Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches.

Cancer disease is one of the most important pathologies in the world, as it causes the death of millions of people, and the cure of this disease is limited in most cases. Rapid spread is one of the most important features of this disease, so many efforts are focused on its early-stage detection and localization. Medicine has made numerous advances in the recent decades with the help of artificial intelligence (AI), reducing costs and saving time.

Design and Application of Automated Algorithms for Diagnosis and Treatment Optimization in Neurodegenerative Diseases.

Neurodegenerative diseases represent a growing healthcare problem, mainly related to an aging population worldwide and thus their increasing prevalence. In particular, Alzheimer's disease (AD) and Parkinson's disease (PD) are leading neurodegenerative diseases. To aid their diagnosis and optimize treatment, we have developed a classification algorithm for AD to manipulate magnetic resonance images (MRI) stored in a large database of patients, containing 1,200 images.

Inventory pooling decisions under demand scenarios in times of COVID-19.

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. The aim of this research is to propose a novel inventory pooling model to help determine order sizes and safety inventories in local hospital warehouses. The current study attempts to portray the availability of pharmaceutical items in public hospitals facing COVID-19 challenges.

[Tuberculous pericarditis an infrequent extrapulmonary manifestation of TB].

Extrapulmonary tuberculosis (TB) contributes to 15% of total cases, representing a great diagnostic and therapeutic challenge. Pericardial involvement is present in 1 to 2% of TB patients and is considered an unusual presentation form of TB. We report a 67-year-old male presenting with fever and progressive dyspnea.

Lot-Size Models with Uncertain Demand Considering Its Skewness/Kurtosis and Stochastic Programming Applied to Hospital Pharmacy with Sensor-Related COVID-19 Data.

Governments have been challenged to provide timely medical care to face the COVID-19 pandemic. Under this pandemic, the demand for pharmaceutical products has changed significantly. Some of these products are in high demand, while, for others, their demand falls sharply.

Compressive spectral image fusion via a single aperture high throughput imaging system.

Spectral image fusion techniques combine the detailed spatial information of a multispectral (MS) image and the rich spectral information of a hyperspectral (HS) image into a high-spatial and high-spectral resolution image. Due to the data deluge entailed by such images, new imaging modalities have exploited their intrinsic correlations in such a way that, a computational algorithm can fuse them from few multiplexed linear projections. The latter has been coined compressive spectral image fusion.

Snapshot compressive spectral depth imaging from coded aberrations.

Compressive spectral depth imaging (CSDI) is an emerging technology aiming to reconstruct spectral and depth information of a scene from a limited set of two-dimensional projections. CSDI architectures have conventionally relied on stereo setups that require the acquisition of multiple shots attained via dynamically programmable spatial light modulators (SLM). This work proposes a snapshot CSDI architecture that exploits both phase and amplitude modulation and uses a single image sensor.

Managing slow-moving item: a zero-inflated truncated normal approach for modeling demand.

This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages.

A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis.

Background: This work presents a forecast model for non-typhoidal salmonellosis outbreaks.

Method: This forecast model is based on fitted values of multivariate regression time series that consider diagnosis and estimation of different parameters, through a very flexible statistical treatment called generalized auto-regressive and moving average models (GSARIMA).

Results: The forecast model was validated by analyzing the cases of serovar Enteritidis in Sydney Australia (2014-2016), the environmental conditions and the consumption of high-risk food as predictive variables.

Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018.

In the current supplement, we are proud to present seventeen relevant contributions from the 6th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2018), which was held during April 25-27, 2018 in Granada (Spain). These contributions have been chosen because of their quality and the importance of their findings.

A Methodology for Data-Driven Decision-Making in the Monitoring of Particulate Matter Environmental Contamination in Santiago of Chile.

Atmospheric pollution derives mainly from anthropogenic activities that use combustion and may lead to adverse effects in exposed populations. It is generally accepted that air contamination causes cardiovascular and pulmonary morbidity in addition to increased mortality after exposure, but other epidemiological associations have also been described, including cancer as well as reproductive and immunological toxicity. Thus the concentration of chemicals in the air must be controlled.

Primary Conjunctival Rhabdomyosarcoma in a Pediatric Patient.

Rhabdomyosarcomas are neoplasms with a high degree of malignancy and arise from the embryonic mesenchyme. They represent approximately 5% of all pediatric tumors and their main locations are the head and neck (45%), trunk (40%), and extremities (15%). Twenty-five percent to 30% of the head and neck rhabdomyosarcomas appear in the orbit; however, its origin from the conjunctiva is rare, with few case reports published in the literature.

Modeling lot-size with time-dependent demand based on stochastic programming and case study of drug supply in Chile.

