GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining

Statisticalandclustering analyses ofgeneexpression results from high-density microarray experiments produce lists of hundreds of genes regulated differentially, or with particular expression profiles, in the conditions under study. Independent of the microarray platforms and analysis methods used, these lists must be biologically interpreted to gain a better knowledge of the patho-physiological phenomena involved. To this end, numerous biological annotations are available within heterogeneous and widely distributed databases. Although several tools have been developed for annotating lists of genes, most of them do not give methods for evaluating the relevance of the annotations provided, or for estimating the functional bias introduced by the gene set on the array used to identify the gene list considered. We developed Genome Functional INtegrated Discoverer (GFINDer ), a web server able to automatically provide large-scale lists of user-classified genes with functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list and calculates statistical significance values for each category. Moreover, GFINDer enables the functional classification of genes according to mined functional categories and the statistical analysis is of the classifications obtained, aiding better interpretationof microarray experiment results. GFINDer is available online at http://www.medinfopoli.polimi.it/GFINDer/

Parallel formation of differently sized groups in a robotic swarm

Swarm robotics is a branch of collective robotics focused on the study of relatively large groups of robots with limited sensing and communication capabilities. One of the main benefits of such systems is their potential for parallelism. To achieve parallelism in real-world scenarios, it is important to be able to split the swarm into appropriately sized groups for different concurrent tasks

Anomaly detection in quasi-periodic energy consumption data series: a comparison of algorithms

The diffusion of domotics solutions and of smart appliances and meters enables the monitoring of energy consumption at a very fine level and the development of forecasting and diagnostic applications. Anomaly detection (AD) in energy consumption data streams helps identify data points or intervals in which the behavior of an appliance deviates from normality and may prevent energy losses and break downs. Many statistical and learning approaches have been applied to the task, but the need remains of comparing their performances with data sets of different characteristics. This paper focuses on anomaly detection on quasi-periodic energy consumption data series and contrasts 12 statistical and machine learning algorithms tested in 144 different configurations on 3 data sets containing the power consumption signals of fridges. The assessment also evaluates the impact of the length of the series used for training and of the size of the sliding window employed to detect the anomalies. The generalization ability of the top five methods is also evaluated by applying them to an appliance different from that used for training. The results show that classical machine learning methods (Isolation Forest, One-Class SVM and Local Outlier Factor) outperform the best neural methods (GRU/LSTM autoencoder and multistep methods) and generalize better when applied to detect the anomalies of an appliance different from the one used for training

Exponential distribution of long heart beat intervals during atrial fibrillation and their relevance for white noise behaviour in power spectrum

The statistical properties of heart beat intervals of 130 long-term surface electrocardiogram recordings during atrial fibrillation (AF) are investigated. We find that the distribution of interbeat intervals exhibits a characteristic exponential tail, which is absent during sinus rhythm, as tested in a corresponding control study with 72 healthy persons. The rate of the exponential decay lies in the range 3-12 Hz and shows diurnal variations. It equals, up to statistical uncertainties, the level of the previously uncovered white noise part in the power spectrum, which is also characteristic for AF. The overall statistical features can be described by decomposing the intervals into two statistically independent times, where the first one is associated with a correlated process with 1/f noise characteristics, while the second one belongs to an uncorrelated process and is responsible for the exponential tail. It is suggested to use the rate of the exponential decay as a further parameter for a better classification of AF and for the medical diagnosis. The relevance of the findings with respect to a general understanding of AF is pointed out

Arándanos: modificaciones en la calidad de frutos refrigerados previa fertilización foliar con calcio y potasio

El arándano presenta un comportamiento climatérico, caracterizado por un alza en la actividad respiratoria y producción de etileno durante la madurez. Es importante considerar que el comportamiento en postcosecha puede ser distinto entre variedades, ya que éstas pueden presentar un metabolismo diferente en relación a la respiración y producción de etileno, susceptibilidad a pudriciones, firmeza a la cosecha y postcosecha, relación azúcar/ácidos, etc. Sin embargo, existe un punto común para todas ellas, y es que se caracterizan por ser muy perecibles después de cosecha. Entre las principales causas de deterioro en arándano están: pudriciones, deshidratación, pérdida de firmeza, pérdida de apariencia, desarrollo de desórdenes y calidad sensorial. El estudio se realizó durante la temporada 2021 en una plantación de 13 años de edad, de la variedad O´Neil, ubicado en la Estación Experimental “Julio Hirschhorn”, perteneciente a la Facultad de Ciencias Agrarias y Forestales de la Universidad Nacional de la Plata.Facultad de Ciencias Agrarias y Forestale

Arándanos: modificaciones en la calidad de frutos refrigerados previa fertilización foliar con calcio y potasio

El arándano presenta un comportamiento climatérico, caracterizado por un alza en la actividad respiratoria y producción de etileno durante la madurez. Es importante considerar que el comportamiento en postcosecha puede ser distinto entre variedades, ya que éstas pueden presentar un metabolismo diferente en relación a la respiración y producción de etileno, susceptibilidad a pudriciones, firmeza a la cosecha y postcosecha, relación azúcar/ácidos, etc. Sin embargo, existe un punto común para todas ellas, y es que se caracterizan por ser muy perecibles después de cosecha. Entre las principales causas de deterioro en arándano están: pudriciones, deshidratación, pérdida de firmeza, pérdida de apariencia, desarrollo de desórdenes y calidad sensorial. El estudio se realizó durante la temporada 2021 en una plantación de 13 años de edad, de la variedad O´Neil, ubicado en la Estación Experimental “Julio Hirschhorn”, perteneciente a la Facultad de Ciencias Agrarias y Forestales de la Universidad Nacional de la Plata.Facultad de Ciencias Agrarias y Forestale

An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) in home healthcare is proposed. The whole system consists of two-lead electrocardiogram acquisition using conductive textile electrodes located in bed, baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problems of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we are proposing a method for the optimal lead selection. An optimal EDR signal among the three EDR signals derived from each lead (and arctangent of their ratio) is selected by estimating the instantaneous frequency using the Hilbert transform, and then choosing the signal with minimum variation of the instantaneous frequency. The proposed algorithm was tested on 15 male subjects, and we obtained satisfactory respiration signals that showed high correlation (r2 > 0.8) with the signal acquired from the chest-belt respiration sensor