Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant Activity of Fucoidan Modified with Gallic Acid Using the Redox Method

Antioxidant compounds decrease the amount of intracellular reactive oxygen species (ROS) and, consequently, reduce the deleterious effects of ROS in osteoblasts. Here, we modified a 21 kDa fucoidan (FucA) with gallic acid (GA) using the redox method, to potentiate its antioxidant/protective capacity on pre-osteoblast-like cells (MC3T3) against oxidative stress. The 20 kDa FucA-GA contains 37 ± 3.0 mg GA per gram of FucA. FucA-GA was the most efficient antioxidant agent in terms of total antioxidant capacity (2.5 times), reducing power (five times), copper chelation (three times), and superoxide radical scavenging (2 times). Exposure of MC3T3 cells to H2O2 increased ROS levels and activated caspase-3 along with caspase-9. In addition, the cell viability decreased approximately 80%. FucA-GA also provided the most effective protection against oxidative damage caused by H2O2. Treatment with FucA-GA (1.0 mg/mL) increased cell viability (~80%) and decreased intracellular ROS (100%) and caspase activation (~80%). In addition, Fuc-GA (0.1 mg/mL) abolished H2O2-induced oxidative stress in zebra fish embryos. Overall, FucA-GA protected MC3T3 cells from oxidative stress and could represent a possible adjuvant for the treatment of bone fragility by counteracting oxidative phenomena.
Fuzzy-Based Privacy-Preserving Scheme of Low Consumption and High Effectiveness for IoTs: A Repeated Game Model

In the Internet of things (IoTs), data transmission via network coding is highly vulnerable to intra-generation and inter-generation pollution attacks. To mitigate such attacks, some resource-intensive privacy-preserving schemes have been adopted in the previous literature. In order to balance resource consumption and data-privacy-preserving issues, a novel fuzzy-based privacy-preserving scheme is proposed. Our scheme is constructed on a T-S fuzzy trust theory, and network coding data streams are routed in optimal clusters formulated by a designed repeated game model to defend against pollution attacks. In particular, the security of our scheme relies on the hardness of the discrete logarithm. Then, we prove that the designed repeated game model has a subgame-perfect Nash equilibrium, and the model can improve resource utilization efficiency under the condition of data security. Simulation results show that the running time of the proposed privacy-preserving scheme is less than 1 s and the remaining energy is higher than 4 J when the length of packets is greater than 400 and the number of iterations is 100. Therefore, our scheme has higher time and energy efficiency than those of previous studies. In addition, the effective trust cluster formulation scheme (ETCFS) can formulate an optimal cluster more quickly under a kind of camouflage attack.
The post-separation well-being of children and parents. What roles do physical custody arrangements and stepparents play?

ABSTRACT This study investigated the relationships between different types of post-separation families (physical custody arrangements x stepfamily status) and parents’ and children’s well-being. Using data from waves 3–12 of the German Family Panel, random- and fixed-effects regression models were estimated for two analytical samples of 1,199 resident parents and 1,129 children aged 7–17. No statistically significant relationships between physical custody arrangements and parents’ depressiveness and life satisfaction were found, but living with a partner was shown to be positively related to parental life satisfaction. Neither physical custody arrangements nor stepfamily status were related to children’s mental and physical health.
A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem

Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 & ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.
Gum Hydrocolloids Reinforced Silver Nanoparticle Sponge for Catalytic Degradation of Water Pollutants

The accumulation of organic contaminants including dyes in aquatic systems is of significant environmental concern, necessitating the development of affordable and sustainable materials for the treatment/elimination of these hazardous pollutants. Here, a green synthesis strategy has been used to develop a self-assembled gum kondagogu-sodium alginate bioconjugate sponge adorned with silver nanoparticles, for the first time. The properties of the nanocomposite sponge were then analyzed using FTIR, TGA, SEM, and MicroCT. The ensued biobased sponge exhibited hierarchical microstructure, open cellular pores, good shape memory, and mechanical properties. It merges the attributes of an open cellular porous structure with metal nanoparticles and are envisaged to be deployed as a sustainable catalytic system for reducing contaminants in the aqueous environment. This nanocomposite sponge showed enhanced catalytic effectiveness (km values up to 37 min−1 g−1 and 44 min−1 g−1 for methylene blue and 4-nitrophenol, respectively), antibacterial properties, reusability, and biodegradability (65% biodegradation in 28 days).
An Effective Shrinkage Control Method for Tooth Profile Accuracy Improvement of Micro-Injection-Molded Small-Module Plastic Gears

