Access to Expensive Therapies and Diagnostics for Kidney Care in Switzerland.

Key Points: Inconsistent responses to the prior approval process for similar patients may lead to inequities in access to optimal care. The prior authorizations process leads to frustration among nephrologists and may contribute to moral distress. The prior authorizations process may lead to important delays in kidney care.

DLSIA: Deep Learning for Scientific Image Analysis.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales.

Long-term, non-invasive FTIR detection of low-dose ionizing radiation exposure.

Non-invasive methods of detecting radiation exposure show promise to improve upon current approaches to biological dosimetry in ease, speed, and accuracy. Here we developed a pipeline that employs Fourier transform infrared (FTIR) spectroscopy in the mid-infrared spectrum to identify a signature of low dose ionizing radiation exposure in mouse ear pinnae over time. Mice exposed to 0.

Deploying Machine Learning Based Segmentation for Scientific Imaging Analysis at Synchrotron Facilities.

Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges.

Eculizumab in Shiga toxin-producing Escherichia coli hemolytic uremic syndrome: a systematic review.

Background: Infection-associated hemolytic uremic syndrome (IA-HUS), most often due to infection with Shiga toxin-producing bacteria, mainly affects young children. It can be acutely life-threatening, as well as cause long-term kidney and neurological morbidity. Specific treatment with proven efficacy is lacking.

MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies.

Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery.

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering.

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics.

EWALD: A macromolecular diffractometer for the second target station.

Revealing the positions of all the atoms in large macromolecules is powerful but only possible with neutron macromolecular crystallography (NMC). Neutrons provide a sensitive and gentle probe for the direct detection of protonation states at near-physiological temperatures and clean of artifacts caused by x rays or electrons. Currently, NMC use is restricted by the requirement for large crystal volumes even at state-of-the-art instruments such as the macromolecular neutron diffractometer at the Spallation Neutron Source.

Uronate Dehydrogenase: Directed Evolution for Improved Thermal Stability and Mutant CsUDH-inc X-ray Crystal Structure.

contains a uronate dehydrogenase termed CsUDH that can convert uronic acids to their corresponding C1,C6-dicarboxy aldaric acids, an important enzyme reaction applicable for biotechnological use of sugar acids. To increase the thermal stability of this enzyme for biotechnological processes, directed evolution using gene family shuffling was applied, and the hits selected from 2-tier screening of a shuffled gene family library contained in total 16 mutations, only some of which when examined individually appreciably increased thermal stability. Most mutations, while having minimal or no effect on thermal stability when tested in isolation, were found to exhibit synergy when combined; CsUDH-inc containing all 16 mutations had Δ +18 °C, such that was unaffected by incubation for 1 hr at ~70 °C.

Sizes of pure and doped helium droplets from single shot x-ray imaging.

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques.

The regional impact of an epidemic: socioeconomic and demographic data in Java, 1905-1924.

This dataset contains data for the island of Java, Indonesia, at the -level - comparable to present-day . The data concern trends in area of cultivated sugar, total and per-hectare sugar production, crude mortality rates and wages in the period ca. 1909-1924.

High-Performance Hybrid-Global-Deflated-Local Optimization with Applications to Active Learning.

Mathematical optimization lies at the core of many science and industry applications. One important issue with many current optimization strategies is a well-known trade-off between the number of function evaluations and the probability to find the global, or at least sufficiently high-quality local optima. In machine learning (ML), and by extension in active learning - for instance for autonomous experimentation - mathematical optimization is often used to find the underlying uncertain surrogate model from which subsequent decisions are made and therefore ML relies on high-quality optima to obtain the most accurate models.

GPU-accelerated multitiered iterative phasing algorithm for fluctuation X-ray scattering.

The multitiered iterative phasing (MTIP) algorithm is used to determine the biological structures of macromolecules from fluctuation scattering data. It is an iterative algorithm that reconstructs the electron density of the sample by matching the computed fluctuation X-ray scattering data to the external observations, and by simultaneously enforcing constraints in real and Fourier space. This paper presents the first ever MTIP algorithm acceleration efforts on contemporary graphics processing units (GPUs).

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation.

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins.

A bitter epidemic: The impact of the 1918 influenza on sugar production in Java.

Research about the economic consequences of past epidemics has mostly focused on the experience of industrialized countries, thus providing little knowledge about the effects of health shocks on developing economies. We fill this gap by studying the impact of the 1918 influenza in Java, with a new dataset on aggregate food production and district-level figures on (i) sugar production, the major export commodity and the predominant source of labour demand; (ii) agricultural and plantation wages, and (iii) annual crude death rates. The mortality impact of the influenza on Java was high, as crude mortality rates doubled in 1918 relative to the preceding years, but its economic impact was mixed.

Nephromegaly due to Disruption of Nephrons in the Green Iguana (Iguana iguana).

We describe a fatal kidney disease in green iguanas (Iguana iguana), associated with severe nephromegaly. Affected animals have enlarged kidneys, which fill the pelvic cavity, leading to compression of adjacent organs, obstipation and, ultimately, death. The pathological features of this disease have been poorly described and its aetiology is unknown.

Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens.

Multivalent presentation of viral glycoproteins can substantially increase the elicitation of antigen-specific antibodies. To enable a new generation of anti-viral vaccines, we designed self-assembling protein nanoparticles with geometries tailored to present the ectodomains of influenza, HIV, and RSV viral glycoprotein trimers. We first designed trimers tailored for antigen fusion, featuring N-terminal helices positioned to match the C termini of the viral glycoproteins.

Evaluating crystallographic likelihood functions using numerical quadratures.

Intensity-based likelihood functions in crystallographic applications have the potential to enhance the quality of structures derived from marginal diffraction data. Their usage, however, is complicated by the ability to efficiently compute these target functions. Here, a numerical quadrature is developed that allows the rapid evaluation of intensity-based likelihood functions in crystallographic applications.

Minimally disruptive optical control of protein tyrosine phosphatase 1B.

Protein tyrosine phosphatases regulate a myriad of essential subcellular signaling events, yet they remain difficult to study in their native biophysical context. Here we develop a minimally disruptive optical approach to control protein tyrosine phosphatase 1B (PTP1B)-an important regulator of receptor tyrosine kinases and a therapeutic target for the treatment of diabetes, obesity, and cancer-and we use that approach to probe the intracellular function of this enzyme. Our conservative architecture for photocontrol, which consists of a protein-based light switch fused to an allosteric regulatory element, preserves the native structure, activity, and subcellular localization of PTP1B, affords changes in activity that match those elicited by post-translational modifications inside the cell, and permits experimental analyses of the molecular basis of optical modulation.

SAD phasing of XFEL data depends critically on the error model.

A nonlinear least-squares method for refining a parametric expression describing the estimated errors of reflection intensities in serial crystallographic (SX) data is presented. This approach, which is similar to that used in the rotation method of crystallographic data collection at synchrotrons, propagates error estimates from photon-counting statistics to the merged data. Here, it is demonstrated that the application of this approach to SX data provides better SAD phasing ability, enabling the autobuilding of a protein structure that had previously failed to be built.

Diagnostic Accuracy of PET Tracers for the Differentiation of Tumor Progression from Treatment-Related Changes in High-Grade Glioma: A Systematic Review and Metaanalysis.

Posttreatment high-grade gliomas are usually monitored with contrast-enhanced MRI, but its diagnostic accuracy is limited as it cannot adequately distinguish between true tumor progression and treatment-related changes. According to recent Response Assessment in Neuro-Oncology recommendations, PET overcomes this limitation. However, it is currently unknown which tracer yields the best results.

Author Correction: Coherent diffractive imaging of microtubules using an X-ray laser.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Coherent diffractive imaging of microtubules using an X-ray laser.

X-ray free electron lasers (XFELs) create new possibilities for structural studies of biological objects that extend beyond what is possible with synchrotron radiation. Serial femtosecond crystallography has allowed high-resolution structures to be determined from micro-meter sized crystals, whereas single particle coherent X-ray imaging requires development to extend the resolution beyond a few tens of nanometers. Here we describe an intermediate approach: the XFEL imaging of biological assemblies with helical symmetry.

Bone Marrow-Harvesting Technique Influences Functional Heterogeneity of Mesenchymal Stem/Stromal Cells and Cartilage Regeneration.

Background: Connective tissue progenitors (CTPs) from native bone marrow (BM) or their culture-expanded progeny, often referred to as mesenchymal stem/stromal cells, represents a promising strategy for treatment of cartilage injuries. But the cartilage regeneration capacity of these cells remains unpredictable because of cell heterogeneity.

Hypothesis: The harvest technique of BM may highly influence stem cell heterogeneity and, thus, cartilage formation because these cells have distinct spatial localization within BM from the same bone.