Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of personalized classifier based on metastasis and the immune microenvironment to predict the prognosis of osteosarcoma patients

Background Osteosarcoma is a common malignant bone tumor with a poor prognosis. The progression and metastasis of osteosarcoma are significantly influenced by the tumor microenvironment (TME). This study aimed to develop a personalized classifier based on metastasis and immune cells in the TME to achieve better prognostic prediction in osteosarcoma. Methods Firstly, osteosarcoma metastasis-related differentially expressed genes (DEGs) and infiltrating immune cells in the TME were analyzed using a series of bioinformatics methods. The metastasis-related gene signature (MRS) and TME score of osteosarcoma patients were then developed. On this basis, a personalized MRS-TME classifier was constructed and validated in other clinical cohorts and different subgroups. In addition, the relationship between the MRS-related genes and the immune microenvironment was also clarified. Finally, the signaling pathways and immune response genes in osteosarcoma patients among different MRS-TME subgroups were analyzed to explore the underlying molecular mechanism. Results We first identified the metastasis-related DEGs in osteosarcoma, which were primarily involved in the muscle system process, calcium ion homeostasis, cell chemotaxis, and leukocyte migration. A personalized MRS-TME classifier was then constructed by integrating the MRS (10 genes) and TME (six immune cells) scores. The MRS-TME classifier demonstrated a potent capacity of predicting the survival prognosis in diverse osteosarcoma cohorts as well as in the clinical feature subgroups. The MRS score was negatively associated with the TME score, and patients in the MRSlow/TMEhigh subgroup exhibited a better prognosis compared to all other subgroups. Significant differences existed between the cellular signaling pathways and immune response profiles among the different MRS-TME subgroups, especially in relation to the metabolism-related biological processes and the inflammatory response. Conclusions The MRS-TME classifier might be a beneficial tool to aid in the prognostic evaluation and risk stratification of osteosarcoma patients.
Dimerisation of the Yeast K+ Translocation Protein Trk1 Depends on the K+ Concentration

In baker’s yeast (Saccharomyces cerevisiae), Trk1, a member of the superfamily of K-transporters (SKT), is the main K+ uptake system under conditions when its concentration in the environment is low. Structurally, Trk1 is made up of four domains, each similar and homologous to a K-channel α subunit. Because most K-channels are proteins containing four channel-building α subunits, Trk1 could be functional as a monomer. However, related SKT proteins TrkH and KtrB were crystallised as dimers, and for Trk1, a tetrameric arrangement has been proposed based on molecular modelling. Here, based on Bimolecular Fluorescence Complementation experiments and single-molecule fluorescence microscopy combined with molecular modelling; we provide evidence that Trk1 can exist in the yeast plasma membrane as a monomer as well as a dimer. The association of monomers to dimers is regulated by the K+ concentration.
Nonrandomly Missing Data in Multiple Regression: An Empirical Comparison of Common Missing-Data Treatments

This research is an investigation of the effects of nonrandomly missing data in two-predictor regression analyses and the differences in the effectiveness of five common treatments of missing data on estimates of R2 and of each of the two standardized regression weights. Bootstrap samples of 50, 100, and 200 were drawn from three sets of actual field data. Nonrandomly missing data were created within each sample, and the parameter estimates were compared with those obtained from the same samples with no missing data. The results indicated that three imputation procedures (mean substitution, simple and multiple regression imputation) produced biased estimates of R2 and both regression weights. Two deletion procedures (listwise and pairwise) provided accurate parameter estimates with up to 30% of the data missing.
Visual tracking method based on cuckoo search algorithm

Abstract. Cuckoo search (CS) is a new meta-heuristic optimization algorithm that is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. It has been found to be efficient in solving global optimization problems. An application of CS is presented to solve the visual tracking problem. The relationship between optimization and visual tracking is comparatively studied and the parameters’ sensitivity and adjustment of CS in the tracking system are experimentally studied. To demonstrate the tracking ability of a CS-based tracker, a comparative study of tracking accuracy and speed of the CS-based tracker with six “state-of-art” trackers, namely, particle filter, meanshift, PSO, ensemble tracker, fragments tracker, and compressive tracker are presented. Comparative results show that the CS-based tracker outperforms the other trackers.
Fuzzy Cluster-Based Group-Wise Point Set Registration With Quality Assessment

This article studies group-wise point set registration and makes the following contributions: “FuzzyGReg”, which is a new fuzzy cluster-based method to register multiple point sets jointly, and “FuzzyQA”, which is the associated quality assessment to check registration accuracy automatically. Given a group of point sets, FuzzyGReg creates a model of fuzzy clusters and equally treats all the point sets as the elements of the fuzzy clusters. Then, the group-wise registration is turned into a fuzzy clustering problem. To resolve this problem, FuzzyGReg applies a fuzzy clustering algorithm to identify the parameters of the fuzzy clusters while jointly transforming all the point sets to achieve an alignment. Next, based on the identified fuzzy clusters, FuzzyQA calculates the spatial properties of the transformed point sets and then checks the alignment accuracy by comparing the similarity degrees of the spatial properties of the point sets. When a local misalignment is detected, a local re-alignment is performed to improve accuracy. The proposed method is cost-efficient and convenient to be implemented. In addition, it provides reliable quality assessments in the absence of ground truth and user intervention. In the experiments, different point sets are used to test the proposed method and make comparisons with state-of-the-art registration techniques. The experimental results demonstrate the effectiveness of our method. The code is available at https://gitsvn-nt.oru. se/qianfang.liao/FuzzyGRegWithQA
Improved delay‐partitioning approach to robust stability analysis for discrete‐time systems with time‐varying delays and randomly occurring parameter uncertainties

This paper is concerned with the robust stability problem for uncertain discrete‐time systems with interval time‐varying delays and randomly occurring parameter uncertainties. By construction of a suitable Lyapunov–Krasovskii functional and utilization of new zero equalities with delay‐partitioning approach, improved delay‐dependent criteria for the robust stability of the systems are derived in terms of linear matrix inequalities for guaranteeing the asymptotic stability of the concerned systems. The effectiveness and reduction of conservatism of the derived results are demonstrated by three numerical examples. Copyright © 2014 John Wiley & Sons, Ltd.
Permeability and Disintegration Characteristics of Composite Improved Phyllite Soil by Red Clay and Cement

The bearing capacity of the phyllite soil subgrade can be greatly improved by red clay, but the water stability of the modified soil is still poor. Hence, the blended soil has been found to be unsuitable for the construction of high-speed railways. This paper proposes an innovative scheme, by adding appropriate amounts of cement and red clay concurrently, to improve phyllite soil, which achieves a higher bearing capacity of the subgrade immediately after compaction, while also solving the problem of insufficient water stability. Laboratory tests of the permeability and disintegration characteristics of phyllite soils improved by cement, red clay, and both were carried out. The test results show that the permeability coefficient and maximum disintegration rate of soil can be improved effectively by using both red clay and cement. It was found that the optimal combination scheme is to add 3% cement and 40% red clay to phyllite soil by mass. Under the optimal scheme, the permeability coefficient, maximum disintegration rate, and disintegration rate of the improved soil decreased by 90.02%, 90.30%, and 99.02%, respectively, compared with the phyllite soil. The microscopic study shows that the mechanism of red clay blending with phyllite is that the finer particles of red clay infill the pores among the phyllite particles, thus reducing its permeability coefficient. The mechanism of adding cement to the blending soil mainly results from the production of hard-setting new materials and the formation of a cementation network among the soil particles, which not only increases the shear strength of the soil, but also reduces the permeability coefficient and the maximum disintegration ratio of the soil. This work makes full use of the complementary characteristics of red clay and phyllite soil and the advantages of hard-setting new materials, which will provide a new idea for soil improvement of the phyllite soil in the future.
A Unified Engine for Accelerating GNN Weighting/Aggregation Operations, With Efficient Load Balancing and Graph-Specific Caching

Graph neural networks (GNNs) analysis engines are vital for real-world problems that use large graph models. Challenges for a GNN hardware platform include the ability to 1) host a variety of GNNs; 2) handle high sparsity in input vertex feature vectors and the graph adjacency matrix and the accompanying random memory access patterns; and 3) maintain load-balanced computation in the face of uneven workloads, induced by high sparsity and power-law vertex degree distributions. This article proposes GNNIE, an accelerator designed to run a broad range of GNNs. It tackles workload imbalance by 1) splitting vertex feature operands into blocks; 2) reordering and redistributing computations; and 3) using a novel flexible MAC architecture. It adopts a graph-specific, degree-aware caching policy that is well suited to real-world graph characteristics. The policy enhances on-chip data reuse and avoids random memory access to DRAM. GNNIE achieves average speedups of <inline-formula> <tex-math notation="LaTeX">$7197\times $ </tex-math></inline-formula> over a CPU and <inline-formula> <tex-math notation="LaTeX">$17.81\times $ </tex-math></inline-formula> over a GPU over multiple datasets on graph attention networks (GATs), graph convolutional networks (GCNs), GraphSAGE, GINConv, and DiffPool. Compared to prior approaches, GNNIE achieves an average speedup of <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> over HyGCN (which cannot implement GATs) for GCN, GraphSAGE, and GINConv. GNNIE achieves an average speedup of <inline-formula> <tex-math notation="LaTeX">$1.3\times $ </tex-math></inline-formula> over AWB-GCN (which runs only GCNs), despite using <inline-formula> <tex-math notation="LaTeX">$3.4\times $ </tex-math></inline-formula> fewer processing units.
Encapsulating Calendula arvensis (Vaill.) L. Florets: UHPLC-HRMS Insights into Bioactive Compounds Preservation and Oral Bioaccessibility

Wild edible plants, once consumed in times of famine or for health purposes, today represent an interesting dietary supplement, aimed at enriching local dishes and/or formulating healthy nutraceutical products. In fact, the broad content of different, and diversely bioactive, specialized metabolites therein suggests new scenarios of use which, in order to be as functional as possible, must maximize the bioactivity of these compounds while preserving their chemistry. In this context, based on a recent investigation on the metabolic profile of the organs of Calendula arvensis that highlighted that florets are abundant in flavonol glycosides and triterpene saponins, the freeze-drying encapsulation of their alcoholic extract (FE) into maltodextrin (MD) was investigated. FE-MD chemical composition was evaluated using Fourier Transform InfraRed spectroscopy (FTIR), while ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) techniques were employed to unravel FE compound preservation also during in vitro simulated digestion. The establishment of H-bonds between FE compounds and MD hydroxyl groups was in line with FE-MD biocompatibility in Caco-2 cells, while in vitro digestion mostly affected structural integrity and/or diversity. Flavonol compounds underwent deglycosylation and demethylation, while deacylation, beyond oxidation, involved triterpene saponins, which massively preserve their aglycone core.
Fundamentals and Applications of ZnO-Nanowire-Based Piezotronics and Piezo-Phototronics

The piezotronic effect is a coupling effect of semiconductor and piezoelectric properties. The piezoelectric potential is used to adjust the p-n junction barrier width and Schottky barrier height to control carrier transportation. At present, it has been applied in the fields of sensors, human–machine interaction, and active flexible electronic devices. The piezo-phototronic effect is a three-field coupling effect of semiconductor, photoexcitation, and piezoelectric properties. The piezoelectric potential generated by the applied strain in the piezoelectric semiconductor controls the generation, transport, separation, and recombination of carriers at the metal–semiconductor contact or p-n junction interface, thereby improving optoelectronic devices performance, such as photodetectors, solar cells, and light-emitting diodes (LED). Since then, the piezotronics and piezo-phototronic effects have attracted vast research interest due to their ability to remarkably enhance the performance of electronic and optoelectronic devices. Meanwhile, ZnO has become an ideal material for studying the piezotronic and piezo-phototronic effects due to its simple preparation process and better biocompatibility. In this review, first, the preparation methods and structural characteristics of ZnO nanowires (NWs) with different doping types were summarized. Then, the theoretical basis of the piezotronic effect and its application in the fields of sensors, biochemistry, energy harvesting, and logic operations (based on piezoelectric transistors) were reviewed. Next, the piezo-phototronic effect in the performance of photodetectors, solar cells, and LEDs was also summarized and analyzed. In addition, modulation of the piezotronic and piezo-phototronic effects was compared and summarized for different materials, structural designs, performance characteristics, and working mechanisms’ analysis. This comprehensive review provides fundamental theoretical and applied guidance for future research directions in piezotronics and piezo-phototronics for optoelectronic devices and energy harvesting.
Brazilian Multiethnic Association Study of Genetic Variant Interactions among FOS, CASP8, MMP2 and CRISPLD2 in the Risk of Nonsyndromic Cleft Lip with or without Cleft Palate

Associations of CRISPLD2 (cysteine-rich secretory protein LCCL domain containing 2) and genes belonging to its activation pathway, including FOS (Fos proto-oncogene), CASP8 (caspase 8) and MMP2 (matrix metalloproteinase 2), with nonsyndromic orofacial cleft risk, have been reported, but the results are yet unclear. The aim of this study was to evaluate single nucleotide polymorphisms (SNPs) in FOS, CASP8 and MMP2 and to determine their SNP-SNP interactions with CRISPLD2 variants in the risk of nonsyndromic cleft lip with or without cleft palate (NSCL±P) in the Brazilian population. The SNPs rs1046117 (FOS), rs3769825 (CASP8) and rs243836 (MMP2) were genotyped using TaqMan allelic discrimination assays in a case-control sample containing 801 NSCL±P patients (233 nonsyndromic cleft lip only (NSCLO) and 568 nonsyndromic cleft lip and palate (NSCLP)) and 881 healthy controls via logistic regression analysis adjusted for the effects of sex and genomic ancestry proportions with a multiple comparison p value set at ≤0.01. SNP-SNP interactions with rs1546124, rs8061351, rs2326398 and rs4783099 in CRISPLD2 were performed with the model-based multifactor dimensionality reduction test complemented with a 1000 permutation-based strategy. Although the association between FOS rs1046117 and risk of NSCL±P reached only nominal p values, NSCLO risk was significantly higher in carriers of the FOS rs1046117 C allele (OR: 1.28, 95% CI: 1.10–1.64, p = 0.004), TC heterozygous genotype (OR: 1.59, 95% CI: 1.16–2.18, p = 0.003), and in the dominant model (OR: 1.50, 95% CI: 1.10–2.02, p = 0.007). Individually, no significant associations between cleft risk and the SNPs in CASP8 and MMP2 were observed. SNP-SNP interactions involving CRISPLD2 variants and rs1046117 (FOS), rs3769825 (CASP8) and rs243836 (MMP2) yielded several significant p values, mostly driven by FOS rs1046117 and CASP8 rs3769825 in NSCL±P, FOS rs1046117 in NSCLO and CRISPLD2 rs8061351 in NSCLP. Our study is the first in the Brazilian population to reveal the association of FOS rs1046117 with NSCLO risk, and to support that CRISPLD2, CASP8, FOS and MMP2 interactions may be related to the pathogenesis of this common craniofacial malformation.
Real-time maritime scene simulation for ladar sensors

Continuing interest exists in the development of cost-effective synthetic environments for testing Laser Detection and Ranging (ladar) sensors. In this paper we describe a PC-based system for real-time ladar scene simulation of ships and small boats in a dynamic maritime environment. In particular, we describe the techniques employed to generate range imagery accompanied by passive radiance imagery. Our ladar scene generation system is an evolutionary extension of the VIRSuite infrared scene simulation program and includes all previous features such as ocean wave simulation, the physically-realistic representation of boat and ship dynamics, wake generation and simulation of whitecaps, spray, wake trails and foam. A terrain simulation extension is also under development. In this paper we outline the development, capabilities and limitations of the VIRSuite extensions.
EULER PRODUCTS CORRESPONDING TO SIEGEL MODULAR FORMS OF GENUS 2

In this article we construct a theory of Dirichlet series with Euler product expansions corresponding to analytic automorphic forms for the integral symplectic group in genus 2; in Chapter 2 we establish a connection between the eigenvalues of the Hecke operators on the spaces of such forms with the Fourier coefficients of the eigenfunctions (Theorem 2.4.1); in Chapter 3 we demonstrate the possibility of analytic continuation to the entire complex plane and derive a functional equation for Euler products corresponding to the eigenfunctions of the Hecke operators (Theorem 3.1.1). Chapter 1 contains a survey of the present state of the theory of Euler products for Siegel modular forms of arbitrary genus n, including a sketch of the classical Hecke theory for the case n = 1.
Development of a smart spectral analysis method for the determination of mulberry (Morus alba var. nigra L.) juice quality parameters using FT‐IR spectroscopy

