An algorithm for PWL approximations of nonlinear functions

In this report we provide some technical details for some of the results appeared in [Alessio et al.(2005)]. In the first section we provide the proof of continuity of the PPWA function computed with the ”squaring the circle” algorithm stated in ACC 06. Then, we analyze the complexity of the previous algorithm, in terms of the desired level of accuracy in the approximation of the PPWA function

Has natural variability a lagged influence on global temperature? A multi-horizon Granger causality analysis

At present, the role of natural variability in influencing climate behaviour is widely discussed. The generally accepted view is that atmosphere-ocean coupled circulation patterns are able to amplify or reduce temperature increase from interannual to multidecadal time ranges, leaving the principal driving role to anthropogenic forcings. In this framework, the influence of these circulation patterns is considered synchronous with global temperature changes. Here, we would like to investigate if there exists a lagged influence of these indices on temperature. In doing so, an extension of the Granger causality technique, which permits to test both direct and indirect causal influences, is applied. A lagged influence of natural variability is not evident in our analysis, if we except weak influences of some peculiar circulation indices in specific periods

Clarifying the Roles of Greenhouse Gases and ENSO in Recent Global Warming through Their Prediction Performance

Abstract It is well known that natural external forcings and decadal-to-millennial variability drove changes in the climate system throughout the Holocene. Regarding recent times, attribution studies have shown that greenhouse gases (GHGs) determined the trend of temperature (T) in the last half century, while circulation patterns contributed to modify its interannual, decadal, or multidecadal behavior over this period. Here temperature predictions based on vector autoregressive models (VARs) have been used to study the influence of GHGs and El Niño–Southern Oscillation (ENSO) on recent temperature behavior. It is found that in the last decades of steep temperature increase, ENSO shows just a very short-range influence on T, while GHGs are dominant for each forecast horizon. Conversely and quite surprisingly, in the previous quasi-stationary period the influences of GHGs and ENSO are comparable, even at longer range. Therefore, if the recent hiatus in global temperatures should persist into the near future, an enhancement of the role of ENSO can be expected. Finally, the predictive ability of GHGs is more evident in the Southern Hemisphere, where the temperature series is smoother

Recent Trends in Word Sense Disambiguation : A Survey

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First record of cirolanids (Crustacea, Isopoda, Cirolanidae) from the Middle Pliocene of Parma and Reggio Emilia Province (Emilia Romagna, N Italy)

We report two new forms of cirolanid isopods ascribed to Palaega sp. and P. steatopigia n. sp. (Cirolanidae) from the Middle Pliocene of Campore (Salsomaggiore Terme, Parma) and Monticelli di Quattro Castella (Reggio Emilia), Emilia Romagna, N Italy. This is the first record of Palaega Woodward, 1870, reported from the Middle Pliocene of Emilia Romagna and in the paleo-Adriatic Gulf. Palaega steatopigia n. sp. represents the third species described in Italy and in the Mediterranean are

Evidence of mysid swarm behaviour (Crustacea: Malacostraca) from the Cenomanian (Late Cretaceous) of Hakel, Lebanon

A mass mortality assemblage of mysid crustaceans is herein reported from the Cenomanian (Late Cretaceous) sublithographic limestone of Hakel (Lebanon). The studied specimens documented a mass mortality event attesting mysid swarm behaviour, herein briefly described and discussed. Though their poor preservation does not allow a specific assignment, this new record is remarkable, resulting the first formal report of Mysida Boas 1883 (Crustacea, Malacostraca) from the rich crustacean fossil fauna from the Late Cretaceous of Lebanon and from the worldwide Cretaceous record

Relations between zero-inflated variables in trials with horticultural crops

Certain characteristics of some vegetable crops allow multiple harvests during the production cycle; however, to our knowledge, no study has described the behavior of fruit production with progression of the production cycle in vegetable crops with multiple harvests that present data overdispersion. We aimed to characterize the data overdispersion of zero-inflated variables and identify the behavior of these variables during the production cycle of several vegetable crops with multiple harvests. Data from 11 uniformity trials were used without applying treatments; these comprise the database from the Experimental Plants Group at the Federal University of Santa Maria, Brazil. The trials were conducted using four horticultural species grown during different cultivation seasons, cultivation environments, and experimental structures. Although at each harvest, a larger number of basic units with harvest fruit was observed than units without harvest fruit, the basic unit percentage without fruit was high, generating an overdispersion within each individual harvest. The variability within each harvest was high and increased with the evolution of the production cycle of Capsicum annuum, Solanum lycopersicum var. cerasiforme, Phaseolus vulgaris, and Cucurbita pepo species. However, the correlation coefficient between the mean weight and number of harvest fruits tended to remain constant during the crop production cycle. These behaviors show that harvest management should be done individually, at each harvest, such that data overdispersion is reduced

Yin Yang 1 extends the Myc-related transcription factors network in embryonic stem cells

The Yin Yang 1 (YY1) transcription factor is a master regulator of development, essential for early embryogenesis and adult tissues formation. YY1 is the mammalian orthologue of Pleiohomeotic, one of the transcription factors that binds Polycomb DNA response elements in Drosophila melanogaster and mediates Polycomb group proteins (PcG) recruitment to DNA. Despite several publications pointing at YY1 having a similar role in mammalians, others showed features of YY1 that are not compatible with PcG functions. Here, we show that, in mouse Embryonic Stem (ES) cells, YY1 has genome-wide PcG-independent activities while it is still stably associated with the INO80 chromatin-remodeling complex, as well as with novel RNA helicase activities. YY1 binds chromatin in close proximity of the transcription start site of highly expressed genes. Loss of YY1 functions preferentially led to a down-regulation of target genes expression, as well as to an up-regulation of several small non-coding RNAs, suggesting a role for YY1 in regulating small RNA biogenesis. Finally, we found that YY1 is a novel player of Myc-related transcription factors and that its coordinated binding at promoters potentiates gene expression, proposing YY1 as an active component of the Myc transcription network that links ES to cancer cells

Crystal structure analyses facilitate understanding of synthesis protocols in the preparation of 6,6′-dibromo-substituted BINOL compounds

A combination of crystallographic and spectroscopic techniques has been used in order to address thorough purification protocols for a series of atropisomeric 1,1′-binaphthalene-2,2′-diol (BINOL) derivatives to be used as building blocks for chiral nanoscale constructs

Phenotyping the histopathological subtypes of non-small-cell lung carcinoma: how beneficial is radiomics?

The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models' performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model's performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal-Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models' performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate similar to 90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained-although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models' performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models' performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems.It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice