Development of new, potent NAPRT inhibitors by CADD

Nicotinate phosphoribosyltransferase (NAPRT) is the rate-limiting enzyme of the Preiss-Handler NAD biosynthetic pathway. NAPRT is widely distributed across healthy mammalian tissues where the enzyme supports the production of NAD, an essential pyridine nucleotide that acts as redox cofactor in multiple metabolic pathways key for bioenergetics and as substrate for several critical cellular processes. Recently, NAPRT has emerged as a novel therapeutic target against cancer owing to its recognition as a biomarker for the success of NAMPT inhibitors in cancer treatment. Indeed, the lack of objective tumor response to NAMPT inhibitors in clinical trials might reflect NAPRT-mediated resistance to these agents. Interestingly, NAPRT displays marked tumor specificity in terms of expression and its regulation mechanisms. Some tumors show NAPRT gene promoter hypermethylation and therefore do not express the enzyme. An insightful study found that NAPRT is frequently upregulated in ovarian, prostate, pancreatic, and breast cancers. In addition, high protein levels of NAPRT were shown to confer resistance to NAMPT inhibitors in several tumor types whereas the simultaneous inhibition of NAMPT and NAPRT resulted in marked anti-tumor effects both in vitro and in vivo. While numerous potent NAMPT inhibitors are available, the few reported NAPRT inhibitors (NAPRTi) have a low affinity for the enzyme. In this work, computer-aided drug design (CADD) efforts to identify putative NAPRT inhibitors were coupled to state-of-the-art in vitro testing of the compounds to study their capacity to inhibit NAPRT and to sensitize the NAPRT-proficient OVCAR-5 cell line to the NAMPTi FK866. Starting from the crystal structure of NAPRT several structure-based drug design (SBDD) experiments based on molecular docking and molecular dynamics simulations were carried out. In the process, large compound libraries of diverse and drug-like small molecules were virtually screened against the NAPRT structure. The selected in silico hits were subsequently tested through cell-based assays in the NAPRT-proficient OVCAR-5 ovarian carcinoma cell line and on the recombinant NAPRT enzyme. We found different chemotypes that efficiently inhibit the enzyme in the micromolar range concentration and for which direct engagement with the target was verified by differential scanning fluorimetry. Of note, the therapeutic potential of these compounds was evidenced by a synergistic interaction between the NAMPT inhibitor FK866 and the new NAPRTi in terms of decreasing OVCAR-5 intracellular NAD+ levels and cell viability. For example, compound IM 29 can potentiate the effect of FK866 of more than two-fold in reducing intracellular NAD+ levels. These results pave the way for the development of a new generation of potent NAPRT inhibitors with anticancer activity

El tráfico marítimo por el puerto de Santiago de Cuba (1858-1868)

Santiago de Cuba desde su fundación logró establecer fluidas relaciones con el exterior y el resto de la Isla a través de su puerto como vía de comunicación más efectiva. Trataremos de ofrecer una somera visión acerca de la forma en que se manifestaron estos contactos con el Caribe, las Américas, Europa y otros puertos cubanos.\ud Escogimos la década de 1858 -1868 porque ésta comienza con la salida de una crisis económica de influencia mundial y termina en vísperas de otra, de carácter más local, pero también más intensa y devastadora para la región oriental de Cuba: el inicio, el 10 de octubre de 1868 de nuestra primera guerra\ud por la Independencia, la Guerra de los Diez Años

La cárcel y el control social : un desarmadero identitario

Fil: Franco, Jorge. Universidad de Buenos Aires. Facultad de Psicología; Argentina.Con numerosas referencias culturales e históricas, el autor analiza el proceso carcelario como un\nverdadero desarmadero de la identidad, donde el individuo es despojado de todos los roles adquiridos a lo\nlargo del ciclo vital, hasta que no queda ninguno. Esto genera un trastorno mayor que la anulación del\npaso del tiempo calendario: ¿qué hacer al terminar la condena y salir del encierro

Dialogue meetings for the prevention and resolution of conflicts: a way to improve relationships in the educational institution Luis Carlos Galán Sarmiento from Itagüí, Colombia

En el presente ensayo se describen algunas de las mejoras en la convivencia escolar obtenidas en la Institución Educativa Luis Carlos Galán Sarmiento del municipio de Itagüí, (Departamento de Antioquia – Colombia), resultado de la realización de los “Encuentros Dialógicos para la Prevención y Resolución de Conflictos”, como una manera particular de implementar la Actuación Educativa de Éxito Modelo Dialógico de Prevención y Resolución de Conflictos, del Proyecto Comunidades de Aprendizaje.This essay describes some of the improvements in school obtained at the Luis Carlos Galán Sarmiento Educational Institution of the municipality of Itagüí, (Department of Antioquia - Colombia); results of the realization of > as a particular way to implement the successful educational actions Dialogic Model of Prevention and Resolution of Conflicts, of the Learning Communities Project

Object Detection in Data Acquired From Aerial Devices

The object detection task, both in images and in videos, has been the source of extraordinary advances with state-of-the-art architectures that can achieve close to perfect precision on large modern datasets. As a result, since these models are trained on large-scale datasets, most of them can adapt to almost any other real-world scenario if given enough data. Nevertheless, there is a specific scenario, aerial images, in which these models tend to perform worse due to their natural characteristics. The main problem differentiating typical object detection datasets from aerial object detection datasets is the object’s scale that needs to be located and identified. Moreover, factors such as the image’s brightness, object rotation and details, and background colours also play a crucial role in the model’s performance, no matter its architecture. Deep learning models make decisions based on the features they can extract from the training data. This technique works particularly well in standard scenarios, where images portray the object at a standard scale in which the object’s details are precise and allow the model to distinguish it from the other objects and background. However, when considering a scenario where the image is being captured from 50 meters above, the object’s details diminish considerably and, thus, logically, making it harder for deep learning models to extract meaningful features that will allow for the identification and localization of the said object. Nowadays, many surveillance systems use static cameras placed in pre-defined places; however, a more appropriate approach for some scenarios would be using drones to surveil a particular area with a specific route. More specifically, these types of surveillance would be adequate for scenarios where it is not feasible to cover the whole area with static cameras, such as wild forests. The first objective of this dissertation is to gather a dataset that focuses on detecting people and vehicles in wild-forest scenarios. The dataset was captured using a DJI drone in four distinct zones of Serra da Estrela. It contains instances captured under different weather conditions – sunny and foggy – and during different parts of the day – morning, afternoon and evening. In addition, it also includes four different types of terrain, earth, tar, forest, and gravel, and there are two classes of objects, person and vehicle. Later on, the second objective of this dissertation aims to precisely analyze how state-ofthe-art single-frame-based and video object detectors perform in the previously described dataset. The analysis focuses on the models’ performance related to each object class in every terrain. Given this, we can demonstrate the exact situations in which the different models stand out and which ones tend to perform the worse. Finally, we propose two methods based on the results obtained during the first phase of experiments, where each aims to solve a different problem that emerged from applying stateof-the-art models to aerial images. The first method aims to improve the performance of the video object detector models in certain situations by using background removal algorithms to delineate specific areas in which the detectors’ predictions are considered valid. One of the main problems with creating a high-quality dataset from scratch is the intensive and time-consuming annotation process after gathering the data. Regarding this, the second method we propose consists of a self-supervised architecture that aims to tackle the particular scarcity of high-quality aerial datasets. The main idea is to analyze the usefulness of unlabelled data in these problems and thus, avoid the immense time-consuming process of labelling the entirety of a full-scale aerial dataset. The reported results show that even with only a partially labelled dataset, it is possible to use the unlabelled data in a self-supervised matter to improve the model’s performance further.A tarefa de deteção de objetos, tanto em imagem como em vídeo, tem contribuído com inúmeros avanços extraordinários no que toca a arquiteturas inovadoras e ao desenvolvimento de conjuntos de dados cada vez mais completos e de qualidade. Nesse sentido, a maioria dos modelos consegue adaptar-se a quase qualquer cenário do mundo real – se existirem dados suficientes –, uma vez que estes modelos são treinados nestes grandes conjuntos de dados. No entanto, existe um cenário específico – as imagens aéreas –, e que devido às suas caraterísticas naturais, estes modelos tendem a mostrar um desempenho de menor qualidade. Contudo, a diferença de escala do próprio objeto que precisa de ser localizado e identificado é o principal aspeto que marca a diferença entre os conjuntos de imagens típicas e os conjuntos de imagens aéreas. Além disso, fatores como o brilho da imagem, a rotação do objeto, os detalhes do mesmo e as cores de fundo também desempenham um papel crucial no desempenho do modelo, independentemente da sua arquitetura. Modelos de aprendizagem profunda tomam decisões com base nas características que conseguem extrair do conjunto de imagens de treino. Esta técnica funciona particularmente bem em cenários padrão, em que as imagens representam o objeto numa escala normal, onde os detalhes do objeto são precisos e permitem que o modelo o distinga de outros objetos. Contudo, ao considerar um cenário onde a imagem está a ser capturada a 50 metros de altura, os detalhes do objeto diminuem consideravelmente e, portanto, torna-se mais difícil para o modelo extrair as melhores caraterísticas significativas que permitem a identificação e localização do objeto. Atualmente, muitos sistemas de vigilância utilizam câmaras estáticas colocadas em locais pré-definidos; porém, uma abordagem mais apropriada para alguns cenários poderia passar por utilizar drones de modo a vigiar uma determinada área com um percurso pré-definido. Mais especificamente, estes tipos de vigilância seriam adequados a cenários em que não é viável cobrir toda a área com câmaras, tal como florestas. O primeiro objetivo do presente trabalho passa por reunir um conjunto de dados que se foque na deteção de pessoas e veículos em florestas. O conjunto de dados foi capturado com um drone DJI em quatro zonas distintas da Serra da Estrela, e contém gravações que foram capturadas com diferentes condições meteorológicas – sol e nevoeiro – e durante diferentes fases do dia – manhã, tarde e ao anoitecer. Além do mais, contempla também quatro tipos diferentes de terreno, terra, alcatrão, floresta e gravilha, para além de existirem duas classes de objetos, pessoa e veículo. Posteriormente, o segundo objetivo contempla a análise precisa do modo como os detetores de objetos de vídeo e imagem atuam no conjunto de dados anteriormente descrito. A análise centra-se no desempenho dos modelos em relação a cada classe de objeto e a cada terreno. Com isto, conseguimos demonstrar uma perspetiva das situações exatas em que os diferentes tipos de modelos se destacam e quais os que tendem a não ter um desempenho tão adequado. Finalmente, com base nos resultados obtidos durante a primeira fase de experiências, o objetivo final tem como propósito propor dois métodos em que cada um deles visa resolver um problema diferente que surgiu da aplicação destes detetores em imagens aéreas. O primeiro método destaca a utilização de algoritmos de remoção de fundo para melhorar o desempenho dos modelos de deteção de objetos em vídeo em determinadas situações com o objetivo de delimitar áreas específicas nas quais as deteções dos modelos devem ser consideradas válidas. Um dos principais problemas na criação de um conjunto de dados de alta qualidade a partir do zero é o processo intensivo e moroso de anotação após a recolha dos dados. Com respeito a isto, o segundo método proposto consiste numa arquitetura auto-supervisionada que tem como objetivo enfrentar a escassez particular de conjuntos de dados aéreos de alta qualidade. A ideia principal é analisar a utilidade dos dados não anotados nestes projetos e, assim, evitar o processo demorado e custoso de anotar a totalidade de um conjunto de dados aéreos. Os resultados relatados mostram que, mesmo com um conjunto de dados parcialmente anotado, é possível utilizar os dados não anotados numa arquitetura auto-supervisionada para melhorar ainda mais o desempenho do modelo

