Project description:Integrating quantitative morphological and qualitative molecular methods to analyze soil nematode community responses to plant range expansion
Project description:BackgroundThe plantar plate is an important static stabilizer of the lesser metatarsophalangeal joints, and disruptions of the plantar plate can lead to significant instability and lesser toe deformities. In recent years, direct plantar plate repair has been proposed. Although direct repair via a dorsal approach is attractive, a torn plantar plate is small and difficult to access using regular instruments in a restricted operative field.MethodsIn this report, a unique method for plantar plate repairs was used to repair various configurations of plantar plate tears with standard operative instruments that are available in most operating rooms.ResultsUsing this method, 10 patients underwent plantar plate repairs, and the mean follow-up period was 24 (range, 14-38) months. The mean visual analog scale score for pain preoperatively was 4.1 (range, 0-6) and decreased to 0.6 (range, 0-3) at last follow-up. Postoperatively, the mean visual analog scale score for satisfaction was 9.6 (range, 8-10) and the mean American Orthopedic Foot and Ankle Society forefoot score was 88.8 (range, 75-100).ConclusionsOur study proposes an inexpensive and versatile method for plantar plate repair via a dorsal approach that uses standard operative instruments.Trial registrationClinicalTrials.gov , NCT04949685 . July 2, 2021 - Retrospectively registered, LEVEL OF CLINICAL EVIDENCE: 4.
Project description:Advanced air quality control requires real-time monitoring of particulate matter size and concentration, which can only be done using optical instruments. However, such techniques need regular calibration with reference samples. In this study, we suggest that puffball fungus (Lycoperdon pyriforme) spores can be utilized as a reference standard having a monodisperse size distribution. We compare the Lycoperdon pyriforme spores with the other commonly used reference samples, such as Al2O3 powder and polystyrene latex (PSL) microspheres. Here we demonstrate that the puffball spores do not coagulate and, thus, maintain the same particle size in the aerosol state for at least 15 minutes, which is enough for instrument calibration. Moreover, the puffball mushrooms can be stored for several years and no agglomeration of the spores occurs. They are also much cheaper than other calibration samples and no additional devices are needed for aerosol generation since the fungal fruiting body acts as an atomizer itself. The aforementioned features make the fungal spores a highly promising substance for calibration and validation of particle size analyzers, which outperforms the existing, artificially produced particles for aerosol sampling. Furthermore, the L. pyriforme spores are convenient for basic research and development of new optical measurement techniques, taking into account their uniform particle size and absent coagulation in the aerosol.
Project description:Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. We introduce a cryoEM calibration sample that is a mixture of compatible macromolecules that can be used not only for resolution optimization but also provides multiple reference points for evaluating instrument performance, data quality, and image processing workflows in a single experiment. This combined test specimen provides researchers a reference point for validating their cryoEM pipeline, benchmarking their methodologies, and testing new algorithms.
Project description:Cryogenic electron microscopy (cryoEM) is a rapidly growing structural biology modality that has been successful in revealing molecular details of biological systems. However, unlike established biophysical and analytical techniques with calibration standards, cryoEM has lacked comprehensive biological test samples. Here, a cryoEM calibration sample consisting of a mixture of compatible macromolecules is introduced that can not only be used for resolution optimization, but also provides multiple reference points for evaluating instrument performance, data quality and image-processing workflows in a single experiment. This combined test specimen provides researchers with a reference point for validating their cryoEM pipeline, benchmarking their methodologies and testing new algorithms.
Project description:Quantitative Polymerase Chain Reaction (qPCR) is one of central techniques in molecular biology and important tool in medical diagnostics. While being a golden standard qPCR techniques depend on reference measurements and are susceptible to large errors caused by even small changes of reaction efficiency or conditions that are typically not marked by decreased precision. Digital PCR (dPCR) technologies should alleviate the need for calibration by providing absolute quantitation using binary (yes/no) signals from partitions provided that the basic assumption of amplification a single target molecule into a positive signal is met. Still, the access to digital techniques is limited because they require new instruments. We show an analog-digital method that can be executed on standard (real-time) qPCR devices. It benefits from real-time readout, providing calibration-free assessment. The method combines advantages of qPCR and dPCR and bypasses their drawbacks. The protocols provide for small simplified partitioning that can be fitted within standard well plate format. We demonstrate that with the use of synergistic assay design standard qPCR devices are capable of absolute quantitation when normal qPCR protocols fail to provide accurate estimates. We list practical recipes how to design assays for required parameters, and how to analyze signals to estimate concentration.
Project description:Model averaging (MA) is a modelling strategy where the uncertainty in the configuration of selected variables is taken into account by weight-combining each estimate of the so-called 'candidate model'. Some studies have shown that MA enables better prediction, even in high-dimensional cases. However, little is known about the model prediction performance at different types of multicollinearity in high-dimensional data. Motivated by calibration of near-infrared (NIR) instruments,we focus on MA prediction performance in such data. The weighting schemes that we consider are based on the Akaike's information criterion (AIC), Mallows' Cp, and cross-validation. For estimating the model parameters, we consider the standard least squares and the ridge regression methods. The results indicate that MA outperforms model selection methods such as LASSO and SCAD in high-correlation data. The use of Mallows' Cp and cross-validation for the weights tends to yield similar results in all structures of correlation, although the former is generally preferred. We also find that the ridge model averaging outperforms the least-squares model averaging. This research suggests ridge model averaging to build a relatively better prediction of the NIR calibration model.
Project description:The signal levels observed from mass spectrometers coupled by molecular beam sampling to shock tubes are impacted by dynamic pressures in the spectrometer due to rapid pressure changes in the shock tube. Accounting for the impact of the pressure changes is essential if absolute concentrations of species are to be measured. Obtaining such a correction for spectrometers operated with vacuum ultra violet photoionization has been challenging. We present here a new external calibration method which uses VUV-photoionization of CO2 to develop time-dependent corrections to species concentration/time profiles from which kinetic data can be extracted. The experiments were performed with the ICARE-HRRST (high repetition rate shock tube) at the DESIRS beamline of synchrotron SOLEIL. The calibration experiments were performed at temperatures and pressures behind reflected shock waves of 1376 ± 12 K and 6.6 ± 0.1 bar, respectively. Pyrolytic experiments with two aromatic species, toluene (T5 = 1362 ± 22 K, P5 = 6.6 ± 0.2 bar) and ethylbenzene (T5 = 1327 ± 18 K, P5 = 6.7 ± 0.2 bar), are analyzed to test the method. Time dependent concentrations for molecular and radical species were corrected with the new method. The resulting signals were compared with chemical kinetic simulations using a recent mechanism for pyrolytic formation of polycyclic aromatic hydrocarbons. Excellent agreement was obtained between the experimental data and simulations, without adjustment of the model, demonstrating the validity of the external calibration method.
Project description:Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This paper presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs. The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction. The program is available at https://github.com/sshaghayeghs/NMF-DR.
Project description:Accurate assessment of protein-protein interactions (PPIs) is critical to deciphering disease mechanisms and developing novel drugs, and with rapidly growing PPI data, the need for more efficient predictive methods is emerging. We propose here a symmetric logistic matrix factorization (symLMF)-based approach to predict PPIs, especially useful for large PPI networks. Benchmarked against two widely used datasets (Saccharomyces cerevisiae and Homo sapiens benchmarks) and their extended versions, the symLMF-based method proves to outperform most of the state-of-the-art data-driven methods applied to human PPIs, and it shows a performance comparable to those of deep learning methods despite its conceptual and technical simplicity and efficiency. Tests performed on humans, yeast, and tissue (brain and liver)- and disease (neurodegenerative and metabolic disorders)-specific datasets further demonstrate the high capability to capture the hidden interactions. Notably, many "de novo predictions" made by symLMF are verified to exist in PPI databases other than those used for training/testing the method, indicating that the method could be of broad utility as a simple, yet efficient and accurate, tool applicable to PPI datasets.