Application of 3D Zernike descriptors to shape-based ligand similarity searching

Background: The identification of promising drug leads from a large database of compounds is an important step in the preliminary stages of drug design. Although shape is known to play a key role in the molecular recognition process, its application to virtual screening poses significant hurdles both in terms of the encoding scheme and speed. Results: In this study, we have examined the efficacy of the alignment independent three-dimensional Zernike descriptor (3DZD) for fast shape based similarity searching. Performance of this approach was compared with several other methods including the statistical moments based ultrafast shape recognition scheme (USR) and SIMCOMP, a graph matching algorithm that compares atom environments. Three benchmark datasets are used to thoroughly test the methods in terms of their ability for molecular classification, retrieval rate, and performance under the situation that simulates actual virtual screening tasks over a large pharmaceutical database. The 3DZD performed better than or comparable to the other methods examined, depending on the datasets and evaluation metrics used. Reasons for the success and the failure of the shape based methods for specific cases are investigated. Based on the results for the three datasets, general conclusions are drawn with regard to their efficiency and applicability

The Netherlands study of depression in older persons (NESDO); a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>To study late-life depression and its unfavourable course and co morbidities in The Netherlands.</p> <p>Methods</p> <p>We designed the Netherlands Study of Depression in Older Persons (NESDO), a multi-site naturalistic prospective cohort study which makes it possible to examine the determinants, the course and the consequences of depressive disorders in older persons over a period of six years, and to compare these with those of depression earlier in adulthood.</p> <p>Results</p> <p>From 2007 until 2010, the NESDO consortium has recruited 510 depressed and non depressed older persons (≥ 60 years) at 5 locations throughout the Netherlands. Depressed persons were recruited from both mental health care institutes and general practices in order to include persons with late-life depression in various developmental and severity stages. Non-depressed persons were recruited from general practices. The baseline assessment included written questionnaires, interviews, a medical examination, cognitive tests and collection of blood and saliva samples. Information was gathered about mental health outcomes and demographic, psychosocial, biological, cognitive and genetic determinants. The baseline NESDO sample consists of 378 depressed (according to DSM-IV criteria) and 132 non-depressed persons aged 60 through 93 years. 95% had a major depression and 26.5% had dysthymia. Mean age of onset of the depressive disorder was around 49 year. For 33.1% of the depressed persons it was their first episode. 41.0% of the depressed persons had a co morbid anxiety disorder. Follow up assessments are currently going on with 6 monthly written questionnaires and face-to-face interviews after 2 and 6 years.</p> <p>Conclusions</p> <p>The NESDO sample offers the opportunity to study the neurobiological, psychosocial and physical determinants of depression and its long-term course in older persons. Since largely similar measures were used as in the Netherlands Study of Depression and Anxiety (NESDA; age range 18-65 years), data can be pooled thus creating a large longitudinal database of clinically depressed persons with adequate power and a large set of neurobiological, psychosocial and physical variables from both younger and older depressed persons.</p

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

IPA: Internet Personal Assistant

The Internet Personal Assistant (IPA) gives you the power to automatically act on data from the Internet. Create triggers to text you when it starts to snow in your zipcode or email your friend when their soccer team loses. Go even further by creating complex conditions and actions or even build you own data sources and actions to completely customize your triggers. URL website http://cs.carleton.edu/cs_comps/1112/news_agent/final_results/index.htm

Super capacitor energy storage system design for wave energy converter demonstration

Aquaharmonics Inc (AH) intends to develop, build, and perform open ocean testing on a 1:7 scale device. Testing will include data capture and performance optimization in wave climates representative of full scale in potential deployment locations. Test data will be used to develop a scaled device to provide minimized levelized cost of energy (LCOE) for markets with high power demand

WEC array networked microgrid control design and energy storage system requirements

Wave Energy Converter (WEC) technologies transform power from the waves to the electrical grid. WEC system components are investigated that support the performance, stability, and efficiency as part of a WEC array. To this end, Aquaharmonics Inc took home the $1.5 million grand prize in the 2016 U.S. Department of Energy Wave Energy Prize, an 18-month design-build-test competition to increase the energy capture potential of wave energy devices. Aquaharmonics intends to develop, build, and perform open ocean testing on a 1: 7 scale device. Preliminary wave tank testing on the mechanical system of the 1: 20 scale device has yielded a data-set of operational conditions and performance. In this paper, the Hamiltonian surface shaping and power flow control (HSSPFC) method is used in conjunction with scaled wave tank test data to explore the design space for the electrical transmission of energy to the shore-side power grid. Of primary interest is the energy storage system (ESS) that will electrically link the WEC to the shore. Initial analysis results contained in this paper provide a trade-off in storage device performance and design selection

DNA-damage repair deficiency (dDDR) and response to nanoliposomal irinotecan (nal-IRI) in metastatic pancreatic ductal adenocarcinoma (mPDAC)

731 Background: Chemotherapy is the standard of care for patients with mPDAC but there are no biomarkers to aid in treatment selection. Nal-IRI with 5-fluorouracil/folinic acid (FUFA) improves survival over FUFA in the second-line treatment of mPDAC. Nal-IRI is a topoisomerase inhibitor and its action produces DNA damage leading to cell death. We hypothesize that tumors with dDDR, a process that is altered in a subset of patients with PDAC, may be more sensitive to the effects of nal-IRI. Methods: Utilizing the IRB-approved pancreas cancer databases at the University of Miami and Wake Forest University, we identified patients with mPDAC treated with nal-IRI and FUFA who had germline and/or somatic mutation testing. We conducted a retrospective chart review to extract demographic and clinical characteristics including treatments received, response, and survival. Results: Among 31 patients identified, the median age was 66y and 47% were female. Nine patients had a DDR mutation; 6 germline and 3 somatic. Median progression-free survival (PFS) in patients with any germline or somatic DDR mutation was 3.2m vs 3.9m for those without (log-rank p = 0.7). When restricted to germline DDR mutations only, the median PFS was not reached with germline dDDR vs 4m for those without (log-rank p = 0.22). Presence of dDDR was associated with a higher clinical benefit rate (CBR = partial response + stable disease); a DDR mutation was present in 36% of patients who showed clinical benefit vs 15% in those without clinical benefit (p = 0.21). Conclusions: DDR mutations appear to define a subset of patients with mPDAC who may be more sensitive to nal-IRI and FUFA. The PFS and CBR were numerically but not statistically superior, especially in patients with germline DDR mutations. Larger data sets and longer follow-up are needed to confirm this trend