A Comparison of Response Surface Methodology and a One-Factor-At-A-Time Approach as Calibration Techniques for the Bioplume-II Simulation Model of Contaminant Biodegradation

This thesis compared Response Surface Methodology (RSM) to the one-factor-at-a-time approach for calibrating the Bioplume-II simulation model of contaminant biodegradation. The MADE-2 data set from Columbus Air Force Base, Mississippi was used. The one-factor-at-a-time approach reduced the root-mean-squared (RMS) error for the flow to 0.921225 feet in a total of 36 runs of Bioplume-II. The RSM approach reduced the error criterion to 0.918875 in a total of 47 runs. The one-factor-at-a-time approach was unable to reduce the error below 67.1831 parts per billion (ppb) after 21 runs. The RSM approach reduced the RMS error to 67.0327 ppb after 47 runs. The RSM approach allows the modeler to identify parametric regions of improved response in a systematic way that would be extremely difficult to find using the one-factor-at-a-time approach. Limitations of this work included the use of inefficient full factorial designs and the poor assumption of homogeneous parameter values

Electromyography Data Processing Impacts Muscle Synergies during Gait for Unimpaired Children and Children with Cerebral Palsy

Muscle synergies calculated from electromyography (EMG) data identify weighted groups of muscles activated together during functional tasks. Research has shown that fewer synergies are required to describe EMG data of individuals with neurologic impairments. When considering potential clinical applications of synergies, understanding how EMG data processing impacts results and clinical interpretation is important. The aim of this study was to evaluate how EMG signal processing impacts synergy outputs during gait. We evaluated the impacts of two common processing steps for synergy analyses: low pass (LP) filtering and unit variance scaling. We evaluated EMG data collected during barefoot walking from five muscles of 113 children with cerebral palsy (CP) and 73 typically-developing (TD) children. We applied LP filters to the EMG data with cutoff frequencies ranging from 4 to 40 Hz (reflecting the range reported in prior synergy research). We also evaluated the impact of normalizing EMG amplitude by unit variance. We found that the total variance accounted for (tVAF) by a given number of synergies was sensitive to LP filter choice and decreased in both TD and CP groups with increasing LP cutoff frequency (e.g., 9.3 percentage points change for one synergy between 4 and 40 Hz). This change in tVAF can alter the number of synergies selected for further analyses. Normalizing tVAF to a z-score (e.g., dynamic motor control index during walking, walk-DMC) reduced sensitivity to LP cutoff. Unit variance scaling caused comparatively small changes in tVAF. Synergy weights and activations were impacted less than tVAF by LP filter choice and unit variance normalization. These results demonstrate that EMG signal processing methods impact outputs of synergy analysis and z-score based measures can assist in reporting and comparing results across studies and clinical centers

Parents’ Pandemic NICU Experience in the United States: A Qualitative Study

Background Prior to the COVID-19 pandemic, parents of infants in the Neonatal Intensive Care Unit (NICU) frequently reported high levels of stress, uncertainty, and decreased parenting confidence. Early research has demonstrated that parents have had less access to their infants in the hospital due to restrictions on parental presence secondary to the pandemic. It is unknown how parents have perceived their experiences in the NICU since the beginning of the COVID-19 pandemic. The purpose of this study was to describe the lived experience of parents who had an infant in the NICU in the context of the COVID-19 pandemic to inform healthcare providers and policy makers for future development of policies and care planning. Methods The study design was a qualitative description of the impact of the COVID-19 pandemic on parents’ experiences of having an infant in the NICU. Free-text responses to open-ended questions were collected as part of a multi-method study of parents’ experiences of the NICU during the first six months of the pandemic. Participants from the United States were recruited using social media platforms between the months of May and July of 2020. Data were analyzed using a reflexive thematic approach. Findings Free-text responses came from 169 parents from 38 different states in the United States. Three broad themes emerged from the analysis: (1) parents’ NICU experiences during the COVID-19 pandemic were emotionally isolating and overwhelming, (2) policy changes restricting parental presence created disruptions to the family unit and limited family-centered care, and (3) interactions with NICU providers intensified or alleviated emotional distress felt by parents. A unifying theme of experiences of emotional distress attributed to COVID-19 circumstances ran through all three themes. Conclusions Parents of infants in the NICU during the first six months of the COVID-19 pandemic experienced emotional struggles, feelings of isolation, lack of family-centered care, and deep disappointment with system-level decisions. Moving forward, parents need to be considered essential partners in the development of policies concerning care of and access to their infants. Background The COVID-19 pandemic created unprecedented conditions for administrators and clinicians working in Neonatal Intensive Care Units (NICU) and greatly affected parents of infants requiring hospitalization. Prior to the COVID-19 pandemic, parents of infants admitted to a NICU reported high levels of stress, anxiety, uncertainty, and decreased parenting confidence when compared to parents of healthy full-term infants [1,2,3,4,5,6]. Approximately 28–40% of mothers of infants admitted to a NICU were diagnosed with a new mental illness, such as depression or perinatal post-traumatic stress disorder [7]. Fathers of infants requiring NICU hospitalization also reported significant stress and need for reassurance and support [8, 9]

Global and Local Uncertainty Principles for Signals on Graphs

Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluating linear signal transforms. Generalizations of such principles to the graph setting can inform dictionary design for graph signals, lead to algorithms for reconstructing missing information from graph signals via sparse representations, and yield new graph analysis tools. While previous work has focused on generalizing notions of spreads of a graph signal in the vertex and graph spectral domains, our approach is to generalize the methods of Lieb in order to develop uncertainty principles that provide limits on the concentration of the analysis coefficients of any graph signal under a dictionary transform whose atoms are jointly localized in the vertex and graph spectral domains. One challenge we highlight is that due to the inhomogeneity of the underlying graph data domain, the local structure in a single small region of the graph can drastically affect the uncertainty bounds for signals concentrated in different regions of the graph, limiting the information provided by global uncertainty principles. Accordingly, we suggest a new way to incorporate a notion of locality, and develop local uncertainty principles that bound the concentration of the analysis coefficients of each atom of a localized graph spectral filter frame in terms of quantities that depend on the local structure of the graph around the center vertex of the given atom. Finally, we demonstrate how our proposed local uncertainty measures can improve the random sampling of graph signals

Intraoperative Round Window Recordings to Acoustic Stimuli From Cochlear Implant Patients

Acoustically evoked neural and hair cell potentials can be measured from the round window (RW) intraoperatively in the general population of cochlear implant recipients

Ribose 2′-O-methylation provides a molecular signature for the distinction of self and non-self mRNA dependent on the RNA sensor Mda5

The 5'-cap-structures of higher eukaryote mRNAs are ribose 2'-O-methylated. Likewise, a number of viruses replicating in the cytoplasm of eukayotes have evolved 2'-O-methyltransferases to modify autonomously their mRNAs. However, a defined biological role of mRNA 2'-O-methylation remains elusive. Here we show that viral mRNA 2'-O-methylation is critically involved in subversion of type-I-interferon (IFN-I) induction. We demonstrate that human and murine coronavirus 2'-O-methyltransferase mutants induce increased IFN-I expression, and are highly IFN-I sensitive. Importantly, IFN-I induction by 2'-O-methyltransferase-deficient viruses is dependent on the cytoplasmic RNA sensor melanoma differentiation-associated gene 5 (MDA5). This link between MDA5-mediated sensing of viral RNA and mRNA 2'-O-methylation suggests that RNA modifications, such as 2'-O-methylation, provide a molecular signature for the discrimination of self and non-self mRNA

Rescue therapy for vasospasm following aneurysmal subarachnoid hemorrhage:a propensity score-matched analysis with machine learning

OBJECTIVE Rescue therapies have been recommended for patients with angiographic vasospasm (aVSP) and delayed cerebral ischemia (DCI) following subarachnoid hemorrhage (SAH). However, there is little evidence from randomized clinical trials that these therapies are safe and effective. The primary aim of this study was to apply game theory-based methods in explainable machine learning (ML) and propensity score matching to determine if rescue therapy was associated with better 3-month outcomes following post-SAH aVSP and DCI. The authors also sought to use these explainable ML methods to identify patient populations that were more likely to receive rescue therapy and factors associated with better outcomes after rescue therapy. METHODS Data for patients with aVSP or DCI after SAH were obtained from 8 clinical trials and 1 observational study in the Subarachnoid Hemorrhage International Trialists repository. Gradient boosting ML models were constructed for each patient to predict the probability of receiving rescue therapy and the 3-month Glasgow Outcome Scale (GOS) score. Favorable outcome was defined as a 3-month GOS score of 4 or 5. Shapley Additive Explanation (SNAP) values were calculated for each patient-derived model to quantify feature importance and interaction effects. Variables with high S HAP importance in predicting rescue therapy administration were used in a propensity score-matched analysis of rescue therapy and 3-month GOS scores. RESULTS The authors identified 1532 patients with aVSP or DCI. Predictive, explainable ML models revealed that aneurysm characteristics and neurological complications, but not admission neurological scores, carried the highest relative importance rankings in predicting whether rescue therapy was administered. Younger age and absence of cerebral ischemia/ infarction were invariably linked to better rescue outcomes, whereas the other important predictors of outcome varied by rescue type (interventional or noninterventional). In a propensity score-matched analysis guided by SHAP-based variable selection, rescue therapy was associated with higher odds of 3-month GOS scores of 4-5 (OR 1.63, 95% CI 1.22-2.17). CONCLUSIONS Rescue therapy may increase the odds of good outcome in patients with aVSP or DCI after SAH. Given the strong association between cerebral ischemia/infarction and poor outcome, trials focusing on preventative or therapeutic interventions in these patients may be most able to demonstrate improvements in clinical outcomes. Insights developed from these models may be helpful for improving patient selection and trial design

ILC Vertex Tracker R&D

This document summarizes past achievements, current activities and future goals of the R&D program aimed at the design, prototyping and characterization of a full detector module, equipped with monolithic pixel sensors, matching the requirements for the Vertex Tracker at the ILC. We provide a plan of activities to obtain a demonstrator multi-layered vertex tracker equipped with sensors matching the ILC requirements and realistic lightweight ladders in FY11, under the assumption that ILC detector proto-collaborations will be choosing technologies and designs for the Vertex Tracker by that time. The R&D program discussed here started at LBNL in 2004, supported by a Laboratory Directed R&D (LDRD) grant and by funding allocated from the core budget of the LBNL Physics Division and from the Department of Physics at UC Berkeley. Subsequently additional funding has been awarded under the NSF-DOE LCRD program and also personnel have become available through collaborative research with other groups. The aim of the R&D program carried out by our collaboration is to provide a well-integrated, inclusive research effort starting from physics requirements for the ILC Vertex Tracker and addressing Si sensor design and characterization, engineered ladder design, module system issues, tracking and vertex performances and beam test validation. The broad scope of this program is made possible by important synergies with existing know-how and concurrent programs both at LBNL and at the other collaborating institutions. In particular, significant overlaps with LHC detector design, SLHC R&D as well as prototyping for the STAR upgrade have been exploited to optimize the cost per deliverable of our program. This activity is carried out as a collaborative effort together with Accelerator and Fusion Research, the Engineering and the Nuclear Science Divisions at LBNL, INFN and the Department of Physics in Padova, Italy, INFN and the Department of Physics in Torino, Italy and the Department of Physics of Purdue University

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCV

Roflumilast inhibits tumor growth and migration in STK11/LKB1 deficient pancreatic cancer

Pancreatic cancer is a malignant tumor of the digestive system. It is highly aggressive, easily metastasizes, and extremely difficult to treat. This study aimed to analyze the genes that might regulate pancreatic cancer migration to provide an essential basis for the prognostic assessment of pancreatic cancer and individualized treatment. A CRISPR knockout library directed against 915 murine genes was transfected into TB 32047 cell line to screen which gene loss promoted cell migration. Next-generation sequencing and PinAPL.py- analysis was performed to identify candidate genes. We then assessed the effect of serine/threonine kinase 11 (STK11) knockout on pancreatic cancer by wound-healing assay, chick agnosia (CAM) assay, and orthotopic mouse pancreatic cancer model. We performed RNA sequence and Western blotting for mechanistic studies to identify and verify the pathways. After accelerated Transwell migration screening, STK11 was identified as one of the top candidate genes. Further experiments showed that targeted knockout of STK11 promoted the cell migration and increased liver metastasis in mice. Mechanistic analyses revealed that STK11 knockout influences blood vessel morphogenesis and is closely associated with the enhanced expression of phosphodiesterases (PDEs), especially PDE4D, PDE4B, and PDE10A. PDE4 inhibitor Roflumilast inhibited STK11-KO cell migration and tumor size, further demonstrating that PDEs are essential for STK11-deficient cell migration. Our findings support the adoption of therapeutic strategies, including Roflumilast, for patients with STK11-mutated pancreatic cancer in order to improve treatment efficacy and ultimately prolong survival