Novel Colorimetric and Light Scatter Methods to Identify and Manage Peritoneal Dialysis-Associated Peritonitis at the Point-of-Care.

Introduction: Peritoneal dialysis (PD)-related peritonitis (PDRP) is a common cause of transfer to hemodialysis, patient morbidity, and is a risk factor for mortality. Associated patient anxiety can deter selection of PD for renal replacement therapy. Diagnosis relies on hospital laboratory tests; however, this might be achieved earlier if such information was available at the point-of-care (POC), thereby significantly improving outcomes. The presence of culturable microbes and the concentration of leukocytes in effluent both aid peritonitis diagnosis, as specified in the International Society for Peritoneal Dialysis (ISPD) diagnostic guidelines. Here, we report the development of 2 new methods providing such information in simple POC tests. Methods: One approach uses a tetrazolium-based chemical reporting system, primarily focused on detecting bacterial contamination and associated vancomycin-sensitivity. The second approach uses a novel forward light-scatter device (QuickCheck) to provide an instant quantitative cell count directly from PD patient effluent. Results: The tetrazolium approach detected and correctly distinguished laboratory isolates, taking 10 hours to provide non-quantitative results. We compared the technical performance of the light scatter leukocyte counting approach with spectrophotometry, hemocytometer counting and flow cytometry (Sysmex) using patient effluent samples. QuickCheck had high accuracy (94%) and was the most precise (coefficient of variation <4%), showing minimal bias, overall performing similarly to flow cytometry. Conclusion: These complementary new approaches provide a simple means to obtain information to assist diagnosis at the POC. The first provides antibiotic sensitivity following 10 hours incubation, whereas the second optical approach (QuickCheck), provides instant accurate total leukocyte count.

Analyzing global utilization and missed opportunities in debt-for-nature swaps with generative AI.

We deploy a prompt-augmented GPT-4 model to distill comprehensive datasets on the global application of debt-for-nature swaps (DNS), a pivotal financial tool for environmental conservation. Our analysis includes 195 nations and identifies 21 countries that have not yet used DNS before as prime candidates for DNS. A significant proportion demonstrates consistent commitments to conservation finance (0.86 accuracy as compared to historical swaps records). Conversely, 35 countries previously active in DNS before 2010 have since been identified as unsuitable. Notably, Argentina, grappling with soaring inflation and a substantial sovereign debt crisis, and Poland, which has achieved economic stability and gained access to alternative EU conservation funds, exemplify the shifting suitability landscape. The study's outcomes illuminate the fragility of DNS as a conservation strategy amid economic and political volatility.

Continuous daytime and nighttime forecast of atmospheric optical turbulence from numerical weather prediction models.

Future satellite-to-ground optical communication systems will benefit from accurate forecasts of atmospheric optical turbulence; namely for site selection, for the routing and the operation of optical links, and for the design of optical communication terminals. This work presents a numerical approach based on the Weather Research and Forecasting software that enables continuous forecast of the refractive index structure parameter, C n2, vertical profiles. Two different C n2 models are presented and compared. One is based on monitoring the turbulent kinetic energy, while the other is a hybrid model using the Tatarskii equation to depict the free atmosphere region, and the Monin-Obukhov similarity theory for describing the boundary layer. The validity of both models is assessed by using thermosonde measurements from the Terrain-induced Rotor Experiment campaign, and from day and night measurements of the coherence length collected during a six-day campaign at Paranal observatory by a Shack-Hartmann Image Motion Monitor. The novelty of this work is the ability of the presented approach to continuously predict optical turbulence both during daytime and nighttime, and its validation with measurements in day and night conditions.

Demonstrating 24-hour continuous vertical monitoring of atmospheric optical turbulence.

We report what is believed to be the first example of fully continuous, 24-hour vertical monitoring of atmospheric optical turbulence. This is achieved using a novel instrument, the 24-hour Shack-Hartmann Image Motion Monitor (24hSHIMM). Optical turbulence is a fundamental limitation for applications such as free-space optical communications, where it limits the achievable bandwidth, and ground-based optical astronomy, restricting the observational precision. Knowledge of the turbulence enables us to select the best sites, design optical instrumentation and optimise the operation of ground-based optical systems. The 24hSHIMM estimates the vertical optical turbulence coherence length, time, angle and Rytov variance from the measurement of a four-layer vertical turbulence profile and a wind speed profile retrieved from meteorological forecasts. To illustrate our advance we show the values of these parameters recorded during a 36-hour, continuous demonstration of the instrument. Due to its portability and ability to work in stronger turbulence, the 24hSHIMM can also operate in urban locations, providing the field with a truly continuous, versatile turbulence monitor for all but the most demanding of applications.

