Caputo-Hadamard fractional differential equations on time scales: Numerical scheme, asymptotic stability, and chaos.

This study investigates Caputo-Hadamard fractional differential equations on time scales. The Hadamard fractional sum and difference are defined for the first time. A general logarithm function on time scales is used as a kernel function. New fractional difference equations and their equivalent fractional sum equations are presented by the use of fundamental theorems. Gronwall inequality, asymptotical stability conditions, and two discrete-time Mittag-Leffler functions of Hadamard type are obtained. Numerical schemes are provided and chaos in fractional discrete-time logistic equation and neural network equations are reported.

The association between the NAT2 genetic polymorphisms and risk of DILI during anti-TB treatment: a systematic review and meta-analysis.

AIMS: The aim of this study is to evaluate the potential association between N-acetyltransferase type 2 (NAT2) polymorphisms and drug-induced liver injury during anti-TB treatment (AT-DILI). METHODS: We conducted a systematic review and performed a meta-analysis to clarify the role of NAT2 polymorphism in AT-DILI. PubMed, Medline and EMBASE databases were searched for studies published in English to December 31, 2017, on the association between the NAT2 polymorphism and AT-DILI risk. Outcomes were pooled with random-effects meta-analysis. Details were registered in the PROSPERO register (number: CRD42016051722). RESULTS: Thirty-seven studies involving 1527 cases and 7184 controls were included in this meta-analysis. The overall odds ratio (OR) of AT-DILI associated with NAT2 slow acetylator phenotype was 3.15 (95% CI 2.58-3.84, I2  = 51.3%, P = 0.000). The OR varied between different ethnic populations, ranging from 6.42 (95% CI 2.41-17.10, I2  = 2.3%) for the West Asian population to 2.32 (95% CI 0.58-9.24, I2  = 80.3%) for the European population. Within the slow NAT2 genotype, variation was also observed; NAT2*6/*7 was associated with the highest risk of AT-DILI (OR = 1.68, 95% CI 1.09-2.59) compared to the other slow NAT2 acetylators combined. CONCLUSIONS: NAT2 slow acetylation was observed to increase the risk of AT-DILI in tuberculosis patients. Our results support the hypothesis that the slow NAT2 genotype is a risk factor for AT-DILI.

Interlocking intramedullary nail fixation with additional bone grafting from trochanter via a femoral hollow trephine in the treatment of femoral shaft fractures: design and clinical application.

PURPOSE: The purpose of this study was to describe and evaluate the clinical application of the technique of interlocking intramedullary (IM) nailing via an entry point at the tip of greater trochanter using a specially designed femoral hollow trephine to stabilize diaphyseal fractures of the femur. METHODS: From February 2010 to April 2014, 35 consecutive patients with femoral shaft fractures were treated by the therapy of bone grafting from trochanter region with interlocking IM nail. The average age of these 23 male and 12 female patients was 37.5 years (range, 22-67 years). Fractures were classified according to AO classification system (15 type A, 17 type B, 3 type C). Femoral canal reaming and the collection of cancellous bone were simultaneously performed in a single step with the specially designed femoral hollow trephine, followed by regular IM nailing procedure. RESULTS: Of the 35 cases, the mean volume of spongy bone obtained was 5.63 cm3 (range, 3.0-7.0 cm3). Thirty-five patients with femoral shaft fractures had a mean follow-up period of 16.2 months (range, 12-22 months). All patients achieved bony union, at a mean of 5.4 months (range, 4-6 months). No patient developed a delayed union or a nonunion. There were no complications such as infections, injury of vascular and nerve, or heterotopic ossification in hip. CONCLUSIONS: These results indicate that the technique of use of IM nailing with the femoral hollow trephine significantly decreases the occurrence of nonunion in femoral shaft fractures.

Bufalin Inhibits the Differentiation and Proliferation of Cancer Stem Cells Derived from Primary Osteosarcoma Cells through Mir-148a.

BACKGROUND/AIMS: Osteosarcoma (OS) is the second leading cause of cancer-related death in children and young adults. Chemoresistance is the most important cause of treatment failure in OS, largely resulting from presence of cancer stem cells (CSCs). However, CSCs isolated from cancer cell lines do not necessarily represent those from primary human tumors due to accumulation of genetic aberrations that increase with passage number. Therefore, studies on CSCs from primary OS may be more important for understanding the mechanisms driving the chemoresistance of CSCs in OS. METHODS: We established a primary culture of OS cells, known as C1OS, from freshly resected tumor tissue. We further isolated CSCs from C1OS cells (C1OS-CSCs). We analyzed the effects of bufalin, a traditional Chinese medicine, on the stemness of C1OS-CSCs. We also analyzed the microRNA (miR) targets of bufalin on the stemness of C1OS-CSCs. Moreover, we examined these findings in the OS specimen. RESULTS: Bufalin inhibited the stemness of C1OS-CSCs. Moreover, we found that miR-148a appeared to be a target of bufalin, and miR-148a further regulated DNMT1 and p27 to control the stemness of OS cells. This mechanism was further confirmed in OS specimen. CONCLUSION: Our data suggest that bufalin may be a promising treatment for OS, and its function may be conducted through regulation of miR-148a.

Post-transcriptional regulation tends to attenuate the mRNA noise and to increase the mRNA gain.

Post-transcriptional regulation is ubiquitous in prokaryotic and eukaryotic cells, but how it impacts gene expression remains to be fully explored. Here, we analyze a simple gene model in which we assume that mRNAs are produced in a constitutive manner but are regulated post-transcriptionally by a decapping enzyme that switches between the active state and the inactive state. We derive the analytical mRNA distribution governed by a chemical master equation, which can be well used to analyze the mechanism of how post-transcription regulation influences the mRNA expression level including the mRNA noise. We demonstrate that the mean mRNA level in the stochastic case is always higher than that in the deterministic case due to the stochastic effect of the enzyme, but the size of the increased part depends mainly on the switching rates between two enzyme states. More interesting is that we find that in contrast to transcriptional regulation, post-transcriptional regulation tends to attenuate noise in mRNA. Our results provide insight into the role of post-transcriptional regulation in controlling the transcriptional noise.

Prediction of myelopathic level in cervical spondylotic myelopathy using diffusion tensor imaging.

PURPOSE: To investigate the use of a newly designed machine learning-based classifier in the automatic identification of myelopathic levels in cervical spondylotic myelopathy (CSM). MATERIALS AND METHODS: In all, 58 normal volunteers and 16 subjects with CSM were recruited for diffusion tensor imaging (DTI) acquisition. The eigenvalues were extracted as the selected features from DTI images. Three classifiers, naive Bayesian, support vector machine, and support tensor machine, and fractional anisotropy (FA) were employed to identify myelopathic levels. The results were compared with clinical level diagnosis results and accuracy, sensitivity, and specificity were calculated to evaluate the performance of the developed classifiers. RESULTS: The accuracy by support tensor machine was the highest (93.62%) among the three classifiers. The support tensor machine also showed excellent capacity to identify true positives (sensitivity: 84.62%) and true negatives (specificity: 97.06%). The accuracy by FA value was the lowest (76%) in all the methods. CONCLUSION: The classifiers-based method using eigenvalues had a better performance in identifying the levels of CSM than the diagnosis using FA values. The support tensor machine was the best among three classifiers.

Thermodynamics-based models of transcriptional regulation with gene sequence.

Quantitative models of gene regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled or heuristic approximations of the underlying regulatory mechanisms. In this work, we have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence. The proposed model relies on a continuous time, differential equation description of transcriptional dynamics. The sequence features of the promoter are exploited to derive the binding affinity which is derived based on statistical molecular thermodynamics. Experimental results show that the proposed model can effectively identify the activity levels of transcription factors and the regulatory parameters. Comparing with the previous models, the proposed model can reveal more biological sense.

