Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-analysis of the effects of denosumab and romosozumab on bone mineral density and turnover markers in patients with osteoporosis

Purpose To assess the alterations in bone mineral density and bone turnover marker concentrations following the administration of denosumab and romosozumab therapies in patients with osteoporosis. Methods PubMed was searched for studies published until January 28, 2023, that investigated the clinical efficacy and bone turnover marker changes of denosumab and romosozumab in the treatment of osteoporosis, with a minimum follow-up of 3 months in each study. Studies were screened, and data on changes in bone mineral density (BMD), P1NP, and TRACP-5b levels after treatment were extracted and included in the analysis. Results Six studies were analyzed. At 3 months after treatment, the romosozumab group showed greater changes in lumbar BMD and bone turnover markers. BMD of total hip and femoral neck was relatively delayed. Beginning at 6 to 12 months, romosozumab showed greater changes in bone mineral density and markers of bone turnover. Conclusion Both romosozumab and denosumab have antiosteoporotic effects, with greater effects on BMD and bone turnover markers observed within 12 months of romosozumab treatment. Systematic Review Registration https://www.crd. york.ac. uk/prospero, identifier CRD42023395034.
An intelligent hierarchical residual attention learning‐based conjoined twin neural network for Alzheimer's stage detection and prediction

Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning‐based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain‐MRI) are used to detect Alzheimer's in computable research work. To correctly categorize the stages of Alzheimer's disease, discriminative features need to be extracted from the MR images. Recently, many studies have used deep learning methods for the early detection of this disorder. However, overfitting degrades the deep learning method's performance because the dataset's selection images are smaller and imbalanced. Some studies could not reach more discriminative and effectual attention‐aware features for Alzheimer's stage classification to increase the model performance. In this paper, we develop a novel hierarchical residual attention learning‐inspired multistage conjoined twin network (HRAL‐CTNN) to classify the stages of Alzheimer's. We used augmentation approaches to scale insufficient and imbalanced data. The HRAL‐CTNN is efficiently overcoming the issues of not obtaining efficient attention‐aware and generative features for Alzheimer's stage classification. The proposed model solved the problem of redundant features by extracting attentive discriminant features, and scaling imbalance data by data augmentation, after that training and validation using HRAL‐CTNN. The execution of this proposed work has been performed on the ADNI MRI dataset. This work achieved outstanding accuracy of 99.97 ±$$ \pm $$ 0.01% and F1 score of 99.30 ±$$ \pm $$ 0.02% for Alzheimer's stage classification. This model proposed by our group outperformed the existing related studies in terms of the model's performance score.
Phytochemical Profiles and Anticancer Effects of Calophyllum inophyllum L. Extract Relating to Reactive Oxygen Species Modulation on Patient-Derived Cells from Breast and Lung Cancers

Reactive oxygen species (ROS) contribute to cancer growth and metastasis. Using antioxidants to modulate cellular ROS levels is a promisingstrategy for cancer prevention and treatment. Calophyllum inophyllum L. , or tamanu, is a medicinal plant renowned for its anti-inflammatory, antioxidant, and anticancer properties in traditional medicine systems. However, the anticancer effects of C. inophyllum extract on cellular ROS remain unexplored. This study represents the first report on such effects and provides the potential mechanisms underlying the anticancer properties of C. inophyllum extract. The branches of C. inophyllum were extracted, and the extract was comprehensively analyzed for phytochemical constituents, antioxidant capacity, total phenolic content, and total flavonoid content. Subsequently, the extract's potential anticancer properties were evaluated using patient-derived cells from breast and lung cancer. The results revealed that the C. inophyllum extract possesses notable antioxidant activity and demonstrated no cytotoxicity within the initial 24 h of treatment. However, after 72 h, it exhibited significant antiproliferative effects. Moreover, the extract exhibited inhibitory properties against migration and invasion at concentrations below the IC50, which corresponded to the expression of related genes. Notably, these effects correlated with the reduction of intracellular ROS levels. Overall, our findings highlight the anticancer potential of C. inophyllum extract, emphasize its ability to modulate cellular ROS levels and target key molecular pathways involved in cancer progression. This study sheds light on the promising therapeutic implications of C. inophyllum extract as a novel agent for cancer treatment, which is safe for normal cells.
HUMAN RESOURCE ACCOUNTING AND ORGANIZATIONAL PERFORMANCE OF DEPOSIT MONEY BANKS LISTED IN NIGERIA

