Exploring morphometric frontiers: A comprehensive study of otolith growth patterns in brown comber Serranus hepatus (Linnaeus, 1758).

Otoliths are widely employed in marine sciences to gain insights into fish growth, age, migrations, and population structure. This study investigates the relationships between morphometric measurements, otolith characteristics, and length size patterns in the brown comber (Serranus hepatus) from the Gulf of Cádiz, a species discarded in artisanal trawl fisheries. Our findings reveal significant changes in otolith shape indices as fish grow, with symmetry observed between left and right otolith measurements. Otolith size is found to be related to fish size, supporting its use in estimating body length at different life stages. Otolith shape analysis has potential applications in stock identification, detecting catch misreporting, and studying marine predator diets. Combining otolith shape analysis with other data types can clarify relationships among taxa and inform spatial management strategies, contributing to the long-term sustainability of fish populations and the assessment of the impact of management strategies on fish size and growth. This study enhances our understanding of the broader implications of morphometric and otolith analyses in fisheries research and supports the development of more sustainable fisheries management practices.

Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure.

Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface. Thus, their protection has become a priority for national security. Cyber-attacks have become more sophisticated and criminals are able to surpass conventional security systems; therefore, attack detection has become a challenging area. Defensive technologies such as intrusion detection systems (IDSs) are a fundamental part of security systems to protect CI. IDSs have incorporated machine learning (ML) techniques that can deal with broader kinds of threats. Nevertheless, the detection of zero-day attacks and having technological resources to implement purposed solutions in the real world are concerns for CI operators. This survey aims to provide a compilation of the state of the art of IDSs that have used ML algorithms to protect CI. It also analyzes the security dataset used to train ML models. Finally, it presents some of the most relevant pieces of research on these topics that have been developed in the last five years.

LPWAN Key Exchange: A Centralised Lightweight Approach.

The Internet of Things (IoT) is one of the fastest emerging technologies in the industry. It includes diverse applications with different requirements to provide services to users. Secure, low-powered, and long-range transmissions are some of the most vital requirements in developing IoT applications. IoT uses several communication technologies to fulfill transmission requirements. However, Low Powered Wide Area Networks (LPWAN) transmission standards have been gaining attention because of their exceptional low-powered and long-distance transmission capabilities. The features of LPWAN transmission standards make them a perfect candidate for IoT applications. However, the current LPWAN standards lack state-of-the-art security mechanism s because of the limitations of the IoT devices in energy and computational capacity. Most of the LPWAN standards, such as Sigfox, NB-IoT, and Weightless, use static keys for node authentication and encryption. LoRaWAN is the only LPWAN technology providing session key mechanisms for better security. However, the session key mechanism is vulnerable to replay attacks. In this paper, we propose a centralized lightweight session key mechanism for LPWAN standards using the Blom-Yang key agreement (BYka) mechanism. The security of the session key mechanism is tested using the security verification tool Scyther. In addition, an energy consumption model is implemented on the LoRaWAN protocol using the NS3 simulator to verify the energy depletion in a LoRaWAN node because of the proposed session key mechanisms. The proposed session key is also verified on the Mininet-WiFi emulator for its correctness. The analysis demonstrates that the proposed session key mechanism uses a fewer number of transmissions than the existing session key mechanisms in LPWAN and provides mechanisms against replay attacks that are possible in current LPWAN session key schemes.

A Dynamic Algorithm for Interference Management in D2D-Enabled Heterogeneous Cellular Networks: Modeling and Analysis.

To supporting a wider and diverse range of applications, device-to-device (D2D) communication is a key enabler in heterogeneous cellular networks (HetCNets). It plays an important role in fulfilling the performance and quality of service (QoS) requirements for 5G networks and beyond. D2D-enabled cellular networks enable user equipment (UE) to communicate directly, without any or with a partial association with base stations (eNBs). Interference management is one of the critical and complex issues in D2D-enabled HetCNets. Despite the wide adoption of D2D communications, there are very few researchers addressing the problems of mode selection (MS), as well as resource allocation for mutual interference in three-tier cellular networks. In this paper, we first identify and analyze three key factors, namely outage probability, signal-to-interference and noise ratio (SINR), and cell density that influence the performance of D2D-enabled HetCNets. We then propose a dynamic algorithm based on a distance-based approach to minimize the interference and to guarantee QoS for both cellular and D2D communication links. Results obtained show that outage probability is improved by 35% and 49% in eNB and SCeNB links, respectively, when compared with traditional neighbor-based methods. The findings reported in this paper provide some insights into interference management in D2D communications that can help network researchers and engineers contribute to further developing next-generation cellular networks.

