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Accurately detecting small vehicle targets consisting of only a few pixels in remote sensing images (RSIs) remains highly challenging. General vehicle detection networks primarily design complex deep neural networks to learn the features of small targets in remote sensing images. However, while enhancing accuracy, this approach results in an increase in the model's size. To tackle these issues, this paper presents the Restructuring Architecture – You Only Look Once (RA-YOLO) network based on network reconfiguration. By integrating the CA mechanism and adding the small target detection layer (STDL), enhances the extraction of shallow features of small targets. In addition, the feature fusion part in the middle of the original PAFPN layer is rewritten as a Lite-Asymptotic Feature Pyramid Network (L-AFPN) module, which not only enhances the extraction of shallow features of small targets, but also improves the multi-scale feature fusion capability. The results indicate that the RA-YOLO enhances the mean Average Precision (mAP) by 10.1%, 2% and 7.5%, respectively, on the remote sensing datasets DIOR, NWPU VHR-10 and DOTAv1.5-small vehicle. Moreover, the model parameters are reduced from 7.02 M to 5.22 M. Our proposed model shows a good trade-off between accuracy and model size compared with the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Rechargeable zinc–air batteries (ZABs) and electrocatalytic water splitting are two important energy conversion technologies. Although ZABs acting as a power supply to drive water splitting devices can realize self-powered hydrolysis and are of great significance, catalyst design applied for these applications remains a challenge. Herein, different from other reported NiFe2O4-based materials, we used iron foam (IF) as the conductive substrate, as well as the iron source to synthesize self-supported NiFe2O4. The achieved NiFe2O4/IF exhibits outstanding oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) properties compared to those of the bare IF substrate, or even the commercial RuO2/IF and Pt/C/IF. In addition, the assembled liquid/flexible ZAB using NiFe2O4/IF as the air-cathode illustrates excellent electrochemical stability. After this insight and a computed theoretical data analysis, we confirm that the Ni sites acted as the most active catalytic sites during the OER process, while the Fe sites are the most active sites during the HER course. Thus, the gained NiFe2O4/IF is a superior candidate material applied for a self-powered water splitting device. We believe that this work will be an application extension to NiFe2O4-based catalysts and experimental/theoretical basis for other functional materials. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) heterogeneity impacts prognosis, and imaging is a potential indicator. Purpose: To characterize HCC image subtypes in MRI and correlate subtypes with recurrence. Study Type: Retrospective. Population: A total of 440 patients (training cohort = 213, internal test cohort = 140, external test cohort = 87) from three centers. Field Strength/Sequence: 1.5‐T/3.0‐T, fast/turbo spin‐echo T2‐weighted, spin‐echo echo‐planar diffusion‐weighted, contrast‐enhanced three‐dimensional gradient‐recalled‐echo T1‐weighted with extracellular agents (Gd‐DTPA, Gd‐DTPA‐BMA, and Gd‐BOPTA). Assessment: Three‐dimensional volume‐of‐interest of HCC was contoured on portal venous phase, then coregistered with precontrast and late arterial phases. Subtypes were identified using non‐negative matrix factorization by analyzing radiomics features from volume‐of‐interests, and correlated with recurrence. Clinical (demographic and laboratory data), pathological, and radiologic features were compared across subtypes. Among clinical, radiologic features and subtypes, variables with variance inflation factor above 10 were excluded. Variables (P < 0.10) in univariate Cox regression were included in stepwise multivariate analysis. Three recurrence estimation models were built: clinical‐radiologic model, subtype model, hybrid model integrating clinical‐radiologic characteristics, and subtypes. Statistical Tests: Mann–Whitney U test, Kruskal–Wallis H test, chi‐square test, Fisher's exact test, Kaplan–Meier curves, log‐rank test, concordance index (C‐index). Significance level: P < 0.05. Results: Two subtypes were identified across three cohorts (subtype 1:subtype 2 of 86:127, 60:80, and 36:51, respectively). Subtype 1 showed higher microvascular invasion (MVI)‐positive rates (53%–57% vs. 26%–31%), and worse recurrence‐free survival. Hazard ratio (HR) for the subtype is 6.10 in subtype model. Clinical‐radiologic model included alpha‐fetoprotein (HR: 3.01), macrovascular invasion (HR: 2.32), nonsmooth tumor margin (HR: 1.81), rim enhancement (HR: 3.13), and intratumoral artery (HR: 2.21). Hybrid model included alpha‐fetoprotein (HR: 2.70), nonsmooth tumor margin (HR: 1.51), rim enhancement (HR: 3.25), and subtypes (HR: 5.34). Subtype model was comparable to clinical‐radiologic model (C‐index: 0.71–0.73 vs. 0.71–0.73), but hybrid model outperformed both (C‐index: 0.77–0.79). Conclusion: MRI radiomics‐based clustering identified two HCC subtypes with distinct MVI status and recurrence‐free survival. Hybrid model showed superior capability to estimate recurrence. Level of Evidence: 3 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Primary aortic intimal sarcoma is a very rare disease and most patients present with features similar to those of atherosclerotic plaque and thrombus; however, primary aortic intimal sarcoma presents with features similar to those of intramural hematoma (IMH) on CT imaging and clinical presentation had never been previously reported. Here we report a case involving a 49-year-old woman with primary aortic intimal sarcoma masquerading as IMH on radiological images and clinical presentation. We also discuss some of the diagnostic pitfalls and hope that these diagnostic pitfalls will be very useful for clinicians.

Recombinant adeno‐associated virus (AAV) has become a popular platform for many gene therapy applications. The strength of AAV‐based products is a critical quality attribute that affects the efficacy of the drug and is measured as the concentration of vector genomes, or physical titer. Because the dosing of patients is based on the titer measurement, it is critical for manufacturers to ensure that the measured titer of the drug product is close to the actual concentration of the batch. Historically, dosing calculations have been performed using the measured titer, which is reported on the drug product label. However, due to recent regulatory guidance, sponsors are now expected to label the drug product with nominal or "target" titer. This new expectation for gene therapy products can pose a challenge in the presence of process and analytical variability. In particular, the manufacturer must decide if a dilution of the drug substance is warranted at the drug product stage to bring the strength in line with the nominal value. In this paper, we present two straightforward statistical methods to aid the manufacturer in the dilution decision. These approaches use the understanding of process and analytical variability to compute probabilities of achieving the desired drug product titer. We also provide an approach for determining an optimal assay replication strategy for achieving the desired probability of meeting drug product release specifications. [ABSTRACT FROM AUTHOR]

Bioassays are regulated, analytical methods used to ensure proper activity (potency) of biological products at release and during long-term storage. Potency is commonly reported on a relative basis by comparing and calibrating a concentration–response curve from the test material to that of a reference standard material. The relative potency approach depends on an assumption that the two concentration–response curves exhibit similar (equivalent) shapes, except for a potency shift. In certain circumstances, however, biological factors preclude the similarity assumption, and the traditional approach becomes unworkable. The antibody-mediated cytotoxicity assay is one example where the similarity assumption does not always hold. Other examples also arise in the fields of toxicology and pharmacology. In this work, we present a non-constant mean relative potency approach which averages the relative potency across a common range of the concentration-response curves. The proposed method captures the changing nature of the relative potency into a summary statistic that can be reported for batch calibration and quality control purposes. We provide inferential methods for this statistic and summarize the results of a simulation comparing these methods across a number of non-constant relative potency scenarios and assay conditions. [ABSTRACT FROM AUTHOR]

Hit screening, which involves the identification of compounds or targets capable of modulating disease-relevant processes, is an important step in drug discovery. Some assays, such as image-based high-content screenings, produce complex multivariate readouts. To fully exploit the richness of such data, advanced analytical methods that go beyond the conventional univariate approaches should be employed. In this work, we tackle the problem of hit identification in multivariate assays. As with univariate assays, a hit from a multivariate assay can be defined as a candidate that yields an assay value sufficiently far away in distance from the mean or central value of inactives. Viewed another way, a hit is an outlier from the distribution of inactives. A method was developed for identifying multivariate hit in high-dimensional data sets based on principal components and robust Mahalanobis distance (the multivariate analogue to the Z- or T-statistic). The proposed method, termed mROUT (multivariate robust outlier detection), demonstrates superior performance over other techniques in the literature in terms of maintaining Type I error, false discovery rate and true discovery rate in simulation studies. The performance of mROUT is also illustrated on a CRISPR knockout data set from in-house phenotypic screening programme. [ABSTRACT FROM AUTHOR]

Background: Individuals suffering from chronic kidney disease (CKD) frequently face a heightened likelihood of experiencing cardiovascular complications, including heart failure and cardiac mortality. Cardiovascular magnetic resonance feature tracking (CMR-FT) is utilized to assess the micro-contraction function of the myocardium. The objective of this research is to explore the relationship between the left ventricular anatomy, myocardial strain, and the clinical outcomes in patients with CKD. Methods: A total of 77 patients with late-stage CKD were enrolled in this retrospective study. They underwent cardiac magnetic resonance imaging and were followed up, with no history of significant cardiac diseases. The patients were divided into two groups: those with a left ventricular global longitudinal strain (LVGLS) ≥ −15.2% (n = 49) and those with LVGLS < −15.2% (n = 28). The clinical endpoints were defined as hospitalization for heart failure or all-cause mortality. Results: Over an average observation period of 22 ± 9 months, 11 (14%) patients passed away and 30 (39%) were admitted to the hospital for heart failure, with eight encountering both incidents. Those with LVGLS ≥ −15.2% had markedly lower rates of event-free survival concerning heart failure admissions and overall mortality than their counterparts (log-rank P = 0.014). Cox multivariable analysis indicated that reduced LVGLS consistently predicted a higher likelihood of combined outcomes of heart failure admissions and total mortality (HR: 3.40, 95% CI [1.35–8.56], P = 0.009), even when factoring in age, diabetes, left atrial diameter, and left ventricular mass index (LVMI). However, the LVMI showed no significant correlation with the risk of heart failure admissions or overall mortality. Conclusion: Compared to patients with LVGLS < −15.2%, CKD patients with LVGLS ≥ −15.2% have an increased risk of heart failure hospitalization and all-cause mortality. The prognostic role of LVMI in assessing CKD patients among the Asian population requires further investigation. [ABSTRACT FROM AUTHOR]

ABSTRACT In preclinical drug discovery, at the step of lead optimization of a compound, in vivo experimentation can differentiate several compounds in terms of efficacy and potency in a biological system of whole living organisms. For the lead optimization study, it may be desirable to implement a dose–response design so that compound comparisons can be made from nonlinear curves fitted to the data. A dose–response design requires more thought relative to a simpler study design, needing parameters for the number of doses, the dose values, and the sample size per dose. This tutorial illustrates how to calculate statistical power, choose doses, and determine sample size per dose for a comparison of two or more dose–response curves for a future in vivo study. [ABSTRACT FROM AUTHOR]

To address the IoT security problem, in this paper we propose and evaluate the DDoS attack mitigation method based on blockchain, and construct a DDoS abnormal information detection and sharing model. The obtained experimental results show that when the number of decision trees increases, the training time of the DDoS attack detection model based on the RF model grows with a minimum trend of 14 s. The testing time is finally maintained at 1 s, and the recognition accuracy of DDoS attacks keeps improving, ultimately reaching over 99.8%. If the amount of DDoS abnormal traffic information exceeds 100 pieces and 2000 pieces, it only takes 0.1 and 5 s to sign the DDoS abnormal traffic information using ECDSA algorithm digitally. The signature verification only takes 0.1 and 9 s, respectively. And compared to conventional network physical system IoT security architecture, a network physical system IoT security architecture that integrates AI empowerment, blockchain, and SDN integration has a higher joint defense success rate. It can be explained that this scheme will be conducive to promoting joint defense against DDoS attacks and ensuring the security of the Internet of Things. [ABSTRACT FROM AUTHOR]

The aim of the article is to analyze the models of the joint consumption economy from the point of view of finding the applicability of such models in the agro-industrial sector of the economy as tools to increase its efficiency and competitiveness. The authors have described the models of joint consumption economy based on the analysis of the differences between the definitions of “joint consumption economy" and "sharing economy» in the works of researchers. Authors allocate the assets-based, service and financial models of joint consumption economy. The study shows that in the Russian agro-industrial sector there are certain reserves for increasing its efficiency and competitiveness while creating the favorable conditions by State for the continuation of the implementation of the models of joint consumption economy in the near future. These conditions should become the object of further research, as well as the choice of the most appropriate business models for the domestic agro-industrial sector.