Exceptional representations of a double quiver of type A, and Richardson elements in seaweed Lie algebras

In this paper, we study the set of $\Delta$-filtered modules of quasi-hereditary algebras arising from quotients of the double of quivers of type $A$. Our main result is that for any fixed $\Delta$-dimension vector, there is a unique (up to isomorphism) exceptional $\Delta$-filtered module. We then apply this result to show that there is always an open adjoint orbit in the nilpotent radical of a seaweed Lie algebra in \mathrm{gl}_{n}(\field), thus answering positively in this \mathrm{gl}_{n}(\field) case to a question raised independently by Michel Duflo and Dmitri Panyushev. An example of a seaweed Lie algebra in a simple Lie algebra of type $E_{8}$ not admitting an open orbit in its nilpotent radical is given

Contributing Factors to Persistence among African-American and Hispanic Students in Higher Education: A Phenomenological Qualitative Study at a Diverse Small Institution on the East Coast

The purpose for this phenomenological study is to understand the persistence of academically successful African-American and Hispanic students in an ethnically diverse higher education context where services and support targeting minority students do not formally exist. The research for this study has been conducted on campuses where the student body demographics are largely mono-ethnic. It would seem that as institutions of higher education become increasingly diverse, contributing factors to success for students of color may change. Themes that emerged include having a spiritual perspective, having a sense of purpose or the big picture, having support from family, having support from the individuals within the institution, and having self-motivation. With the increasingly competitive nature of higher education and the need for a higher education degree in the marketplace, there is an expectation that academic institutions will address student persistence as well as equal opportunity for success among all students. However, there remains in higher education a certain inequality, especially when considering student persistence and the unique needs of minority students in higher education (Jost, Whitfield, & Jost, 2005). While there may be a variety of reasons for such a reality, recent research and development addressing student persistence among minority students helps to address the issue

Bilinear forms on Grothendieck groups of triangulated categories

We extend the theory of bilinear forms on the Green ring of a finite group developed by Benson and Parker to the context of the Grothendieck group of a triangulated category with Auslander-Reiten triangles, taking only relations given by direct sum decompositions. We examine the non-degeneracy of the bilinear form given by dimensions of homomorphisms, and show that the form may be modified to give a Hermitian form for which the standard basis given by indecomposable objects has a dual basis given by Auslander-Reiten triangles. An application is given to the homotopy category of perfect complexes over a symmetric algebra, with a consequence analogous to a result of Erdmann and Kerner.Comment: arXiv admin note: substantial text overlap with arXiv:1301.470

Butterfly hysteresis loop at non-zero bias field in antiferromagnetic molecular rings: cooling by adiabatic magnetization

At low temperatures, the magnetization of the molecular ferric wheel NaFe$_6$ exhibits a step at a critical field $B_c$ due to a field-induced level-crossing. By means of high-field torque magnetometry we observed a hysteretic behavior at the level-crossing with a characteristic butterfly shape which is analyzed in terms of a dissipative two-level model. Several unusual features were found. The non-zero bias field of the level-crossing suggests the possibility of cooling by adiabatic magnetization.Comment: 4 pages, 5 figures, REVTEX4, to appear in PR

AI for predicting chemical-effect associations at the chemical universe level – deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary information Supplementary data are available at bioRxiv online.Contact jana.schor{at}ufz.deCompeting Interest StatementThe authors have declared no competing interest

AI for predicting chemical-effect associations at the chemical universe level: DeepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn

Effect of large weight reductions on measured and estimated kidney function

BACKGROUND: When patients experience large weight loss, muscle mass may be affected followed by changes in plasma creatinine (pCr). The MDRD and CKD-EPI equations for estimated GFR (eGFR) include pCr. We hypothesised that a large weight loss reduces muscle mass and pCr causing increase in eGFR (creatinine-based equations), whereas measured GFR (mGFR) and cystatin C-based eGFR would be unaffected if adjusted for body surface area. METHODS: Prospective, intervention study including 19 patients. All attended a baseline visit before gastric bypass surgery followed by a visit six months post-surgery. mGFR was assessed during four hours plasma (51)Cr-EDTA clearance. GFR was estimated by four equations (MDRD, CKD-EPI-pCr, CKD-EPI-cysC and CKD-EPI-pCr-cysC). DXA-scans were performed at baseline and six months post-surgery to measure changes in lean limb mass, as a surrogate for muscle mass. RESULTS: Patients were (mean ± SD) 40.0 ± 9.3 years, 14 (74%) were female and 5 (26%) had type 2 diabetes, baseline weight was 128 ± 19 kg, body mass index 41 ± 6 kg/m2 and absolute mGFR 122 ± 24 ml/min. Six months post-surgery weight loss was 27 (95% CI: 23; 30) kg, mGFR decreased by 9 (−17; −2) from 122 ± 24 to 113 ± 21 ml/min (p = 0.024), but corrected for current body surface area (BSA) mGFR was unchanged by 2 (−5; 9) ml/min/1.73 m(2) (p = 0.52). CKD-EPI-pCr increased by 12 (6; 17) and MDRD by 13 (8; 18) (p < 0.001 for both), while CKD-EPI-cysC was unchanged by 2 (−8; 4) ml/min/1.73 m(2) (p = 0.51). Lean limb mass was reduced by 3.5 (−4.4;−2.6; p < 0.001) kg and change in lean limb mass correlated with change in plasma creatinine (R (2) = 0.28, p = 0.032). CONCLUSIONS: Major weight reductions are associated with a reduction in absolute mGFR, which may reflect resolution of glomerular hyperfiltration, while mGFR adjusted for body surface area was unchanged. Estimates of GFR based on creatinine overestimate renal function likely due to changes in muscle mass, whereas cystatin C based estimates are unaffected. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02138565. Date of registration: March 24, 2014