Through its many operations and services, the banking industry significantly raises the level of life in every country, whether it is emerging or developed. The report's creation and presentation are the responsibility of the human resource. Every capital market has to develop public trust in its participants as well as an environment of transparency and trust. Deposit money banks undoubtedly face a number of difficulties, just like any other business or institution in existence. How to maintain excellent performance, what constitutes good performance, and how to quantify it are just a few of the many issues that banks must deal with. Performance management might seriously harm banks if it is neglected or implemented incorrectly. As a consequence of the foregoing, the study examined the impact of human resource accounting on deposit money banks' organizational performance in Nigeria. An ex-post facto research design was used in the study. The financial statements of deposit money banks in Nigeria were used as a source for secondary data. In order to conduct this research, convenience sampling was used, and ten (10) Deposit Money Banks in Nigeria were selected at random from a total of twenty-four (24) banks for a ten-year study period (2012–2021). This corresponds to 100 bank-year observations. Descriptive and inferential statistics were used in the data analysis. According to the study's findings, human resource accounting significantly affected both return on equity and return on capital employed (F- Stat (2,97) = 1.72, p > 0.05, Adj R2 = 0.55 and F- Stat (2,97) = 138.8 and 0.45, respectively). The study found a substantial correlation between organizational performance of deposit money banks registered in Nigeria and human resource accounting. However, it is advised that bank management make use of these various human resource techniques, which will aid them regardless of whether the ongoing business is in danger or is distressed, and which will ultimately have an impact on the profits per share, return on equity, and return on capital employed, which are of the utmost importance.
Clinical Trials of Cellular Therapies in Solid Tumors

Simple Summary Cell therapy approaches, including adoptive cell therapy with engineered T cells remains challenging in the clinical setting of solid tumors. Clinical results have not been encouraging so far, with a general lack of significant therapeutic response and presence of on-target off-tumor toxicity. For this reason, novel cell therapy programs are currently exploring (i) advanced therapy medicinal products capable of increasing T cell affinity or avidity; and (ii) cell therapy in combination with other therapeutic agents. Our review will focus on the current clinical research in this setting, which will likely play a role in improving cancer treatment outcomes in the near future. Abstract In the past years cancer treatments have drastically changed, mainly due to the development of immune checkpoint inhibitors capable of immune modulation in vivo, thus providing major clinical benefit in a number of malignancies. Simultaneously, considerable technical refinements have opened new prospects for the development of immune cell-based medicinal products and unprecedented success with chimeric antigen receptor (CAR)-T cells targeting B-cell hematologic malignancies has been obtained. However, T cell therapies introduced and performed in the field of solid tumors have produced so far only limited responses in selected patient populations. This standstill is attributable to the difficulty in identifying target antigens which are homogeneously expressed by all tumor cells while absent from normal tissues, and the limited T cell persistence and proliferation in a hostile tumor microenvironment that favors immune escape. Replicating the results observed in hematology is a major scientific challenge in solid tumors, and ongoing translational and clinical research is focused on obtaining insight into the mechanisms of tumor recognition and evasion, and how to improve the efficacy of cellular therapies, also combining them with immune checkpoint inhibitors or other agents targeting either the cancer cell or the tumor environment. This paper provides an overview of current adaptive T cell therapy approaches in solid tumors, the research performed to increase their efficacy and safety, and results from ongoing clinical trials.
Ettringite as a factor causing structural strengthening of fluvial sand from the Praski terrace (Warsaw, Poland)

