CAR-NK cell therapy for glioblastoma: what to do next?

Glioblastoma is a malignant tumor with the highest morbidity and mortality in the central nervous system. Conventional surgical resection combined with radiotherapy or chemotherapy has a high recurrence rate and poor prognosis. The 5-year survival rate of patients is less than 10%. In tumor immunotherapy, CAR-T cell therapy represented by chimeric antigen receptor-modified T cells has achieved great success in hematological tumors. However, the application of CAR-T cells in solid tumors such as glioblastoma still faces many challenges. CAR-NK cells are another potential adoptive cell therapy strategy after CAR-T cells. Compared with CAR-T cell therapy, CAR-NK cells have similar anti-tumor effects. CAR-NK cells can also avoid some deficiencies in CAR-T cell therapy, a research hotspot in tumor immunity. This article summarizes the preclinical research status of CAR-NK cells in glioblastoma and the problems and challenges faced by CAR-NK in glioblastoma

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Optimal portfolio selections via ℓ _{1 , 2} -norm regularization

There has been much research about regularizing optimal portfolio selections through ℓ 1 norm and/or ℓ 2-norm squared. The common consensuses are (i) ℓ 1 leads to sparse portfolios and there exists a theoretical bound that limits extreme shorting of assets; (ii) ℓ 2 (norm-squared) stabilizes the computation by improving the condition number of the problem resulting in strong out-of-sample performance; and (iii) there exist efficient numerical algorithms for those regularized portfolios with closed-form solutions each step. When combined such as in the well-known elastic net regularization, theoretical bounds are difficult to derive so as to limit extreme shorting of assets. In this paper, we propose a minimum variance portfolio with the regularization of ℓ 1 and ℓ 2 norm combined (namely ℓ 1 , 2-norm). The new regularization enjoys the best of the two regularizations of ℓ 1 norm and ℓ 2-norm squared. In particular, we derive a theoretical bound that limits short-sells and develop a closed-form formula for the proximal term of the ℓ 1 , 2 norm. A fast proximal augmented Lagrange method is applied to solve the ℓ 1 , 2-norm regularized problem. Extensive numerical experiments confirm that the new model often results in high Sharpe ratio, low turnover and small amount of short sells when compared with several existing models on six datasets. </p

Effects of SC dual pulsed-microwaves irradiation on the bioeffects in rats' organs under a mutually perpendicular incidence

To investigate the potential risk of electromagnetic exposure, systematic studies on the bioeffects caused by single-frequency electromagnetic fields have been carried out with plenty of achievements.However, the bioeffects caused by multiple-frequency fields still need to be further studied. In the resent work, we constructed a dual-sourced irradiation system to allow two microwaves simultaneously incident from two perpendicular directions. Subsequently, the influence of the number of pulses on the blood, brain, testis, heart, and liver tissues of rats under the simultaneous irradiation of S-band and C-band pulsed microwaves was studied, and compared to that caused by the single-sourced exposure with the same electromagnetic exposure dose. The results showed that when the amplitude of the space electric field intensity was 44.84 kV/m and the number of the pulses was 2, 5, 20 and 200, the blood routine, testosterone, alanine aminotransferase and aspartate aminotransferase content of rats in the irradiation groups did not change significantly compared with the control group and sham group (p&gt;0.05). The nuclear area in the brain, heart and liver of rats, as well as the Johnson score of the testis, has not significantly changed after the irradiation (p&gt;0.05), and no pathological changes have been observed in these target tissues. The content of Bax in the above four tissues of rats in the irradiation groups increased nonlinearly with the increase of the number of pulses, which can be described as the non-thermal bioeffect that concerned the intensity of the electric field of the pulsed microwave. When the number of pulses is greater than 2, a significant apoptotic response was detected in the brain and testicle (p˂0.05), while in the liver and heart the pulse number needs to be greater than 20 (p˂0.05). This is mainly because the amplitude of the internal field of the brain and testicle (11.3 kV/m and 10 kV/m respectively) is higher than that of the liver and heart (5.73 kV/m and 5.33 kV/m respectively). Under the same electromagnetic exposure dose, there was no difference between the SC combined irradiation and the S or C individual-irradiation (p&gt;0.05)

A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms

In order to ensure the driving safety of vehicles in windy environments, a wind monitoring and warning system is widely used, in which a wind speed prediction algorithm with better stability and sufficient accuracy is one of the key factors to ensure the smooth operation of the system. In this paper, a novel short-term wind speed forecasting model, combining complementary ensemble empirical mode decomposition (CEEMD), auto-regressive integrated moving average (ARIMA), and support vector machine (SVM) technology, is proposed. Firstly, EMD and CEEMD are used to decompose the measured wind speed sequence into a finite number of intrinsic mode functions (IMFs) and a decomposed residual. Each of the IMF subseries has better linear characteristics. The ARIMA algorithm is adopted to predict each of the subseries. Then, a new subseries is reconstructed using the sum of the predicted errors of all subseries. The high nonlinear features of the reconstructed error subseries are modeled using SVM, which is suitable to process nonlinear data. Finally, the superposition of all prediction results is performed to obtain the final predicted wind speed. To verify the stability and accuracy of the model, two typhoon datasets, measured from the south coast of China, are used to test the proposed methods. The results show that the proposed hybrid model has a better predictive ability than single models and other combined models. The root mean squared errors (RMSEs) of the hybrid model for the three wind speed datasets are 0.839, 0.529, and 0.377, respectively. The combination of CEEMD with ARIMA contributes most of the prediction performance to the hybrid model. It is feasible to apply the hybrid model to wind speed prediction

A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms

In order to ensure the driving safety of vehicles in windy environments, a wind monitoring and warning system is widely used, in which a wind speed prediction algorithm with better stability and sufficient accuracy is one of the key factors to ensure the smooth operation of the system. In this paper, a novel short-term wind speed forecasting model, combining complementary ensemble empirical mode decomposition (CEEMD), auto-regressive integrated moving average (ARIMA), and support vector machine (SVM) technology, is proposed. Firstly, EMD and CEEMD are used to decompose the measured wind speed sequence into a finite number of intrinsic mode functions (IMFs) and a decomposed residual. Each of the IMF subseries has better linear characteristics. The ARIMA algorithm is adopted to predict each of the subseries. Then, a new subseries is reconstructed using the sum of the predicted errors of all subseries. The high nonlinear features of the reconstructed error subseries are modeled using SVM, which is suitable to process nonlinear data. Finally, the superposition of all prediction results is performed to obtain the final predicted wind speed. To verify the stability and accuracy of the model, two typhoon datasets, measured from the south coast of China, are used to test the proposed methods. The results show that the proposed hybrid model has a better predictive ability than single models and other combined models. The root mean squared errors (RMSEs) of the hybrid model for the three wind speed datasets are 0.839, 0.529, and 0.377, respectively. The combination of CEEMD with ARIMA contributes most of the prediction performance to the hybrid model. It is feasible to apply the hybrid model to wind speed prediction

Pseudoaglycone of Spinosyn A

The title compound [systematic name: 9-ethyl-13-hydroxy-14-methyl-2-(3,4,5-trimethoxy-6-methyltetrahydro-2H-pyran-2-yloxy)-3,3a,5b,6,9,10,11,12,13,14,16a,16b-dodecahydro-1H-as-indaceno[3,2-d][1]oxacyclododecine-7,15(2H,5aH)-dione], C33H50O9, was obtained by hydrolysis of Spinosyn A. The fused cyclopentene ring adopts a twisted conformation, while the fused cyclohexene and cyclopentane rings are in envelope conformations with the same C atom at the flaps. In the crystal, molecules are linked by O&amp;#8212;H...O and C&amp;#8212;H...O hydrogen bonds into a layer parallel to the ab plane