Array programming with NumPy

Network dynamics underlying OFF responses in the auditory cortex

Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of recurrent interactions at the network level. To test this hypothesis, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli, and compared them to linear single-cell and network models. While the single-cell model explained some prominent features of the data, it could not capture the structure across stimuli and trials. In contrast, the network model accounted for the low-dimensional organization of population responses and their global structure across stimuli, where distinct stimuli activated mostly orthogonal dimensions in the neural state-space.

Mechanistic Origin of Superionic Lithium Diffusion in Anion-Disordered Li6PS5 X Argyrodites

The rational development of fast-ion-conducting solid electrolytes for all-solid-state lithium-ion batteries requires understanding the key structural and chemical principles that give some materials their exceptional ionic conductivities. For the lithium argyrodites Li6PS5X (X = Cl, Br, or I), the choice of the halide, X, strongly affects the ionic conductivity, giving room-temperature ionic conductivities for X = {Cl,Br} that are ×103 higher than for X = I. This variation has been attributed to differing degrees of S/X anion disorder. For X = {Cl,Br}, the S/X anions are substitutionally disordered, while for X = I, the anion substructure is fully ordered. To better understand the role of substitutional anion disorder in enabling fast lithium-ion transport, we have performed a first-principles molecular dynamics study of Li6PS5I and Li6PS5Cl with varying amounts of S/X anion-site disorder. By considering the S/X anions as a tetrahedrally close-packed substructure, we identify three partially occupied lithium sites that define a contiguous three-dimensional network of face-sharing tetrahedra. The active lithium-ion diffusion pathways within this network are found to depend on the S/X anion configuration. For anion-disordered systems, the active site-site pathways give a percolating three-dimensional diffusion network; whereas for anion-ordered systems, critical site-site pathways are inactive, giving a disconnected diffusion network with lithium motion restricted to local orbits around S positions. Analysis of the lithium substructure and dynamics in terms of the lithium coordination around each sulfur site highlights a mechanistic link between substitutional anion disorder and lithium disorder. In anion-ordered systems, the lithium ions are pseudo-ordered, with preferential 6-fold coordination of sulfur sites. Long-ranged lithium diffusion would disrupt this SLi6 pseudo-ordering, and is, therefore, disfavored. In anion-disordered systems, the pseudo-ordered 6-fold S-Li coordination is frustrated because of Li-Li Coulombic repulsion. Lithium positions become disordered, giving a range of S-Li coordination environments. Long-ranged lithium diffusion is now possible with no net change in S-Li coordination numbers. This gives rise to superionic lithium transport in the anion-disordered systems, effected by a concerted string-like diffusion mechanism.

ParaMol: A Package for Automatic Parameterization of Molecular Mechanics Force Fields

The ensemble of structures generated by molecular mechanics (MM) simulations is determined by the functional form of the force field employed and its parameterization. For a given functional form, the quality of the parameterization is crucial and will determine how accurately we can compute observable properties from simulations. While accurate force field parameterizations are available for biomolecules, such as proteins or DNA, the parameterization of new molecules, such as drug candidates, is particularly challenging as these may involve functional groups and interactions for which accurate parameters may not be available. Here, in an effort to address this problem, we present ParaMol, a Python package that has a special focus on the parameterization of bonded and nonbonded terms of druglike molecules by fitting to ab initio data. We demonstrate the software by deriving bonded terms' parameters of three widely known drug molecules, viz. aspirin, caffeine, and a norfloxacin analogue, for which we show that, within the constraints of the functional form, the methodologies implemented in ParaMol are able to derive near-ideal parameters. Additionally, we illustrate the best practices to follow when employing specific parameterization routes. We also determine the sensitivity of different fitting data sets, such as relaxed dihedral scans and configurational ensembles, to the parameterization procedure, and discuss the features of the various weighting methods available to weight configurations. Owing to ParaMol's capabilities, we propose that this software can be introduced as a routine step in the protocol normally employed to parameterize druglike molecules for MM simulations.

Coupled oscillators coordinate collective germline growth

Developing oocytes need large supplies of macromolecules and organelles. A conserved strategy for accumulating these products is to pool resources of oocyte-associated germline nurse cells. In Drosophila, these cells grow more than 100-fold to boost their biosynthetic capacity. No previously known mechanism explains how nurse cells coordinate growth collectively. Here, we report a cell cycle-regulating mechanism that depends on bidirectional communication between the oocyte and nurse cells, revealing the oocyte as a critical regulator of germline cyst growth. Transcripts encoding the cyclin-dependent kinase inhibitor, Dacapo, are synthesized by the nurse cells and actively localized to the oocyte. Retrograde movement of the oocyte-synthesized Dacapo protein to the nurse cells generates a network of coupled oscillators that controls the cell cycle of the nurse cells to regulate cyst growth. We propose that bidirectional nurse cell-oocyte communication establishes a growth-sensing feedback mechanism that regulates the quantity of maternal resources loaded into the oocyte.

Visualizing anatomically registered data with brainrender

Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

Automated Rational Strain Construction Based on High-Throughput Conjugation

Molecular cloning is the core of synthetic biology, as it comprises the assembly of DNA and its expression in target hosts. At present, however, cloning is most often a manual, time-consuming, and repetitive process that highly benefits from automation. The automation of a complete rational cloning procedure, i.e., from DNA creation to expression in the target host, involves the integration of different operations and machines. Examples of such workflows are sparse, especially when the design is rational (i.e., the DNA sequence design is fixed and not based on randomized libraries) and the target host is less genetically tractable (e.g., not sensitive to heat-shock transformation). In this study, an automated workflow for the rational construction of plasmids and their subsequent conjugative transfer into the biotechnological platform organism Corynebacterium glutamicum is presented. The whole workflow is accompanied by a custom-made software tool. As an application example, a rationally designed library of transcription factor-biosensors based on the regulator Lrp was constructed and characterized. A sensor with an improved dynamic range was obtained, and insights from the screening provided evidence for a dual regulator function of C. glutamicum Lrp.

EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of ‘omic data.

IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

Correction of Directional Effects in VEGETATION NDVI Time-Series

Land surface reflectance measurements from the VEGETATION program (SPOT-4, SPOT-5 and PROBA-V satellites) have led to the acquisition of consistent time-series of Normalized Difference Vegetation Index (NDVI) at a global scale. The wide imaging swath (>2000 km) of the family of VEGETATION space-borne sensors ensures a daily coverage of the Earth at the expense of a varying observation and illumination geometries between successive orbit overpasses for a given target located on the ground. Such angular variations infer saw-like patterns on time-series of reflectance and NDVI. The presence of directional effects is not a real issue provided that they can be properly removed, which supposes an appropriate BRDF (Bidirectional Reflectance Distribution Function) sampling as offered by the VEGETATION program. An anisotropy correction supports a better analysis of the temporal shapes and spatial patterns of land surface reflectance values and vegetation indices such as NDVI. Herein we describe a BRDF correction methodology, for the purpose of the Copernicus Global Land Service framework, which includes notably an adaptive data accumulation window and provides uncertainties associated with the NDVI computed with normalized reflectance. Assessing the general performance of the methodology in comparing time-series between normalized and directional NDVI reveals a significant removal of the high-frequency noise due to directional effects. The proposed methodology is computationally efficient to operate at a global scale to deliver a BRDF-corrected NDVI product based on long-term Time-Series of VEGETATION sensor and its follow-on with the Copernicus Sentinel-3 satellite constellation.

Metabolic Capacity of the Antarctic Cyanobacterium Phormidium pseudopriestleyi That Sustains Oxygenic Photosynthesis in the Presence of Hydrogen Sulfide

Sulfide inhibits oxygenic photosynthesis by blocking electron transfer between H₂O and the oxygen-evolving complex in the D1 protein of Photosystem II. The ability of cyanobacteria to counter this effect has implications for understanding the productivity of benthic microbial mats in sulfidic environments throughout Earth history. In Lake Fryxell, Antarctica, the benthic, filamentous cyanobacterium Phormidium pseudopriestleyi creates a 1–2 mm thick layer of 50 µmol L⁻¹ O₂ in otherwise sulfidic water, demonstrating that it sustains oxygenic photosynthesis in the presence of sulfide. A metagenome-assembled genome of P. pseudopriestleyi indicates a genetic capacity for oxygenic photosynthesis, including multiple copies of psbA (encoding the D1 protein of Photosystem II), and anoxygenic photosynthesis with a copy of sqr (encoding the sulfide quinone reductase protein that oxidizes sulfide). The genomic content of P. pseudopriestleyi is consistent with sulfide tolerance mechanisms including increasing psbA expression or directly oxidizing sulfide with sulfide quinone reductase. However, the ability of the organism to reduce Photosystem I via sulfide quinone reductase while Photosystem II is sulfide-inhibited, thereby performing anoxygenic photosynthesis in the presence of sulfide, has yet to be demonstrated.

Metabolic Characterization of Plasma and Cyst Fluid from Cystic Precursors to Pancreatic Cancer Patients Reveal Metabolic Signatures of Bacterial Infection

Pancreatic cancer is the seventh leading cause of cancer-related death worldwide, with a 5 year survival rate as low as 9%. One factor complicating the management of pancreatic cancer is the lack of reliable tools for early diagnosis. While up to 50% of the adult population has been shown to develop precancerous pancreatic cysts, limited and insufficient approaches are currently available to determine whether a cyst is going to progress into pancreatic cancer. Recently, we used metabolomics approaches to identify candidate markers of disease progression in patients diagnosed with intraductal papillary mucinous neoplasms (IPMNs) undergoing pancreatic resection. Here, we enrolled an independent cohort to verify the candidate markers from our previous study with orthogonal quantitative methods in plasma and cyst fluid from serous cystic neoplasm and IPMN (either low- or high-grade dysplasia or pancreatic ductal adenocarcinoma). We thus validated these markers with absolute quantitative methods through the auxilium of stable isotope-labeled internal standards in a new independent cohort. Finally, we identified novel markers of IPMN status and disease progression-including amino acids, carboxylic acids, conjugated bile acids, free and carnitine-conjugated fatty acids, purine oxidation products, and trimethylamine-oxide. We show that the levels of these metabolites of potential bacterial origin correlated with the degree of bacterial enrichment in the cyst, as determined by 16S RNA. Overall, our findings are interesting per se, owing to the validation of previous markers and identification of novel small molecule signatures of IPMN and disease progression. In addition, our findings further fuel the provoking debate as to whether bacterial infections may represent an etiological contributor to the development and severity of the disease in pancreatic cancer, in like fashion to other cancers (e.g., Helicobacter pylori and gastric cancer).

Causal discovery using compression-complexity measures

Causal inference is one of the most fundamental problems across all domains of science. We address the problem of inferring a causal direction from two observed discrete symbolic sequences X and Y. We present a framework which relies on lossless compressors for inferring context-free grammars (CFGs) from sequence pairs and quantifies the extent to which the grammar inferred from one sequence compresses the other sequence. We infer X causes Y if the grammar inferred from X better compresses Y than in the other direction. To put this notion to practice, we propose three models that use the Compression-Complexity Measures (CCMs) - Lempel-Ziv (LZ) complexity and Effort-To-Compress (ETC) to infer CFGs and discover causal directions without demanding temporal structures. We evaluate these models on synthetic and real-world benchmarks and empirically observe performances competitive with current state-of-the-art methods. Lastly, we present two unique applications of the proposed models for causal inference directly from pairs of genome sequences belonging to the SARS-CoV-2 virus. Using numerous sequences, we show that our models capture causal information exchanged between genome sequence pairs, presenting novel opportunities for addressing key issues in sequence analysis to investigate the evolution of virulence and pathogenicity in future applications.

Machine Learning in Futures Markets

In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

Bacterial Growth Control Mechanisms Inferred from Multivariate Statistical Analysis of Single-Cell Measurements

Analysis of single-cell measurements of bacterial growth and division often relied on testing preconceived models of cell size control mechanisms. Such an approach could limit the scope of data analysis and prevent us from uncovering new information. Here, we take an "agnostic" approach by applying regression methods to multiple simultaneously measured cellular variables, which allow us to infer dependencies among those variables from their apparent correlations. Besides previously observed correlations attributed to particular cell size control mechanisms, we identify dependencies that point to potentially new mechanisms. In particular, cells born smaller than their sisters tend to grow faster and make up for the size difference acquired during division. We also find that sister cells are correlated beyond what single-cell, size-control models predict. These trends are consistently found in repeat experiments, although the dependencies vary quantitatively. Such variation highlights the sensitivity of cell growth to environmental variations and the limitation of currently used experimental setups.

Human ORC/MCM density is low in active genes and correlates with replication time but does not delimit initiation zones

Eukaryotic DNA replication initiates during S phase from origins that have been licensed in the preceding G1 phase. Here, we compare ChIP-seq profiles of the licensing factors Orc2, Orc3, Mcm3, and Mcm7 with gene expression, replication timing, and fork directionality profiles obtained by RNA-seq, Repli-seq, and OK-seq. Both, the origin recognition complex (ORC) and the minichromosome maintenance complex (MCM) are significantly and homogeneously depleted from transcribed genes, enriched at gene promoters, and more abundant in early- than in late-replicating domains. Surprisingly, after controlling these variables, no difference in ORC/MCM density is detected between initiation zones, termination zones, unidirectionally replicating regions, and randomly replicating regions. Therefore, ORC/MCM density correlates with replication timing but does not solely regulate the probability of replication initiation. Interestingly, H4K20me3, a histone modification proposed to facilitate late origin licensing, was enriched in late-replicating initiation zones and gene deserts of stochastic replication fork direction. We discuss potential mechanisms specifying when and where replication initiates in human cells.

Dipolar coupling of nanoparticle-molecule assemblies: An efficient approach for studying strong coupling

Strong light-matter interactions facilitate not only emerging applications in quantum and non-linear optics but also modifications of properties of materials. In particular, the latter possibility has spurred the development of advanced theoretical techniques that can accurately capture both quantum optical and quantum chemical degrees of freedom. These methods are, however, computationally very demanding, which limits their application range. Here, we demonstrate that the optical spectra of nanoparticle-molecule assemblies, including strong coupling effects, can be predicted with good accuracy using a subsystem approach, in which the response functions of different units are coupled only at the dipolar level. We demonstrate this approach by comparison with previous time-dependent density functional theory calculations for fully coupled systems of Al nanoparticles and benzene molecules. While the present study only considers few-particle systems, the approach can be readily extended to much larger systems and to include explicit optical-cavity modes.

SWIFT: A Deep Learning Approach to Prediction of Hypoxemic Events in Critically-Ill Patients Using SpO2 Waveform Prediction.. [PMID: 33688661 PMCID: PMC7941630 DOI: 10.1101/2021.02.25.21252234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO₂Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO₂) waveforms 5 and 30 minutes in the future using only prior SpO₂ values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO₂ waveforms with average MSE below .0007. SWIFT provides information on both occurrence and magnitude of potential hypoxemic events 30 minutes in advance, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

Neuronal SKN-1B modulates nutritional signalling pathways and mitochondrial networks to control satiety

The feeling of hunger or satiety results from integration of the sensory nervous system with other physiological and metabolic cues. This regulates food intake, maintains homeostasis and prevents disease. In C. elegans, chemosensory neurons sense food and relay information to the rest of the animal via hormones to control food-related behaviour and physiology. Here we identify a new component of this system, SKN-1B which acts as a central food-responsive node, ultimately controlling satiety and metabolic homeostasis. SKN-1B, an ortholog of mammalian NF-E2 related transcription factors (Nrfs), has previously been implicated with metabolism, respiration and the increased lifespan incurred by dietary restriction. Here we show that SKN-1B acts in two hypothalamus-like ASI neurons to sense food, communicate nutritional status to the organism, and control satiety and exploratory behaviours. This is achieved by SKN-1B modulating endocrine signalling pathways (IIS and TGF-β), and by promoting a robust mitochondrial network. Our data suggest a food-sensing and satiety role for mammalian Nrf proteins.

Deciding when and how much to eat is important for maintaining health and preventing disease. It requires an intricate molecular level of communication between our nervous, physiological, and metabolic systems. These signals stimulate food intake, and afterwards the feeling of satiety which makes us stop eating. We have studied these phenomena using the simple nematode worm C. elegans which has a fully mapped nervous system and quantifiable food-related behaviours. In C. elegans, chemosensory neurons sense food and communicate this to the rest of the animal via hormones to control food-related behaviour and associated physiological changes. Here we identify a new central node of this system, the C. elegans gene SKN-1B, which acts in two sensory neurons to sense food, communicate food-status to the rest of the worm, and control satiety and exploratory behaviours. It does this by altering hormonal signalling (Insulin and Transforming Growth Factor-β), and by promoting a strong mitochondrial network. The mammalian equivalents of SKN-1B are the NF-E2 related transcription factors (Nrfs), which have previously been implicated with metabolism and respiration. Our data suggest a new food-sensing and satiety role for mammalian Nrf proteins.

DATAD: a Python-based X-ray diffraction simulation code for arbitrary texture and arbitrary deformation

DATAD, a Python-based X-ray diffraction simulation code, has been developed for simulating one- and two-dimensional diffraction patterns of a polycrystalline specimen with an arbitrary texture under an arbitrary deformation state and an arbitrary detection geometry. Pixelated planar and cylindrical detectors can be used. The basic principles and key components of the code are presented along with the usage of DATAD. As validation and application cases, X-ray diffraction patterns of single-crystal and polycrystalline specimens with or without texture, or applied strain, on a planar or cylindrical detector are simulated.

Multi-scale Simulation of Equilibrium Step Fluctuations on Cu(111) Surfaces

Understanding the nature of active sites is a non-trivial task, especially when the catalyst is sensitively affected by chemical reactions and environmental conditions. The challenge lies on capturing explicitly the dynamics of catalyst evolution during reactions. Despite the complexity of catalyst reconstruction, we can untangle them into several elementary processes, of which surface diffusion is of prime importance. By applying density functional theory-kinetic Monte Carlo (DFT-KMC) simulation employed with cluster expansion (CE), we investigated the microscopic mechanism of surface diffusion of Cu with defects such as steps and kinks. Based on the result, the energetics obtained from CE have shown good agreement with DFT calculations. Various diffusion events during the step fluctuations are discussed as well. Aside from the adatom attachment, the diffusion along the step edge is found to be the dominant mass transport mechanism, indicated by the lowest activation energy. We also calculated time correlation functions at 300, 400, and 500 K. However, the time exponent in the correlation function does not strictly follow the power law behavior due to the limited step length, which inhibits variation in the kink density.

The pyruvate-lactate axis modulates cardiac hypertrophy and heart failure

The metabolic rewiring of cardiomyocytes is a widely accepted hallmark of heart failure (HF). These metabolic changes include a decrease in mitochondrial pyruvate oxidation and an increased export of lactate. We identify the mitochondrial pyruvate carrier (MPC) and the cellular lactate exporter monocarboxylate transporter 4 (MCT4) as pivotal nodes in this metabolic axis. We observed that cardiac assist device-induced myocardial recovery in chronic HF patients was coincident with increased myocardial expression of the MPC. Moreover, the genetic ablation of the MPC in cultured cardiomyocytes and in adult murine hearts was sufficient to induce hypertrophy and HF. Conversely, MPC overexpression attenuated drug-induced hypertrophy in a cell-autonomous manner. We also introduced a novel, highly potent MCT4 inhibitor that mitigated hypertrophy in cultured cardiomyocytes and in mice. Together, we find that alteration of the pyruvate-lactate axis is a fundamental and early feature of cardiac hypertrophy and failure.

Use of computer vision for analysis of image datasets from high temperature plasma experiments

Great strides have been made in improving the quality of x-ray radiographs in high energy density plasma experiments, enabled in part by innovations in engineering and manufacturing of integrated circuits and materials. As a consequence, the radiographs of today are filled with a great deal of detail, but few of these features are extracted in a systematic way. Analysis techniques familiar to plasma physicists tend toward brittle 1D lineout or Fourier transform type analyses. The techniques applied to process our data have not kept pace with improvements in the quality of our data. Fortunately, the field of computer vision has a wealth of tools to offer, which have been widely used in industrial imaging and, more recently, adopted in biological imaging. We demonstrate the application of computer vision techniques to the analysis of x-ray radiographs from high energy density plasma experiments, as well as give a brief tutorial on the computer vision techniques themselves. These tools robustly extract 2D contours of shocks, boundaries of inhomogeneities, and secondary flows, thereby allowing for increased automation of analysis, as well as direct and quantitative comparisons with simulations.

Creating clear and informative image-based figures for scientific publications

Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in 3 fields; plant sciences, cell biology, and physiology (n = 580 papers). Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.

Oncotherapeutic Protein Kinase Inhibitors Associated With Pro-Arrhythmic Liability

BACKGROUND

Ibrutinib is a protein kinase inhibitor that has been widely successful in treating multiple common variations of B-cell cancers. However, an unfortunate side effect of ibrutinib is that it predisposes patients to development of atrial fibrillation.

OBJECTIVES

The purpose of this study was to assess other commonly prescribed protein kinase inhibitors for similar pro-arrhythmic liability.

METHODS

This study comprehensively evaluated data from the U.S. Food and Drug Administration adverse events reporting system and determined the reporting of cardiac arrhythmia attributed to kinase inhibitor therapy using a multivariable logistic regression model. We evaluated 3,663,300 case reports containing 23,067 cases of atrial fibrillation and 66,262 cases of cardiac arrhythmia. In total, 32 protein kinase inhibitors were evaluated, almost all of which are oncotherapeutics.

RESULTS

Seven protein kinase inhibitors were associated with a significant increase in the odds of atrial fibrillation (ibrutinib, ponatinib, nilotinib, ribociclib, trametinib, osimertinib, and idelalisib). Assessment of broader pro-arrhythmic toxicity suggested a ventricular-specific liability for nilotinib and a bradyarrhythmia risk with alectinib and crizotinib.

CONCLUSIONS

Compounds that result in the inhibition of a number of protein kinases are associated with an increased risk of cardiac rhythm disturbances. The mechanisms driving the arrhythmogenic effects remain to be discovered, but this study presents an important step in identifying and prioritizing the study of these protein kinase signaling pathways.

Epigenomic tensor predicts disease subtypes and reveals constrained tumor evolution

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (decomposition and classification of epigenomic tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences among tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic, and prognostic strategies.

Leistico et al. apply tensor decomposition and classification methods to integrate information from hierarchical heterogenous epigenomic datasets and identify histone modification patterns that discriminate disease conditions, tissue types, and differentiation states. Leiomyomas are shown to possess alterations in distal enhancers and large-scale regions confined to chromatin contact domains.

pyFOOMB: Python framework for object oriented modeling of bioprocesses

Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black-box bioprocess modeling with non-linear regression for model parameter estimation. The presented pyFOOMB package enables a guided and flexible implementation of bioprocess models in the form of ordinary differential equation systems (ODEs). By building on Python as powerful and multi-purpose programing language, ODEs can be formulated in an object-oriented manner, which facilitates their modular design, reusability, and extensibility. Once the model is implemented, seamless integration and analysis of the experimental data is supported by various Python packages that are already available. In particular, for the iterative workflow of experimental data generation and subsequent model parameter estimation we employed the concept of replicate model instances, which are linked by common sets of parameters with global or local properties. For the description of multi-stage processes, discontinuities in the right-hand sides of the differential equations are supported via event handling using the freely available assimulo package. Optimization problems can be solved by making use of a parallelized version of the generalized island approach provided by the pygmo package. Furthermore, pyFOOMB in combination with Jupyter notebooks also supports education in bioprocess engineering and the applied learning of Python as scientific programing language. Finally, the applicability and strengths of pyFOOMB will be demonstrated by a comprehensive collection of notebook examples.

A choreography of centrosomal mRNAs reveals a conserved localization mechanism involving active polysome transport

Local translation allows for a spatial control of gene expression. Here, we use high-throughput smFISH to screen centrosomal protein-coding genes, and we describe 8 human mRNAs accumulating at centrosomes. These mRNAs localize at different stages during cell cycle with a remarkable choreography, indicating a finely regulated translational program at centrosomes. Interestingly, drug treatments and reporter analyses reveal a common translation-dependent localization mechanism requiring the nascent protein. Using ASPM and NUMA1 as models, single mRNA and polysome imaging reveals active movements of endogenous polysomes towards the centrosome at the onset of mitosis, when these mRNAs start localizing. ASPM polysomes associate with microtubules and localize by either motor-driven transport or microtubule pulling. Remarkably, the Drosophila orthologs of the human centrosomal mRNAs also localize to centrosomes and also require translation. These data identify a conserved family of centrosomal mRNAs that localize by active polysome transport mediated by nascent proteins.

Centrosomes function as microtubule organizing centers where several mRNAs accumulate. By employing high-throughput single molecule FISH screening, the authors discover that 8 human mRNAs localize to centrosomes with unique cell cycle dependent patterns using an active polysome targeting mechanism.

Monitoring E. coli Cell Integrity by ATR-FTIR Spectroscopy and Chemometrics: Opportunities and Caveats

During recombinant protein production with E. coli, the integrity of the inner and outer membrane changes, which leads to product leakage (loss of outer membrane integrity) or lysis (loss of inner membrane integrity). Motivated by current Quality by Design guidelines, there is a need for monitoring tools to determine leakiness and lysis in real-time. In this work, we assessed a novel approach to monitoring E. coli cell integrity by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Various preprocessing strategies were tested in combination with regression (partial least squares, random forest) or classification models (partial least squares discriminant analysis, linear discriminant analysis, random forest, artificial neural network). Models were validated using standard procedures, and well-performing methods were additionally scrutinized by removing putatively important features and assessing the decrease in performance. Whereas the prediction of target compound concentration via regression was unsuccessful, possibly due to a lack of samples and low sensitivity, random forest classifiers achieved prediction accuracies of over 90% within the datasets tested in this study. However, strong correlations with untargeted spectral regions were revealed by feature selection, thereby demonstrating the need to rigorously validate chemometric models for bioprocesses, including the evaluation of feature importance.

The Distribution of Several Genomic Virulence Determinants Does Not Corroborate the Established Serotyping Classification of Bacillus thuringiensis

Bacillus thuringiensis, commonly referred to as Bt, is an object of the lasting interest of microbiologists due to its highly effective insecticidal properties, which make Bt a prominent source of biologicals. To categorize the exuberance of Bt strains discovered, serotyping assays are utilized in which flagellin serves as a primary seroreactive molecule. Despite its convenience, this approach is not indicative of Bt strains' phenotypes, neither it reflects actual phylogenetic relationships within the species. In this respect, comparative genomic and proteomic techniques appear more informative, but their use in Bt strain classification remains limited. In the present work, we used a bottom-up proteomic approach based on fluorescent two-dimensional difference gel electrophoresis (2D-DIGE) coupled with liquid chromatography/tandem mass spectrometry(LC-MS/MS) protein identification to assess which stage of Bt culture, vegetative or spore, would be more informative for strain characterization. To this end, the proteomic differences for the israelensis-attributed strains were assessed to compare sporulating cultures of the virulent derivative to the avirulent one as well as to the vegetative stage virulent bacteria. Using the same approach, virulent spores of the israelensis strain were also compared to the spores of strains belonging to two other major Bt serovars, namely darmstadiensis and thuringiensis. The identified proteins were analyzed regarding the presence of the respective genes in the 104 Bt genome assemblies available at open access with serovar attributions specified. Of 21 proteins identified, 15 were found to be encoded in all the present assemblies at 67% identity threshold, including several virulence factors. Notable, individual phylogenies of these core genes conferred neither the serotyping nor the flagellin-based phylogeny but corroborated the reconstruction based on phylogenomics approaches in terms of tree topology similarity. In its turn, the distribution of accessory protein genes was not confined to the existing serovars. The obtained results indicate that neither gene presence nor the core gene sequence may serve as distinctive bases for the serovar attribution, undermining the notion that the serotyping system reflects strains' phenotypic or genetic similarity. We also provide a set of loci, which fit in with the phylogenomics data plausibly and thus may serve for draft phylogeny estimation of the novel strains.

Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools

Despite Degenerative Cervical Myelopathy (DCM) being the most common form of spinal cord injury, effective methods to evaluate patients for its presence and severity are only starting to appear. Evaluation of patient images, while fast, is often unreliable; the pathology of DCM is complex, and clinicians often have difficulty predicting patient prognosis. Automated tools, such as the Spinal Cord Toolbox (SCT), show promise, but remain in the early stages of development. To evaluate the current state of an SCT automated process, we applied it to MR imaging records from 328 DCM patients, using the modified Japanese Orthopedic Associate scale as a measure of DCM severity. We found that the metrics extracted from these automated methods are insufficient to reliably predict disease severity. Such automated processes showed potential, however, by highlighting trends and barriers which future analyses could, with time, overcome. This, paired with findings from other studies with similar processes, suggests that additional non-imaging metrics could be added to achieve diagnostically relevant predictions. Although modeling techniques such as these are still in their infancy, future models of DCM severity could greatly improve automated clinical diagnosis, communications with patients, and patient outcomes.

Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods

Successfully applied in the carbon research area, sun-induced chlorophyll fluorescence (SIF) has raised the interest of researchers from the water research domain. However, current works focused on the empirical relationship between SIF and plant transpiration (T), while the mechanistic linkage between them has not been fully explored. Two mechanism methods were developed to estimate T via SIF, namely the water-use efficiency (WUE) method and conductance method based on the carbon–water coupling framework. The T estimated by these two methods was compared with T partitioned from eddy covariance instrument measured evapotranspiration at four different sites. Both methods showed good performance at the hourly (R2 = 0.57 for the WUE method and 0.67 for the conductance method) and daily scales (R2 = 0.67 for the WUE method and 0.78 for the conductance method). The developed mechanism methods provide theoretical support and have a great potential basis for deriving ecosystem T by satellite SIF observations.

Efficient implementation of the single-reference algebraic diagrammatic construction theory for charged excitations: Applications to the TEMPO radical and DNA base pairs

We present an efficient implementation of the second- and third-order single-reference algebraic diagrammatic construction (ADC) theory for electron attachment and ionization energies and spectra [EA/IP-ADC(n), n = 2, 3]. Our new EA/IP-ADC program features spin adaptation for closed-shell systems, density fitting for efficient handling of the two-electron integral tensors, and vectorized and parallel implementation of tensor contractions. We demonstrate capabilities of our efficient implementation by applying the EA/IP-ADC(n) (n = 2, 3) methods to compute the photoelectron spectrum of the (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO) radical, as well as the vertical and adiabatic electron affinities of TEMPO and two DNA base pairs (guanine-cytosine and adenine-thymine). The spectra and electron affinities computed using large diffuse basis sets with up to 1028 molecular orbitals are found to be in good agreement with the best available results from the experiment and theoretical simulations.

Early risk assessment for COVID-19 patients from emergency department data using machine learning

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.

PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies

BACKGROUND

Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation.

RESULTS

In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures.

CONCLUSIONS

In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.

Real-time observation of structure and dynamics during the liquid-to-solid transition of FUS LC

A subset of the proteins found in pathological protein fibrils also exhibit tendencies for liquid-liquid phase separation (LLPS) both in vitro and in cells. The mechanisms underlying the connection between these phase transitions have been challenging to study due to the heterogeneous and dynamic nature of the states formed during the maturation of LLPS protein droplets into gels and solid aggregates. Here, we interrogate the liquid-to-solid transition of the low-complexity domain of the RNA-binding protein FUS (FUS LC), which has been shown to adopt LLPS, gel-like, and amyloid states. We employ magic-angle-spinning NMR spectroscopy, which has allowed us to follow these transitions in real time and with residue-specific resolution. We observe the development of β-sheet structure through the maturation process and show that the final state of FUS LC fibrils produced after LLPS is distinct from that grown from fibrillar seeds. We also apply our methodology to FUS LC G156E, a clinically relevant FUS mutant that exhibits accelerated fibrillization rates. We observe significant changes in dynamics during the transformation of the FUS LC G156E construct and begin to unravel the sequence specific contributions to this phenomenon with computational studies of the phase-separated state of FUS LC and FUS LC G156E.

Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.

Most metabolomics analyses rely upon matching observed fragmentation mass spectra to library spectra for structural annotation or compare spectra with each other through network analysis. As a key part of such processes, scoring functions are used to assess the similarity between pairs of fragment spectra. No studies have so far proposed scores fundamentally different to the popular cosine-based similarity score, despite the fact that its limitations are well understood. We propose a novel spectral similarity score known as Spec2Vec which adapts algorithms from natural language processing to learn relationships between peaks from co-occurrences across large spectra datasets. We find that similarities computed with Spec2Vec i) correlate better to structural similarity than cosine-based scores, ii) subsequently gives better performance in library matching tasks, and iii) is computationally more scalable than cosine-based scores. Given the central place of similarity scoring in key metabolomics analysis tasks such as library matching and spectral networking, we expect Spec2Vec to make a broad impact in all fields that rely upon untargeted metabolomics.

Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research

Understanding the relationship between fiducial registration error (FRE) and target registration error (TRE) is important for the correct use of interventional guidance systems. Whilst it is well established that TRE is statistically independent of FRE, system users still struggle against the intuitive assumption that a low FRE indicates a low TRE. We present the SciKit-Surgery Fiducial Registration Educational Demonstrator and describe its use. SciKit-SurgeryFRED was developed to enable remote teaching of key concepts in image registration. SciKit-SurgeryFRED also supports research into user interface design for image registration systems. SciKit-SurgeryFRED can be used to enable remote tutorials covering the statistics relevant to image guided interventions. Students are able to place fiducial markers on pre and intra-operative images and observe the effects of changes in marker geometry, marker count, and fiducial localisation error on TRE and FRE. SciKit-SurgeryFRED also calculates statistical measures for the expected values of TRE and FRE. Because many registrations can be performed quickly the students can then explore potential correlations between the different statistics. SciKit-SurgeryFRED also implements a registration based game, where participants are rewarded for complete treatment of a clinical target, whilst minimising the treatment margin. We used this game to perform a remote study on registration and simulated ablation, measuring how user performance changes depending on what error statistics are made available. The results support the assumption that knowing the exact value of target registration error leads to better treatment. Display of other statistics did not have a significant impact on the treatment performance.

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

BACKGROUND

Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance.

RESULTS

We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model.

CONCLUSIONS

Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

Nonlinear Band Gap Dependence of Mixed Pb-Sn 2D Ruddlesden-Popper PEA2Pb1-xSnxI4 Perovskites

Two-dimensional (2D) Ruddlesden-Popper perovskites (RPPs) of the form PEA₂Pb_1-xSn_xI₄ can be used as the tunable active layer in photovoltaics, as the passivating layer for 3D perovskite photovoltaics or in light emitting diodes. Here, we show a nonlinear band gap behavior with Sn content in mixed phase 2D RPPs. Density functional theory calculations (with and without spin-orbit coupling) are employed to study the effects of the short-range ordering of Pb and Sn in PEA₂Pb_1-xSn_xI₄ compositions with x = 0, 0.25, 0.5, 0.75, and 1. Analysis of the partial density of states shows that the energy mismatch of the Pb 6s and Sn 5s states in the valence band maximum determines the nonlinearity of the band gap, leading to a bowing parameter of 0.35-0.38 eV. This research provides a critical insight for the design of future metal alloy 2D perovskite materials. The positions of the tunable energy band discontinuity may point to intraband transitions of interest to device engineers.

Sulfur Metabolites Play Key System-Level Roles in Modulating Denitrification

Nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) colonize diverse anoxic environments, including soil subsurface, groundwater, and wastewater. NRB and SRB compete for resources, and their interplay has major implications on the global cycling of nitrogen and sulfur species, with undesirable outcomes in some contexts.

Competition between nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) for resources in anoxic environments is generally thought to be governed largely by thermodynamics. It is now recognized that intermediates of nitrogen and sulfur cycling (e.g., hydrogen sulfide, nitrite, etc.) can also directly impact NRB and SRB activities in freshwater, wastewater, and sediment and therefore may play important roles in competitive interactions. Here, through comparative transcriptomic and metabolomic analyses, we have uncovered mechanisms of hydrogen sulfide- and cysteine-mediated inhibition of nitrate respiratory growth for the NRB Intrasporangium calvum C5. Specifically, the systems analysis predicted that cysteine and hydrogen sulfide inhibit growth of I. calvum C5 by disrupting distinct steps across multiple pathways, including branched-chain amino acid (BCAA) biosynthesis, utilization of specific carbon sources, and cofactor metabolism. We have validated these predictions by demonstrating that complementation with BCAAs and specific carbon sources relieves the growth inhibitory effects of cysteine and hydrogen sulfide. We discuss how these mechanistic insights give new context to the interplay and stratification of NRB and SRB in diverse environments.

IMPORTANCE Nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) colonize diverse anoxic environments, including soil subsurface, groundwater, and wastewater. NRB and SRB compete for resources, and their interplay has major implications on the global cycling of nitrogen and sulfur species, with undesirable outcomes in some contexts. For instance, the removal of reactive nitrogen species by NRB is desirable for wastewater treatment, but in agricultural soils, NRB can drive the conversion of nitrates from fertilizers into nitrous oxide, a potent greenhouse gas. Similarly, the hydrogen sulfide produced by SRB can help sequester and immobilize toxic heavy metals but is undesirable in oil wells where competition between SRB and NRB has been exploited to suppress hydrogen sulfide production. By characterizing how reduced sulfur compounds inhibit growth and activity of NRB, we have gained systems-level and mechanistic insight into the interplay of these two important groups of organisms and drivers of their stratification in diverse environments.

An Efficient Backward/Forward Sweep Algorithm for Power Flow Analysis through a Novel Tree-Like Structure for Unbalanced Distribution Networks

The increase of distributed energy resources (DERs) in low voltage (LV) distribution networks requires the ability to perform an accurate power flow analysis (PFA) in unbalanced systems. The characteristics of a well performing power flow algorithm are the production of accurate results, robustness and quick convergence. The current study proposes an improvement to an already used backward-forward sweep (BFS) power flow algorithm for unbalanced three-phase distribution networks. The proposed power flow algorithm can be implemented in large systems producing accurate results in a small amount of time using as little computational resources as possible. In this version of the algorithm, the network is represented in a tree-like structure, instead of an incidence matrix, avoiding the use of redundant computations and the storing of unnecessary data. An implementation of the method was developed in Python programming language and tested for 3 IEEE feeder test cases (the 4 bus feeder, the 13 bus feeder and the European Low Voltage test feeder), ranging from a low (4) to a very high (907) buses number, while including a wide variety of components witnessed in LV distribution networks.

Comparison between optical and digital blur using near visual acuity

In a low-cost laboratory setup, we compared visual acuity (VA) for stimuli rendered with Zernike aberrations to an equivalent optical dioptric defocus in emmetropic individuals using a relatively short observing distance of 60 cm. The equivalent spherical refractive error of + 1, + 2 or + 4 D, was applied in the rendering of Landolt Rings. Separately, the refractive error was introduced dioptrically in: (1) unchanged Landolt Rings with an added external lens (+ 1, + 2 or + 4 D) at the subject's eye; (2) same as (1) but with an added accommodation and a vertex distance adjustment. To compare all three approaches, we examined VA in 10 healthy men. Stimuli were observed on a PC CRT screen. For all three levels of refractive error, the pairwise comparison did not show a statistically significant difference between digital blur and accommodation-plus-vertex-distance-adjusted dioptric blur (p < 0.204). The best agreement, determined by Bland–Altman analysis, was measured for + 4 D and was in line with test–retest limits for examination in the clinical population. Our results show that even for a near observing distance, it is possible to use digitally rendered defocus to replicate dioptric blur without a significant change in VA in emmetropic subjects.

A Do-It-Yourself Hyperspectral Imager Brought to Practice with Open-Source Python

Commercial hyperspectral imagers (HSIs) are expensive and thus unobtainable for large audiences or research groups with low funding. In this study, we used an existing do-it-yourself push-broom HSI design for which we provide software to correct for spectral smile aberration without using an optical laboratory. The software also corrects an aberration which we call tilt. The tilt is specific for the particular imager design used, but correcting it may be beneficial for other similar devices. The tilt and spectral smile were reduced to zero in terms of used metrics. The software artifact is available as an open-source Github repository. We also present improved casing for the imager design, and, for those readers interested in building their own HSI, we provide print-ready and modifiable versions of the 3D-models required in manufacturing the imager. To our best knowledge, solving the spectral smile correction problem without an optical laboratory has not been previously reported. This study re-solved the problem with simpler and cheaper tools than those commonly utilized. We hope that this study will promote easier access to hyperspectral imaging for all audiences regardless of their financial status and availability of an optical laboratory.

An All-Photonic Molecular Amplifier and Binary Flip-flop

A chemical system is proposed that is capable of amplifying small optical inputs into large changes in internal composition, based on a feedback interaction between switchable fluorescence and visible-light photoswitching. This system would demonstrate bifurcating reaction kinetics under irradiation and reach one of two stable photostationary states depending on the initial composition of the system. This behavior would allow the system to act as a chemical realization of the flip-flop circuit, the fundamental element in sequential logic and binary memory storage. We use detailed numerical modeling to demonstrate the feasibility of the proposed behavior based on known molecular phenomena and comment on some of the conditions required to realize this system.

A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation

Cluster analysis is widely applied in the neuropsychological field for exploring patterns in cognitive profiles, but traditional hierarchical and non-hierarchical approaches could be often poorly effective or even inapplicable on certain type of data. Moreover, these traditional approaches need the initial specification of the number of clusters, based on a priori knowledge not always owned. For this reason, we proposed a novel method for cognitive clustering through the affinity propagation (AP) algorithm. In particular, we applied the AP clustering on the regression residuals of the Mini Mental State Examination scores—a commonly used screening tool for cognitive impairment—of a cohort of 49 Parkinson’s disease, 48 Progressive Supranuclear Palsy and 44 healthy control participants. We found four clusters, where two clusters (68 and 30 participants) showed almost intact cognitive performance, one cluster had a moderate cognitive impairment (34 participants), and the last cluster had a more extensive cognitive deficit (8 participants). The findings showed, for the first time, an intra- and inter-diagnostic heterogeneity in the cognitive profile of Parkinsonisms patients. Our novel method of unsupervised learning could represent a reliable tool for supporting the neuropsychologists in understanding the natural structure of the cognitive performance in the neurodegenerative diseases.