Data-driven discovery of molecular photoswitches with multioutput Gaussian processes.

Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset.

Generative model-enhanced human motion prediction.

The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD). Here, we formulate a new OoD benchmark based on the Human3.6M and Carnegie Mellon University (CMU) motion capture datasets, and introduce a hybrid framework for hardening discriminative architectures to OoD failure by augmenting them with a generative model. When applied to current state-of-the-art discriminative models, we show that the proposed approach improves OoD robustness without sacrificing in-distribution performance, and can theoretically facilitate model interpretability. We suggest human motion predictors ought to be constructed with OoD challenges in mind, and provide an extensible general framework for hardening diverse discriminative architectures to extreme distributional shift. The code is available at: https://github.com/bouracha/OoDMotion.

The human gut symbiont Ruminococcus gnavus shows specificity to blood group A antigen during mucin glycan foraging: Implication for niche colonisation in the gastrointestinal tract.

The human gut symbiont Ruminococcus gnavus displays strain-specific repertoires of glycoside hydrolases (GHs) contributing to its spatial location in the gut. Sequence similarity network analysis identified strain-specific differences in blood-group endo-ß-1,4-galactosidase belonging to the GH98 family. We determined the substrate and linkage specificities of GH98 from R. gnavus ATCC 29149, RgGH98, against a range of defined oligosaccharides and glycoconjugates including mucin. We showed by HPAEC-PAD and LC-FD-MS/MS that RgGH98 is specific for blood group A tetrasaccharide type II (BgA II). Isothermal titration calorimetry (ITC) and saturation transfer difference (STD) NMR confirmed RgGH98 affinity for blood group A over blood group B and H antigens. The molecular basis of RgGH98 strict specificity was further investigated using a combination of glycan microarrays, site-directed mutagenesis, and X-ray crystallography. The crystal structures of RgGH98 in complex with BgA trisaccharide (BgAtri) and of RgGH98 E411A with BgA II revealed a dedicated hydrogen network of residues, which were shown by site-directed mutagenesis to be critical to the recognition of the BgA epitope. We demonstrated experimentally that RgGH98 is part of an operon of 10 genes that is overexpresssed in vitro when R. gnavus ATCC 29149 is grown on mucin as sole carbon source as shown by RNAseq analysis and RT-qPCR confirmed RgGH98 expression on BgA II growth. Using MALDI-ToF MS, we showed that RgGH98 releases BgAtri from mucin and that pretreatment of mucin with RgGH98 confered R. gnavus E1 the ability to grow, by enabling the E1 strain to metabolise BgAtri and access the underlying mucin glycan chain. These data further support that the GH repertoire of R. gnavus strains enable them to colonise different nutritional niches in the human gut and has potential applications in diagnostic and therapeutics against infection.

Mapping Materials and Molecules.

The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.

Constrained Bayesian optimization for automatic chemical design using variational autoencoders.

Automatic Chemical Design is a framework for generating novel molecules with optimized properties. The original scheme, featuring Bayesian optimization over the latent space of a variational autoencoder, suffers from the pathology that it tends to produce invalid molecular structures. First, we demonstrate empirically that this pathology arises when the Bayesian optimization scheme queries latent space points far away from the data on which the variational autoencoder has been trained. Secondly, by reformulating the search procedure as a constrained Bayesian optimization problem, we show that the effects of this pathology can be mitigated, yielding marked improvements in the validity of the generated molecules. We posit that constrained Bayesian optimization is a good approach for solving this kind of training set mismatch in many generative tasks involving Bayesian optimization over the latent space of a variational autoencoder.

A Central Small RNA Regulatory Circuit Controlling Bacterial Denitrification and N2O Emissions.

