Confounding variables can degrade generalization performance of radiological deep learning models

Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one hospital or one group of hospitals will work equally well at different hospitals. Before these tools are used for computer-aided diagnosis in real-world clinical settings, we must verify their ability to generalize across a variety of hospital systems. A cross-sectional design was used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396 from 12,904 patients), and Indiana (n=3,807 from 3,683 patients). In 3 / 5 natural comparisons, performance on chest x-rays from outside hospitals was significantly lower than on held-out x-rays from the original hospital systems. CNNs were able to detect where an x-ray was acquired (hospital system, hospital department) with extremely high accuracy and calibrate predictions accordingly. The performance of CNNs in diagnosing diseases on x-rays may reflect not only their ability to identify disease-specific imaging findings on x-rays, but also their ability to exploit confounding information. Estimates of CNN performance based on test data from hospital systems used for model training may overstate their likely real-world performance

Expression and accumulation of the two-domain odorant-binding protein AaegOBP45 in the ovaries of blood-fed Aedes aegypti

BACKGROUND: \ud Aedes aegypti mosquitoes are the main vectors of dengue viruses. Despite global efforts to reduce the prevalence of dengue using integrated vector management strategies, innovative alternatives are necessary to help prevent virus transmission. Detailed characterizations of Ae. aegypti genes and their products provide information about the biology of mosquitoes and may serve as foundations for the design of new vector control methods.\ud FINDINGS: \ud We studied the Ae. aegypti gene, AAEL010714, that encodes a two-domain odorant-binding protein, AaegOBP45. The predicted gene structure and sequence were validated, although single nucleotide polymorphisms were observed. Transcriptional and translational products accumulate in the ovaries of blood fed females and are not detected or are at low abundance in other tissues.\ud CONCLUSIONS: \ud We validated the Ae. aegypti AAEL010714 gene sequence and characterized the expression profile of a two-domain OBP expressed in ovaries. We propose that AaegOBP45 function as a component of the mosquito eggshell.São Paulo Research Foundation (FAPESP) 2006/00331-3CNPq 143113/2006-2NIH NIAID (AI29746

Hyaluronic acid hydrogels reinforced with laser spun bioactive glass micro- and nanofibres doped with lithium

The repair of articular cartilage lesions in weight-bearing joints remains as a significant challenge due to the low regenerative capacity of this tissue. Hydrogels are candidates to repair lesions as they have similar properties to cartilage extracellular matrix but they are unable to meet the mechanical and biological requirements for a successful outcome. Here, we reinforce hyaluronic acid (HA) hydrogels with 13-93-lithium bioactive glass micro- and nanofibres produced by laser spinning. The glass fibres are a reinforcement filler and a platform for the delivery of therapeutic lithium-ions. The elastic modulus of the composites is more than three times higher than in HA hydrogels. Modelling of the reinforcement corroborates the experimental results. ATDC5 chondrogenic cells seeded on the composites are viable and more proliferation occurs on the hydrogels containing fibres than in HA hydrogels alone. Furthermore, the chondrogenic behavior on HA constructs with fibres containing lithium is more marked than in hydrogels with no-lithium fibres.Xunta de Galicia | Ref. ED431B 2016/042Xunta de Galicia | Ref. POS-A/2013/161Xunta de Galicia | Ref. ED481D 2017/010Xunta de Galicia | Ref. ED481B 2016/047-

Wild dogs at stake: deforestation threatens the only Amazon endemic canid, the short-eared dog (Atelocynus microtis)

The persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog Atelocynus microtis is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species

A deeply branching thermophilic bacterium with an ancient acetyl-CoA pathway dominates a subsurface ecosystem

