An empirical analysis of the relationship among price, demand and CO2 emissions in the Spanish electricity market.

CO2 emissions play a crucial role in international politics. Countries enter into agreements to reduce the amount of pollution emitted into the atmosphere. Energy generation is one of the main contributors to pollution and is generally considered the main cause of climate change. Despite the interest in reducing CO2 emissions, few studies have focused on investigating energy pricing technologies. This article analyzes the technologies used to meet the demand for electricity from 2016 to 2021. The analysis is based on data provided by the Spanish Electricity System regulator, using statistical and clustering techniques. The objective is to establish the relationship between the level of pollution of electricity generation technologies and the hourly price and demand. Overall, the results suggest that there are two distinct periods with respect to the technologies used in the studied years, with a trend toward the use of cleaner technologies and a decrease in power generation using fossil fuels. It is also surprising that in the years 2016 to 2018, the most polluting technologies offered the cheapest prices.

Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images.

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

Trocar-site incisional hernia after laparoscopic colorectal surgery: a significant problem? Incidence and risk factors from a single-center cohort.

BACKGROUND: Trocar-site incisional hernia (TSIH) after laparoscopic surgery has been scarcely studied. TSIH incidence and risk factors have never been properly studied for laparoscopic colorectal surgery. METHODS: A retrospective analytic study in a tertiary hospital was performed including patients who underwent elective laparoscopic colorectal surgery between 2014 and 2016. Clinical and radiological TSIH were analyzed. RESULTS: 272 patients with a mean age of 70.7 years were included. 205 (75.4%) underwent surgery for a malignant disease. The most common procedure was right colectomy (108 patients, 39.7%). After a mean follow-up of 30.8 months 64 (23.5%) patients developed a TSIH. However, only 7 out of 64 (10.9%) patients with a TSIH underwent incisional hernia repair. That means that 2.6% of all the patients underwent TSIH repair. 44 (68.8%) patients had TSIH in the umbilical Hasson trocar. In the multivariate analysis, the existence of an umbilical Hasson trocar orifice was the only statistically significant risk factor for TSIH development. CONCLUSIONS: Incidence of TSIH was high, although few patients underwent incisional hernia repair. Most TSIH were observed in the umbilical Hasson trocar, which was the only risk factor for TSIH development in the multivariate analysis. Efforts should be addressed to avoid TSIH in the umbilical Hasson trocar.

Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance.

Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often involves metastasis with its consequent threat for patients. DNA methylation-derived databases have become an interesting primary source for supervised knowledge extraction regarding breast cancer. Unfortunately, the study of DNA methylation involves the processing of hundreds of thousands of features for every patient. DNA methylation is featured by High Dimension Low Sample Size which has shown well-known issues regarding feature selection and generation. Autoencoders (AEs) appear as a specific technique for conducting nonlinear feature fusion. Our main objective in this work is to design a procedure to summarize DNA methylation by taking advantage of AEs. Our proposal is able to generate new features from the values of CpG sites of patients with and without recurrence. Then, a limited set of relevant genes to characterize breast cancer recurrence is proposed by the application of survival analysis and a pondered ranking of genes according to the distribution of their CpG sites. To test our proposal we have selected a dataset from The Cancer Genome Atlas data portal and an AE with a single-hidden layer. The literature and enrichment analysis (based on genomic context and functional annotation) conducted regarding the genes obtained with our experiment confirmed that all of these genes were related to breast cancer recurrence.

A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation.

Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes involved in breast cancer can be identified by immunohistochemistry. However, this method has problems arising from the intra-observer and inter-observer variability in the assessment of pathologic variables, which may result in misleading conclusions. Using an optimal selection of preprocessing techniques may help to reduce observer variability. Deep learning has emerged as a powerful technique for any tasks related to machine learning such as classification and regression. The aim of this work is to use autoencoders (neural networks commonly used to feed deep learning architectures) to improve the quality of the data for developing immunohistochemistry signatures with prognostic value in breast cancer. Our testing on data from 222 patients with invasive non-special type breast carcinoma shows that an automatic binarization of experimental data after autoencoding could outperform other classical preprocessing techniques (such as human-dependent or automatic binarization only) when applied to the prognosis of breast cancer by immunohistochemical signatures.

Characterization of manganese-dependent peroxidase isoenzymes from the ligninolytic fungus Phanerochaete flavido-alba.

Phanerochaete flavido-alba is able to decolorize and detoxify olive oil wastewater (OMW) in a process in which simple and polymeric phenols are removed. An unusual acidic MnP is accumulated during the degradation course. This microorganism produces two families of MnPs. MnP1 has an apparent molecular weight of 45 kDa and is secreted as a mixture of isoenzymes with pI ranging from 5.6 to 4.75. MnP2, which is produced as an unique isoenzyme, has an apparent molecular weight of 55.6 Mr and an unusual acidic pI lower than 2.8. The higher specific peroxidase activity for purified MnP2 was for Mn2+ oxidation. Hydroquinone and methylhydroquinone oxidation by MnP2 was Mn2+ dependent, in reaction mixtures without exogenous H2O2. Conversely, ABTS oxidation was Mn2+ independent. Two different DNA fragments (mnpA and mnpB), amplified by PCR, using MnP2 N-terminal sequence and oligonucleotides deduced from two conserved sequences of other MnPs, code for MnPs that belong to the P. chrysosporium mnp2 subfamily on the basis of intron position. The structure of mnpA and mnpB seems to be related to known manganese peroxidase genes, but mnpA encodes an Alanine instead of a Serine (Ser168) regarded as invariant within typical MnPs.

[Malposition of central venous catheters in hospitalized patients]. / Malposición de catéteres venosos centrales en pacientes hospitalizados.

BACKGROUND: At the time of inserting a central catheter, it is recommended to check that its distal tip is positioned outside the heart cavities. Our objective was to determine the degree of fulfillment of this recommendation. PATIENTS AND METHOD: Cross-sectional study on patients carrying a central venous catheter who were admitted to conventional areas of a university hospital. We considered as "risk catheter" any catheter whose distal tip was located within the cardiac cavities. RESULTS: 34 out of 362 hospitalized patients had a central venous catheter. Fifty per cent of them were found to have a "risk catheter". In spite of its X-ray confirmation, no patient underwent a subsequent modification of the distal tip position. CONCLUSIONS: We have noticed a high percentage of patients with central venous catheters whose insertion supposed a cardiovascular risk. Unawareness of the serious complications that may arise from this fact probably justifies a high prevalence of misplacements.