Anisotropic uniaxial pressure response of the Mott insulator Ca2RuO4

We have investigated the in-plane uniaxial pressure effect on the antiferromagnetic Mott insulator Ca2RuO4 from resistivity and magnetization measurements. We succeeded in inducing the ferromagnetic metallic phase at lower critical pressure than by hydrostatic pressure, indicating that the flattening distortion of the RuO6 octahedra is more easily released under in-plane uniaxial pressure. We also found a striking in-plane anisotropy in the pressure responses of various magnetic phases: Although the magnetization increases monotonically with pressure diagonal to the orthorhombic principal axes, the magnetization exhibits peculiar dependence on pressure along the in-plane orthorhombic principal axes. This peculiar dependence can be explained by a qualitative difference between the uniaxial pressure effects along the orthorhombic a and b axes, as well as by the presence of twin domain structures.Comment: Accepted for publication in Phys. Rev.

Morphological and cytological studies on the process of growth to the strains of inbred mice

　我国で分離育成した近交系マウスの医学生物学面への実用化の検索目的のーつとして， NC系およびKK系の産仔生後発育とその生殖細胞について形態学的細胞学的観察を行なった. 1. 体重増加は三段階に分けられ生後35-40日令と生後55-60日令に 体重増加の屈折転向点がある.雌雄個体の体重の差および両系統聞の体重の差は生後40日令頃から生ずる. 2. 畢丸降下日令はNC系22-25日， KK系24-28日で両系統聞に有意差が認められた. 3. 腔関口日令はNC系30日前後， KK系32-35日令で両系統聞に有意差がみられた. 4. 腫関口日から正常な性週期をくり返すまでにはKK系は非常に不安定である1週期の長さはNC系4.91±0.26 日， KK系5.80±3.80日であり，両者聞に有意差が認められた. 5. 卵巣重量の増加は生後30-35日令に成熟量IC 達する. 6. 宰丸重量の増加は生後50-55日令までの増加が急速でありそれ以後緩慢である. 7. 精細管口径の発達は生後25日令までは最も急速であり生後55-60日令はやや緩慢で，それ以後は口径は一定する. 8. 両系統における雄の性成熟期は生後50-55日令，雌は30-35日令であり，体の成熟期はこれ等の時期より遅いと考えられた. 9. 精細管内の精子形成期は性成熟期の10-15目前に一応完了するものと考えられ，NC系40日令，KK系45 日令である. 10. 卵巣内の成熟鴻胞は性成熟期の5目前に観察され，NC系25日令， KK系30日令である.多卵性溜胞および多核性漉胞は一般に僅少であるが，他の崎乳動物と同じく幼令期に多く観察された.これは卵巣内のホノレモン調整の不均衡の時期である幼若期に多発すると考えられた.The present study was undertaken to investigate morphologically and cytologically the process of growth of mice of NC and KK strain. The data presented in this study are summarized as follows : 1) Body weight is separated into three stages in the process of growth. Remarkable change in growth of body weight occurred in 35 to 40 days (male) and 55 to 60 days (female) of age after birth. 2) Age of descensus testis was found to be 22 to 25 days in NC strain and 24 to 28 days in KK stram. 3) Age of vaginal opening was 30 days in NC strain and 32 to 35 days in KK strain. 4) Sexual cycle of mice generally was variable by strain. NC strain showed a considerably normal sexual cycle in contrast to KK strain. 5) Ovary weight reached a maximum of growth in 30 to 35 days of age， while testis weight reached a maximum growth in 50 to 55 days in both strains. 6) Seminiferous tubules of mice of both strains showed a marked development by 25 days after birth and slowed down from 25 days to 60 days. The diameter of seminiferous tubules showed a constant value in 60 days of age. 7) Microscopical observations revealed that healthy spermatozoa were observed in seminiferous tubules and epididymis in 40 days of age in NC strain and in 45 days in KK strain. Normal majure follicles proceeding prior to the maturation course is abundant in 25 days of age in NC strain and in 30 days of age in KK strain. Polyovular follicles were rare in occurrence showing no difference by strain. Especially， immature mice showed a comparative large number of polyovular follicles and polynuclear follicles in comparison with mature mice

A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma

The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with the overall survival and other clinicopathological variables; and included a combination of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) artificial neural networks, gene-set enrichment analysis (GSEA), Cox regression and other machine learning and predictive analytics modeling [C5.0 algorithm, logistic regression, Bayesian Network, discriminant analysis, random trees, tree-AS, Chi-squared Automatic Interaction Detection CHAID tree, Quest, classification and regression (C&R) tree and neural net)]. From an initial 54,613 gene-probes, a set of 488 genes and a final set of 16 genes were defined. Secondly, two identified markers of the immune checkpoint, PD-L1 (CD274) and IKAROS (IKZF4), were validated in an independent series from Tokai University, and the immunohistochemical expression was quantified, using a machine-learning-based Weka segmentation. High PD-L1 associated with poor overall and progression-free survival, non-GCB phenotype, Epstein–Barr virus infection (EBER+), high RGS1 expression and several clinicopathological variables, such as high IPI and absence of clinical response. Conversely, high expression of IKAROS was associated with a good overall and progression-free survival, GCB phenotype and a positive clinical response to treatment. Finally, the set of 16 genes (PAF1, USP28, SORT1, MAP7D3, FITM2, CENPO, PRCC, ALDH6A1, CSNK2A1, TOR1AIP1, NUP98, UBE2H, UBXN7, SLC44A2, NR2C2AP and LETM1), in combination with PD-L1, IKAROS, BCL2, MYC, CD163 and TNFAIP8, predicted the survival outcome of DLBCL with an overall accuracy of 82.1%. In conclusion, building predictive models of DLBCL is a feasible analytical strategy

