Anthropometric, hemodynamic, metabolic, and renal responses during 5 days of food and water deprivation

Background: Although there is considerable research in the field of fasting and fluid restriction, little is known about the impact of food and water deprivation (FWD) on body circumferences and vital parameters. Methods: During 5 days of FWD in 10 healthy adults, hemodynamic, metabolic, and renal parameters, such as weight, 5 circumferences at neck, waist, hip, chest at axilla, chest at nipples, and 1 new oblique hip circumference were measured daily. For each circumference, new quotients of daily circumference-to-weight decrease were calculated. The set of employed parameters quantified and monitored dieting persons' compliance and efficacy of the method. Results: The values of blood pressure, heart rate, hemoglobin oxygen saturation, glucose, K(+), Na(+), Cl(-), urea, creatinine, and serum osmolality proved to be stable. The mean creatinine clearance increased up to 167%. The mean daily weight decrease (1,390 ± 60 g) demonstrated the effectiveness of FWD in weight reduction. The daily decrease of all measured circumferences and the values of the corresponding circumference-to-weight decrease quotients reflected considerable volume decrease in all measured body parts per day and kg of weight loss during FWD. Conclusion: The intervention of 5 FWD days in 10 healthy adults was found to be safe, decreased weight and all measured circumferences, and improved renal function considerably

Data Under Siege: The Quest for the Optimal Convolutional Autoencoder in Side-Channel Attacks

Encryption is a method to keep our data safe from third parties. However, side-channel information may be leaked during encryption due to physical properties. This information can be used in side-channel attacks to recover critical values such as the secret encryption key. To this end, it is necessary to understand the robustness of implementations to assess the security of data handled by a device. Side-channel attacks are one such method which allow researchers to evaluate the robustness of implementations using appropriate metrics.In the security community, machine learning is playing a prominent role in the study of side-channel attacks. A notable example of this is the use of Convolutional Autoencoders (CAE) as a preprocessing step on the measurements. In this work we study in depth the problem of finding the most suitable architecture of such Convolutional Autoencoders. To this end, Optuna is used to explore the CAE hyperparameter space. This process allows us to identify hyperparameters that outperform state-of-the-art autoencoders, reducing the needed traces for a succesful attack by approximately 37% in the presence of Gaussian noise and reducing the trainable parameters needed to attack desynchronization by a factor of 29. In addition to the promising results, experiments carried out in this paper allow a better understanding of the hyperparameter space in the field of side channel attacks, providing a solid base for future use of CAE in this specific domain

A computational index derived from whole-genome copy number analysis is a novel tool for prognosis in early stage lung squamous cell carcinoma.

AbstractSquamous cell carcinoma of the lung is remarkable for the extent to which the same chromosomal abnormalities are detected in individual tumours. We have used next generation sequencing at low coverage to produce high resolution copy number karyograms of a series of 89 non-small cell lung tumours specifically of the squamous cell subtype. Because this methodology is able to create karyograms from formalin-fixed paraffin-embedded material, we were able to use archival stored samples for which survival data were available and correlate frequently occurring copy number changes with disease outcome. No single region of genomic change showed significant correlation with survival. However, adopting a whole-genome approach, we devised an algorithm that relates to total genomic damage, specifically the relative ratios of copy number states across the genome. This algorithm generated a novel index, which is an independent prognostic indicator in early stage squamous cell carcinoma of the lung

FOXO1 controls protein synthesis and transcript abundance of mutant polyglutamine proteins, preventing protein aggregation

FOXO1, a transcription factor downstream of the insulin/insulin like growth factor axis, has been linked to protein degradation. Elevated expression of FOXO orthologs can also prevent the aggregation of cytosine adenine guanine (CAG)-repeat disease causing polyglutamine (polyQ) proteins but whether FOXO1 targets mutant proteins for degradation is unclear. Here, we show that increased expression of FOXO1 prevents toxic polyQ aggregation in human cells while reducing FOXO1 levels has the opposite effect and accelerates it. Although FOXO1 indeed stimulates autophagy, its effect on polyQ aggregation is independent of autophagy, ubiquitin–proteasome system (UPS) mediated protein degradation and is not due to a change in mutant polyQ protein turnover. Instead, FOXO1 specifically downregulates protein synthesis rates from expanded pathogenic CAG repeat transcripts. FOXO1 orchestrates a change in the composition of proteins that occupy mutant expanded CAG transcripts, including the recruitment of IGF2BP3. This mRNA binding protein enables a FOXO1 driven decrease in pathogenic expanded CAG transcript- and protein levels, thereby reducing the initiation of amyloidogenesis. Our data thus demonstrate that FOXO1 not only preserves protein homeostasis at multiple levels, but also reduces the accumulation of aberrant RNA species that may co-contribute to the toxicity in CAG-repeat diseases

Synthesis and solution properties of a temperature-responsive PNIPAM–b-PDMS–b-PNIPAM triblock copolymer

In this paper, we report the synthesis and self-assembly of a novel thermoresponsive PNIPAM60–b-PDMS70–b-PNIPAM60 triblock copolymer in aqueous solution. The copolymer used a commercially available precursor modified with an atom transfer radical polymerization (ATRP) initiator to produce an ABA triblock copolymer via ATRP. Small-angle neutron scattering (SANS) was used to shed light on the structures of nanoparticles formed in aqueous solutions of this copolymer at two temperatures, 25 and 40 °C. The poly(dimethylsiloxane) block is very hydrophobic and poly(N-isopropylacrylamide) (PNIPAM) is thermoresponsive. SANS data at 25 °C indicates that the solutions of PNIPAM–b-PDMS–b-PNIPAM copolymers form well-defined aggregates with presumably core–shell structures below cloud point temperature. The scattering curves originating from nanoparticles formed at 40 °C in 100% D2O or 100% H2O were successfully fitted with the Beaucage model describing aggregates with hierarchical structure

