Identification and measurement of intensive economic growth in a Roman imperial province.

A key question in economic history is the degree to which preindustrial economies could generate sustained increases in per capita productivity. Previous studies suggest that, in many preindustrial contexts, growth was primarily a consequence of agglomeration. Here, we examine evidence for three different socioeconomic rates that are available from the archaeological record for Roman Britain. We find that all three measures show increasing returns to scale with settlement population, with a common elasticity that is consistent with the expectation from settlement scaling theory. We also identify a pattern of increase in baseline rates, similar to that observed in contemporary societies, suggesting that this economy did generate modest levels of per capita productivity growth over a four-century period. Last, we suggest that the observed growth is attributable to changes in transportation costs and to institutions and technologies related to socioeconomic interchange. These findings reinforce the view that differences between ancient and contemporary economies are more a matter of degree than kind.

Real-World Evidence of the Long-Term Effectiveness of 0.2 µg/Day Fluocinolone Acetonide Implant in Persistent and Recurrent Diabetic Macular Edema - A Single Center Study.

Purpose: To report the long-term functional, anatomical and safety outcomes of 0.2 µg/day fluocinolone acetonide 0.19mg in patients with persistent or recurrent diabetic macular edema (DME). Methods: Retrospective, observational, single-center study of patients with recurrent or persistent DME. All patients received 0.2 µg/day of fluocinolone acetonide 0.19mg, and data were collected at baseline and months 1, 3, 6, 12, 24 and 36 after implantation. Outcomes measured included best-corrected visual acuity (BCVA), central macular thickness (CMT), intraocular pressure (IOP), and safety outcomes. Results: A total of 28 eyes from 28 patients were included. The mean age was 66.5 years (95% CI 62.8-70.2) with a mean duration of DME of 8.8 years (95% CI 7.7-10.0). Only two eyes were phakic. Mean follow-up was 25.4 months (95% CI 21.2-29.6). Mean BCVA at baseline was 48.6 ETDRS letters (95% CI 41.3-55.8) and improved as early as month 1 of follow-up with a mean gain in BCVA of 7.8 (95% CI 4.3-11.3) ETDRS letters (p<0.001). Statistically significant improvements in BCVA were also observed at months 6, 12 and 24. At baseline, patients had a mean CMT of 530.5µm (95% CI 463.0-598.0), and a decrease in CMT was observed, starting at the first month of follow-up (mean CMT reduction of -170.5µm, 95% CI -223.8- -117.1; p<0.001). Statistically significant decreases in CMT were also observed at months 6, 12, 24, and 36, with the maximum decrease observed at month 12 (p<0.001). Mean IOP at baseline was 16.4mmHg (95% CI 15.3-17.5) and nine eyes (32.1%) had an IOP ≥21mmHg during follow-up. Conclusion: Our results support the effectiveness and safety profile of fluocinolone acetonide. Although additional long-term real-world evidence is required, fluocinolone acetonide may represent a safe strategy for daily, low-dose, sustained and localized release to the posterior segment of the eye, providing both functional and anatomical benefits in DME.

Implicit racial biases are lower in more populous more diverse and less segregated US cities.

Implicit biases - differential attitudes towards members of distinct groups - are pervasive in human societies and create inequities across many aspects of life. Recent research has revealed that implicit biases are generally driven by social contexts, but not whether they are systematically influenced by the ways that humans self-organize in cities. We leverage complex system modeling in the framework of urban scaling theory to predict differences in these biases between cities. Our model links spatial scales from city-wide infrastructure to individual psychology to predict that cities that are more populous, more diverse, and less segregated are less biased. We find empirical support for these predictions in U.S. cities with Implicit Association Test data spanning a decade from 2.7 million individuals and U.S. Census demographic data. Additionally, we find that changes in cities' social environments precede changes in implicit biases at short time-scales, but this relationship is bi-directional at longer time-scales. We conclude that the social organization of cities may influence the strength of these biases.

The multispecies microbial cluster of Fusobacterium, Parvimonas, Bacteroides and Faecalibacterium as a precision biomarker for colorectal cancer diagnosis.

The incidence of colorectal cancer (CRC) has increased worldwide, and early diagnosis is crucial to reduce mortality rates. Therefore, new noninvasive biomarkers for CRC are required. Recent studies have revealed an imbalance in the oral and gut microbiomes of patients with CRC, as well as impaired gut vascular barrier function. In the present study, the microbiomes of saliva, crevicular fluid, feces, and non-neoplastic and tumor intestinal tissue samples of 93 CRC patients and 30 healthy individuals without digestive disorders (non-CRC) were analyzed by 16S rRNA metabarcoding procedures. The data revealed that Parvimonas, Fusobacterium, and Bacteroides fragilis were significantly over-represented in stool samples of CRC patients, whereas Faecalibacterium and Blautia were significantly over-abundant in the non-CRC group. Moreover, the tumor samples were enriched in well-known periodontal anaerobes, including Fusobacterium, Parvimonas, Peptostreptococcus, Porphyromonas, and Prevotella. Co-occurrence patterns of these oral microorganisms were observed in the subgingival pocket and in the tumor tissues of CRC patients, where they also correlated with other gut microbes, such as Hungatella. This study provides new evidence that oral pathobionts, normally located in subgingival pockets, can migrate to the colon and probably aggregate with aerobic bacteria, forming synergistic consortia. Furthermore, we suggest that the group composed of Fusobacterium, Parvimonas, Bacteroides, and Faecalibacterium could be used to design an excellent noninvasive fecal test for the early diagnosis of CRC. The combination of these four genera would significantly improve the reliability of a discriminatory test with respect to others that use a single species as a unique CRC biomarker.

Information synergy maximizes the growth rate of heterogeneous groups.

Collective action and group formation are fundamental behaviors among both organisms cooperating to maximize their fitness and people forming socioeconomic organizations. Researchers have extensively explored social interaction structures via game theory and homophilic linkages, such as kin selection and scalar stress, to understand emergent cooperation in complex systems. However, we still lack a general theory capable of predicting how agents benefit from heterogeneous preferences, joint information, or skill complementarities in statistical environments. Here, we derive general statistical dynamics for the origin of cooperation based on the management of resources and pooled information. Specifically, we show how groups that optimally combine complementary agent knowledge about resources in statistical environments maximize their growth rate. We show that these advantages are quantified by the information synergy embedded in the conditional probability of environmental states given agents' signals, such that groups with a greater diversity of signals maximize their collective information. It follows that, when constraints are placed on group formation, agents must intelligently select with whom they cooperate to maximize the synergy available to their own signal. Our results show how the general properties of information underlie the optimal collective formation and dynamics of groups of heterogeneous agents across social and biological phenomena.

Evidence of COVID-19 fatalities in Swedish neighborhoods from a full population study.

The COVID-19 pandemic has highlighted a debate about whether marginalized communities suffered the disproportionate brunt of the pandemic's mortality. Empirical studies addressing this question typically suffer from statistical uncertainties and potential biases associated with uneven and incomplete reporting. We use geo-coded micro-level data for the entire population of Sweden to analyze how local neighborhood characteristics affect the likelihood of dying with COVID-19 at individual level, given the individual's overall risk of death. We control for several individual and regional characteristics to compare the results in specific communities to overall death patterns in Sweden during 2020. When accounting for the probability to die of any cause, we find that individuals residing in socioeconomically disadvantaged neighborhoods were not more likely to die with COVID-19 than individuals residing elsewhere. Importantly, we do find that individuals show a generally higher probability of death in these neighborhoods. Nevertheless, ethnicity is an important explanatory factor for COVID-19 deaths for foreign-born individuals, especially from East Africa, who are more likely to pass away regardless of residential neighborhood.

Integration of urban science and urban climate adaptation research: opportunities to advance climate action.

There is a growing recognition that responding to climate change necessitates urban adaptation. We sketch a transdisciplinary research effort, arguing that actionable research on urban adaptation needs to recognize the nature of cities as social networks embedded in physical space. Given the pace, scale and socioeconomic outcomes of urbanization in the Global South, the specificities and history of its cities must be central to the study of how well-known agglomeration effects can facilitate adaptation. The proposed effort calls for the co-creation of knowledge involving scientists and stakeholders, especially those historically excluded from the design and implementation of urban development policies.

Learning increases growth and reduces inequality in shared noisy environments.

Stochastic multiplicative dynamics characterize many complex natural phenomena such as selection and mutation in evolving populations, and the generation and distribution of wealth within social systems. Population heterogeneity in stochastic growth rates has been shown to be the critical driver of wealth inequality over long time scales. However, we still lack a general statistical theory that systematically explains the origins of these heterogeneities resulting from the dynamical adaptation of agents to their environment. In this paper, we derive population growth parameters resulting from the general interaction between agents and their environment, conditional on subjective signals each agent perceives. We show that average wealth-growth rates converge, under specific conditions, to their maximal value as the mutual information between the agent's signal and the environment, and that sequential Bayesian inference is the optimal strategy for reaching this maximum. It follows that when all agents access the same statistical environment, the learning process attenuates growth rate disparities, reducing the long-term effects of heterogeneity on inequality. Our approach shows how the formal properties of information underlie general growth dynamics across social and biological phenomena, including cooperation and the effects of education and learning on life history choices.

Assessment of heterogeneity among participants in the Parkinson's Progression Markers Initiative cohort using α-synuclein seed amplification: a cross-sectional study.

BACKGROUND: Emerging evidence shows that α-synuclein seed amplification assays (SAAs) have the potential to differentiate people with Parkinson's disease from healthy controls. We used the well characterised, multicentre Parkinson's Progression Markers Initiative (PPMI) cohort to further assess the diagnostic performance of the α-synuclein SAA and to examine whether the assay identifies heterogeneity among patients and enables the early identification of at-risk groups. METHODS: This cross-sectional analysis is based on assessments done at enrolment for PPMI participants (including people with sporadic Parkinson's disease from LRRK2 and GBA variants, healthy controls, prodromal individuals with either rapid eye movement sleep behaviour disorder (RBD) or hyposmia, and non-manifesting carriers of LRRK2 and GBA variants) from 33 participating academic neurology outpatient practices worldwide (in Austria, Canada, France, Germany, Greece, Israel, Italy, the Netherlands, Norway, Spain, the UK, and the USA). α-synuclein SAA analysis of CSF was performed using previously described methods. We assessed the sensitivity and specificity of the α-synuclein SAA in participants with Parkinson's disease and healthy controls, including subgroups based on genetic and clinical features. We established the frequency of positive α-synuclein SAA results in prodromal participants (RBD and hyposmia) and non-manifesting carriers of genetic variants associated with Parkinson's disease, and compared α-synuclein SAA to clinical measures and other biomarkers. We used odds ratio estimates with 95% CIs to measure the association between α-synuclein SAA status and categorical measures, and two-sample 95% CIs from the resampling method to assess differences in medians between α-synuclein SAA positive and negative participants for continuous measures. A linear regression model was used to control for potential confounders such as age and sex. FINDINGS: This analysis included 1123 participants who were enrolled between July 7, 2010, and July 4, 2019. Of these, 545 had Parkinson's disease, 163 were healthy controls, 54 were participants with scans without evidence of dopaminergic deficit, 51 were prodromal participants, and 310 were non-manifesting carriers. Sensitivity for Parkinson's disease was 87·7% (95% CI 84·9-90·5), and specificity for healthy controls was 96·3% (93·4-99·2). The sensitivity of the α-synuclein SAA in sporadic Parkinson's disease with the typical olfactory deficit was 98·6% (96·4-99·4). The proportion of positive α-synuclein SAA was lower than this figure in subgroups including LRRK2 Parkinson's disease (67·5% [59·2-75·8]) and participants with sporadic Parkinson's disease without olfactory deficit (78·3% [69·8-86·7]). Participants with LRRK2 variant and normal olfaction had an even lower α-synuclein SAA positivity rate (34·7% [21·4-48·0]). Among prodromal and at-risk groups, 44 (86%) of 51 of participants with RBD or hyposmia had positive α-synuclein SAA (16 of 18 with hyposmia, and 28 of 33 with RBD). 25 (8%) of 310 non-manifesting carriers (14 of 159 [9%] LRRK2 and 11 of 151 [7%] GBA) were positive. INTERPRETATION: This study represents the largest analysis so far of the α-synuclein SAA for the biochemical diagnosis of Parkinson's disease. Our results show that the assay classifies people with Parkinson's disease with high sensitivity and specificity, provides information about molecular heterogeneity, and detects prodromal individuals before diagnosis. These findings suggest a crucial role for the α-synuclein SAA in therapeutic development, both to identify pathologically defined subgroups of people with Parkinson's disease and to establish biomarker-defined at-risk cohorts. FUNDING: PPMI is funded by the Michael J Fox Foundation for Parkinson's Research and funding partners, including: Abbvie, AcureX, Aligning Science Across Parkinson's, Amathus Therapeutics, Avid Radiopharmaceuticals, Bial Biotech, Biohaven, Biogen, BioLegend, Bristol-Myers Squibb, Calico Labs, Celgene, Cerevel, Coave, DaCapo Brainscience, 4D Pharma, Denali, Edmond J Safra Foundation, Eli Lilly, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Insitro, Janssen Neuroscience, Lundbeck, Merck, Meso Scale Discovery, Neurocrine Biosciences, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, VanquaBio, Verily, Voyager Therapeutics, and Yumanity.

Central and peripheral α-synuclein in Parkinson disease detected by seed amplification assay.

OBJECTIVES: Detection of α-synuclein aggregates by seed amplification is a promising Parkinson disease biomarker assay. Understanding intraindividual relationships of α-synuclein measures could inform optimal biomarker development. The objectives were to test accuracy of α-synuclein seed amplification assay in central (cerebrospinal fluid) and peripheral (submandibular gland) sources, compare to total α-synuclein measures, and investigate within-subject relationships. METHODS: The Systemic Synuclein Sampling Study aimed to characterize α-synuclein in multiple tissues and biofluids within Parkinson disease subjects (n = 59) and compared to healthy controls (n = 21). Motor and non-motor measures and dopamine transporter scans were obtained. Four measures of α-synuclein were compared: seed amplification assay in cerebrospinal fluid and formalin-fixed paraffin-embedded submandibular gland, total α-synuclein quantified in biofluids using enzyme-linked immunoassay, and aggregated α-synuclein in submandibular gland detected by immunohistochemistry. Accuracy of seed amplification assay for Parkinson disease diagnosis was examined and within-subject α-synuclein measures were compared. RESULTS: Sensitivity and specificity of α-synuclein seed amplification assay for Parkinson disease diagnosis was 92.6% and 90.5% in cerebrospinal fluid, and 73.2% and 78.6% in submandibular gland, respectively. 25/38 (65.8%) Parkinson disease participants were positive for both cerebrospinal fluid and submandibular gland seed amplification assay. Comparing accuracy for Parkinson disease diagnosis of different α-synuclein measures, cerebrospinal fluid seed amplification assay was the highest (Youden Index = 83.1%). 98.3% of all Parkinson disease cases had ≥1 measure of α-synuclein positive. INTERPRETATION: α-synuclein seed amplification assay (cerebrospinal fluid>submandibular gland) had higher sensitivity and specificity compared to total α-synuclein measures, and within-subject relationships of central and peripheral α-synuclein measures emerged.

Cryo-plasma FIB/SEM volume imaging of biological specimens.

Serial focussed ion beam scanning electron microscopy (FIB/SEM) enables imaging and assessment of subcellular structures on the mesoscale (10 nm to 10 µm). When applied to vitrified samples, serial FIB/SEM is also a means to target specific structures in cells and tissues while maintaining constituents' hydration shells for in situ structural biology downstream. However, the application of serial FIB/SEM imaging of non-stained cryogenic biological samples is limited due to low contrast, curtaining, and charging artefacts. We address these challenges using a cryogenic plasma FIB/SEM. We evaluated the choice of plasma ion source and imaging regimes to produce high-quality SEM images of a range of different biological samples. Using an automated workflow we produced three-dimensional volumes of bacteria, human cells, and tissue, and calculated estimates for their resolution, typically achieving 20-50 nm. Additionally, a tag-free localisation tool for regions of interest is needed to drive the application of in situ structural biology towards tissue. The combination of serial FIB/SEM with plasma-based ion sources promises a framework for targeting specific features in bulk-frozen samples (>100 µm) to produce lamellae for cryogenic electron tomography.

Structure-function studies can improve binding affinity of cohesin-dockerin interactions for multi-protein assemblies.

The cellulosome is an elaborate multi-enzyme structure secreted by many anaerobic microorganisms for the efficient degradation of lignocellulosic substrates. It is composed of multiple catalytic and non-catalytic components that are assembled through high-affinity protein-protein interactions between the enzyme-borne dockerin (Doc) modules and the repeated cohesin (Coh) modules present in primary scaffoldins. In some cellulosomes, primary scaffoldins can interact with adaptor and cell-anchoring scaffoldins to create structures of increasing complexity. The cellulosomal system of the ruminal bacterium, Ruminococcus flavefaciens, is one of the most intricate described to date. An unprecedent number of different Doc specificities results in an elaborate architecture, assembled exclusively through single-binding-mode type-III Coh-Doc interactions. However, a set of type-III Docs exhibits certain features associated with the classic dual-binding mode Coh-Doc interaction. Here, the structure of the adaptor scaffoldin-borne ScaH Doc in complex with the Coh from anchoring scaffoldin ScaE is described. This complex, unlike previously described type-III interactions in R. flavefaciens, was found to interact in a dual-binding mode. The key residues determining Coh recognition were also identified. This information was used to perform structure-informed protein engineering to change the electrostatic profile of the binding surface and to improve the affinity between the two modules. The results show that the nature of the residues in the ligand-binding surface plays a major role in Coh recognition and that Coh-Doc affinity can be manipulated through rational design, a key feature for the creation of designer cellulosomes or other affinity-based technologies using tailored Coh-Doc interactions.

Okapi-EM: A napari plugin for processing and analyzing cryogenic serial focused ion beam/scanning electron microscopy images.

An emergent volume electron microscopy technique called cryogenic serial plasma focused ion beam milling scanning electron microscopy (pFIB/SEM) can decipher complex biological structures by building a three-dimensional picture of biological samples at mesoscale resolution. This is achieved by collecting consecutive SEM images after successive rounds of FIB milling that expose a new surface after each milling step. Due to instrumental limitations, some image processing is necessary before 3D visualization and analysis of the data is possible. SEM images are affected by noise, drift, and charging effects, that can make precise 3D reconstruction of biological features difficult. This article presents Okapi-EM, an open-source napari plugin developed to process and analyze cryogenic serial pFIB/SEM images. Okapi-EM enables automated image registration of slices, evaluation of image quality metrics specific to pFIB-SEM imaging, and mitigation of charging artifacts. Implementation of Okapi-EM within the napari framework ensures that the tools are both user- and developer-friendly, through provision of a graphical user interface and access to Python programming.

The Effect of Glycol Side Chains on the Assembly and Microstructure of Conjugated Polymers.

Conjugated polymers with glycol-based chains, are emerging as a material class with promising applications as organic mixed ionic-electronic conductors, particularly in bioelectronics and thermoelectrics. However, little is still known about their microstructure and the role of the side chains in determining intermolecular interactions and polymer packing. Here, we use the combination of electrospray deposition and scanning tunneling microscopy to determine the microstructure of prototypical glycolated conjugated polymers (pgBTTT and p(g2T-TT)) with submonomer resolution. Molecular dynamics simulations of the same surface-adsorbed polymers exhibit an excellent agreement with the experimental images, allowing us to extend the characterization of the polymers to the atomic scale. Our results prove that, similarly to their alkylated counterparts, glycolated polymers assemble through interdigitation of their side chains, although significant differences are found in their conformation and interaction patterns. A model is proposed that identifies the driving force for the polymer assembly in the tendency of the side chains to adopt the conformation of their free analogues, i.e., polyethylene and polyethylene glycol, for alkyl or ethylene glycol side chains, respectively. For both classes of polymers, it is also demonstrated that the backbone conformation is determined to a higher degree by the interaction between the side chains rather than by the backbone torsional potential energy. The generalization of these findings from two-dimensional (2D) monolayers to three-dimensional thin films is discussed, together with the opportunity to use this type of 2D study to gain so far inaccessible, subnm-scale information on the microstructure of conjugated polymers.

Generation of a Library of Carbohydrate-Active Enzymes for Plant Biomass Deconstruction.

In nature, the deconstruction of plant carbohydrates is carried out by carbohydrate-active enzymes (CAZymes). A high-throughput (HTP) strategy was used to isolate and clone 1476 genes obtained from a diverse library of recombinant CAZymes covering a variety of sequence-based families, enzyme classes, and source organisms. All genes were successfully isolated by either PCR (61%) or gene synthesis (GS) (39%) and were subsequently cloned into Escherichia coli expression vectors. Most proteins (79%) were obtained at a good yield during recombinant expression. A significantly lower number (p < 0.01) of proteins from eukaryotic (57.7%) and archaeal (53.3%) origin were soluble compared to bacteria (79.7%). Genes obtained by GS gave a significantly lower number (p = 0.04) of soluble proteins while the green fluorescent protein tag improved protein solubility (p = 0.05). Finally, a relationship between the amino acid composition and protein solubility was observed. Thus, a lower percentage of non-polar and higher percentage of negatively charged amino acids in a protein may be a good predictor for higher protein solubility in E. coli. The HTP approach presented here is a powerful tool for producing recombinant CAZymes that can be used for future studies of plant cell wall degradation. Successful production and expression of soluble recombinant proteins at a high rate opens new possibilities for the high-throughput production of targets from limitless sources.

Evidence and theory for lower rates of depression in larger US urban areas.

It is commonly assumed that cities are detrimental to mental health. However, the evidence remains inconsistent and at most, makes the case for differences between rural and urban environments as a whole. Here, we propose a model of depression driven by an individual's accumulated experience mediated by social networks. The connection between observed systematic variations in socioeconomic networks and built environments with city size provides a link between urbanization and mental health. Surprisingly, this model predicts lower depression rates in larger cities. We confirm this prediction for US cities using four independent datasets. These results are consistent with other behaviors associated with denser socioeconomic networks and suggest that larger cities provide a buffer against depression. This approach introduces a systematic framework for conceptualizing and modeling mental health in complex physical and social networks, producing testable predictions for environmental and social determinants of mental health also applicable to other psychopathologies.

Alpha-synuclein research: defining strategic moves in the battle against Parkinson's disease.

With the advent of the genetic era in Parkinson's disease (PD) research in 1997, α-synuclein was identified as an important player in a complex neurodegenerative disease that affects >10 million people worldwide. PD has been estimated to have an economic impact of $51.9 billion in the US alone. Since the initial association with PD, hundreds of researchers have contributed to elucidating the functions of α-synuclein in normal and pathological states, and these remain critical areas for continued research. With this position paper the authors strive to achieve two goals: first, to succinctly summarize the critical features that define α-synuclein's varied roles, as they are known today; and second, to identify the most pressing knowledge gaps and delineate a multipronged strategy for future research with the goal of enabling therapies to stop or slow disease progression in PD.