Framework-Topology-Controlled Singlet Fission in Metal-Organic Frameworks.

Singlet fission (SF) has been explored as a viable route to improve photovoltaic performance by producing more excitons. Efficient SF is achieved through a high degree of interchromophoric coupling that facilitates electron superexchange to generate triplet pairs. However, strongly coupled chromophores often form excimers that can serve as an SF intermediate or a low-energy trap site. The succeeding decoherence process, however, requires an optimum electronic coupling to facilitate the isolation of triplet production from the initially prepared correlated triplet pair. Conformational flexibility and dielectric modulation can provide a means to tune the SF mechanism and efficiency by modulating the interchromophoric electronic interaction. Such a strategy cannot be easily adopted in densely stacked traditional organic solids. Here, we show that the assembly of the SF-active chromophores around well-defined pores of solution-stable metal-organic frameworks (MOFs) can be a great platform for a modular SF process. A series of three new MOFs, built out from 9,10-bis(ethynylenephenyl)anthracene-derived struts, show a topology-defined packing density and conformational flexibility of the anthracene core to dictate the SF mechanism. Various steady-state and transient spectroscopic data suggest that the initially prepared singlet population can prefer either an excimer-mediated SF or a direct SF (both through a virtual charge-transfer (CT) state). These solution-stable frameworks offer the tunability of the dielectric environment to facilitate the SF process by stabilizing the CT state. Given that MOFs are a great platform for various photophysical and photochemical developments, generating a large population of long-lived triplets can expand their utilities in various photon energy conversion schemes.

Compilation of the Antimicrobial Compounds Produced by Burkholderia Sensu Stricto.

Due to the increase in multidrug-resistant microorganisms, the investigation of novel or more efficient antimicrobial compounds is essential. The World Health Organization issued a list of priority multidrug-resistant bacteria whose eradication will require new antibiotics. Among them, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae are in the "critical" (most urgent) category. As a result, major investigations are ongoing worldwide to discover new antimicrobial compounds. Burkholderia, specifically Burkholderia sensu stricto, is recognized as an antimicrobial-producing group of species. Highly dissimilar compounds are among the molecules produced by this genus, such as those that are unique to a particular strain (like compound CF66I produced by Burkholderia cepacia CF-66) or antimicrobials found in a number of species, e.g., phenazines or ornibactins. The compounds produced by Burkholderia include N-containing heterocycles, volatile organic compounds, polyenes, polyynes, siderophores, macrolides, bacteriocins, quinolones, and other not classified antimicrobials. Some of them might be candidates not only for antimicrobials for both bacteria and fungi, but also as anticancer or antitumor agents. Therefore, in this review, the wide range of antimicrobial compounds produced by Burkholderia is explored, focusing especially on those compounds that were tested in vitro for antimicrobial activity. In addition, information was gathered regarding novel compounds discovered by genome-guided approaches.

Burkholderia orbicola sp. nov., a novel species within the Burkholderia cepacia complex.

Genome analysis of strains placed in the NCBI genome database as Burkholderia cenocepacia defined nine genomic species groups. The largest group (259 strains) corresponds to B. cenocepacia and the second largest group (58 strains) was identified as "Burkholderia servocepacia", a Burkholderia species classification which has not been validly published. The publication of "B. servocepacia" did not comply with Rule 27 and Recommendation 30 from the International Code of Nomenclature of Prokaryotes (ICNP) and have errors in the type strain name and the protologue describing the novel species. Here, we correct the position of this species by showing essential information that meets the criteria defined by ICNP. After additional analysis complying with taxonomic criteria, we propose that the invalid "B. servocepacia" be renamed as Burkholderia orbicola sp. nov. The original study proposing "B. servocepacia" was misleading, because this name derives from the Latin "servo" meaning "to protect/watch over", and the authors proposed this based on the beneficial biocontrol properties of several strains within the group. However, it is clear that "B. servocepacia" isolates are capable of opportunistic infection, and the proposed name Burkholderia orbicola sp. nov. takes into account these diverse phenotypic traits. The type strain is TAtl-371 T (= LMG 30279 T = CM-CNRG 715 T).

