On the non-equivalence of Lorenz System and Chen System

In this paper, we prove that the Chen system with a set of chaotic parameters is not smoothly equivalent to the Lorenz system with any parameters

BEC-BCS Crossover with Feshbach Resonance for a Three-Hyperfine-Species Model

We consider the behavior of an ultracold Fermi gas across a narrow Feshbach resonance, where the occupation of the closed channel may not be negligible. While the corrections to the single-channel formulae associated with the nonzero chemical potential and with particle conservation have been considered in the existing literature, there is a further effect, namely the "inter-channel Pauli exclusion principle" associated with the fact that a single hyperfine species may be common to the two channels. We focus on this effect and show that, as intuitively expected, the resulting corrections are of order $E_F/\eta$, where $E_F$ is the Fermi energy of the gas in the absence of interactions and $\eta$ is the Zeeman energy difference between the two channels. We also consider the related corrections to the fermionic excitation spectrum, and briefly discuss the collective modes of the system

Breast cancer-derived Dickkopf1 inhibits osteoblast differentiation and osteoprotegerin expression: Implication for breast cancer osteolytic bone metastases

Most breast cancer metastases in bone form osteolytic lesions, but the mechanisms of tumor-induced bone resorption and destruction are not fully understood. Although it is well recognized that Wnt/Β-catenin signaling is important for breast cancer tumorigenesis, the role of this pathway in breast cancer bone metastasis is unclear. Dickkopf1 (Dkk1) is a secreted Wnt/Β-catenin antagonist. In the present study, we demonstrated that activation of Wnt/Β-catenin signaling enhanced Dkk1 expression in breast cancer cells and that Dkk1 overexpression is a frequent event in breast cancer. We also found that human breast cancer cell lines that preferentially form osteolytic bone metastases exhibited increased levels of Wnt/Β-catenin signaling and Dkk1 expression. Moreover, we showed that breast cancer cell-produced Dkk1 blocked Wnt3A-induced osteoblastic differentiation and osteoprotegerin (OPG) expression of osteoblast precursor C2C12 cells and that these effects could be neutralized by a specific anti-Dkk1 antibody. In addition, we found that breast cancer cell conditioned media were able to block Wnt3A-induced NF-kappaB ligand reduction in C2C12 cells. Finally, we demonstrated that conditioned media from breast cancer cells in which Dkk1 expression had been silenced via RNAi were unable to block Wnt3A-induced C2C12 osteoblastic differentiation and OPG expression. Taken together, these results suggest that breast cancer-produced Dkk1 may be an important mechanistic link between primary breast tumors and secondary osteolytic bone metastases. © 2008 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/60217/1/23625_ftp.pd

Rapid in vivo measurement of B-amyloid reveals biphasic clearance kinetics in an Alzheimer\u27s mouse model

Accumulation of ?-amyloid peptide is a key step in Alzheimer?s disease pathogenesis. Yuede et al. propose a novel method to track ?-amyloid levels in vivo

Expression of SORL1 and a novel SORL1 splice variant in normal and Alzheimers disease brain

<p>Abstract</p> <p>Background</p> <p>Variations in sortilin-related receptor (SORL1) expression and function have been implicated in Alzheimers Disease (AD). Here, to gain insights into SORL1, we evaluated SORL1 expression and splicing as a function of AD and AD neuropathology, neural gene expression and a candidate single nucleotide polymorphism (SNP).</p> <p>Results</p> <p>To identify SORL1 splice variants, we scanned each of the 46 internal SORL1 exons in human brain RNA samples and readily found SORL1 isoforms that lack exon 2 or exon 19. Quantification in a case-control series of the more abundant isoform lacking exon 2 (delta-2-SORL1), as well as the "full-length" SORL1 (FL-SORL1) isoform containing exon 2 showed that expression of FL-SORL1 was reduced in AD individuals. Moreover, FL-SORL1 was reduced in cognitively intact individuals with significant AD-like neuropathology. In contrast, the expression of the delta-2-SORL1 isoform was similar in AD and non-AD brains. The expression of FL-SORL1 was significantly associated with synaptophysin expression while delta-2-SORL1 was modestly enriched in white matter. Lastly, FL-SORL1 expression was associated with rs661057, a SORL1 intron one SNP that has been associated with AD risk. A linear regression analysis found that rs661057, synaptophysin expression and AD neuropathology were each associated with FL-SORL1 expression.</p> <p>Conclusion</p> <p>These results confirm that FL-SORL1 expression declines in AD and with AD-associated neuropathology, suggest that FL-SORL1 declines in cognitively-intact individuals with AD-associated neuropathology, identify a novel SORL1 splice variant that is expressed similarly in AD and non-AD individuals, and provide evidence that an AD-associated SNP is associated with SORL1 expression. Overall, these results contribute to our understanding of SORL1 expression in the human brain.</p

Predicting the Most Deleterious Missense Nonsynonymous Single-Nucleotide Polymorphisms of Hennekam Syndrome-Causing CCBE1 Gene, in Silico Analysis