Shareholder wars at Banco Português de Investimento

This case study aims to describe the impacts created on Banco Português de Investimento’s governance when supervision of the largest 120 European banks switched from National Banks to the European Central Bank. When this occurred, BPI was informed that its exposure to Angola, through one of its subsidiaries- Banco de Fomento Angolano-, surpassed the limit of the large risks imposed by the ECB in €3 billion. The urge for solutions to avoid daily sanctions by the ECB triggered a fight for control between the bank’s key shareholders, La Caixa and Santoro Finance, given that Isabel dos Santos, daughter of Angola’s president, was a key shareholder both in BPI and in Banco de Fomento Angolanothrough a company named Unitel. The Governance of the Bank comprising a shareholders voting rights limit, the number of Shareholder’s evolution, the negotiation process that included a Portuguese Government intervention, the sale of part of BFA to Unitel and the tender offer launched by La Caixa in order to control BPI are discussed in detail to provide the reader all the information required to assess on whether or not all good Governance principles were followed throughout the process

Stochastic monotonicity of dependent variables given their sum

Given a finite set of independent random variables, assume one can observe their sum, and denote with s its value. Efron in 1965, and Lehmann in 1966, described conditions on the involved variables such that each of them stochastically increases in the value s, i.e., such that the expected value of any non-decreasing function of the variable increases as s increases. In this paper, we investigate conditions such that this stochastic monotonicity property is satisfied when the assumption of independence is removed. Comparisons in the stronger likelihood ratio order are considered as well