Abstract Recently, the application of Fourier transform infrared (FT‐IR) spectroscopy as a noninvasive technique combined with chemometric methods has been widely noted for quality evaluation of agricultural products. Mulberry (Morus alba var. nigra L.) is a native fruit of Iran and there is limited information about its quality characteristics. The present study aims at assessing a nondestructive optical method for determining the internal quality of mulberry juice. To do so, first, FT‐IR spectra were acquired in the spectral range 1000–8333 nm. Then, the principal component analysis (PCA) was used to extract the principal components (PCs) which were given as inputs to three predictive models (support vector regression (SVR), partial least square (PLS), and artificial neural network (ANN)) to predict the internal parameters of the mulberry juice. The performance of predictive models showed that SVR got better results for the prediction of ascorbic acid (R 2 = .84, RMSE = 0.29), acidity (R 2 = .71, RMSE = 0.0004), phenol (R 2 = .35, RMSE = 0.19), total anthocyanin (R 2 = .93, RMSE = 5.85), and browning (R 2 = .89, RMSE = 0.062) compared to PLS and ANN. However, the ANN predicted the parameters TSS (R 2 = .98, RMSE = 0.003) and pH (R 2 = .99, RMSE = 0.0009) better than the other two models. The results indicated that a good prediction performance was obtained using the FT‐IR technique along with SVR and this method could be easily adapted to detect the quality parameters of mulberry juice.
Urdu Translation and Validation of Adapted Self-Report Altruism Scale

The current study aims to translate and validate the adapted Self-report Altruism Scale tithe Urdu language for Pakistani people. Self-report altruism scale adapted by Peter Witt and Chris Boleman (2009) is one of the well-cited measures of altruism. The translation of the scale is done by using MAPI guidelines (MAPI Research Trust, 2012). Appropriateness of translation is confirmed on a sample of 30 adults selected through a purposive sampling technique. The empirical equivalence of the Urdu and English versions of the scale is confirmed by finding the correlation between the original and translated version of the scale through the test-retest phase. It is carried out on a sample of 40 adults by categorizing them into four groups (10 participants in each group). Exploratory Factor Analysis reconfirms the factor structure of translated version on a sample of 320 adults. 14 items are subjected to principle component analysis and the resulting scree plot and Eigenvalues evidence of single factor solution which accounted for 29.67% of the total items variance. Furthermore, confirmatory factor analysis validates the factor structure on an independent sample of 400 adults through confirmatory factor analysis (CFA). The results regarding internal consistency and construct validity yielded a self-report altruism scale as a promising indigenous measure of altruism. The findings indicated adequate equivalence between both versions. No discrepancies are found between both versions.
Holographic lithography for biomedical applications

Fabrication of scaffolds for cell growth with appropriate mechanical characteristics is top-most important for successful creation of tissue. Due to ability of fast fabrication of periodic structures with a different period, the holographic lithography technique is a suitable tool for scaffolds fabrication. The scaffolds fabricated by holographic lithography can be used in various biomedical investigations such as the cellular adhesion, proliferation and viability. These investigations allow selection of the suitable material and geometry of scaffolds which can be used in creation of tissue. Scaffolds fabricated from di-acrylated poly(ethylene glycol) (PEG-DA-258) over a large area by holographic lithography technique are presented in this paper. The PEG-DA scaffolds fabricated by holographic lithography showed good cytocompatibility for rabbit myogenic stem cells. It was observed that adult rabbit muscle-derived myogenic stem cells grew onto PEG-DA scaffolds. They were attached to the pillars and formed cell-cell interactions. It demonstrates that the fabricated structures have potential to be an interconnection channel network for cell-to-cell interactions, flow transport of nutrients and metabolic waste as well as vascular capillary ingrowth. These results are encouraging for further development of holographic lithography by improving its efficiency for microstructuring three-dimensional scaffolds out of biodegradable hydrogels