Compared to conventional, ecological intensive management promotes beneficial proteolytic soil microbial communities for agro-ecosystem functioning under climate change-induced rain regimes

Projected climate change and rainfall variability will affect soil microbial communities, biogeochemical cycling and agriculture. Nitrogen (N) is the most limiting nutrient in agroecosystems and its cycling and availability is highly dependent on microbial driven processes. In agroecosystems, hydrolysis of organic nitrogen (N) is an important step in controlling soil N availability. We analyzed the effect of management (ecological intensive vs. conventional intensive) on N-cycling processes and involved microbial communities under climate change-induced rain regimes. Terrestrial model ecosystems originating from agroecosystems across Europe were subjected to four different rain regimes for 263 days. Using structural equation modelling we identified direct impacts of rain regimes on N-cycling processes, whereas N-related microbial communities were more resistant. In addition to rain regimes, management indirectly affected N-cycling processes via modifications of N-related microbial community composition. Ecological intensive management promoted a beneficial N-related microbial community composition involved in N-cycling processes under climate change-induced rain regimes. Exploratory analyses identified phosphorus-associated litter properties as possible drivers for the observed management effects on N-related microbial community composition. This work provides novel insights into mechanisms controlling agro-ecosystem functioning under climate change

Transit Photometry as an Exoplanet Discovery Method

Photometry with the transit method has arguably been the most successful exoplanet discovery method to date. A short overview about the rise of that method to its present status is given. The method's strength is the rich set of parameters that can be obtained from transiting planets, in particular in combination with radial velocity observations; the basic principles of these parameters are given. The method has however also drawbacks, which are the low probability that transits appear in randomly oriented planet systems, and the presence of astrophysical phenomena that may mimic transits and give rise to false detection positives. In the second part we outline the main factors that determine the design of transit surveys, such as the size of the survey sample, the temporal coverage, the detection precision, the sample brightness and the methods to extract transit events from observed light curves. Lastly, an overview over past, current and future transit surveys is given. For these surveys we indicate their basic instrument configuration and their planet catch, including the ranges of planet sizes and stellar magnitudes that were encountered. Current and future transit detection experiments concentrate primarily on bright or special targets, and we expect that the transit method remains a principal driver of exoplanet science, through new discoveries to be made and through the development of new generations of instruments.Comment: Review chapte

Automatic Detection of Malignant Masses in Digital Mammograms Based on a MCET-HHO Approach

Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy.Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique applied to mammography images, based on the Harris Hawks Optimization (HHO) algorithm, in order to identify regions of interest (ROIs) that contain malignant masses. The method of minimum cross entropy thresholding (MCET) is used to select the optimal threshold values for the segmentation. For the development of this work, four mammography images were used (all with presence of a malignant tumor), in their two views, craniocaudal (CC) and mediolateral oblique (MLO), obtained from the Digital Database for Screening Mammography (DDSM). Finally, the ROIs calculated were compared with the original ROIs of the database through a series of metrics, to evaluate the behavior of the algorithm. According to the results obtained, where it is shown that the agreement between the original ROIs and the calculated ROIs is significantly high, it is possible to conclude that the proposal of the MCET-HHO algorithm allows the automatic identification of ROIs containing malignant tumors in mammography images with significant accuracy

The efficacy of various machine learning models for multi-class classification of RNA-seq expression data

Late diagnosis and high costs are key factors that negatively impact the care of cancer patients worldwide. Although the availability of biological markers for the diagnosis of cancer type is increasing, costs and reliability of tests currently present a barrier to the adoption of their routine use. There is a pressing need for accurate methods that enable early diagnosis and cover a broad range of cancers. The use of machine learning and RNA-seq expression analysis has shown promise in the classification of cancer type. However, research is inconclusive about which type of machine learning models are optimal. The suitability of five algorithms were assessed for the classification of 17 different cancer types. Each algorithm was fine-tuned and trained on the full array of 18,015 genes per sample, for 4,221 samples (75 % of the dataset). They were then tested with 1,408 samples (25 % of the dataset) for which cancer types were withheld to determine the accuracy of prediction. The results show that ensemble algorithms achieve 100% accuracy in the classification of 14 out of 17 types of cancer. The clustering and classification models, while faster than the ensembles, performed poorly due to the high level of noise in the dataset. When the features were reduced to a list of 20 genes, the ensemble algorithms maintained an accuracy above 95% as opposed to the clustering and classification models.Comment: 12 pages, 4 figures, 3 tables, conference paper: Computing Conference 2019, published at https://link.springer.com/chapter/10.1007/978-3-030-22871-2_6

Random UV-C mutagenesis of Scheffersomyces (formerly Pichia) stipitis NRRL Y-7124 to improve anaerobic growth on lignocellulosic sugars

Scheffersomyces (formerly Pichia) stipitis NRRL Y-7124 was mutagenized using UV-C irradiation to produce yeast strains for anaerobic conversion of lignocellulosic sugars to ethanol. UV-C irradiation potentially produces large numbers of random mutations broadly and uniformly over the whole genome to generate unique strains. Wild-type cultures of S. stipitis NRRL Y-7124 were subjected to UV-C (234 nm) irradiation targeted at approximately 40% cell survival. When surviving cells were selected in sufficient numbers via automated plating strategies and cultured anaerobically on xylose medium for 5 months at 28°C, five novel mutagenized S. stipitis strains were obtained. Variable number tandem repeat analysis revealed that mutations had occurred in the genome, which may have produced genes that allowed the anaerobic utilization of xylose. The mutagenized strains were capable of growing anaerobically on xylose/glucose substrate with higher ethanol production during 250- to 500-h growth than a Saccharomyces cerevisiae yeast strain that is the standard for industrial fuel ethanol production. The S. stipitis strains resulting from this intense multigene mutagenesis strategy have potential application in industrial fuel ethanol production from lignocellulosic hydrolysates

