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The prognostic relevance of diagnosis to treatment interval (DTI) in patients with newly diagnosed mantle cell lymphoma (MCL) is unknown. Hence, we sought to evaluate the impact of DTI on outcomes in MCL using 3 large datasets (1) the University of Iowa/Mayo Clinic Specialized Program of Research Excellence Molecular Epidemiology Resource, (2) patients enrolled in the ALL Age Asthma Cohort/CALGB 50403, and (3) a multisitecohort of patients with MCL. Patients were a priori divided into 2 groups, 0 to 14 days (short DTI) and 15 to 60 days (long DTI). The patients in whom observation was deemed appropriate were excluded. One thousand ninety-seven patients newly diagnosed with MCL and available DTI were included in the study. The majority (73%) had long DTI (n=797). Patients with short DTI had worse eastern cooperative oncology group performance status (ECOG PS ≥2), higher lactate dehydrogenase, bone marrow involvement, more frequent B symptoms, higher MCL International Prognostic Index (MIPI ≥6.2), and were less likely to receive intensive induction therapy than long DTI group. The median progression-free survival (2.5 years vs 4.8 years, p

In order to improve the optimal setting temperature problem to achieve the global optimum of product performance, costs and benefits. In this article, a hierarchical structure optimal setting approach of production indexes for the RHFTF is proposed. It is composed of three layers with different functions to obtain the temperature control setting model of the RHFTF. In the first layer, the bi-feature Gaussian mixture model clustering (BFGMMC) algorithm of loading plan is proposed to optimize the setting of a limited number of slabs. In the second layer, the type-2 fuzzy rule interpolation (T2FRI) setting method is developed to obtain the optimal setting curve. Meanwhile, an improved KH (Kóczy-Hirota) α-cut distance (IKHCD) algorithm is proposed to get the miss information between any two adjacent interpolation points. In the third layer, knowledge feedforward compensation of rule matrices (KFCRM) algorithm is presented to improve the anti-interference ability of the setting model. The results of the study can demonstrate that the proposed method improves the accuracy of the model and optimizes the control strategy. Furthermore, the experimental results show that the proposed method meets the process technical requirements.

Predicting future movements of a stock could be usually better performed within multiple models of analysis. However, different methods of prediction will show completely varied results even in case of a same stock. Due to varied logics and progression process, each model will return different results. These results could have implied certain aspects or properties that the stock owns, and they would reflect keys to acknowledge analyzers and decision makers to in practical. It is imperative for analyzers or decision makers to compare results from different models and algorithms. This paper performs a comparative statement of how would different prediction models or algorithms differentiate in results of predicting a stock price. Furthermore, the researcher will include a comparation of applying separately linear regression and Nonlinear Regression, to analysis on the stock Apple (NASDAQ: AAPL) and compare their prediction outcomes. In addition, the researcher will evaluate the performance according to RMSE (Root-Mean-Square-Deviation) results.

Endiandra macrocarpa, a new species of Endiandra (Lauraceae) from Yunnan Province of south-western China, is here described and illustrated, based on morphological evidence. Compared to other Endiandra species occurring in south China and the adjacent regions in Indochina, this species is mainly characterised by its much larger ellipsoidal fruits (up to 11 × 6 cm), as well as glabrous branchlets and puberulent inflorescences.

Analysts are recognized for their expertise in predicting industry growth, yet little is known about whether CEOs learn from analysts’ insights to guide investment decisions. Focusing on conglomerates where CEOs are underinformed about segment growth opportunities, we find that CEOs learn industry insights from analysts to adjust internal capital allocation. The extent of learning increases when analysts have closer proximity to CEOs or expertise in segments where CEOs face larger internal knowledge gaps. CEOs likely learn from analysts through private communications, as the insights learned are not yet publicly available, difficult to replace with other sources, and persistently impactful for firms. CEOs also exploit conference calls as another way to learn from analysts. As a result, learning analysts’ insights enhances firm value. We employ brokerage mergers/closures as a quasi-experiment to address endogeneity concerns. Overall, our study provides novel evidence on a learning channel through which analysts add value to firms. JEL Classifications: D80; D83; G10; G31.

We utilize the exclusive feature of the Marangoni effect to create a special nucleation and growth environment, and oriented centimetre-size Zn-MOF crystals are synthesized.

