Miracle-dz/UniFAR-FTUs huggingface dataset:
Biodegradable Electrospun Polyester-Urethane Nanofiber Scaffold: Codelivery Investigation of Doxorubicin-Ezetimibe and Its Synergistic Effect on Prostate Cancer Cell Line

Electrospun nanofiber-mediated drug-delivery systems are extensively applied for the controlled delivery of anticancer agents and localized cancer chemotherapy. In this work, we assessed the synergistic anticancer effect of polyurethane-polycaprolactone (PU-PCL) nanofibers (NFs) on prostate cancer cell line (PC3) and evaluate their antitumor activity in vitro. Polyurethane-polycaprolactone (PU-PCL) nanofibers were prepared by electrospinning and used for codelivery of doxorubicin hydrochloride (DOX) and ezetimibe (EZ). The morphology, weight loss, and swelling ability of the PU-PCL nanofibers were characterized. Drug release test was performed by UV-vis spectroscopy in a phosphate buffer at pH 7.4 at 37°C. The viability of the PC3 prostate cancer cells was evaluated by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay for the different periods of cell incubation. PC3 prostate cancer cells were treated with different concentrations of 0-50 μg/ml of DOX, EZ, DOX-loaded NFs, EZ-loaded NFs, and DOX-EZ coloaded NFs for 48 hours. The results demonstrated that electrospun PU-PCL NFs showed significant synergistic therapeutic efficacy on prostate cancer cells. This study proposes that the DOX-EZ codelivery using PU-PCL nanofibrous scaffold could be used as a possible approach for anticancer drug delivery for the treatment of prostate cancer.
Transforming Growth Factor-β Concerning Malarial Infection and Severity: A Systematic Review and Meta-Analysis

Transforming growth factor-β (TGF-β) is important in the pathophysiology of malaria, but its role in acute and severe malaria is largely unknown. As a result, this study used a meta-analysis approach to investigate the difference in TGF-β levels between several groups of malaria patients and healthy controls. The systematic review protocol was registered at PROSPERO (ID: CRD42022318864). From inception to 7 March 2022, studies that reported TGF-β levels in patients with uncomplicated and healthy controls and patients with severe and uncomplicated malaria were searched in PubMed, Scopus and Embase. The assessment of the quality of the included studies was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. Qualitative and quantitative syntheses were performed to narratively describe and quantitatively pool the mean difference (MD) in TGF-β levels between uncomplicated malaria and healthy controls, and between severe and uncomplicated malaria, using a random-effects model. A total of 1027 relevant articles were identified, and 13 studies were included for syntheses. The meta-analysis results show 233 patients with uncomplicated malaria and 239 healthy controls. Patients with uncomplicated malaria (233 cases) had lower mean TGF-β levels than healthy controls (239 cases; p < 0.01, pooled MD = −14.72 pg/mL, 95% confidence interval (95% CI) = −20.46 to 8.99 pg/mL, I2 = 98.82%, seven studies). The meta-analysis found no difference in mean TGF-β levels between patients with severe malaria (367 cases) and patients with uncomplicated malaria (180 cases; p = 0.11, pooled MD = −6.07 pg/mL, 95% CI = −13.48 to 1.35 pg/mL, I2 = 97.73%, six studies). The meta-analysis demonstrated decreased TGF-β levels in patients with uncomplicated malaria compared to healthy controls. In addition, no difference in TGF-β levels was found between patients with severe and uncomplicated malaria. More research is needed to determine whether TGF-β levels could be a candidate marker for malarial infection or disease severity.
Efficient face recognition method based on DCT and LDA

It has been demonstrated that the linear discriminant analysis (LDA) is an effective approach in face recognition tasks. However, due to the high dimensionality of an image space, many LDA based approaches first use the principal component analysis (PCA) to project an image into a lower dimensional space, then peeform the LDA transform to extract discriminant feature. But some useful discriminant information to the following LDA transform will be lost in the PCA step. To overcome these defects, a face recognition method based on the discrete cosine transform (DCT) and the LDA is proposed. First the DCT is used to achieve dimension reduction, then LDA transform is performed on the lower space to extract features. Two face databases are used to test our method and the correct recognition rates of 97.5 % and 96.0% are obtained respectively. The performance of the proposed method is compared with that of the PCA + LDA method and the results show that the method proposed outperforms the PCA + LDA method.
Randomness in topological models

There are two aspects of randomness in topological models. In the first one, topological idealization of random patterns found in the Nature can be regarded as planar representations of three-dimensional lattices and thus reconstructed in the space. Another aspect of randomness is related to graphs in which some properties are determined in a random way. For example, combinatorial properties of graphs: number of vertices, number of edges, and connections between them can be regarded as events in the defined probability space. Random-graph theory deals with a question: at what connection probability a particular property reveals. Combination of probabilistic description of planar graphs and their spatial reconstruction creates new opportunities in structural form-finding, especially in the inceptive, the most creative, stage.
Relationship of Lysosomal Storage Diseases (LSD) with Autophagy

Lysosomes are organelles that break down damaged components or structures that have completed their task in the cell by the hydrolytic enzymes they contain and take part in the last step of the autophagic pathway. Autophagy, an evolutionarily conserved lysosomal pathway, is activated under stress conditions such as amino acid starvation, accumulation of unfolded proteins, or viral infection. The main task of autophagy is to remove damaged proteins, organelles and microorganisms that constitute the source of infection. During this degradation mechanism, autophagosomes are formed as a result of the interaction of lysosomes and autophagic vesicles carrying damaged proteins, and this formation is tightly controlled by cellular mechanisms. Damage during this interaction can disrupt cellular homeostasis and interfere with the survival of the organism. Lysosomal Storage Diseases (LDH) is one of the most common human genetic diseases that develop because of mutations on genes encoding lysosomal enzymes. To date, more than 70 LDHs have been identified. LDH is mainly caused by functional disorders of enzymes or lysosome-associated proteins in lysosomes. These disorders may cause undigested macromolecules to accumulate in the cells, compromising the proper functioning of the organs in which they are located. LDH can cause many systemic damages, especially in the nervous system, skeletal system, and reticuloendothelial system, especially in the early stages of the disease. Modulation and reactivation of autophagy is considered a new therapeutic approach for LDH. In this review, the general mechanisms of the relationship between lysosomal storage diseases and autophagy were evaluated together with treatment approaches.
Epoxyeicosatrienoic Acids Inhibit the Activation of Murine Fibroblasts by Blocking the TGF-β1-Smad2/3 Signaling in a PPARγ-Dependent Manner

Background Epoxyeicosatrienoic acids (EETs), the metabolite of arachidonic acid by cytochrome P450 (CYP), reportedly serve as a vital endogenous protective factor in several chronic diseases. EETs are metabolized by soluble epoxide hydrolase (sEH). We have observed that prophylactic blocking sEH alleviates bleomycin- (BLM-) induced pulmonary fibrosis (PF) in mice. However, the underlying mechanism and therapeutic effects of EETs on PF remain elusive. Objective In this study, we investigated the effect of CYP2J2/EETs on the activation of murine fibroblasts and their mechanisms. Results we found that administration of the sEH inhibitor (TPPU) 7 days after the BLM injection also reversed the morphology changes and collagen deposition in the lungs of BLM-treated mice, attenuating PF. Fibroblast activation is regarded as a critical role of PF. Therefore, we investigated the effects of EETs on the proliferation and differentiation of murine fibroblasts. Results showed that the overexpression of CYP2J2 reduced the cell proliferation and the expressions of α-SMA and PCNA induced by transforming growth factor- (TGF-) β1 in murine fibroblasts. Then, we found that EETs inhibited the proliferation and differentiation of TGF-β1-treated-NIH3T3 cells and primary murine fibroblasts. Mechanistically, we found that 14,15-EET disrupted the phosphorylation of Smad2/3 murine fibroblasts by activating PPARγ, which was completely abolished by a PPARγ inhibitor GW9662. Conclusion our study shows that EETs inhibit the activation of murine fibroblasts by blocking the TGF-β1-Smad2/3 signaling in a PPARγ-dependent manner. Regulating CYP2J2-EET-sEH metabolic pathway may be a potential therapeutic option in PF.
The topological spherical space form problem I

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Quantitative scanning acoustic microscopy investigation of cortical bone using a multilayer analysis method

An in vitro scanning acoustic microscopy measurement technique is described to assess the acoustical properties of human bone tissue at the level of individual lamellae. Contrast influences caused by defocus, surface edges and inclinations, respectively are separated or eliminated. The microscopic acoustic impedances of the remaining data points are determined by a dynamic calibration method. Cortical bone samples of the human femur embedded in polymethyl-methacrylate (PMMA) were investigated at a frequency of 900 MHz. The resulting impedance maps showed a strong structural dependence and the mean values were found to be in the range from 2 to 6 Mrayl, which is significantly lower than macroscopic bulk values of the bone compound measured by acoustic low frequency methods.
A coupled Maxwell integrodifferential model for magnetization processes

A mathematical model for instationary magnetization processes is considered, where the underlying spatial domain includes electrically conducting and nonconducting regions. The model accounts for the magnetic induction law that couples the given electrical voltage with the induced electrical current in the induction coil. By a theorem of Showalter on degenerate parabolic equations, theorems on existence, uniqueness, and regularity of the solution to the associated Maxwell integrodifferential system are proved.
From deterministic cellular automata to coupled map lattices

A general mathematical method is presented for the systematic construction of coupled map lattices (CMLs) out of deterministic cellular automata (CAs). The entire CA rule space is addressed by means of a universal map for CAs that we have recently derived and that is not dependent on any freely adjustable parameters. The CMLs thus constructed are termed real-valued deterministic cellular automata (RDCA) and encompass all deterministic CAs in rule space in the asymptotic limit κ → 0 of a continuous parameter κ. Thus, RDCAs generalize CAs in such a way that they constitute CMLs when κ is finite and nonvanishing. In the limit κ → ∞ all RDCAs are shown to exhibit a global homogeneous fixed-point that attracts all initial conditions. A new bifurcation is discovered for RDCAs and its location is exactly determined from the linear stability analysis of the global quiescent state. In this bifurcation, fuzziness gradually begins to intrude in a purely deterministic CA-like dynamics. The mathematical method presented allows to get insight in some highly nontrivial behavior found after the bifurcation.
Using AAEHS-Net as an Attention-Based Auxiliary Extraction and Hybrid Subsampled Network for Semantic Segmentation

Semantic segmentation based on deep learning has undergone remarkable advancements in recent years. However, due to the neglect of the shallow features, the problems of inaccurate segmentation have persisted. To address this issue, a semantic segmentation network-attention-based auxiliary extraction and hybrid subsampled network (AAEHS-Net) is suggested in this study. To extract more deep information and the shallow features, the complementary and enhanced extraction module (CEEM) is utilized by the network. As a result, the edge segmentation of the model is improved. Moreover, to reduce the loss of features, a hybrid subsampled module (HSM) is introduced. Meanwhile, global max pool and global avg pool module (GAGM) is designed as an attention module to enhance the features with global and important information and maintain feature continuity. The proposed AAEHS-Net is evaluated on three datasets: the aerial drone image dataset, the Massachusetts roads dataset, and the Massachusetts buildings dataset. On the three datasets, AAEHS-Net achieves 1.15%, 0.88%, and 2.1% higher accuracy than U-Net, reaching 90.12%, 96.23%, and 95.15%, respectively. At the same time, our proposed network has obtained the best values for all evaluation metrics in three datasets compared to the currently popular algorithms.
Mac-2-binding protein glycosylation isomer is useful to predict muscle cramps in patients with chronic liver disease

Muscle cramps are frequently overlooked and worsen the quality of life in patients with chronic liver disease (CLD). Therefore, a valuable biomarker for predicting muscle cramps is required in the clinical setting. This study aimed to investigate whether the serum Mac-2-binding protein glycosylation isomer (M2BPGi) levels, a reliable liver fibrosis marker, could predict muscle cramps in patients with CLD. This retrospective study included 80 patients with CLD. Muscle cramps were assessed using a questionnaire regarding their presence, frequency, pain severity, and duration. The associated predictors were analyzed using logistic regression analysis. The diagnostic accuracy and optimal cutoff values were evaluated using receiver operating characteristic curves. Of the 80 patients, 55% had muscle cramps and showed significantly higher serum M2BPGi levels than those without them (4.54 cutoff index [COI] vs 2.20; P = .001). Multivariate analysis revealed that M2BPGi (odds ratio [ORs], 1.19; 95% confidence interval, 1.003–1.42; P = .046) was independently associated with the presence of muscle cramps. The optimal COI value for predicting muscle cramps was 3.95, and the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 61.4%, 80.6%, 79.4%, 63.0%, and 70.0%, respectively. Patients with a COI value ≥3.95 had a 2-fold higher incidence of muscle cramps than patients with a COI value <3.95 (79% vs 37%; P < .001). M2BPGi levels were also associated with the duration of muscle cramps. Serum M2BPGi appears useful as a biomarker for predicting muscle cramps in patients with CLD.
On weight-symmetric 3-coloured digraphs

We consider particular weighted directed graphs with edges having colour red, blue or green such that each red edge has weight 1, each blue edge has weight $ -1 $ −1 and each green edge has weight $ \mathrm {i} $ i (the imaginary unit). Such a directed graph is called a 3-coloured digraph (for short, a 3-CD). Every mixed graph and every signed graph can be interpreted as a 3-CD. We first study some structural properties of 3-CDs by means of the eigenvalues of the adjacency matrix. In particular, we give spectral criteria for singularity of such digraphs. Second, we consider weight-symmetric 3-CDs, i.e. those 3-CDs that are switching isomorphic to their negation. It follows that the class of weight-symmetric 3-CDs is included in the class of 3-CDs whose spectrum is symmetric (with respect to the origin). We give some basic properties and several constructions of weight-symmetric 3-CDs and establish constructions of 3-CDs which have symmetric spectrum but are not weight-symmetric.
Applications of Cayley graphs, bilinearity, and higher-order residues to cryptology

First, we focus on the discrete-logarithm problem, showing an algorithm that works in optimal time (up to logarithmic factors) and uses only a small amount of space. Our algorithm is a modification of the classic Pollard rho algorithm, introducing explicit randomization of the parameters for the updating steps of the algorithm. In proving that the algorithm works as claimed, we see several intermediate and related results of independent interest. Next, we present the concept of hierarchical identity-based encryption (HIBE) schemes. An IBE scheme is one in which any string can be used as a public key (e.g., the recipient's e-mail address); private keys are distributed by one central authority. An HIBE scheme is the hierarchical analogue of an IBE; among its many advantages those that come from splitting the key-distribution burden as well as those that come from escrow-related applications. We present an example of an HIBE scheme, give security definitions, and mention some of the applications of HIBE schemes. Finally, we describe some cryptologic applications of the rth-power-residue symbol (a higher-order analogue of the Jacobi symbol). We present an encryption scheme in which recipients only need an rth root of unity to decrypt. By simply changing the value of r and distributing roots to a new group, controlling who receives such roots, the set of decrypters can be changed without generating a new modulus (N = pq). We present other applications of the system as well as another application of the rth-power-residue symbol: how to speed up the elliptic curve method for factoring numbers of the form N = pqr.
Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height

Predicting significant wave height (SWH) is significant for coastal energy evaluation and utilization, port construction, and shipping planning. It has been reported that SWH is difficult to forecast for the complex marine conditions and chaos in nature. Current methods either require reliable prior information or reach the upper limit of prediction accuracy. To this end, this paper proposes a wavelet-based residual network to predict SWH with high accuracy. First, the time-series data of wave-related factors collected by the ocean buoy station is decomposed using the wavelet transformation. Then, the transformation results are used as the inputs to train the residual neural network. Finally, the data obtained from the NOAA’s National Data Buoy Center is used to prove the outperformed prediction accuracy of the proposed method. The analysis results suggested that wavelet transformation can improve the prediction performance of the neural network, and the proposed model achieves better performance compared with several other deep neural network schemes.
Orbital-FFLO state in a chain of high spin ultracold atoms

Recent experiments with Yb-173 and Sr-87 isotopes provide new possibilities to study high spin two-orbital systems. Within these experiments part of the atoms are excited to a higher energy metastable electronic state mimicking an additional internal (orbital) degree of freedom. The interaction between the atoms depends on the orbital states, therefore four different scattering channels can be identified in the system characterized by four independent couplings. When the system is confined into a one-dimensional chain the scattering lengths can be tuned by changing the transverse confinement, and driven through four resonances. Using the new available experimental data of the scattering lengths we analyze the phase diagram of the one-dimensional system as the couplings are tuned via transverse confinement, and the populations of the two orbital states are changed. We found that three orders compete showing power law decay: a state with dominant density wave fluctuations, another one with spin density fluctuations, and a third one characterized by exotic Fulde-Ferrell-Larkin-Ovchinnikov-like pairs consisting one atom in the electronic ground state and one in the excited state. We also show that sufficiently close to the resonances the compressibility of the system starts to diverge indicating that the emerging order is unstable and collapses to a phase separated state with a first order phase transition.
Effect of Dietary Salt Intake on Risk of Gastric Cancer: A Systematic Review and Meta-Analysis of Case-Control Studies

Aim: The effect of dietary salt intake on the risk of gastric cancer is not clear. A meta-analysis was performed to estimate the association between dietary salt intake and the risk of gastric cancer. Methods: Three major databases were searched to retrieve case-control studies published in English before 1 July 2022. Random effects model analysis was used to obtain the pooled odds ratios (ORs) and 95% confidence intervals (CIs) of the association between dietary salt intake and risk of gastric cancer. Subgroup analyses were used to identify possible sources of heterogeneity. Results: Thirty-eight case-control studies were included in this meta-analysis (total population: n = 37,225). The pooled ORs showed a significantly positive association between high salt intake and gastric cancer compared with low salt intake (OR = 1.55, 95% CI (1.45, 1.64); p < 0.001). In subgroup meta-analysis for geographic region, estimation method for dietary salt intake and the source of controls, this association was not changed. Conclusion: Higher dietary salt intake increased the risk of gastric cancer. This study has implications for the prevention of gastric cancer.