Cloning, tissue and ontogenetic expression of the taurine transporter in the flatfish Senegalese sole (Solea senegalensis)

Flatfish species seem to require dietary taurine for normal growth and development. Although dietary taurine supplementation has been recommended for flatfish, little is known about the mechanisms of taurine absorption in the digestive tract of flatfish throughout ontogeny. This study described the cloning and ontogenetic expression of the taurine transporter (TauT) in the flatfish Senegalese sole (Solea senegalensis). Results showed a high similarity between TauT in Senegalese sole and other vertebrates, but a change in TauT amino acid sequences indicates that taurine transport may differ between mammals and fish, reptiles or birds. Moreover, results showed that Senegalese sole metamorphosis is an important developmental trigger to promote taurine transport in larvae, especially in muscle tissues, which may be important for larval growth. Results also indicated that the capacity to uptake dietary taurine in the digestive tract is already established in larvae at the onset of metamorphosis. In Senegalese sole juveniles, TauT expression was highest in brain, heart and eye. These are organs where taurine is usually found in high concentrations and is believed to play important biological roles. In the digestive tract of juveniles, TauT was more expressed in stomach and hindgut, indicating that dietary taurine is quickly absorbed when digestion begins and taurine endogenously used for bile salt conjugation may be recycled at the posterior end of the digestive tract. Therefore, these results suggest an enterohepatic recycling pathway for taurine in Senegalese sole, a process that may be important for maintenance of the taurine body levels in flatfish species

Distribution of mosquitoes in the South East of Argentina and first report on the analysis based on 18S rDNA and COI sequences

Although Mar del Plata is the most important city on the Atlantic coast of Argentina, mosquitoes inhabiting such area are almost uncharacterized. To increase our knowledge in their distribution, we sampled specimens of natural populations. After the morphological identification based on taxonomic keys, sequences of DNA from small ribosomal subunit (18S rDNA) and cytochrome c oxidase I (COI) genes were obtained from native species and the phylogenetic analysis of these sequences were done. Fourteen species from the genera Uranotaenia, Culex, Ochlerotatus and Psorophora were found and identified. Our 18S rDNA and COI-based analysis indicates the relationships among groups at the supra-species level in concordance with mosquito taxonomy. The introduction and spread of vectors and diseases carried by them are not known in Mar del Plata, but some of the species found in this study were reported as pathogen vectors

Locomotion disorders and skin and claw lesions in gestating sows housed in dynamic versus static groups

Lameness and lesions to the skin and claws of sows in group housing are commonly occurring indicators of reduced welfare. Typically, these problems are more common in group housing than in individual housing systems. Group management type (dynamic versus static) and stage of gestation influence the behavior of the animals, which in turn influences the occurrence of these problems. The present study compared prevalence, incidence and mean scores of lameness and skin and claw lesions in static versus dynamic group housed sows at different stages of gestation during three consecutive reproductive cycles. A total of 10 Belgian sow herds were monitored; 5 in which dynamic groups and 5 in which static groups were utilized. All sows were visually assessed for lameness and skin lesions three times per cycle and the claws of the hind limbs were assessed once per cycle. Lameness and claw lesions were assessed using visual analogue scales. Static groups, in comparison with dynamic groups, demonstrated lower lameness scores (P<0.05) and decreased skin lesion prevalence (24.9 vs. 47.3%, P<0.05) at the end of gestation. There was no difference between treatment group regarding claw lesion prevalence with 75.5% of sows demonstrating claw lesions regardless of group management. Prevalences of lameness (22.4 vs. 8.9%, P<0.05) and skin lesions (46.6 vs. 4.4%, P<0.05) were highest during the group-housed phase compared to the individually housed phases. Although the prevalence of lameness and skin lesions did not differ three days after grouping versus at the end of the group-housing phase, their incidence peaked during the first three days after moving from the insemination stalls to the group. In conclusion, the first three days after grouping was the most risky period for lameness incidence, but there was no significant difference between static or dynamic group management

An ultrahot Neptune in the Neptune desert

About one out of 200 Sun-like stars has a planet with an orbital period shorter than one day: an ultra-short-period planet (Sanchis-ojeda et al. 2014; Winn et al. 2018). All of the previously known ultra-short-period planets are either hot Jupiters, with sizes above 10 Earth radii (Re), or apparently rocky planets smaller than 2 Re. Such lack of planets of intermediate size (the "hot Neptune desert") has been interpreted as the inability of low-mass planets to retain any hydrogen/helium (H/He) envelope in the face of strong stellar irradiation. Here, we report the discovery of an ultra-short-period planet with a radius of 4.6 Re and a mass of 29 Me, firmly in the hot Neptune desert. Data from the Transiting Exoplanet Survey Satellite (Ricker et al. 2015) revealed transits of the bright Sun-like star \starname\, every 0.79 days. The planet's mean density is similar to that of Neptune, and according to thermal evolution models, it has a H/He-rich envelope constituting 9.0^(+2.7)_(-2.9)% of the total mass. With an equilibrium temperature around 2000 K, it is unclear how this "ultra-hot Neptune" managed to retain such an envelope. Follow-up observations of the planet's atmosphere to better understand its origin and physical nature will be facilitated by the star's brightness (Vmag=9.8)