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CHQ- SocioEmo: Identifying Social and Emotional Support Needs in Consumer-Health Questions
General public, often called consumers, are increasingly seeking health information online. To be satisfactory, answers to health-related questions often have to go beyond informational needs. Automated approaches to consumer health question answering should be able to recognize the need for social and emotional support. Recently, large scale datasets have addressed the issue of medical question answering and highlighted the challenges associated with question classification from the standpoint of informational needs. However, there is a lack of annotated datasets for the non-informational needs. We introduce a new dataset for non-informational support needs, called CHQ-SocioEmo. The Dataset of Consumer Health Questions was collected from a community question answering forum and annotated with basic emotions and social support needs. This is the first publicly available resource for understanding non-informational support needs in consumer health-related questions online. We benchmark the corpus against multiple state-of-the-art classification models to demonstrate the dataset’s effectiveness.
Identifying genomic data use with the Data Citation Explorer
Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data that was not formally cited. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.
Cell based assay identifies TLR2 and TLR4 stimulating impurities in Interferon beta
Immunogenicity can have devastating consequences on the safety and efficacy of therapeutic proteins. Therefore, evaluating and mitigating the risk of product immunogenicity is critical for the development these products. This study, showed that Betaseron and Extavia, which are reported to be more immunogenic among IFNβ products in clinical usage, contain residual innate immune response modulating impurities (IIRMIs) capable of activating NF-κB and induced expression of infammatory mediators. These IIRMIs were undetectable in Rebif or Avonex. The stimulatory efect was attributed solely to IIRMIs because it was evident in murine cells lacking the interferon receptor (IFNAR). The IIRMIs in Betaseron and Extavia triggered NF-κB activation in HEK-293 cells bearing TLR2 and TLR4 in MyD88 dependent manner. Importantly, the IIRMIs in Betaseron induced up-regulation of IL-6, IL-1β, and ccl5 in the skin of IFNAR knock out mice following subcutaneous administration. This indicates that trace level IIRMIs in Betaseron could contribute to the higher immunogenicity rates seen in clinics. Together these data suggest that cell based assays can reveal subtle but clinically relevant differences in IIRMIs following manufacturing changes or between products with the same active ingredients but different manufacturing processes. Appreciating these differences may inform immunogenicity risk assessments.
Structural Analysis of Inhibitor Binding to CAMKK1 Identifies Features Necessary for Design of Specific Inhibitors
The calcium/calmodulin-dependent protein kinases (CAMKKs) are upstream activators of CAMK1 and CAMK4 signalling and have important functions in neural development, maintenance and signalling, as well as in other aspects of biology such as Ca 2+ signalling in the cardiovascular system. To support the development of specific inhibitors of CAMKKs we have determined the crystal structure of CAMKK1 with two ATP-competitive inhibitors. The structures reveal small but exploitable differences between CAMKK1 and CAMKK2, despite the high sequence identity, which could be used in the generation of specific inhibitors. Screening of a kinase inhibitor library revealed molecules that bind potently to CAMKK1. Isothermal titration calorimetry revealed that the most potent inhibitors had binding energies largely dependent on favourable enthalpy. Together, the data provide a foundation for future inhibitor development activities.
A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures
Abstract Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.
Identifying Bayesian optimal experiments for uncertain biochemical pathway models
Abstract Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.
Integrative path modeling and QTL mapping identify maturity, stem strength, and cell wall composition driving lettuce resistance to Sclerotinia minor
Lettuce ( Lactuca sativa ) is highly vulnerable to Sclerotinia minor , the pathogen causing lettuce drop. Breeding for resistance is the most effective control strategy; however, full resistance has not been achieved, and current partial resistance sources are often linked with undesirable traits, such as early bolting. This study aimed to unravel the genetic basis of partial resistance to S. minor and its relationship with plant maturity (bolting), stem mechanical strength (SMS), and cell wall composition (CWC) using a recombinant inbred line (RIL) population derived from a cross between the susceptible iceberg cv. ‘Salinas’ and the resistant oil-seed accession PI 251246. Field evaluations indicated that resistance was linked to earlier bolting, stronger stems, and higher pentose content. Path analysis demonstrated that earlier-maturing plants exhibited increased resistance through enhanced SMS and modified CWC, particularly with higher xylose and lower arabinose levels. Further analysis indicated a significant relationship between syringyl lignin content and resistance, especially in plants with varying bolting responses. Three key quantitative trait loci (QTLs) on linkage groups (LG) 2, 6, and 7 were consistently associated with resistance, bolting, and SMS. Importantly, residual QTL analysis revealed that the resistance locus on LG7 acted independently of maturity, suggesting a distinct resistance mechanism. Callose synthase emerged as a key candidate gene within the LG7 resistance QTL, located near - but distinct from - genes associated with plant maturity and flowering. These findings provide valuable insights into decoupling resistance from early bolting, suggesting a pathway for breeding lettuce cultivars with improved disease resistance and delayed bolting.
Gene network analysis identifies dysregulated pathways in an autism spectrum disorder caused by mutations in Transcription Factor 4
Not provided.
FTIR imaging identifies alterations in lung tissue structure and biochemical composition in human idiopathic pulmonary fibrosis
Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, and fatal lung disease characterized by damage to the epithelial tissue and a reduced ability of the alveoli to repair themselves. This impaired repair process leads to abnormal accumulation of extracellular matrix (ECM), resulting in scarring and stiffening of lung tissue. Fourier transform infrared imaging (FTIRI) is a promising technique for imaging the biochemical changes related to fibrotic changes in a label-free and non-destructive manner, which can be analyzed to mark the progression of IPF. In this study, FTIRI was used to image human lung tissue biopsies with IPF and control biopsies without disease. In-depth spectral analyses were performed to observe the biochemical changes in the tissue composition using FTIRI. The parameters that were analyzed included collagen structure, total lipid content, lipid chain length, and phospholipids. Results showed a significant increase in lipid content in IPF compared to control, where long chain lipids dominated and phospholipids were reduced. Minor changes in collagen structure were also observed in IPF, likely attributed to the excess formation of extracellular matrix in the disease. These findings indicate that FTIRI has the potential to be a promising diagnostic technique to understand the molecular changes during IPF, as analysis of infrared data can reveal detailed biochemical information regarding disease progression and provide spatial insights on the molecular changes across the IPF lung tissue.
Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs
Not provided.
Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning
Agricultural practices affect soil microbes which are critical to soil health and sustainable agriculture. To understand prokaryotic and fungal assembly under agricultural practices, we use machine learning-based methods. We show that fertility source is the most pronounced factor for microbial assembly especially for fungi, and its effect decreases with soil depths. Fertility source also shapes microbial co-occurrence patterns revealed by machine learning, leading to fungi-dominated modules sensitive to fertility down to 30 cm depth. Tillage affects soil microbiomes at 0-20 cm depth, enhancing dispersal and stochastic processes but potentially jeopardizing microbial interactions. Cover crop effects are less pronounced and lack depth-dependent patterns. Machine learning reveals that the impact of agricultural practices on microbial communities is multifaceted and highlights the role of fertility source over the soil depth. Machine learning overcomes the linear limitations of traditional methods and offers enhanced insights into the mechanisms underlying microbial assembly and distributions in agriculture soils.
Targeted proteomic assays for quantitation of proteins identified by proteogenomic analysis of ovarian cancer
Here, mass spectrometry (MS) based targeted proteomic methods such as selected reaction monitoring (SRM) are becoming the method of choice for preclinical verification of candidate protein biomarkers. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute has investigated the standardization and analytical validation of the SRM assays and demonstrated robust analytical performance on different instruments across different laboratories. An Assay Portal has also been established by CPTAC to provide the research community a resource consisting of large set of targeted MS-based assays, and a depository to share assays publicly, providing that assays meet the guidelines proposed by CPTAC. Herein, we report 98 SRM assays covering 70 candidate protein biomarkers previously reported as associated with ovarian cancer that have been thoroughly characterized according to the CPTAC Assay Characterization Guidance Document. The experiments, methods and results for characterizing these SRM assays for their MS response, repeatability, selectivity, stability, and reproducible detection of endogenous analytes are described in detail.
Spectromicroscopy of C 60 and azafullerene C 59 N: Identifying surface adsorbed water
C 60 fullerene crystals may serve as important catalysts for interstellar organic chemistry. To explore this possibility, the electronic structures of free-standing powders of C 60 and (C 59 N) 2 azafullerenes are characterized using X-ray microscopy with near-edge X-ray adsorption fine structure (NEXAFS) spectroscopy, closely coupled with density functional theory (DFT) calculations. This is supported with X-ray photoelectron spectroscopy (XPS) measurements and associated core-level shift DFT calculations. We compare the oxygen 1s spectra from oxygen impurities in C 60 and C 59 N, and calculate a range of possible oxidized and hydroxylated structures and associated formation barriers. These results allow us to propose a model for the oxygen present in these samples, notably the importance of water surface adsorption and possible ice formation. Water adsorption on C 60 crystal surfaces may prove important for astrobiological studies of interstellar amino acid formation.
Identifying low-coverage surface species on supported noble metal nanoparticle catalysts by DNP-NMR
DNP-NMR spectroscopy has been applied to enhance the signal for organic molecules adsorbed on γ-Al 2 O 3 -supported Pd nanoparticles. In addition, by offering >2500-fold time savings, the technique enabled the observation of 13 C- 13 C cross-peaks for low coverage species, which were assigned to products from oxidative degradation of methionine adsorbed on the nanoparticle surface.
Identifying Cu( ii )–amyloid peptide binding intermediates in the early stages of aggregation by resonance Raman spectroscopy: a simulation study
The aggregation of amyloid beta (Aβ) peptides plays a crucial role in the pathology and etiology of Alzheimer's disease. Experimental evidence shows that copper ion is an aggregation-prone species with the ability to coordinately bind to Aβ and further induce the formation of neurotoxic Aβ oligomers. However, the detailed structures of Cu(II)–Aβ complexes have not been illustrated, and the kinetics and dynamics of the Cu(II) binding are not well understood. Two Cu(II)–Aβ complexes have been proposed to exist under physiological conditions, and another two might exist at higher pH values. By using ab initio simulations for the spontaneous resonance Raman and time domain stimulated resonance Raman spectroscopy signals, we obtained the characteristic Raman vibronic features of each complex. Finally, these signals contain rich structural information with high temporal resolution, enabling the characterization of transient states during the fast Cu–Aβ binding and interconversion processes.
Towards identifying the active sites on RuO 2 (110) in catalyzing oxygen evolution
While the surface atomic structure of RuO 2 has been well studied in ultra high vacuum, much less is known about the interaction between water and RuO 2 in aqueous solution. In this work, in situ surface X-ray scattering measurements combined with density functional theory (DFT) were used to determine the surface structural changes on single-crystal RuO2(110) as a function of potential in acidic electrolyte. The redox peaks at 0.7, 1.1 and 1.4 V vs. reversible hydrogen electrode (RHE) could be attributed to surface transitions associated with the successive deprotonation of –H 2 O on the coordinatively unsaturated Ru sites (CUS) and hydrogen adsorbed to the bridging oxygen sites. At potentials relevant to the oxygen evolution reaction (OER), an –OO species on the Ru CUS sites was detected, which was stabilized by a neighboring –OH group on the Ru CUS or bridge site. Combining potential-dependent surface structures with their energetics from DFT led to a new OER pathway, where the deprotonation of the –OH group used to stabilize –OO was found to be rate-limiting.