<div><p>A nearly complete genome sequence of <em>Candidatus</em> ‘Acetothermum autotrophicum’, a presently uncultivated bacterium in candidate division OP1, was revealed by metagenomic analysis of a subsurface thermophilic microbial mat community. Phylogenetic analysis based on the concatenated sequences of proteins common among 367 prokaryotes suggests that <em>Ca.</em> ‘A. autotrophicum’ is one of the earliest diverging bacterial lineages. It possesses a folate-dependent Wood-Ljungdahl (acetyl-CoA) pathway of CO₂ fixation, is predicted to have an acetogenic lifestyle, and possesses the newly discovered archaeal-autotrophic type of bifunctional fructose 1,6-bisphosphate aldolase/phosphatase. A phylogenetic analysis of the core gene cluster of the acethyl-CoA pathway, shared by acetogens, methanogens, some sulfur- and iron-reducers and dechlorinators, supports the hypothesis that the core gene cluster of <em>Ca.</em> ‘A. autotrophicum’ is a particularly ancient bacterial pathway. The habitat, physiology and phylogenetic position of <em>Ca.</em> ‘A. autotrophicum’ support the view that the first bacterial and archaeal lineages were H₂-dependent acetogens and methanogenes living in hydrothermal environments.</p> </div

A Study of Generative Large Language Model for Medical Research and Healthcare

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billion words of mixed clinical and English text with a GPT-3 architecture of 20 billion parameters. GatorTronGPT improves biomedical natural language processing for medical research. Synthetic NLP models trained using GatorTronGPT generated text outperform NLP models trained using real-world clinical text. Physicians Turing test using 1 (worst) to 9 (best) scale shows that there is no significant difference in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare

The Deuteron Spin-dependent Structure Function g1d and its First Moment

We present a measurement of the deuteron spin-dependent structure function g1d based on the data collected by the COMPASS experiment at CERN during the years 2002-2004. The data provide an accurate evaluation for Gamma_1^d, the first moment of g1d(x), and for the matrix element of the singlet axial current, a0. The results of QCD fits in the next to leading order (NLO) on all g1 deep inelastic scattering data are also presented. They provide two solutions with the gluon spin distribution function Delta G positive or negative, which describe the data equally well. In both cases, at Q^2 = 3 (GeV/c)^2 the first moment of Delta G is found to be of the order of 0.2 - 0.3 in absolute value.Comment: fits redone using MRST2004 instead of MRSV1998 for G(x), correlation matrix adde

Fabrication and characterization of Eri silk fibers-based sponges for biomedical application

Cocoon-derived semi-domesticated Eri silk fibers still lack exploitation for tissue engineering applications due to their poor solubility using conventional methods. The present work explores the ability to process cocoon fibers of non-mulberry Eri silk (Samia/Philosamia ricini) into sponges through a green approach using ionic liquid (IL) â 1-buthyl-imidazolium acetate as a solvent. The formation of β-sheet structures during Eri silk/IL gelation was acquired by exposing the Eri silk/IL gels to a saturated atmosphere composed of two different solvents: (i) isopropanol/ethanol (physical stabilization) and (ii) genipin, a natural crosslinker, dissolved in ethanol (chemical crosslinking). The sponges were then obtained by freeze-drying. This approach promotes the formation of both stable and ordered non-crosslinked Eri silk fibroin matrices. Moreover, genipin-crosslinked silk fibroin sponges presenting high height recovery capacity after compression, high swelling degree and suitable mechanical properties for tissue engineering applications were produced. The incorporation of a model drug â ibuprofen â and the corresponding release study from the loaded sponges demonstrated the potential of using these matrices as effective drug delivery systems. The assessment of the biological performance of ATDC5 chondrocyte-like cells in contact with the developed sponges showed the promotion of cell adhesion and proliferation, as well as extracellular matrix production within two weeks of culture. Spongesâ intrinsic properties and biological findings open up their potential use for biomedical applications.The authors SSS, DSC, MBO, NMO acknowledge financial support from Portuguese Foundation for Science and Technology – FCT (Grants SFRH/BPD/45307/2008, SFRH/BPD/85790/2012, SFRH/BD/71396/2010 and SFRH/BD/73172/2010, respectively), ‘‘Fundo Social Europeu” – FSE, and ‘‘Programa Diferencial de Potencial Humano POPH”. This work is also financially supported by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n REGPOT-CT2012-316331-POLARIS and from Fundação para a Ciência e Tecnologia (FCT) through the project ENIGMA – PTDC/EQU-EPR/121491/2010. The laboratory work of SCK is supported by Department of Biotechnology and Indian Council of Medical Research, Govt of India. SCK and RLR acknowledge their short visits either Institutes. SCK is also grateful to 3B´ s Research Group- Biomaterials, Biodegradables and Biomimetics, University of Minho, Portugal for providing facilities during his short visit