The archaeology of a landslide: Unravelling the Azores earthquake disaster of 1522 and its consequences

The multidisciplinary research described here shows how archaeologists can help reconstruct past seismic episodes and understand the subsequent relief operation, rehabilitation, and reconstruction processes. In October 1522, a major earthquake and landslide struck the then capital of the Azores, Vila Franca do Campo, 1500 km from the European mainland. Damage was extensive, destroying key monuments, affecting most of the inhabited area, and leaving few survivors among the early colonists. The results from twenty-six archaeological trenches, geological and geoarchaeological investigations, and documentary analysis are reviewed here. Distinctive archaeological deposits are identified and explained, using the high density of artefacts and the erosional contact between the landslide and the pre-1522 palaeosol to reconstruct the episode in detail

Errors in chromosome segregation during oogenesis and early embryogenesis

Errors in chromosome segregation occurring during human oogenesis and early embryogenesis are very common. Meiotic chromosome development during oogenesis is subdivided into three distinct phases. The crucial events, including meiotic chromosome pairing and recombination, take place from around 11 weeks until birth. Oogenesis is then arrested until ovulation, when the first meiotic division takes place, with the second meiotic division not completed until after fertilization. It is generally accepted that most aneuploid fetal conditions, such as trisomy 21 Down syndrome, are due to maternal chromosome segregation errors. The underlying reasons are not yet fully understood. It is also clear that superimposed on the maternal meiotic chromosome segregation errors, there are a large number of mitotic errors taking place post-zygotically during the first few cell divisions in the embryo. In this chapter, we summarise current knowledge of errors in chromosome segregation during oogenesis and early embryogenesis, with special reference to the clinical implications for successful assisted reproduction

Global and decomposition evolutionary support vector machine approaches for time series forecasting

Multi-step ahead Time Series Forecasting (TSF) is a key tool for support- ing tactical decisions (e.g., planning resources). Recently, the support vector machine emerged as a natural solution for TSF due to its nonlinear learning capabilities. This paper presents two novel Evolutionary Support Vector Machine (ESVM) methods for multi-step TSF. Both methods are based on an Estimation Distribution Algorithm (EDA) search engine that automatically performs a simultaneous variable (number of inputs) and model (hyperparameters) selection. The Global ESVM (GESVM) uses all past patterns to fit the support vector machine, while the Decomposition ESVM (DESVM) separates the series into trended and stationary effects, using a distinct ESVM to forecast each effect and then summing both predictions into a sin- gle response. Several experiments were held, using six time series. The proposed approaches were analyzed under two criteria and compared against a recent Evolu- tionary Artificial Neural Network (EANN) and two classical forecasting methods, Holt-Winters and ARIMA. Overall, the DESVM and GESVM obtained competitive and high quality results. Furthermore, both ESVM approaches consume much less computational effort when compared with EANN.The authors wish to thank Ramon Sagarna for introducing the subject of EDA. The work of P. Cortez was supported by FEDER (program COMPETE and FCT) under project FCOMP-01-0124-FEDER-022674

Stratification of Patients With Sjögren’s Syndrome and Patients With Systemic Lupus Erythematosus According to Two Shared Immune Cell Signatures, With Potential Therapeutic Implications

OBJECTIVE: Similarities in the clinical and laboratory features of patients with primary Sjögren's syndrome (pSS) and systemic lupus erythematosus (SLE) have led to attempts to treat pSS and SLE patients with similar biologic therapeutics. However, the results of many clinical trials are disappointing, and no biologic treatments are licensed in pSS, while few are available for SLE patients with refractory disease. Identifying shared immunological features between pSS and SLE could lead to better treatment selection using a stratification approach. METHODS: Immune-phenotyping of 29 immune-cell subsets in peripheral blood from patients with pSS (n=45), SLE (n=29) and secondary SS associated with SLE (SLE/SS) (n=14) with low disease activity or in clinical remission, and sex-matched healthy controls (n=31), was performed using flow cytometry. Data were analysed using supervised machine learning (balanced random forest, sparse partial least squares discriminant analysis), logistic regression and multiple t-tests. Patients were stratified by k-means clustering, and clinical trajectory analysis. RESULTS: Patients with pSS and SLE had a similar immunological architecture despite having different clinical presentations and prognosis. K-means cluster analysis of the combined pSS, SLE and SLE/SS patient cohorts identified two endotypes characterized by distinct immune-cell profiles which spanned patient diagnoses. Logistic regression and machine learning models identified a signature of eight T-cell subsets that differentiated between the two endotypes with high accuracy (AUC=0.9979). Baseline and five-year clinical trajectory analysis identified differential damage scores and disease activity between the two endotypes. CONCLUSION: An immune-cell toolkit could differentiate patients across diseases with high accuracy for targeted therapeutic approaches

SCIB1, a huIgG1 antibody DNA vaccination, combined with PD-1 blockade induced efficient therapy of poorly immunogenic tumors

Purpose: We have previously shown that supraoptimal signaling of high avidity T cells leads to high expression of PD-1 and inhibition of proliferation. This study was designed to see if this effect could be mitigated by combining a vaccine that stimulates high avidity T cells with PD-1 blockade. Experimental Design: We investigated the anti-tumor effect of a huIgG1 antibody DNA vaccine (SCIB1) and PD-1 blockade. Results: Vaccination of HLA-DR4 transgenic mice with SCIB1 induced high frequency and avidity T cell responses that resulted in survival (40%) of mice with established B16F1-DR4 tumors. SCIB1 vaccination was associated with increased infiltration of CD4 and CD8 T cells within the tumor but was also associated with upregulation of PD-L1 within the tumor environment. PD-1 blockade also resulted in increased CD8 T cell infiltration and an anti-tumor response with 50% of mice showing long term survival. In line with our hypothesis that PD-1/PD-L1 signaling results in inhibition of proliferation of high avidity T cells at the tumor site, the combination of PD-1 blockade with vaccination, enhanced the number and proliferation of the CD8 tumor infiltrate. This resulted in a potent anti-tumor response with 80% survival of the mice. Conclusions: There is a benefit in combining PD-1 blockade with vaccines that induce high avidity T cell responses and in particular with SCIB1

Pleiotropic functions of the tumor- and metastasis-suppressing Matrix Metalloproteinase-8 in mammary cancer in MMTV-PyMT transgenic mice

Matrix metalloproteinase-8 (MMP-8; neutrophil collagenase) is an important regulator of innate immunity which has onco-suppressive actions in numerous tumor types