A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework.

Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.

Knowledge distillation driven instance segmentation for grading prostate cancer.

Prostate cancer (PCa) is one of the deadliest cancers in men, and identifying cancerous tissue patterns at an early stage can assist clinicians in timely treating the PCa spread. Many researchers have developed deep learning systems for mass-screening PCa. These systems, however, are commonly trained with well-annotated datasets in order to produce accurate results. Obtaining such data for training is often time and resource-demanding in clinical settings and can result in compromised screening performance. To address these limitations, we present a novel knowledge distillation-based instance segmentation scheme that allows conventional semantic segmentation models to perform instance-aware segmentation to extract stroma, benign, and the cancerous prostate tissues from the whole slide images (WSI) with incremental few-shot training. The extracted tissues are then used to compute majority and minority Gleason scores, which, afterward, are used in grading the PCa as per the clinical standards. The proposed scheme has been thoroughly tested on two datasets, containing around 10,516 and 11,000 WSI scans, respectively. Across both datasets, the proposed scheme outperforms state-of-the-art methods by 2.01% and 4.45%, respectively, in terms of the mean IoU score for identifying prostate tissues, and 10.73% and 11.42% in terms of F1 score for grading PCa according to the clinical standards. Furthermore, the applicability of the proposed scheme is tested under a blind experiment with a panel of expert pathologists, where it achieved a statistically significant Pearson correlation of 0.9192 and 0.8984 with the clinicians' grading.

Study of the Effect of Research Octane Number on the Auto-Ignition of Lubricant Oil Surrogates (n-Hexadecane).

Engine oil is considered one of the sources for pre-ignition in downsized boosted direct injection spark-ignited engines. When interacting with fuel sprayed in the combustion chamber, engine oil forms an ignitable mixture and can cause an ignition event before firing the spark plug. Because high research octane number (RON) fuels are difficult to auto-ignite and tend to suppress the knock in an internal combustion engine, studying their interaction with engine oil is essential. Hence, in the current study, a suitable lubricant oil surrogate, namely, n-hexadecane, is mixed with iso-octane and n-heptane at different concentrations to investigate the auto-ignition behavior at elevated pressures. Five sets of fuels (PRF0, PRF20, PRF50, PRF80, and PRF100) were prepared to get a wide range of RONs and blended with n-hexadecane at 15, 25, 35, and 45% mixture concentrations (vol %). These experiments were conducted in a constant volume combustion chamber, keeping the initial temperature constant at 300 °C. A single droplet of the mixture was suspended on a thermocouple bead to record the droplet's lifetime temperature. It was observed that hexadecane mixed with PRF0, PRF20, PRF50, and PRF80 showed similar auto-ignition behaviors. The time of ignition (TI) for these mixtures initially increased until 25% concentration of the fuel in n-hexadecane, and further addition of fuels to 35% and higher concentrations showed a gradual decrease in TI. Ignition of mixtures with 35% and 45% fuel concentrations is attributed to n-heptane, as its low temperature chemistry is the dominant factor in its high reactivity compared to iso-octane. TI increased with the increasing concentration of PRF100 mixtures in hexadecane, unlike other PRF fuels tested in this study. This is because iso-octane is a high RON fuel with a higher auto-ignition temperature, making it challenging to auto-ignite.

Transient Cell Cycle Induction in Cardiomyocytes to Treat Subacute Ischemic Heart Failure.

BACKGROUND: The regenerative capacity of the heart after myocardial infarction is limited. Our previous study showed that ectopic introduction of 4 cell cycle factors (4F; CDK1 [cyclin-dependent kinase 1], CDK4 [cyclin-dependent kinase 4], CCNB [cyclin B1], and CCND [cyclin D1]) promotes cardiomyocyte proliferation in 15% to 20% of infected cardiomyocytes in vitro and in vivo and improves cardiac function after myocardial infarction in mice. METHODS: Using temporal single-cell RNA sequencing, we aimed to identify the necessary reprogramming stages during the forced cardiomyocyte proliferation with 4F on a single cell basis. Using rat and pig models of ischemic heart failure, we aimed to start the first preclinical testing to introduce 4F gene therapy as a candidate for the treatment of ischemia-induced heart failure. RESULTS: Temporal bulk and single-cell RNA sequencing and further biochemical validations of mature human induced pluripotent stem cell-derived cardiomyocytes treated with either LacZ or 4F adenoviruses revealed full cell cycle reprogramming in 15% of the cardiomyocyte population at 48 hours after infection with 4F, which was associated mainly with sarcomere disassembly and metabolic reprogramming (n=3/time point/group). Transient overexpression of 4F, specifically in cardiomyocytes, was achieved using a polycistronic nonintegrating lentivirus (NIL) encoding 4F; each is driven by a TNNT2 (cardiac troponin T isoform 2) promoter (TNNT2-4Fpolycistronic-NIL). TNNT2-4Fpolycistronic-NIL or control virus was injected intramyocardially 1 week after myocardial infarction in rats (n=10/group) or pigs (n=6-7/group). Four weeks after injection, TNNT2-4Fpolycistronic-NIL-treated animals showed significant improvement in left ventricular ejection fraction and scar size compared with the control virus-treated animals. At 4 months after treatment, rats that received TNNT2-4Fpolycistronic-NIL still showed a sustained improvement in cardiac function and no obvious development of cardiac arrhythmias or systemic tumorigenesis (n=10/group). CONCLUSIONS: This study provides mechanistic insights into the process of forced cardiomyocyte proliferation and advances the clinical feasibility of this approach by minimizing the oncogenic potential of the cell cycle factors owing to the use of a novel transient and cardiomyocyte-specific viral construct.