Carbon materials have excellent catalytic effects on the hydrogen storage performance of MgH2. Here, carbon-supported Ni3S2 (denoted as Ni3S2@C) was synthesized by a facile chemical route using ion exchange resin and nickel acetate tetrahydrate as raw materials and then introduced to improve the hydrogen storage properties of MgH2. The results indicated the addition of 10 wt.% Ni3S2@C prepared by macroporous ion exchange resin can effectively improve the hydrogenation/dehydrogenation kinetic properties of MgH2. At 100 °C, the dehydrogenated MgH2 Ni3S2@C-4 composite could absorb 5.68 wt.% H2. Additionally, the rehydrogenated MgH2 Ni3S2@C-4 sample could release 6.35 wt.% H2 at 275 °C. The dehydrogenation/hydrogenation enthalpy changes of MgH2 Ni3S2@C-4 were calculated to be 78.5 kJ mol−1/−74.7 kJ mol−1, i.e., 11.0 kJ mol−1/7.3 kJ mol−1 lower than those of MgH2. The improvement in the kinetic properties of MgH2 was ascribed to the multi-phase catalytic action of C, Mg2Ni, and MgS, which were formed by the reaction between Ni3S2 contained in the Ni3S2@C catalyst and Mg during the first hydrogen absorption–desorption process.

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The COVID-19 pandemic broke the normal of school education and sounded the horn for future education reform. This paper designs the online and offline mixed teaching mode of the SPOC platform in the postpandemic era according to the actual situation of teaching at application-oriented colleges and universities, and the blended teaching mode is applied to real teaching scenarios. The teaching effect and students’ behaviors are analyzed in such scenarios. Empirical research shows that the situational teaching model based on mixed SPOC for course design, teaching resources are more abundant, and task assessments are more reasonable. Through the questionnaire survey, about 95% of the students think that the learning mode based on SPOC has improved their learning interest, autonomous learning ability, and problem-solving ability, which is much higher than the assessment of traditional courses. The exploratory practice has shown that the online and offline blended teaching mode based on SPOC can dramatically improve teaching quality.

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Ganoderic acid A (GAA) exhibited neuron protection in in vitro epilepsy study, but no study has been done in vivo. Rats were administered (i.p.) pentylenetetrazole daily for 28 days to induce seizure. Rats with grade II or above of epileptic score were divided into three groups and given placebo, sodium valproate, or GAA treatment, respectively, for 7 days. The electrical signals of brain were monitored with electroencephalography (EGG); epileptic behavior was assessed using the Racine scale; morphological changes and apoptosis rate of cortical neurons were assessed with H&E staining and TUNEL staining, respectively. Protein expression of calcium-sensing receptor, p-ERK, p-JNK, and p-p38 in hippocampal tissue and Bcl-2, cleaved caspase-3, and Bax in cortical tissues was observed by Western blot and immunohistochemistry assay, respectively. After GAA treatment, apparent seizure-like EEG with significant arrhythmic disorder and spike waves was reduced or disappeared, and wave amplitude of EEG was reduced significantly. GAA showed similar effect with sodium valproate treatments on epilepsy. There were an apparent improvement of the epileptic behavior and a significant increase in the epileptic latency and shortening of the epileptic duration in the treatment group compared to control. GAA treatment ameliorated the nuclear pyknosis of neurons which appeared seriously in the epilepsy group. GAA treatment significantly reduced the cortical neuron apoptosis of epilepsy and the expression of calcium-sensing receptor, p-P38, p-JNK, cleaved caspase-3, and Bax but increased the expression of both p-ERK and Bcl-2. In conclusion, GAA treatment showed strong antiepileptic effect by decreasing apoptosis in cortical neuron and the expression of calcium-sensing receptor and stimulating the MAPK pathway.

Combinatorial testing typically considers a single input model and creates a single test set that achieves t-way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy t-way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.

Designing quantum algorithms with a speedup over their classical analogs is a central challenge in quantum information science. Motivated by recent experimental observations of a superlinear quantum speedup in solving the Maximum Independent Set problem on certain unit-disk graph instances [Ebadi et al., Science 376, 6598 (2022)], we develop a theoretical framework to analyze the relative performance of the optimized quantum adiabatic algorithm and a broad class of classical Markov chain Monte Carlo algorithms. We outline conditions for the quantum adiabatic algorithm to achieve a quadratic speedup on hard problem instances featuring flat low-energy landscapes and provide example instances with either a quantum speedup or slowdown. We then introduce an additional local Hamiltonian with no sign problem to the optimized adiabatic algorithm to achieve a quadratic speedup over a wide class of classical simulated annealing, parallel tempering, and quantum Monte Carlo algorithms in solving these hard problem instances. Finally, we use this framework to analyze the experimental observations., 9+27 pages, 5+8 figures