Global atmospheric loading of the climate-active gas nitrous oxide (N2O) continues to increase. A significant proportion of anthropogenic N2O emissions arises from microbial transformation of nitrogen-based fertilizers during denitrification, making microbial N2O emissions a key target for greenhouse gas reduction strategies. The genetic, physiological, and environmental regulation of microbially mediated N2O flux is poorly understood and therefore represents a critical knowledge gap in the development of successful mitigation approaches. We have previously mapped the transcriptional landscape of the model soil-denitrifying bacterium Paracoccus denitrificans Here, we show that a single bacterial small RNA (sRNA) can control the denitrification rate of P. denitrificans by stalling denitrification at nitrite reduction to limit production of downstream pathway intermediates and N2O emissions. Overexpression of sRNA-29 downregulates nitrite reductase and limits NO and N2O production by cells. RNA sequencing (RNA-seq) analysis revealed 53 genes that are controlled by sRNA-29, one of which is a previously uncharacterized GntR-type transcriptional regulator. Overexpression of this regulator phenocopies sRNA-29 overexpression and allows us to propose a model whereby sRNA-29 enhances levels of the regulator to repress denitrification under appropriate conditions. Our identification of a new regulatory pathway controlling the core denitrification pathway in bacteria highlights the current chasm in knowledge regarding genetic regulation of this pivotal biogeochemical process, which needs to be closed to support future biological and chemical N2O mitigation strategies.IMPORTANCE N2O is an important greenhouse gas and a major cause of ozone depletion. Denitrifying bacteria play vital roles in the production and consumption of N2O in many environments. Complete denitrification consists of the conversion of a soluble N-oxyanion, nitrate (NO3-), to an inert gaseous N-oxide, dinitrogen (N2). Incomplete denitrification can occur if conditions are prohibitive, for example, under conditions of low soil copper concentrations, leading to emission of N2O rather than N2 Although enzymatically well characterized, the genetic drivers that regulate denitrification in response to environmental and physiological cues are not fully understood. This study identified a new regulatory sRNA-based control mechanism for denitrification in the model denitrifying bacterium P. denitrificans Overexpression of this sRNA slows the rate of denitrification. This report highlights that there are gaps in understanding the regulation of this important pathway which need to be filled if strategies for N2O mitigation can be rationally and carefully developed.

Light chain renal amyloidosis with prominent giant cells.

Clinical diagnosis of amyloidosis may be very challenging because signs, symptoms, and laboratory study results can be highly variable and may overlap with other disease entities. Amyloid has characteristic features on kidney biopsy, involving glomeruli, vessels, and/or interstitium as typically amorphous waxy material that is periodic acid-Schiff pale and Congo Red birefringent under polarized light. Electron microscopy demonstrates characteristic randomly oriented fibrils. However, in rare cases, amyloid may present with atypical morphologic features on kidney biopsy, closely mimicking other histopathologic diagnoses. We present a case of light chain (AL) κ amyloidosis with an unusual inflammatory infiltrate including prominent multinucleated giant cells in the interstitium and at the glomerular hilus. Amyloid was apparent within giant cells on Congo Red staining, as well as on ultrastructural evaluation. Together with prior studies of tumoral nonrenal amyloid and renal amyloid A, we suggest that the amyloid fibril constituents κ and serum amyloid A have some predilection for inciting the rare multinucleated giant cell reaction.

Glomerular basement membrane lipidosis in Alagille syndrome.

Alagille syndrome is characterized by a paucity of interlobular bile ducts with chronic cholestasis, cardiac, skeletal, and eye abnormalities and is associated predominantly with JAG1 mutations. Various renal abnormalities have been sporadically described. The classic renal histopathology described in Alagille syndrome is mesangiolipidosis, with lipid deposits predominately confined to the mesangium and minimal deposition within the glomerular basement membrane (GBM). We report a 5-year-old girl with Alagille syndrome who presented with persistent subnephrotic proteinuria and renal tubular acidosis. A renal biopsy showed GBM irregularities (mimicking membranous glomerulonephritis), mesangial sclerosis, and focal segmental glomerulosclerosis (FSGS) on light microscopy. Electron microscopy revealed few lipid inclusions within the mesangium but extensive inclusions along the GBM. These findings are mostly consistent with those reported previously in Alagille syndrome. However, the histologic distribution of lipid vacuoles is seemingly reversed in this patient and is uniquely accompanied by FSGS, emphasizing the spectrum of renal histopathology seen in Alagille syndrome. The proteinuria observed in this patient is likely attributed to significant GBM lipid deposition, which over time may contribute to the development of FSGS.