Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory

The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information ($\Phi$) in the brain is related to the level of consciousness. IIT proposes that to quantify information integration in a system as a whole, integrated information should be measured across the partition of the system at which information loss caused by partitioning is minimized, called the Minimum Information Partition (MIP). The computational cost for exhaustively searching for the MIP grows exponentially with system size, making it difficult to apply IIT to real neural data. It has been previously shown that if a measure of $\Phi$ satisfies a mathematical property, submodularity, the MIP can be found in a polynomial order by an optimization algorithm. However, although the first version of $\Phi$ is submodular, the later versions are not. In this study, we empirically explore to what extent the algorithm can be applied to the non-submodular measures of $\Phi$ by evaluating the accuracy of the algorithm in simulated data and real neural data. We find that the algorithm identifies the MIP in a nearly perfect manner even for the non-submodular measures. Our results show that the algorithm allows us to measure $\Phi$ in large systems within a practical amount of time

Effects of valproic acid on the cell cycle and apoptosis through acetylation of histone and tubulin in a scirrhous gastric cancer cell line

<p>Abstract</p> <p>Background</p> <p>Management of peritoneal dissemination is the most critical problem in gastric cancer. This study was performed to investigate the inhibitory effects of valproic acid (VPA) on a highly peritoneal-seeding cell line of human scirrhous gastric cancer, OCUM-2MD3, and to explore the mechanism and the potential of VPA.</p> <p>Methods</p> <p>The effects of VPA on the growth of OCUM-2MD3 cells were assessed by MTT assay. In addition, paclitaxel (PTX) was combined with VPA to evaluate their synergistic effects. HDAC1 and HDAC2 expression were evaluated by western blotting in OCUM-2MD3 cells and other gastric cancer cell lines (TMK-1, MKN-28). The acetylation status of histone H3 and α-tubulin after exposure to VPA were analyzed by western blotting. The activities of cell cycle regulatory proteins and apoptosis-modulating proteins were also examined by western blotting. The effects of VPA <it>in vivo </it>were evaluated in a xenograft model, and apoptotic activity was assessed by TUNEL assay.</p> <p>Results</p> <p>OCUM-2MD3 cells showed high levels of HDAC1 and HDAC2 expression compared with TMK-1 and MKN-28. The concentration of VPA required for significant inhibition of cell viability (<it>P </it>< 0.05) was 5 mM at 24 h and 0.5 mM at 48 h and 72 h. The inhibition of VPA with PTX showed dose-dependent and combinatorial effects. VPA increased acetyl-histone H3, acetyl-α-tubulin, and p21WAF1 levels accompanied by upregulation of p27, caspase 3, and caspase 9, and downregulation of bcl-2, cyclin D1, and survivin. In the xenograft model experiment, the mean tumor volume of the VPA-treated group was significantly reduced by 36.4%, compared with that of the control group at 4 weeks after treatment (<it>P </it>< 0.01). The apoptotic index was significantly higher in the VPA-treated group (42.3% ± 3.5%) than in the control group (7.7% ± 2.5%) (<it>P </it>< 0.001).</p> <p>Conclusions</p> <p>VPA induced dynamic modulation of histone H3 and α-tubulin acetylation in relation with the anticancer effect and the enhancement of PTX in the OCUM-2MD3 cell line. Therefore, VPA in combination with PTX is expected to be a promising therapy for peritoneal dissemination of scirrhous gastric cancer.</p

Genetic Predisposition to Ischemic Stroke

Background and Purpose—The prediction of genetic predispositions to ischemic stroke (IS) may allow the identification of individuals at elevated risk and thereby prevent IS in clinical practice. Previously developed weighted multilocus genetic risk scores showed limited predictive ability for IS. Here, we investigated the predictive ability of a newer method, polygenic risk score (polyGRS), based on the idea that a few strong signals, as well as several weaker signals, can be collectively informative to determine IS risk.Methods—We genotyped 13 214 Japanese individuals with IS and 26 470 controls (derivation samples) and generated both multilocus genetic risk scores and polyGRS, using the same derivation data set. The predictive abilities of each scoring system were then assessed using 2 independent sets of Japanese samples (KyushuU and JPJM data sets).Results—In both validation data sets, polyGRS was shown to be significantly associated with IS, but weighted multilocus genetic risk scores was not. Comparing the highest with the lowest polyGRS quintile, the odds ratios for IS were 1.75 (95% confidence interval, 1.33–2.31) and 1.99 (95% confidence interval, 1.19–3.33) in the KyushuU and JPJM samples, respectively. Using the KyushuU samples, the addition of polyGRS to a nongenetic risk model resulted in a significant improvement of the predictive ability (net reclassification improvement=0.151; P<0.001).Conclusions—The polyGRS was shown to be superior to weighted multilocus genetic risk scores as an IS prediction model. Thus, together with the nongenetic risk factors, polyGRS will provide valuable information for individual risk assessment and management of modifiable risk factors

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Practical PHI Toolbox

<p>This MATLAB toolbox provides codes for computing practical measures of integrated information proposed in Oizumi et al., 2016, PLoS Comp Biol and Oizumi et al., 2016, PNAS and codes for efficiently searching the Minimum Information Partition (MIP) proposed in Hidaka & Oizumi, 2018, PLoS ONE and Kitazono et al., 2018, Entropy (Queyranne’s algorithm). Please see Readme.docx for more details. </p><p>The codes for Queyranne’s algorithm were written by Shohei Hidaka at JAIST (Japan Advanced Institute of Science and Technology).<br></p><p><br></p> <p> </p> <p>References</p> <p>[1] Oizumi, M., Amari, S, Yanagawa, T., Fujii, N., & Tsuchiya, N. (2016). Measuring integrated information from the decoding perspective. PLoS Comput Biol, 12(1), e1004654. </p> <p> </p> <p>[2] Oizumi, M., Tsuchiya, N., & Amari, S. (2016). Unified framework for information integration based on information geometry. Proceedings of the National Academy of Sciences, 113(51), 14817-14822. </p> <p> </p> <p>[3] Hidaka, S., & Oizumi, M. (2018). Fast and exact search for the partition with minimal information loss. PLoS one, 13(9), e0201126.</p> <p> </p> <p>[4] Kitazono, J., Kanai, R., Oizumi, M. (2018). Efficient algorithms for searching the minimum information partition in integrated information theory. Entropy, 20, 173.</p

Practical PHI Toolbox

<p>This MATLAB toolbox provides codes for computing practical measures of integrated information proposed in Oizumi et al., 2016, PLoS Comp Biol and Oizumi et al., 2016, PNAS and codes for efficiently searching the Minimum Information Partition (MIP) proposed in Hidaka & Oizumi, 2018, PLoS ONE and Kitazono et al., 2018, Entropy (Queyranne’s algorithm). Please see Readme.docx for more details. </p><p>The codes for Queyranne’s algorithm were written by Shohei Hidaka at JAIST (Japan Advanced Institute of Science and Technology).<br></p><p><br></p> <p> </p> <p>References</p> <p>[1] Oizumi, M., Amari, S, Yanagawa, T., Fujii, N., & Tsuchiya, N. (2016). Measuring integrated information from the decoding perspective. PLoS Comput Biol, 12(1), e1004654. </p> <p> </p> <p>[2] Oizumi, M., Tsuchiya, N., & Amari, S. (2016). Unified framework for information integration based on information geometry. Proceedings of the National Academy of Sciences, 113(51), 14817-14822. </p> <p> </p> <p>[3] Hidaka, S., & Oizumi, M. (2018). Fast and exact search for the partition with minimal information loss. PLoS one, 13(9), e0201126.</p> <p> </p> <p>[4] Kitazono, J., Kanai, R., Oizumi, M. (2018). Efficient algorithms for searching the minimum information partition in integrated information theory. Entropy, 20, 173.</p