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. Specifically, we modulate the spectral-depth information in two steps. Firstly, a deformable mirror (DM) is used as a phase modulator to induce a focal length sweeping while simultaneously introducing a controlled aberration. The phase-modulated wavefront is then spatially modulated and spectrally dispersed by a digital micromirror device (DMD) and a prism, respectively. Therefore, each depth plane is modulated by a variable phase and binary code. Complimentary, we also propose a computational methodology to recover the underlying spectral depth hypercube efficiently. Through simulations and our experimental proof-of-concept implementation, we demonstrate that the proposed computational imaging system is a viable approach to capture spectral-depth hypercubes from a single image.

Deviation from Equilibrium Thermodynamics of an Asphaltene Model Compound during Compression-Expansion Experiments at Fluid-Fluid Interfaces.

Asphaltenes play a crucial role in crude oil behavior, and model compounds are often used to capture, mimic, and predict certain interfacial properties. In previous works, sorption of an asphaltene model compound (C5PeC11) was studied using surface pressure isotherms, where a deviation from the expected thermodynamic behavior of the interface during decane-water and air-water compression experiments was observed but not explained. In this work, the interfacial behavior of C5PeC11 was assessed at the decane-water and the air-water interfaces using a multiscale approach that includes: compression-expansion experiments on rectangular and radial Langmuir troughs, dynamic interfacial stress relaxation, and fluorescence microscopy imaging. Connections between molecular and microscopic phenomena strongly suggest that the nonthermodynamic response can be explained through a dynamic effect whose origin lies in the predominance of intermolecular forces in C5PeC11 molecules over the mechanical compression force applied. When aggregation begins at the air-water interface, stable structures are formed, and the nonthermodynamic phenomenon is not observed in subsequent compressions. However, at the decane-water interface, the initial aggregation is not consolidated due to the effect of the oil phase on the free energy of the interface allowing the high reproducibility of the dynamic effect in subsequent compression cycles. These results highlight the need to probe interfacial systems at various length scales to adequately separate equilibrium thermodynamics from dynamic responses.

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. These changes in the random demand patterns are connected with changes in the skewness (asymmetry) and kurtosis of their data distribution. Such changes are critical to determining optimal lots and inventory costs. The lot-size model helps to make decisions based on probabilistic demand when calculating the optimal costs of supply using two-stage stochastic programming. The objective of this study is to evaluate how the skewness and kurtosis of the distribution of demand data, collected through sensors, affect the modeling of inventories of hospital pharmacy products helpful to treat COVID-19. The use of stochastic programming allows us to obtain results under demand uncertainty that are closer to reality. We carry out a simulation study to evaluate the performance of our methodology under different demand scenarios with diverse degrees of skewness and kurtosis. A case study in the field of hospital pharmacy with sensor-related COVID-19 data is also provided. An algorithm that permits us to use sensors when submitting requests for supplying pharmaceutical products in the hospital treatment of COVID-19 is designed. We show that the coefficients of skewness and kurtosis impact the total costs of inventory that involve order, purchase, holding, and shortage. We conclude that the asymmetry and kurtosis of the demand statistical distribution do not seem to affect the first-stage lot-size decisions. However, demand patterns with high positive skewness are related to significant increases in expected inventories on hand and shortage, increasing the costs of second-stage decisions. Thus, demand distributions that are highly asymmetrical to the right and leptokurtic favor high total costs in probabilistic lot-size systems.

[Tuberculous pericarditis an infrequent extrapulmonary manifestation of TB]. / Pericarditis tuberculosa: una manifestación extrapulmonar infrecuente de TBC.

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. A chest CAT scan showed a bilateral pleural effusion and an extensive pericardial effusion. An echocardiogram showed signs of tamponade. Therefore, an emergency pericardiectomy was performed. The pathological report of pericardial tissue showed caseating necrosis and its Koch culture was positive. The patient was treated with anti-tuberculous drugs with a favorable evolution.

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.

Probing Interfacial Structure and Dynamics of Model and Natural Asphaltenes at Fluid-Fluid Interfaces.

