Efficient Dynamic Compressor Optimization in Natural Gas Transmission Systems

The growing reliance of electric power systems on gas-fired generation to balance intermittent sources of renewable energy has increased the variation and volume of flows through natural gas transmission pipelines. Adapting pipeline operations to maintain efficiency and security under these new conditions requires optimization methods that account for transients and that can quickly compute solutions in reaction to generator re-dispatch. This paper presents an efficient scheme to minimize compression costs under dynamic conditions where deliveries to customers are described by time-dependent mass flow. The optimization scheme relies on a compact representation of gas flow physics, a trapezoidal discretization in time and space, and a two-stage approach to minimize energy costs and maximize smoothness. The resulting large-scale nonlinear programs are solved using a modern interior-point method. The proposed optimization scheme is validated against an integration of dynamic equations with adaptive time-stepping, as well as a recently proposed state-of-the-art optimal control method. The comparison shows that the solutions are feasible for the continuous problem and also practical from an operational standpoint. The results also indicate that our scheme provides at least an order of magnitude reduction in computation time relative to the state-of-the-art and scales to large gas transmission networks with more than 6000 kilometers of total pipeline

Large neighborhood search for the double traveling salesman problem with multiple stacks

This paper considers a complex real-life short-haul/long haul pickup and delivery application. The problem can be modeled as double traveling salesman problem (TSP) in which the pickups and the deliveries happen in the first and second TSPs respectively. Moreover, the application features multiple stacks in which the items must be stored and the pickups and deliveries must take place in reserve (LIFO) order for each stack. The goal is to minimize the total travel time satisfying these constraints. This paper presents a large neighborhood search (LNS) algorithm which improves the best-known results on 65% of the available instances and is always within 2% of the best-known solutions

Intrathecal treatment of neoplastic meningitis due to breast cancer with a slow-release formulation of cytarabine

DepoCyte is a slow-release formulation of cytarabine designed for intrathecal administration. The goal of this multi-centre cohort study was to determine the safety and efficacy of DepoCyte for the intrathecal treatment of neoplastic meningitis due to breast cancer. DepoCyte 50 mg was injected once every 2 weeks for one month of induction therapy; responding patients were treated with an additional 3 months of consolidation therapy. All patients had metastatic breast cancer and a positive CSF cytology or neurologic findings characteristic of neoplastic meningitis. The median number of DepoCyte doses was 3, and 85% of patients completed the planned 1 month induction. Median follow up is currently 19 months. The primary endpoint was response, defined as conversion of the CSF cytology from positive to negative at all sites known to be positive, and the absence of neurologic progression at the time the cytologic conversion was documented. The response rate among the 43 evaluable patients was 28% (CI 95%: 14–41%); the intent-to-treat response rate was 21% (CI 95%: 12–34%). Median time to neurologic progression was 49 days (range 1–515(+)); median survival was 88 days (range 1–515(+)), and 1 year survival is projected to be 19%. The major adverse events were headache and arachnoiditis. When drug-related, these were largely of low grade, transient and reversible. Headache occurred on 11% of cycles; 90% were grade 1 or 2. Arachnoiditis occurred on 19% of cycles; 88% were grade 1 or 2. DepoCyte demonstrated activity in neoplastic meningitis due to breast cancer that is comparable to results reported with conventional intrathecal agents. However, this activity was achieved with one fourth as many intrathecal injections as typically required in conventional therapy. The every 2 week dose schedule is a major advantage for both patients and physicians. © 2001 Cancer Research Campaign http://www.bjcancer.co

Res Medica, April 1967, Special Issue – Lauder Brunton Centenary Symposium on Angina Pectoris

WelcomeHistorical SessionOpening AddressLauder BruntonHistory of Angina Pathophysiological SessionThe Pathology of AnginaExperimental Studies on the Myocardial Collateral CirculationFirst DiscussionCoronary Blood Flow and Myocardial Metabolism in Angina PectorisCardiac Function in Patients with AnginaSecond Discussion Therapeutic SessionThe Modern EpidemicIs Angina Preventable?Third DiscussionChest Pain, Exercise Electrocardiography and Coronary Arteriography(Correlative Studies in Angina PectorisPrognosis of Angina PectorisPanel DiscussionSumming U

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10-20), ER-negative BC (P=1.1 × 10-13), BRCA1-associated BC (P=7.7 × 10-16) and triple negative BC (P-diff=2 × 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10-3) and ABHD8 (P<2 × 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing