Versatility of Bicoronal flap approach in Head and neck surgeries

Bicoronal approach popularised by Tessier is one of the versatile approaches for skulland frontal region (1-6).In this article we present our experience regarding Bicoronal flapapproach in 3 different cases. Each patient had different pathologies in frontal region forwhich the same approach had been used. We also describe in detail about the incision, itsindications and contra indications, advantages and disadvantages. Incision was made in hairbearing area. Hence post operatively, cosmetic results were appealing in all the patients 9. Itpreserves the supraorbital neurovascular bundle, so complaints related to that are avoided. Inthis article, we discuss about the individual patient, merits and demerits of this particularapproach in each patient. 

Spatial intratumoral heterogeneity and temporal clonal evolution in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is among the most common malignancies, but little is known about its spatial intratumoral heterogeneity (ITH) and temporal clonal evolutionary processes. To address this, we performed multiregion whole-exome sequencing on 51 tumor regions from 13 ESCC cases and multiregion global methylation profiling for 3 of these 13 cases. We found an average of 35.8% heterogeneous somatic mutations with strong evidence of ITH. Half of the driver mutations located on the branches of tumor phylogenetic trees targeted oncogenes, including PIK3CA, NFE2L2 and MTOR, among others. By contrast, the majority of truncal and clonal driver mutations occurred in tumor-suppressor genes, including TP53, KMT2D and ZNF750, among others. Interestingly, phyloepigenetic trees robustly recapitulated the topological structures of the phylogenetic trees, indicating a possible relationship between genetic and epigenetic alterations. Our integrated investigations of spatial ITH and clonal evolution provide an important molecular foundation for enhanced understanding of tumorigenesis and progression in ESCC

Neuronal and astroglial correlates underlying spatiotemporal Intrinsic Optical Signal in the rat hippocampal slice

Widely used for mapping afferent activated brain areas in vivo, the label-free intrinsic optical signal (IOS) is mainly ascribed to blood volume changes subsequent to glial glutamate uptake. By contrast, IOS imaged in vitro is generally attributed to neuronal and glial cell swelling, however the relative contribution of different cell types and molecular players remained largely unknown. We characterized IOS to Schaffer collateral stimulation in the rat hippocampal slice using a 464-element photodiode-array device that enables IOS monitoring at 0.6 ms time-resolution in combination with simultaneous field potential recordings. We used brief half-maximal stimuli by applying a medium intensity 50 Volt-stimulus train within 50 ms (20 Hz). IOS was primarily observed in the str. pyramidale and proximal region of the str. radiatum of the hippocampus. It was eliminated by tetrodotoxin blockade of voltage-gated Na+ channels and was significantly enhanced by suppressing inhibitory signaling with gamma-aminobutyric acid(A) receptor antagonist picrotoxin. We found that IOS was predominantly initiated by postsynaptic Glu receptor activation and progressed by the activation of astroglial Glu transporters and Mg2+-independent astroglial N-methyl-D-aspartate receptors. Under control conditions, role for neuronal K+/Cl- cotransporter KCC2, but not for glial Na+/K+/Cl- cotransporter NKCC1 was observed. Slight enhancement and inhibition of IOS through non-specific Cl- and volume-regulated anion channels, respectively, were also depicted. High-frequency IOS imaging, evoked by brief afferent stimulation in brain slices provide a new paradigm for studying mechanisms underlying IOS genesis. Major players disclosed this way imply that spatiotemporal IOS reflects glutamatergic neuronal activation and astroglial response, as observed within the hippocampus. Our model may help to better interpret in vivo IOS and support diagnosis in the future

Behavioral analysis in cybersecurity using machine learning: a study based on graph representation, class imbalance and temporal dissection

The main goal of this thesis is to improve behavioral cybersecurity analysis using machine learning, exploiting graph structures, temporal dissection, and addressing imbalance problems.This main objective is divided into four specific goals: OBJ1: To study the influence of the temporal resolution on highlighting micro-dynamics in the entity behavior classification problem. In real use cases, time-series information could be not enough for describing the entity behavior classification. For this reason, we plan to exploit graph structures for integrating both structured and unstructured data in a representation of entities and their relationships. In this way, it will be possible to appreciate not only the single temporal communication but the whole behavior of these entities. Nevertheless, entity behaviors evolve over time and therefore, a static graph may not be enoughto describe all these changes. For this reason, we propose to use a temporal dissection for creating temporal subgraphs and therefore, analyze the influence of the temporal resolution on the graph creation and the entity behaviors within. Furthermore, we propose to study how the temporal granularity should be used for highlighting network micro-dynamics and short-term behavioral changes which can be a hint of suspicious activities. OBJ2: To develop novel sampling methods that work with disconnected graphs for addressing imbalanced problems avoiding component topology changes. Graph imbalance problem is a very common and challenging task and traditional graph sampling techniques that work directly on these structures cannot be used without modifying the graph’s intrinsic information or introducing bias. Furthermore, existing techniques have shown to be limited when disconnected graphs are used. For this reason, novel resampling methods for balancing the number of nodes that can be directly applied over disconnected graphs, without altering component topologies, need to be introduced. In particular, we propose to take advantage of the existence of disconnected graphs to detect and replicate the most relevant graph components without changing their topology, while considering traditional data-level strategies for handling the entity behaviors within. OBJ3: To study the usefulness of the generative adversarial networks for addressing the class imbalance problem in cybersecurity applications. Although traditional data-level pre-processing techniques have shown to be effective for addressing class imbalance problems, they have also shown downside effects when highly variable datasets are used, as it happens in cybersecurity. For this reason, new techniques that can exploit the overall data distribution for learning highly variable behaviors should be investigated. In this sense, GANs have shown promising results in the image and video domain, however, their extension to tabular data is not trivial. For this reason, we propose to adapt GANs for working with cybersecurity data and exploit their ability in learning and reproducing the input distribution for addressing the class imbalance problem (as an oversampling technique). Furthermore, since it is not possible to find a unique GAN solution that works for every scenario, we propose to study several GAN architectures with several training configurations to detect which is the best option for a cybersecurity application. OBJ4: To analyze temporal data trends and performance drift for enhancing cyber threat analysis. Temporal dynamics and incoming new data can affect the quality of the predictions compromising the model reliability. This phenomenon makes models get outdated without noticing. In this sense, it is very important to be able to extract more insightful information from the application domain analyzing data trends, learning processes, and performance drifts over time. For this reason, we propose to develop a systematic approach for analyzing how the data quality and their amount affect the learning process. Moreover, in the contextof CTI, we propose to study the relations between temporal performance drifts and the input data distribution for detecting possible model limitations, enhancing cyber threat analysis.Programa de Doctorado en Ciencias y Tecnologías Industriales (RD 99/2011) Industria Zientzietako eta Teknologietako Doktoretza Programa (ED 99/2011

Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials