Miracle-dz/UniFAR-FTUs huggingface dataset:
The Value of 18F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules

Purpose To establish an 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) mathematical prediction model to improve the diagnosis of solitary pulmonary nodules (SPNs). Materials and Methods We retrospectively reviewed 177 consecutive patients who underwent 18F-FDG PET/CT for evaluation of SPNs. The mathematical model was established by logistic regression analysis. The diagnostic capabilities of the model were calculated, and the areas under the receiver operating characteristic curve (AUC) were compared with Mayo and VA model. Results The mathematical model was y = exp⁡(x)/[1 + exp⁡(x)], x = −7.363 + 0.079 × age + 1.900 × lobulation + 1.024 × vascular convergence + 1.530 × pleural retraction + 0.359 × the maximum of standardized uptake value (SUVmax). When the cut-off value was set at 0.56, the sensitivity, specificity, and accuracy of our model were 86.55%, 74.14%, and 81.4%, respectively. The area under the receiver operating characteristic curve (AUC) of our model was 0.903 (95% confidence interval (CI): 0.860 to 0.946). The AUC of our model was greater than that of the Mayo model, the VA model, and PET (P < 0.05) and has no difference with that of PET/CT (P > 0.05). Conclusion The mathematical predictive model has high accuracy in estimating the malignant probability of patients with SPNs.
The Efficacy and Safety of Long-term Pirfenidone Therapy in Patients with Idiopathic Pulmonary Fibrosis

Objective Pirfenidone (PFD) is often used for years, but the efficacy and safety of long-term PFD therapy in patients with idiopathic pulmonary fibrosis (IPF) are not fully understood. Methods and Patients We retrospectively evaluated 46 patients with IPF who received PFD between February 2009 and August 2014. The efficacy and safety of PFD therapy were compared between 2 groups: long-term therapy patients who received PFD for over 1 year (group L, n=30, 65%) and short-term therapy patients who could not receive PFD for more than 1 year due to worsening of their condition or side effects (group S, n=16, 35%). Results The median age of the 46 patients was 70.5 years, and the median baseline % predicted forced vital capacity (%FVC) was 70.0%. The changes in the FVC in group L were -120 mL and -170 mL at 12 and 24 months after receiving PFD, respectively. The respective median survival times after PFD therapy in groups L and S were 1,612 days and 285 days (p<0.001). The patients in group L experienced a longer time free of acute exacerbation of IPF than those in group S (947 days vs. 145 days, p=0.001). A multivariate analysis revealed that %FVC <60% was a predictor of the inability to receive PFD for over 1 year (odds ratio 0.240, 95% confidence interval 0.060-0.958; p=0.043). With regard to grade 3-5 adverse events, only one patient exhibited grade 3 hyponatremia. Conclusion Long-term PFD therapy is effective, with few severe adverse events.
Production of Staphylococcal Complement Inhibitor (SCIN) and Other Immune Modulators during the Early Stages of Staphylococcus aureus Biofilm Formation in a Mammalian Cell Culture Medium

Immune modulators are known to be produced by matured biofilms and during different stages of planktonic growth of Staphylococcus aureus. Little is known about immune modulator production during the early stages of biofilm formation, thus raising the following question: how does S. aureus protect itself from the innate immune responses at these stages? Therefore, we determined the production of the following immune modulators: chemotaxis inhibitory protein of staphylococci (CHIPS); staphylococcal complement inhibitor (SCIN); formyl peptide receptor-like 1 inhibitor; gamma-hemolysin component B; leukocidins D, E, and S; staphylococcal superantigen-like proteins 1, 3, 5, and 9; and staphylococcal enterotoxin A. ABSTRACT Immune modulators are known to be produced by matured biofilms and during different stages of planktonic growth of Staphylococcus aureus. Little is known about immune modulator production during the early stages of biofilm formation, thus raising the following question: how does S. aureus protect itself from the innate immune responses at these stages? Therefore, we determined the production of the following immune modulators: chemotaxis inhibitory protein of staphylococci (CHIPS); staphylococcal complement inhibitor (SCIN); formyl peptide receptor-like 1 inhibitor; gamma-hemolysin component B; leukocidins D, E, and S; staphylococcal superantigen-like proteins 1, 3, 5, and 9; and staphylococcal enterotoxin A. Production was determined during in vitro biofilm formation in Iscove's modified Dulbecco's medium at different time points using a competitive Luminex assay and mass spectrometry. Both methods demonstrated the production of the immune modulators SCIN and CHIPS during the early stages of biofilm formation. The green fluorescence protein promoter fusion technology confirmed scn (SCIN) and, to a lesser extent, chp (CHIPS) transcription during the early stages of biofilm formation. Furthermore, we found that SCIN could inhibit human complement activation induced by early biofilms, indicating that S. aureus is able to modulate the innate immune system already during the early stages of biofilm formation in vitro. These results form a stepping stone toward elucidating the role of immune modulators in the establishment of biofilms in vivo and present opportunities to develop preventive strategies.
Multi-institutional study of the variability in target delineation for six targets commonly treated with radiosurgery

Abstract Background: Although accurate delineation of the target is a key factor of success in radiosurgery there are no consensus guidelines for target contouring. Aim: The aim of the present study was therefore to quantify the variability in target delineation and discuss the potential clinical implications, for six targets regarded as common in stereotactic radiosurgery. Material and methods: Twelve Gamma Knife centers participated in the study by contouring the targets and organs at risks and performing the treatment plans. Analysis of target delineation variability was based on metrics defined based on agreement volumes derived from overlapping structures following a previously developed method. The 50% agreement volume (AV50), the common and the encompassing volumes as well as the Agreement Volume Index (AVI) were determined. Results: Results showed that the lowest AVI (0.16) was found for one of the analyzed metastases (range of delineated volumes 1.27–3.33 cm3). AVI for the other two metastases was 0.62 and 0.37, respectively. Corresponding AVIs for the cavernous sinus meningioma, pituitary adenoma and vestibular schwannoma were 0.22, 0.37 and 0.50. Conclusions: This study showed that the variability in the contouring was much higher than expected and therefore further work in standardizing the contouring practice in radiosurgery is warranted.
Parainfluenza Virus Type 3 Pneumonia in Bone Marrow Transplant Recipients: Multiple Small Nodules in High-Resolution Lung Computed Tomography Scans Provide a Radiological Clue to Diagnosis

Abstract We report the findings of high-resolution chest computed tomography of 6 hematopoietic stem cell transplant recipients with parainfluenza virus type 3 pneumonia who were not infected with any other pathogens. All patients had multiple small nodules (diameter, <5 mm) without cavitation in a peribronchial distribution. Changes preceded microbiological diagnosis in 4 of 6 cases.
Dual-Clustering Maximum Entropy with Application to Classification and Word Embedding

Maximum Entropy (ME), as a general-purpose machine learning model, has been successfully applied to various fields such as text mining and natural language processing. It has been used as a classification technique and recently also applied to learn word embedding. ME establishes a distribution of the exponential form over items (classes/words). When training such a model, learning efficiency is guaranteed by globally updating the entire set of model parameters associated with all items at each training instance. This creates a significant computational challenge when the number of items is large. To achieve learning efficiency with affordable computational cost, we propose an approach named Dual-Clustering Maximum Entropy (DCME). Exploiting the primal-dual form of ME, it conducts clustering in the dual space and approximates each dual distribution by the corresponding cluster center. This naturally enables a hybrid online-offline optimization algorithm whose time complexity per instance only scales as the product of the feature/word vector dimensionality and the cluster number. Experimental studies on text classification and word embedding learning demonstrate that DCME effectively strikes a balance between training speed and model quality, substantially outperforming state-of-the-art methods.
A Randomized Clinical Trial of Metformin to Treat Autosomal Dominant Polycystic Kidney Disease

Background: Metformin inhibits cyclic AMP generation and activates AMP-activated protein kinase (AMPK), which inhibits the cystic fibrosis transmembrane conductance regulator and Mammalian Target of Rapamycin pathways. Together these effects may reduce cyst growth in autosomal dominant polycystic kidney disease (ADPKD). Methods: A phase II, double-blinded randomized placebo-controlled trial of 26 months duration. Participants will include nondiabetic adults (n = 96) aged 18–60 years, with an estimated glomerular filtration rate (eGFR) ≥50 mL/min/1.73 m2 and ADPKD, recruited from university-based practices in Baltimore and Boston. Participants will be randomized in 1: 1 ratio to metformin or placebo at 500 mg once daily, increased every 2 weeks to a maximum of 1,000 mg twice daily as tolerated. Dose is decreased if eGFR falls to 30–45 mL/min/1.73 m2 and discontinued at eGFR < 30 mL/min/1.73 m2. Results: The primary outcomes are safety, assessed by the rates of hypoglycemia, elevated lactic acid levels, adverse events, and tolerability assessed by the Gastrointestinal Severity Rating Scale and maximum tolerated dose of study medication. Secondary outcomes include changes in total kidney and liver volumes, pain, and health-related quality of life, and changes in urinary metabolomic biomarkers. Conclusions: Results of this trial will provide important information on the feasibility, safety, and tolerability of long-term use of metformin in patients with ADPKD and provide preliminary information regarding its efficacy in slowing disease progression. Furthermore, results may support or refute the hypothesis that metformin effects on disease progression are mediated through the activation of the AMPK pathway. These results will be essential for the justification and design of a full-scale efficacy trial.
Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this work, we build a neural network model for the task of ranking clarification questions. Our model is inspired by the idea of expected value of perfect information: a good question is one whose expected answer will be useful. We study this problem using data from StackExchange, a plentiful online resource in which people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We create a dataset of clarification questions consisting of 77K posts paired with a clarification question (and answer) from three domains of StackExchange: askubuntu, unix and superuser. We evaluate our model on 500 samples of this dataset against expert human judgments and demonstrate significant improvements over controlled baselines.
Fast-Tracking Stationary MOMDPs for Adaptive Management Problems

Adaptive management is applied in conservation and natural resource management, and consists of making sequential decisions when the transition matrix is uncertain. Informally described as ’learning by doing’, this approach aims to trade off between decisions that help achieve the objective and decisions that will yield a better knowledge of the true transition matrix. When the true transition matrix is assumed to be an element of a finite set of possible matrices, solving a mixed observability Markov decision process (MOMDP) leads to an optimal trade-off but is very computationally demanding. Under the assumption (common in adaptive management) that the true transition matrix is stationary, we propose a polynomial-time algorithm to find a lower bound of the value function. In the corners of the domain of the value function (belief space), this lower bound is provably equal to the optimal value function. We also show that under further assumptions, it is a linear approximation of the optimal value function in a neighborhood around the corners. We evaluate the benefits of our approach by using it to initialize the solvers MO-SARSOP and Perseus on a novel computational sustainability problem and a recent adaptive management data challenge. Our approach leads to an improved initial value function and translates into significant computational gains for both solvers.
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks

Spiking neural networks (SNNs) are positioned to enable spatio-temporal information processing and ultra-low power event-driven neuromorphic hardware. However, SNNs are yet to reach the same performances of conventional deep artificial neural networks (ANNs), a long-standing challenge due to complex dynamics and non-differentiable spike events encountered in training. The existing SNN error backpropagation (BP) methods are limited in terms of scalability, lack of proper handling of spiking discontinuities, and/or mismatch between the rate-coded loss function and computed gradient. We present a hybrid macro/micro level backpropagation (HM2-BP) algorithm for training multi-layer SNNs. The temporal effects are precisely captured by the proposed spike-train level post-synaptic potential (S-PSP) at the microscopic level. The rate-coded errors are defined at the macroscopic level, computed and back-propagated across both macroscopic and microscopic levels. Different from existing BP methods, HM2-BP directly computes the gradient of the rate-coded loss function w.r. t tunable parameters. We evaluate the proposed HM2-BP algorithm by training deep fully connected and convolutional SNNs based on the static MNIST [14] and dynamic neuromorphic N-MNIST [26]. HM2-BP achieves an accuracy level of 99.49% and 98.88% for MNIST and N-MNIST, respectively, outperforming the best reported performances obtained from the existing SNN BP algorithms. Furthermore, the HM2-BP produces the highest accuracies based on SNNs for the EMNIST [3] dataset, and leads to high recognition accuracy for the 16-speaker spoken English letters of TI46 Corpus [16], a challenging patio-temporal speech recognition benchmark for which no prior success based on SNNs was reported. It also achieves competitive performances surpassing those of conventional deep learning models when dealing with asynchronous spiking streams.
Efficiently Answering Technical Questions - A Knowledge Graph Approach

More and more users prefer to ask their technical questions online. For machines, understanding a question is nontrivial. Current approaches lack explicit background knowledge.In this paper, we introduce a novel technical question understanding approach to recommending probable solutions to users. First, a knowledge graph is constructed which contains abundant technical information, and an augmented knowledge graph is built on the basis of the knowledge graph, to link the knowledge graph and documents. Then we develop a light weight question driven mechanism to select candidate documents. To improve the online performance, we propose an index-based random walk to support the online search. We use comprehensive experiments to evaluate the effectiveness of our approach on a large scale of real-world query logs. Our system outperforms main-stream search engine and the state-of-art information retrieval methods. Meanwhile, extensive experiments confirm the efficiency of our index-based online search mechanism.
Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations

We present a system to infer and execute a human-readable program from a real-world demonstration. The system consists of a series of neural networks to perform perception, program generation, and program execution. Leveraging convolutional pose machines, the perception network reliably detects the bounding cuboids of objects in real images even when severely occluded, after training only on synthetic images using domain randomization. To increase the applicability of the perception network to new scenarios, the network is formulated to predict in image space rather than in world space. Additional networks detect relationships between objects, generate plans, and determine actions to reproduce a real-world demonstration. The networks are trained entirely in simulation, and the system is tested in the real world on the pick-and-place problem of stacking colored cubes using a Baxter robot.
[Biomechanical assessment of newly-designed proximal femoral medial buttress plate for treatment of reverse oblique femoral intertrochanteric fracture].

Objective To evaluate the biomechanical properties of proximal femoral medial buttress plate (PFMBP) for fixing the reverse oblique intertrochanteric fractures by comparing with proximal femoral locking compression plate (PFLCP) and proximal femoral nail antirotation (PFNA). Methods Eighteen synthetic femoral bone models (Synbone) were divided into 3 groups (group PFLCP, group PFNA, and group PFMBP), 6 models in each group; an AO 31-A3.1 reverse oblique femoral intertrochanteric fracture was made based on the same criterion. After being fixed and embeded, the axial load testing, torsion testing, and axial load-to-failure testing were performed on each model. The axial displacement of different loads, torque of different torsion angles, and failure load of each model were recorded, and the stiffness of axial load and torsion were calculated. Results The axial stiffness in groups PFLCP, PFNA, and PFMBP were (109.42±30.14), (119.13±29.14), and (162.05±22.05) N/mm respectively, showing significant differences between groups ( P<0.05). There were significant differences in torque between different torsion angles in the same group, as well as in the torque between groups at the same torsional angle ( P<0.05). The torsion stiffness in groups PFLCP, PFNA, and PFMBP were (1.45±0.44), (1.10±0.13), and (1.36±0.32) N·mm/deg respectively; there were significant differences when compared groups PFLCP and PFMBP with group PFNA ( P<0.05), but no significant difference was found between group PFLCP and group PFMBP ( P>0.05). The failure loads of groups PFLCP, PFNA, and PFMBP were (1 408.88± 0.17), (1 696.56±0.52), and (2 154.65±0.10) N respectively, showing significant differences between groups ( P<0.05). Conclusion The newly-designed PFMBP is better than PFNA and PFLCP in axial load stiffness and torsion stiffness for fixing reverse oblique intertrochanteric fracture by biomechanical test, indicating that reconstruction of medial stability is a key element for unstable intertrochanteric fracture.
Within-Subject Correlation Analysis to Detect Functional Areas Associated With Response Inhibition

Functional areas in fMRI studies are often detected by brain-behavior correlation, calculating across-subject correlation between the behavioral index and the brain activity related to a function of interest. Within-subject correlation analysis is also employed in a single subject level, which utilizes cognitive fluctuations in a shorter time period by correlating the behavioral index with the brain activity across trials. In the present study, the within-subject analysis was applied to the stop-signal task, a standard task to probe response inhibition, where efficiency of response inhibition can be evaluated by the stop-signal reaction time (SSRT). Since the SSRT is estimated, by definition, not in a trial basis but from pooled trials, the correlation across runs was calculated between the SSRT and the brain activity related to response inhibition. The within-subject correlation revealed negative correlations in the anterior cingulate cortex and the cerebellum. Moreover, the dissociation pattern was observed in the within-subject analysis when earlier vs. later parts of the runs were analyzed: negative correlation was dominant in earlier runs, whereas positive correlation was dominant in later runs. Regions of interest analyses revealed that the negative correlation in the anterior cingulate cortex, but not in the cerebellum, was dominant in earlier runs, suggesting multiple mechanisms associated with inhibitory processes that fluctuate on a run-by-run basis. These results indicate that the within-subject analysis compliments the across-subject analysis by highlighting different aspects of cognitive/affective processes related to response inhibition.
Identification of potential crucial genes and pathways associated with vein graft restenosis based on gene expression analysis in experimental rabbits

Occlusive artery disease (CAD) is the leading cause of death worldwide. Bypass graft surgery remains the most prevalently performed treatment for occlusive arterial disease, and veins are the most frequently used conduits for surgical revascularization. However, the clinical efficacy of bypass graft surgery is highly affected by the long-term potency rates of vein grafts, and no optimal treatments are available for the prevention of vein graft restenosis (VGR) at present. Hence, there is an urgent need to improve our understanding of the molecular mechanisms involved in mediating VGR. The past decade has seen the rapid development of genomic technologies, such as genome sequencing and microarray technologies, which will provide novel insights into potential molecular mechanisms involved in the VGR program. Ironically, high throughput data associated with VGR are extremely scarce. The main goal of the current study was to explore potential crucial genes and pathways associated with VGR and to provide valid biological information for further investigation of VGR. A comprehensive bioinformatics analysis was performed using high throughput gene expression data. Differentially expressed genes (DEGs) were identified using the R and Bioconductor packages. After functional enrichment analysis of the DEGs, protein–protein interaction (PPI) network and sub-PPI network analyses were performed. Finally, nine potential hub genes and fourteen pathways were identified. These hub genes may interact with each other and regulate the VGR program by modulating the cell cycle pathway. Future studies focusing on revealing the specific cellular and molecular mechanisms of these key genes and pathways involved in regulating the VGR program may provide novel therapeutic targets for VGR inhibition.