comprises a set of differential equations that describe the relationship between an IoT network and multiple IoT devices. Based on the Lotka-Volterra model, in this article, we propose a model in which the predators are the non-precision IoT devices and the prey are the precision IoT devices. Furthermore, a third species is introduced, the maintenance staff, which will impact the interaction between both species, helping the prey to survive within the ecosystem. This is the first Lotka-Volterra model that is applied in the field of IoT. Our work establishes a proof of concept in the field and opens a wide spectrum of applications for biology models to be applied in IoT.

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building.

Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

Energy Optimization Using a Case-Based Reasoning Strategy.

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring.

Real-time Localization Systems have been postulated as one of the most appropriated technologies for the development of applications that provide customized services. These systems provide us with the ability to locate and trace users and, among other features, they help identify behavioural patterns and habits. Moreover, the implementation of policies that will foster energy saving in homes is a complex task that involves the use of this type of systems. Although there are multiple proposals in this area, the implementation of frameworks that combine technologies and use Social Computing to influence user behaviour have not yet reached any significant savings in terms of energy. In this work, the CAFCLA framework (Context-Aware Framework for Collaborative Learning Applications) is used to develop a recommendation system for home users. The proposed system integrates a Real-Time Localization System and Wireless Sensor Networks, making it possible to develop applications that work under the umbrella of Social Computing. The implementation of an experimental use case aided efficient energy use, achieving savings of 17%. Moreover, the conducted case study pointed to the possibility of attaining good energy consumption habits in the long term. This can be done thanks to the system's real time and historical localization, tracking and contextual data, based on which customized recommendations are generated.

Energy Efficiency in Public Buildings through Context-Aware Social Computing.

The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings.

Nutritional composition of quinoa leafy greens: An underutilized plant-based food with the potential of contributing to current dietary trends.

Quinoa (Chenopodium quinoa Willd.) leafy greens (QLGs) are plant-based foods of high nutritional value that have been scarcely studied. In this work, the nutritional and functional composition of three QLGs varieties was evaluated. A protein content higher than 35 g 100 g-1 dw with a well-balanced essential amino acid composition was found making them a good source of vegetable protein. In addition, elevated contents of dietary fibre and minerals, higher than those detected in quinoa seeds and other leafy vegetables, were found. The lipid profile showed higher contents of linoleic (C18:2, ω6) (20.2 %) and linolenic acids (C18:3, ω3) (52.8 %) with low ω6/ ω3 ratios (∼0.4/1). A total sugar content <1 g 100 g-1 dw was found for all varieties tested, lower than that obtained in seeds. The saponin content varied between 0.76 and 0.87 %. Also, high values of total phenolic compounds (969.8-1195.4 mg gallic acid 100 g-1), mainly hydroxycinnamic acids and flavonoids, and great antioxidant activities (7.64-8.90 g Trolox kg-1) were found. Multivariate analysis here used allowed us to classify the samples according to the quinoa variety evaluated, and the sequential stepwise multiple regression applied revealed that the PUFA and sucrose contents negatively influenced the protein content while the palmitic acid content affected positively this parameter. Overall, this study shows that QLGs are promising nutritious and functional plant-based foods supporting the necessity of promoting their cultivation, commercialization, and consumption.

Enhanced Electrocatalysis on Copper Nanostructures: Role of the Oxidation State in Sulfite Oxidation.

The influence of surface morphology and the oxidation state on the electrocatalytic activity of nanostructured electrodes is well recognized, yet disentangling their individual roles in specific reactions remains challenging. Here, we investigated the electrooxidation of sulfite ions in an alkaline environment using cyclic voltammetry on copper oxide nanostructured electrodes with different oxidation states and morphologies but with similar active areas. To this aim, we synthesized nanostructured Cu films made of nanoparticles or nanorods on top of glassy carbon electrodes. Our findings showed an enhanced sensitivity and a lower detection threshold when utilizing Cu(I) over Cu(II). Density functional theory-based thermochemical analysis revealed the underlying oxidation mechanism, indicating that while the energy gain associated with the process is comparable for both oxide surfaces, the desorption energy barrier for the resulting sulfate molecules is three times higher on Cu(II). This becomes the limiting step of the reaction kinetics and diminishes the overall electrooxidation efficiency. Our proposed mechanism relies on the tautomerization of hydroxyl groups confined on the surface of Cu-based electrodes. This mechanism might be applicable to electrochemical reactions involving other sulfur compounds that hold technological significance.

Therapeutic strategy for spinal muscular atrophy by combining gene supplementation and genome editing.

Defect in the SMN1 gene causes spinal muscular atrophy (SMA), which shows loss of motor neurons, muscle weakness and atrophy. While current treatment strategies, including small molecules or viral vectors, have shown promise in improving motor function and survival, achieving a definitive and long-term correction of SMA's endogenous mutations and phenotypes remains highly challenging. We have previously developed a CRISPR-Cas9 based homology-independent targeted integration (HITI) strategy, enabling unidirectional DNA knock-in in both dividing and non-dividing cells in vivo. In this study, we demonstrated its utility by correcting an SMA mutation in mice. When combined with Smn1 cDNA supplementation, it exhibited long-term therapeutic benefits in SMA mice. Our observations may provide new avenues for the long-term and efficient treatment of inherited diseases.

Unique nutritional features that distinguish Amaranthus cruentus L. and Chenopodium quinoa Willd seeds.

Univariate (Analysis of Variance_ANOVA) and multivariate (Principal Component Analysis (PCA) and Canonical Discriminant Analysis (CDA)) analyses were performed in order to classify and authenticate the seeds from different varieties of quinoa (Chenopodium quinoa Will.), and amaranth (Amaranthus cruentus L.). The univariate analysis showed differences between species for sucrose, K, Ca, unsaturated fatty acids, and the ω6/ω3 ratio. Nevertheless, to strengthen this classification, a PCA was applied separating the samples in 2 groups; group 1, formed by quinoa seeds, presented higher contents of margaroleic, eicosadienoic, behenic, erucic, linolenic, linoleic, and gadoleic acids, proteins, sucrose, and total sugars. Group 2, formed by amaranth seeds, showed positive values for Mn, Mg, Fe, P, Zn, Ca, fiber, glucose, and ω6/ω3 ratio. Furthermore, the CDA models developed resulted in a probability of event of 100% when classifying the samples in the groups quinoa or amaranth, highlighting the good sensitivity of the models used.

An overview of machine learning and deep learning techniques for predicting epileptic seizures.

Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has shown potential as a cost-effective alternative for rapid diagnosis. In this study, we review the current state of machine learning in the detection and prediction of epileptic seizures. The objective of this study is to portray the existing machine learning methods for seizure prediction. Internet bibliographical searches were conducted to identify relevant literature on the topic. Through cross-referencing from key articles, additional references were obtained to provide a comprehensive overview of the techniques. As the aim of this paper aims is not a pure bibliographical review of the subject, the publications here cited have been selected among many others based on their number of citations. To implement accurate diagnostic and treatment tools, it is necessary to achieve a balance between prediction time, sensitivity, and specificity. This balance can be achieved using deep learning algorithms. The best performance and results are often achieved by combining multiple techniques and features, but this approach can also increase computational requirements.

Peripheral Ulcerative Keratopathy following Systemic Treatment with Bortezomib: A Case Report.

A 60-year-old patient was diagnosed with multiple myeloma in November 2020, and started treatment in December 2020 with bortezomib, dexamethasone, and thalidomide. Four months later, he presented to the ophthalmology emergency department with inflammation and pain in the left eye. Examination of the anterior segment revealed severe, mixed anterior squamous blepharitis with significant bilateral palpebral inflammation, dysfunction of the meibomian glands, and several styes on both eyelids bilaterally. A peripheral ulcerative keratopathy was detected while examining the left eye cornea, with an inferior infiltrate and significant thinning, approximately 5 mm in length, at the limbal margin (Figure 1). Tear break-up time was shortened bilaterally. In addition to palpebral hygiene and oral treatment with doxycycline 100 mg every 24 hours, the patient was prescribed the following topical treatment without preservatives: artificial tears with lipid emulsion, netilmicin 0,3% 0,4 ml eye drops every 6 hours and dexamethasone 1 mg/ml every 8 hours.

Rapid degeneration of iPSC-derived motor neurons lacking Gdap1 engages a mitochondrial-sustained innate immune response.