Asphaltenes are largely responsible for crude oil interfacial behavior. Due to their complex molecular nature, studying connections between interfacial properties and molecular structure is challenging, and these connections remain unclear. Several groups have reported on the interfacial behavior of asphaltenes, but a unified picture of both interfacial dynamics and thermodynamics is still missing. We seek to establish connections between asphaltene interfacial morphology and interfacial dynamics by combining interfacial dilatational deformation with microscopic structural imaging analysis. Understanding the behavior of natural asphaltene samples is made difficult by the inherent molecular variability. Therefore, we have also studied the behavior of an asphaltene model compound to draw fundamental structure-property relationships. This work contains simultaneous interfacial deformation and microscopy in systems of natural and model asphaltenes at air-water and decane-water interfaces. How the dynamics of natural asphaltenes influences the morphological and thermodynamic state of the air-water and decane-water interfaces is discussed based on the deviations observed between isotropic and anisotropic deformations. Areas where model asphaltenes can help us to understand the behavior of natural asphaltenes are identified such as its high surface pressure activity and aggregation character. An aggregation mechanism for model and natural asphaltenes is proposed based on an observed relationship between microscopic and millimetric aggregates.

Cupriavidus agavae sp. nov., a species isolated from Agave L. rhizosphere in northeast Mexico.

During the isolation of bacteria from the Agave L. rhizosphere in northeast Mexico, four strains with similar BOX-PCR patterns were collected. The 16S rRNA gene sequences of all four strains were very similar to each other and that of the type strains of Cupriavidus metallidurans CH34T (98.49 % sequence similarity) and Cupriavidus necator N-1T (98.35 %). The genome of strain ASC-9842T was sequenced and compared to those of other Cupriavidus species. ANIb and ANIm values with the most closely related species were lower than 95%, while the in silico DNA-DNA hybridization values were also much lower than 70 %, consistent with the proposal that they represent a novel species. This conclusion was supported by additional phenotypic and chemotaxonomic analyses. Therefore, the name Cupriavidus agavae sp. nov. is proposed with the type strain ASC-9842T (=LMG 26414T=CIP 110327T).

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. We propose that monitoring of air quality may be achieved by employing data analytics to generate information within the context of data-driven decision making to prevent and/or adequately alert the population about possible critical episodes of air contamination. In this paper, we propose a methodology for monitoring particulate matter pollution in Santiago of Chile which is based on bivariate control charts with heavy-tailed asymmetric distributions. This methodology is useful for monitoring environmental risk when the particulate matter concentrations follow bivariate Birnbaum-Saunders or Birnbaum-Saunders-t-Student distributions. A case study with real particulate matter pollution from Santiago is provided, which shows that the methodology is suitable to alert early episodes of extreme air pollution. The results are in agreement with the critical episodes reported with the current model used by the Chilean health authority.

Draft Genome of Burkholderia cenocepacia TAtl-371, a Strain from the Burkholderia cepacia Complex Retains Antagonism in Different Carbon and Nitrogen Sources.

Burkholderia cenocepacia TAtl-371 was isolated from the rhizosphere of a tomato plant growing in Atlatlahucan, Morelos, Mexico. This strain exhibited a broad antimicrobial spectrum against bacteria, yeast, and fungi. Here, we report and describe the improved, high-quality permanent draft genome of B. cenocepacia TAtl-371, which was sequenced using a combination of PacBio RS and PacBio RS II sequencing methods. The 7,496,106 bp genome of the TAtl-371 strain is arranged in three scaffolds, contains 6722 protein-coding genes, and 99 RNA only-encoding genes. Genome analysis revealed genes related to biosynthesis of antimicrobials such as non-ribosomal peptides, siderophores, chitinases, and bacteriocins. Moreover, analysis of bacterial growth on different carbon and nitrogen sources shows that the strain retains its antimicrobial ability.

[The controversial Burkholderia cepacia complex, a group of plant growth promoting species and plant, animals and human pathogens]. / El controvertido complejo Burkholderia cepacia, un grupo de especies promotoras del crecimiento vegetal y patógenas de plantas, animales y humanos.