An effective method to control the non-linear shrinkage of micro-injection molded small-module plastic gears by combining multi-objective optimization with Moldflow simulation is proposed. The accuracy of the simulation model was verified in a micro-injection molding experiment using reference process parameters. The maximum shrinkage (Y1), volume shrinkage (Y2), addendum diameter shrinkage (Y3), and root circle diameter shrinkage (Y4) were utilized as optimization objectives to characterize the non-linear shrinkage of the studied gear. An analysis of the relationship between key process parameters and the optimization objectives was undertaken using a second-order response surface model (RSM-Quadratic). Finally, multi-objective optimization was carried out using the non-dominated sorting genetic algorithm-II (NSGA-II). The error rates for the key shrinkage dimensions were all below 2%. The simulation results showed that the gear shrinkage variables, Y1, Y2, Y3, and Y4, were reduced by 5.60%, 8.23%, 11.71%, and 11.39%, respectively. Moreover, the tooth profile inclination deviation (fHαT), the profile deviation (ffαT), and the total tooth profile deviation (FαT) were reduced by 47.57%, 23.43%, and 49.96%, respectively. Consequently, the proposed method has considerable potential for application in the high-precision and high-efficiency manufacture of small-module plastic gears.
HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging

Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin–eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.
Development of multi-polarization-mode undulator

A novel undulator has been developed for the soft X‐ray beamline BL17SU of SPring‐8. Experiments to be done at the BL17SU require linearly/circularly polarized light and fast helicity switching of circular polarized light. In addition, it must have low power density on axis to avoid a heat load problem. Therefore, the undulator has three operation modes; it works as a helical undulator for circular polarized light, figure‐8 undulator for linear polarized light and asymmetric figure‐8 undulator for fast helicity switching. In order to realize three types of undulator, the novel undulator consists of electromagnets and permanent magnets. The magnetic field has been measured at each operation mode and performances have been calculated from the measurement results.
H ∞  model reduction for discrete‐time positive systems with inhomogeneous initial conditions

This paper addresses the H ∞ model reduction problem of discrete‐time positive linear systems with inhomogeneous initial conditions. For an asymptotically stable positive system with non‐zero initial condition, our goal is to approximate it by a reduced‐order initial‐valued positive system without introducing significant error. We establish a necessary and sufficient condition for the existence of a desired reduced‐order model such that the output error between the original system and the reduced‐order one is bounded by a weighted sum of the magnitude of the input and that of the initial condition. Moreover, based on congruent transformation and the dual form of bounded real lemma, several equivalent conditions are derived in terms of LMIs and an iterative convex optimization algorithm is developed accordingly. Finally, an illustrative example is presented to show the effectiveness of the proposed methods. Copyright © 2013 John Wiley & Sons, Ltd.
Vitamin D Analogs Bearing C-20 Modifications Stabilize the Agonistic Conformation of Non-Responsive Vitamin D Receptor Variants

The Vitamin D receptor (VDR) plays a key role in calcium homeostasis, as well as in cell proliferation and differentiation. Among the large number of VDR ligands that have been developed, we have previously shown that BXL-62 and Gemini-72, two C-20-modified vitamin D analogs are highly potent VDR agonists. In this study, we show that both VDR ligands restore the transcriptional activities of VDR variants unresponsive to the natural ligand and identified in patients with rickets. The elucidated mechanisms of action underlying the activities of these C-20-modified analogs emphasize the mutual adaptation of the ligand and the VDR ligand-binding pocket.
Na+/H+-exchanger 1 Enhances Antitumor Activity of Engineered NK-92 Natural Killer Cells

Adoptive cell transfer (ACT) immunotherapy has remarkable efficacy against some hematologic malignancies. However, its efficacy in solid tumors is limited by the adverse tumor microenvironment (TME) conditions, most notably that acidity inhibits T and natural killer (NK) cell mTOR complex 1 (mTORC1) activity and impairs cytotoxicity. In several reported studies, systemic buffering of tumor acidity enhanced the efficacy of immune checkpoint inhibitors. Paradoxically, we found in a MYC–driven hepatocellular carcinoma model that systemic buffering increased tumor mTORC1 activity, negating inhibition of tumor growth by anti-PD1 treatment. Therefore, in this proof-of-concept study, we tested the metabolic engineering of immune effector cells to mitigate the inhibitory effect of tumor acidity while avoiding side effects associated with systemic buffering. We first overexpressed an activated RHEB in the human NK cell line NK-92, thereby rescuing acid-blunted mTORC1 activity and enhancing cytolytic activity. Then, to directly mitigate the effect of acidity, we ectopically expressed acid extruder proteins. Whereas ectopic expression of carbonic anhydrase IX (CA9) moderately increased mTORC1 activity, it did not enhance effector function. In contrast, overexpressing a constitutively active Na+/H+-exchanger 1 (NHE1; SLC9A1) in NK-92 did not elevate mTORC1 but enhanced degranulation, target engagement, in vitro cytotoxicity, and in vivo antitumor activity. Our findings suggest the feasibility of overcoming the inhibitory effect of the TME by metabolically engineering immune effector cells, which can enhance ACT for better efficacy against solid tumors. Significance: This study demonstrates the feasibility of metabolic engineering immune effector cells to overcome inhibition in the TME, an approach that could enhance the efficacy of adoptive transfer immunotherapy.
Live Drosophila melanogaster Larvae Deter Oviposition by Drosophila suzukii

Simple Summary The invasive insect pest, Drosophila suzukii Matsumura or spotted-wing drosophila (SWD) lays its eggs in soft and stone fruit. Eggs hatch into larvae, which feed on fruit, causing fruit collapse and significant economic losses worldwide. Current control methods rely primarily on foliar insecticide applications, which are not sustainable long-term solutions. In nature, D. suzukii interacts with and encounters other Drosophila species, especially towards the end of the growing season when ripening fruits are scarce. We showed previously that D. suzukii were deterred from laying eggs on artificial media exposed to egg laying Drosophila melanogaster, its sister species. It was hypothesized that a signal was left by D. melanogaster which deterred D. suzukii from laying eggs. This study aimed to identify from which D. melanogaster life stage the egg laying deterrent signal originated and we showed that the presence of live D. melanogaster larvae on the egg laying media deter D. suzukii from laying eggs. Drosophila melanogaster cuticular hydrocarbons were examined as the signal source, but no evidence was found for their involvement. These results have improved our understanding of the interspecific interactions between D. suzukii and other Drosophila species and could provide new innovative approaches to D. suzukii management strategies. Abstract The worldwide invasive insect pest, Drosophila suzukii Matsumura (spotted-wing Drosophila), lays eggs in soft and stone fruit before harvest. Hatched larvae cause fruit collapse and significant economic losses. Current control methods rely primarily on foliar insecticide applications, which are not sustainable long-term solutions due to regulatory restrictions and the risk of insecticide resistance developing. We showed before that D. suzukii were deterred from laying eggs on artificial media previously visited by its sister species—Drosophila melanogaster. In the current study, laboratory choice test experiments were conducted to identify which D. melanogaster life stage (eggs, larvae, or adult) deterred D. suzukii oviposition. We demonstrated that the presence of live D. melanogaster larvae on the egg-laying media consistently deterred D. suzukii oviposition. Drosophila melanogaster cuticular hydrocarbons (CHCs) were examined as candidate for the oviposition deterrent. CHCs of larval and adult D. melanogaster and D. suzukii were analyzed. In both species, the composition of the CHCs of larvae was similar to that of adults, although quantities present were much lower. Furthermore, the CHC profiles of the two species were markedly different. However, when assayed as deterrents in the laboratory choice test experiment, CHC extracts from D. melanogaster did not deter oviposition by D. suzukii.
Cross sectional study to estimate the morbidity pattern in ascetics visited to a largest religious gathering in west central part of India

Background: Ascetics are otherworldly creatures that have thought of being ethereal. To be spiritual one has to be physically fit because a healthy body has an intimate connection with spiritual health. Methods: Cross sectional survey was accomplished in a festive gathering to estimate morbidity pattern among ascetics in central India. Results: The sample size was 960. 352 participants (36.6%) were either overweight or obese, 284 participants (29.6%) had hypertension, 236 (24.6%) were diagnosed with cataract and 194 (20.2%) had anemia, Diabetes was found in 58 participants (6%) and 28 (2.9%) had fever. Conclusion: Obesity (and overweight) and hypertension were the top two morbidities came across during study. Morbidities were found to be more associated with socio-demographical factors than spirituality.
Coupling between a deuteron and a lattice

We recently put forth a new fundamental lattice Hamiltonian based on an underlying picture of electrons and deuterons as elementary Dirac particles. Within this model there appears a term in which lattice vibrations are coupled to internal nuclear transitions. This is interesting as it has the potential to provide a connection between experiment and models that describe coherent energy transfer between two-level systems and an oscillator. In this work we describe a calculation of the coupling matrix element in the case of the deuteron based on the old empirical Hamada-Johnston model for the nucleon-nucleon interaction. The triplet S and D states of the the deuteron in the rest frame couples to a singlet P state through this new interaction. The singlet P state in this calculation is a virtual state with an energy of 125 MeV, and a coupling matrix element for $z$-directed motion given by $2.98 \times 10^{-3} ~M_J c \hat{P}_z$.
A Review of: “Statistics in Clinical Vaccine Trials, by J. Nauta”

The focus of this book is the standard statistical methods employed in clinical trials developing vaccines. The descriptions of the methods are concise and clear. They are always supported by worked out numeric examples; often the SAS code is supplied as well. The book is strewn with problems and stories taken from within the context of real vaccine trials. This makes the material easy to read, the explanations are accessible, and the practical knowledge furnished can serve as a true guide in the day to day work of a newly hired biostatistician in the vaccine field. Although it is a relatively slim volume, it touches upon nearly every aspect related to the statistics of a vaccine trial, whether it is a phase I small study, or a phase III efficacy trial involving tens of thousands of subjects. The book contains considerations about design of the trial and sample size calculations, goes through the analysis of immunogenicity data and the type of endpoints commonly used, and addresses the types of assays associated with data collection. The author has dedicated whole chapters to vaccine efficacy trials with their own set of issues and to the analysis of safety data; both are pertinent topics in the context of vaccine trials and the process of taking a new vaccine to the market. In the developed countries, vaccines have been conceivably the most effective tool in fighting infectious diseases and mortality, especially among newborns and infants. Smallpox and polio are virtually eradicated, while other childhood diseases like diphtheria, measles, pertussis, and rubella no longer present a risk for widespread outbreaks. While most of the vaccines, in use currently, have been invented and developed during the past two centuries, there is a revival in the field due to an increased understanding of the protective immune response, novel technologies to predict in vivo expressions of proteins for identification of vaccine targets, and new techniques for vaccine delivery. The potential for therapeutic vaccines like against Alzheimer’s disease and stroke or certain types of cancer is fueling further the growing interest. In light of the increasing need for biostatisticians with expertise in vaccine trials, the book is an excellent start for anyone who is entering the field, like new graduates or transfers from other areas of statistics. It also can be used by medical doctors or epidemiologists who desire a better understanding of the methods involved in the design and analysis of vaccine trials. There is a thing or two for seasoned practitioners, like estimating a protection curve from challenge study data (section 8.2) or the note on fold changes and baseline imbalances (section 3.3.3), as well as the sensitivity analysis example found in Appendix E. The book starts with two chapters addressing the basic concepts of vaccine immunology and the two types of immune response commonly measured—humoral
Parallel dynamics of disordered Ising spin systems on random graphs

We examine the non‐equilibrium dynamics of bond‐disordered Ising spin systems on random graphs with arbitrary degree distribution, using generating functional techniques. The dynamic order parameter is a measure describing the likelihood of a spin path, given a path of perturbations or external fields, which has a clear physical interpretation. For short times the order parameter set can be calculated exactly, while for longer times this is not possible and approximation schemes must be devised to study the dynamics. We study one such scheme and compare our results with simulations.