Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing of Energy Absorption Capability of Sandwich Structures Based on Metal Foams for Design of Protective Helmets

Purpose of this study is investigation of energy absorption capability of the sandwich structures composed of combination of polystyrene and metal foam element and their suitability as new structure for design of protective helmets. Two types of the metal foams were experimentally tested and evaluated: Alporas (Shinko Wire Ltd., Japan) and Aluhab (Aluinvent Plc., Hungary). Samples of the sandwich structure are composed of two layers: bottom expanded polystyrene (EPS 200S) layer and upper metal foam layer which are glued together. Prepared samples are tested using a drop tower experiment to measure sample response (acceleration, reaction force) at different strain rates and energies. From acceleration/time history the Head Injury Criterion (HIC) is calculated as significant parameters in terms of protective helmets. Moreover, measured and derived characteristics are compared with pure EPS samples to obtain comparison of deformation behaviour between conventional structure for protective helmets and designed sandwich structures.
Application of Evidence-Based Methods to Construct Mechanism-Driven Chemical Assessment Frameworks

The workshop titled “Application of evidence-based methods to construct mechanism-driven chemical assessment frameworks” was co-organized by the Evidence-based Toxicology Collaboration and the European Food Safety Authority (EFSA) and hosted by EFSA at its headquarters in Parma, Italy on October 2 and 3, 2019. The goal was to explore integration of systematic review with mechanistic evidence evaluation. Participants were invited to work on concrete products to advance the exploration of how evidence-based approaches can support the development and application of adverse outcome pathways (AOP) in chemical risk assessment. The workshop discussions were centered around three related themes: 1) assessing certainty in AOPs, 2) literature-based AOP development, and 3) integrating certainty in AOPs and non-animal evidence into decision frameworks. Several challenges, mostly related to methodology, were identified and largely determined the workshop recommendations. The workshop recommendations included the comparison and potential alignment of processes used to develop AOP and systematic review methodology, including the translation of vocabulary of evidence-based methods to AOP and vice versa, the development and improvement of evidence mapping and text mining methods and tools, as well as a call for a fundamental change in chemical risk and uncertainty assessment methodology if to be conducted based on AOPs and new approach methodologies (NAM). The usefulness of evidence-based approaches for mechanism-based chemical risk assessments was stressed, particularly the potential contribution of the rigor and transparency inherent to such approaches in building stakeholders’ trust for implementation of NAM evidence and AOPs into chemical risk assessment.
RagRug: A Toolkit for Situated Analytics

We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualization with data streams from the Internet of Things using distributed dataflow. To this aim, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities.
In Vivo Modeling of Tumor Heterogeneity for Immuno‐Oncology Studies: Failures, Improvements, and Hopes

Murine tumor modeling is fundamental for the preclinical development of anti‐cancer therapies. Use of immunocompetent mouse models is becoming increasingly relevant as we gain more knowledge of how cancer cells interact with the immune system in the tumor microenvironment and how we can harness the immune system to fight tumors. However, there are few intrinsically immunogenic preclinical tumor models, and the vast majority either do not respond to therapy or do not faithfully predict the responses of the therapy when applied in the clinic. Here, we discuss the limitations of commonly used murine tumor models in immuno‐oncology and strategies to improve their immunogenicity and mutational burden to more accurately reflect the heterogeneity of patient tumors. © 2022 Wiley Periodicals LLC.
Global kelp forest restoration: past lessons, present status, and future directions

Kelp forest ecosystems and their associated ecosystem services are declining around the world. In response, marine managers are working to restore and counteract these declines. Kelp restoration first started in the 1700s in Japan and since then has spread across the globe. Restoration efforts, however, have been largely disconnected, with varying methodologies trialled by different actors in different countries. Moreover, a small subset of these efforts are ‘afforestation’, which focuses on creating new kelp habitat, as opposed to restoring kelp where it previously existed. To distil lessons learned over the last 300 years of kelp restoration, we review the history of kelp restoration (including afforestation) around the world and synthesise the results of 259 documented restoration attempts spanning from 1957 to 2020, across 16 countries, five languages, and multiple user groups. Our results show that kelp restoration projects have increased in frequency, have employed 10 different methodologies and targeted 17 different kelp genera. Of these projects, the majority have been led by academics (62%), have been conducted at sizes of less than 1 ha (80%) and took place over time spans of less than 2 years. We show that projects are most successful when they are located near existing kelp forests. Further, disturbance events such as sea‐urchin grazing are identified as regular causes of project failure. Costs for restoration are historically high, averaging hundreds of thousands of dollars per hectare, therefore we explore avenues to reduce these costs and suggest financial and legal pathways for scaling up future restoration efforts. One key suggestion is the creation of a living database which serves as a platform for recording restoration projects, showcasing and/or re‐analysing existing data, and providing updated information. Our work establishes the groundwork to provide adaptive and relevant recommendations on best practices for kelp restoration projects today and into the future.
Long-Term Cannabis Use and Cognitive Reserves and Hippocampal Volume in Midlife.

OBJECTIVE Cannabis use is increasing among midlife and older adults. This study tested the hypotheses that long-term cannabis use is associated with cognitive deficits and smaller hippocampal volume in midlife, which is important because midlife cognitive deficits and smaller hippocampal volume are risk factors for dementia. METHODS Participants are members of a representative cohort of 1,037 individuals born in Dunedin, New Zealand, in 1972-1973 and followed to age 45, with 94% retention. Cannabis use and dependence were assessed at ages 18, 21, 26, 32, 38, and 45. IQ was assessed at ages 7, 9, 11, and 45. Specific neuropsychological functions and hippocampal volume were assessed at age 45. RESULTS Long-term cannabis users showed IQ decline from childhood to midlife (mean=-5.5 IQ points), poorer learning and processing speed relative to their childhood IQ, and informant-reported memory and attention problems. These deficits were specific to long-term cannabis users because they were either not present or were smaller among long-term tobacco users, long-term alcohol users, midlife recreational cannabis users, and cannabis quitters. Cognitive deficits among long-term cannabis users could not be explained by persistent tobacco, alcohol, or other illicit drug use, childhood socioeconomic status, low childhood self-control, or family history of substance dependence. Long-term cannabis users showed smaller hippocampal volume, but smaller hippocampal volume did not statistically mediate cannabis-related cognitive deficits. CONCLUSIONS Long-term cannabis users showed cognitive deficits and smaller hippocampal volume in midlife. Research is needed to ascertain whether long-term cannabis users show elevated rates of dementia in later life.
The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers

Significance Stream/river carbon dioxide (CO2) emission has significant spatial and seasonal variations critical for understanding its macroecosystem controls and plumbing of the terrestrial carbon budget. We relied on direct fluvial CO2 partial pressure measurements and seasonally varying gas transfer velocity and river network surface area estimates to resolve reach-level seasonal variations of the flux at the global scale. The percentage of terrestrial primary production (GPP) shunted into rivers that ultimately contributes to CO2 evasion increases with discharge across regions, due to a stronger response in fluvial CO2 evasion to discharge than GPP. This highlights the importance of hydrology, in particular water throughput, in terrestrial–fluvial carbon transfers and the need to account for this effect in plumbing the terrestrial carbon budget.
Degenerated Hole Doping and Ultra‐Low Lattice Thermal Conductivity in Polycrystalline SnSe by Nonequilibrium Isovalent Te Substitution

Tin mono‐selenide (SnSe) exhibits the world record of thermoelectric conversion efficiency ZT in the single crystal form, but the performance of polycrystalline SnSe is restricted by low electronic conductivity (σ) and high thermal conductivity (κ), compared to those of the single crystal. Here an effective strategy to achieve high σ and low κ simultaneously is reported on p‐type polycrystalline SnSe with isovalent Te ion substitution. The nonequilibrium Sn(Se1−xTex) solid solution bulks with x up to 0.4 are synthesized by the two‐step process composed of high‐temperature solid‐state reaction and rapid thermal quenching. The Te ion substitution in SnSe realizes high σ due to the 103‐times increase in hole carrier concentration and effectively reduced lattice κ less than one‐third at room temperature. The large‐size Te ion in Sn(Se1−xTex) forms weak SnTe bonds, leading to the high‐density formation of hole‐donating Sn vacancies and the reduced phonon frequency and enhanced phonon scattering. This result—doping of large‐size ions beyond the equilibrium limit—proposes a new idea for carrier doping and controlling thermal properties to enhance the ZT of polycrystalline SnSe.
Nup98 Is Subverted from Annulate Lamellae by Hepatitis C Virus Core Protein to Foster Viral Assembly

Nup98 is an essential component of the nuclear pore that also participates in annulate lamella pore structures localized in the cytosol. Nup98 is involved in HCV assembly, though its role remains elusive. ABSTRACT Nup98, an essential component of the nuclear pore that also participates in annulate lamella pore structures localized in the cytosol, is involved in hepatitis C virus (HCV) assembly. Here, we combined confocal microscopy and biochemical assays to study the interplay between Nup98, core (i.e., the HCV capsid protein), and viral genomes. Our results show that in HCV-infected cells, core protein is necessary and sufficient to induce relocalization of Nup98 from annulate lamellae to lipid droplet-apposed areas in which core/NS5A and HCV genomic RNA [(+)RNA] are clustered to promote viral assembly. Furthermore, we found that Nup98 interacts with HCV RNA and that upon Nup98 downregulation, the viral (+)RNA genome was specifically excluded from areas that contain active translating ribosomes and the core and NS5A proteins. Altogether, these results indicate that Nup98 is recruited by HCV core from annulate lamellae to viral assembly sites to locally increase the concentration of (+)RNA genome, which may favor its encapsidation into nascent virions. IMPORTANCE Nup98 is an essential component of the nuclear pore that also participates in annulate lamella pore structures localized in the cytosol. Nup98 is involved in HCV assembly, though its role remains elusive. Here, we show that Nup98 is retrieved from annulate lamellae during HCV infection. We demonstrate that Nup98 interacts with viral genome within infected cells and that these interactions are essential to maintain viral (+)RNAs in subcellular regions promoting viral replication, assembly, and translation. Importantly, we also show that HCV core nucleocapsid protein is the viral component responsible for the retrieval of Nup98 protein from annulate lamellae, hence allowing an enrichment of Nup98 complexed with viral (+)RNAs in core protein-containing areas. Altogether, our results indicate that Nup98 is recruited from annulate lamellae to viral assembly sites by HCV core protein to promote viral assembly, which highlights a novel virus-induced subversion mechanism of nuclear pore complex components.
Accelerated Distributed Approximate Newton Method

Distributed second-order optimization, as an effective strategy for training large-scale machine learning systems, has been widely investigated due to its low communication complexity. However, the existing distributed second-order optimization algorithms, including distributed approximate Newton (<monospace>DANE</monospace>), accelerated inexact DANE (<monospace>AIDE</monospace>), and statistically preconditioned accelerated gradient (<monospace>SPAG</monospace>), are all required to precisely solve an expensive subproblem up to the target precision. Therefore, this causes these algorithms to suffer from high computation costs and this hinders their development. In this article, we design a novel distributed second-order algorithm called the accelerated distributed approximate Newton (<monospace>ADAN</monospace>) method to overcome the high computation costs of the existing ones. Compared with <monospace>DANE</monospace>, <monospace>AIDE</monospace>, and <monospace>SPAG</monospace>, which are constructed based on the relative smooth theory, <monospace>ADAN’s</monospace> theoretical foundation is built upon the inexact Newton theory. The different theoretical foundations lead to handle the expensive subproblem efficiently, and steps required to solve the subproblem are independent of the target precision. At the same time, <monospace>ADAN</monospace> resorts to the acceleration and can effectively exploit the objective function’s curvature information, making <monospace>ADAN</monospace> to achieve a low communication complexity. Thus, <monospace>ADAN</monospace> can achieve both the communication and computation efficiencies, while <monospace>DANE</monospace>, <monospace>AIDE</monospace>, and <monospace>SPAG</monospace> can achieve only the communication efficiency. Our empirical study also validates the advantages of <monospace>ADAN</monospace> over extant distributed second-order algorithms.
A pharmacoeconomic study comparing the use of mycophenolate mofetil or cyclophosphamide as induction therapy in lupus nephritis patients in Egypt

Introduction/objectives Lupus nephritis (LN) is a major and serious complication of systemic lupus erythematosus (SLE) and is associated with morbidity and mortality. The difference in drug cost between mycophenolate mofetil (MMF) and cyclophosphamide (CYC) inducing regimens becomes a relevant and realistic issue, especially in developing countries. Thus, this study aims to estimate and compare the costs and outcomes of CYC and MMF for better allocation of resources to reduce the burden on the Egyptian healthcare system. Methods A prospective, parallel observational study was conducted at Kasr Al-Aini Hospital between 2018 and 2020. One hundred and twenty-two LN patients were followed up monthly during the study period. Remission and maintenance of renal remission were assessed at 6 and 12 months from the start of therapy. Total direct medical costs associated with both regimens were examined. We applied the cost-minimization analysis method from governmental perspective. Besides, a prospective evaluation of reported changes in health-related quality of life using SF-36 was included in our study. Results There was no statistically significant difference in treatment response at 6 and 12 months (RR 0.6 [0.26;1.43] and 0.8 [0.27;2.33]), respectively, as well as the incidence of infection episodes between MMF and CYC group (71.4% versus 70.45%, p > 0.05). The average direct medical expenditures per patient in the MMF group were approximately one and half times more than the CYC group (2339.69 $ versus 1329.03 $, p <0.001). Conclusion The CYC arm was associated with lower costs than the MMF arm, with equally effective outcomes indicating that CYC is an attractive treatment option.
Comparative Transcriptome Analysis of Superficial and Deep Partial-Thickness Burn Wounds in Yorkshire vs Red Duroc Pigs.

Hypertrophic scars are a common negative outcome of deep partial-thickness burn wounds resulting in increased dermal thickness, wound area contracture, and inflammation of the affected area. The red Duroc and Yorkshire porcine breeds are common large animal models for studying dermal wounds due to their structural similarities to human skin; however, the porcine transcriptomic profiles of dermal burn wounds and healing process are not well known. In response, a longitudinal transcriptomic comparative study was conducted comparing red Duroc and Yorkshire superficial and DPT burn wounds to their respective control uninjured tissue. Using next-generation RNA-sequencing, total RNAs were isolated from burn wound tissue harvested at 0, 3, 7, 15, 30, and 60 days post-burn and mRNA-seq and gene expression read counts were generated. Significant differentially expressed genes relative to uninjured tissue were defined and active biological processes were determined using gene set enrichment analyses. Additionally, collagen deposition, α-SMA protein concentration, epidermal and dermal thickness measurements, and wound area changes in response to burn injury were characterized. Overall, the red Duroc pigs, in response to both burn wound types, elicited a more robust and prolonged inflammatory immune response, fibroblast migration and proliferation as well as heightened levels of extracellular matrix modulation relative to respective burn types in the Yorkshire pigs. Collectively, the red Duroc deep partial-thickness burn wounds produce a greater degree of hypertrohic scar like response compared to Yorkshire DPT burn wounds. These findings will facilitate future porcine burn studies down-selecting treatment targets and determining effects of novel therapeutic strategies.
Accelerated Partially Shared Dictionary Learning With Differentiable Scale-Invariant Sparsity for Multi-View Clustering

Multiview dictionary learning (DL) is attracting attention in multiview clustering due to the efficient feature learning ability. However, most existing multiview DL algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multiview clustering because of gaps between views. This article proposes an efficient multiview DL algorithm for multiview clustering, which uses the partially shared DL model with a flexible ratio of shared sparse coefficients to excavate both consistency and complementarity in the multiview data. In particular, a differentiable scale-invariant function is used as the sparsity regularizer, which considers the absolute sparsity of coefficients as the $\ell _{0}$ norm regularizer but is continuous and differentiable almost everywhere. The corresponding optimization problem is solved by the proximal splitting method with extrapolation technology; moreover, the proximal operator of the differentiable scale-invariant regularizer can be derived. The synthetic experiment results demonstrate that the proposed algorithm can recover the synthetic dictionary well with reasonable convergence time costs. Multiview clustering experiments include six real-world multiview datasets, and the performances show that the proposed algorithm is not sensitive to the regularizer parameter as the other algorithms. Furthermore, an appropriate coefficient sharing ratio can help to exploit consistent information while keeping complementary information from multiview data and thus enhance performances in multiview clustering. In addition, the convergence performances show that the proposed algorithm can obtain the best performances in multiview clustering among compared algorithms and can converge faster than compared multiview algorithms mostly.
A Real-time Pedestr ian Detection based on AGMM and HOG for Embedded Surveillance

Pedestrian detection (PD) is an essential task in various applications and sliding window-based methods utilizing HOG (Histogram of Oriented Gradients) or HOG-like descriptors have been shown to be very effective for accurate PD. However, due to exhaustive search across images, PD methods based on sliding window usually require heavy computational time. In this paper, we propose a real-time PD method for embedded visual surveillance with fixed backgrounds. The proposed PD method employs HOG descriptors as many PD methods does, but utilizes selective search so that it can save processing time significantly. The proposed selective search is guided by restricting searching to candidate regions extracted from Adaptive Gaussian Mixture Model (AGMM)-based background subtraction technique. Moreover, approximate computation of HOG descriptor and implementation in fixed-point arithmetic mode contributes to reduction of processing time further. Possible accuracy degradation due to approximate computation is compensated by applying an appropriate one among three offline trained SVM classifiers according to sizes of candidate regions. The experimental results show that the proposed PD method significantly improves processing speed without noticeable accuracy degradation compared to the original HOG-based PD and HOG with cascade SVM so that it is a suitable real-time PD implementation for embedded surveillance systems.
FliL ring enhances the function of periplasmic flagella

Significance How flagella sense complex environments and control bacterial motility remain fascinating questions. Here, we deploy cryo-electron tomography to determine in situ structures of the flagellar motor in wild-type and mutant cells of Borrelia burgdorferi, revealing that three flagellar proteins (FliL, MotA, and MotB) form a unique supramolecular complex in situ. Importantly, FliL not only enhances motor function by forming a ring around the stator complex MotA/MotB in its extended, active conformation but also facilitates assembly of the stator complex around the motor. Our in situ data provide insights into how cooperative remodeling of the FliL–stator supramolecular complex helps regulate the collective ion flux and establishes the optimal function of the flagellar motor to guide bacterial motility in various environments.