Gastrointestinal stromal tumor masquerading as a lung neoplasm. A case presentation and literature review

Gastrointestinal stromal tumors (GISTs) are rare neoplasms of the gastrointestinal tract. Their incidence in the esophagus is 1%–3%. Never has a GIST been documented to directly invade the lung. We report a primary esophageal GIST with direct invasion into the lung parenchyma, presenting predominantly with respiratory symptoms. We include a retrospective literature review. Although the principle 'common things are common' usually guides our everyday clinical practice, this case emphasizes that rare entities can mimic common pathologies and underlines the importance of having a clearly defined differential diagnostic list which should be meticulously scrutinized

LIVAS: A 3-D multi-wavelength aerosol/cloud database based on CALIPSO and EARLINET

We present LIVAS (LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies), a 3-D multi-wavelength global aerosol and cloud optical database, optimized to be used for future space-based lidar end-to-end simulations of realistic atmospheric scenarios as well as retrieval algorithm testing activities. The LIVAS database provides averaged profiles of aerosol optical properties for the potential spaceborne laser operating wavelengths of 355, 532, 1064, 1570 and 2050 nm and of cloud optical properties at the wavelength of 532 nm. The global database is based on CALIPSO observations at 532 and 1064 nm and on aerosol-type-dependent backscatter- and extinction-related Ångström exponents, derived from EARLINET (European Aerosol Research Lidar Network) ground-based measurements for the UV and scattering calculations for the IR wavelengths, using a combination of input data from AERONET, suitable aerosol models and recent literature. The required spectral conversions are calculated for each of the CALIPSO aerosol types and are applied to CALIPSO backscatter and extinction data corresponding to the aerosol type retrieved by the CALIPSO aerosol classification scheme. A cloud optical database based on CALIPSO measurements at 532 nm is also provided, neglecting wavelength conversion due to approximately neutral scattering behavior of clouds along the spectral range of LIVAS. Averages of particle linear depolarization ratio profiles at 532 nm are provided as well. Finally, vertical distributions for a set of selected scenes of specific atmospheric phenomena (e.g., dust outbreaks, volcanic eruptions, wild fires, polar stratospheric clouds) are analyzed and spectrally converted so as to be used as case studies for spaceborne lidar performance assessments. The final global data set includes 4-year (1 January 2008–31 December 2011) time-averaged CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) data on a uniform grid of 1° × 1° with the original high vertical resolution of CALIPSO in order to ensure realistic simulations of the atmospheric variability in lidar end-to-end simulations

Ground/space, passive/active remote sensing observations coupled with particle dispersion modelling to understand the inter-continental transport of wildfire smoke plumes

During the 2017 record-breaking burning season in Canada/United States, intense wild fires raged during the first week of September in the Pacific northwestern region (British Columbia, Alberta, Washington, Oregon, Idaho, Montana and northern California) burning mostly temperate coniferous forests. The heavy loads of smoke particles emitted in the atmosphere reached the Iberian Peninsula (IP) a few days later on 7 and 8 September. Satellite imagery allows to identify two main smoke clouds emitted during two different periods that were injected and transported in the atmosphere at several altitude levels. Columnar properties on 7 and 8 September at two Aerosol Robotic Network (AERONET) mid-altitude, background sites in northern and southern Spain are: aerosol optical depth (AOD) at 440 nm up to 0.62, Ångström exponent of 1.6–1.7, large dominance of small particles (fine mode fraction >0.88), low absorption AOD at 440 nm (0.98). Profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) show the presence of smoke particles in the stratosphere during the transport, whereas the smoke is only observed in the troposphere at its arrival over the IP. Portuguese and Spanish ground lidar stations from the European Aerosol Research Lidar Network/Aerosols, Clouds, and Trace gases Research InfraStructure Network (EARLINET/ACTRIS) and the Micro-Pulse Lidar NETwork (MPLNET) reveal smoke plumes with different properties: particle depolarization ratio and color ratio, respectively, of 0.05 and 2.5 in the mid troposphere (5–9 km) and of 0.10 and 3.0 in the upper troposphere (10–13 km). In the mid troposphere the particle depolarization ratio does not seem time-dependent during the transport whereas the color ratio seems to increase (larger particles sediment first). To analyze the horizontal and vertical transport of the smoke from its origin to the IP, particle dispersion modelling is performed with the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) parameterized with satellite-derived biomass burning emission estimates from the Global Fire Assimilation System (GFAS) of the Copernicus Atmosphere Monitoring Service (CAMS). Three compounds are simulated: carbon monoxide, black carbon and organic carbon. The results show that the first smoke plume which travels slowly reaches rapidly (~1 day) the upper troposphere and lower stratosphere (UTLS) but also shows evidence of large scale horizontal dispersion, while the second plume, entrained by strong subtropical jets, reaches the upper troposphere much slower (~2.5 days). Observations and dispersion modelling all together suggest that particle depolarization properties are enhanced during their vertical transport from the mid to the upper troposphere.Spanish groups acknowledge the Spanish Ministry of Economy and Competitivity (MINECO) (ref. CGL2013-45410-R, CGL2014-52877-R, CGL2014-55230-R, TEC2015-63832-P, CGL2015-73250-JIN, CGL2016-81092-R and CGL2017-85344-R)European Union through H2020 programme ACTRIS-2, grant 654109European Union through H2020 programme EUNADICS-AV, grant 723986European Union through H2020 programme GRASP-ACE, grant 77834

An automatic observation-based aerosol typing method for EARLINET

We present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with lit-erature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59% (minimum) and 90% (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80 %. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite