On the Design of an ECOC-Compliant Genetic Algorithm

Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches

Probability-based Dynamic Time Warping and Bag-of-Visual-and-Depth-Words for Human Gesture Recognition in RGB-D

We present a methodology to address the problem of human gesture segmentation and recognition in video and depth image sequences. A Bag-of-Visual-and-Depth-Words (BoVDW) model is introduced as an extension of the Bag-of-Visual-Words (BoVW) model. State-of-the-art RGB and depth features, including a newly proposed depth descriptor, are analysed and combined in a late fusion form. The method is integrated in a Human Gesture Recognition pipeline, together with a novel probability-based Dynamic Time Warping (PDTW) algorithm which is used to perform prior segmentation of idle gestures. The proposed DTW variant uses samples of the same gesture category to build a Gaussian Mixture Model driven probabilistic model of that gesture class. Results of the whole Human Gesture Recognition pipeline in a public data set show better performance in comparison to both standard BoVW model and DTW approach

Phase I study of plitidepsin in combination with bortezomib and dexamethasone in patients with relapsed/refractory multiple myeloma

Previous studies showed antitumor activity for plitidepsin plus dexamethasone (DXM) in relapsed/refractory multiple myeloma (r/r MM), and in vitro synergism with bortezomib (BTZ) or DXM against MM cells. This phase I trial evaluated plitidepsin (3-h intravenous infusion Day 1 and 15), BTZ (subcutaneous bolus Day 1, 4, 8, and 11), and DXM (orally Day 1, 8, 15, and 22), every 4 weeks in 36 r/r MM patients. Twenty-two patients were treated using a standard dose escalation design (10 at the recommended dose [RD] cohort), and 14 additional patients were treated to expand the RD cohort. No dose-limiting toxicities (DLTs) occurred during dose escalation. The highest dose level evaluated (plitidepsin 5.0 mg/m2 , BTZ 1.3 mg/m2 , DXM 40.0 mg) was the RD for phase II studies. Results shown herein are focused on this RD. Two patients had DLTs (grade 3 diarrhea, and grade 3 nausea/vomiting refractory to antiemetic therapy). Grade ? 3 hematological toxicity (thrombocytopenia 46%, anemia 33%, and neutropenia 17%) was manageable and did not result in treatment discontinuation. Transient and manageable grade 3 ALT increase (26%) was the most common biochemical abnormality. At the RD cohort, overall response rate was 22.2% (95%CI, 6.4%-47.6%), including one stringent complete response, one very good partial response, and two partial responses in r/r patients to BTZ and/or lenalidomide. The clinical benefit rate was 77.8% (95%CI, 52.4-93.6%). No major pharmacokinetic drug-drug interaction was found. In conclusion, the triple combination of plitidepsin, BTZ, and DXM showed an acceptable safety profile and had moderate activity in adult patients with r/r MM.FUNDING: The study was funded by Pharma Mar, S.A. ACKNOWLEDGMENTS: The authors thank the patients, their families, and investigators teams for their participation in this phase I clinical trial

Detection of severe obstructive sleep apnea through voice analysis

tThis paper deals with the potential and limitations of using voice and speech processing to detect Obstruc-tive Sleep Apnea (OSA). An extensive body of voice features has been extracted from patients whopresent various degrees of OSA as well as healthy controls. We analyse the utility of a reduced set offeatures for detecting OSA. We apply various feature selection and reduction schemes (statistical rank-ing, Genetic Algorithms, PCA, LDA) and compare various classifiers (Bayesian Classifiers, kNN, SupportVector Machines, neural networks, Adaboost). S-fold crossvalidation performed on 248 subjects showsthat in the extreme cases (that is, 127 controls and 121 patients with severe OSA) voice alone is able todiscriminate quite well between the presence and absence of OSA. However, this is not the case withmild OSA and healthy snoring patients where voice seems to play a secondary role. We found that thebest classification schemes are achieved using a Genetic Algorithm for feature selection/reduction

Effects of intubation timing in patients with COVID-19 throughout the four waves of the pandemic : a matched analysis