The Burkholderia cepacia complex is a group of 22 species, which are known as opportunistic pathogens in immunocompromised people, especially those suffering from cystic fibrosis. It is also found in nosocomial infections and is difficult to eradicate due to intrinsic resistance to several antibiotics. The species have large genomes (up to 9 Mbp), distributed into 2-5 replicons. These features significantly contribute to genome plasticity, which makes them thrive in different environments like soil, water, plants or even producing nodules in legume plants. Some B. cepacia complex species are beneficial in bioremediation, biocontrol and plant-growth promotion. However, because the B. cepacia complex is involved in human infection, its use in agriculture is restricted. B. cepacia complex is being constantly studied due to the health problems that it causes and because of its agricultural potential. In this review, the history of B. cepacia complex and the most recently published information related to this complex are revised.

Broad-spectrum antimicrobial activity by Burkholderia cenocepacia TAtl-371, a strain isolated from the tomato rhizosphere.

The Burkholderia cepacia complex (Bcc) comprises a group of 24 species, many of which are opportunistic pathogens of immunocompromised patients and also are widely distributed in agricultural soils. Several Bcc strains synthesize strain-specific antagonistic compounds. In this study, the broad killing activity of B. cenocepacia TAtl-371, a Bcc strain isolated from the tomato rhizosphere, was characterized. This strain exhibits a remarkable antagonism against bacteria, yeast and fungi including other Bcc strains, multidrug-resistant human pathogens and plant pathogens. Genome analysis of strain TAtl-371 revealed several genes involved in the production of antagonistic compounds: siderophores, bacteriocins and hydrolytic enzymes. In pursuit of these activities, we observed growth inhibition of Candida glabrata and Paraburkholderia phenazinium that was dependent on the iron concentration in the medium, suggesting the involvement of siderophores. This strain also produces a previously described lectin-like bacteriocin (LlpA88) and here this was shown to inhibit only Bcc strains but no other bacteria. Moreover, a compound with an m/z 391.2845 with antagonistic activity against Tatumella terrea SHS 2008T was isolated from the TAtl-371 culture supernatant. This strain also contains a phage-tail-like bacteriocin (tailocin) and two chitinases, but the activity of these compounds was not detected. Nevertheless, the previous activities are not responsible for the whole antimicrobial spectrum of TAtl-371 seen on agar plates, suggesting the presence of other compounds yet to be found. In summary, we observed a diversified antimicrobial activity for strain TAtl-371 and believe it supports the biotechnological potential of this Bcc strain as a source of new antimicrobials.

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. Additionally, information extraction and acquisition of large number of samples in RNA-Seq still entails very high costs in terms of time and computational resources.This paper proposes a new model to find the gene signature of breast cancer cell lines through the integration of heterogeneous data from different breast cancer datasets, obtained from microarray and RNA-Seq technologies. Consequently, data integration is expected to provide a more robust statistical significance to the results obtained. Finally, a classification method is proposed in order to test the robustness of the Differentially Expressed Genes when unseen data is presented for diagnosis. RESULTS: The proposed data integration allows analyzing gene expression samples coming from different technologies. The most significant genes of the whole integrated data were obtained through the intersection of the three gene sets, corresponding to the identified expressed genes within the microarray data itself, within the RNA-Seq data itself, and within the integrated data from both technologies. This intersection reveals 98 possible technology-independent biomarkers. Two different heterogeneous datasets were distinguished for the classification tasks: a training dataset for gene expression identification and classifier validation, and a test dataset with unseen data for testing the classifier. Both of them achieved great classification accuracies, therefore confirming the validity of the obtained set of genes as possible biomarkers for breast cancer. Through a feature selection process, a final small subset made up by six genes was considered for breast cancer diagnosis. CONCLUSIONS: This work proposes a novel data integration stage in the traditional gene expression analysis pipeline through the combination of heterogeneous data from microarrays and RNA-Seq technologies. Available samples have been successfully classified using a subset of six genes obtained by a feature selection method. Consequently, a new classification and diagnosis tool was built and its performance was validated using previously unseen samples.

Localization and role of MYO-1, an endocytic protein in hyphae of Neurospora crassa.

