In addition, we found suggestive evidence that some individuals may have embellished their smoking history to gain entry to the study. Given the prominent effect of smoking on longevity, we conclude that DNA methylation may be a useful tool for guiding and incentivizing contingency management-based approaches for smoking cessation.Multiple myeloma is a complex hematologic malignancy, and despite a survival improvement related to the growing number of available therapeutic options since 2000s, it remains an incurable disease with most patients experiencing relapse. However, therapeutic options for this disease are constantly evolving and immunotherapy is becoming the mainstay of the therapeutic armamentarium of Multiple Myeloma (MM), starting with monoclonal antibodies (MoAbs) as elotuzumab, daratumumab and isatuximab. Elotuzumab, the first in class targeting SLAMF7, in combination with lenalidomide and dexamethasone and daratumumab, directed against CD38, in combination with Rd and with bortezomib and dexamethasone (Vd), have been approved for the treatment of relapsed/refractory MM (RRMM) after they demonstrated excellent efficacy. More recently, another anti-CD38 MoAb named isatuximab was approved by FDA in combination with pomalidomide-dexamethasone (Pd) in the same setting. Many phase II and III trials with regimens containing these MoAbs are ongoing, and when available, preliminary data are very encouraging. In this review we will describe the results of major clinical studies that have been conducted with elotuzumab, daratumumab and isatuximab in RRMM, focusing on phase III trials. Moreover, we will summarized the emerging MoAbs-based combinations in the RRMM landscape.SUMOylation is a reversible posttranslational modification pathway catalyzing the conjugation of small ubiquitin-related modifier (SUMO) proteins to lysine residues of distinct target proteins. SUMOylation modifies a wide variety of cellular regulators thereby affecting a multitude of key processes in a highly dynamic manner. The SUMOylation pathway displays a hallmark in cellular stress-adaption, such as heat or redox stress. It has been proposed that enhanced cellular SUMOylation protects the brain during ischemia, however, little is known about the specific regulation of the SUMO system and the potential target proteins during cardiac ischemia and reperfusion injury (I/R). By applying left anterior descending (LAD) coronary artery ligation and reperfusion in mice, we detect dynamic changes in the overall cellular SUMOylation pattern correlating with decreased SUMO deconjugase activity during I/R injury. Further, unbiased system-wide quantitative SUMO-proteomics identified a sub-group of SUMO targets exhibiting significant alterations in response to cardiac I/R. Notably, transcription factors that control hypoxia- and angiogenesis-related gene expression programs, exhibit altered SUMOylation during ischemic stress adaptation. Moreover, several components of the ubiquitin proteasome system undergo dynamic changes in SUMO conjugation during cardiac I/R suggesting an involvement of SUMO signaling in protein quality control and proteostasis in the ischemic heart. Altogether, our study reveals regulated candidate SUMO target proteins in the mouse heart, which might be important in coping with hypoxic/proteotoxic stress during cardiac I/R injury.Sixty-nine Lactic Acid Bacteria (LAB) and bifidobacteria were isolated and identified from Italian dairy products (raw milk, cream, butter, soft cheese and yoghurt) to find new antimicrobial compounds to use as food bio-preservatives. All the isolates were preliminarily screened by the deferred antagonism method for bacteriocin production. Afterwards, to evaluate the release of bacteriocin in liquid medium, the Cell-Free Supernatant Fluid (CFSF) of the best producers was tested by agar well diffusion assay. The study allowed the selection of three bacteriocin producing strains (Enterococcus faecium E23, Bifidobacterium thermophilum B23 and Lactobacillus bulgaricus L21), endowed with the strongest and broadest inhibitory capability against the pathogen Listeria monocytogenes. The molecular characteristics and the chemical-physical properties of both producers and the respective bacteriocins were studied and compared. The results showed that E. check details faecium E23 was the best producer strain and its class IIa bacteriocins, called enterocin E23, exhibited a good spectrum of activity towards L. monocytogenes. Enterocin E23 was stable over a wide range of pH and at low temperatures for at least four months and, for this reason, it can be employed in refrigerated foods for the control of L. monocytogenes, the major concern in dairy products.With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% mAP@0.5 in the MS COCO dataset than YOLOv4-tiny model.