Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Condition Monitoring of Induction Motors

Induction motors (IMs) are commonly used in various industrial applications. A spectrum synch (SS) technique is proposed in this chapter for early IM defect detection using electric current signals; fault detection in this work will focus on defects in rolling element bearings and rotor bars, which together account for more than half of IM imperfections. In bearing fault detection, the proposed SS technique will highlight the peakedness of the fault frequency components distributed over several fault related local bands. These bands are synchronized to form a fault information spectrum to accentuate fault features. A central kurtosis indicator is proposed to extract representative features from the fault information spectrum and formulate a fault index for incipient IM fault diagnosis. The effectiveness of the developed SS technique is tested on IMs with broken rotor bars and with damaged bearings.
Mechanical Property Evaluation of Electrodeposited Nanocrystalline Metals by Micro-testing

Electrodeposition is a very important technology in the fabrication of micro-compo‐ nents for micro-electro-mechanical systems (MEMS) or integrated circuits. Evalua‐ tions of the materials used in these devices as 3D components should be conducted using micro-sized specimens due to the sample size effect on the practical use of the components. Nanocrystalline metals could be deposited using an electrodeposition method with supercritical CO 2 emulsion. Our experiment on the micro-specimens provides information on micro-mechanical testing of electrodeposited metals in‐ cluding the effect of sample size, grain size, and anisotropic structures on mechani‐ cal properties. In this chapter, recent studies on crystal growth in electrodeposition of metals and its evaluation using micron-sized testing will be presented.
Training for Rapid Interpretation of Voluminous Multimodal Data

Abstract : Previous research has not specifically addressed rapid decision-making based on large amounts of data, although a large body of research has identified various biases and characteristic errors in human decision making that promote economy of information processing. The purpose of this series of experiments was to determine whether seven known characteristic error types operate in rapid decision-making, and to determine whether training to identify key contexts in which these errors are likely to occur can reduce their occurrence. Student volunteers across four experiments performed a simulated incident detection task using information in a variety of formats. Results supported the primary hypothesis that each of seven types of characteristic errors of interest occur in rapid decision making. A reduced tendency to commit false alarms occurs as a result of general alerting to the presence of error traps, although individuals who received specialized training to reduce the occurrence of characteristic errors performed no better than a control group. Team task performance greatly reduced undesirable false alarm errors, but also reduced desirable incident hits. Teams that received training reported the fewest hits and errors, although it was unclear whether this was a result of training or the effect of alerting observed in individuals.
Solving Large Problems with Heuristic Search: General-Purpose Parallel External-Memory Search

Classic best-first heuristic search algorithms, like A*, record every unique state they encounter in RAM, making them infeasible for solving large problems. In this paper, we demonstrate how best-first search can be scaled to solve much larger problems by exploiting disk storage and parallel processing and, in some cases, slightly relaxing the strict best-first node expansion order. Some previous disk-based search algorithms abandon best-first search order in an attempt to increase efficiency. We present two case studies showing that A*, when augmented with Delayed Duplicate Detection, can actually be more efficient than these non-best-first search orders. First, we present a straightforward external variant of A*, called PEDAL, that slightly relaxes best-first order in order to be I/O efficient in both theory and practice, even on problems featuring real-valued node costs. Because it is easy to parallelize, PEDAL can be faster than in-memory IDA* even on domains with few duplicate states, such as the sliding-tile puzzle. Second, we present a variant of PEDAL, called PE2A*, that uses partial expansion to handle problems that have large branching factors. When tested on the problem of Multiple Sequence Alignment, PE2A* is the first algorithm capable of solving the entire Reference Set 1 of the standard BAliBASE benchmark using a biologically accurate cost function. This work shows that classic best-first algorithms like A* can be applied to large real-world problems. We also provide a detailed implementation guide with source code both for generic parallel disk-based best-first search and for Multiple Sequence Alignment with a biologically accurate cost function. Given its effectiveness as a general-purpose problem-solving method, we hope that this makes parallel and disk-based search accessible to a wider audience.
DNA barcoding of marine fish species from Rongcheng Bay, China

Rongcheng Bay is a coastal bay of the Northern Yellow Sea, China. To investigate and monitor the fish resources in Rongcheng Bay, 187 specimens from 41 different species belonging to 28 families in nine orders were DNA-barcoded using the mitochondrial cytochrome c oxidase subunit I gene (COI). Most of the fish species could be discriminated using this COI sequence with the exception of Cynoglossus joyneri and Cynoglossus lighti. The average GC% content of the 41 fish species was 47.3%. The average Kimura 2-parameter genetic distances within the species, genera, families, and orders were 0.21%, 5.28%, 21.30%, and 23.63%, respectively. Our results confirmed that the use of combined morphological and DNA barcoding identification methods facilitated fish species identification in Rongcheng Bay, and also established a reliable DNA barcode reference library for these fish. DNA barcodes will contribute to future efforts to achieve better monitoring, conservation, and management of fisheries in this area.
Multi-Mapping Image-to-Image Translation with Central Biasing Normalization

Recent advances in image-to-image translation have seen a rise in approaches generating diverse images through a single network. To indicate the target domain for a one-to-many mapping, the latent code is injected into the generator network. However, we found that the injection method leads to mode collapse because of normalization strategies. Existing normalization strategies might either cause the inconsistency of feature distribution or eliminate the effect of the latent code. To solve these problems, we propose the consistency within diversity criteria for designing the multi-mapping model. Based on the criteria, we propose central biasing normalization to inject the latent code information. Experiments show that our method can improve the quality and diversity of existing image-to-image translation models, such as StarGAN, BicycleGAN, and pix2pix.
BVDV in Australian alpacas: natural infection and clinical profiles following co‐mingling with a persistently infected heifer

BACKGROUND Although predominantly a disease of cattle, bovine viral diarrhoea virus (BVDV) is known to infect other ruminant and camelid species such as sheep and alpacas. The aims of this study were to determine if BVDV-naive alpacas would become acutely infected and seroconvert to the predominant Australian strain of BVDV following co-mingling with a BVDV-1c persistently infected (PI) heifer and to determine what, if any, clinical signs, haematological responses and selected biochemical changes occur with acute BVDV-1c infections in alpacas. METHODS A PI heifer and four alpacas co-mingled for 2 weeks. Weekly blood samples were collected and twice weekly clinical examinations were performed on the alpacas. RESULTS Serum analysis by antibody ELISA indicated that all four alpacas were positive for BVDV-specific antibodies between 35 and 54 days after mixing with the BVDV-1c PI heifer. Viral antigen was detected by antigen ELISA in two alpacas on days 21 and 35 after initial mixing. In general, all the physical clinical parameters measured were normal. Serum biochemical and haematological analyses in two of the alpacas revealed marginally low sodium, chloride and elevated potassium concentrations, a lymphocytosis, monocytosis and a neutrophilia at some point during the study period in either one or both of the alpacas. CONCLUSION This study showed that infection in Australian alpacas readily occurs when a BVDV-1c PI bovine co-mingles with naive alpacas and that acute infections are clinically mild and undetectable without serological testing.
Impact of RNA integrity and blood sample storage conditions on the gene expression analysis

Background The reliability of RNA sequencing (RNA-seq) output is affected by the quality of RNAs, which is in turn dependent on the quality of samples. Therefore, the purposes of this study were to reconsider the threshold of the RNA integrity number (RIN) and propose a simple and efficient storage scheme of blood samples for RNA-seq. Patients and methods The RNAs were extracted from blood samples that were stored at different conditions and used for sequencing. The bioinformatic analyses were performed to evaluate the impact of RNA integrity and blood sample storage conditions on the gene expression analysis. Results Our outcomes showed that the samples with RIN values more than 5.3 scarcely affected the quantitative results of RNA-seq, and the influence of inherent cellular physiological processes on RNA-seq output could be negligible. Conclusion The blood samples stored at 4°C within 7 days with RIN values more than 5.3 were available for RNA-seq.
Bioceramics and bone healing

Calcium phosphates have long been used as synthetic bone grafts. Recent studies have shown that the modulation of composition and textural properties, such as nano-, micro- and macro-porosity, is a powerful strategy to control and synchronize material resorption and bone formation. Biomimetic calcium phosphates, which closely mimic the composition and structure of bone mineral, can be produced using low-temperature processing routes, and offer the possibility to modulate the material properties to a larger extent than conventional high temperature sintering processes. Advanced technologies open up new possibilities in the design of bioceramics for bone regeneration; 3D-printing technologies, in combination with the development of hybrid materials with enhanced mechanical properties, supported by finite element modelling tools, are expected to enable the design and fabrication of mechanically competent patient-specific bone grafts. The association of ions, drugs and cells allows leveraging of the osteogenic potential of bioceramic scaffolds in compromised clinical situations, where the intrinsic bone regeneration potential is impaired. Cite this article: EFORT Open Rev 2018;3 DOI: 10.1302/2058-5241.3. 170056
The secret sauce in efficient and precise static analysis: the beauty of distributive, summary-based static analyses (and how to master them)

In this paper I report on experiences gained from more than five years of extensively designing static code analysis tools- in particular such ones with a focus on security- to scale to real-world projects within an industrial context. Within this time frame, my team and I were able to design static-analysis algorithms that yield both largely improved precision and performance compared to previous approaches. I will give a number of insights regarding important design decisions that made this possible. In particular, I argue that summary-based static-analysis techniques for distributive problems, such as IFDS, IDE and WPDS have been unduly under-appreciated. As my experience shows, those techniques can tremendously benefit both precision and performance, if one uses them in a well-informed way, using carefully designed abstract domains. As one example, I will explain how in previous work on BOOMERANG we were able to decompose pointer analysis, a static analysis problem that is actually not distributive, into sub-problems that are distributive. This yields an implementation that is both highly precise and efficient. This breakthrough, along with the use of a demand-driven program-analysis design, has recently allowed us to implement practical static analysis tools such as the crypto-misuse checker CogniCrypt, which can analyze the entire Maven-Central repository with more than 200.000 binaries in under five days, although its analysis is flow-sensitive, field-sensitive, and fully context-sensitive.
A Theory of Diagnostic Interpretation in Supervised Classification

Interpretable deep learning is a fundamental building block towards safer AI, especially when the deployment possibilities of deep learning-based computer-aided medical diagnostic systems are so eminent. However, without a computational formulation of black-box interpretation, general interpretability research rely heavily on subjective bias. Clear decision structure of the medical diagnostics lets us approximate the decision process of a radiologist as a model - removed from subjective bias. We define the process of interpretation as a finite communication between a known model and a black-box model to optimally map the black box's decision process in the known model. Consequently, we define interpretability as maximal information gain over the initial uncertainty about the black-box's decision within finite communication. We relax this definition based on the observation that diagnostic interpretation is typically achieved by a process of minimal querying. We derive an algorithm to calculate diagnostic interpretability. The usual question of accuracy-interpretability tradeoff, i.e. whether a black-box model's prediction accuracy is dependent on its ability to be interpreted by a known source model, does not arise in this theory. With multiple example simulation experiments of various complexity levels, we demonstrate the working of such a theoretical model in synthetic supervised classification scenarios.
Zinc oxide nanoparticles induce toxicity in CAL 27 oral cancer cell lines by activating PINK1/Parkin-mediated mitophagy

Background Tongue squamous cell carcinoma (tongue cancer) is one of the most common malignancies in the oral maxillofacial region. The tumor easily relapses after surgery, and the prognosis remains poor. Recently, zinc oxide nanoparticles (ZnO NPs) were shown to target multiple cancer cell types. In this study, we aimed to elucidate the anticancer effect of ZnO NPs on CAL 27 human tongue cancer cells and identify the role of PINK1/Parkin-mediated mitophagy in this effect. Materials and methods We analyzed the dose-dependent cytotoxic effects of ZnO NPs on CAL 27 cells. Cells were cultured in media containing 0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 μg/mL ZnO NPs for 24 h. We further examined the intracellular reactive oxygen species levels, monodansylcadaverine intensity and mitochondrial membrane potential following the administration of 25 μg/mL ZnO NPs for 4, 8, 12, or 24 h and investigated the role of PINK1/Parkin-mediated mitophagy in ZnO NP-induced toxicity in CAL 27 cells. Results The viability of CAL 27 cells decreased after treatment with increasing ZnO NP concentrations. The inhibitory concentration 50% of the ZnO NPs was calculated as 25 μg/mL. The ZnO NPs increased the intracellular reactive oxygen species levels and decreased the mitochondrial membrane potential in a time-dependent manner as well as activated the PINK1/Parkin-mediated mitophagy process in CAL 27 cells. Conclusion Based on our findings, ZnO NPs may possess potential anticancer activity toward tongue cancer cells.
Anodal and Cathodal tDCS Over the Right Frontal Eye Fields Impacts Spatial Probability Processing Differently in Pro- and Anti-saccades

Learning regularities that exist in the environment can help the visual system achieve optimal efficiency while reducing computational burden. Using a pro- and anti-saccade task, studies have shown that probabilistic information regarding spatial locations can be a strong modulator of frontal eye fields (FEF) activities and consequently alter saccadic behavior. One recent study has also shown that FEF activities can be modulated by transcranial direct current stimulation, where anodal tDCS facilitated prosaccades but cathodal tDCS prolonged antisaccades. These studies together suggest that location probability and tDCS can both alter FEF activities and oculomotor performance, yet how these two modulators interact with each other remains unclear. In this study, we applied anodal or cathodal tDCS over right FEF, and participants performed an interleaved pro- and anti-saccade task. Location probability was manipulated in prosaccade trials but not antisaccade trials. We observed that anodal tDCS over rFEF facilitated prosaccdes toward low-probability locations but not to high-probability locations; whereas cathodal tDCS facilitated antisaccades away from the high-probability location (i.e., same location as the low-probability locations in prosaccades). These observed effects were specific to rFEF as tDCS over the SEF in a separate control experiment did not yield similar patterns. These effects were also more pronounced in low-performers who had slower saccade reaction time. Together, we conclude that (1) the overlapping spatial endpoint between prosaccades (i.e., toward low-probability location) and antisaccades (i.e., away from high-probability location) possibly suggest an endpoint-selective mechanism within right FEF, (2) anodal tDCS and location probability cannot be combined to produce a bigger facilitative effect, and (3) anodal rFEF tDCS works best on low-performers who had slower saccade reaction time. These observations are consistent with the homeostasis account of tDCS effect and FEF functioning.
The Application of Medical Thermography to Discriminate Neuroischemic Toe Ulceration in the Diabetic Foot

This study aimed to determine whether thermal imaging can detect temperature differences between healthy feet, nonulcerated neuroischemic feet, and neuroischemic feet with toe ulcers in patients with type 2 diabetes mellitus (T2DM). Participants were prospectively divided into 3 groups: T2DM without foot problems; a healthy, nonulcerated neuroischemic group, and an ulcerated neuroischemic group. Thermal images of the feet were obtained with automated segmentation of regions of interest. Thermographic images from 43 neuroischemic feet, 21 healthy feet, and 12 neuroischemic feet with active ulcer in one of the toes were analyzed. There was a significant difference in toe temperatures between the 3 groups (P = .001), that is, nonulcerated neuroischemic (n = 181; mean temperature = 27.7°C [±2.16 SD]) versus neuroischemic ulcerated (n = 12; mean temperature = 28.7°C [±3.23 SD]), and healthy T2DM group (n = 104; mean temperature = 24.9°C [±5.04 SD]). A post hoc analysis showed a significant difference in toe temperatures between neuroischemic nonulcerated and healthy T2DM groups (P = .001), neuroischemic ulcerated and healthy groups (P = .001). However, no significant differences in toe temperatures were identified between the ulcerated neuroischemic and nonulcerated neuroischemic groups (P = .626). There were no significant differences between the ulcerated toes (n = 12) and the nonulcerated toes (n = 57) of the same foot in the ulcerated neuroischemic group (P = .331). Toe temperatures were significantly higher in neuroischemic feet with or without ulceration compared with healthy feet in patients with T2DM. There were no significant differences in temperatures of ulcerated toes and the nonulcerated toes of the same foot, implying that all the toes of the same foot could potentially be at risk of developing complications, which can be potentially detected by infrared thermography.
Comparative Study of Ex Vivo Transmucosal Permeation of Pioglitazone Nanoparticles for the Treatment of Alzheimer’s Disease

Pioglitazone has been reported in the literature to have a substantial role in the improvement of overall cognition in a mouse model. With this in mind, the aim of this study was to determine the most efficacious route for the administration of Pioglitazone nanoparticles (PGZ-NPs) in order to promote drug delivery to the brain for the treatment of Alzheimer’s disease. PGZ-loaded NPs were developed by the solvent displacement method. Parameters such as mean size, polydispersity index, zeta potential, encapsulation efficacy, rheological behavior, and short-term stability were evaluated. Ex vivo permeation studies were then carried out using buccal, sublingual, nasal, and intestinal mucosa. PGZ-NPs with a size around of 160 nm showed high permeability in all mucosae. However, the permeation and prediction parameters revealed that lag-time and vehicle/tissue partition coefficient of nasal mucosa were significantly lower than other studied mucosae, while the diffusion coefficient and theoretical steady-state plasma concentration of the drug were higher, providing biopharmaceutical results that reveal more favorable PGZ permeation through the nasal mucosa. The results suggest that nasal mucosa represents an attractive and non-invasive pathway for PGZ-NPs administration to the brain since the drug permeation was demonstrated to be more favorable in this tissue.
Antimicrobial endotoxin‐neutralizing peptides promote keratinocyte migration via P2X7 receptor activation and accelerate wound healing in vivo

Wound healing is a complex process that is essential to provide skin homeostasis. Infection with pathogenic bacteria such as Staphylococcus aureus can lead to chronic wounds, which are challenging to heal. Previously, we demonstrated that the antimicrobial endotoxin‐neutralizing peptide Pep19‐2.5 promotes artificial wound closure in keratinocytes. Here, we investigated the mechanism of peptide‐induced cell migration and if Pep19‐2.5 accelerates wound closure in vivo.
Immunization Against Group B Streptococci vs. Intrapartum Antibiotic Prophylaxis in Peripartum Pregnant Women and their Neonates: A Review

Group B streptococcus (GBS) is an organism that has an immense global impact on neonatal morbidity and mortality. GBS is known to colonize the gastrointestinal and genitourinary tracts of infected pregnant women. Transmission to the neonate is achieved during labor and delivery. Complications of neonatal infection include sepsis, meningitis, and pneumonia. Preventative measures are needed to reduce the disease burden. The current method of prophylaxis being utilized is intrapartum antibiotics. Though effective, it presents limitations that include a required, lengthy duration of treatment, increased antibiotic resistance, and lack of late-onset disease coverage. Recent studies are exploring alternative methods of prophylaxis such as vaccination. Vaccination provides coverage of both early and late-onset disease to infected women who are unable to complete the required four hours of antibiotic course intrapartum, women with resistance to antibiotic infections, and women delivering in resource-poor areas without access to antibiotics. An investigational CRM197 conjugated trivalent vaccine has shown a good immunogenicity profile against the three most prevalent serotypes. The vaccine induced antibodies freely cross the placenta and persist in a neonate for up to 90 days after birth. Additionally, the vaccine possesses a safety profile free of any serious systemic adverse effects and only mild pain at the injection site. Future studies should focus on investigating the effect of the vaccine on maternal rectovaginal acquisition and subsequent vaginal colonization, as well as the expansion of vaccine coverage to all 10 serotypes identified worldwide in order to facilitate its use in resource-poor areas of the world with inadequate antibiotic availability. If available, such a vaccine can serve as an effective supplement to current guidelines of antibiotic prophylaxis against GBS in peripartum women and their neonates.