Effect of climatic oscillations on small pelagic fisheries and its economic profit in the Gulf of Cadiz.

Several studies have shown the effect of climatic oscillations on fisheries. Small pelagic fish are of special global economic importance and very sensitive to fluctuations in the physical environment in which they live. The main goal of this study was to explore the relationship between the North Atlantic Oscillation (NAO), the East Atlantic pattern (EA), and the Arctic Oscillation (AO) on the landings and first sale prices of the most representative small pelagic commercial species of the purse-seine fisheries in the Gulf of Cadiz (North East Atlantic), the European anchovy Engraulis encrasicolus and the European sardine Sardine pilchardus. Generalised linear models (GLMs) with different data transformations and distribution errors were generated to analyse these relationships. The best results of the models were obtained by applying a moving average of order 3 to the dataset with a double weighted median. Our results demonstrate relationships between NAO, AO, and EA and European anchovy and sardine landings. These cause an indirect effect on the first sale price in markets through catch variations, which affect the price according to the law of supply and demand. The limitations of this study and management implications are discussed.

Development and characterization of two equine formulations towards SARS-CoV-2 proteins for the potential treatment of COVID-19.

In the current global emergency due to SARS-CoV-2 outbreak, passive immunotherapy emerges as a promising treatment for COVID-19. Among animal-derived products, equine formulations are still the cornerstone therapy for treating envenomations due to animal bites and stings. Therefore, drawing upon decades of experience in manufacturing snake antivenom, we developed and preclinically evaluated two anti-SARS-CoV-2 polyclonal equine formulations as potential alternative therapy for COVID-19. We immunized two groups of horses with either S1 (anti-S1) or a mixture of S1, N, and SEM mosaic (anti-Mix) viral recombinant proteins. Horses reached a maximum anti-viral antibody level at 7 weeks following priming, and showed no major adverse acute or chronic clinical alterations. Two whole-IgG formulations were prepared via hyperimmune plasma precipitation with caprylic acid and then formulated for parenteral use. Both preparations had similar physicochemical and microbiological quality and showed ELISA immunoreactivity towards S1 protein and the receptor binding domain (RBD). The anti-Mix formulation also presented immunoreactivity against N protein. Due to high anti-S1 and anti-RBD antibody content, final products exhibited high in vitro neutralizing capacity of SARS-CoV-2 infection, 80 times higher than a pool of human convalescent plasma. Pre-clinical quality profiles were similar among both products, but clinical efficacy and safety must be tested in clinical trials. The technological strategy we describe here can be adapted by other producers, particularly in low- and middle-income countries.

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications.

BACKGROUND: Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. OBJECTIVES: This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. METHODS: We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. RESULTS: The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. CONCLUSION: We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.

[Puerperal insanity:a comparative reading of Argentina and Colombia, 1880-1950]. / Las locuras puerperales: una lectura comparada entre Argentina y Colombia, 1880-1950.

Our goal is to understand the appearance and spread of forms of puerperal insanity in Argentina and Colombia in the late nineteenth and early twentieth century, as well as their decline or disappearance around the 1940s. This is a historical and hermeneutical study, which uses the concepts of "field of visibility" and "ecological niche" for a transitory disease. There was no correlation between pregnancy, childbirth and puerperium and the state of delirium that led to commitment, which was attributed to predisposing factors; furthermore, forms of puerperal insanity were nosographically distinct due to their unique etiopathogeneses. As clinical cases of puerperal insanity started to emerge, the disciplinary field of obstetrics converged with psychiatry, with the former exerting more weight.

El objetivo es comprender la aparición y propagación de locuras puerperales en Argentina y Colombia, a finales del siglo XIX y principios del XX, así como su decadencia o desvanecimiento hacia la década de 1940-1950. Investigación histórico-hermenéutica, según los conceptos de "campo de visibilidad" y "nicho ecológico" de una enfermedad transitoria. No existió correlación entre embarazo, parto y puerperio con el estado delirante que motivaba la internación, atribuido a factores predisponentes y, asimismo, tuvieron una autonomía nosográfica en virtud de etiopatogenias singulares. Al tiempo que empezó a emerger el tipo clínico locura puerperal, se entrecruzaron el campo disciplinar de la obstetricia con el alienismo, con una mayor preponderancia del primero.

