Miracle-dz/UniFAR-FTUs huggingface dataset:
High Tumor Mutation Burden and DNA Repair Gene Mutations are Associated with Primary Resistance to Crizotinib in ALK-Rearranged Lung Cancer

Background About 20% of patients with ALK-rearranged non-small cell lung cancer (NSCLC) develop acquired resistance to tyrosine kinase inhibitor (TKI) during the first 6 months. This study aimed to examine the molecular mechanisms of early TKI resistance and prognosis in ALK-rearranged NSCLC. Methods Ten patients with ALK-rearranged NSCLC were included: five who developed rapid resistance to crizotinib (progression-free survival (PFS) ≤3 months) and five who exhibited a good response to crizotinib (PFS ≥36 months). The tumor specimens were subjected to whole-exome sequencing (WES). The validation cohort included 19 patients with ALK-rearranged NSCLC who received crizotinib; targeted sequencing of 43 selected genes was performed. The effect of the TP53 G245S mutation on crizotinib sensitivity was tested in H3122 cells. Results Mutations in DNA repair-associated genes were identified in primary resistance to crizotinib. Patients with a poor response to crizotinib harbored a greater burden of somatic mutations than those with a good response [median somatic mutations, 136 (range, 72–180) vs 31 (range, 10–48)]. Compared with the patients carrying wild-type TP53 or TP53 exon 3 deletion, 29 patients with TP53 G245S mutation showed a shorter survival time (P < 0.05), with a median PFS of 3 (95% CI: 1.9–4.1) months and a median overall survival of 7 (95% CI: 3.4–10.5) months. TP53 mutation promoted the proliferation of EML4-ALK-rearranged H3122 cells by approximately 3 folds (P < 0.001). H3122 cells with TP53 mutant were more sensitive to crizotinib compared with control cells. Conclusion A higher mutation burden and mutations in DNA repair gene, including TP53, were potentially associated with primary resistance to crizotinib in ALK-rearranged NSCLC. An immune-checkpoint inhibition strategy could be examined, which might overcome primary resistance to crizotinib in ALK-rearranged NSCLC.
Styphnolobium japonicum (L.) Schott Flower Extract Alleviates Oxidative Stress and Inflammatory Factors in the Adjuvant-Induced Arthritis Rat Model

Introduction This research was to evaluate the beneficial effects of Styphnolobium japonicum (L.) Schott flower extract (SJF) on the adjuvant-induced arthritis rat model. Methods Arthritis was evoked by injection of complete Freund’s adjuvant (CFA) in the hind paw. SJF (150 or 300 mg/kg/day) or Celecoxib (5 mg/kg/day) were administered intragastrically from the 0th day to the 28th day. The arthritis symptoms (paw edema, arthritic scores, mechanical hyperalgesia, and thermal hyperalgesia), inflammation biomarkers (RT and CRP), related enzymes (MMP1 and MMP13), oxidative stress markers (CAT, SOD, GPx, and MDA), and inflammatory cytokines (IL-6, IFN-γ, TNF-α, and IL-1β) of SJF-treated CFA rats were evaluated. Results CFA rats exhibited severe arthritis symptoms, increased oxidative stress, and inflammatory cytokines. Interestingly, SJF treatment relieving arthritis symptoms and restored body weight gain compared with those in the CFA group. SJF treatment decreased the levels of CRP, RF, MMP1, and MMP13 in the CFA group. Besides, SJF treatment increased the activities of antioxidant enzymes and decreased the MDA content and inflammatory cytokines compared with those in the CFA group. Moreover, SJF could increase the mRNA expression of GPx-1 and CAT and inhibit the mRNA expression of IL-6 and TNF-α in the ankle tissue of CFA rats.
Screening of novel Midkine binding protein by BioID2-based proximity labeling

ABSTRACT Midkine (MK), a heparin-binding growth factor, is associated with the poor prognosis of the pediatric tumor, neuroblastoma. MK would be a druggable target as many studies showed inhibition of its function in various cancers suppressed tumor developments. To establish the therapy targeting MK, identification of its binding partners, and elucidation of its intracellular signaling are needed. It was reported that exogenous MK induced phosphorylation of ribosomal protein S6 (RPS6) downstream of mTOR signaling. Using RPS6 phosphorylation as a marker of MK response, we searched for MK reactive cell lines. We found that MK cell lines expressing less MK tended to respond better to MK. Next, using an MK reactive neuroblastoma cell line, MK-knocked down SH-SY5Y cells, we employed a proximity-dependent biotin identification method, which was invented to evaluate protein-protein interactions by biotinylation. We confirmed that secreted MK fused to the biotin ligase BioID2 (MK-BioID2) was able to biotinylate proteins from the cells. Biotinylated proteins were identified by liquid chromatography-mass spectrometry analyses. Twenty five proteins were found to be overlapped after three independent experiments, among which insulin-like growth binding protein 2 (IGFBP2) was further analyzed. IGFBP2 was indeed detected with immunoblotting after streptavidin pull down of MK-BioID2 labeled cell extract of MK-knocked down SH-SY5Y cells. Our study suggests that the BioID2 method is useful to identify binding partners of growth factors.
Potential degradation of methylene blue (MB) by nano-metallic particles: A kinetic study and possible mechanism of MB degradation

The degradation of methylene blue (MB) in an aqueous solution by nano-metallic particles (NMPs) was studied to evaluate the possibility of applying NMPs to remove MB from the wastewater. Scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS) were used to characterize the synthesized NMPs before and after the reaction. The effects of the NMP dosage, the initial pH, the initial concentration of MB and the amount of H2O2 on the MB degradation outcomes were studied. The highest removal rate of MB was achieved to be 100% with an initial MB concentration of 5 mg/L, followed by 99.6% with an initial concentration of 10 mg/L under the following treatment conditions: dose of NMP of 0.15 g/L, concentration of H2O2-100 mM and a temperature of 25C. The SEM analysis revealed that the nano particles were not spherical in shape. FTIR spectra shows occurrence of metal oxides on the surfaces of the NMPs. The XPS analyses results represent that Fe, Zn, N, Ca, C and O were occurred on the surfaces of the NMPs. The degradation of MB was suitable for the pseudo-first-order kinetics.
Nasopharyngeal pneumococcal carriage in South Asian infants: Results of observational cohort studies in vaccinated and unvaccinated populations

Background Nasopharyngeal pneumococcal carriage (NPC) is a prerequisite for invasive pneumococcal disease and reduced carriage of vaccine serotypes is a marker for the protection offered by the pneumococcal conjugate vaccine (PCV). The present study reports NPC during the first year of life in a vaccinated (with PCV10) cohort in Bangladesh and an unvaccinated cohort in India. Methods A total of 450 and 459 infants were recruited from India and Bangladesh respectively within 0-7 days after birth. Nasopharyngeal swabs were collected at baseline, 18 and 36 weeks after birth. The swabs were processed for pneumococcal culture and identification of serotypes by the Quellung test and polymerase chain reaction (PCR). An identical protocol was applied at both sites. Results Prevalence of NPC was 48% in the Indian and 54.8% in the Bangladeshi cohort at 18 weeks. It increased to 53% and 64.8% respectively at 36 weeks. The average prevalence of vaccine serotypes was higher in the Indian cohort (17.8% vs 9.8% for PCV-10 and 26.1% vs17.6% for PCV-13) with 6A, 6B, 19F, 23F, and 19A as the common serotypes. On the other hand, the prevalence of non-vaccine serotypes was higher (43.6% vs 27.1% for non-PCV13) in the Bangladeshi cohort with 34, 15B, 17F, and 35B as the common serotypes. Overcrowding was associated with increased risk of pneumococcal carriage. The present PCV-13 vaccine would cover 28%-30% and 47%-48% serotypes in the Bangladeshi and Indian cohorts respectively. Conclusions South Asian infants get colonised with pneumococci early in infancy; predominantly vaccine serotypes in PCV naïve population (India) and non-vaccine serotypes in the vaccinated population (Bangladesh). These local findings are important to inform the public health policy and the development of higher valent pneumococcal vaccines.
Effect of Missed Doses on the Therapeutic Effect of Methotrexate for Rheumatoid Arthritis: A Pharmacokinetic Modeling Study

Introduction Patients rarely, if ever, take their medications exactly as prescribed. The extent to which missed doses interfere with a drug’s therapeutic effect remains unclear. Methods After weekly oral dosing of methotrexate (MTX) for rheumatoid arthritis, its polyglutamate derivatives (MTXglu) accumulate in red blood cells, where they are markers for the drug’s therapeutic effectiveness. We used Medication Event Monitoring System data and pharmacokinetic modeling to analyze whether missing MTX doses causes the MTXglu level in red blood cells to fall below the range associated with the drug’s clinical effect. Results For patients initiating oral MTX, the threshold for clinical effectiveness and the steady state level were reached in medians of 6 weeks and 22 weeks, respectively. For patients at steady state who discontinued MTX, the MTXglu level fell below the therapeutic threshold after a median of 3 weeks. After initiating MTX, single missed doses did not cause a loss of therapeutic effect in the median patient if they occurred after 10 weeks, while runs of ≥3 consecutive missed doses did cause the MTXglu level to fall below the therapeutic threshold. Conclusion While there is considerable variation between patients, pharmacokinetic modeling indicates that instances of isolated single missed doses of MTX typically will not cause polyglutamated methotrexate levels in red blood cells to fall below the range associated with the therapeutic effect. Runs of ≥3 consecutive missed doses, however, are typically expected to result in a loss of the therapeutic effect.
Effect of Uncaria rhynchophylla against Thioacetamide-Induced Acute Liver Injury in Rat

Both oxidative stress (OS) and inflammation are two fundamental pathological processes of acute liver injury (ALI). The current work is to investigate the effect and possible mechanism of Uncaria rhynchophylla (UR) on thioacetamide- (TAA-) induced ALI in rats. UR (100 and 200 mg/kg) was orally administrated with TAA (200 mg/kg of bodyweight, intraperitoneal injection) for 3 consecutive days. ALI was confirmed using histological examination and the factors associated with OS and liver function activity measured in serum. Moreover, expressions of inflammation and collagen-related proteins were measured by the Western blot analysis. Myeloperoxidase (MPO), which mediates OS in the ALI control group, was manifested by a significant rise compared with the normal group. UR significantly reduced AST, ALT, and ammonia levels in serum. The nuclear factor-κB (NF-κB) activation induced by TAA led to increase both inflammatory mediators and cytokines. Whereas, UR administration remarkably suppressed such an overexpression. UR supplementation improved matrix metalloproteinases (MMPs) such as MMP-1, -2, and -8. In contrast, tissue inhibitors of metalloproteinases- (TIMP-) 1 level increased significantly by UR treatment. In addition, the histopathological analysis showed that the liver tissue lesions were improved obviously by UR treatment. UR may ameliorate the effects of TAA-induced ALI in rats by suppressing both OS through MPO activation and proinflammatory factors through NF-κB activation. In conclusion, UR exhibited a potent hepatoprotective effect on ALI through the suppression of OS.
Evaluation of Teachers' Educational Technology Ability Based on Fuzzy Clustering Generalized Regression Neural Network

The improvement of teachers' educational technology ability is one of the main methods to improve the management efficiency of colleges and universities in China, and the scientific evaluation of teachers' ability is of great significance. In view of this, this study proposes an evaluation model of teachers' educational technology ability based on the fuzzy clustering generalized regression neural network. Firstly, the comprehensive evaluation structure system of teachers' educational technology ability is constructed, and then the prediction method of teachers' ability based on fuzzy clustering algorithm is analysed. On this basis, the optimization prediction method of fuzzy clustering generalized regression neural network is proposed. Finally, the application effect of fuzzy clustering generalized regression neural network in the evaluation of teachers' educational technology ability is analysed. The results show that the evaluation system of teachers' educational technology ability proposed in this study is scientific and reasonable; fuzzy clustering generalized regression neural network model can better accurately predict the ability of teachers' educational technology and can quickly realize global optimization. According to the fitness analysis results of the fuzzy clustering generalized regression neural network model, the model converges after the 20th iteration and the fitness value remains about 1.45. Therefore, the fuzzy clustering generalized regression neural network has stronger adaptability and has been optimized to a certain extent. The average evaluation accuracy of fuzzy clustering generalized regression neural network model is 98.44%, and the evaluation results of the model are better than other algorithms. It is hoped that this study can provide some reference value for the evaluation of teachers' educational technology ability in colleges and universities in China.
Global sales and operations planning: A multinational manufacturing company perspective

The purpose of this paper is to analyse the global Sales and Operations Planning (S&OP) process and investigate the steps to support consolidated business planning in worldwide operations and large-scale supply chains. The paper conducts a case study at a multinational manufacturing company applying an abductive approach. It combines the deductive logic from theory and the inductive logic from field observation in an attempt to elaborate further on theory on global S&OP. The analysis is structured and guided by a novel framework for global S&OP, which is developed based on the theoretical background and the case study findings. The research findings characterise the S&OP process for global operations and identify challenges related to the need to synchronise the subsidiaries’ S&OP efforts worldwide to deal with different contingencies of these subsidiaries, and to manage and analyse a large amount of information gathered. The research reveals how the subsidiaries’ performance is analysed by top executives along the global S&OP process, feeding strategic initiatives in the organisation and identifying business opportunities like benchmarking among subsidiaries, synergies with other management practices, and global gains. This paper offers a novel investigation of the global steps on S&OP in a real-life setting, offering a well-documented characterisation of the process that goes beyond the traditional local approach. Moreover, it is the first study to reveal challenges and expected outcomes of such a global perspective for S&OP. The theoretical advancements of S&OP research offered herein aid scholars, opening avenues for middle-range theorising, highlighting the cross-disciplinary nature of the domain, and discussing the use of concepts from related disciplines like Economics, Psychology, and Information Systems. The research findings can also assist executives, especially from multinational manufacturers, in their efforts to consolidate global planning.
Dust Removal from 3D Point Cloud Data in Mine Plane Areas Based on Orthogonal Total Least Squares Fitting and GA-TELM

With the further development of the construction of “smart mine,” the establishment of three-dimensional (3D) point cloud models of mines has become very common. However, the truck operation caused the 3D point cloud model of the mining area to contain dust points, and the 3D point cloud model established by the Context Capture modeling software is a hollow structure. The previous point cloud denoising algorithms caused holes in the model. In view of the above problems, this paper proposes the point cloud denoising method based on orthogonal total least squares fitting and two-layer extreme learning machine improved by genetic algorithm (GA-TELM). The steps are to separate dust points and ground points by orthogonal total least squares fitting and use GA-TELM to repair holes. The advantages of the proposed method are listed as follows. First, this method could denoise without generating holes, which solves engineering problems. Second, GA-TELM has a better effect in repairing holes compared with the other methods considered in this paper. Finally, this method starts from actual problems and could be used in mining areas with the same problems. Experimental results demonstrate that it can remove dust spots in the flat area of the mine effectively and ensure the integrity of the model.
Packing of elastic rings with friction

We study the deformations of elastic filaments confined within slowly shrinking circular boundaries, under contact forces with friction. We perform computations with a spring-lattice model that deforms like a thin inextensible filament of uniform bending stiffness. Early in the deformation, two lobes of the filament make contact. If the friction coefficient is small enough, one lobe slides inside the other; otherwise, the lobes move together or one lobe bifurcates the other. There follows a sequence of deformations that is a mixture of spiralling and bifurcations, primarily the former with small friction and the latter with large friction. With zero friction, a simple model predicts that the maximum curvature and the total elastic energy scale as the wall radius to the −3/2 and −2 powers, respectively. With non-zero friction, the elastic energy follows a similar scaling but with a prefactor up to eight times larger, due to delayering and bending with a range of small curvatures. For friction coefficients as large as 1, the deformations are qualitatively similar with and without friction at the outer wall. Above 1, the wall friction case becomes dominated by buckling near the wall.
Learning by Examples Based on Multi-level Optimization