Charcot-Marie-Tooth disease is a chronic hereditary motor and sensory polyneuropathy targeting Schwann cells and/or motor neurons. Its multifactorial and polygenic origin portrays a complex clinical phenotype of the disease with a wide range of genetic inheritance patterns. The disease-associated gene GDAP1 encodes for a mitochondrial outer membrane protein. Mouse and insect models with mutations in Gdap1 have reproduced several traits of the human disease. However, the precise function in the cell types affected by the disease remains unknown. Here, we use induced-pluripotent stem cells derived from a Gdap1 knockout mouse model to better understand the molecular and cellular phenotypes of the disease caused by the loss-of-function of this gene. Gdap1-null motor neurons display a fragile cell phenotype prone to early degeneration showing (1) altered mitochondrial morphology, with an increase in the fragmentation of these organelles, (2) activation of autophagy and mitophagy, (3) abnormal metabolism, characterized by a downregulation of Hexokinase 2 and ATP5b proteins, (4) increased reactive oxygen species and elevated mitochondrial membrane potential, and (5) increased innate immune response and p38 MAP kinase activation. Our data reveals the existence of an underlying Redox-inflammatory axis fueled by altered mitochondrial metabolism in the absence of Gdap1. As this biochemical axis encompasses a wide variety of druggable targets, our results may have implications for developing therapies using combinatorial pharmacological approaches and improving therefore human welfare. A Redox-immune axis underlying motor neuron degeneration caused by the absence of Gdap1. Our results show that Gdap1-/- motor neurons have a fragile cellular phenotype that is prone to degeneration. Gdap1-/- iPSCs differentiated into motor neurons showed an altered metabolic state: decreased glycolysis and increased OXPHOS. These alterations may lead to hyperpolarization of mitochondria and increased ROS levels. Excessive amounts of ROS might be the cause of increased mitophagy, p38 activation and inflammation as a cellular response to oxidative stress. The p38 MAPK pathway and the immune response may, in turn, have feedback mechanisms, leading to the induction of apoptosis and senescence, respectively. CAC, citric acid cycle; ETC, electronic transport chain; Glc, glucose; Lac, lactate; Pyr, pyruvate.

Adverse events of antibody-drug conjugates on the ocular surface in cancer therapy.

Antibody-drug conjugates consist of a monoclonal antibody attached to a cytotoxic therapeutic molecule by a connector. This association allows a highly specific therapy, which increases their effectiveness and decreases their potential toxicity. This new therapy emerged approximately 20 years ago; since then, numerous combinations have appeared in the field of treatment-related neoplasms as an alternative for patients who do not achieve good results with conventional treatment options. Adverse effects of these drugs on the ocular surface are frequent and varied. Their prevalence ranges from 20 to 90% depending on the drug and administration condition, probably due to multiple receptor-mediated factors or mechanisms not mediated by specific receptors, such as macropinocytosis. These adverse events can greatly limit patients' comfort; thus, the objectives of this article were, in the first place, to compile the information currently available on different types of adverse effects of antibody-drug conjugates on the ocular surface, including pathophysiology, prevalence, and treatment, and in second place, to contribute to the correct identification and management of these events, which will result in a lower rate of cessation of treatment, which is necessary for the survival of candidate patients.

Myc Supports Self-Renewal of Basal Cells in the Esophageal Epithelium.

It is widely believed that cellular senescence plays a critical role in both aging and cancer, and that senescence is a fundamental, permanent growth arrest that somatic cells cannot avoid. Here we show that Myc plays an important role in self-renewal of esophageal epithelial cells, contributing to their resistance to cellular senescence. Myc is homogeneously expressed in basal cells of the esophageal epithelium and Myc positively regulates their self-renewal by maintaining their undifferentiated state. Indeed, Myc knockout induced a loss of the undifferentiated state of esophageal epithelial cells resulting in cellular senescence while forced MYC expression promoted oncogenic cell proliferation. A superoxide scavenger counteracted Myc knockout-induced senescence, therefore suggesting that a mitochondrial superoxide takes part in inducing senescence. Taken together, these analyses reveal extremely low levels of cellular senescence and senescence-associated phenotypes in the esophageal epithelium, as well as a critical role for Myc in self-renewal of basal cells in this organ. This provides new avenues for studying and understanding the links between stemness and resistance to cellular senescence.

In vivo partial reprogramming alters age-associated molecular changes during physiological aging in mice.

Partial reprogramming by expression of reprogramming factors (Oct4, Sox2, Klf4 and c-Myc) for short periods of time restores a youthful epigenetic signature to aging cells and extends the life span of a premature aging mouse model. However, the effects of longer-term partial reprogramming in physiologically aging wild-type mice are unknown. Here, we performed various long-term partial reprogramming regimens, including different onset timings, during physiological aging. Long-term partial reprogramming lead to rejuvenating effects in different tissues, such as the kidney and skin, and at the organismal level; duration of the treatment determined the extent of the beneficial effects. The rejuvenating effects were associated with a reversion of the epigenetic clock and metabolic and transcriptomic changes, including reduced expression of genes involved in the inflammation, senescence and stress response pathways. Overall, our observations indicate that partial reprogramming protocols can be designed to be safe and effective in preventing age-related physiological changes. We further conclude that longer-term partial reprogramming regimens are more effective in delaying aging phenotypes than short-term reprogramming.