Adaptation of Saccharomyces cerevisiae Cells to High Ethanol Concentration and Changes in Fatty Acid Composition of Membrane and Cell Size

BACKGROUND: Microorganisms can adapt to perturbations of the surrounding environment to grow. To analyze the adaptation process of the yeast Saccharomyces cerevisiae to a high ethanol concentration, repetitive cultivation was performed with a stepwise increase in the ethanol concentration in the culture medium. METHODOLOGY/PRINCIPAL FINDINGS: First, a laboratory strain of S. cerevisiae was cultivated in medium containing a low ethanol concentration, followed by repetitive cultivations. Then, the strain repeatedly cultivated in the low ethanol concentration was transferred to medium containing a high ethanol concentration and cultivated repeatedly in the same high-ethanol-concentration medium. When subjected to a stepwise increase in ethanol concentration with the repetitive cultivations, the yeast cells adapted to the high ethanol concentration; the specific growth rate of the adapted yeast strain did not decrease during repetitive cultivation in the medium containing the same ethanol concentration, while that of the non-adapted strain decreased during repetitive cultivation. A comparison of the fatty acid composition of the cell membrane showed that the contents in oleic acid (C(18:1)) in ethanol-adapted and non-adapted strains were similar, but the content of palmitic acid (C(16:0)) in the ethanol-adapted strains was lower than that in the non-adapted strain in media containing ethanol. Moreover, microscopic observation showed that the mother cells of the adapted yeast were significantly larger than those of the non-adapted strain. CONCLUSIONS: Our results suggest that activity of cell growth defined by specific growth rate of the yeast cells adapted to stepwise increase in ethanol concentration did not decrease during repetitive cultivation in high-ethanol-concentration medium. Moreover, fatty acid content of cell membrane and the size of ethanol-adapted yeast cells were changed during adaptation process. Those might be the typical phenotypes of yeast cells adapted to high ethanol concentration. In addition, the difference in sizes of the mother cell between the non-adapted and ethanol strains suggests that the cell size, cell cycle and adaptation to ethanol are thought to be closely correlated

Spinal afferent neurons projecting to the rat lung and pleura express acid sensitive channels

BACKGROUND: The acid sensitive ion channels TRPV1 (transient receptor potential vanilloid receptor-1) and ASIC3 (acid sensing ion channel-3) respond to tissue acidification in the range that occurs during painful conditions such as inflammation and ischemia. Here, we investigated to which extent they are expressed by rat dorsal root ganglion neurons projecting to lung and pleura, respectively. METHODS: The tracer DiI was either injected into the left lung or applied to the costal pleura. Retrogradely labelled dorsal root ganglion neurons were subjected to triple-labelling immunohistochemistry using antisera against TRPV1, ASIC3 and neurofilament 68 (marker for myelinated neurons), and their soma diameter was measured. RESULTS: Whereas 22% of pulmonary spinal afferents contained neither channel-immunoreactivity, at least one is expressed by 97% of pleural afferents. TRPV1(+)/ASIC3(- )neurons with probably slow conduction velocity (small soma, neurofilament 68-negative) were significantly more frequent among pleural (35%) than pulmonary afferents (20%). TRPV1(+)/ASIC3(+ )neurons amounted to 14 and 10% respectively. TRPV1(-)/ASIC3(+ )neurons made up between 44% (lung) and 48% (pleura) of neurons, and half of them presumably conducted in the A-fibre range (larger soma, neurofilament 68-positive). CONCLUSION: Rat pleural and pulmonary spinal afferents express at least two different acid-sensitive channels that make them suitable to monitor tissue acidification. Patterns of co-expression and structural markers define neuronal subgroups that can be inferred to subserve different functions and may initiate specific reflex responses. The higher prevalence of TRPV1(+)/ASIC3(- )neurons among pleural afferents probably reflects the high sensitivity of the parietal pleura to painful stimuli

Positioning the principles of precision medicine in care pathways for allergic rhinitis and chronic rhinosinusitis - A EUFOREA-ARIA-EPOS-AIRWAYS ICP statement.