Joint subchannel pairing and power control for cognitive radio networks with amplify-and-forward relaying.

Dynamic spectrum sharing has drawn intensive attention in cognitive radio networks. The secondary users are allowed to use the available spectrum to transmit data if the interference to the primary users is maintained at a low level. Cooperative transmission for secondary users can reduce the transmission power and thus improve the performance further. We study the joint subchannel pairing and power allocation problem in relay-based cognitive radio networks. The objective is to maximize the sum rate of the secondary user that is helped by an amplify-and-forward relay. The individual power constraints at the source and the relay, the subchannel pairing constraints, and the interference power constraints are considered. The problem under consideration is formulated as a mixed integer programming problem. By the dual decomposition method, a joint optimal subchannel pairing and power allocation algorithm is proposed. To reduce the computational complexity, two suboptimal algorithms are developed. Simulations have been conducted to verify the performance of the proposed algorithms in terms of sum rate and average running time under different conditions.

Defining biological networks for noise buffering and signaling sensitivity using approximate Bayesian computation.

Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.

Facet impact of CeO2@C 2D core-shell structure on electrochemical reaction kinetic factor and efficient detection of nitrite.

Fine-tuning the surface structure of transition metal oxides at the atomic level is a promising way to improve the catalytic properties of materials. However, the influence of crystal surface structure on electrode reaction kinetics is still limited. In this study, we propose an in-situ synthesis strategy to obtain two-dimensional carbon/cerium oxide core-shell nanosheets by thermal decomposition of Ce-MOF nanosheets grown on the surface of carbon nanostructures, and fine-tuning the surface structure by introducing oxygen vacancies through defect engineering during the oxide nucleation process is conducted to obtain controllable exposed {111} and {110} surface CeO2@C composites. Both experiments and theoretical calculations show that the {110} -dominated nanocomplex (CeO2@C-350S) has better kinetic behavior and catalytic activity due to its abundant surface defects, which is manifested in higher active surface area, richer carrier concentration, and better promotion of diffusion and adsorption. In addition, CeO2@C-350S electrode has an extremely wide linear range and good stability in the electrochemical detection of nitrite. After 1000 times of the accelerated cycle experiments, CeO2@C-350S electrode still maintains 79.3 % of its initial current response, and recovers to 87.3 % after 10 min of stopping the test. The electrode stability is excellent, which is attributed to the clever carbon shell structure of the material. This synthesis strategy can be extended to other carbon-based oxide composite catalysts to improve the electrocatalytic performance and overall stability by adjusting the surface structure.

Prior-Guided Adversarial Learning With Hypergraph for Predicting Abnormal Connections in Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished the classification task, but few of them can accurately evaluate the changing characteristics of brain connectivity. In this work, a prior-guided adversarial learning with hypergraph (PALH) model is proposed to predict abnormal brain connections using triple-modality medical images. Concretely, a prior distribution from anatomical knowledge is estimated to guide multimodal representation learning using an adversarial strategy. Also, the pairwise collaborative discriminator structure is further utilized to narrow the difference in representation distribution. Moreover, the hypergraph perceptual network is developed to effectively fuse the learned representations while establishing high-order relations within and between multimodal images. Experimental results demonstrate that the proposed model outperforms other related methods in analyzing and predicting AD progression. More importantly, the identified abnormal connections are partly consistent with previous neuroscience discoveries. The proposed model can evaluate the characteristics of abnormal brain connections at different stages of AD, which is helpful for cognitive disease study and early treatment.

Estimating Addiction-Related Brain Connectivity by Prior-Embedding Graph Generative Adversarial Networks.

The study of nicotine addiction mechanism is of great significance in both nicotine withdrawal and brain science. The detection of addiction-related brain connectivity using functional magnetic resonance imaging (fMRI) is a critical step in study of this mechanism. However, it is challenging to accurately estimate addiction-related brain connectivity due to the low-signal-to-noise ratio of fMRI and the issue of small sample size. In this work, a prior-embedding graph generative adversarial network (PG-GAN) is proposed to capture addiction-related brain connectivity accurately. By designing a dual-generator-based scheme, the addiction-related connectivity generator is employed to learn the feature map of addiction connection, while the reconstruction generator is used for sample reconstruction. Moreover, a bidirectional mapping mechanism is designed to maintain the consistency of sample distribution in the latent space so that addiction-related brain connectivity can be estimated more accurately. The proposed model utilizes prior knowledge embeddings to reduce the search space so that the model can better understand the latent distribution for the issue of small sample size. Experimental results demonstrate the effectiveness of the proposed PG-GAN.

Addiction-related brain networks identification via Graph Diffusion Reconstruction Network.

Functional magnetic resonance imaging (fMRI) provides insights into complex patterns of brain functional changes, making it a valuable tool for exploring addiction-related brain connectivity. However, effectively extracting addiction-related brain connectivity from fMRI data remains challenging due to the intricate and non-linear nature of brain connections. Therefore, this paper proposed the Graph Diffusion Reconstruction Network (GDRN), a novel framework designed to capture addiction-related brain connectivity from fMRI data acquired from addicted rats. The proposed GDRN incorporates a diffusion reconstruction module that effectively maintains the unity of data distribution by reconstructing the training samples, thereby enhancing the model's ability to reconstruct nicotine addiction-related brain networks. Experimental evaluations conducted on a nicotine addiction rat dataset demonstrate that the proposed GDRN effectively explores nicotine addiction-related brain connectivity. The findings suggest that the GDRN holds promise for uncovering and understanding the complex neural mechanisms underlying addiction using fMRI data.

BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment.

Effective connectivity estimation plays a crucial role in understanding the interactions and information flow between different brain regions. However, the functional time series used for estimating effective connectivity is derived from certain software, which may lead to large computing errors because of different parameter settings and degrade the ability to model complex causal relationships between brain regions. In this paper, a brain diffuser with hierarchical transformer (BDHT) is proposed to estimate effective connectivity for mild cognitive impairment (MCI) analysis. To our best knowledge, the proposed brain diffuser is the first generative model to apply diffusion models to the application of generating and analyzing multimodal brain networks. Specifically, the BDHT leverages structural connectivity to guide the reverse processes in an efficient way. It makes the denoising process more reliable and guarantees effective connectivity estimation accuracy. To improve denoising quality, the hierarchical denoising transformer is designed to learn multi-scale features in topological space. By stacking the multi-head attention and graph convolutional network, the graph convolutional transformer (GraphConformer) module is devised to enhance structure-function complementarity and improve the ability in noise estimation. Experimental evaluations of the denoising diffusion model demonstrate its effectiveness in estimating effective connectivity. The proposed model achieves superior performance in terms of accuracy and robustness compared to existing approaches. Moreover, the proposed model can identify altered directional connections and provide a comprehensive understanding of parthenogenesis for MCI treatment.

Anti-osteoporosis activity of casticin in ovariectomized rats.

