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Self-supervised multi-task representation learning for sequential medical images
No full textSelf-supervised representation learning has achieved promising results for downstream visual tasks in natural images. However, its use in the medical domain, where there is an underlying anatomical structural similarity, remains underexplored. To address this shortcoming, we propose a self-supervised multi-task representation learning framework for sequential 2D medical images, which explicitly aims to exploit the underlying structures via multiple pretext tasks. Unlike the current state-of-the-art methods, which are designed to only pre-train the encoder for instance discrimination tasks, the proposed framework can pre-train the encoder and the decoder at the same time for dense prediction tasks. We evaluate the representations extracted by the proposed framework on two public whole heart segmentation datasets from different domains. The experimental results show that our proposed framework outperforms MoCo V2, a strong representation learning baseline. Given only a small amount of labeled data, the segmentation networks pre-trained by the proposed framework on unlabeled data can achieve better results than their counterparts trained by standard supervised approaches
Towards safe and personalized autonomous driving: decision-making and motion control with DPF and CDT techniques
No full textIn this paper, a novel approach of decision-making and motion control is designed for realizing safe and personalized driving of autonomous vehicles. A new lane-change intention generation model and a new lane-change decision making algorithm are proposed. The feature of the proposed decision-making module is that the interactions between the ego vehicle and other surrounding vehicles are represented by dynamic potential field (DPF) and embedded in the gap acceptance model to ensure the safety and personalization during driving. In addition, an integrated trajectory planning and tracking control algorithm, which incorporates the artificial potential field (APF) and constrained Delaunay triangulation (CDT) into the model predictive control (MPC) framework, is developed. The newly developed integrated controller allows efficient execution of the expected motion. The proposed approach is tested under different driving conditions and further compared with an existing baseline method. The results show that the proposed approach is able to make safe and personalized decisions, and execute motion control more efficiently for automated driving under dynamic situations, validating its feasibility and effectiveness
Pancreatic cancer microenvironment and cellular composition: Current understandings and therapeutic approaches
No full textPancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human solid tumors, despite great efforts in improving therapeutics over the past few decades. In PDAC, the distinct characteristic of the tumor microenvironment (TME) is the main barrier for developing effective treatments. PDAC TME is characterized by a dense stroma, cancer-associated fibroblasts, and immune cells populations that crosstalk to the subpopulations of neoplastic cells that include cancer stem cells (CSCs). The heterogeneity in TME is also exhibited in the diversity and dynamics of acellular components, including the Extracellular matrix (ECM), cytokines, growth factors, and secreted ligands to signaling pathways. These contribute to drug resistance, metastasis, and relapse in PDAC. However, clinical trials targeting TME components have often reported unexpected results and still have not benefited patients. The failures in those trials and various efforts to understand the PDAC biology demonstrate the highly heterogeneous and multi-faceted TME compositions and the complexity of their interplay within TME. Hence, further functional and mechanistic insight is needed. In this review, we will present a current understanding of PDAC biology with a focus on the heterogeneity in TME and crosstalk among its components. We also discuss clinical challenges and the arising therapeutic opportunities in PDAC research
Effects of antibiotic resistance, drug target attainment, bacterial pathogenicity and virulence, and antibiotic access and affordability on outcomes in neonatal sepsis: an international microbiology and drug evaluation prospective substudy (BARNARDS)
No full textBackground
Sepsis is a major contributor to neonatal mortality, particularly in low-income and middle-income countries (LMICs). WHO advocates ampicillin–gentamicin as first-line therapy for the management of neonatal sepsis. In the BARNARDS observational cohort study of neonatal sepsis and antimicrobial resistance in LMICs, common sepsis pathogens were characterised via whole genome sequencing (WGS) and antimicrobial resistance profiles. In this substudy of BARNARDS, we aimed to assess the use and efficacy of empirical antibiotic therapies commonly used in LMICs for neonatal sepsis.
Methods
In BARNARDS, consenting mother–neonates aged 0–60 days dyads were enrolled on delivery or neonatal presentation with suspected sepsis at 12 BARNARDS clinical sites in Bangladesh, Ethiopia, India, Pakistan, Nigeria, Rwanda, and South Africa. Stillborn babies were excluded from the study. Blood samples were collected from neonates presenting with clinical signs of sepsis, and WGS and minimum inhibitory concentrations for antibiotic treatment were determined for bacterial isolates from culture-confirmed sepsis. Neonatal outcome data were collected following enrolment until 60 days of life. Antibiotic usage and neonatal outcome data were assessed. Survival analyses were adjusted to take into account potential clinical confounding variables related to the birth and pathogen. Additionally, resistance profiles, pharmacokinetic–pharmacodynamic probability of target attainment, and frequency of resistance (ie, resistance defined by in-vitro growth of isolates when challenged by antibiotics) were assessed. Questionnaires on health structures and antibiotic costs evaluated accessibility and affordability.
Findings
Between Nov 12, 2015, and Feb 1, 2018, 36 285 neonates were enrolled into the main BARNARDS study, of whom 9874 had clinically diagnosed sepsis and 5749 had available antibiotic data. The four most commonly prescribed antibiotic combinations given to 4451 neonates (77·42%) of 5749 were ampicillin–gentamicin, ceftazidime–amikacin, piperacillin–tazobactam–amikacin, and amoxicillin clavulanate–amikacin. This dataset assessed 476 prescriptions for 442 neonates treated with one of these antibiotic combinations with WGS data (all BARNARDS countries were represented in this subset except India). Multiple pathogens were isolated, totalling 457 isolates. Reported mortality was lower for neonates treated with ceftazidime–amikacin than for neonates treated with ampicillin–gentamicin (hazard ratio [adjusted for clinical variables considered potential confounders to outcomes] 0·32, 95% CI 0·14–0·72; p=0·0060). Of 390 Gram-negative isolates, 379 (97·2%) were resistant to ampicillin and 274 (70·3%) were resistant to gentamicin. Susceptibility of Gram-negative isolates to at least one antibiotic in a treatment combination was noted in 111 (28·5%) to ampicillin–gentamicin; 286 (73·3%) to amoxicillin clavulanate–amikacin; 301 (77·2%) to ceftazidime–amikacin; and 312 (80·0%) to piperacillin–tazobactam–amikacin. A probability of target attainment of 80% or more was noted in 26 neonates (33·7% [SD 0·59]) of 78 with ampicillin–gentamicin; 15 (68·0% [3·84]) of 27 with amoxicillin clavulanate–amikacin; 93 (92·7% [0·24]) of 109 with ceftazidime–amikacin; and 70 (85·3% [0·47]) of 76 with piperacillin–tazobactam–amikacin. However, antibiotic and country effects could not be distinguished. Frequency of resistance was recorded most frequently with fosfomycin (in 78 isolates [68·4%] of 114), followed by colistin (55 isolates [57·3%] of 96), and gentamicin (62 isolates [53·0%] of 117). Sites in six of the seven countries (excluding South Africa) stated that the cost of antibiotics would influence treatment of neonatal sepsis.
Interpretation
Our data raise questions about the empirical use of combined ampicillin–gentamicin for neonatal sepsis in LMICs because of its high resistance and high rates of frequency of resistance and low probability of target attainment. Accessibility and affordability need to be considered when advocating antibiotic treatments with variance in economic health structures across LMICs.
Funding
The Bill & Melinda Gates Foundation
Bipartite graph reasoning GANs for person image generation
No full textWe present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task. The proposed graph generator mainly consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively. Specifically, the proposed Bipartite Graph Reasoning (BGR) block aims to reason the crossing long-range relations between the source pose and the target pose in a bipartite graph, which mitigates some challenges caused by pose deformation. Moreover, we propose a new Interaction-and-Aggregation (IA) block to effectively update and enhance the feature representation capability of both person’s shape and appearance in an interactive way. Experiments on two challenging and public datasets, i.e., Market-1501 and DeepFashion, show the effectiveness of the proposed BiGraphGAN in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/BiGraphGAN
Integration of symptomatic, demographical and diet-related comorbidities data with SARS-CoV-2 antibody rapid diagnostic tests during epidemiological surveillance: a cross-sectional study in Jakarta, Indonesia
No full textObjectives Affordable options for COVID-19 epidemiological surveillance are needed. Virus detection by reverse transcription-PCR (RT-PCR) is sensitive but costly, and antigen-based rapid diagnostic tests (RDTs) are cheap but with reduced sensitivity; both detect current infection but not exposure. RDT-IgM/IgG antibodies to SARS-CoV-2 detect exposure but have poor sensitivity for current infection. We investigated if the integration of symptomatic, demographical and diet-related comorbidities data with antibody RDTs improves their potential to assess infection rates in addition to exposure, thereby broadening their utility for surveillance.
Design We conducted a cross-sectional study using data from community surveillance for SARS-CoV-2. Health workers collected nasopharyngeal swabs for RT-PCR and RDT antigen assessments and venous blood for RDT-IgM/IgG from symptomatic and asymptomatic persons. Data on age, gender, contact history, symptoms (ie, fever, cough, runny nose, sore throat, headache, dyspnoea and diarrhoea), diet-related comorbidities (ie, diabetes and hypertension) and chest radiology were collected.
Setting High-risk communities in Jakarta, Indonesia, in May 2020.
Participants 343 community members’ data were included.
Outcome measures RDT-IgM/IgG sensitivity, specificity and predictive values and area under receiver operating characteristic curve for RT-PCR positivity using RDT results alone and in combination with other predictors, including symptom components derived from principal component analysis.