Hennekam lymphangiectasia-lymphedema syndrome has been linked to single-nucleotide polymorphisms in the CCBE1 (collagen and calcium-binding EGF domains 1) gene. Several bioinformatics methods were used to find the most dangerous nsSNPs that could affect CCBE1 structure and function. Using state-of-the-art in silico tools, this study examined the most pathogenic nonsynonymous single-nucleotide polymorphisms (nsSNPs) that disrupt the CCBE1 protein and extracellular matrix remodeling and migration. Our results indicate that seven nsSNPs, rs115982879, rs149792489, rs374941368, rs121908254, rs149531418, rs121908251, and rs372499913, are deleterious in the CCBE1 gene, four (G330E, C102S, C174R, and G107D) of which are the highly deleterious, two of them (G330E and G107D) have never been seen reported in the context of Hennekam syndrome. Twelve missense SNPs, rs199902030, rs267605221, rs37517418, rs80008675, rs116596858, rs116675104, rs121908252, rs147974432, rs147681552, rs192224843, rs139059968, and rs148498685, are found to revert into stop codons. Structural homology-based methods and sequence homology-based tools revealed that 8.8% of the nsSNPs are pathogenic. SIFT, PolyPhen2, M-CAP, CADD, FATHMM-MKL, DANN, PANTHER, Mutation Taster, LRT, and SNAP2 had a significant score for identifying deleterious nsSNPs. The importance of rs374941368 and rs200149541 in the prediction of post-translation changes was highlighted because it impacts a possible phosphorylation site. Gene-gene interactions revealed CCBE1's association with other genes, showing its role in a number of pathways and coexpressions. The top 16 deleterious nsSNPs found in this research should be investigated further in the future while researching diseases caused CCBE1 gene specifically HS. The FT web server predicted amino acid residues involved in the ligand-binding site of the CCBE1 protein, and two of the substitutions (R167W and T153N) were found to be involved. These highly deleterious nsSNPs can be used as marker pathogenic variants in the mutational diagnosis of the HS syndrome, and this research also offers potential insights that will aid in the development of precision medicines. CCBE1 proteins from Hennekam syndrome patients should be tested in animal models for this purpose. © 2021 Khyber Shinwari et al.The work was carried out within the framework of state research at the Institute of Immunology and Physiology of the Ural Branch of the Russian Academy of Sciences, project number AAAA-A21-121012090091-6

In Silico Analysis Revealed Five Novel High-Risk Single-Nucleotide Polymorphisms (rs200384291, rs201163886, rs193141883, rs201139487, and rs201723157) in ELANE Gene Causing Autosomal Dominant Severe Congenital Neutropenia 1 and Cyclic Hematopoiesis

Single-nucleotide polymorphisms in the ELANE (Elastase, Neutrophil Expressed) gene are associated with severe congenital neutropenia, while the ELANE gene provides instructions for making a protein called neutrophil elastase. We identified disease susceptibility single-nucleotide polymorphisms (SNPs) in the ELANE gene using several computational tools. We used cutting-edge computational techniques to investigate the effects of ELANE mutations on the sequence and structure of the protein. Our study suggested that eight nsSNPs (rs28931611, rs57246956, rs137854448, rs193141883, rs201723157, rs201139487, rs137854451, and rs200384291) are the most deleterious in ELANE gene and disturb protein structure and function. The mutants F218L, R34W, G203S, R193W, and T175M have not yet been identified in patients suffering from SCN and cyclic hematopoiesis, while C71Y, P139R, C151Y, G214R, and G203C reported in our study are already associated with both of the disorders. These mutations are shown to destabilize structure and disrupt ELANE protein activation, splicing, and folding and might diminish trypsin-like serine protease efficiency. Prediction of posttranslation modifications highlighted the significance of deleterious nsSNPs because some of nsSNPs affect potential phosphorylation sites. Gene-gene interactions showed the relation of ELANE with other genes depicting its importance in numerous pathways and coexpressions. We identified the deleterious nsSNPs, constructed mutant protein structures, and evaluated the impact of mutation by employing molecular docking. This research sheds light on how ELANE failure upon mutation results in disease progression, including congenital neutropenia, and validation of these novel predicted nsSNPs is required through the wet lab. © 2022 Khyber Shinwari et al.Ural Branch, Russian Academy of Sciences, UB RAS: AAAAA-A21- 121012090091-6The work was carried out in accordance with the state order of the Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, AAAAA-A21- 121012090091-6

Smokeless Tobacco Use and the Risk of Head and Neck Cancer: Pooled Analysis of US Studies in the INHANCE Consortium

Previous studies on smokeless tobacco use and head and neck cancer (HNC) have found inconsistent and often imprecise estimates, with limited control for cigarette smoking. Using pooled data from 11 US case-control studies (1981–2006) of oral, pharyngeal, and laryngeal cancers (6,772 cases and 8,375 controls) in the International Head and Neck Cancer Epidemiology (INHANCE) Consortium, we applied hierarchical logistic regression to estimate odds ratios and 95% confidence intervals for ever use, frequency of use, and duration of use of snuff and chewing tobacco separately for never and ever cigarette smokers. Ever use (versus never use) of snuff was strongly associated with HNC among never cigarette smokers (odds ratio (OR) = 1.71, 95% confidence interval (CI): 1.08, 2.70), particularly for oral cavity cancers (OR = 3.01, 95% CI: 1.63, 5.55). Although ever (versus never) tobacco chewing was weakly associated with HNC among never cigarette smokers (OR = 1.20, 95% CI: 0.81, 1.77), analyses restricted to cancers of the oral cavity showed a stronger association (OR = 1.81, 95% CI: 1.04, 3.17). Few or no associations between each type of smokeless tobacco and HNC were observed among ever cigarette smokers, possibly reflecting residual confounding by smoking. Smokeless tobacco use appears to be associated with HNC, especially oral cancers, with snuff being more strongly associated than chewing tobacco

A study on decision-making of food supply chain based on big data

As more and more companies have captured and analyzed huge volumes of data to improve the performance of supply chain, this paper develops a big data harvest model that uses big data as inputs to make more informed production decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates products demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the analytical framework has vast potential for supporting support decision making by extracting value from big data