Background: Postmenopausal osteoporosis (PMPO) is the most familiar type of osteoporosis, a silent bone disease. Casticin, a natural flavonoid constituent, improves osteoporosis in animal model. Nevertheless, the potential mechanism remains to be further explored. Methods: A model of PMPO was established in rats treated with ovariectomy (OVX) and RAW 264.7 cells induced with receptor activator of nuclear factor kappa-B ligand (RANKL). The effect and potential mechanism of casticin on PMPO were addressed by pathological staining, measurement of bone mineral density (BMD), three-point bending test, serum biochemical detection, filamentous-actin (F-actin) ring staining, TRAcP staining, reverse transcription quantitative polymerase chain reaction, western blot and examination of oxidative stress indicators. Results: The casticin treatment increased the femoral trabecular area, bone maturity, BMD, elastic modulus, maximum load, the level of calcium and estrogen with the reduced concentrations of alkaline phosphatase (ALP) and tumor necrosis factor (TNF)-α in OVX rats. An enhancement in the F-actin ring formation, TRAcP staining and the relative mRNA expression of NFATc1 and TRAP was observed in RANKL-induced RAW 264.7 cells, which was declined by the treatment of casticin. Moreover, the casticin treatment reversed the reduced the relative protein expression of Nrf2 and HO-1 and the concentrations of superoxide dismutase and glutathione peroxidase, and the increased content of malondialdehyde both in vivo and in vitro. Conclusion: Casticin improved bone density, bone biomechanics, the level of calcium and estrogen, the release of pro-inflammatory factor and oxidative stress to alleviate osteoporosis, which was associated with the upregulation of Nrf2/HO-1 pathway.

3D Multimodal Fusion Network with Disease-induced Joint Learning for Early Alzheimer's Disease Diagnosis.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

Liver X receptor α inhibits osteosarcoma cell proliferation through up-regulation of FoxO1.

BACKGROUND: Osteosarcoma is the most common primary bone malignancy of adolescents and young adults. METHODS: We analyzed liver X receptor α (LXRα) mRNA expression in 16 pairs of human osteosarcoma tissues and adjacent noncancerous tissues. Moreover, we investigated LXRα's potential role in regulating cell proliferation in Saos-2 and U2OS cells. RESULTS: We found that activation of LXRα, a member of nuclear receptor, was able to inhibit cell proliferation in Saos-2 and U2OS cells. At the molecular level, our results further revealed that expression of tumor suppressor gene, FoxO1, was up-regulated by LXRα activation. LXRα activates FoxO1 transcription through a direct binding on its promoter region. CONCLUSION: LXRα acts as a tumor suppressor for osteosarcoma, which may offer a new way in molecular targeting cancer treatment.

Production scheduling of prefabricated components considering delivery methods.

To address the processing scheduling problem involving multiple molds, components, and floors, we propose the Genetic Grey Wolf Optimizer (GGA) as a means to optimize the production scheduling of components in a production line. This approach combines the Grey Wolf algorithm with the genetic algorithm. Previous methods have overlooked the storage requirements arising from the delivery characteristics of prefabricated components, often resulting in unnecessary storage costs. Intelligent algorithms have been demonstrated to be effective in production scheduling, and thus, to enhance the efficiency of prefabricated component production scheduling, our study presents a model incorporating a production objective function. This model takes into account production resources and delivery characteristics constraints. Subsequently, we develop a hybrid algorithm, combining the grey wolf algorithm with the genetic algorithm, to search for the optimal solution with a minimal storage cost. We validate the model using a case study, and the experimental results demonstrate that GAGWO successfully identifies the best precast production schedule. Furthermore, the precast production plan, considering the delivery method, is found to be reasonable.

Prefabricated concrete components combination schemes selection based on comprehensive benefits analysis.

In prefabricated buildings, there are numerous types of prefabricated components, forming a complex combination of schemes that are difficult to select. Therefore, this article takes prefabricated components combination schemes as the object. By constructing the evaluation index system through four aspects of assembly rate, cost, duration, and carbon footprint, then using the fuzzy gray correlation projection method to evaluate and select. A residential in Wuhan, China, was enlisted to conduct a case study to show the application of the proposed method. Results indicate that among the six choices, the L scheme is optimal, and the selection order of the prefabricated components in different scenarios is ranked. The results reveal that the method has good applicability, simultaneously provides a reasonable and effective reference for each participant of the assembled building when making scheme comparison, and also provides a new method for the evaluation study of prefabricated component combination schemes.