Coupling different methods for overcoming the class imbalance problem

Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical. Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches. To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature. Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357

Early invasive strategy (< 24 hours) in patients with acute coronary syndromes: Weighing the evidence

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Monitoring and modelling interactions between the montagna dei fiori aquifer and the castellano stream (Central Apennines, Italy)

Groundwater is the most used water resource around the world, but due to population growth and climate change the alluvial lowland aquifers are often polluted and over-exploited. Thus, more and more frequently water managers need to shift their attention to mountain regions to identify groundwater resources for drinking purposes. This study presents a monitoring and modelling approach that allowed to quantify the inflow from the "Montagna dei Fiori" fractured aquifer to the Castellano stream. Continuous monitoring of flow discharge and temperature during an entire hydrological year (2018-2019) at two monitoring stations along the stream allowed to discriminate between the baseflow (on average, 0.891 m3/s) and the run-off (on average, 0.148 m3/s) components. A hydrogeological basin-wide numerical flow model (using MODFLOW-2005) was set up using information from hydrogeological and geomechanical surveys. The model was calibrated using the daily baseflow observations made in the Castellano stream (R2 = 0.75). The calibrated model allowed to quantify groundwater/surface water interactions. After an automated sensitivity analysis (using MODFLOW-2000), the recharge was found to be the most uncertain parameter, followed by the hydraulic conductivity zonation. This methodology could be applied in other mountain regions where groundwater monitoring networks are usually lacking to improve water resources management

The mobility of Atlantic baric depressions leading to intense precipitation over Italy: a preliminary statistical analysis

International audienceThe speed of Atlantic surface depressions, occurred during the autumn and winter seasons and that lead to intense precipitation over Italy from 1951 to 2000, was investigated. Italy was divided into 5 regions as documented in previous climatological studies (based on Principal Component Analysis). Intense precipitation events were selected on the basis of in situ rain gauge data and clustered according to the region that they hit. For each intense precipitation event we tried to identify an associated surface depression and we tracked it, within a large domain covering the Mediterranean and Atlantic regions, from its formation to cyclolysis in order to estimate its speed. "Depression speeds" were estimated with 6-h resolution and clustered into slow and non-slow classes by means of a threshold, coinciding with the first quartile of speed distribution and depression centre speeds were associated with their positions. Slow speeds occurring over an area including Italy and the western Mediterranean basin showed frequencies higher than 25%, for all the Italian regions but one. The probability of obtaining by chance the observed more than 25% success rate was estimated by means of a binomial distribution. The statistical reliability of the result is confirmed for only one region. For Italy as a whole, results were confirmed at 95% confidence level. Stability of the statistical inference, with respect to errors in estimating depression speed and changes in the threshold of slow depressions, was analysed and essentially confirmed the previous results

Visually driven analysis of movement data by progressive clustering

The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, that is, sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, (1) is difficult to build, (2) would require much time to compute, and (3) might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of ‘cheap’ distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real data set. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories

Biochar as plant growth promoter: Better off alone or mixed with organic amendments?

Biochar is nowadays largely used as a soil amendment and is commercialized worldwide. However, in temperate agro-ecosystems the beneficial effect of biochar on crop productivity is limited, with several studies reporting negative crop responses. In this work, we studied the effect of 10 biochar and 9 not pyrogenic organic amendments (NPOA), using pure and in all possible combinations on lettuce growth (Lactuca sativa). Organic materials were characterized by 13C-CPMAS NMR spectroscopy and elemental analysis (pH, EC, C, N, C/N and H/C ratios). Pure biochars and NPOAs have variable effects, ranging from inhibition to strong stimulation on lettuce growth. For NPOAs, major inhibitory effects were found with N poor materials characterized by high C/N and H/C ratio. Among pure biochars, instead, those having a low H/C ratio seem to be the best for promoting plant growth. When biochars and organic amendments were mixed, non-additive interactions, either synergistic or antagonistic, were prevalent. However, the mixture effect on plant growth was mainly dependent on the chemical quality of NPOAs, while biochar chemistry played a secondary role. Synergisms were prevalent when N rich and lignin poor materials were mixed with biochar. On the contrary, antagonistic interactions occurred when leaf litter or woody materials were mixed with biochar. Further research is needed to identify the mechanisms behind the observed non-additive effects and to develop biochar-organic amendment combinations that maximize plant productivity in different agricultural systems

