Sharp detection of oscillation packets in rich time-frequency representations of neural signals

Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets’ precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations’ dynamics

Fecal Microbiota Transplantation in Inflammatory Bowel Disease

Inflammatory bowel diseases represent a complex array of diseases of incompletely known etiology that led to gastrointestinal tract chronic inflammation. In inflammatory bowel disease, a promising method of treatment is represented by fecal microbiota transplantation (FMT), FMT has shown its increasing effectiveness and safety in recent years for recurrent CDI; moreover, it showed real clinical benefits in treating SARS-CoV-2 and CDI co-infection. Crohn’s disease and ulcerative colitis are characterized by immune dysregulation, resulting in digestive tract damage caused by immune responses. Most current therapeutic strategies are associated with high costs and many adverse effects by directly targeting the immune response, so modifying the microbial environment by FMT offers an alternative approach that could indirectly influence the host’s immune system in a safe way. Studies outline the endoscopic and clinical improvements in UC and CD in FMT patients versus control groups. This review outlines the multiple benefits of FMT in the case of IBD by improving patients unbalanced gut, therefore improving endoscopic and clinical symptomatology. We aim to emphasize the clinical importance and benefits of FMT in order to prevent flares or complications of IBD and to highlight that further validation is needed for establishing a clinical protocol for FMT in IBD

Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture

The growing need for food worldwide requires the development of a high-performance, high-productivity, and sustainable agriculture, which implies the introduction of new technologies into monitoring activities related to control and decision-making. In this regard, this paper presents a hierarchical structure based on the collaboration between unmanned aerial vehicles (UAVs) and federated wireless sensor networks (WSNs) for crop monitoring in precision agriculture. The integration of UAVs with intelligent, ground WSNs, and IoT proved to be a robust and efficient solution for data collection, control, analysis, and decisions in such specialized applications. Key advantages lay in online data collection and relaying to a central monitoring point, while effectively managing network load and latency through optimized UAV trajectories and in situ data processing. Two important aspects of the collaboration were considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level for the transmission of the relevant data. The experiments were carried out at a Romanian research institute where different crops and methods are developed. The results demonstrate that the collaborative UAV–WSN–IoT approach increases the performances in both precision agriculture and ecological agriculture

A Collaborative UAV-WSN Network for Monitoring Large Areas

Large-scale monitoring systems have seen rapid development in recent years. Wireless sensor networks (WSN), composed of thousands of sensing, computing and communication nodes, form the backbone of such systems. Integration with unmanned aerial vehicles (UAVs) leads to increased monitoring area and to better overall performance. This paper presents a hybrid UAV-WSN network which is self-configured to improve the acquisition of environmental data across large areas. A prime objective and novelty of the heterogeneous multi-agent scheme proposed here is the optimal generation of reference trajectories, parameterized after inter- and intra-line distances. The main contribution is the trajectory design, optimized to avoid interdicted regions, to pass near predefined way-points, with guaranteed communication time, and to minimize total path length. Mixed-integer description is employed into the associated constrained optimization problem. The second novelty is the sensor localization and clustering method for optimal ground coverage taking into account the communication information between UAV and a subset of ground sensors (i.e., the cluster heads). Results show improvements in both network and data collection efficiency metrics by implementing the proposed algorithms. These are initially evaluated by means of simulation and then validated on a realistic WSN-UAV test-bed, thus bringing significant practical value

Fecal Microbiota Transplantation in Liver Cirrhosis

The human gastrointestinal tract houses a diverse array of probiotic and pathogenic bacteria and any alterations in this microbial composition can exert a significant influence on an individual’s well-being. It is well-established that imbalances in the gut microbiota play a pivotal role in the development of liver diseases. In light of this, a new adjuvant therapy for liver diseases could be regulating the intestinal microbiota. Through fecal microbiota transplantation, patients whose microbiomes are compromised are treated with stool from healthy donors in an attempt to restore a normal microbiome and alleviate their symptoms. A review of cross-sectional studies and case reports suggests that fecal microbiota transplants may offer effective treatment for chronic liver diseases. Adding to the potential of this emerging therapy, recent research has indicated that fecal microbiota transplantation holds promise as a therapeutic approach specifically for liver cirrhosis. By introducing a diverse range of beneficial microorganisms into the gut, this innovative treatment aims to address the microbial imbalances often observed in cirrhotic patients. While further validation is still required, these preliminary findings highlight the potential impact of fecal microbiota transplantation as a novel and targeted method for managing liver cirrhosis. We aimed to summarize the current state of understanding regarding this procedure, as a new therapeutic method for liver cirrhosis, as well as to explain its clinical application and future potential

Cotnari vineyard - a gift of hydraulic foehn?

Cotnari vineyard is the northernmost wine-growing vineyard of our country, located close enough to the northern limit of the expanding region of the grape vine. Within this study, we analyse one of the climatic factors that, in our view, contributes to the climatic favorability of this region for the vine plantations: the foehn. More precisely, the hydraulic foehn, which is not mentioned in the Romanian climatology literature, although the mechanism associated with the development of this type of foehn wind, is described for more than 40 years in the international climatological literature. We bring arguments for the manifestation of the hydraulic foehn during winter 2013-2014 in the related region of Cotnary vineyard on the basis of measurements accomplished at Paşcani climatologic station and at other 11 points of hourly monitoring temperature of Dealu Mare-Hârlău region. Additionally, radio-sounding data, synoptic maps and other synoptic data of some of the meteorological indicators of foehn development are used. Local generating mechanism, periods of occurrence, synoptic conditions associated to its generation, the intensity of temperature difference recorded within the region and the possible effect of its development in the increase of the region’s favourability for the vine-growing, are analysed in our stud

THE EFFECT OF THE TREATMENTS WITH GAMMA RADIATIONS ON THE CONTENT OF NUCLEIC ACIDS TO THE SPECIES OF HYPERICUM PERFORATUM L. AND ECHINACEA PURPUREA (L) MOENCH

The gamma radiations determined to the species of Echinacea purpurea (L) Moench on increase of the quantity of DNA, comparatively to control and to the species of Hypericum perforatum (L.), the decrease of the quantity of DNA (excepting the30 Gy dose witch hand a stimulative effect)

A study of autoencoders as a feature extraction technique for spike sorting

Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current methods have yet to achieve satisfactory performance and many investigators favour sorting manually, even though it is an intensive undertaking that requires prolonged allotments of time. To automate the process, a diverse array of machine learning techniques has been applied. The performance of these techniques depends however critically on the feature extraction step. Here, we propose deep learning using autoencoders as a feature extraction method and evaluate extensively the performance of multiple designs. The models presented are evaluated on publicly available synthetic and real “in vivo” datasets, with various numbers of clusters. The proposed methods indicate a higher performance for the process of spike sorting when compared to other state-of-the-art techniques