Miracle-dz/UniFAR-FTUs huggingface dataset:
Insights into Infusion-Based Targeted Drug Delivery in the Brain: Perspectives, Challenges and Opportunities

Targeted drug delivery in the brain is instrumental in the treatment of lethal brain diseases, such as glioblastoma multiforme, the most aggressive primary central nervous system tumour in adults. Infusion-based drug delivery techniques, which directly administer to the tissue for local treatment, as in convection-enhanced delivery (CED), provide an important opportunity; however, poor understanding of the pressure-driven drug transport mechanisms in the brain has hindered its ultimate success in clinical applications. In this review, we focus on the biomechanical and biochemical aspects of infusion-based targeted drug delivery in the brain and look into the underlying molecular level mechanisms. We discuss recent advances and challenges in the complementary field of medical robotics and its use in targeted drug delivery in the brain. A critical overview of current research in these areas and their clinical implications is provided. This review delivers new ideas and perspectives for further studies of targeted drug delivery in the brain.
Comparison between the System Cascading Collapse Based Hidden Failure and Fault Chain Theory in the Determination of Sensitive Transmission Lines

This paper presents a computationally accurate technique used to determine the estimated average probability of a system cascading collapse in association with the effect of hidden failure on a protection system. This includes an accurate calculation of the probability of hidden failure as it will provide considerable effect on the results of the estimated average probability of system cascading collapse. The estimated average probability of a system cascading collapse is then used to determine the sensitive transmission contributing to a higher risk of a system cascading collapse. This information is important because it will assist the utility to determine the critical transmission lines before the occurrence of critical power system cascading collapse. Comparative study has been done with other techniques to verify the effectiveness of the proposed method used in the determination of sensitive transmission lines.
Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

With the rapid expansion of the use of smartphone devices, malicious attacks against Android mobile devices have increased. The Android system adopted a wide range of sensitive applications such as banking applications; therefore, it is becoming the target of malware that exploits the vulnerabilities of the security system. A few studies proposed models for the detection of mobile malware. Nevertheless, improvements are required to achieve maximum efficiency and performance. Hence, we implemented machine learning and deep learning approaches to detect Android-directed malicious attacks. The support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were applied to identify malware in mobile environments. The cybersecurity system was tested with two Android mobile benchmark datasets. The correlation was calculated to find the high-percentage significant features of these systems in the protection against attacks. The machine learning and deep learning algorithms successfully detected the malware on Android applications. The SVM algorithm achieved the highest accuracy (100%) using the CICAndMal2017 dataset. The LSTM model also achieved a high percentage accuracy (99.40%) using the Drebin dataset. Additionally, by calculating the mean error, mean square error, root mean square error, and Pearson correlation, we found a strong relationship between the predicted values and the target values in the validation phase. The correlation coefficient for the SVM method was R2 = 100% using the CICAndMal2017 dataset, and LSTM achieved R2 = 97.39% in the Drebin dataset. Our results were compared with existing security systems, showing that the SVM, LSTM, and CNN-LSTM algorithms are of high efficiency in the detection of malware in the Android environment.
From Macroscopy to Ultrastructure: An Integrative Approach to Pulmonary Pathology

Pathology and radiology are complimentary tools, and their joint application is often crucial in obtaining an accurate diagnosis in non-neoplastic pulmonary diseases. However, both come with significant limitations of their own: Computed Tomography (CT) can only visualize larger structures due to its inherent–relatively–poor resolution, while (histo) pathology is often limited due to small sample size and sampling error and only allows for a 2D investigation. An innovative approach of inflating whole lung specimens and subjecting these subsequently to CT and whole lung microCT allows for an accurate matching of CT-imaging and histopathology data of exactly the same areas. Systematic application of this approach allows for a more targeted assessment of localized disease extent and more specifically can be used to investigate early mechanisms of lung diseases on a morphological and molecular level. Therefore, this technique is suitable to selectively investigate changes in the large and small airways, as well as the pulmonary arteries, veins and capillaries in relation to the disease extent in the same lung specimen. In this perspective we provide an overview of the different strategies that are currently being used, as well as how this growing field could further evolve.
Structural Analysis of the Black-Legged Tick Saliva Protein Salp15

Salp15 is one of the proteins in the saliva of the tick Ixodes scapularis. Together with other biomolecules injected into the mammalian host at the biting site, it helps the tick to sustain its blood meal for days. Salp15 interferes with the cellular immune response of the mammalian host by inhibiting the activation of CD4+ T-lymphocytes. This function is co-opted by pathogens that use the tick as a vector and invade the host when the tick bites, such as Borrelia burgdorferi, the causative agent of Lyme borreliosis. Because of the immunity-suppressing role of Salp15, it has been proposed as a candidate for therapeutic applications in disorders of the immune system. The protein is produced as a 135-residue long polypeptide and secreted without its N-terminal signal 1–21 sequence. Detailed structural studies on Salp15 are lacking because of the difficulty in producing large amounts of the folded protein. We report the production of Salp15 and its structural analysis by NMR. The protein is monomeric and contains a flexible N-terminal region followed by a folded domain with mixed α + β secondary structures. Our results are consistent with a three-dimensional structural model derived from AlphaFold, which predicts the formation of three disulfide bridges and a free C-terminal cysteine.
Aligning digitalisation and sustainable development? Evidence from the analysis of worldviews in sustainability reports

Funding information German Federal Ministry of Education and Research, Grant/Award Number: 01UU1705A Abstract The digitalisation of industrial production and the actual positive and negative consequences for sustainable development are not yet sufficiently understood. This study describes and evaluates the linkages between corporate digitalisation and sustainability management based on qualitative data analysis of sustainability reports of DAX 30 companies and applying the concept of sustainability worldviews. The results show a predominate worldview of weak or business-centred sustainability on digitalisation, which could potentially pose a threat to sustainable development. In particular, the focus on customer demands without stakeholder involvement and the worldview of digitalisation as a way of doing ‘business as-usual’ but in a more effective way reproduces unsustainable economic patterns. A holistic sustainable approach on digitalisation should also include possible negative impacts like increased resource consumption which is not the case yet in the studied companies. Different types of ‘sustainability worldviews on digitalisation’ can be distinguished. While ‘Pioneer’ companies can inform policy-making, the other types of ‘Intermediates’, ‘Indecisive’ and ‘Laggards’ could be addressed by information exchange, support and regulation to promote a more sustainable worldview on corporate digitalisation. The ‘Unsustainable’ digitalisation type would be the most difficult to address with soft policy instruments and requires a more regulated approach.
18F-Fluorodeoxyglucose PET/CT for Early Prediction of Outcomes in Patients with Advanced Lung Adenocarcinomas and EGFR Mutations Treated with First-Line EGFR-TKIs

Simple Summary Epithelial growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the first-line therapy for patients with advanced-stage lung adenocarcinoma with EGFR mutations. However, 17–31% of these patients do not respond to therapy, making early evaluation of treatment response crucial. This prospective study investigates the value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for timely prediction of response and survival of these patients. We evaluated 30 patients with stage IIIB/IV lung adenocarcinomas and EGFR mutations, receiving first-line EGFR-TKI therapy. 18F-FDG PET/CT was performed before and two weeks after initiation of treatment. Positron Emission Tomography Response Criteria in Solid Tumors served as an independent predictor of non-progressive disease; baseline and change of metabolic tumor volume represented independent predictors of progression-free survival and overall survival, respectively. Therefore, 18F-FDG PET/CT is an early predictor of outcomes and individual prognosis of patients with stage IIIB/IV lung adenocarcinomas and EGFR mutations receiving first-line EGFR-TKI therapy. Abstract This study aims to investigate the role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in early prediction of response and survival following epithelial growth factor receptor (EGFR)–tyrosine kinase inhibitor (TKI) therapy in patients with advanced lung adenocarcinomas and EGFR mutations. Thirty patients with stage IIIB/IV lung adenocarcinomas and EGFR mutations receiving first-line EGFR-TKIs were prospectively evaluated between November 2012 and May 2015. EGFR mutations were quantified by delta cycle threshold (dCt). 18F-FDG PET/CT was performed before and 2 weeks after treatment initiation. PET response was assessed based on PET Response Criteria in Solid Tumors (PERCIST). Baseline and percentage changes in the summed standardized uptake value, metabolic tumor volume (bsumMTV and ΔsumMTV, respectively), and total lesion glycolysis of ≤5 target lesions/patient were calculated. The association between parameters (clinical and PET) and non-progression disease after 3 months of treatment in CT based on the Response Evaluation Criteria in Solid Tumors Version 1.1 (nPD3mo), progression-free survival (PFS), and overall survival (OS) were tested. The median follow-up time was 19.6 months. The median PFS and OS were 12.0 and 25.3 months, respectively. The PERCIST criteria was an independent predictor of nPD3mo (p = 0.009), dCt (p = 0.014) and bsumMTV (p = 0.014) were independent predictors of PFS, and dCt (p = 0.014) and ΔsumMTV (p = 0.005) were independent predictors of OS. 18F-FDG PET/CT achieved early prediction of outcomes in patients with advanced lung adenocarcinomas and EGFR mutations receiving EGFR-TKIs.
Voltage Control Strategies for Active Distribution Networks with Multiple Distributed Energy Resources - A Survey

With the growing penetration of distributed energy resources on the network the operation regime has been changed from passive to active which brings a number of impacts to the network including voltage control, power flow management and protection issues etc. As one of the dominant impact, the voltage control strategy has become a key enabling technology for the deployment of renewable energy systems. This paper provides a comprehensive review on voltage control for active distribution networks, the efficiency and practicability have been studied in details.
Evaluating the success of a conservation reintroduction: The case of bull trout in the Wallowa River

Bull trout in the Wallowa River watershed were considered extirpated in the 1950s. In 1997, bull trout from the adjacent Imnaha River watershed were reintroduced into the Wallowa River watershed. We evaluated whether bull trout are currently present in the Wallowa River watershed and, if so, whether they appear to be the result of the 1997 reintroduction. From 2010 to 2018, we captured 181 Salvelinus spp. The majority (64.5%) of these individuals were bull trout. Bull trout in the Wallowa River watershed were more genetically similar to those from the Imnaha River watershed (pairwise FST = 0.102) than to the other populations we examined. They also exhibited genetic evidence of a recent bottleneck (observed heterozygosity was 0.598, significantly greater than expected). Modeled estimates of size (541–581 mm), survival (<0.0001–0.0015 probability) and age (14–22 years) for the reintroduced fish indicated the bull trout captured in the Wallowa River watershed were unlikely to be remnants from 1997. These results suggest the 1997 reintroduction has resulted in natural reproduction and recolonization of bull trout in the Wallowa River watershed. Whether the abundance and genetic diversity of these bull trout is sufficient to allow the population to persist over an ecological time period is unclear.
Facing COVID-19 challenges: What is so special in family businesses?

PurposeThis study aims to explore the main drivers that family businesses possess to strengthen their resilience during the COVID-19 crisis.Design/methodology/approachThis study followed a quantitative method analysis through a multiple regression analysis based on a sample of 570 Italian family firms.FindingsThe results showed that job quality and innovation significantly stimulate family firms' resilience during the COVID-19 crisis.Practical implicationsThe study has several academic implications. Firstly, the study contributes to family firm research by extending the studies on factors that significantly influence the concept of resilience; secondly, the work contributes to crisis management, offering suggestions to help other firms exceed the COVID-19 crisis.Originality/valueThe present study clarifies the role of family firms' resilience, and it reveals how job quality and innovation play a meaningful role during the COVID-19 crisis.
How Many Palabras? Codeswitching and Lexical Diversity in Spanish-English Picture Books

Bilingual picture books have been growing in popularity, with caregivers, teachers, and researchers increasingly interested in understanding how picture books might be able to support the learning of words in two languages. In this study, we present the first evaluation of the quantity and quality of text contained within bilingual picture books in English and Spanish targeted to children ages 0–9 and available to parents in the United States. We focus specifically on a sample of codeswitching books (N = 45) which present text in one language embedded in another language. All books were transcribed and evaluated for (1) the number of words and utterances presented in each language; (2) the quality and complexity of text presented in each language; and (3) how switching occurred between the two languages. Results showed that although picture books in our sample presented predominantly English text and more complex English sentences, relatively more unique words were presented in Spanish. Furthermore, picture books in our sample presented frequent switching between languages, particularly within utterances. We suggest that bilingual picture books provide children with potentially enriching yet asymmetrical opportunities for learning in each language.
Understanding the Evolution of Mitochondrial Genomes in the Green Macroalgal Genus Ulva (Ulvophyceae, Chlorophyta)

To gain more insights into the evolution of mitochondrial genomes (mitogenomes or mtDNAs) in the green macroalgal genus Ulva (Ulvophyceae, Chlorophyta), we sequenced seven Ulva mitogenomes from six species as well as one Percursaria mitogenome as outgroup, and compared them with the available Ulva mtDNA data. Our comparative analyses unveiled many novel findings. First, the Ulva mitogenomes shared a total of 62 core genes including 29 protein-coding genes (PCGs), three ribosomal RNA genes (rRNAs), 26 transfer RNA genes (tRNAs), three conserved free-standing open reading frames (orfs), and one putative RNA subunit of RNase P (rnpB). The rrn5 gene previously unrecognized is present in all sequenced ulvalean mitogenomes, which is situated between trnG(ucc) and trnW(cca). Second, the evolution of tRNAs in Ulva mitogenomes is related to different processes, including duplication, transposition, remolding, degeneration, loss and recruitment of tRNAs. The duplication of three tRNAs, i.e., trnT1(ugu), trnI1(gau), and trnM2(cau), was observed in Ulva mitogenomes. Third, the DNA-directed RNA polymerases (rpos), belonging to single-subunit DNA-dependent RNA polymerase (ssRNAP) family, are common in ulvalean mitogenomes. A total of three full-length and 55 split rpos have been detected in these 33 ulvalean mitogenomes. Fourth, six types of group I/II introns are detected at 29 insertion sites which are related to seven host genes (atp1, cox1, cox2, nad3, nad5, rnl, and rns) in these ulvalean mitogenomes. One group IB intron, i.e., intron cox1-214 which carried a GIY-YIG homing endonuclease (GHE), was observed for the first time in Ulva organelle genomes. Finally, phylogenomic analyses based on mitogenome dataset showed that the Ulva was split into two sister clades, representing Ulva lineage I and II, which was consistent to the results based on plastid genome dataset. Our study provides more important findings to better understand the evolution of mitochondrial genome in green algae.
Effectiveness Evaluation of Dual-Polarized OAM Multiplexing Employing SC-FDE in Urban Street Canyon Environments

This study investigates the system capacity of dual-polarized orbital angular momentum (OAM) multiplexing that employs single-carrier with frequency domain equalization (FDE) in urban street canyon environments. To overcome the impact of multipaths, two types of FDE, designed with and without the consideration of the cross-polarization interference (XPI), are introduced. We derive the effects of the residual inter-mode interference (IMI), inter-symbol interference (ISI), and XPI when applying such FDE and incorporate their impacts into the system capacity analysis. Moreover, we adopt the urban street canyon model based on a ray-tracing simulation, which enables performance evaluation in a realistic multipath environment. The numerical results show that although the computational complexity is increased, the consideration of the XPI in the FDE weight design improves the system capacity, and its effectiveness is remarkable in the cases of short link distances.
Hedgehog Signalling Modulates Immune Response and Protects against Experimental Autoimmune Encephalomyelitis

The Hedgehog (Hh) pathway is essential for the embryonic development and homeostatic maintenance of many adult tissues and organs. It has also been associated with some functions of the innate and adaptive immune system. However, its involvement in the immune response has not been well determined. Here we study the role of Hh signalling in the modulation of the immune response by using the Ptch-1-LacZ+/− mouse model (hereinafter referred to as ptch+/−), in which the hemizygous inactivation of Patched-1, the Hh receptor gene, causes the constitutive activation of Hh response genes. The in vitro TCR stimulation of spleen and lymph node (LN) T cells showed increased levels of Th2 cytokines (IL-4 and IL-10) in ptch+/−cells compared to control cells from wild-type (wt) littermates, suggesting that the Th2 phenotype is favoured by Hh pathway activation. In addition, CD4+ cells secreted less IL-17, and the establishment of the Th1 phenotype was impaired in ptch+/− mice. Consistently, in response to an inflammatory challenge by the induction of experimental autoimmune encephalomyelitis (EAE), ptch+/− mice showed milder clinical scores and more minor spinal cord damage than wt mice. These results demonstrate a role for the Hh/ptch pathway in immune response modulation and highlight the usefulness of the ptch+/− mouse model for the study of T-cell-mediated diseases and for the search for new therapeutic strategies in inflammatory diseases.
DeepShake: Shaking Intensity Prediction Using Deep Spatiotemporal RNNs for Earthquake Early Warning

We propose a deep spatiotemporal recurrent neural network, DeepShake, to project future shaking intensity directly from current ground-motion observations. DeepShake is a network-based forecasting model, able to predict future shaking intensity at all stations within a network given previously measured ground shaking. The model is not given any a priori knowledge of station locations; instead, it learns wave propagation amplitudes and delays solely from training data. We developed DeepShake with the 35,679 earthquakes from the 2019 Ridgecrest sequence. Tasked with alerting for modified Mercalli intensity (MMI) IV+ shaking on 3568 validation earthquakes at least 5 s in advance, DeepShake achieves an equal error rate of 11.4%. For the Mw 7.1 earthquake that hit Ridgecrest on 5 July 2019, DeepShake was able to provide targeted alerts to all stations inside the network 5 s prior to the arrival of MMI IV+ waveforms. DeepShake demonstrates that deep spatiotemporal neural networks can effectively provide one-step earthquake early warning with reasonable accuracy and latency.
A Comparative Study of Autonomous Object Detection Algorithms in the Maritime Environment Using a UAV Platform

Maritime operations rely heavily on surveillance and require reliable and timely data that can inform decisions and planning. Critical information in such cases includes the exact location of objects in the water, such as vessels, persons, and others. Due to the unique characteristics of the maritime environment, the location of even inert objects changes through time, depending on the weather conditions, water currents, etc. Unmanned aerial vehicles (UAVs) can be used to support maritime operations by providing live video streams and images from the area of operations. Machine learning algorithms can be developed, trained, and used to automatically detect and track objects of specific types and characteristics. EFFECTOR is an EU-funded project, developing an Interoperability Framework for maritime surveillance. Within the project, we developed an embedded system that employs machine learning algorithms, allowing a UAV to autonomously detect objects in the water and keep track of their changing position through time. Using the on-board computation unit of the UAV, we ran and present the results of a series of comparative tests among possible architecture sizes and training datasets for the detection and tracking of objects in the maritime environment. We tested architectures based on their efficiency, accuracy, and speed. A combined solution for training the datasets is suggested, providing optimal efficiency and accuracy.