Estrogen-dependent dynamic profile of eNOS-DNA associations in prostate cancer

In previous work we have documented the nuclear translocation of endothelial NOS (eNOS) and its participation in combinatorial complexes with Estrogen Receptor Beta (ERβ) and Hypoxia Inducible Factors (HIFs) that determine localized chromatin remodeling in response to estrogen (E2) and hypoxia stimuli, resulting in transcriptional regulation of genes associated with adverse prognosis in prostate cancer (PCa). To explore the role of nuclear eNOS in the acquisition of aggressive phenotype in PCa, we performed ChIP-Sequencing on chromatin-associated eNOS from cells from a primary tumor with poor outcome and from metastatic LNCaP cells. We found that: 1. the eNOS-bound regions (peaks) are widely distributed across the genome encompassing multiple transcription factors binding sites, including Estrogen Response Elements. 2. E2 increased the number of peaks, indicating hormone-dependent eNOS re-localization. 3. Peak distribution was similar with/without E2 with ≈ 55% of them in extragenic DNA regions and an intriguing involvement of the 5′ domain of several miRs deregulated in PCa. Numerous potentially novel eNOS-targeted genes have been identified suggesting that eNOS participates in the regulation of large gene sets. The parallel finding of downregulation of a cluster of miRs, including miR-34a, in PCa cells associated with poor outcome led us to unveil a molecular link between eNOS and SIRT1, an epigenetic regulator of aging and tumorigenicity, negatively regulated by miR-34a and in turn activating eNOS. E2 potentiates miR-34a downregulation thus enhancing SIRT1 expression, depicting a novel eNOS/SIRT1 interplay fine-tuned by E2-activated ER signaling, and suggesting that eNOS may play an important role in aggressive PCa

Projectile-Residual-Target-Ion Scattering After Single Ionization of Helium by Slow Proton Impact

We have measured fully differential single ionization cross sections for 75 keV p+He collisions. At this relatively small projectile velocity, signatures of the projectile-residual-target-ion interaction, which are not observable for fast projectiles and for electron impact, are revealed rather sensitively. In fact, this interaction appears to be more important than the postcollision interaction, which so far was assumed to be the most important factor in higher-order effects for slow ion impact. These features are not well reproduced by our three-distorted-wave calculations

Ensemble of convolutional neural networks to improve animal audio classification

Abstract In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of producing better classification accuracy than other state-of-the-art approaches without ad hoc parameter optimization. We present an ensemble of classifiers that performs competitively on different types of animal audio datasets using the same set of classifiers and parameter settings. To produce this general-purpose ensemble, we ran a large number of experiments that fine-tuned pretrained convolutional neural networks (CNNs) for different audio classification tasks (bird, bat, and whale audio datasets). Six different CNNs were tested, compared, and combined. Moreover, a further CNN, trained from scratch, was tested and combined with the fine-tuned CNNs. To the best of our knowledge, this is the largest study on CNNs in animal audio classification. Our results show that several CNNs can be fine-tuned and fused for robust and generalizable audio classification. Finally, the ensemble of CNNs is combined with handcrafted texture descriptors obtained from spectrograms for further improvement of performance. The MATLAB code used in our experiments will be provided to other researchers for future comparisons at https://github.com/LorisNanni

Whole-genome doubling drives oncogenic loss of chromatin segregation.

Whole-genome doubling (WGD) is a recurrent event in human cancers and it promotes chromosomal instability and acquisition of aneuploidies &lt;sup&gt;1-8&lt;/sup&gt; . However, the three-dimensional organization of chromatin in WGD cells and its contribution to oncogenic phenotypes are currently unknown. Here we show that in p53-deficient cells, WGD induces loss of chromatin segregation (LCS). This event is characterized by reduced segregation between short and long chromosomes, A and B subcompartments and adjacent chromatin domains. LCS is driven by the downregulation of CTCF and H3K9me3 in cells that bypassed activation of the tetraploid checkpoint. Longitudinal analyses revealed that LCS primes genomic regions for subcompartment repositioning in WGD cells. This results in chromatin and epigenetic changes associated with oncogene activation in tumours ensuing from WGD cells. Notably, subcompartment repositioning events were largely independent of chromosomal alterations, which indicates that these were complementary mechanisms contributing to tumour development and progression. Overall, LCS initiates chromatin conformation changes that ultimately result in oncogenic epigenetic and transcriptional modifications, which suggests that chromatin evolution is a hallmark of WGD-driven cancer