Mascarpone, a soft-spread cheese, is an unripened dairy product manufactured by the thermal-acidic coagulation of milk cream. Due to the mild flavor and creamy consistency, it is a base ingredient in industrial, culinary, and homemade preparations (e.g., it is a key constituent of a widely appreciated Italian dessert ‘Tiramisù’). Probably due to this relevance as an ingredient rather than as directly consumed foodstuff, mascarpone has not been often the subject of detailed studies. To the best of our knowledge, no investigation has been carried out on the volatile compounds contributing to the mascarpone cheese aroma profile. In this study, we analyzed the Volatile Organic Compounds (VOCs) in the headspace of different commercial mascarpone cheeses by two different techniques Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME GC-MS) and Proton-Transfer Reaction-Mass Spectrometry coupled to a Time of Flight mass analyzer (PTR-ToF-MS). We coupled these two approaches due to the complementarity of the analytical potential-efficient separation and identification of the analytes on the one side (HS-SPME GC-MS), and effective, fast quantitative analysis without any sample preparation on the other (PTR-ToF-MS). A total of 27 VOCs belonging to different chemical classes (9 ketones, 5 alcohols, 4 organic acids, 3 hydrocarbons, 2 furans, 1 ester, 1 lactone, 1 aldehyde, and 1 oxime) have been identified by HS-SPME GC-MS, while PTR-ToF-MS allowed a rapid snapshot of volatile diversity confirming the aptitude to rapid noninvasive quality control and the potential in commercial sample differentiation. Ketones (2-heptanone and 2-pentanone, in particular) are the most abundant compounds in mascarpone headspace, followed by 2-propanone, 2-nonanone, 2-butanone, 1-pentanol, 2-ethyl-1-hexanol, furfural and 2-furanmethanol. The study also provides preliminary information on the differentiation of the aroma of different brands and product types.This study explored factors affecting parents’ intentions to use physical violence (PV) to discipline their children in the future. The theory of planned behavior (TPB) guided selection of variables. A sample of 1337 preschool children’s parents from nine kindergartens located in a county of Henan Province, China were selected by stratified random cluster sampling. Data on parents’ attitudes, subjective norms, and perceived behavioral control over PV, intentions to engage in PV to discipline their preschool children in the future, self-reported PV behavior toward their children during the past three months, and demographic characteristics were collected via a paper-based questionnaire. Multivariable logistic regression analyses examined putative predictors of parents’ intentions to use physically violent discipline. Nearly three-quarters of the sample said they definitely will not use violent discipline, while 23.4% either said they would use it, or did not rule it out. Logistic regression analysis showed that parents’ lower level of perceived behavioral control over using violence (OR 4.17; 95% CI 2.659, 6.551), attitudes that support PV (OR 2.23; 95% CI 1.555, 3.203), and having been physically violent with their children during the past three months (OR 1.62; 95% CI 1.032, 2.556) were significantly associated with parents’ tendency either to include, or not exclude, the use of violent discipline. Parents’ subjective norms regarding PV had no significant impact on their intentions (p > 0.05). The influence of TPB constructs varied according to parents’ gender. Intervention programs that aim to reduce violent discipline should focus both on increasing parents’ perceived behavioral control over PV and changing their attitudes toward physically violent practices, especially among mothers and parents who have already used PV to discipline their children.It is becoming increasingly important to understand the mechanism of regulatory elements on target genes in long-range genomic distance. 3C (chromosome conformation capture) and its derived methods are now widely applied to investigate three-dimensional (3D) genome organizations and gene regulation. Digestion-ligation-only Hi-C (DLO Hi-C) is a new technology with high efficiency and cost-effectiveness for whole-genome chromosome conformation capture. Here, we introduce the DLO Hi-C tool, a flexible and versatile pipeline for processing DLO Hi-C data from raw sequencing reads to normalized contact maps and for providing quality controls for different steps. It includes more efficient iterative mapping and linker filtering. We applied the DLO Hi-C tool to different DLO Hi-C datasets and demonstrated its ability in processing large data with multithreading. The DLO Hi-C tool is suitable for processing DLO Hi-C and in situ DLO Hi-C datasets. It is convenient and efficient for DLO Hi-C data processing.In the early 20th century, a series of epidemics across equatorial Africa brought African sleeping sickness (human African trypanosomiasis, HAT) to the attention of the European colonial administrations. selleck screening library This disease presented an exciting challenge for microbiologists across Europe to study the disease, discover the pathogen and search for an effective treatment. In 1923, the first “remedy for tropical diseases”-Suramin-manufactured by Bayer AG came onto the market under the brand name “Germanin.” The development and life cycle of this product-which today is still the medicine of choice for Trypanosoma brucei (T.b), hodesiense infections-reflect medical progress as well as the successes and failures in fighting the disease in the context of historic political changes over the last 100 years.The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder-decoder is named SegNet, and consists of a hierarchical correspondence of encode-decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.