bluuebunny/crossref_metadata_2025 huggingface dataset:
A Mini Review on Molecules Inducing Caspase-Independent Cell Death: A New Route to Cancer Therapy
Authors: Bhadra Kakali
Published: September 2022

Most anticancer treatments trigger tumor cell death through apoptosis, where initiation of proteolytic action of caspase protein is a basic need. But under certain circumstances, apoptosis is prevented by the apoptosis inhibitor proteins, survivin and Hsp70. Several drugs focusing on classical programmed death of the cell have been reported to have low anti-tumorogenic potency due to mutations in proteins involved in the caspase-dependent programmed cell death with intrinsic and extrinsic pathways. This review concentrates on the role of anti-cancer drug molecules targeting alternative pathways of cancer cell death for treatment, by providing a molecular basis for the new strategies of novel anti-cancer treatment. Under these conditions, active agents targeting alternative cell death pathways can be considered as potent chemotherapeutic drugs. Many natural compounds and other small molecules, such as inorganic and synthetic compounds, including several repurposing drugs, are reported to cause caspase-independent cell death in the system. However, few molecules indicated both caspase-dependent as well caspase-free cell death in specific cancer lines. Cancer cells have alternative methods of caspase-independent programmed cell death which are equally promising for being targeted by small molecules. These small molecules may be useful leads for rational therapeutic drug design, and can be of potential interest for future cancer-preventive strategies.
Sequential Isolation of Microglia and Astrocytes from Young and Aged Adult Mouse Brains for Downstream Transcriptomic Analysis
Authors: Buenaventura Ruchelle G. , Harvey Alex C. , Burns Mark P. , Main Bevan S.
Published: September 2022

In aging, the brain is more vulnerable to injury and neurodegenerative disease, but the mechanisms responsible are largely unknown. Evidence now suggests that neuroinflammation, mediated by resident brain astrocyte and microglia populations, are key players in the generation of inflammatory responses and may influence both age related processes and the initiation/progression of neurodegeneration. Consequently, targeting these cell types individually and collectively may aid in the development of novel disease-modifying therapies. We have optimized and characterized a protocol for the effective sequential isolation of both microglia and astrocytes from the adult mouse brain in young and aged mice. We demonstrate a technique for the sequential isolation of these immune cells by using magnetic beads technology, optimized to increase yield and limit potential artifacts in downstream transcriptomic applications, including RNA-sequencing pipelines. This technique is versatile, cost-effective, and reliable for the study of responses within the same biological context, simultaneously being advantageous in reducing mice numbers required to assess cellular responses in normal and age-related pathological conditions.
Octahedral Molybdenum Cluster-Based Nanomaterials for Potential Photodynamic Therapy
Authors: Tavares Marina Rodrigues , Kirakci Kaplan , Kotov Nikolay , Pechar Michal , Lang Kamil , Pola Robert , Etrych Tomáš
Published: September 2022

Photo/radiosensitizers, such as octahedral molybdenum clusters (Mo6), have been intensively studied for photodynamic applications to treat various diseases. However, their delivery to the desired target can be hampered by its limited solubility, low stability in physiological conditions, and inappropriate biodistribution, thus limiting the therapeutic effect and increasing the side effects of the therapy. To overcome such obstacles and to prepare photofunctional nanomaterials, we employed biocompatible and water-soluble copolymers based on N-(2-hydroxypropyl)methacrylamide (pHPMA) as carriers of Mo6 clusters. Several strategies based on electrostatic, hydrophobic, or covalent interactions were employed for the formation of polymer-cluster constructs. Importantly, the luminescent properties of the Mo6 clusters were preserved upon association with the polymers: all polymer-cluster constructs exhibited an effective quenching of their excited states, suggesting a production of singlet oxygen (O2(1Δg)) species which is a major factor for a successful photodynamic treatment. Even though the colloidal stability of all polymer-cluster constructs was satisfactory in deionized water, the complexes prepared by electrostatic and hydrophobic interactions underwent severe aggregation in phosphate buffer saline (PBS) accompanied by the disruption of the cohesive forces between the cluster and polymer molecules. On the contrary, the conjugates prepared by covalent interactions notably displayed colloidal stability in PBS in addition to high luminescence quantum yields, suggesting that pHPMA is a suitable nanocarrier for molybdenum cluster-based photosensitizers intended for photodynamic applications.
High Strength Die-Attach Joint Formation by Pressureless Sintering of Organic Amine Modified Ag Nanoparticle Paste
Authors: Shen Xingwang , Li Junjie , Xi Shuang
Published: September 2022

Sintered silver (Ag) die-attach has attracted much attention in power systems with high power density and high operating temperature. In this paper, we proposed a novel surface modification method for Ag nanoparticles with organic amines as a coating agent for enhancing the pressureless sintering performance. This work systematically introduced the Ag nanoparticle modification process, Ag paste preparation, and sintering process and compared the changes in the sintering performance of Ag nanoparticles after modification with four different alkyl chain lengths of amines. The study showed that the sintered films of Ag nanoparticle pastes modified with n-octylamine (NOA) can achieve the lowest resistivity of the sintered film and the highest shear strength of the bonded joints. The resistivity of the sintered Ag film is affected by the grain size and microscopic morphology, and the strength of the bonded joints is also related to the sintering density and the amount of organic residues. The thermal behavior of the Ag particles coated with different amines is measured by thermal analysis. Finally, the mechanism of NOA-modified Ag nanoparticles to improve the sintering performance is proposed. This study can provide effective data and theoretical support for the further promotion and application of nano-Ag pressureless sintering.
Thin Films of Nonlinear Metallic Amorphous Composites
Authors: Daryakar Navid , David Christin
Published: September 2022

We studied the nonlinear optical response of metallic amorphous composite layers in terms of a self-phase-modulated, third-order Kerr nonlinearity. A nonlinear effective medium theory was used to describe low densities of gold and iridium nanoparticles embedded in an equally nonlinear host material. The fill fraction strongly influences the effective nonlinear susceptibility of the materials, increasing it by orders of magnitude in the case of gold due to localized surface plasmonic resonances. The enhancement of the nonlinear strength in amorphous composites with respect to the bulk material has an upper limit in metallic composites as dominating absorption effects take over at higher fill factors. Both saturated and induced absorption in the thin films of amorphous composites were observed depending on the selected frequency and relative position to the resonant frequency of electron excitation in the metallic inclusions. We demonstrated the depths to which thin films are affected by nonlinear enhancement effects.
Silver and Hyaluronic Acid-Coated Gold Nanoparticles Modulate the Metabolism of a Model Human Gut Bacterium Lactobacillus casei
Authors: Huang Wenqian , Zhang Yirong , Li Zhi , Li Minjie , Li Fangfang , Mortimer Monika , Guo Liang-Hong
Published: September 2022

Medical applications of nanotechnology are promising in creating efficient and targeted therapies. However, so far, nanodrug design has not taken into consideration possible effects on human microbiota. The beneficial functions of bacteria could be stimulated by nanodrugs while negative effects on beneficial bacteria could cause risks to human health. Here, simulated intestinal fluid (IF) was optimized for culturing a human commensal and probiotic bacterial strain, Lactobacillus casei, to study the effects of medically relevant NPs—Ag and hyaluronic acid-coated Au NPs (HA-Au NPs)—in conditions pertinent to the gastrointestinal tract. When cultivated either aerobically or anaerobically, the specific growth rates of L. casei were ~0.2 h−1 in IF and ~0.4 h−1 in the standard medium of lactobacilli (MRS). Ag NPs inhibited the growth of L. casei in IF at lower concentrations (EC50 ~ 65 and 15 mg/L in aerobic and anaerobic conditions, respectively) than in MRS (EC50 > 100 mg/L), likely caused by differences in the composition of the two media and different intrinsic growth rates of bacteria in IF and MRS. Ag NP dissolution in IF and MRS did not explain the differences in growth inhibition, implying NP-specific effects. HA-Au NPs were not growth-inhibitory to L. casei up to 250 mg/L. Still, both NPs at sub-growth-inhibitory concentrations suppressed the expression of bacteriocin genes in L. casei, suggesting an inhibitory effect of NPs on the probiotic properties of L. casei, i.e., its competitiveness in microbial communities. However, HA-Au NPs did not appear to affect or even stimulated the immunomodulatory properties of L. casei in human intestinal epithelial cells. Thus, medically relevant NPs at low, sub-bacteriostatic levels can affect the metabolism of beneficial human bacteria and potentially induce changes in the microbiota and immune signaling.
Facile Synthesis of Black Phosphorus Nanosheet@NaReF4 Nanocomposites for Potential Bioimaging
Authors: Wang Dongya , Qin Jingcan , Zhang Chuan , Li Yuehua
Published: September 2022

Black phosphorus nanomaterials (BPN) have been well developed in tumor therapy. However, lack of diagnostic function limits the development of BPN in biomedicine. Lanthanide-doped nanoparticles are considered as versatile materials for fluorescence or magnetic resonance imaging. Integration of BPN with lanthanide-doped nanoparticles was rarely reported owing to the complex synthesis processes and poor modification effect. Herein, we report a simple and general method for synthesizing BPN@NaReF4 (Re: Gd or Y, Yb, Er) nanocomposite. TEM and XRD characterization confirm efficient combination of BPN and NaGdF4 or NaYF4:Yb,Er (18.2 mol %) after one-step mixing. The FTIR and XPS spectra were used to prove the generation of PO43−-Gd and P-Gd coordination bonds and clarify ligand exchange mechanism. The anchored nanoparticles on BPN were stable and become hydrophilic. The prepared BPN@NaGdF4 exhibit the signals of photoacoustic and magnetic resonance imaging. The obtained BPN@NaYF4:Yb,Er (18.2 mol %) have the potential in fluorescence bioimaging. We believe that this work will expand the applications of BPN in diagnosis and therapy together.
Optical Absorption in N-Dimensional Colloidal Quantum Dot Arrays: Influence of Stoichiometry and Applications in Intermediate Band Solar Cells
Authors: Hahn Rebeca V. H. , Rodríguez-Bolívar Salvador , Rodosthenous Panagiotis , Skibinsky-Gitlin Erik S. , Califano Marco , Gómez-Campos Francisco M.
Published: September 2022

We present a theoretical atomistic study of the optical properties of non-toxic InX (X = P, As, Sb) colloidal quantum dot arrays for application in photovoltaics. We focus on the electronic structure and optical absorption and on their dependence on array dimensionality and surface stoichiometry motivated by the rapid development of experimental techniques to achieve high periodicity and colloidal quantum dot characteristics. The homogeneous response of colloidal quantum dot arrays to different light polarizations is also investigated. Our results shed light on the optical behaviour of these novel multi-dimensional nanomaterials and identify some of them as ideal building blocks for intermediate band solar cells.
Changes in Group B Streptococcus Colonization among Pregnant Women before and after the Onset of the COVID-19 Pandemic in Brazil
Authors: Costa Natália Silva , Rio-Tinto André , Pinto Isabella Bittencourt Ferreira , dos Santos Silva Alvim Danielle Cristina , de Assis Rocha Amanda , Oliveira Laura Maria Andrade , Botelho Ana Caroline Nunes , Fracalanzza Sergio Eduardo Longo , Teixeira Lucia Martins , Rezende-Filho Jorge , Marinho Penélope Saldanha , Amim Júnior Joffre , Taylor Stephen , Thomas Steve , Pinto Tatiana Castro Abreu
Published: September 2022

Group B Streptococcus (GBS) is a leading cause of neonatal infections. The genitourinary and gastrointestinal tract of pregnant women are the main source of transmission to newborns. This work investigated the prevalence and characterized GBS from pregnant women in Rio de Janeiro, Brazil, comparing the periods before (January 2019 to March 2020; 521) and during (May 2020 to March 2021; 285) the COVID-19 pandemic. GBS was detected in 10.8% of anovaginal samples. Considering scenarios before and during the pandemic, GBS colonization rate significantly decreased (13.8% vs. 5.3%; p = 0.0001). No clinical and sociodemographic aspect was associated with GBS carriage (p > 0.05). A total of 80%, 13.8% and 4.6% GBS strains were non-susceptible to tetracycline, erythromycin and clindamycin, respectively. Serotype Ia was the most frequent (47.7%), followed by V (23.1%), II (18.4%), III (7.7%) and Ib (3.1%). An increasing trend of serotypes Ib and V, as well as of antimicrobial resistance rates, and a decreasing trend of serotypes II and III, were observed after the pandemic onset, albeit not statistically significant (p > 0.05). The reduction in GBS colonization rates and alterations in GBS serotypes and resistance profiles during the pandemic were not due to changes in the sociodemographic profile of the population. Considering that control and preventive measures related to the COVID-19 pandemic onset have impacted other infectious diseases, these results shed light on the need for the continuous surveillance of GBS among pregnant women in the post-pandemic era.
MRSA in Humans, Pets and Livestock in Portugal: Where We Came from and Where We Are Going
Authors: Silva Vanessa , Monteiro Andreia , Pereira José Eduardo , Maltez Luís , Igrejas Gilberto , Poeta Patrícia
Published: September 2022

Over the years, molecular typing of methicillin-resistant S. aureus (MRSA) has allowed for the identification of endemic MRSA strains and pathogenic strains. After reaching a peak of predominance in a given geographic region, MRSA strains are usually replaced by a new strain. This process is called clonal replacement and is observed worldwide. The worldwide spread of hospital-associated MRSA (HA-MRSA), community-associated MRSA (CA-MRSA) and livestock-associated MRSA (LA-MRSA) clones over the last few decades has allowed this microorganism to be currently considered a pandemic. In Portugal, most HA-MRSA infections are associated with EMRSA-15 (S22-IV), New York/Japan (ST5-II) and Iberian (ST247-I) clones. Regarding the strains found in the community, many of them are frequently associated with the hospital environment, namely the Pediatric, Brazilian and Iberian clones. On the other hand, a strain that is typically found in animals, MRSA clonal complex (CC) 398, has been described in humans as colonizing and causing infections. The ST398 clone is found across all animal species, particularly in farm animals where the economic impact of LA-MRSA infections can have disastrous consequences for industries. In contrast, the EMRSA-15 clone seems to be more related to companion animals. The objective of this review is to better understand the MRSA epidemiology because it is, undoubtedly, an important public health concern that requires more attention, in order to achieve an effective response in all sectors.
Myopericarditis in a Male Adolescent Following the Second Shot of COVID-19 Pfizer m-RNA Vaccine: Typical Example and Analysis of 110 Single Case Reports
Authors: Piras Alessandra , Melis Giada , Cugusi Lucia , Bassareo Pier Paolo
Published: September 2022

One of the most powerful weapons against COVID-19 is vaccines. After the worldwide spread of the disease, m-RNA vaccines were authorized not only in adult patients, but also in children and adolescents aged 12–18. Since then, alarming reports of cases of myocarditis and/or pericarditis have been noted, primarily involving males after the second vaccine shot. A typical example of myopericarditis occurring in an adolescent a few days after the second shot of an m-RNA vaccine is described here. An in-depth review of all 110 single case reports published up to July 2022 with related features and outcomes is also presented. This is the first extensive analysis focused solely on a significant number of single case reports, which have usually been excluded from systematic reviews and meta-analyses carried out in the field. The analysis presented here confirms that most cases occurred in males after the second injection of an m-RNA vaccine. Cases were mild and responsive to the usual medical treatment. What is newly reported is that not only adolescents, but also older people, especially females, were affected by this adverse event.
Beyond the Altruistic Donor: Embedding Solidarity in Organ Procurement Policies
Authors: Martínez-López María Victoria , Díaz-Cobacho Gonzalo , Liedo Belén , Rueda Jon , Molina-Pérez Alberto
Published: September 2022

Altruism and solidarity are concepts that are closely related to organ donation for transplantation. On the one hand, they are typically used for encouraging people to donate. On the other hand, they also underpin the regulations in force in each country to different extents. They are often used indistinctly and equivocally, despite the different ethical implications of each concept. This paper aims to clarify to what extent we can speak of altruism and solidarity in the predominant models of organ donation. It also raises the ethical question of whether these categories are adequate as a basis for such models, bearing in mind that organs are a scarce resource and that a shortage of them may mean that fewer lives are saved or improved.
Double Beta Decay: A Shell Model Approach
Authors: Horoi Mihai
Published: September 2022

Studies of weak interaction in nuclei are important tools for testing different aspects of the fundamental symmetries of the Standard Model. Neutrinoless double beta decay offers an unique venue of investigating the possibility that neutrinos are Majorana fermions and that the lepton number conservation law is violated. Here, I use a shell model approach to calculate the nuclear matrix elements needed to extract the lepton-number-violating parameters of a few nuclei of experimental interest from the latest experimental lower limits of neutrinoless double beta decay half-lives. The analysis presented here could reveal valuable information regarding the dominant neutrinoless double beta decay mechanism if experimental half-life data become available for different isotopes. A complementary shell model analysis of the two-neutrino double beta decay nuclear matrix elements and half-lives is also presented.
Phenotypic Variability of Root System Architecture Traits for Drought Tolerance among Accessions of Citron Watermelon (Citrullus lanatus var. citroides (L.H. Bailey)
Authors: Mandizvo Takudzwa , Odindo Alfred Oduor , Mashilo Jacob , Sibiya Julia , Beck-Pay Sascha Lynn
Published: September 2022

Citron watermelon (Citrullus lanatus var. citroides) is a drought-tolerant cucurbit crop widely grown in sub-Saharan Africa in arid and semi-arid environments. The species is a C3 xerophyte used for multiple purposes, including intercropping with maize, and has a deep taproot system. The deep taproot system plays a key role in the species’ adaptation to dry conditions. Understanding the root system development of this crop could be useful to identify traits for breeding water-use efficient and drought-tolerant varieties. This study compared the root system architecture of citron watermelon accessions under water-stress conditions. Nine selected and drought-tolerant citron watermelon accessions were grown under non-stress (NS) and water stress (WS) conditions using the root rhizotron procedure in a glasshouse. The following root system architecture (RSA) traits were measured: root system width (RSW), root system depth (RSD), convex hull area (CHA), total root length (TRL), root branch count (RBC), total root volume (TRV), leaf area (LA), leaf number (LN), first seminal root length (FSRL), seminal root angle (SRA), root dry mass (RDM), shoot dry mass (SDM), root–shoot mass ratio (RSM), root mass ratio (RMR), shoot mass ratio (SMR) and root tissue density (RTD). The data collected on RSA traits were subjected to an analysis of variance (ANOVA), correlation and principal component analyses. ANOVA revealed a significant (p < 0.05) accession × water stress interaction effect for studied RSA traits. Under WS, RDM exhibited significant and positive correlations with RSM (r = 0.65), RMR (r = 0.66), RSD (r = 0.66), TRL (r = 0.60), RBC (r = 0.72), FSRL (r = 0.73) and LN (r = 0.70). The principal component analysis revealed high loading scores for the following RSA traits: RSW (0.89), RSD (0.97), TRL (0.99), TRV (0.90), TRL (0.99), RMR (0.96) and RDM (0.76). In conclusion, the study has shown that the identified RSA traits could be useful in crop improvement programmes for citron watermelon genotypes with enhanced drought adaptation for improved yield performance under drought-prone environments.
Significant Shear Failure Difference among Additively Manufactured Polymers Using Different Techniques
Authors: Xu Luoyu Roy , Wang Qinglin , Ni Yinxu , Zhang Gonghe , Liu Fenghua , Zheng Xiaodong , Liu Yang
Published: September 2022

Because additively manufactured materials are increasingly being used in load-bearing structures, strength research has become critical. Surprisingly, numerous studies have reported the tensile strength measurements, but only a few studies have presented meaningful results for the shear strength measurements of additively manufactured polymers. Hence, this paper proposes a combined experimental and numerical investigation of a new interlayer shear strength measurement approach, and it targeted the applications of the same polyamide (PA12) specimens made with fused deposition modeling (FDM) and selective laser sintering (SLS). A necking-shaped shear specimen was developed to measure the pure shear strengths with the aid of a three-dimensional (3D) finite element analysis. The results showed that the specimens made with FDM and SLS exhibited totally different shear failure behaviors. The ultimate shear strength of the FDM-PA specimens had more than a 32% increase over that of the SLS-PA specimens. An interface mechanics assumption was employed to explore the different shear failure mechanisms with the support of a fractography analysis.
Polyimide Layers with High Refractivity and Surface Wettability Adapted for Lowering Optical Losses in Solar Cells
Authors: Barzic Andreea Irina , Albu Raluca Marinica , Hulubei Camelia , Mahmoud Samy F. , Abu Ali Ola A. , El-Bahy Zeinhom M. , Stoica Iuliana
Published: September 2022

The performance of photovoltaics with superstrate configuration is limited by the rigidity and low refractivity of a classical glass cover. In this work, two polyimides (PIs) and two copolyimides combined in the main chain cycloaliphatic moieties, aromatic sequences, chalcogen atoms, and having/lacking fluorine atoms, are proposed as shielding covers for solar cells. The samples containing small cycloaliphatic moieties displayed high transmittance above 80% at 550 nm. The refractive index values under changeable wavelengths and temperatures were shown to influence the magnitude of the reflection losses. At the sample interface with the transparent electrode, optical losses were reduced (~0.26%) in comparison to the classical glass (~0.97%). The samples with the best optical features were further subjected to a surface treatment to render the self-cleaning ability. For this, a new approach was used residing in irradiation with the diffuse coplanar surface barrier discharge (DCSBD), followed by spraying with a commercial substance. Scanning electron microscopy and atomic force microscopy scans show that the surface characteristics were changed after surface treatment, as indicated by the variations in root mean square roughness, surface area ratio, and surface bearing index values. The proposed PI covers diminish the optical losses caused by total internal reflection and soiling, owing to their adapted refractivity and superhydrophobic surfaces (contact angles > 150°), and open up new perspectives for modern photovoltaic technologies.
Gallic Acid Crosslinked Gelatin and Casein Based Composite Films for Food Packaging Applications
Authors: Bhatia Saurabh , Al-Harrasi Ahmed , Al-Azri Mohammed Said , Ullah Sana , Makeen Hafiz A. , Meraya Abdulkarim M. , Albratty Mohammed , Najmi Asim , Anwer Md. Khalid
Published: September 2022

In the current work, we fabricated gelatin–casein-based edible films (GC-EFs) crosslinked with gallic acid (GA). We analyzed the physiochemical characteristics, crystallinity, thermal stability, and surface properties of the EFs using Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). It was found that the edible films possessed a semi-crystalline structure. Addition of GA enhanced the thermal stability of the edible films as well as the surface properties of the films. It was found that a higher concentration of GA (4–5% w/v) significantly improved the surface properties, observed in the surface and cross-sectional examination of SEM micrographs. EFs containing higher amounts of GA showed more compact and denser structures with smoother and more homogeneous surfaces than the control samples. In addition, swelling degree (SD), thickness, water solubility (WS), moisture content (MC), and water vapor permeability (WVP) were found to be low in EFs containing more GA concentration. Mechanical parameters revealed that the Young modulus (Ym) and tensile strength (TS) increased with a rise in GA concentration, and elongation at break (EB) reduced with a rise in GA concentration. In transparency and color analysis, it was observed that GA positively affected the transparency of the edible films.
An End-to-End Recurrent Neural Network for Radial MR Image Reconstruction
Authors: Oh Changheun , Chung Jun-Young , Han Yeji
Published: September 2022

Recent advances in deep learning have contributed greatly to the field of parallel MR imaging, where a reduced amount of k-space data are acquired to accelerate imaging time. In our previous work, we have proposed a deep learning method to reconstruct MR images directly from k-space data acquired with Cartesian trajectories. However, MRI utilizes various non-Cartesian trajectories, such as radial trajectories, with various numbers of multi-channel RF coils according to the purpose of an MRI scan. Thus, it is important for a reconstruction network to efficiently unfold aliasing artifacts due to undersampling and to combine multi-channel k-space data into single-channel data. In this work, a neural network named ‘ETER-net’ is utilized to reconstruct an MR image directly from k-space data acquired with Cartesian and non-Cartesian trajectories and multi-channel RF coils. In the proposed image reconstruction network, the domain transform network converts k-space data into a rough image, which is then refined in the following network to reconstruct a final image. We also analyze loss functions including adversarial and perceptual losses to improve the network performance. For experiments, we acquired k-space data at a 3T MRI scanner with Cartesian and radial trajectories to show the learning mechanism of the direct mapping relationship between the k-space and the corresponding image by the proposed network and to demonstrate the practical applications. According to our experiments, the proposed method showed satisfactory performance in reconstructing images from undersampled single- or multi-channel k-space data with reduced image artifacts. In conclusion, the proposed method is a deep-learning-based MR reconstruction network, which can be used as a unified solution for parallel MRI, where k-space data are acquired with various scanning trajectories.
Feasibility of Brachial Occlusion Technique for Beat-to-Beat Pulse Wave Analysis
Authors: Matera Lukas , Sajgalik Pavol , Fabian Vratislav , Mikhailov Yegor , Zemanek David , Johnson Bruce D.
Published: September 2022

Czech physiologist Penaz tried to overcome limitations of invasive pulse-contour methods (PCM) in clinical applications by a non-invasive method (finger mounted BP cuff) for continuous arterial waveform detection and beat-to-beat analysis. This discovery resulted in significant interest in human physiology and non-invasive examination of hemodynamic parameters, however has limitations because of the distal BP recording using a volume-clamp method. Thus, we propose a validation of beat-to-beat signal analysis acquired by novel a brachial occlusion-cuff (suprasystolic) principle and signal obtained from Finapres during a forced expiratory effort against an obstructed airway (Valsalva maneuver). Twelve healthy adult subjects [2 females, age = (27.2 ± 5.1) years] were in the upright siting position, breathe through the mouthpiece (simultaneously acquisition by brachial blood pressure monitor and Finapres) and at a defined time were asked to generate positive mouth pressure for 20 s (Valsalva). For the purpose of signal analysis, we proposed parameter a “Occlusion Cuff Index” (OCCI). The assumption about similarities between measured signals (suprasystolic brachial pulse waves amplitudes and Finapres’s MAP) were proved by averaged Pearson’s correlation coefficient (r- = 0.60, p < 0.001). The averaged Pearson’s correlation coefficient for the comparative analysis of OCCI between methods was r- = 0.88, p < 0.001. The average percent change of OCCI during maneuver: 8% increase, 19% decrease and percent change of max/min ratio is 35%. The investigation of brachial pulse waves measured by novel brachial blood pressure monitor shows positive correlation with Finapres and the parameter OCCI shows promise as an index, which could describe changes during beat-to-beat cardiac cycles.
Design of Compact Mid-Infrared Cooled Echelle Spectrometer Based on Toroidal Uniform-Line-Spaced (TULS) Grating
Authors: Wang Qingyu , Shen Honghai , Liu Weiqi , Zhang Jingzhong , Meng Lingtong
Published: September 2022

A traditional flat-panel spectrometer does not allow high-resolution observation and miniaturization simultaneously. In this study, a compact, high-resolution cross-dispersion spectrometer was designed based on the theoretical basis of echelle grating for recording an infrared spectrum. To meet the high-resolution observation and miniaturization design requirements, a reflective immersion grating was used as the primary spectroscopic device. To compress the beam aperture of the imaging system, the order-separation device of the spectrometer adopted toroidal uniform line grating, which had both imaging and dispersion functions in the spectrometer. The aberration balance condition of the toroidal uniform line grating was analyzed based on the optical path difference function of the concave grating, and dispersion characteristics of the immersed grating and thermal design of the infrared lens were discussed based on the echelle grating. An immersion echelle spectrometer optical system consisting of a culmination system, an immersed echelle grating, and a converged system was used. The spectrometer was based on the asymmetrical Czerny-Turner and Littrow mount designs, and it was equipped with a 320 × 256 pixel detector array. The designed wavelength range was 3.7–4.8 μm, the F-number was 4, and the central wavelength resolution was approximately 30,000. An infrared cooling detector was used. The design results showed that, in the operating band range, the root implied that the square diameter of the spectrometer spot diagram was less than 30 μm, the energy was concentrated in a pixel size range, and the spectrometer system design met the requirements.
Meter Wave Polarization-Sensitive Array Radar for Height Measurement Based on MUSIC Algorithm
Authors: Wang Guoxuan , Zheng Guimei , Wang Hongzhen , Chen Chen
Published: September 2022

Obtaining good measurement performance with meter wave radar has always been a difficult problem. Especially in low-elevation areas, the multipath effect seriously affects the measurement accuracy of meter wave radar. The generalized multiple signal classification (MUSIC) algorithm is a well-known measurement method that dose not require decorrelation processing. The polarization-sensitive array (PSA) has the advantage of polarization diversity, and the polarization smoothing generalized MUSIC algorithm demonstrates good angle estimation performance in low-elevation areas when based on a PSA. Nevertheless, its computational complexity is still high, and the estimation accuracy and discrimination success probability need to be further improved. In addition, it cannot estimate the polarization parameters. To solve these problems, a polarization synthesis steering vector MUSIC algorithm is proposed in this paper. First, the MUSIC algorithm is used to obtain the spatial spectrum of the meter wave PSA. Second, the received data are properly deformed and classified. The Rayleigh–Ritz method is used to decompose the angle to realize the decoupling of polarization and the direction of the arrival angle. Third, the geometric relationship and prior information of the direct wave and the reflected wave are used to continue dimension reduction processing to reduce the computational complexity of the algorithm. Finally, the geometric relationship is used to obtain the target height measurement results. Extensive simulation results illustrate the accuracy and superiority of the proposed algorithm.
Application and Challenges of IoT Healthcare System in COVID-19
Authors: Al-Atawi Abdullah A. , Khan Faheem , Kim Cheong Ghil
Published: September 2022

The importance of the IoT is increasing in every field of life, and it especially has a significant role in improving the efficiency of the healthcare system. Its demand further increased during COVID-19 to facilitate the patient remotely from their home digitally. Every time the COVID-19 patient visited the doctor for minor complications, it increased the risk of spreading the virus and the cost for the patient. Another alarming situation arose when a patient was in a critical position and may not claim an emergency service from the nearby healthcare system, increasing the death rate. The IoT uses healthcare services to properly monitor COVID-19 patients by using the interconnected network to overcome these issues. Through the IoT, the patient is facilitated by the health care system without spreading the virus, decreasing the death ratio during COVID-19. This paper aims to discuss different applications, technologies, and challenges of the IoT healthcare system, related to COVID-19. Different databases were searched using keywords in PubMed, ResearchGate, Scopus, ACM, Springer, Elsevier, Google Scholar, etc. This paper is trying to discuss, identify, and highlight the useful applications of the IoT healthcare system to provide guidelines to the researchers, healthcare institutions, and scientists to overcomes the hazards of COVID-19 pandemics. Hence, IoT is beneficial by identifying the symptoms of COVID-19 patients and by providing better treatments that use the healthcare system efficiently. At the end of the paper, challenges and future work are discussed, along with useful suggestions through which scientists can benefit from the IoT healthcare system during COVID-19 and in a severe pandemic. The survey paper is not limited to the healthcare system and COVID-19, but it can be beneficial for future pandemics or in a worse situation.
Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging
Authors: Huang Shuan-Yu , Mukundan Arvind , Tsao Yu-Ming , Kim Youngjo , Lin Fen-Chi , Wang Hsiang-Chen
Published: September 2022

Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.
Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices
Authors: Kaushik Keshav , Bhardwaj Akashdeep , Dahiya Susheela , Maashi Mashael S. , Al Moteri Moteeb , Aljebreen Mohammed , Bharany Salil
Published: September 2022

Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically. This research aims to present a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive or negative thoughts. A dataset containing reviews of 3150 users has been used in this research work. Initially, a word cloud of positive and negative reviews was plotted, which gave a lot of insight from the text data. After that, a deep learning model using a multinomial naive Bayesian classifier was built and trained using 80% of the dataset. Then the remaining 20% of the dataset was used to test the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, outperforming three.
Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition
Authors: Bento Nuno , Rebelo Joana , Barandas Marília , Carreiro André V. , Campagner Andrea , Cabitza Federico , Gamboa Hugo
Published: September 2022

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.
Power Equipment Fault Diagnosis Method Based on Energy Spectrogram and Deep Learning
Authors: Liu Yiyang , Li Fei , Guan Qingbo , Zhao Yang , Yan Shuaihua
Published: September 2022

With the development of industrial manufacturing intelligence, the role of rotating machinery in industrial production and life is more and more important. Aiming at the problems of the complex and changeable working environment of rolling bearings and limited computing ability, fault feature information cannot be effectively extracted, and the current deep learning model is difficult to be compatible with lightweight and high efficiency. Therefore, this paper proposes a fault detection method for power equipment based on an energy spectrum diagram and deep learning. Firstly, a novel two-dimensional time-frequency feature representation method and energy spectrum feature map based on wavelet packet transform is proposed, and an energy spectrum feature map dataset is made for subsequent diagnosis. This method can realize multi-resolution analysis, fully extract the feature information contained in the fault signal, and accelerate the convergence of the subsequent diagnosis model. Secondly, a lightweight residual dense convolutional neural network model (LR-DenseNet) is proposed. This model combines the advantages of residual learning and a dense connection, and can not only extract deep features more easily, but can also effectively use shallow features. Then, based on the lightweight residual dense convolutional neural network model, an LR-DenseSENet model is proposed. By introducing the transfer learning strategy and adding the channel domain, an attention mechanism is added to the channel feature fusion layer, with the accuracy of detection up to 99.4%, and the amount of parameter calculation greatly reduced to one-fifth of that of VGG. Finally, through an experimental analysis, it is verified that the fault detection model designed in this paper based on the combination of an energy spectrum feature map and LR-DenseSENet achieves a satisfactory detection effect.
Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach
Authors: Mukherjee Pratyusa , Garg Hitendra , Pradhan Chittaranjan , Ghosh Soumik , Chowdhury Subrata , Srivastava Gautam
Published: September 2022

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.
The Use of Soil Moisture and Pore-Water Pressure Sensors for the Interpretation of Landslide Behavior in Small-Scale Physical Models
Authors: Peranić Josip , Čeh Nina , Arbanas Željko
Published: September 2022

This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation constructions’ behavior under static and seismic actions”, and its main components are briefly described with the particular focus on the sensor network that allows monitoring changes in soil moisture and pore-water pressure (pwp). The technical characteristics of the sensors and the measurement methods used to provide the metrics are described in detail. Some data on the hydraulic and mechanical responses obtained from the conducted tests on slope models built from different soil types under different test conditions are presented and interpreted in the context of rainfall-induced landslides. The results show that the sensor network used is suitable for monitoring changes in the soil moisture and pwp in the model, both in terms of the transient rainfall infiltration through partially saturated soil and in terms of the rise in the water table and pwp build-up under fully saturated conditions. It is shown how simultaneous monitoring of soil moisture and pwp can be used to reconstruct stress paths that the monitored points undergo during different test phases. Finally, some peculiarities related to hydraulic hysteresis and surface erosion that were observed in some of tests are discussed, as well as possible difficulties in achieving and maintaining the targeted initial moisture distribution in slope models.
A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques
Authors: Paepae Thulane , Bokoro Pitshou , Kyamakya Kyandoghere
Published: September 2022

Harmful cyanobacterial bloom (HCB) is problematic for drinking water treatment, and some of its strains can produce toxins that significantly affect human health. To better control eutrophication and HCB, catchment managers need to continuously keep track of nitrogen (N) and phosphorus (P) in the water bodies. However, the high-frequency monitoring of these water quality indicators is not economical. In these cases, machine learning techniques may serve as viable alternatives since they can learn directly from the available surrogate data. In the present work, a random forest, extremely randomized trees (ET), extreme gradient boosting, k-nearest neighbors, a light gradient boosting machine, and bagging regressor-based virtual sensors were used to predict N and P in two catchments with contrasting land uses. The effect of data scaling and missing value imputation were also assessed, while the Shapley additive explanations were used to rank feature importance. A specification book, sensitivity analysis, and best practices for developing virtual sensors are discussed. Results show that ET, MinMax scaler, and a multivariate imputer were the best predictive model, scaler, and imputer, respectively. The highest predictive performance, reported in terms of R2, was 97% in the rural catchment and 82% in an urban catchment.