The objective of this paper is to propose a lot-sizing methodology for an inventory system that faces time-dependent random demands and that seeks to minimize total cost as a function of order, purchase, holding and shortage costs. A two-stage stochastic programming framework is derived to optimize lot-sizing decisions over a time horizon. To this end, we simulate a demand time-series by using a generalized autoregressive moving average structure.

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level.

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world.

Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling.

Background: Nowadays, many public repositories containing large microarray gene expression datasets are available. However, the problem lies in the fact that microarray technology are less powerful and accurate than more recent Next Generation Sequencing technologies, such as RNA-Seq. In any case, information from microarrays is truthful and robust, thus it can be exploited through the integration of microarray data with RNA-Seq data.

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

Motivation: Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar.

Electronic and transport properties in circular graphene structures with a pentagonal disclination.

: We investigate the electronic and transport properties of circular graphene structures (quantum dots) that include a pentagonal defect. In our calculations, we employ a tight-binding model determining total and local density of states, transmission function and participation number. For the closed structure, we observe that the effect of the defect is concentrated mainly on energies near to zero, which is characteristic of edge states in graphene.

One-qubit quantum gates in a circular graphene quantum dot: genetic algorithm approach.

The aim of this work was to design and control, using genetic algorithm (GA) for parameter optimization, one-charge-qubit quantum logic gates σx, σy, and σz, using two bound states as a qubit space, of circular graphene quantum dots in a homogeneous magnetic field. The method employed for the proposed gate implementation is through the quantum dynamic control of the qubit subspace with an oscillating electric field and an onsite (inside the quantum dot) gate voltage pulse with amplitude and time width modulation which introduce relative phases and transitions between states. Our results show that we can obtain values of fitness or gate fidelity close to 1, avoiding the leakage probability to higher states.

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques.

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy.

[Hyponatremia on admission to the emergency room as a risk factor for hospital mortality].

Background: Patients who develop hyponatremia during their hospitalization have higher hospital mortality.

Aim: To determine if the presence of hyponatremia on admission to the emergency room is a risk factor for hospital mortality.

Patients And Methods: Two hundred forty five patients consecutively admitted to the emergency room and then transferred to the Medicine Department, where they finally died, were matched for age and gender with 245 control subjects admitted to the emergency room and hospitalized in the Medicine Department at the same time, but survived.

Bloch oscillations in carbon nanotubes.

Bloch oscillations arise when electrons are in a one-dimensional linear chain of atoms under a constant electric field. In this paper we show numerically that electrons in different types of carbon nanotubes show oscillations with a Bloch frequency proportional to the constant electric field applied along the nanotube axis. We show these oscillations, calculating the quadratic displacement as a function of the electric field.

[Factors related to non-participation in a breast cancer early detection program].

Objective: To determine the causes of non-participation in a breast cancer early detection program for women in the northern area of Almería (Spain).

Methods: We performed a case-control study. A sample of women included in a breast cancer early detection program between October 2002 and February 2004 was chosen.

Surfactantless synthesis and textural properties of self-assembled mesoporous SnO(2).

Ordered surfactantless self-assembled, mesoporous SnO(2) adsorbents, consisting of tubular voids of nanometric sizes, are prepared by the sol-gel processing of tin (IV) tetra-tert-amyloxide, Sn(OAm(t))(4), whose molecules have been previously chelated with acetylacetone in the absence of water, to modulate their reactivity and to promote an incipient self-assembling of -O-Sn-O oligomeric species; ultimately, the necessary amount of water to induce the hydrolysis-condensation reactions is added to this aged sol, then producing tubular pore templates within the SnO(2) xerogel network. A collection of mesoporous SnO(2) xerogels of assorted structural properties has been obtained after calcination in air of precursory gels proceeding from an aged mixture of Sn(OAm(t))(4) and acetylacetone at temperatures in the range 200-1000 degrees C. N(2) sorption isotherms measured on these SnO(2) solids evidence mesoporous structures of diverse textural characteristics (i.

Physicochemical aspects of novel surfactantless, self-templated mesoporous SnO2 thin films.

A novel method of synthesis consisting of the production of ordered arrangements of tubular pores distributed inside SnO2 annealed thin films, which are prepared from a rotating disk process carried out at 2000-3500 rpm, is herein described. The main novelty is that no surfactant molecules are required in order to create these ordered pore structures; the templating entities are supramolecular assemblies of oligomeric chains formed during the extra-long aging allowed to the sol-gel processing of tin(IV) tetra-tert-amiloxide, Sn(OAm(t))4, chelated with acetylacetone molecules. Low angle X-ray diffraction peaks of SnO2 thin films calcined at 500 degrees C clearly certify the existence of ordered mesostructures when employing the right H2O/Sn(OAm(t))4 molar ratio during the SnO2 sol-gel synthesis.