Results There were 24 PCR-confirmed infections. RDT-IgM/IgG-positive tests were associated with infection (OR 10.8, 95% CI 4.43 to 26.4, p<0.001) with an area under the curve (AUC) of 0.708% and 50% sensitivity, 91.5% specificity, 30.8% positive predictive value (PPV) and 96.1% negative predictive value (NPV). RDT results combined with age, gender, contact history, symptoms and comorbidities increased the AUC to 0.787 and yielded 62.5% sensitivity, 87.0% specificity, 26.6% PPV and 96.9% NPV.
Conclusions SARS-CoV-2 RDT-IgM/IgG results integrated with other predictors may be an affordable tool for epidemiological surveillance for population-based COVID-19 exposure and current infection, especially in groups with outbreaks or high transmission
Diabetic mitochondria are resistant to palmitoyl CoA inhibition of respiration, which is detrimental during ischemia
No full textThe bioactive lipid intermediate palmitoyl CoA (PCoA) can inhibit mitochondrial ADP/ATP transport, though the physiological relevance of this regulation remains unclear. We questioned whether myocardial ischemia provides a pathological setting in which PCoA regulation of ADP/ATP transport would be beneficial, and secondly, whether the chronically elevated lipid content within the diabetic heart could make mitochondria less sensitive to the effects of PCoA. PCoA acutely decreased ADP-stimulated state 3 respiration and increased the apparent Km for ADP twofold. The half maximal inhibitory concentration (IC50) of PCoA in control mitochondria was 22 µM. This inhibitory effect of PCoA on respiration was blunted in diabetic mitochondria, with no significant difference in the Km for ADP in the presence of PCoA, and an increase in the IC50 to 32 µM PCoA. The competitive inhibition by PCoA was localised to the phosphorylation apparatus, particularly the ADP/ATP carrier (AAC). During ischemia, the AAC imports ATP into the mitochondria, where it is hydrolysed by reversal of the ATP synthase, regenerating the membrane potential. Addition of PCoA dose-dependently prevented this wasteful ATP hydrolysis for membrane repolarisation during ischemia, however, this beneficial effect was blunted in diabetic mitochondria. Finally, using 31P-magnetic resonance spectroscopy we demonstrated that diabetic hearts lose ATP more rapidly during ischemia, with a threefold higher ATP decay rate compared with control hearts. In conclusion, PCoA plays a role in protecting mitochondrial energetics during ischemia, by preventing wasteful ATP hydrolysis. However, this beneficial effect is blunted in diabetes, contributing to the impaired energy metabolism seen during myocardial ischemia in the diabetic heart
EP 3 368 868 Coriolis flowmeter
No full textA Coriolis flowmeter has a driver that oscillates a conduit, a first sensor configured to generate a first sensor signal indicative of movement of the conduit at a first location, and a second sensor configured to generate a second sensor signal indicative of movement of the conduit at a second location. The first and second locations are arranged so a phase difference between the first and second signals when the conduit is oscillated by the driver is related to a mass flow rate of the fluid through the flowmeter. A digital signal processor includes a plurality of detectors tuned to a set of different frequencies. The detectors are configured to analyze the first sensor signal in parallel and generate an output indicative of how closely an actual frequency of the first sensor signal matches the frequency to which the respective detector is tuned
Forecasting for battery storage: choosing the error metric
No full textWe describe our approach to the Western Power Distribution (WPD) Presumed Open Data (POD) 6 MWh battery storage capacity forecasting competition, in which we finished second. The competition entails two distinct forecasting aims to maximise the daily evening peak reduction and using as much solar photovoltaic energy as possible. For the latter, we combine a Bayesian (MCMC) linear regression model with an average generation distribution. For the former, we introduce a new error metric that allows even a simple weighted average combined with a simple linear regression model to score very well using the competition performance metric
Strategies for constructing and operating DNA origami linear actuators
No full textLinear actuators are ubiquitous components at all scales of engineering. DNA nanotechnology offers a unique opportunity for bottom-up assembly at the molecular scale, providing nanoscale precision with multiple methods for constructing and operating devices. In this paper, DNA origami linear actuators with up to 200 nm travel, based on a rail threading a topologically locked slider, are demonstrated. Two strategies, one- and two-pot assembly, are demonstrated whereby the two components are folded from one or two DNA scaffold strands, respectively. In order to control the position of the slider on the rail, the rail and the inside of the slider are decorated with single-stranded oligonucleotides with distinct sequences. Two positioning strategies, based on diffusion and capture of signaling strands, are used to link the slider reversibly to determined positions on the rail with high yield and precision. These machine components provide a basis for applications in molecular machinery and nanoscale manufacture including programmed chemical synthesis