: Zawrzykraj, P., Rydelek. , P., Bąkowska, A. and Cabalski, K. 2022. Ettringite as a factor causing structural strengthening of fluvial sand from the Praski terrace (Warsaw, Poland). Acta Geologica Polonica , 72 (4), 519–528. Warszawa. Engineering activity may lead to uncontrolled changes in the geological environment. This paper presents an example of structural changes in fluvial sand of the Praski terrace (in Warsaw) caused by the activity of a temporary concrete batching plant. Our investigations made it possible to identify the material responsible for the structural anomalies observed in the bottom of the trench excavation. The compound responsible for the cementation phenomenon was identified as ettringite – hydrated calcium aluminosulphate: Ca 6 Al 2 [(OH) 12 (SO 4 ) 3 ]·26H 2 O. The source of ettringite were most probably significant volumes of contaminants coming from the temporary concrete batching plant (e.g., from the rinsing of concrete mixers and/or installations for concrete storage and transportation). While penetrating into the ground, ettringite caused extensive cementation of the soil mass, mainly in the saturation zone. As a result, the mineral (chemical) composition of the inter-grain space changed and the structure of the sand was strengthened. The estimated zone of volumetric changes in soil properties was about 6 thousand m 3 . However, analysis of the chemical composition of groundwater for its potential sulphate contamination, did not reveal any anomalous concentrations of sulphates.
Genome-Wide Characterization of PEBP Gene Family and Functional Analysis of TERMINAL FLOWER 1 Homologs in Macadamia integrifolia

Edible Macadamia is one of the most important commercial nut trees cultivated in many countries, but its large tree size and long juvenile period pose barriers to commercial cultivation. The short domestication period and well-annotated genome of Macadamia integrifolia create great opportunities to breed commercial varieties with superior traits. Recent studies have shown that members of the phosphatidylethanolamine binding protein (PEBP) family play pivotal roles in regulating plant architecture and flowering time in various plants. In this study, thirteen members of MiPEBP were identified in the genome of M. integrifolia, and they are highly similarity in both motif and gene structure. A phylogenetic analysis divided the MiPEBP genes into three subfamilies: MFT-like, FT-like and TFL1-like. We subsequently identified two TERMINAL FLOWER 1 homologues from the TFL1-like subfamily, MiTFL1 and MiTFL1-like, both of which were highly expressed in stems and vegetative shoots, while MiTFL1-like was highly expressed in young leaves and early flowers. A subcellular location analysis revealed that both MiTFL1 and MiTFL1-like are localized in the cytoplasm and nucleus. The ectopic expression of MiTFL1 can rescue the early-flowering and terminal-flower phenotypes in the tfl1–14 mutant of Arabidopsis thaliana, and it indicates the conserved functions in controlling the inflorescence architecture and flowering time. This study will provide insight into the isolation of PEBP family members and the key targets for breeding M. integrifolia with improved traits in plant architecture and flowering time.
Lignocellulosic Bionanomaterials for Biosensor Applications

The rapid population growth, increasing global energy demand, climate change, and excessive use of fossil fuels have adversely affected environmental management and sustainability. Furthermore, the requirements for a safer ecology and environment have necessitated the use of renewable materials, thereby solving the problem of sustainability of resources. In this perspective, lignocellulosic biomass is an attractive natural resource because of its abundance, renewability, recyclability, and low cost. The ever-increasing developments in nanotechnology have opened up new vistas in sensor fabrication such as biosensor design for electronics, communication, automobile, optical products, packaging, textile, biomedical, and tissue engineering. Due to their outstanding properties such as biodegradability, biocompatibility, non-toxicity, improved electrical and thermal conductivity, high physical and mechanical properties, high surface area and catalytic activity, lignocellulosic bionanomaterials including nanocellulose and nanolignin emerge as very promising raw materials to be used in the development of high-impact biosensors. In this article, the use of lignocellulosic bionanomaterials in biosensor applications is reviewed and major challenges and opportunities are identified.
Design and co-simulation of a fuzzy gain-scheduled PID controller based on particle swarm optimization algorithms for a quad tilt wing unmanned aerial vehicle