Artificial Intelligence Analysis of the Gene Expression of Follicular Lymphoma Predicted the Overall Survival and Correlated with the Immune Microenvironment Response Signatures

Follicular lymphoma (FL) is the second most common lymphoma in Western countries. FL is characterized by being incurable, usually having an indolent clinical course with frequent relapses, and an eventual patient’s death or transformation to Diffuse Large B-cell Lymphoma. The immune response and the tumoral immune microenvironment, including FOXP3+Tregs, PD-1+TFH cells, TNFRSF14 (HVEM), and BTLA play a role in the pathogenesis. We aimed to analyze the gene expression of FL by Artificial Intelligence (machine learning, deep learning), to identify genes associated with the prognosis of the patients and with the microenvironment in terms of overall survival (OS). A series of 184 cases of the GSE16131 dataset was analyzed by multilayer perceptron (MLP) and radial basis function (RBF) neural networks. In the analysis, MLP and RBF had a synergistic effect. From an initial set of 22,215 genes probes, a final set of 43 genes was highlighted. These 43 genes predicted the OS and correlated with the immune microenvironment: in a multivariate Cox analysis, 18 genes were associated with a poor prognosis (namely, MED8, KRT19, CDC40, SLC24A2, PRB1, KIAA0100, EVA1B, KLK10, TMEM70, BTN2A3P, TRPM4, MED6, FRYL, CBFA2T2, RANBP9, BNIP2, PTP4A2 and ALDH1L1) and 25 genes were associated with a good prognosis of the patients. Gene set enrichment analysis (GSEA) confirmed these findings and showed a typical sinusoidal-like shape. Some of the most relevant genes for poor OS were EVA1B, KRT19, BTN2A3P, KLK10, TRPM4, TMEM70, and SLC24A2 (hazard risk = from 1.7 to 4.3, p < 0.005) and for good OS, these were TDRD12 and ZNF230 (HR = 0.34 and 0.28, p < 0.001). EVA1B, KRT19, BTN2AP3, KLK10, and TRPM4 also associated with M2-like macrophage markers including CD163, MRC1 (CD206), and IL10 in the core enrichment for dead OS outcome by GSEA and to poor OS by Kaplan–Meier with Log rank test. The scientific literature showed that some of these genes also play a role in other types of cancer. In conclusion, by Artificial Intelligence, we have identified new biomarkers with prognostic relevance in FL

The Use of the Random Number Generator and Artificial Intelligence Analysis for Dimensionality Reduction of Follicular Lymphoma Transcriptomic Data

Follicular lymphoma (FL) is one of the most frequent subtypes of non-Hodgkin lymphomas. This research predicted the prognosis of 184 untreated follicular lymphoma patients (LLMPP GSE16131 series), using gene expression data and artificial intelligence (AI) neural networks. A new strategy based on the random number generation was used to create 120 different and independent multilayer perceptron (MLP) solutions, and 22,215 gene probes were ranked according to their averaged normalized importance for predicting the overall survival. After dimensionality reduction, the final neural network architecture included (1) newly identified predictor genes related to cell adhesion and migration, cell signaling, and metabolism (EPB41L4B, MOCOS, SPIN2A, BTD, SRGAP3, CTNS, PRB1, L1CAM, and CEP57); (2) the international prognostic index (IPI); and (3) other relevant immuno-oncology, immune microenvironment, and checkpoint markers (CD163, CSF1R, FOXP3, PDCD1, TNFRSF14 (HVEM), and IL10). The performance of this neural network was good, with an area under the curve (AUC) of 0.89. A comparison with other machine learning techniques (C5 tree, logistic regression, Bayesian network, discriminant analysis, KNN algorithms, LSVM, random trees, SVM, tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network) was also made. In conclusion, the overall survival of follicular lymphoma was predicted with a neural network with high accuracy

Copy Number Alteration and Mutational Profile of High-Grade B-Cell Lymphoma with MYC and BCL2 and/or BCL6 Rearrangements, Diffuse Large B-Cell Lymphoma with MYC-Rearrangement, and Diffuse Large B-Cell Lymphoma with MYC-Cluster Amplification.

The authors thank the technicians and scientists of the Genomics Core Facility of the IDIBAPS for their assistance in performing the NGS analysis.Diffuse large B-cell lymphoma (DLBCL) with MYC alteration is classified as high-grade B-cell lymphoma with MYC and BCL2 and/or BCL6 rearrangements (double/triple-hit lymphoma; DHL/THL), DLBCL with MYC rearrangement (single-hit lymphoma; SHL) and DLBCL with MYC-cluster amplification (MCAD). To elucidate the genetic features of DHL/THL, SHL, and MCAD, 23 lymphoma cases from Tokai University Hospital were analyzed. The series included 10 cases of DHL/THL, 10 cases of SHL and 3 cases of MCAD. The analysis used whole-genome copy number microarray analysis (OncoScan) and a custom-made next-generation sequencing (NGS) panel of 115 genes associated with aggressive B-cell lymphomas. The copy number alteration (CNA) profiles were similar between DHL/THL and SHL. MCAD had fewer CNAs than those of DHL/THL and SHL, except for +8q24. The NGS profile characterized DHL/THL with a higher "mutation burden" than SHL (17 vs. 10, p = 0.010), and the most relevant genes for DHL/THL were BCL2 and SOCS1, and for SHL was DTX1. MCAD was characterized by mutations of DDX3X, TCF3, HLA-A, and TP53, whereas MYC was unmutated. In conclusion, DHL/THL, SHL, and MCAD have different profiles.S