The primary aim of our study was to investigate the association between intubation timing and hospital mortality in critically ill patients with COVID-19-associated respiratory failure. We also analysed both the impact of such timing throughout the first four pandemic waves and the influence of prior non-invasive respiratory support on outcomes. This is a secondary analysis of a multicentre, observational and prospective cohort study that included all consecutive patients undergoing invasive mechanical ventilation due to COVID-19 from across 58 Spanish intensive care units (ICU) participating in the CIBERESUCICOVID project. The study period was between 29 February 2020 and 31 August 2021. Early intubation was defined as that occurring within the first 24 h of intensive care unit (ICU) admission. Propensity score (PS) matching was used to achieve balance across baseline variables between the early intubation cohort and those patients who were intubated after the first 24 h of ICU admission. Differences in outcomes between early and delayed intubation were also assessed. We performed sensitivity analyses to consider a different timepoint (48 h from ICU admission) for early and delayed intubation. Of the 2725 patients who received invasive mechanical ventilation, a total of 614 matched patients were included in the analysis (307 for each group). In the unmatched population, there were no differences in mortality between the early and delayed groups. After PS matching, patients with delayed intubation presented higher hospital mortality (27.3% versus 37.1%, p =0.01), ICU mortality (25.7% versus 36.1%, p=0.007) and 90-day mortality (30.9% versus 40.2%, p=0.02) when compared to the early intubation group. Very similar findings were observed when we used a 48-hour timepoint for early or delayed intubation. The use of early intubation decreased after the first wave of the pandemic (72%, 49%, 46% and 45% in the first, second, third and fourth wave, respectively; first versus second, third and fourth waves p<0.001). In both the main and sensitivity analyses, hospital mortality was lower in patients receiving high-flow nasal cannula (n=294) who were intubated earlier. The subgroup of patients undergoing NIV (n=214) before intubation showed higher mortality when delayed intubation was set as that occurring after 48 h from ICU admission, but not when after 24 h. In patients with COVID-19 requiring invasive mechanical ventilation, delayed intubation was associated with a higher risk of hospital mortality. The use of early intubation significantly decreased throughout the course of the pandemic. Benefits of such an approach occurred more notably in patients who had received high-flow nasal cannul

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

Effects of Platelet-Rich Plasma (PRP) on a Model of Renal Ischemia-Reperfusion in Rats.

Renal ischemia-reperfusion injury is a major cause of acute renal failure, causing renal cell death, a permanent decrease of renal blood flow, organ dysfunction and chronic kidney disease. Platelet-rich plasma (PRP) is an autologous product rich in growth factors, and therefore able to promote tissue regeneration and angiogenesis. This product has proven its efficacy in multiple studies, but has not yet been tested on kidney tissue. The aim of this work is to evaluate whether the application of PRP to rat kidneys undergoing ischemia-reperfusion reduces mid-term kidney damage. A total of 30 monorrenal Sprague-Dawley male rats underwent renal ischemia-reperfusion for 45 minutes. During ischemia, PRP (PRP Group, n = 15) or saline solution (SALINE Group, n = 15) was administered by subcapsular renal injection. Control kidneys were the contralateral organs removed immediately before the start of ischemia in the remaining kidneys. Survival, body weight, renal blood flow on Doppler ultrasound, kidney weight, kidney volume, blood biochemistry and histopathology were determined for all subjects and kidneys, as applicable. Correlations between these variables were searched for. The PRP Group showed significantly worse kidney blood flow (p = 0.045) and more histopathological damage (p<0.0001). Correlations were found between body weight, kidney volume, kidney weight, renal blood flow, histology, and serum levels of creatinine and urea. Our study provides the first evidence that treatment with PRP results in the deterioration of the kidney's response to ischemia-reperfusion injury

Distinguishing outcomes of neonatal intestinal volvulus: review of our experience over the last 20 years

Aim: There are two types of intestinal volvulus: midgut (MGV) and segmental (SV). Patients with different types of intestinal volvulus are often included in the same case series, which may affect the perception of how severe "intestinal volvuli" are. We aimed to compare both types of intestinal volvulus. Methods: This is a retrospective observational study including all patients with MGV and SV up to 28 days of life admitted to a tertiary hospital in Spain over a 20-year-period (1999-2019). A comparison between groups and a logistic regression model for mortality were done. Results: We identified 32 patients: 23 MGV and 9 SV. Malrotation was exclusive of MGV. Prenatal diagnosis, cystic fibrosis, and intestinal resection were significantly more frequent in SV. Surgery was performed at a significantly lower age in SV. The mortality observed in acute MGV with intestinal compromise (41.7%) is four times higher than the mortality of SV (11.1%). The overall mortality of all MGV patients (21.7%) is almost twice that of SV. Mortality was best predicted by the presence of hemodynamic instability (OR 27.5 95% CI 2.50-302.17; p = 0.007). Conclusion: SV and MGV have a different clinical presentation. Hemodynamic instability is the major risk factor for death

On the Design of an ECOC-Compliant Genetic Algorithm

Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches

Renal blood flow before and one week after ischemia-reperfusion.

<p>Renal blood flow before and one week after ischemia-reperfusion.</p