Inference on the symmetry point-based optimal cut-off point and associated sensitivity and specificity with application to SARS-CoV-2 antibody data

Acknowledgments. This work was supported by grants PID2019-104681RB-I00. Data courtesy of Dr Konstantina Kontopoulou.In the presence of a continuous response test/biomarker, it is often necessary to identify a cut-off point value to aid binary classification between diseased and non-diseased subjects. The symmetry-point approach which maximizes simultaneously both types of correct classification is one way to determine an optimal cut-off point. In this article, we study methods for constructing confidence intervals independently for the symmetry point and its corresponding sensitivity, as well as respective joint nonparametric confidence regions. We illustrate using data on the generation of antibodies elicited two weeks post-injection after the second dose of the Pfizer/BioNTech vaccine in adult healthcare workers

Diseño y análisis de ladrillo con incorporación de caucho reciclado para aumentar la resistencia a esfuerzos horizontales en zona sísmicas, Trujillo 2022

Se realizó la presente investigación con el objetivo de Diseñar y analizar el ladrillo con incorporación de caucho reciclado para aumentar la resistencia a esfuerzos horizontales en zonas sísmicas, Trujillo 2022, se aplicó un diseño experimental en la cual se usaron 40 ladrillo sólidos de arcilla, divididos en 4 grupos de 10 unidades, ladrillo patrón, ladrillo con 5%, 10% y 15% de incorporación de caucho reciclado. Se empleó como instrumento a las fichas de observación para registrar los datos obtenidos. Los resultados reflejan que el porcentaje óptimo de incorporación de caucho reciclado es de 5%, los desplazamientos máximos tanto en el análisis lineal y no lineal, lo tiene 15% con un resultado de 0.488 mm y 15.677 mm, respectivamente, mientras que los desplazamientos mínimos en el análisis lineal y no lineal manifiestan que el 5% tiene el menor desplazamiento con 0.396 mm. En cuanto a las derivas ocurre lo mismo, la menor deriva la obtuvo el 5% con 0.000159 y la mayor fue del 15% con 0.000195. En conclusión, el caucho reciclado mejora las propiedades del ladrillo al agregar el 5%; las derivas y desplazamientos del análisis lineal, cumplen con lo establecido en la norma del RNE E.030 Diseño Sismorresistente

Scatter signatures in SFDI data enable breast surgical margin delineation via ensemble learning

Margin assessment in gross pathology is becoming feasible as various explanatory deep learning-powered methods are able to obtain models for macroscopic textural information, tissue microstructure, and local surface optical properties. Unfortunately, each different method seems to lack enough diagnostic power to perform an adequate classification on its own. This work proposes using several separately trained deep convolutional networks, and averaging their responses, in order to achieve a better margin assessment. Qualitative leave-one-out cross-validation results are discussed for a cohort of 70 samples.Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15- 00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23), Spanish Ministry of Education, Culture, and Sports (FPU16/05705)

Automated surgical margin assessment in breast conserving surgery using SFDI with ensembles of self-confident deep convolutional networks

With an adequate tissue dataset, supervised classification of tissue optical properties can be achieved in SFDI images of breast cancer lumpectomies with deep convolutional networks. Nevertheless, the use of a black-box classifier in current ex vivo setups provides output diagnostic images that are inevitably bound to show misclassified areas due to inter- and intra-patient variability that could potentially be misinterpreted in a real clinical setting. This work proposes the use of a novel architecture, the self-introspective classifier, where part of the model is dedicated to estimating its own expected classification error. The model can be used to generate metrics of self-confidence for a given classification problem, which can then be employed to show how much the network is familiar with the new incoming data. A heterogenous ensemble of four deep convolutional models with self-confidence, each sensitive to a different spatial scale of features, is tested on a cohort of 70 specimens, achieving a global leave-one-out cross-validation accuracy of up to 81%, while being able to explain where in the output classification image the system is most confident.Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15- 00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23), Spanish Ministry of Education, Culture, and Sports (FPU16/05705)

Dedifferentiation of committed epithelial cells into stem cells in vivo

Summary Cellular plasticity contributes to the regenerative capacity of plants, invertebrates, teleost fishes, and amphibians. In vertebrates, differentiated cells are known to revert into replicating progenitors, but these cells do not persist as stable stem cells. We now present evidence that differentiated airway epithelial cells can revert into stable and functional stem cells in vivo. Following the ablation of airway stem cells, we observed a surprising increase in the proliferation of committed secretory cells. Subsequent lineage tracing demonstrated that the luminal secretory cells had dedifferentiated into basal stem cells. Dedifferentiated cells were morphologically indistinguishable from stem cells and they functioned as well as their endogenous counterparts to repair epithelial injury. Indeed, single secretory cells clonally dedifferentiated into multipotent stem cells when they were cultured ex vivo without basal stem cells. In contrast, direct contact with a single basal stem cell was sufficient to prevent secretory cell dedifferentiation. In analogy to classical descriptions of amphibian nuclear reprogramming, the propensity of committed cells to dedifferentiate was inversely correlated to their state of maturity. This capacity of committed cells to dedifferentiate into stem cells may play a more general role in the regeneration of many tissues and in multiple disease states, notably cancer

Modeling and synthesis of breast cancer optical property signatures with generative models

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.This work was supported in part by the National Cancer Institute, US National Institutes of Health, under grants R01 CA192803 and F31 CA196308, by the Spanish Ministry of Science and Innovation under grant FIS2010-19860, by the Spanish Ministry of Science, Innovation and Universities under grants TEC2016-76021-C2-2-R and PID2019-107270RB-C21, by the Spanish Minstry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III via DTS17-00055, by IDIVAL under grants INNVAL 16/02, and INNVAL 18/23, and by the Spanish Ministry of Education, Culture, and Sports with PhD grant FPU16/05705, as well as FEDER funds

Unscheduled DNA replication in G1 causes genome instability and damage signatures indicative of replication collisions

DNA replicates once per cell cycle. Interfering with the regulation of DNA replication initiation generates genome instability through over-replication and has been linked to early stages of cancer development. Here, we engineer genetic systems in budding yeast to induce unscheduled replication in a G1- like cell cycle state. Unscheduled G1 replication initiates at canonical S-phase origins. We quantifiy the composition of replisomes in G1- and S-phase and identified firing factors, polymerase α, and histone supply as factors that limit replication outside S-phase. G1 replication per se does not trigger cellular checkpoints. Subsequent replication during S-phase, however, results in overreplication and leads to chromosome breaks and chromosome-wide, strandbiased occurrence of RPA-bound single-stranded DNA, indicating head-to-tail replication collisions as a key mechanism generating genome instability upon G1 replication. Low-level, sporadic induction of G1 replication induces an identical response, indicating findings from synthetic systems are applicable to naturally occurring scenarios of unscheduled replication initiation

Outcomes and risk score for distal pancreatectomy with celiac axis resection (DP-CAR) : an international multicenter analysis

Background: Distal pancreatectomy with celiac axis resection (DP-CAR) is a treatment option for selected patients with pancreatic cancer involving the celiac axis. A recent multicenter European study reported a 90-day mortality rate of 16%, highlighting the importance of patient selection. The authors constructed a risk score to predict 90-day mortality and assessed oncologic outcomes. Methods: This multicenter retrospective cohort study investigated patients undergoing DP-CAR at 20 European centers from 12 countries (model design 2000-2016) and three very-high-volume international centers in the United States and Japan (model validation 2004-2017). The area under receiver operator curve (AUC) and calibration plots were used for validation of the 90-day mortality risk model. Secondary outcomes included resection margin status, adjuvant therapy, and survival. Results: For 191 DP-CAR patients, the 90-day mortality rate was 5.5% (95 confidence interval [CI], 2.2-11%) at 5 high-volume (1 DP-CAR/year) and 18% (95 CI, 9-30%) at 18 low-volume DP-CAR centers (P=0.015). A risk score with age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) score, multivisceral resection, open versus minimally invasive surgery, and low- versus high-volume center performed well in both the design and validation cohorts (AUC, 0.79 vs 0.74; P=0.642). For 174 patients with pancreatic ductal adenocarcinoma, the R0 resection rate was 60%, neoadjuvant and adjuvant therapies were applied for respectively 69% and 67% of the patients, and the median overall survival period was 19months (95 CI, 15-25months). Conclusions: When performed for selected patients at high-volume centers, DP-CAR is associated with acceptable 90-day mortality and overall survival. The authors propose a 90-day mortality risk score to improve patient selection and outcomes, with DP-CAR volume as the dominant predictor

Road‐risk: metodología para la identificación de puntos conflictivos por riesgos múltiples en infraestructuras viarias tras episodios torrenciales

La comunicación recoge los contenidos de una metodología aplicada que permite cartografiar aquellos puntos en el recorrido de una infraestructura viaria que pueden quedar bloqueados por riesgos múltiples de funcionamiento simultáneo, tras unos episodios de precipitaciones de alta intensidad. Se incorporan dos modelos predictivos para identificar los puntos con riesgo de movimientos en masa, descalzamiento del firme y/o encharcamiento y generación de balsas. Se ha diseñado igualmente una aplicación informática que permite aplicar los criterios de predicción obtenidos y cartografiar de forma automatizada los puntos conflictivos en infraestructuras distintas a las utilizadas como área de estudio.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech