Restoring the Fluctuation-Dissipation Theorem in Kardar-Parisi-Zhang Universality Class through a New Emergent Fractal Dimension.

The Kardar-Parisi-Zhang (KPZ) equation describes a wide range of growth-like phenomena, with applications in physics, chemistry and biology. There are three central questions in the study of KPZ growth: the determination of height probability distributions; the search for ever more precise universal growth exponents; and the apparent absence of a fluctuation-dissipation theorem (FDT) for spatial dimension d>1. Notably, these questions were answered exactly only for 1+1 dimensions. In this work, we propose a new FDT valid for the KPZ problem in d+1 dimensions. This is achieved by rearranging terms and identifying a new correlated noise which we argue to be characterized by a fractal dimension dn. We present relations between the KPZ exponents and two emergent fractal dimensions, namely df, of the rough interface, and dn. Also, we simulate KPZ growth to obtain values for transient versions of the roughness exponent α, the surface fractal dimension df and, through our relations, the noise fractal dimension dn. Our results indicate that KPZ may have at least two fractal dimensions and that, within this proposal, an FDT is restored. Finally, we provide new insights into the old question about the upper critical dimension of the KPZ universality class.

Health-related quality of life in men with oligometastatic prostate cancer following metastases-directed stereotactic body radiotherapy: Real-world data from the E2-RADIatE OligoCare cohort.

Objective: To evaluate the impact of metastases-directed stereotactic body radiotherapy (SBRT) on health-related quality of life (HRQoL) in men with oligometastatic prostate cancer (PCa) using real-world data from the OligoCare cohort. Materials and methods: OligoCare is a pragmatic, observational cohort designed to assess the impact of metastases-directed SBRT on patients with oligometastatic disease (OMD). We report an interim analyses of the secondary endpoint HRQoL, assessed using the EORTC QLQ-C30, within six months of metastases-directed SBRT for oligometastatic disease in men with PCa among the first 1600 registered patients. HRQoL data collection was optional within the OligoCare cohort. To compare HRQoL between baseline and first follow-up assessment, a Wilcoxon signed-rank test was used. A multiple linear regression model was used to explore the HRQoL associations with predefined factors. Results: Out of the 1600 registered patients, 658 were treated for oligometastatic PCa, of which 233 had baseline QoL data and 132 patients had both baseline and follow-up HRQoL data. At baseline, most patients had a WHO performance status of 0 or 1 (87 %), were de-novo oligometastatic (79 %), had one metastasis (90 %), and had a good overall global health status (mean 80.81, SD16.11, IQR 75-92). 51 % received hormonal therapy as concomitant systemic treatment. Patients with comorbidities as assessed by the Charlson Comorbidity index had a worse global health status at baseline (-4.88, 95 % CI:-9.35, -0.42). No clinically meaningful significant difference in global health status was observed at first assessment following SBRT (median 3.0 months) compared with baseline (mean difference 2.27, 95 % CI:-1.54, 6.08). Upon evaluating the proportions, meaningful clinically important differences (a 10-point or more difference) was observed in, 17 % and 11 % of the patients reporting deterioration and improvement of global health status, respectively. Conclusion: Metastases-directed stereotactic body radiotherapy had no negative impact on global HRQoL within the first six months after treatment.

Sequential epidemic-like spread between agglomerates of self-propelled agents in one dimension.

Motile organisms can form stable agglomerates such as cities or colonies. In the outbreak of a highly contagious disease, the control of large-scale epidemic spread depends on factors like the number and size of agglomerates, travel rate between them, and disease recovery rate. While the emergence of agglomerates permits early interventions, it also explains longer real epidemics. In this work, we study the spread of susceptible-infected-recovered (SIR) epidemics (or any sort of information exchange by contact) in one-dimensional spatially structured systems. By working in one dimension, we establish a necessary foundation for future investigation in higher dimensions and mimic micro-organisms in narrow channels. We employ a model of self-propelled particles which spontaneously form multiple clusters. For a lower rate of stochastic reorientation, particles have a higher tendency to agglomerate and therefore the clusters become larger and less numerous. We examine the time evolution averaged over many epidemics and how it is affected by the existence of clusters through the eventual recovery of infected particles before reaching new clusters. New terms appear in the SIR differential equations in the last epidemic stages. We show how the final number of ever-infected individuals depends nontrivially on single-individual parameters. In particular, the number of ever-infected individuals first increases with the reorientation rate since particles escape sooner from clusters and spread the disease. For higher reorientation rate, travel between clusters becomes too diffusive and the clusters too small, decreasing the number of ever-infected individuals.

Automation of Caenorhabditis elegans lifespan assay using a simplified domain synthetic image-based neural network training strategy.

Performing lifespan assays with Caenorhabditis elegans (C. elegans) nematodes manually is a time consuming and laborious task. Therefore, automation is necessary to increase productivity. In this paper, we propose a method to automate the counting of live C. elegans using deep learning. The survival curves of the experiment are obtained using a sequence formed by an image taken on each day of the assay. Solving this problem would require a very large labeled dataset; thus, to facilitate its generation, we propose a simplified image-based strategy. This simplification consists of transforming the real images of the nematodes in the Petri dish to a synthetic image, in which circular blobs are drawn on a constant background to mark the position of the C. elegans. To apply this simplification method, it is divided into two steps. First, a Faster R-CNN network detects the C. elegans, allowing its transformation into a synthetic image. Second, using the simplified image sequence as input, a regression neural network is in charge of predicting the count of live nematodes on each day of the experiment. In this way, the counting network was trained using a simple simulator, avoiding labeling a very large real dataset or developing a realistic simulator. Results showed that the differences between the curves obtained by the proposed method and the manual curves are not statistically significant for either short-lived N2 (p-value log rank test 0.45) or long-lived daf-2 (p-value log rank test 0.83) strains.

Towards generalization for Caenorhabditis elegans detection.

The nematode Caenorhabditis elegans (C. elegans) is of significant interest for research into neurodegenerative diseases, aging, and drug screening. However, conducting these assays manually is a tedious and time-consuming process. This paper proposes a methodology to achieve a generalist C. elegans detection algorithm, as previous work only focused on dataset-specific detection, tailored exclusively to the characteristics and appearance of the images in a given dataset. The main aim of our study is to achieve a solution that allows for robust detection, regardless of the image-capture system used, with the potential to serve as a basis for the automation of numerous assays. These potential applications include worm counting, worm tracking, motion detection and motion characterization. To train this model, a dataset consisting of a wide variety of appearances adopted by C. elegans has been curated and dataset augmentation methods have been proposed and evaluated, including synthetic image generation. The results show that the model achieves an average precision of 89.5% for a wide variety of C. elegans appearances that were not used during training, thereby validating its generalization capabilities.

Mixtures of self-propelled particles interacting with asymmetric obstacles.

In the presence of an obstacle, active particles condensate into a surface "wetting" layer due to persistent motion. If the obstacle is asymmetric, a rectification current arises in addition to wetting. Asymmetric geometries are therefore commonly used to concentrate microorganisms like bacteria and sperms. However, most studies neglect the fact that biological active matter is diverse, composed of individuals with distinct self-propulsions. Using simulations, we study a mixture of "fast" and "slow" active Brownian disks in two dimensions interacting with large half-disk obstacles. With this prototypical obstacle geometry, we analyze how the stationary collective behavior depends on the degree of self-propulsion "diversity," defined as proportional to the difference between the self-propulsion speeds, while keeping the average self-propulsion speed fixed. A wetting layer rich in fast particles arises. The rectification current is amplified by speed diversity due to a superlinear dependence of rectification on self-propulsion speed, which arises from cooperative effects. Thus, the total rectification current cannot be obtained from an effective one-component active fluid with the same average self-propulsion speed, highlighting the importance of considering diversity in active matter.

Changes over time in the causes of death with a functioning graft in kidney transplantation recipients.

INTRODUCTION: Death with a functioning graft (DWFG) is the most frequent cause of loss of kidney transplantation (KT). OBJECTIVE: To analyze the evolution of the causes of DWFG and the frequency of the types of cancer causing DWFG. METHODS: Retrospective study of KT in Andalusia from 1984 to 2018. We analyzed the evolution according to eras (1984-1995; 1996-2007; 2008-2018) and according to post-transplant period (early death: first year post-KT; late death: after first year post-KT). RESULTS: A total of 9905 KT were performed, registering 1861 DWFG. The most frequent causes were cardiovascular disease (25.1%), infections (21.5%) and cancer (19.9%). In early death we did not observe changes, and infections were always the main cause. In late death, cardiovascular death decreased (1984-1995: 35.2%, 1996-2007: 22.6%, 2008-2018: 23.9%), but infections (1984-1995: 12.5%, 1996-2007: 18.3%, 2008-2018: 19.9%) and, above all, cancer-related deaths increased (1984-1995: 21.8%, 1996-2007: 29%, 2008-2018: 26.8%) (P < .001). In the multivariable analysis for late death due to cardiovascular disease, recipient age, retransplantation, diabetes, and the first period were risk factors, while the risk of late death due to cancer and infections was associated with recent eras. In the first year after transplantation, the most frequent neoplasia causing DWFG was post-transplant lymphoproliferative disease, and after the first year, it was lung cancer, without differences when it was analyzed by eras. CONCLUSIONS: Despite the greater comorbidity of the recipients, cardiovascular deaths have decreased. Cancer has been the main cause of late death in recent years. Lung cancer is the most frequent malignancy that causes DWFG in our transplant patients.

TREXIO: A file format and library for quantum chemistry.

TREXIO is an open-source file format and library developed for the storage and manipulation of data produced by quantum chemistry calculations. It is designed with the goal of providing a reliable and efficient method of storing and exchanging wave function parameters and matrix elements, making it an important tool for researchers in the field of quantum chemistry. In this work, we present an overview of the TREXIO file format and library. The library consists of a front-end implemented in the C programming language and two different back-ends: a text back-end and a binary back-end utilizing the hierarchical data format version 5 library, which enables fast read and write operations. It is compatible with a variety of platforms and has interfaces for Fortran, Python, and OCaml programming languages. In addition, a suite of tools have been developed to facilitate the use of the TREXIO format and library, including converters for popular quantum chemistry codes and utilities for validating and manipulating data stored in TREXIO files. The simplicity, versatility, and ease of use of TREXIO make it a valuable resource for researchers working with quantum chemistry data.

The urban structure and pedestrian injuries: A typological analysis of pedestrian crashes in the city of Hermosillo, Mexico.

OBJECTIVE: In this study, we aim to identify social typologies of pedestrian crashes considering demographics, health impacts, involved vehicle, temporality of the collision, and place of impact in Hermosillo, Mexico. METHODS: A socio-spatial analysis was performed by using local urban planning information and vehicles-pedestrian crashes records collected by the police department (N = 950) between 2014 and 2017. Multiple Correspondence Analysis and Hierarchical Cluster Analysis were used to determine typologies. Geographical distribution of typologies was obtained with spatial analysis techniques. RESULTS: The results suggest there are four typologies, which portray the physical vulnerability of pedestrians, which reflect the vulnerability to collisions associated to the variables age, gender, and street speed limits. Findings show that children are more likely to be injured during weekends in residential zones (Typology 1), while older females are more likely to be injured during the first three days of the week (Monday - Wednesday) in the downtown area (Typology 2). Injured males during the afternoon in arterial streets represented the most frequent cluster (Typology 3). Also, males were likely to be severely injured by heavy trucks during nighttime in peri-urban areas (Typology 4). These findings indicate that vulnerability and risk exposure vary according to the type of pedestrian involved in the crash, which are linked to the types of places they visit. CONCLUSIONS: The design of the built environment plays a major role in the number of pedestrian injuries particularly when it favors motor vehicles over pedestrians or non-motorized vehicles. Because traffic crashes are considered preventable events, cities must embrace a diversity of mobility modes and incorporate the appropriate infrastructures that safeguard the lives of all their travelers, especially pedestrians.

Automatic segmentation of Caenorhabditis elegans skeletons in worm aggregations using improved U-Net in low-resolution image sequences.

Pose estimation of C. elegans in image sequences is challenging and even more difficult in low-resolution images. Problems range from occlusions, loss of worm identity, and overlaps to aggregations that are too complex or difficult to resolve, even for the human eye. Neural networks, on the other hand, have shown good results in both low-resolution and high-resolution images. However, training in a neural network model requires a very large and balanced dataset, which is sometimes impossible or too expensive to obtain. In this article, a novel method for predicting C. elegans poses in cases of multi-worm aggregation and aggregation with noise is proposed. To solve this problem we use an improved U-Net model capable of obtaining images of the next aggregated worm posture. This neural network model was trained/validated using a custom-generated dataset with a synthetic image simulator. Subsequently, tested with a dataset of real images. The results obtained were greater than 75% in precision and 0.65 with Intersection over Union (IoU) values.

Differences in Longevity and Temperature-Driven Extrinsic Incubation Period Correlate with Varying Dengue Risk in the Arizona-Sonora Desert Region.

Dengue transmission is determined by a complex set of interactions between the environment, Aedes aegypti mosquitoes, dengue viruses, and humans. Emergence in new geographic areas can be unpredictable, with some regions having established mosquito populations for decades without locally acquired transmission. Key factors such as mosquito longevity, temperature-driven extrinsic incubation period (EIP), and vector-human contact can strongly influence the potential for disease transmission. To assess how these factors interact at the edge of the geographical range of dengue virus transmission, we conducted mosquito sampling in multiple urban areas located throughout the Arizona-Sonora desert region during the summer rainy seasons from 2013 to 2015. Mosquito population age structure, reflecting mosquito survivorship, was measured using a combination of parity analysis and relative gene expression of an age-related gene, SCP-1. Bloodmeal analysis was conducted on field collected blood-fed mosquitoes. Site-specific temperature was used to estimate the EIP, and this predicted EIP combined with mosquito age were combined to estimate the abundance of "potential" vectors (i.e., mosquitoes old enough to survive the EIP). Comparisons were made across cities by month and year. The dengue endemic cities Hermosillo and Ciudad Obregon, both in the state of Sonora, Mexico, had higher abundance of potential vectors than non-endemic Nogales, Sonora, Mexico. Interestingly, Tucson, Arizona consistently had a higher estimated abundance of potential vectors than dengue endemic regions of Sonora, Mexico. There were no observed city-level differences in species composition of blood meals. Combined, these data offer insights into the critical factors required for dengue transmission at the ecological edge of the mosquito's range. However, further research is needed to integrate an understanding of how social and additional environmental factors constrain and enhance dengue transmission in emerging regions.

Molecular evidence of widespread benzimidazole drug resistance in Ancylostoma caninum from domestic dogs throughout the USA and discovery of a novel ß-tubulin benzimidazole resistance mutation.

Ancylostoma caninum is an important zoonotic gastrointestinal nematode of dogs worldwide and a close relative of human hookworms. We recently reported that racing greyhound dogs in the USA are infected with A. caninum that are commonly resistant to multiple anthelmintics. Benzimidazole resistance in A. caninum in greyhounds was associated with a high frequency of the canonical F167Y(TTC>TAC) isotype-1 ß-tubulin mutation. In this work, we show that benzimidazole resistance is remarkably widespread in A. caninum from domestic dogs across the USA. First, we identified and showed the functional significance of a novel benzimidazole isotype-1 ß-tubulin resistance mutation, Q134H(CAA>CAT). Several benzimidazole resistant A. caninum isolates from greyhounds with a low frequency of the F167Y(TTC>TAC) mutation had a high frequency of a Q134H(CAA>CAT) mutation not previously reported from any eukaryotic pathogen in the field. Structural modeling predicted that the Q134 residue is directly involved in benzimidazole drug binding and that the 134H substitution would significantly reduce binding affinity. Introduction of the Q134H substitution into the C. elegans ß-tubulin gene ben-1, by CRISPR-Cas9 editing, conferred similar levels of resistance as a ben-1 null allele. Deep amplicon sequencing on A. caninum eggs from 685 hookworm positive pet dog fecal samples revealed that both mutations were widespread across the USA, with prevalences of 49.7% (overall mean frequency 54.0%) and 31.1% (overall mean frequency 16.4%) for F167Y(TTC>TAC) and Q134H(CAA>CAT), respectively. Canonical codon 198 and 200 benzimidazole resistance mutations were absent. The F167Y(TTC>TAC) mutation had a significantly higher prevalence and frequency in Western USA than in other regions, which we hypothesize is due to differences in refugia. This work has important implications for companion animal parasite control and the potential emergence of drug resistance in human hookworms.

Targeting VCAM-1: a therapeutic opportunity for vascular damage.

INTRODUCTION: The vascular cell adhesion molecule (VCAM-1) is a transmembrane sialoglycoprotein detected in activated endothelial and vascular smooth muscle cells involved in the adhesion and transmigration of inflammatory cells into damaged tissue. Widely used as a pro-inflammatory marker, its potential role as a targeting molecule has not been thoroughly explored. AREAS COVERED: We discuss the current evidence supporting the potential targeting of VCAM-1 in atherosclerosis, diabetes, hypertension and ischemia/reperfusion injury. EXPERT OPINION: There is emerging evidence that VCAM-1 is more than a biomarker and may be a promising therapeutic target for vascular diseases. While there are neutralizing antibodies that allow preclinical research, the development of pharmacological tools to activate or inhibit this protein are required to thoroughly assess its therapeutic potential.

Nodal Yield <15 Is Associated With Reduced Survival in Esophagectomy and Is a Quality Metric.

BACKGROUND: Surgical resection after neoadjuvant therapy remains the cornerstone of curative management of esophageal adenocarcinoma and is frequently used for squamous cell carcinoma. The optimal extent of lymphadenectomy and whether increasing lymph node yields confer a survival benefit remains unclear. Guidelines suggest resecting and examining a minimum of 15 lymph nodes at esophagectomy. This study assessed the impact of lymph node yield and lymph node ratio (LNR) on survival, identifying factors influencing nodal yield and radicality of resection. METHODS: All patients undergoing esophagectomy with curative intent at a single institution (stage 1-4 inclusive) from January 1, 2010, to December 31, 2020, were reviewed. Clinical and pathologic variables were interrogated. LNR was calculated by dividing positive lymph nodes by the total nodes resected. RESULTS: Esophagectomy was performed in 397 patients, with 288 undergoing minimally invasive esophagectomy (MIE). Margin status (hazard ratio [HR], 1.80; 95% CI, 1.15-2.83; P < .01), nodal yield <15 (HR, 1.98; 95% CI, 1.29-3.04; P = .002), and elevated LNR (HR, 8.16; 95% CI, 2.89-23.06; P < .001) predicted survival. MIE had higher nodal yields compared with open procedures (30.7 vs 25.3, P < .001). Patients undergoing neoadjuvant chemoradiotherapy had lower nodal yields compared with those with no neoadjuvant therapy and those with neoadjuvant chemotherapy (26.4 vs 30.6 vs 36.8, respectively; P < .001). Regression analysis determined a LNR of <0.05 was associated with a survival benefit. CONCLUSIONS: Textbook lymphadenectomy is associated with improved survival. Low lymph node yield and a high LNR are associated with reduced overall survival. A LNR of <0.05 is associated with significant survival benefit. A minimum nodal yield of 15 should remain the standard of care.

Wetting dynamics by mixtures of fast and slow self-propelled particles.

We study active surface wetting using a minimal model of bacteria that takes into account the intrinsic motility diversity of living matter. A mixture of "fast" and "slow" self-propelled Brownian particles is considered in the presence of a wall. The evolution of the wetting layer thickness shows an overshoot before stationarity and its composition evolves in two stages, equilibrating after a slow elimination of excess particles. Nonmonotonic evolutions are shown to arise from delayed avalanches towards the dilute phase combined with the emergence of a transient particle front.

Effects of Trimetazidine on Right Ventricular Function and Ventricular Remodeling in Patients with Pulmonary Artery Hypertension: A Randomised Controlled Trial.

BACKGROUND: Pulmonary artery hypertension (PAH) is a chronic and progressive disease. Although current therapy has improved the disease prognosis, PAH has a poor survival rate. The key feature leading to disease progression and death is right ventricular (RV) failure. METHODS AND RESULTS: We assessed the role of trimetazidine, a fatty acid beta-oxidation (FAO) inhibitor, in right ventricular function, remodeling, and functional class in PAH patients, with a placebo-controlled double-blind, case-crossover trial. Twenty-seven PAH subjects were enrolled, randomized, and assigned to trimetazidine or placebo for three months and then reallocated to the other study arm. The primary endpoint was RV morphology and function change after three months of treatment. Secondary endpoints were the change in exercise capacity assessed by a 6 min walk test after three months of treatment and the change in pro-BNP and Galectin-3 plasma levels after three months. Trimetazidine use was safe and well-tolerated. After three months of treatment, patients in the trimetazidine group showed a small but significant reduction of RV diastolic area, and a substantial increase in the 6 min walk distance (418 vs. 438 mt, p = 0.023), without significant changes in biomarkers. CONCLUSIONS: A short course of trimetazidine is safe and well-tolerated on PAH patients, and it is associated with significant increases in the 6MWT and minor but significant improvement in RV remodeling. The therapeutic potential of this drug should be evaluated in larger clinical trials.

Analysis of a C. elegans lifespan prediction method based on a bimodal neural network and uncertainty estimation.

In recent decades, assays with the nematode Caenorhabditis elegans (C. elegans) have enabled great advances to be made in research on aging. However, performing these assays manually is a laborious task. To solve this problem, numerous C. elegans assay automation techniques are being developed to increase throughput and accuracy. In this paper, a method for predicting the lifespan of C. elegans nematodes using a bimodal neural network is proposed and analyzed. Specifically, the model uses the sequence of images and the count of live C. elegans up to the current day to predict the lifespan curve termination. This network has been trained using a simulator to avoid the labeling costs of training such a model. In addition, a method for estimating the uncertainty of the model predictions has been proposed. Using this uncertainty, a criterion has been analyzed to decide at what point the assay could be halted and the user could rely on the model's predictions. The method has been analyzed and validated using real experiments. The results show that uncertainty is reduced from the mean lifespan and that most of the predictions obtained do not present statistically significant differences with respect to the curves obtained manually.