This paper deals with the systematic design and hardware co-simulation of a fuzzy gain-scheduled proportional–integral–derivative (GS-PID) controller for a quad tilt wing (QTW) type of unmanned aerial vehicles (UAVs) based on different variants of the particle swarm optimization (PSO) algorithm. The fuzzy PID gains scheduling problem for the stabilization of the roll, pitch and yaw dynamics of the QTW vehicle is formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. PSO algorithms with variable inertia weight (PSO-In), PSO with constriction factor (PSO-Co) and PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim further to improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. The robustness of the designed PSO-based fuzzy GS-PID controllers under actuators faults is shown on the non-linear model of the QTW. All optimized fuzzy GS-PID controllers are then co-simulated within a processor-in-the-loop (PIL) framework based on an embedded NI myRIO-1900 board and a host PC. Such a proposed software (SW) and hardware (HW) computer aided design (CAD) platform is based on the Control Design and Simulation (CDSim) module of the LabVIEW environment as well as a set-up Network Streams-based data communication protocol. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based fuzzy gains scheduled PID controllers for the QTW’s attitude flight stabilization.
A Cautionary Tale: Mediation Analysis Applied to Censored Survival Data

Recent advances in causal mediation analysis have formalized conditions for estimating direct and indirect effects from empirical studies in various contexts. These approaches have been extended to a number of models for a survival outcome including the accelerated failure time (AFT) model. In this setting, it has been suggested that under standard assumptions, the "difference" and "product" methods produce equivalent estimates of the indirect effects of exposure on the survival outcome. We formally show that these two methods may produce substantially different estimates in the presence of censoring, due to a form of model misspecification. Specifically, we establish that while the product method remains valid under standard assumptions in the presence of independent censoring, the difference method can be biased in the presence of such censoring whenever the error distribution of the AFT model fails to be collapsible upon marginalizing over the mediator. This will invariably be the case for most choices of mediator and outcome error distributions. A notable exception arises in case of normal mediator-normal outcome where we show consistency of both difference and product estimators in the presence of independent censoring. These results are confirmed in simulation studies and two data applications.
Green Is Not the Same as Green: Differentiating Between the Association of Trees and Open Green Spaces With Children’s Brain Structure in the Netherlands

There is accumulating evidence for positive effects of green spaces on mental and brain health. Here we investigated whether differentiating the types of green spaces may be relevant. On longitudinal data of children (N = 95) from the Netherlands, we quantified the link between green space exposure at home from birth onwards and MRI brain structure at 12.5 years. We differentiated between green space resulting from trees versus open green spaces and also associated visibility of sky (sky view factor) with brain structure (200 m buffer around home address). We observed a positive association between grey matter volume in different prefrontal clusters and green open space coverage as well as sky view, but a negative association within prefrontal clusters for tree cover density. Most importantly, in the medial prefrontal cortex, the only region in which all three analyses overlapped, the visibility of sky was the most important predictor. Our findings advance knowledge on health-promoting, evidence-based urban planning.
Recycling of solution spaces in multipreconditioned FETI methods applied to structural dynamics

This article presents a new method to recycle the solution space of an adaptive multipreconditioned finite element tearing and interconnecting algorithm in the case where the same operator is solved for multiple right‐hand sides like in linear structural dynamics. It accelerates the computation from the second time step on by applying a coarse space that is generated from Ritz approximations of local eigenproblems, using the solution space of the first time step. These eigenproblems are known to provide very efficient coarse spaces but must usually be solved a priori at high computational cost. Their Ritz approximations are much smaller and less expensive to solve. Recycling methods based on Ritz approximations of global eigenproblems have been published for classical finite element tearing and interconnecting algorithms, but their efficient application to multipreconditioned variants is not possible. This article also presents the application of a simpler recycling procedure, which reuses plain solution spaces, to adaptive multipreconditioned finite element tearing and interconnecting. Numerical results of the application of the presented methods to four test cases are shown. The new Ritz approximation method leads to coarse spaces, which turn out to be as efficient as those obtained from solving the unreduced eigenproblems. It is the most efficient recycling method currently available for multipreconditioned dual domain decomposition techniques.
Adaptive neural network control of an arm-string system with actuator fault based on a PDE model

In this paper, an adaptive boundary controller for an undersea detection robot system with actuator failure, unknown disturbance and boundary deflection constraint is proposed. Using Hamilton’s principle, a partial differential equation (PDE) model is established for the detection system, which consists of a rigid arm, a flexible string and a sensor. Considering the actuator failure, a fault-tolerant scheme is proposed to tackle it. To handle the unknown disturbance, we employ radial basis function (RBF) neural networks (NNs) to neutralize the boundary uncertain nonlinear disturbance. The proposed adaptive controller includes a proportional–derivative (PD) feedback structure, a fault-tolerant strategy and a NN control scheme. By choosing an appropriate Lyapunov-Krasovskii function and applying LaSalle’s Invariance Principle, the asymptotic stability of the closed-loop system is rigorously proven. Simulation results validate the proposed controller.
Dynamic Modeling of a Generator With Anisotropic Nonlinear Permanent Magnets in Finite Element Method Software

In this article, a method of dynamic modeling of nonlinear permanent magnets (PMs) with recoil lines in 2-D finite element analysis (FEA) software was presented. COMSOL Multiphysics 6.0 FEA software was used in this study. The method is implemented through the variable utilities. The simulation results of a spoke-type synchronous generator for a wind turbine with anisotropic aluminum–nickel–cobalt (Alnico) 5, 8B, and 9 grades were used to exemplify the model and compared. The proposed methodology can be used for the simulation of nonlinear PMs with recoil lines and includes reversible and irreversible losses of magnetization of nonlinear PMs. The effect of the magnetic field from the stator winding on nonlinear PMs during normal operation and short circuits was studied. The modeling results were compared to the model without any demagnetization and a previous study with recoil lines and averaged minimum magnetic flux points. The no-load (NL) voltages were compared before and after a demagnetization. The dynamic model showed considerable demagnetization of Alnico magnets during normal operational and three-phase short circuits. Alnico 5 and 9 showed higher sensitivity to short-circuit currents and the short-circuit currents caused remagnetization of the upper part of the magnet in the opposite direction. The anisotropy of the PM implemented in the model improved the magnetic field simulation inside the magnet and partially protected the magnet from demagnetization by inclined fields. At last, the method was experimentally verified by tests on an iron core.
Continuity pattern-based sparse Bayesian learning for inverse synthetic aperture radar imaging

Abstract. This paper considers the problem of block-sparse recovery for two-dimensional inverse synthetic aperture radar (ISAR) imaging. According to the scatterer distribution of the target scene in ISAR image, the continuity pattern in both range and cross-range domains should be considered. Therefore, the sparsity of each grid cell is controlled by four neighboring hyperparameters and the relevance between neighboring coefficients is determined by coupling parameters, which are data-dependent, so the estimation is done adaptively by an expectation–maximization algorithm. To model the pattern dependencies among neighboring scatterers on range-Doppler domain, we develop the data-dependent coupling parameters method to capture continuity pattern of ISAR signals. Simulation results show that the proposed method can achieve improvement in terms of entropy, image entropy, and image contrast. Moreover, our algorithm effectively improves reconstruction of target scene in noiseless and noisy case compared with other methods.