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Ballico, E.,Huh, S.,Malaspina, F. Academic Press 2016 Journal of algebra Vol.450 No.-
<P>We classify globally generated vector bundles on P-1 x P-1 x P-1 with small first Chern class, i.e. c(1) = (a(1), a(2), a(3)), a(i) <= 2. Our main method is to investigate the associated smooth curves to globally generated vector bundles via the Hartshorne-Serre correspondence. (C) 2015 Elsevier Inc. All rights reserved.</P>
Globally generated vector bundles of rank 2 on a smooth quadric threefold
Ballico, E.,Huh, S.,Malaspina, F. North-Holland Pub. Co 2014 Journal of pure and applied algebra Vol.218 No.2
We investigate the existence of globally generated vector bundles of rank 2 with c<SUB>1</SUB>@?3 on a smooth quadric threefold and determine their Chern classes. As an automatic consequence, every rank 2 globally generated vector bundle on Q with c<SUB>1</SUB>=3 is an odd instanton up to twist.
Walsh-Messinger, Julie,Jiang, Haoran,Lee, Hyejoo,Rothman, Karen,Ahn, Hongshik,Malaspina, Dolores Elsevier/North Holland Biomedical Press 2019 Psychiatry Research Vol. No.
<P><B>Abstract</B></P> <P>This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (<I>n</I> = 60), schizoaffective disorder (<I>n</I> = 19), bipolar disorder (<I>n</I> = 20), unipolar depression (<I>n</I> = 14), and healthy controls (<I>n</I> = 51) into psychiatric diagnostic categories. The Random Forest machine learning algorithm, which showed best efficacy (92.9% SD: 0.06), was used to generate variable importance ranking of positive, negative, and general psychopathology symptoms, cognitive indexes, global assessment of function (GAF), and parental ages at birth for sorting participants into diagnostic categories. Symptoms were ranked most influential for separating cases from healthy controls, followed by cognition and maternal age. To separate schizophrenia/schizoaffective disorder from bipolar/unipolar depression, GAF was most influential, followed by cognition and paternal age. For classifying schizophrenia from all other psychiatric disorders, low GAF and paternal age were similarly important, followed by cognition, psychopathology and maternal age. Controls misclassified as schizophrenia cases showed lower nonverbal abilities, mild negative and general psychopathology symptoms, and younger maternal or older paternal age. The importance of symptoms for classification of cases and lower GAF for diagnosing schizophrenia, notably more important and distinct from cognition and symptoms, concurs with current practices. The high importance of parental ages is noteworthy and merits further study.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Machine-learning algorithms estimated importance of multilevel data for diagnostic classification. </LI> <LI> Symptoms were most influential for differentiating psychiatric cases from healthy controls. </LI> <LI> Function was most important for separating the schizophrenias from affective disorder cases. </LI> <LI> Function and paternal age were equally important for separating schizophrenia from all other cases. </LI> <LI> Misclassified controls had mild symptoms, lower cognition, and/or younger mothers/older fathers. </LI> </UL> </P>
A motor neuron strategy to save time and energy in neurodegeneration: adaptive protein stoichiometry
Zucchi, Elisabetta,Lu, Ching‐,Hua,Cho, Yunju,Chang, Rakwoo,Adiutori, Rocco,Zubiri, Irene,Ceroni, Mauro,Cereda, Cristina,Pansarasa, Orietta,Greensmith, Linda,Malaspina, Andrea,Petzold, Axel Raven Press [etc.] 2018 Journal of neurochemistry Vol.146 No.5
<P><B>Abstract</B></P><P>Neurofilament proteins (Nf) are a biomarker of disease progression in amyotrophic lateral sclerosis (ALS). This study investigated whether there are major differences in expression from <I>in vivo</I> measurements of neurofilament isoforms, from the light chain, NfL (68 kDa), compared with larger proteins, the medium chain (NfM, 150 kDa) and the heavy (NfH, 200‐210 kDa) chains in ALS patients and healthy controls. New immunological methods were combined with Nf subunit stoichiometry calculations and Monte Carlo simulations of a coarse‐grained Nf brush model. Based on a physiological Nf subunit stoichiometry of 7 : 3 : 2 (NfL:NfM:NfH), we found an ‘adaptive’ Nf subunit stoichiometry of 24 : 2.4 : 1.6 in ALS. Adaptive Nf stoichiometry preserved NfL gyration radius in the Nf brush model. The energy and time requirements for Nf translation were 56 ± 27k ATP (5.6 h) in control subjects compared to 123 ± 102k (12.3 h) in ALS with ‘adaptive’ (24:2.4:1.6) Nf stoichiometry (not significant) and increased significantly to 355 ± 330k (35.5 h) with ‘luxury’ (7:3:2) Nf subunit stoichiometry (<I>p</I> < 0.0001 for each comparison). Longitudinal disease progression‐related energy consumption was highest with a ‘luxury’ (7:3:2) Nf stoichiometry. Therefore, an energy and time‐saving option for motor neurons is to shift protein expression from larger to smaller (cheaper) subunits, at little or no costs on a protein structural level, to compensate for increased energy demands.</P><P/>