Precision medicine (PM) is increasingly recognized as the way forward for optimizing patient care. Introduced in the field of oncology, it is now considered of major interest in other medical domains like allergy and chronic airway diseases, which face an urgent need to improve the level of disease control, enhance patient satisfaction and increase effectiveness of preventive interventions. The combination of personalized care, prediction of treatment success, prevention of disease and patient participation in the elaboration of the treatment plan is expected to substantially improve the therapeutic approach for individuals suffering from chronic disabling conditions. Given the emerging data on the impact of patient stratification on treatment outcomes, European and American regulatory bodies support the principles of PM and its potential advantage over current treatment strategies. The aim of the current document was to propose a consensus on the position and gradual implementation of the principles of PM within existing adult treatment algorithms for allergic rhinitis (AR) and chronic rhinosinusitis (CRS). At the time of diagnosis, prediction of success of the initiated treatment and patient participation in the decision of the treatment plan can be implemented. The second-level approach ideally involves strategies to prevent progression of disease, in addition to prediction of success of therapy, and patient participation in the long-term therapeutic strategy. Endotype-driven treatment is part of a personalized approach and should be positioned at the tertiary level of care, given the efforts needed for its implementation and the high cost of molecular diagnosis and biological treatment

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Balanced Twin Auto-Encoder for IoT Intrusion Detection

Intrusion detection systems (IDSs) provide an ef-fective solution for protecting loT systems. However, due to the massive number of loT devices (in billions) and their heterogeneity, IDSs face challenges posed by the complexity of loT data such as correlation-based features, high dimensions, and imbalance. To address these problems, this paper proposes a novel neural network architecture, called Balanced Twin Auto-Encoder (BTAE) which consists of three components, i.e., an encoder, a hermaphrodite, and a decoder. The encoder of BTAE first aims to transfer the input data into the latent space before data samples (pre-images) are translated into this space by different translation vectors. In addition, the data of the skewed labels are also generated in the latent space to address the problem of imbalanced data in which the number of attack samples is often significantly lower than those of the benign samples. Second, the hermaphrodite component serves as a bridge to move the data from the encoder to the decoder. Third, the decoder tries to copy the distribution of the samples in the latent space. BTAE is trained by a supervised learning technique, and its data representation extracted from the decoder can well distinguish the attack from the normal data. The experiments on five loT botnet datasets show that BTAE outperforms three existing groups of methods, e.g., the typical supervised learning, the well-known sampling, and the state-of-the-art representation learning. In addition, the false alarm rate (FAR) of BTAE applied for loT intrusion detection is less than equal to 1.2%

Twin Variational Auto-Encoder for Representation Learning in IoT Intrusion Detection

Intrusion detection systems (IDSs) play a pivotal role in defending IoT systems. However, developing a robust and efficient IDS is challenging due to the rapid and continuing evolving of various forms of cyber-attacks as well as a massive number of low-end IoT devices. In this paper, we introduce a novel deep learning architecture based on auto-encoders that allows to develop a robust intrusion detection system. Specifically, we propose a novel neural network architecture called Twin Variational Auto-Encoder (TVAE) for representation learning. TVAE includes a variational Auto-Encoder (VAE) and an Auto-Encoder (AE) that share a common stage where the decoder of the VAE is used as the encoder of the AE. The TVAE is trained in an unsupervised manner to effectively transform the original representation of data at the input of the VAE into a new representation at the output of the AE. In the new representation space, the difference between normal and attack data is more distinguishable. A variant of TVAE, namely Twin Sparse Variational Auto-Encoder (TSVAE) is also introduced by imposing a sparsity constraint on the representation units. The effectiveness of TVAE and TSVAE is evaluated using popular IDS and IoT botnet datasets. The simulation results show that the accuracy of TVAE and TSVAE can achieve the best results on six datasets, which is higher than those of state-of-the-art AE and VAE variants. We also investigate various characteristics of TVAE in the latent space as well as in the data extraction process. Besides applications on the IoT IDS, TVAE can also be applicable to all conventional network IDSs