Miracle-dz/UniFAR-FTUs huggingface dataset:
A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images

Purpose The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data. Methods A weakly supervised learning-based method was proposed to detect fluorescein leakage without the need for manual annotation of leakage areas. To enhance the representation of the network, a residual attention module (RAM) was designed as the core component of the proposed generator. Moreover, class activation maps (CAMs) were used to define a novel anomaly mask loss to facilitate more accurate learning of leakage areas. In addition, sensitivity, specificity, accuracy, area under the curve (AUC), and dice coefficient (DC) were used to evaluate the performance of the methods. Results The proposed method reached a sensitivity of 0.73 ± 0.04, a specificity of 0.97 ± 0.03, an accuracy of 0.95 ± 0.05, an AUC of 0.86 ± 0.04, and a DC of 0.87 ± 0.01 on the HRA data set; a sensitivity of 0.91 ± 0.02, a specificity of 0.97 ± 0.02, an accuracy of 0.96 ± 0.03, an AUC of 0.94 ± 0.02, and a DC of 0.85 ± 0.03 on Zhao's publicly available data set; and a sensitivity of 0.71 ± 0.04, a specificity of 0.99 ± 0.06, an accuracy of 0.87 ± 0.06, an AUC of 0.85 ± 0.02, and a DC of 0.78 ± 0.04 on Rabbani's publicly available data set. Conclusions The experimental results showed that the proposed method achieves better performance on fluorescence leakage detection and can detect one image within 1 second and thus has great potential value for clinical diagnosis and treatment of retina-related diseases, such as diabetic retinopathy and malarial retinopathy. Translational Relevance The proposed weakly supervised learning-based method that automates the detection of fluorescence leakage can facilitate the assessment of retinal-related diseases.
Organization, feasibility and patient appreciation of a follow-up consultation in surgical critically ill patients with favorable baseline quality of life and prolonged ICU-stay: a pilot study

ABSTRACT Objectives Intensive care unit (ICU) survivors are often left with impairments in physical, mental and cognitive functioning (Post-Intensive Care Syndrome (PICS)). We evaluated the organization, the feasibility for caregivers and patients and the patients’ appreciation of a post-ICU consultation aiming to detect these PICS-symptoms. Methods A single-center prospective observational pilot study was conducted during an 18 month-period in the surgical ICU of a tertiary care hospital. Consecutive adult patients with an ICU-stay of ≥8 days and a favorable baseline quality of life (utility index ≥0.6 on EQ-5D-3 L) were eligible for inclusion. A post-ICU follow-up consultation consisting of a structured interview was scheduled 3 months after hospital discharge. Characteristics of the consultation (CG) and no consultation group (NCG) were compared. P-values <0.05 were considered significant. Results Of 133 eligible patients, 85 (64%) consented for the study and 42 (49%) attended the consultation. A total of 148 phone calls were made to schedule the consultations. Consultations took a median of 68 (61–74) minutes. Compared to CG-patients, NCG-patients were more often discharged to a care facility (P = 0.003) and had more problems with mobility (P = 0.014), self-care (P < 0.001) and usual activities (P = 0.005) after 3 months. At least one PICS-related problem was documented in all patients in the CG and NCG. Thirty-four CG-patients (81%) appreciated the initiative. Conclusion Organizing an ICU-follow-up consultation was difficult and feasibility was low, but most attending patients appreciated the initiative. Better developed structures for ICU-follow-up are needed in view of the high number of PICS-related problems documented.
Brachial plexus bridging with specific extracellular matrix-modified chitosan/silk scaffold: a new expand of tissue engineered nerve graft

Objective. Brachial plexus injuries (BPIs) result in serious dysfunction, especially brachial plexus defects which are currently treated using autologous nerve graft (autograft) transplantation. With the development of tissue engineering, tissue engineered nerve grafts (TENGs) have emerged as promising alternatives to autografts but have not yet been widely applied to the treatment of BPIs. Herein, we developed a TENG modified with extracellular matrix generated by skin-derived precursor Schwann cells (SKP-SCs) and expand its application in upper brachial plexus defects in rats. Approach. SKP-SCs were co-cultured with chitosan neural conduits or silk fibres and subjected to decellularization treatment. Ten bundles of silk fibres (five fibres per bundle) were placed into a conduit to obtain the TENG, which was used to bridge an 8 mm gap in the upper brachial plexus. The efficacy of this treatment was examined for TENG-, autograft- and scaffold-treated groups at several times after surgery using immunochemical staining, behavioural tests, electrophysiological measurements, and electron microscopy. Main results. Histological analysis conducted two weeks after surgery showed that compared to scaffold bridging, TENG treatment enhanced the growth of regenerating axons. Behavioural tests conducted four weeks after surgery showed that TENG-treated rats performed similarly to autograft-treated ones, with a significant improvement observed in both cases compared with the scaffold treatment group. Electrophysiological and retrograde tracing characterizations revealed that the target muscles were reinnervated in both TENG and autograft groups, while transmission electron microscopy and immunohistochemical staining showed the occurrence of the superior myelination of regenerated axons in these groups. Significance. Treatment with the developed TENG allows the effective bridging of proximal nerve defects in the upper extremities, and the obtained results provide a theoretical basis for clinical transformation to expand the application scope of TENGs.
The introduction of the six-again rule has increased acceleration intensity across all positions in the National Rugby League competition

ABSTRACT The impact of the six-again rule change on the movement of National Rugby League (NRL) athletes was examined. Player Global Navigation Satellite System (GNSS) data (10 Hz) was collected from 42 athletes who competed in 56 matches across the 2019 to 2021 NRL seasons. Maximal mean speed (m·min−1) and acceleration (m·s−2) were established across a 10 s to 10-min duration via raw GNSS files, with subsequent intercept (mean estimates) and slope values determined via power law analysis. The distributions of match distance (m) and impulse (kN·s−1) were established during ball-in-play time. To determine the significance between positions and seasons under different rules, linear mixed models were used. Effects were described using standardised effect sizes (ES) with 90% confidence limits (CL). Acceleration intercepts (power law-derived) across all positions were substantially greater (>0.6 SD) following the introduction of the six-again rule in the 2020 (mean ± SD; 1.02 ± 0.10 m·s−2) and 2021 seasons (1.05 ± 0.08 m·s−2) compared to the 2019 season (0.91 ± 0.07 m·s−2). Mean acceleration during ball-in-play time was greater in 2020 (ES; 90% CL = 0.75; ± 0.32) compared to 2019. The acceleration requirements of rugby league increased across all positional groups following the modification in NRL competition rules. Practitioners should tailor training programs for athletes to reflect the increased acceleration intensity found under the revised competition format.
Identification of B Cell and T Cell Epitopes Using Synthetic Peptide Combinatorial Libraries

This article presents a combinatorial library method that consists of the synthesis and screening of mixture‐based synthetic combinatorial libraries of peptide molecules to identify B and T cell epitopes. The protocols employ peptide libraries to identify peptides recognized by MAbs and T cells. The first protocol uses a positional scanning peptide library made up of hexapeptides to identify antigenic determinants recognized by MAbs. The 120 mixtures in the hexapeptide library are tested for their inhibitory activity in a competitive ELISA. The second protocol uses a decapeptide library to identify T cell peptide ligands. The 200 mixtures of the decapeptide library are tested for their ability to induce T cell activation. Support protocols cover optimization of the assay conditions for each MAb or T cell, to achieve the best level of sensitivity and reproducibility, and preparation of a hexapeptide library, along with deconvolution approaches. © 2022 Wiley Periodicals LLC.
Effect of iron deficiency without anaemia on days alive and out of hospital in patients undergoing valvular heart surgery

Comprehensive evidence regarding the treatment of non‐anaemic iron deficiency in patients undergoing valvular heart surgery is lacking. This study aimed to investigate the association between non‐anaemic iron deficiency and postoperative outcomes in these patients. We retrospectively analysed 321 patients of which 180 (56%) had iron deficiency (defined as serum ferritin < 100 ng.ml‐1 or < 300 ng.ml‐1 with transferrin saturation < 20%). While the iron‐deficient group had lower pre‐operative haemoglobin levels than the non‐iron deficient group (median (IQR [range]) 134 (127–141 [120–172]) g.l‐1, 143 (133–150 [120–179]) g.l‐1, p = 0.001), there was no between‐group difference in allogeneic red blood cell transfusion. Median (IQR [range]) days alive and out of hospital at postoperative day 90 was 1 day shorter in the iron‐deficient group (80 (77–82 [9–85]) days vs. 81 (79–83 [0–85]) days, p = 0.026). In multivariable analysis, only cardiopulmonary bypass duration (p = 0.032) and intra‐operative allogeneic red blood cell transfusion (p = 0.011) were significantly associated with reduced days alive and out of hospital at postoperative day 90. Iron deficiency did not exert any adverse influence on secondary outcomes except length of hospital stay. Our findings indicate that non‐anaemic iron deficiency alone is not associated with adverse effects in patients undergoing valvular heart surgery when it does not translate into an increased risk of allogeneic transfusion.
Clinical, serological and genetic predictors of response to immunotherapy in anti-IgLON5 disease.

Anti-IgLON5 disease is a newly defined clinical entity characterized by a progressive course with high disability and mortality rate. While precise pathogenetic mechanisms remain unclear, features characteristic of both autoimmune and neurodegenerative diseases were reported. Data on immunotherapy are limited and its efficacy remains controversial. In this study we retrospectively investigated an anti-IgLON5 disease cohort with special focus on clinical, serological, and genetic predictors of the immunotherapy response and long-term outcome. Patients were recruited from the GENERATE (GErman NEtwork for REsearch on AuToimmune Encephalitis) registry. Along with clinical parameters, anti-IgLON5 immunoglobulin (Ig)G in serum and CSF, anti-IgLON5 IgG1-4, IgA, and IgM in serum, neurofilament light chain and glial fibrillary acidic protein in serum as well as human leukocyte antigen-genotypes were determined. We identified 53 patients (symptom onset 63.8 ± 10.3 years, female:male 1:1.5). The most frequent initial clinical presentations were bulbar syndrome, hyperkinetic syndrome or isolated sleep disorder (at least one symptom present in 38% [20/53]). At the time of diagnosis, the majority of patients had a generalized multi-systemic phenotype; nevertheless 21% (11/53) still had an isolated brainstem syndrome and/or a characteristic sleep disorder only. About a third of patients (28% [15/53]) reported subacute disease onset and 51% (27/53) relapse-like exacerbations during the disease course. Inflammatory CSF changes were evident in 37% (19/51) and increased blood-CSF-barrier permeability in 46% (21/46). CSF cell count significantly decreased, while serum anti-IgLON5 IgG titer increased with disease duration. The presence of human leukocyte antigen-DRB1*10:01 (55% [24/44]) was associated with higher serum anti-IgLON5 IgG titers. Neurofilament light chain and glial fibrillary acidic protein in serum were substantially increased (71.1 ± 103.9 pg/ml and 126.7 ± 73.3 pg/ml, respectively). First-line immunotherapy of relapse-like acute-to-subacute exacerbation episodes resulted in improvement in 41% (11/27) of patients and early initiation within the first six weeks was a predictor for therapy response. Sixty-eight percent (36/53) of patients were treated with long-term immunotherapy and 75% (27/36) of these experienced no further disease progression (observation period of 20.2 ± 15.4 months). Long-term immunotherapy initiation during the first year after onset and low pre-treatment neurofilament light chain were significant predictors for a better outcome. In conclusion, subacute disease onset and early inflammatory CSF changes support the primary role of autoimmune mechanisms at least at initial stages of anti-IgLON5 disease. Early immunotherapy, prior to advanced neurodegeneration, is associated with a better long-term clinical outcome. Low serum neurofilament light chain at treatment initiation may serve as a potential biomarker of the immunotherapy response.
Synergistic hepatoprotective effects of ω-3 and ω-6 fatty acids from Indian flax and sesame seed oils against CCl4-induced oxidative stress-mediated liver damage in rats

Abstract Flaxseed (FS) and sesame seed (SS) are traditional and functional foods in traditional Indian medicine for treating various disorders. The present study investigated the hepatoprotective effects of bioactive-fatty acids (FAs) from FS and SS against carbon tetrachloride (CCl4)-induced hepatic damage in rats. Pre and post-treatments for 28 consecutive days significantly increased the activities of in vivo antioxidant enzymes such as catalase (CAT), superoxide dismutase (SOD), and peroxidase (POX), whereas, lipid peroxidation (LPO) activity was markedly decreased in a dose-dependent manner in liver and kidneys. A significant reduction was observed in the hematological parameters like aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and total bilirubin in the serum of post-treated animals compared to the negative control. The results were confirmed histopathologically. The results suggested that the ω-3 and ω-6 FAs from flaxseed oil (FSO) and sesame seed oil (SSO), respectively, showed potential synergistic hepatoprotective and antioxidant effects that were mediated mainly by ω-3 and ω-6 FAs present in the respective seed oils. Graphical Abstract Highlights ALA and LA ω-3 and ω-6 FAs rich oils from FSO an SSO respectively, mitigated the hepatotoxicity induced by CCl4. The in vitro and in vivo studies have revealed that the combination of FSO + SSO significantly increased the hepatic antioxidant enzymes like CAT, SOD, POX, and decreased MDA levels in the liver and kidneys of CCl4 induced rats. Co-treatment of FSO + SSO shown more synergistic antioxidant potential compared to their individual treatments.
Next practice, not best practice: using virtual worlds in new product development in the automotive industry

Involving customers in the development of new products development is crucial to market success. However, many companies, especially within the technology-oriented automotive industry, fail to do so. In this paper, we examine how web-based virtual worlds offer fresh perspectives for integrating customers into product development. We argue that companies must look to 'next practice, not best practice' if they hope to outperform the market. We also investigate the steps involved in such projects, and the factors that make them possible. We conducted interviews with managers from three leading car manufacturers - Audi AG, BMW Group and Daimler AG - and experts from industry-related research institutes and service companies. Our findings demonstrate that virtual worlds support more efficient and effective new product development projects, especially in their early and late phases.
Biocompatible phospholipid‐based nanovesicular drug delivery system of ketoprofen: Systematic development, optimization, and preclinical evaluation

The present work involved development of phospholipid‐based permeation enhancing nanovesicles (PENVs) for topical delivery of ketoprofen. Screening of phospholipids and process parameters was performed. Central composite design was used for optimization of factors, that is, amount (%, w/w) of phospholipid and ethanol at three levels. The optimized nanovesicles (NVs) were loaded with different terpenes and then incorporated into a gel base. Optimized NVs exhibited 69% entrapment efficiency, 51% transmittance, 328 nm mean vesicle size, and polydispersity index of 0.25. In vitro release kinetics evaluation indicated best fitting as per Korsemeyer–Peppa's model and drug release via Fickian‐diffusion mechanism. The optimized NVs loaded with mint terpene showed minimal degree of deformability and maximal elasticity as compared with the conventional NVs and liposomes. Rheology and texture analysis indicated pseudoplastic flow and smooth texture of the vesicle gel formulation. Ex vivo permeation studies across Wistar rat skin indicated low penetration (0.43‐fold decrease) and high skin retention (4.26‐fold increase) of ketoprofen from the optimized PENVs gel vis‐à‐vis the conventional gel. Skin irritancy study indicated lower scores for PENVs gel construing its biocompatible nature. Stability studies confirmed cold storage is best suitable for vesicle gel, and optimized PENVs were found to be suitable for topical delivery of ketoprofen.
Enhance Topics Analysis based on Keywords Properties

Topic Modelling is one of the most prevalent text analysis technique used to explore and retrieve collection of documents. The evaluation of the topic model algorithms is still a very challenging tasks due to the absence of gold-standard list of topics to compare against for every corpus. In this work, we present a specificity score based on keywords properties that is able to select the most informative topics. This approach helps the user to focus on the most informative topics. In the experiments, we show that we are able to compress the state-of-the-art topic modelling results of different factors with an information loss that is much lower than the solution based on the recent coherence score presented in literature.
The Cross-Evaluation of Machine Learning-Based Network Intrusion Detection Systems

Enhancing Network Intrusion Detection Systems (NIDS) with supervised Machine Learning (ML) is tough. ML-NIDS must be trained and evaluated, operations requiring data where benign and malicious samples are clearly labeled. Such labels demand costly expert knowledge, resulting in a lack of real deployments, as well as on papers always relying on the same outdated data. The situation improved recently, as some efforts disclosed their labeled datasets. However, most past works used such datasets just as a ‘yet another’ testbed, overlooking the added potential provided by such availability. In contrast, we promote using such existing labeled data to cross-evaluate ML-NIDS. Such approach received only limited attention and, due to its complexity, requires a dedicated treatment. We hence propose the first cross-evaluation model. Our model highlights the broader range of realistic use-cases that can be assessed via cross-evaluations, allowing the discovery of still unknown qualities of state-of-the-art ML-NIDS. For instance, their detection surface can be extended—at no additional labeling cost. However, conducting such cross-evaluations is challenging. Hence, we propose the first framework, XeNIDS, for reliable cross-evaluations based on Network Flows. By using XeNIDS on six well-known datasets, we demonstrate the concealed potential, but also the risks, of cross-evaluations of ML-NIDS.
BinMLM: Binary Authorship Verification with Flow-aware Mixture-of-Shared Language Model

Binary authorship analysis is a significant problem in many software engineering applications. In this paper, we formulate a binary authorship verification task to accurately reflect the real-world working process of software forensic experts. It aims to determine whether an anonymous binary is developed by a specific programmer with a small set of support samples, and the actual developer may not belong to the known candidate set but from the wild. We propose an effective binary authorship verification framework, BinMLM. BinMLM trains the RNN language model on consecutive opcode traces extracted from the control-flow-graph (CFG) to characterize the candidate developers' programming styles. We build a mixture-of-shared architecture with multiple shared encoders and author-specific gate layers, which can learn the developers' combination preferences of universal programming patterns and alleviate the problem of low training resources. Through an optimization pipeline of external pre-training, joint training, and fine-tuning, our framework can eliminate additional noise and accurately distill developers' unique styles. Extensive experiments show that BinMLM achieves promising results on Google Code Jam (GCJ) and Codeforces datasets with different numbers of programmers and supporting samples. It significantly outperforms the baselines built on the state-of-the-art feature set (4.73% to 19.46% improvement) and remains robust in multi-author collaboration scenarios. Furthermore, Bin-MLM can perform organization-level verification on a real-world APT malware dataset, which can provide valuable auxiliary information for exploring the group behind the APT attack.
Assouad type dimensions of parabolic Julia sets

We prove that the Assouad dimension of a parabolic Julia set is max{1, h} where h is the Hausdorff dimension of the Julia set. Since h may be strictly less than 1, this provides examples where the Assouad and Hausdorff dimension are distinct. The box and packing dimensions of the Julia set are known to coincide with h and, moreover, h can be characterised by a topological pressure function. This distinctive behaviour of the Assouad dimension invites further analysis of the Assouad type dimensions, including the Assouad and lower spectra. We compute all of the Assouad type dimensions for parabolic Julia sets and the associated h-conformal measure. Further, we show that if a Julia set has a Cremer point, then the Assouad dimension is 2. Mathematics Subject Classification 2020: 28A80, 37F10.
Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for selected applications in stock and cryptocurrency trading

We investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency trading. More specifically, we build on the generalized setting à la Fontaine and Friedman [6] (where the reward weighting mechanism is not specified a priori , but embedded in the learning process) by complementing it with computational speed-ups, and adding the cumulative reward’s discount factor to the learning process. Firstly, we verify that the resulting Multi-Objective algorithm generalizes well, and we provide preliminary statistical evidence showing that its prediction is more stable than the corresponding Single-Objective strategy’s. Secondly, we show that the Multi-Objective algorithm has a clear edge over the corresponding Single-Objective strategy when the reward mechanism is sparse (i.e., when non-null feedback is infrequent over time). Finally, we discuss the generalization properties of the discount factor. The entirety of our code is provided in open source format.