SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

Prof J Cohen-Mansfield), and Minerva Center for Interdisciplinary Study of End of Life (Prof J Cohen-Mansfield),should consider dementia in older people without known dementia who have frequent admissions or who develop delirium. Delirium is common in people with dementia and contributes to cognitive decline. In hospital, care including appropriate sensory stimulation, ensuring fluid intake, and avoiding infections might reduce delirium incidence.Acting now on dementia prevention, intervention, and care will vastly improve living and dying for individuals with dementia and their families, and thus society.

People with dementia are usually older, often have co-morbidities and may need help in coping with these illnesses. A third of older people now die with dementia and all professionals working in endof-life care need to make this a central part of their planning and communication. In this commission, we have detailed evidence-based approaches to dementia and its symptoms. Services should be available, scalable and give value. As there are limited resources, professionals and services need to use what works, not use what is ineffective, and be aware of the difference. Overall, there is good potential for prevention and, once someone develops dementia, for care to be high-quality, accessible, and give value to an under-served, growing population. Effective dementia prevention and care could transform the future for society and vastly improve living and dying for individuals with dementia and their families. Acting now on what we already know can make this difference happen. Key Messages 1 There are increasing numbers of people with dementia globally although incidence in some countries has decreased. 2 Be ambitious about prevention: We recommend energetically treating hypertension in middle aged and older people without dementia to reduce dementia incidence. Interventions for other risk factors, including more childhood education, exercise, maintaining social engagement, reducing smoking, and management of hearing loss, depression, diabetes and obesity; may have the potential of delaying or preventing a third of dementias. 3 Treat cognitive symptoms: To maximise cognition, people with Alzheimer's dementia or Dementia with Lewy Bodies should be offered Cholinesterase Inhibitors (ChEIs)at all stages, or memantine for severe dementia. ChEIs are not effective in Mild Cognitive Impairment. 4 Individualise dementia care: Good dementia care spans medical, social and supportive care, should be tailored to unique individual and cultural needs, preferences, priorities, and should incorporate support for the family carers 5 Care for family carers. Family carers are at high risk of depression. Effective interventions reduce the risk and treat the symptoms, include START (Strategies for Relatives) or REACH (Resources for Enhancing Alzheimer's Caregiver Health intervention) and should be made available. 6 Plan for the future. People with dementia and their families value discussions about the future and decisions about possible attorneys to make decisions. Clinicians should consider capacity to make different types of decisions at diagnosis. 7 Protect people with dementia. People with dementia and society require protection from possible risks of the condition, including self-neglect, vulnerability including to exploitation, managing money, driving or using weapons. Risk assessment and management at all stages of the disease is essential but it should be balanced against the persons' right to autonomy. 8 Manage neuropsychiatric symptoms. Management of the neuropsychiatric symptoms of dementia including agitation, low mood or psyc...

Eleven susceptibility loci for late-onset Alzheimer’s disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer’s disease cases and 37,154 controls. In stage 2,11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer’s disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10−8) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer’s disease.

The affiliation for Evgeni Burovski was given as Higher School of Economics; the correct affiliation is National Research University, Higher School of Economics. In Box 1, "SciPy is an open-source package that builds on the strengths of Python and Numeric, providing a wide range of fast scientific and numeric functionality" was used as the box title; this has been moved to the beginning of the box text and a new title has been provided: "Excerpt from the SciPy 0.1 release announcement (typos corrected), posted 20 August 2001 on the Python-list mailing list. " From the original first sentence of this box, "(text following the % symbol indicates that a typo in the original text has been corrected in the version reproduced here)" has been deleted, and "% hanker to Hankel" and "% Netwon to Newton" have been deleted from the ends of the special functions row and the optimization row, respectively. In the first sentence of the ndimage section of Box 2, "nonlinear filter" has been changed to plural. At the end of the first paragraph of the section "SciPy matures, " "The library was expanded carefully, with the patience affordable in open-source projects and via best practices common in industry" has been changed to "The library was expanded carefully, with the patience affordable in open-source projects and via best practices, which are increasingly common in the scientific Python ecosystem and industry. " In Table 2, "Inequality constraint" has been changed to plural. In the "Nonlinear optimization: global minimization" section, "scipy.optimize.differentialevolution" had been changed to "scipy.optimize.differential_evolution. " In the first sentence of the section "Maintainers and contributors, " "SciPy developer guide" has been changed to "SciPy contributor guide" and the URL has been changed from

Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members. EADI. This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) including funding from MEL (Metropole européenne de Lille), ERDF (European Regional Development Fund) and Conseil Régional Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS-I, RS-II, RS-III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the

To determine whether the presenilin 1 (PS1), presenilin 2 (PS2) and amyloid beta-protein precursor (APP) mutations linked to familial Alzheimer's disease (FAD) increase the extracellular concentration of amyloid beta-protein (A beta) ending at A beta 42(43) in vivo, we performed a blinded comparison of plasma A beta levels in carriers of these mutations and controls. A beta 1-42(43) was elevated in plasma from subjects with FAD-linked PS1 (P < 0.0001), PS2N1411 (P = 0.009), APPK670N,M671L (P < 0.0001), and APPV7171 (one subject) mutations. A beta ending at A beta 42(43) was also significantly elevated in fibroblast media from subjects with PS1 (P < 0.0001) or PS2 (P = 0.03) mutations. These findings indicate that the FAD-linked mutations may all cause Alzhelmer's disease by increasing the extracellular concentration of A beta 42(43), thereby fostering cerebral deposition of this highly amyloidogenic peptide.

The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study (GWAS) of late-onset Alzheimer disease (LOAD) using a 3 stage design consisting of a discovery stage (Stage 1) and two replication stages (Stages 2 and 3). Both joint and meta-analysis analysis approaches were used. We obtained genome-wide significant results at MS4A4A [rs4938933; Stages 1+2, meta-analysis (PM) = 1.7 × 10−9, joint analysis (PJ) = 1.7 × 10−9; Stages 1–3, PM = 8.2 × 10−12], CD2AP (rs9349407; Stages 1–3, PM = 8.6 × 10−9), EPHA1 (rs11767557; Stages 1–3 PM = 6.0 × 10−10), and CD33 (rs3865444; Stages 1–3, PM = 1.6 × 10−9). We confirmed that CR1 (rs6701713; PM = 4.6×10−10, PJ = 5.2×10−11), CLU (rs1532278; PM = 8.3 × 10−8, PJ = 1.9×10−8), BIN1 (rs7561528; PM = 4.0×10−14; PJ = 5.2×10−14), and PICALM (rs561655; PM = 7.0 × 10−11, PJ = 1.0×10−10) but not EXOC3L2 are LOAD risk loci1–3.