Learning by examples, which learns to solve a new problem by looking into how similar problems are solved, is an effective learning method in human learning. When a student learns a new topic, he/she finds out exemplar topics that are similar to this new topic and studies the exemplar topics to deepen the understanding of the new topic. We aim to investigate whether this powerful learning skill can be borrowed from humans to improve machine learning as well. In this work, we propose a novel learning approach called Learning By Examples (LBE). Our approach automatically retrieves a set of training examples that are similar to query examples and predicts labels for query examples by using class labels of the retrieved examples. We propose a three-level optimization framework to formulate LBE which involves three stages of learning: learning a Siamese network to retrieve similar examples; learning a matching network to make predictions on query examples by leveraging class labels of retrieved similar examples; learning the ``ground-truth'' similarities between training examples by minimizing the validation loss. We develop an efficient algorithm to solve the LBE problem and conduct extensive experiments on various benchmarks where the results demonstrate the effectiveness of our method on both supervised and few-shot learning.
More Powerful Selective Inference for the Graph Fused Lasso

ABSTRACT The graph fused lasso—which includes as a special case the one-dimensional fused lasso—is widely used to reconstruct signals that are piecewise constant on a graph, meaning that nodes connected by an edge tend to have identical values. We consider testing for a difference in the means of two connected components estimated using the graph fused lasso. A naive procedure such as a z-test for a difference in means will not control the selective Type I error, since the hypothesis that we are testing is itself a function of the data. In this work, we propose a new test for this task that controls the selective Type I error, and conditions on less information than existing approaches, leading to substantially higher power. We illustrate our approach in simulation and on datasets of drug overdose death rates and teenage birth rates in the contiguous United States. Our approach yields more discoveries on both datasets. Supplementary materials for this article are available online.
The density distribution and the physical origins of density intermittency in sub- to trans-Alfvénic supersonic turbulence

The probability density function (PDF) of the logarithmic density contrast, s = ln(ρ/ρ0), with gas density ρ and mean density ρ0, for hydrodynamical supersonic turbulence is well-known to have significant signatures of intermittency that monotonically increase with the turbulent Mach number, M. By studying the massand volume-weighted s-PDF for an ensemble of 16 subto trans-Alfv́enic mean-field, supersonic, isothermal turbulence simulations, relevant to molecular gas in the cool interstellar medium, we show that a more intricate picture emerges for the intermittency of s. Using four independent measures of the intermittency we find hydrodynamical-like intermittency in the highly magnetised plasma for M . 4. However, for M & 4, the signatures of intermittency disappear, leaving approximately lognormal s-statistics – exactly the opposite of hydrodynamical turbulence in the high-M limit. To understand the M . 4 intermittency we use one-dimensional (1D) pencil beams to explore the dynamics along and across the mean magnetic field, B0. We discuss kinetic, density and magnetic field fluctuations from the pencil beams, and identify physical sources of intermittency as single, strong shocks coupled to fast magnetosonic compressions that form along B0 and create large, volume-poor under-densities. These under-densities contribute significantly to the skewness of the s-PDF. We confirm this result independently using 1D fluid shock simulations. We discuss the Gaussianisation of the M & 4 s-fields through the lens of two phenomenologies: the self-similarity of the s-field and homogenisation of the dynamical timescales between the overand under-dense regions in the compressible gas.
Reactive Answer Set Programming

Logic Production System (LPS) is a logic-based framework for modelling reactive behaviour. Based on abductive logic programming, it combines reactive rules with logic programs, a database and a causal theory that specifies transitions between the states of the database. This paper proposes a systematic mapping of the Kernel of this framework (called KELPS) into an answer set program (ASP). For this purpose a new variant of KELPS with finite models, called n-distance KELPS, is introduced. A formal definition of the mapping from this n-distance KELPS to ASP is given and proven sound and complete. The Answer Set Programming paradigm allows to capture additional behaviours to the basic reactivity of KELPS, in particular proactive, pre-emptive and prospective behaviours. These are all discussed and illustrated with examples. Then a hybrid framework is proposed that integrates KELPS and ASP, allowing to combine the strengths of both paradigms.
Nonemptiness of severi varieties on enriques surfaces

Abstract Let $(S,L)$ be a general polarised Enriques surface, with L not numerically 2-divisible. We prove the existence of regular components of all Severi varieties of irreducible nodal curves in the linear system $|L|$ , that is, for any number of nodes $\delta =0, \ldots , p_a(L)-1$ . This solves a classical open problem and gives a positive answer to a recent conjecture of Pandharipande–Schmitt, under the additional condition of non-2-divisibility.