Tandem mass spectrometry has found widespread application as a powerful tool for the characterization of linear and branched oligosaccharides. Though the technique has been applied to the analysis of cyclic oligosaccharides as well, the underlying fragmentation mechanisms have hardly been investigated. This study focuses on the mechanistic aspects of the gas-phase dissociation of protonated β-cyclodextrins. Elucidation of the dissociation mechanisms is supported by tandem mass spectrometric experiments and by experiments on di- and trimethylated cyclodextrin derivatives. The fragmentation pathway comprises the linearization of the macrocyclic structure as the initial step of the decomposition, followed by the elimination of glucose subunits and the subsequent release of water and formaldehyde moieties from the glucose monomer and dimer fragment ions. Linearization of the macrocycle occurs due to proton-driven scission of the glycosidic bond adjacent to carbon atom C1 in conjunction with the formation of a new hydroxy group. The resulting ring-opened structure further decomposes in charge-independent processes forming either zwitterionic fragments, a 1,4-anhydroglucose moiety, or a new macrocyclic structure, that is lost as a neutral, and an oxonium ion. Since the hydroxy group formed at the ring-opening site can be regarded as the non-reducing end of the linearized structure, the fragment ion nomenclature commonly used for linear and branched oligosaccharides, which relies on the designation of a reducing and a non-reducing end, can also be applied to the description of fragment ions derived from cyclic structures.The Kováts retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independent of many experimental variables and, as such, is considered a near-universal descriptor of retention time on a chromatography column. The Kováts retention indices of a large number of molecules have been determined experimentally. The "NIST 20 GC Method/Retention Index Library" database has collected and, more importantly, curated retention indices of a subset of these compounds resulting in a highly valued reference database. The experimental data in the library form an ideal data set for training machine learning models for the prediction of retention indices of unknown compounds. In this article, we describe the training of a graph neural network model to predict the Kováts retention index for compounds in the NIST library and compare this approach with previous work [1]. We predict the Kováts retention index with a mean unsigned error of 28 index units as compared to 44, the putative best result using a convolutional neural network [1]. The NIST library also incorporates an estimation scheme based on a group contribution approach that achieves a mean unsigned error of 114 compared to the experimental data. Our method uses the same input data source as the group contribution approach, making its application straightforward and convenient to apply to existing libraries. Our results convincingly demonstrate the predictive powers of systematic, data-driven approaches leveraging deep learning methodologies applied to chemical data and for the data in the NIST 20 library outperform previous models.Stand-alone electrospray ionization mass spectrometry (ESI-MS) has been advancing through enhancements in throughput, selectivity and sensitivity of mass spectrometers. Unlike traditional MS techniques which usually require extensive offline sample preparation and chromatographic separation, many sample preparation techniques are now directly coupled with stand-alone MS to enable outstanding throughput for bioanalysis. In this review, we summarize the different sample clean-up and/or analyte enrichment strategies that can be directly coupled with ESI-MS and nano-ESI-MS for the analysis of biological fluids. The overview covers the hyphenation of different sample preparation techniques including solid phase extraction (SPE), solid phase micro-extraction (SPME), slug flow micro-extraction/nano-extraction (SFME/SFNE), liquid extraction surface analysis (LESA), extraction electrospray, extraction using digital microfluidics (DMF), and electrokinetic extraction (EkE) with ESI-MS and nano-ESI-MS.As companion animals, dogs and cats live in close contact with humans, generating the possibility of interspecies pathogen transmission events. Equine origin H3N8 and avian origin H5N1 influenza virus have been reported in dogs and cats respectively since 2004 with outbreaks associated with different strains recorded for both species in Asia and North America. To date, there have been no reports of influenza viruses from companion animals in South America. To fill this gap in knowledge, we performed active epidemiological surveillance in shelters that received abandoned animals, backyard production systems and veterinary clinics between May 2017 and January 2019 to estimate the burden of influenza infection in cats and dogs in the central region of Chile. Blood samples, oropharyngeal swabs or both were collected for influenza A virus detection by RT-qPCR, NP-ELISA, and hemagglutination inhibition assay. Logistic regression models were performed to assess the association between NP-ELISA-positivity and variables including sex and animal origin. The percentage of ELISA-positive samples was 43.5 % (95 % CI 37.0-50.1) and 23.3 % (95 % CI 10.6-42.7) for dogs and cats, respectively. No association was found between NP-ELISA results and sex or animal origin for either dogs or cats. click here Two ELISA positive samples showed hemagglutination inhibition titers against pandemic H1N1 influenza. One dog sample tested positive by RT-qPCR, indicating an overall RT-qPCR positivity in dogs of 1.1 % (95 % CI 0.05-6.7). None of the tested cat samples were positive by this assay. To analyze the effect of surgical technique and other risk factors on severe postoperative complications in dogs with cranial cruciate ligament disease (CCLD). A cohort study of 255 dogs (287 stifles) surgically treated for CCLD at two veterinary university hospitals (2011-2016) was performed. The electronic medical records were reviewed and dog owners and referring veterinarians contacted for additional information. The complications were classified as minor, major and catastrophic, where major and catastrophic were considered severe. A multivariable Cox proportional hazards model was applied to assess risk factors for severe postoperative complications. Three surgical techniques were used; lateral fabellotibial suture (LFS, 141 stifles), tibial plateau leveling osteotomy (TPLO, 77 stifles) and tibial tuberosity advancement (TTA, 69 stifles). The most common severe postoperative complications were surgical site infections or complications related to the surgical implant. Severe postoperative complications occurred in 31 % of the stifles treated with TPLO, 22 % of the stifles treated with LFS and 25 % of the stifles treated with TTA.