Las locuras puerperales: una lectura comparada entre Argentina y Colombia, 1880-1950 / Puerperal insanity: a comparative reading of Argentina and Colombia, 1880-1950

Resumen El objetivo es comprender la aparición y propagación de locuras puerperales en Argentina y Colombia, a finales del siglo XIX y principios del XX, así como su decadencia o desvanecimiento hacia la década de 1940-1950. Investigación histórico-hermenéutica, según los conceptos de "campo de visibilidad" y "nicho ecológico" de una enfermedad transitoria. No existió correlación entre embarazo, parto y puerperio con el estado delirante que motivaba la internación, atribuido a factores predisponentes y, asimismo, tuvieron una autonomía nosográfica en virtud de etiopatogenias singulares. Al tiempo que empezó a emerger el tipo clínico locura puerperal, se entrecruzaron el campo disciplinar de la obstetricia con el alienismo, con una mayor preponderancia del primero.

Abstract Our goal is to understand the appearance and spread of forms of puerperal insanity in Argentina and Colombia in the late nineteenth and early twentieth century, as well as their decline or disappearance around the 1940s. This is a historical and hermeneutical study, which uses the concepts of "field of visibility" and "ecological niche" for a transitory disease. There was no correlation between pregnancy, childbirth and puerperium and the state of delirium that led to commitment, which was attributed to predisposing factors; furthermore, forms of puerperal insanity were nosographically distinct due to their unique etiopathogeneses. As clinical cases of puerperal insanity started to emerge, the disciplinary field of obstetrics converged with psychiatry, with the former exerting more weight.

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU.

To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance. Alternatively, Deep Learning offers state of the art capabilities in certain prediction tasks and research suggests deep neural networks are able to outperform traditional techniques. Nevertheless, a main impediment for the adoption of Deep Learning in healthcare is its reduced interpretability, for in this field it is crucial to gain insight into the why of predictions, to assure that models are actually learning relevant features instead of spurious correlations. To address this, we propose a deep multi-scale convolutional architecture trained on the Medical Information Mart for Intensive Care III (MIMIC-III) for mortality prediction, and the use of concepts from coalitional game theory to construct visual explanations aimed to show how important these inputs are deemed by the network. Results show our model attains a ROC AUC of 0.8735 (± 0.0025) which is competitive with the state of the art of Deep Learning mortality models trained on MIMIC-III data, while remaining interpretable. Supporting code can be found at https://github.com/williamcaicedo/ISeeU.

Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology.

Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of prediabetes and type 2 diabetes mellitus (T2DM) using wearable technology. An artificial intelligence model was developed based on adaptive-neuro fuzzy interference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence and calories). The data was collected using an advanced wearable body vest. The real-time data was combined with manual recordings of blood glucose, height, weight, age and sex. The model analyzed the data alongside a clinical knowledge-base. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines and protocols. The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.

Perfil epidemiológia en niños de 5 a 12 años para higiene oral, caries y gingivitis de escuelas del área comunitaria de la Escuela Colombiana de Medicina / Epidemiological profiles in children from 5 to 12 years old for oral hygiene, dental caries and gingivitis from community schools of Colombian Medicine School

El propósito de este estudio fue el de evaluar la situación actual higiene oral, caries, gingivitis de las escuelas del área de influencia de la Escuela Colombiana de Medicina, que vienen recibiendo atención odontológica desde 1985 dentro del programa del área comunitaria, con el fin de planificar mejor los resultados de toda índole que actualmente invierte la institución en el desarrollo del proceso de enseñanza-aprendizaje y tener elementos de planeación de estrategias dentro del marco de la ley 100 de seguridad social de 1993. Para llevar a cabo este estudio de tipo descriptivo de corte, se tomaron como universo los 1.300 alumnos vinculados al prograsma educativo de las escuelas del área de influencia de la Escuela Colombina de Medicina: Fundación Ana Restrepo del corral, Fundación Santa María, Concentración, Escolar San Isidro y concentración Escolar San Benito. Posteriormente se realizó un muestreo estratificado donde se obtuvo una muestra estadísticamente representativa con una confiabilidad del 99, un margen de error del 3 y equivalente a 710 alumnos distribuidos por género, edad y escuela.