The subapical endocytic collar is a prominent feature of hyphae of Neurospora crassa. It comprises a dynamic collection of actin patches associated with a number of proteins required for endocytosis, namely, ARP-2/3 complex, fimbrin, coronin, etc. We presently show that MYO-1 is another key component of this endocytic collar. A myo-1 sequence was identified in the genome of N. crassa and used it to generate a strain with a myo-1-sgfp allele under the ccg1 promoter. Examination of living hyphae by confocal microscopy, revealed MYO-1-GFP located mainly as a dynamic collection of small patches arranged in collar-like fashion in the hyphal subapex. Dual tagging showed MYO-1-GFP partially colocalized with two other endocytic proteins, fimbrin and coronin. MYO-1 was also present during septum formation. By recovering a viable strain, albeit severely inhibited, after deletion of myo-1, it was possible to investigate the phenotypic consequences of the elimination of MYO-1. Deletion of myo-1 caused a severe reduction in growth rate (95%), near absence of aerial mycelium and no conidiation. A reduced uptake of the lipophilic dye FM4-64 indicated a deficiency in endocytosis in the Δmyo-1 mutant. Hyphae were produced by the Δmyo-1 mutant but their morphogenesis was severely affected; hyphal morphology was distorted displaying irregular periods of isotropic and polarized growth. The morphological alterations were accompanied, and presumably caused, by a disruption in the organization and dynamics of a myosin-deprived actin cytoskeleton that, ultimately, compromised the stability and function of the Spitzenkörper as a vesicle supply center.

The SrkA Kinase Is Part of the SakA Mitogen-Activated Protein Kinase Interactome and Regulates Stress Responses and Development in Aspergillus nidulans.

Fungi and many other eukaryotes use specialized mitogen-activated protein kinases (MAPK) of the Hog1/p38 family to transduce environmental stress signals. In Aspergillus nidulans, the MAPK SakA and the transcription factor AtfA are components of a central multiple stress-signaling pathway that also regulates development. Here we characterize SrkA, a putative MAPK-activated protein kinase, as a novel component of this pathway. ΔsrkA and ΔsakA mutants share a derepressed sexual development phenotype. However, ΔsrkA mutants are not sensitive to oxidative stress, and in fact, srkA inactivation partially suppresses the sensitivity of ΔsakA mutant conidia to H2O2, tert-butyl-hydroperoxide (t-BOOH), and menadione. In the absence of stress, SrkA shows physical interaction with nonphosphorylated SakA in the cytosol. We show that H2O2 induces a drastic change in mitochondrial morphology consistent with a fission process and the relocalization of SrkA to nuclei and mitochondria, depending on the presence of SakA. SakA-SrkA nuclear interaction is also observed during normal asexual development in dormant spores. Using SakA and SrkA S-tag pulldown and purification studies coupled to mass spectrometry, we found that SakA interacts with SrkA, the stress MAPK MpkC, the PPT1-type phosphatase AN6892, and other proteins involved in cell cycle regulation, DNA damage response, mRNA stability and protein synthesis, mitochondrial function, and other stress-related responses. We propose that oxidative stress induces DNA damage and mitochondrial fission and that SakA and SrkA mediate cell cycle arrest and regulate mitochondrial function during stress. Our results provide new insights into the mechanisms by which SakA and SrkA regulate the remodelling of cell physiology during oxidative stress and development.

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. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.

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. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. RESULTS: The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. AVAILABILITY: The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.

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. In this paper, deep learning models (DL) are used to present a novel method for detecting and localizing cancerous zones in WSI images, using tissue patch overlay to improve performance results. A novel overlapping methodology is proposed and discussed, together with different alternatives to evaluate the labels of the patches overlapping in the same zone to improve detection performance. The goal is to strengthen the labeling of different areas of an image with multiple overlapping patch testing. The results show that the proposed method improves the traditional framework and provides a different approach to cancer detection. The proposed method, based on applying 3x3 step 2 average pooling filters on overlapping patch labels, provides a better result with a 12.9% correction percentage for misclassified patches on the HUP dataset and 15.8% on the CINIJ dataset. In addition, a filter is implemented to correct isolated patches that were also misclassified. Finally, a CNN decision threshold study is performed to analyze the impact of the threshold value on the accuracy of the model. The alteration of the threshold decision along with the filter for isolated patches and the proposed method for overlapping patches, corrects about 20% of the patches that are mislabeled in the traditional method. As a whole, the proposed method achieves an accuracy rate of 94.6%. The code is available at https://github.com/sergioortiz26/Cancer_overlapping_filter_WSI_images.