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Background: Morphological features of cancer cell nuclei are routinely used to assess disease severity and prognosis, and cancer nuclear morphology has been linked to genomic alterations. Quantitative analyses of the nuclear features of cancer cells and other tumor-resident cell types, such as cancer-associated fibroblasts (CAFs), may reveal novel biomarkers for prognosis and treatment response. Here, we applied a pan-cancer nucleus detection and segmentation algorithm and a cell classification model to hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of breast cancer specimens, enabling the measurement of morphological features of nuclei of multiple cell types within a tumor. Methods: Convolutional Neural Network models for 1) nucleus detection and segmentation and 2) cell classification were deployed on H&E-stained WSIs from The Cancer Genome Atlas (TCGA) breast cancer dataset (primary surgical resections; N=890). Separate models were trained to segment regions of stromal subtypes, such as inflamed and fibroblastic stroma. Nuclear features (area, axis length, eccentricity, color, and texture) were computed and aggregated across each slide to summarize slide-level nuclear morphology for each cell type. Next-generation sequencing-based metrics of genomic instability (N=774) and gene expression (N=868) were acquired and paired with TCGA WSIs. Gene set enrichment analysis was performed using the Molecular Signatures Database. Spearman correlation compared nuclear features to genomic instability metrics. Linear regression was used to assess the relationship between nuclear features and bulk gene expression. Multivariable Cox regression with age and ordinal tumor stage as covariates was used to find association between overall survival (OS) and nuclear features. All reported results were significant (p< 0.05) when adjusted for false discovery rate via the Benjamini-Hochberg procedure. Results: Variation in cancer cell nuclear area, a quantitative metric related to pathologist-assessed nuclear pleomorphism, was calculated by the standard deviation of the nuclear area of cancer cells across a WSI. This feature was associated with genomic instability, as measured by aneuploidy score (r=0.448) and homologous recombination deficiency score (r=0.382), and reduced OS. In contrast, the variability in fibroblast and lymphocyte nuclear areas did not correlate with either metric of genomic instability (all r< 0.1, p>0.05). Furthermore, an association between variation in cancer cell nuclear area with the expression of cell cycle and proliferation pathway genes was observed, suggesting that increased nuclear size heterogeneity may indicate a more aggressive cancer phenotype. Features quantifying CAF nuclear morphology were also assessed, revealing that CAF nucleus shape (larger minor axis length) was associated with lower OS, as well as the expression of gene sets involved in extracellular matrix remodeling and degradation. Conclusions: The nuclear morphologies of breast cancer cells and CAFs reflect underlying genomic and transcriptomic properties of the tumor and correlates with patient outcome. The application of digital pathology analysis of breast cancer histopathology slides enables the integrative study of genomics, transcriptomics, tumor morphology, and overall survival to support research into disease biology research and biomarker discovery. Citation Format: John Abel, Christian Kirkup, Filip Kos, Ylaine Gerardin, Sandhya Srinivasan, Jacqueline Brosnan-Cashman, Ken Leidal, Sanjana Vasudevan, Deepta Rajan, Suyog Jain, Aaditya Prakash, Harshith Padigela, Jake Conway, Neel Patel, Benjamin Trotter, Limin Yu, Amaro Taylor-Weiner, Emma L. Krause, Matthew Bronnimann, Laura Chambre, Ben Glass, Chintan Parmar, Stephanie Hennek, Archit Khosla, Murray Resnick, Andrew H. Beck, Michael Montalto, Fedaa Najdawi, Michael G. Drage, Ilan Wapinski. AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-08.

[Image: see text] Antimicrobial drug resistance is a looming health crisis facing us in the modern era, and new drugs are urgently needed to combat this growing problem. Synthetic mimics of antimicrobial peptides have recently emerged as a promising class of compounds for the treatment of persistent microbial infections. In the current study, we investigate five cyclic N-alkylated amphiphilic 2,5-diketopiperazines against 15 different strains of bacteria and fungi, including drug-resistant clinical isolates. Several of the 2,5-diketopiperazines displayed activities similar or superior to antibiotics currently in clinical use, with activities coupled to both the cationic and hydrophobic substituents. All possible stereoisomers of the lead peptide were prepared, and the effects of stereochemistry and amphiphilicity were investigated via 1D and 2D NMR spectroscopy, solution dynamics, and membrane interaction modeling. Clear differences in solution structures and membrane interaction potentials explain the differences seen in the bioactivity and physicochemical properties of each stereoisomer.

Maximizing the discrepancy between expected and actual outcomes during exposure (i.e., expectancy violation) is thought to optimize inhibitory learning. The current study examined Craske et al.’s suggestion that engaging in cognitive restructuring (CR) before exposure prematurely reduces expectancy and mitigates outcomes. Participants ( N = 93) with claustrophobia were randomly assigned to either 15 minutes of CR before exposure ( CR Before) or 15 minutes of CR after exposure ( CR After). Although the CR Before condition experienced greater expectancy reduction before exposure than the CR After condition, both groups experienced similar overall expectancy reduction by the end of the intervention. Groups experienced similar gains, with large significant improvement at posttreatment and follow-up. Results suggest that both cognitive therapy and exposure therapy lead to expectancy reduction, but that the order of these interventions does not impact outcome. Clinicaltrials.org registration #NCT03628105.

Pattern formation has been extensively studied in the context of evolving (time-dependent) domains in recent years, with domain growth implicated in ameliorating problems of pattern robustness and selection, in addition to more realistic modelling in developmental biology. Most work to date has considered prescribed domains evolving as given functions of time, but not the scenario of concentration-dependent dynamics, which is also highly relevant in a developmental setting. Here, we study such concentration-dependent domain evolution for reaction-diffusion systems to elucidate fundamental aspects of these more complex models. We pose a general form of one-dimensional domain evolution, and extend this to $N$-dimensional manifolds under mild constitutive assumptions in lieu of developing a full tissue-mechanical model. In the 1D case, we are able to extend linear stability analysis around homogeneous equilibria, though this is of limited utility in understanding complex pattern dynamics in fast growth regimes. We numerically demonstrate a variety of dynamical behaviours in 1D and 2D planar geometries, giving rise to several new phenomena, especially near regimes of critical bifurcation boundaries such as peak-splitting instabilities. For sufficiently fast growth and contraction, concentration-dependence can have an enormous impact on the nonlinear dynamics of the system both qualitatively and quantitatively. We highlight crucial differences between 1D evolution and higher dimensional models, explaining obstructions for linear analysis and underscoring the importance of careful constitutive choices in defining domain evolution in higher dimensions. We raise important questions in the modelling and analysis of biological systems, in addition to numerous mathematical questions that appear tractable in the one-dimensional setting, but are vastly more difficult for higher-dimensional models., Comment: 22 pages, 12 figures

A revised total synthesis of aurachin D (

Fingerprints are complex and individually unique patterns in the skin. Established prenatally, the molecular and cellular mechanisms that guide fingerprint ridge formation and their intricate arrangements are unknown. Here we show that fingerprint ridges are epithelial structures that undergo a truncated hair follicle developmental program and fail to recruit a mesenchymal condensate. Their spatial pattern is established by a Turing reaction-diffusion system, based on signaling between EDAR, WNT, and antagonistic BMP pathways. These signals resolve epithelial growth into bands of focalized proliferation under a precociously differentiated suprabasal layer. Ridge formation occurs as a set of waves spreading from variable initiation sites defined by the local signaling environments and anatomical intricacies of the digit, with the propagation and meeting of these waves determining the type of pattern that forms. Relying on a dynamic patterning system triggered at spatially distinct sites generates the characteristic types and unending variation of human fingerprint patterns.

Background: Neoadjuvant treatment (NAT) combining chemotherapy and HER2-targeted agents is frequently administered to HER2-positive (HER2+) breast cancer (BC) patients, with some experiencing a pathological complete response (pCR) and others having residual disease measured by the residual cancer burden (RCB) score. Here, we use a physics-guided machine learning (ML)-based approach to extract fiber-level collagen features from hematoxylin and eosin (H&E)-stained whole slide images (WSIs) and identify collagen-related associations with treatment response in HER2+ patients receiving NAT. Methods: Clinical data and specimens from stage II-III HER2+ BC patients enrolled on the De-escalation to Adjuvant Antibodies Post-pCR to Neoadjuvant THP (DAPHNe; NCT03716180) clinical trial and treated with neoadjuvant paclitaxel/trastuzumab/pertuzumab were analyzed. An ML-based model trained to identify regions of BC tissue as invasive carcinoma, ductal carcinoma in situ (DCIS), diffuse inflammatory infiltrate, stroma, necrosis, or normal tissue was deployed on WSIs of H&E-stained diagnostic core needle biopsies (N=89) to generate tissue overlays. Additional tissue areas were computed from the tissue model predictions using heatmap transformation, including tumor nests (continuous regions predicted as invasive cancer epithelium or DCIS), tumor nest borders (stromal region boundaries 10 μm from tumor nests), and bulk tumor borders (stromal region boundaries 300 μm from aggregated tumor nests). A separate ML-based model trained to identify fiber-level collagen features in WSIs of H&E-stained specimens was also deployed to generate collagen overlays. A fiber feature extraction pipeline was utilized to characterize properties of all identified collagen fibers in the WSI (on the order of hundreds of thousands per slide), including length, width, tortuosity, and angle. These fiber features were then assessed based on their position within the tumor (e.g. relative to the tumor nest border). Combinatorial features (e.g. angle of fibers with respect to tumor boundary) were then explored univariately for associations (N=609) with treatment response. Patients with pCR (RCB=0; N=53) were considered responders, while all other cases (RCBI-III; N=36) were designated non-responders. Due to the small size of the cohort analyzed here, raw p-values are reported. Results: Using estrogen receptor status as a clinical covariate, a logistic regression-based univariate analysis of 609 collagen-associated features revealed six features to strongly associate with pCR (p< 0.05, AUC≥0.75; Table 1). Notable feature themes were identified: 1) fiber tortuosity in tumor nest borders and tumor borders, 2) angle of fibers in tumor border with respect to tumor boundary, and 3) distribution patterns of fiber width in tumor nest borders. The presence of fibers perpendicular to tumor boundary tangents was negatively associated with pCR, as was higher fiber tortuosity and thickness in tumor nest borders. Conclusions: Improved prediction of response to NAT in patients with BC is needed to determine appropriate treatment strategies for each patient. Here, using ML-based models to identify tissue features and collagen fibers, we identify collagen-associated features, measured directly from WSIs of H&E-stained diagnostic BC biopsies, that negatively correlate with pCR. Additional development of this strategy, including the addition of cell identification models and known clinical information, is underway to further refine this novel predictive model. Citation Format: Tan H. Nguyen, Mohammad Mirzadeh, Aaditya Prakash, Emma L. Krause, Jun Zhang, Michael Pyle, Esther R. Ogayo, Harry C. Cramer, Busem Binboga Kurt, Jacqueline Brosnan-Cashman, Michael G. Drage, Stuart Schnitt, Andrew H. Beck, Michael Montalto, Ilan Wapinski, Laura Chambre, Sara Tolaney, Adrienne Waks, Justin Lee, Elizabeth A. Mittendorf. Quantitative analysis of fiber-level collagen features in H&E whole-slide images predicts neoadjuvant therapy response in patients with HER2+ breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-02-09.

Søderberg electrodes feature prominently in the operation of metallurgical electrical furnaces. The electrode material must bake before entering the furnace; failure to bake will lower the efficiency of the process and may cause physical harm to the furnace itself through a soft breakage. As such, ensuring that the baking isotherm remains within the region of the electrode outside of the furnace is essential. We propose a mathematical model for a Søderberg electrode taking into account the heat, mass, and current transfer mechanisms at play, along with realistic boundary conditions on the outside of the electrode that are strongly heterogeneous in height. The resulting model describes a strongly heterogeneous cylindrical “thermistor" which moves slowly downward and is acted on by current clamps which provide Joule heating. Although it is often ignored in the literature on thermistor problems, we find that the Péclet number resulting from the downward motion strongly influences the position of the baking isotherm. Aside from some specific reductions leading to analytical solutions, the general form of the model is complicated enough to require numerical simulations. Still, our modeling approach provides us with a qualitative understanding of many aspects of the Søderberg electrode baking process and permits us to identify three parameters of key importance to the positioning of the baking isotherm. In particular, our results suggest desired ranges for the lowering rate of the electrode (in terms of a Péclet number), the radius of the electrode, and the strength of the Joule heating due to an applied current, which are the three aspects which may be controlled (to varying degrees) in industrial applications.

Migrant and refugee populations have been identified as among the most vulnerable to COVID-19 due to what medical anthropologists have described as structural vulnerability We argue significant differences exist between migrant groups and offer lessons for society at large We develop the concept of viral encounters to frame an analysis of social narratives, representations, and practices involved in coping with threats of transmission and practices of prevention Specifically, the globalized city of Prato offers a case study due to its unique relationship with COVID-19 Instead of a COVID-19 epicenter, however, Prato emerged as a contagion exception particularly as related to its Chinese migrant community We use a place-based framework to argue that the threat of xenophobia, preparedness with quarantine, and the will of solidarity motivated an entire migrant community to take action—well before the nationwide lockdown began and extending beyond its conclusion We combine virtual ethnography with health data as well as evidence of xenophobia and solidarity to offer an analysis We argue that the effects of solidarity reconfigure dominant ideologies of individualism, open space for collective orientation toward a human economy, and offer potential to alleviate detrimental impacts of pandemics Copyright © 2020 by the Society for Applied Anthropology

Hyperbolic reaction-diffusion equations have recently attracted attention both for their application to a variety of biological and chemical phenomena, and for their distinct features in terms of propagation speed and novel instabilities not present in classical two-species reaction-diffusion systems. We explore the onset of diffusive instabilities and resulting pattern formation for such systems. Starting with a rather general formulation of the problem, we obtain necessary and sufficient conditions for the Turing and wave instabilities in such systems, thereby classifying parameter spaces for which these diffusive instabilities occur. We find that the additional temporal terms do not strongly modify the Turing patterns which form or parameters which admit them, but only their regions of existence. This is in contrast to the case of additional space derivatives, where past work has shown that resulting patterned structures are sensitive to second-order cross-diffusion and first-order advection. We also show that additional temporal terms are necessary for the emergence of spatiotemporal patterns under the wave instability. We find that such wave instabilities exist for parameters which are mutually exclusive to those parameters leading to stationary Turing patterns. This implies that wave instabilities may occur in cases where the activator diffuses faster than the inhibitor, leading to routes to spatial symmetry breaking in reaction-diffusion systems which are distinct from the well studied Turing case.

Enterococcus faecalis and Enterococcus faecium are Gram-positive commensal gut bacteria that can also cause fatal infections. To study clinically relevant multi-drug resistant E. faecalis and E. faecium strains, methods are needed to overcome physical (thick cell wall) and enzymatic barriers that limit the transfer of foreign DNA and thus prevent facile genetic manipulation. Enzymatic barriers to DNA uptake identified in E. faecalis and E. faecium include type I, II and IV restriction modification systems and CRISPR-Cas. This review examines E. faecalis and E. faecium DNA defence systems and the methods with potential to overcome these barriers. DNA defence system bypass will allow the application of innovative genetic techniques to expedite molecular-level understanding of these important, but somewhat neglected, pathogens.

Enterococcus faecalis is a gram-positive organism responsible for serious infections in humans, but as with many bacterial pathogens, resistance has rendered a number of commonly used antibiotics ineffective. Here, we report the cryo-EM structure of the E. faecalis 70S ribosome to a global resolution of 2.8 Å. Structural differences are clustered in peripheral and solvent exposed regions when compared with Escherichia coli, whereas functional centres, including antibiotic binding sites, are similar to other bacterial ribosomes. Comparison of intersubunit conformations among five classes obtained after three-dimensional classification identifies several rotated states. Large ribosomal subunit protein bL31, which forms intersubunit bridges to the small ribosomal subunit, assumes different conformations in the five classes, revealing how contacts to the small subunit are maintained throughout intersubunit rotation. A tRNA observed in one of the five classes is positioned in a chimeric pe/E position in a rotated ribosomal state. The 70S ribosome structure of E. faecalis now extends our knowledge of bacterial ribosome structures and may serve as a basis for the development of novel antibiotic compounds effective against this pathogen.

In vitro bone formation by mesenchymal stromal cells encapsulated in type-1 collagen hydrogels is demonstrated after a 28-day in vitro culture period. Analysis of the hydrogels is carried out by X-ray microcomputed tomography, histology, and immunohistochemistry, which collectively demonstrates that bone formation in the hydrogels was quantifiably proportional to the initial collagen concentration, and subsequently the population density of seeded cells. This was established by varying the initial collagen concentration at a constant cell seeding density (3 × 105 cells/0.3 mL hydrogel), and separately varying cell seeding density at a constant collagen concentration (1 mg/mL). Using these data, a mathematical model is presented for the total hydrogel volume and mineralization volume based on the observed linear contraction dynamics of cell-seeded collagen gels. The model parameters are fitted by comparing the predictions of the mathematical model for the hydrogel and mineralized volumes on day 28 with the experimental data. The model is then used to predict the hydrogel and mineralization volumes for a range of hydrogel collagen concentrations and cell seeding densities, providing comprehensive input/output descriptors for generating mineralized hydrogels for bone tissue engineering. It is proposed that this quantitative approach will be a useful tool for generating in vitro manufactured bone tissue, defining input parameters that yield predictable output measures of tissue maturation.

This paper develops theories of place in relation to migration to argue that the diasporic population of Chinese migrants in Prato, Italy, live not only ‘out of place’ due in no small part to margi...

Biosecurity strategies that aim to restrict the spread of invasive pests can benefit from knowing where new incursions have come from. This knowledge can be acquired using genomic databanks, by comparing genetic variation in incursion samples against reference samples. Here we use genomic databanks to investigate domestic incursions of two mosquito species in Australia, and assess the performance of genomic tracing methods when databank samples were collected some time ago or are genetically similar. We used a deep learning method to trace a 2021 invasion of Aedes aegypti in Tennant Creek, Northern Territory, to Townsville, Queensland, and to trace two years of Ae. albopictus incursions to two specific islands in the Torres Strait. We observed high precision of tracing despite 30-70 generations separating incursion and reference samples. Targeted assays also provided additional information on the origin of the Tennant Creek Ae. aegypti, in this case by comparing Wolbachia infection data and mitochondrial DNA variation. Patterns of relatedness and inbreeding indicated that Tennant Creek was likely invaded by one family of Ae. aegypti, whereas Torres Strait incursions involved distinct kinship groups. Our results highlight the value of genomic databanks that remain informative over years and for a range of biological conditions, and demonstrate how additional targeted assays (e.g. Wolbachia) can improve inferences.Key MessageGenomic tracing can provide valuable information on pest incursions and new invasions.Evolution will lead to increasing differences between databanks and extant populations.We tested how well genomic databanks could trace incursions sampled 30-70 generations later and where genetic differentiation was low.We show that tracing methods are robust for a wide range of conditions, and report specific incursion origins for two Aedes species.Our results suggest that genomic databanks will remain informative over years and for a range of invasive systems.

Tooth classes are a mammalian innovation that has contributed to the evolutionary success of mammals. However, our understanding of how tooth classes have evolved and diversified remains limited. Here, we use the evolutionary radiation of noctilionoid bats, the most diverse clade of mammals in terms of diet type, as a model system to show how the tooth developmental program evolved during the adaptation to new diet types. We combined morphological, developmental, cellular, and modeling approaches, to investigate the developmental differences between two tooth classes, molars and premolars and the mechanisms driving their diversification. We demonstrate that tooth classes develop through independent developmental cascades that deviate from classical models. Then we showed that the dramatic diversification of tooth number and size is driven by the modulation of the growth rate of the jaw, explaining the rapid gain/loss of teeth during the evolution of this clade. Finally, we propose a mathematical model that recapitulates the successive appearance of tooth buds and supports the hypothesis that growth acts as a key driver of the evolution of tooth number and size by tinkering with reaction/diffusion processes. Our results demonstrate developmental independence between mammalian tooth classes and provide a mechanism to explain their rapid diversification. More broadly, these results reveal how simple modifications of one developmental mechanism by another can drive the evolution of repeated structures during adaptive radiations.

Elucidating pattern forming processes is an important problem in the physical, chemical and biological sciences. Turing's contribution, after being initially neglected, eventually catalysed a huge amount of work from mathematicians, physicists, chemists and biologists aimed towards understanding how steady spatial patterns can emerge from homogeneous chemical mixtures due to the reaction and diffusion of different chemical species. While this theory has been developed mathematically and investigated experimentally for over half a century, many questions still remain unresolved. This theme issue places Turing's theory of pattern formation in a modern context, discussing the current frontiers in foundational aspects of pattern formation in reaction-diffusion and related systems. It highlights ongoing work in chemical, synthetic and developmental settings which is helping to elucidate how important Turing's mechanism is for real morphogenesis, while highlighting gaps that remain in matching theory to reality. The theme issue also surveys a variety of recent mathematical research pushing the boundaries of Turing's original theory to more realistic and complicated settings, as well as discussing open theoretical challenges in the analysis of such models. It aims to consolidate current research frontiers and highlight some of the most promising future directions. This article is part of the theme issue ‘Recent progress and open frontiers in Turing’s theory of morphogenesis’.

Introduction In Germany, there are more than 35 million women. The German Women's Health Report being published in December 2020 by the Federal Health Reporting Unit at the Robert Koch Institute describes their health status. Methods The report uses a broad data basis and shows differences and similarities in the health of women and men (‘differences between') as well as within the group of women (‘differences within'). It includes an overview on their health status, health behaviour and health care. Focus chapters are devoted to health over the life course, specific groups of women, e.g. women with disabilities, and specific health topics, e.g. sexual and reproductive health. Results Women's average life expectancy has been increasing for many decades. Two thirds of women rate their health as good or very good. The most common cause of death among women are cardiovascular diseases, accounting for around 40% of all deaths. Nevertheless, they are still considered as ‘male' diseases, thus women often underestimate their risk of disease. Women are in general more health conscious than men, e.g. they have a healthier diet. Women use prevention measures, health promotion and health care services more often. Two thirds of those in need of care are women and it is mainly women who are responsible for the care of others. Data gaps exist for certain conditions such as benign gynaecological diseases as well as for certain groups of women, e.g. migrant women. Conclusions In Germany, the health status and health care for women are at a high level. But inequalities in the opportunities for a healthy life exist: socio-economic factors such as age, education, occupation, income, family type, migration background and many other aspects have an important impact on their health status. The report provides current data and information for policymakers, science and practice and raises the awareness and the need for sex and gender-appropriate health promotion, prevention and care.

At the end of 2019 a newly emerged betacoronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the cause of an outbreak of severe pneumonia, subsequently termed COVID-19, in a number of patients in Wuhan, China. Subsequently, SARS-CoV-2 rapidly spread globally, resulting in a pandemic that has to date infected over 200 million individuals and resulted in more than 4.3 million deaths. While SARS-CoV-2 results in severe disease in 13.8%, with increasing frequency of severe disease with age, over 80% of infections are asymptomatic or mild. The immune response is an important determinant of outcome following SARS-CoV-2 infection. While B cell and T cell responses are associated with control of infection and protection against subsequent challenge with SARS-CoV-2, failure to control viral replication and the resulting hyperinflammation are associated with severe COVID-19. Towards the end of 2020, several variants of concern emerged that demonstrate increased transmissibility and/or evasion of immune responses from prior SARS-CoV-2 infection. This article reviews what is known about the humoral and cellular immune responses to SARS-CoV-2 and how mutation and structural/functional changes in the emerging variants of concern impact upon the immune protection from prior infection or vaccination.

The obligate intracellular bacterium Chlamydia trachomatis (C. trachomatis) is responsible for the most common bacterial sexually transmitted infection and is the leading cause of preventable blindness, representing a major global health burden. While C. trachomatis infection is currently treatable with broad-spectrum antibiotics, there would be many benefits of a chlamydia-specific therapy. Previously, we have identified a small-molecule lead compound JO146 [Boc-Val-Pro-ValP(OPh)2] targeting the bacterial serine protease HtrA, which is essential in bacterial replication, virulence and survival, particularly under stress conditions. JO146 is highly efficacious in attenuating infectivity of both human (C. trachomatis) as well as koala (C. pecorum) species in vitro and in vivo, without host cell toxicity. Herein, we present our continuing efforts on optimizing JO146 by modifying the N-capping group as well as replacing the parent peptide structure with the 2-pyridone scaffold at P3/P2. The drug optimization process was guided by molecular modelling, enzyme and cell-based assays. Compound 18b from the pyridone series showed improved inhibitory activity against CtHtrA by 5-fold and selectivity over human neutrophil elastase (HNE) by 109-fold compared to JO146, indicating that 2-pyridone is a suitable bioisostere of the P3/P2 amide/proline for developing CtHtrA inhibitors. Most pyridone-based inhibitors showed superior anti-chlamydial potency to JO146 especially at lower doses (25 and 50 μM) in C. trachomatis and C. pecorum cell culture assays. Modifications of the N-capping group of the peptidyl inhibitors did not have much influence on the anti-chlamydial activities, providing opportunities for more versatile alterations and future optimization. In summary, we present 2-pyridone based analogues as a new generation of non-peptidic CtHtrA inhibitors, which hold better promise as anti-chlamydial drug candidates.

In the nearly seven decades since the publication of Alan Turing's work on morphogenesis, enormous progress has been made in understanding both the mathematical and biological aspects of his proposed reaction-diffusion theory. Some of these developments were nascent in Turing's paper, and others have been due to new insights from modern mathematical techniques, advances in numerical simulations, and extensive biological experiments. Despite such progress, there are still important gaps between theory and experiment, with many examples of biological patterning where the underlying mechanisms are still unclear. Here we review modern developments in the mathematical theory pioneered by Turing, showing how his approach has been generalized to a range of settings beyond the classical two-species reaction-diffusion framework, including evolving and complex manifolds, systems heterogeneous in space and time, and more general reaction-transport equations. While substantial progress has been made in understanding these more complicated models, there are many remaining challenges that we highlight throughout. We focus on the mathematical theory, and in particular linear stability analysis of `trivial' base states. We emphasise important open questions in developing this theory further, and discuss obstacles in using these techniques to understand biological reality., 21 pages, 6 figures

Background and Purpose— Stroke etiology drives thrombus composition. We thus hypothesized that endovascular treatment shows different efficacy in cardioembolic versus noncardioembolic large-vessel occlusions (LVOs). Methods— Procedural characteristics, grade of reperfusion, and functional outcome at discharge and 90 days were compared between patients with cardioembolic versus noncardioembolic LVO from the GSR-ET (German Stroke Registry–Endovascular Treatment; n=2589). To determine associations with functional outcome, adjusted odds ratios and 95% CIs were calculated using ordinal multivariable logistic regression models adjusting for potential baseline confounder variables. Results— Endovascular treatment of cardioembolic LVO had a higher rate of successful reperfusion (85.6% versus 81.0%; P =0.002) and a higher rate of complete reperfusion after a single thrombectomy pass (45.7% versus 38.1%; P Conclusions— These results provide evidence for higher efficacy of endovascular treatment in cardioembolic LVO compared with noncardioembolic LVO.

OBJECTIVECommon interbody graft options for anterior cervical discectomy and fusion (ACDF) include structural allograft and polyetheretherketone (PEEK). PEEK has gained popularity due to its radiolucency and its elastic modulus, which is similar to that of bone. The authors sought to compare the rates of pseudarthrosis, a lack of solid bone growth across the disc space, and the need for revision surgery with the use of grafts made of allogenic bone versus PEEK.METHODSThe authors retrospectively reviewed 127 cases in which patients had undergone a 1-level ACDF followed by at least 1 year of radiographic follow-up. Data on age, sex, body mass index, tobacco use, pseudarthrosis, and the reoperation rate for pseudarthrosis were collected. These data were analyzed by performing a Pearson’s chi-square test.RESULTSOf 127 patients, 56 had received PEEK implants and 71 had received allografts. Forty-six of the PEEK implants (82%) were stand-alone devices. There were no significant differences between the 2 treatment groups with respect to patient age, sex, or body mass index. Twenty-nine (52%) of 56 patients with PEEK implants demonstrated radiographic evidence of pseudarthrosis, compared to 7 (10%) of 71 patients with structural allografts (p < 0.001, OR 9.82; 95% CI 3.836–25.139). Seven patients with PEEK implants required reoperation for pseudarthrosis, compared to 1 patient with an allograft (p = 0.01, OR 10.00; 95% CI 1.192–83.884). There was no significant difference in tobacco use between the PEEK and allograft groups (p = 0.586).CONCLUSIONSThe results of this study demonstrate that the use of PEEK devices in 1-level ACDF is associated with a significantly higher rate of radiographically demonstrated pseudarthrosis and need for revision surgery compared with the use of allografts. Surgeons should be aware of this when deciding on interbody graft options, and reimbursement policies should reflect these discrepancies.

Numerous studies have demonstrated that SARS-CoV-2 can be inactivated by ultraviolet (UV) radiation. However, there are few data available on the relative efficacy of different wavelengths of UV radiation and visible light, which complicates assessments of UV decontamination interventions. The present study evaluated the effects of monochromatic radiation at 16 wavelengths from 222 nm through 488 nm on SARS-CoV-2 in liquid aliquots and dried droplets of water and simulated saliva. The data were used to generate a set of action spectra which quantify the susceptibility of SARS-CoV-2 to genome damage and inactivation across the tested wavelengths. UVC wavelengths (≤280 nm) were most effective for inactivating SARS-CoV-2, although inactivation rates were dependent on sample type. Results from this study suggest that UV radiation can effectively inactivate SARS-CoV-2 in liquids and dried droplets, and provide a foundation for understanding the factors which affect the efficacy of different wavelengths in real-world settings.

BACKGROUND AND PURPOSE: Morphological changes of the corpus callosum have been associated with a large number of congenital neurocognitive and psychiatric disorders. Focal defects or notches of the dorsal surface of the corpus callosum have not been well characterized. Our purpose was the following; 1) to characterize the dorsal contour of the corpus callosum during the life span, 2) to characterize the relationship of contour deviations to neighboring vessels, and 3) to determine whether contour deviations are congenital or acquired. MATERIALS AND METHODS: We retrospectively reviewed normal sagittal T1-weighted brain MR images. A “notch” was defined as a concavity in the dorsal surface at least 1 mm in depth. The corpus callosum was considered to be “undulating” if there were >2 notches, including an anterior and posterior notch. The presence of a pericallosal artery and its relationship to a notch were assessed. RESULTS: We reviewed 1639 MR imaging studies, spanning 0–89 years of age. A total of 1102 notches were identified in 823 studies; 344 (31%) were anterior, 660 (60%) were posterior, and 98 (9%), undulating. There was a positive correlation between the prevalence (P < .001) and depth (P = .028) of an anterior notch and age and a negative correlation between the prevalence of a posterior notch and age (P < .001). There was no difference between patient sex and corpus callosum notching (P = .884). Of the 823 studies with notches, 490 (60%) were associated with a pericallosal artery (P < .001). CONCLUSIONS: The prevalence and depth of notches in the anterior corpus callosum increase significantly with age; this finding suggests that most notches are acquired. There is a significant positive association between the presence of a corpus callosum notch and adjacent pericallosal arteries, suggesting that this may play a role in notch formation.

During periods of rest, hippocampal place cells feature bursts of activity called sharp-wave ripples (SWRs). Heuristic approaches have revealed that a small fraction of SWRs appear to "simulate" trajectories through the environment, called awake hippocampal replay. However, the functional role of a majority of these SWRs remains unclear. We find, using Bayesian model comparison of state-space models to characterize the spatiotemporal dynamics embedded in SWRs, that almost all SWRs of foraging rodents simulate such trajectories. Furthermore, these trajectories feature momentum, or inertia in their velocities, that mirrors the animals' natural movement, in contrast to replay events during sleep, which lack such momentum. Last, we show that past analyses of replayed trajectories for navigational planning were biased by the heuristic SWR sub-selection. Our findings thus identify the dominant function of awake SWRs as simulating trajectories with momentum and provide a principled foundation for future work on their computational function.