Thoracic Surgery Consultations in COVID-19 Critically Ill Patients: Beyond Conservative Approach.

INTRODUCTION: Iatrogenic pneumothoracis, barotraumas, and tracheoesophageal fistulae, especially after prolonged intubation, and tracheal stenosis are all entities involving thoracic surgeons' consultation and management. With the surge of COVID-19 cases particularly in the critical care settings, various types of complications have been observed that require intervention from thoracic surgeons. METHODS AND MATERIALS: A retrospective study was conducted in an academic healthcare institute in the Eastern Province of Saudi Arabia. We included all COVID-19 cases admitted to ICU in the period between March 15, 2020, and August 15, 2020, requiring thoracic surgery consultation and management. Non-COVID-19 critical cases and iatrogenic pneumothorax were excluded. RESULTS: Of 122 patients who were admitted to ICU with COVID-19, 18 patients (14.75%) required thoracic surgery consultation and management. We discovered a significant association between the outcomes and reintubation rates and the rate of pneumothorax occurrence. The survival analysis showed improvement in patients who had thoracostomy tube insertion as a management than the group who were treated conservatively. On the other hand, there was a significant difference between the COVID ICU group who had thoracic complication and those who did not regarding the length of hospital stay. CONCLUSION: Noniatrogenic pneumothorax, subcutaneous emphysema, and mediastinal emphysema are well-known thoracic entities, but their presence in the context of COVID-19 disease is a harbinger for worse prognosis and outcomes. The presence of pneumothorax may be associated with better prognosis and outcome compared to surgical and mediastinal emphysema.

Mole fraction measurement through a transparent quarl burner using filtered Rayleigh scattering.

A filtered Rayleigh scattering system is developed and applied to measure the mole fraction of methane in a methane-air swirl flow through a transparent conical quartz quarl. Light scattering from the location where the laser beam is incident on the surface of the quarl is orders of magnitudes larger than Rayleigh scattering from the gas mixture of interest. This diffusive scattering is suppressed using molecular absorption by an iodine cell and using spatial filtering by an optical aperture. Residual stray light accounted for up to 5% of the total signal and had to be removed for accurate measurements. The flow consisted of a nonpremixed mixture of methane and air in the central jet surrounded by a strong swirling air flow. Measurements were conducted at a height of 4 mm from the fuel tube's exit for six different conditions of the swirl flow to demonstrate the ability of the instrument to study the effects of swirl strength and fuel flow rate on the mixing process. By using a four-leg pulse stretcher to allow higher laser energies in the probe volume, large collection optics and a reference iodine cell to monitor laser wavelength variations, standard deviations of ∼0.006 in air and ∼0.012 in a laminar methane flow were achieved for mole fraction measurements.

High-speed Rayleigh-Raman measurements with subframe burst gating.

A 10-kHz one-dimensional Rayleigh-CH4 Raman instrument capable of achieving highly precise measurement of temperature and methane mole fraction is demonstrated. The system uses a pulse-burst laser as the light source and back-illuminated electron-multiplied CCD cameras as the detectors. The cameras are operated in the subframe burst gating mode, to combine a high sampling rate, low noise, and a slow readout. The improved precision of this configuration is demonstrated by measuring temperature and methane mole fractions in ambient temperature gas mixtures and in a non-premixed inverse diffusion flame.

Integrated In†Situ Characterization of a Molten Salt Catalyst Surface: Evidence of Sodium Peroxide and Hydroxyl Radical Formation.

Sodium-based catalysts (such as Na2 WO4 ) were proposed to selectively catalyze OH radical formation from H2 O and O2 at high temperatures. This reaction may proceed on molten salt state surfaces owing to the lower melting point of the used Na salts compared to the reaction temperature. This study provides direct evidence of the molten salt state of Na2 WO4 , which can form OH radicals, using in situ techniques including X-ray diffraction (XRD), scanning transmission electron microscopy (STEM), laser induced fluorescence (LIF) spectrometry, and ambient-pressure X-ray photoelectron spectroscopy (AP-XPS). As a result, Na2 O2 species, which were hypothesized to be responsible for the formation of OH radicals, have been identified on the outer surfaces at temperatures of ≥800 °C, and these species are useful for various gas-phase hydrocarbon reactions, including the selective transformation of methane to ethane.

Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach.

BACKGROUND AND OBJECTIVE: Fast intravascular ultrasound (IVUS) video processing is required for calcium volume computation during the planning phase of percutaneous coronary interventional (PCI) procedures. Nonlinear multiresolution techniques are generally applied to improve the processing time by down-sampling the video frames. METHODS: This paper presents four different segmentation methods for calcium volume measurement, namely Threshold-based, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) embedded with five different kinds of multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, and Gaussian pyramid). This leads to 20 different kinds of combinations. IVUS image data sets consisting of 38,760 IVUS frames taken from 19 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec.). The performance of these 20 systems is compared with and without multiresolution using the following metrics: (a) computational time; (b) calcium volume; (c) image quality degradation ratio; and (d) quality assessment ratio. RESULTS: Among the four segmentation methods embedded with five kinds of multiresolution techniques, FCM segmentation combined with wavelet-based multiresolution gave the best performance. FCM and wavelet experienced the highest percentage mean improvement in computational time of 77.15% and 74.07%, respectively. Wavelet interpolation experiences the highest mean precision-of-merit (PoM) of 94.06 ± 3.64% and 81.34 ± 16.29% as compared to other multiresolution techniques for volume level and frame level respectively. Wavelet multiresolution technique also experiences the highest Jaccard Index and Dice Similarity of 0.7 and 0.8, respectively. Multiresolution is a nonlinear operation which introduces bias and thus degrades the image. The proposed system also provides a bias correction approach to enrich the system, giving a better mean calcium volume similarity for all the multiresolution-based segmentation methods. After including the bias correction, bicubic interpolation gives the largest increase in mean calcium volume similarity of 4.13% compared to the rest of the multiresolution techniques. The system is automated and can be adapted in clinical settings. CONCLUSIONS: We demonstrated the time improvement in calcium volume computation without compromising the quality of IVUS image. Among the 20 different combinations of multiresolution with calcium volume segmentation methods, the FCM embedded with wavelet-based multiresolution gave the best performance.

Pleuropulmonary blastoma. A rare childhood malignancy.

Pleuropulmonary blastoma PPB is an extremely rare, intrathoracic neoplasm of early childhood with unfavorable outcome. We present a case of a 4-year-old boy with progressive dyspnea due to tension pneumothorax. After chest tube insertion, the CT scan showed large multilocular cystic lesions containing air and solid areas involving the right lower lobe, and its related pleura. Thoracotomy was carried out, and the cyst was removed with the pleural solid areas. Histopathological examination confirmed the diagnosis of PPB type II. Postoperatively, the patient was scheduled to start chemotherapy in a specialized pediatric oncology center to complete the treatment.

New deformable registration technique using scale space and curve evolution theory and a finite element based validation framework.

In this paper, we present a novel and accurate approach for nonrigid registration. New feature descriptors are built as voxel signatures using scale space theory. These descriptors are used to capture the global motion of the imaged object. Local deformations are modelled through an evolution process of equi-spaced closed curves/surfaces (iso-contours/surfaces) which are generated using fast marching level sets and are matched using the built feature descriptors. The performance of the proposed approach is validated using the finite element method. Both 2D and 3D tissue deformations cases are simulated, and the registration accuracy is quantified by co-registering the deformed images with the original ones and comparing the recovered mesh point displacements with the simulated ones. The evaluation results show the potential of the proposed approach in handling local deformation better than some conventional approaches.

Malignancy: Identification of Predictors of Disease Status and Progression in Patients with Myeloma (MM).

Multiple studies have attempted to recognize the best markers of disease activity and outcome in myeloma (MM). Our objective was to identify the best variables that can reflect MM disease status. Design and methods: The data obtained from all the following tests were included in the analysis: serum levels of the 2 growth factors known to be crucial for MM growth (i.e. IL-6, and sIL-6R), routine peripheral blood data (Hb%, serum calcium, albumin, CRP, B2m, LDH) and bone marrow plasma cell (BMPC)%, as well as the age and sex of patients. The study was conducted on 21 cases of MM under chemotherapy (aged 48-74 years; M/F = 13/8) and 12 matched normal individuals. The patients were categorized into 2 groups according to their clinical status: Group#1 (n = 16; cases in plateau/stable phase), and Group#2 (n = 5; advanced/refractory cases). Results: Student t-test confirms that serum IL-6 and sIL-6R are the most statistically different variables upon comparing cases in plateau phase (Group#1) with those of advanced disease (Group#2). Stepwise discriminant analysis of data has resulted in a function that is composed of the 2 most salient variables (i.e. serum IL-6, sIL-6R). The proposed function was highly significant (p = 0.0000) with Wilk's Lambda = 0.02538. The diagnostic capability of the proposed function was very high (percent of grouped cases that were classified correctly= 100%). Conclusion: measurement of serum IL-6 and sIL-6R gives the best prediction of disease activity in patients with MM.