The Death of Rocket Science in the 21st Century

AbstractAs we enter the 21st century, the lack of a space propulsion science discipline – involving the development of new emerging physical theories and models – within the bounds of Rocket Science forbids any rapid development of ideas and concepts toward new frontiers in spaceflight and implies a stagnate death in the advancement of Rocket Science as a whole. Specifically, the conventional disciplines in Rocket Science lacks foresight into the physics of acceleration to include the nature of gravity and inertia, which is foremost needed for the progression of spaceflight. In this paper is discussed various topics toward the understanding that space propulsion science is not a major player in Rocket Science, but must become so, if Rocket Science is to evolve the necessary new frontiers needed for future space exploration

Bayesian integration of isotope ratio for geographic sourcing of castor beans

pre-printRecent years have seen an increase in the forensic interest associated with the poison ricin, which is extracted from the seeds of the Ricinus communis plant. Both light element (C, N, O, and H) and strontium (Sr) isotope ratios have previously been used to associate organic material with geographic regions of origin. We present a Bayesian integration methodology that can more accurately predict the region of origin for a castor bean than individual models developed independently for light element stable isotopes or Sr isotope ratios. Our results demonstrate a clear improvement in the ability to correctly classify regions based on the integrated model with a class accuracy of 60.9 ± 2.1% versus 55.9 ± 2.1% and 40.2 ± 1.8% for the light element and strontium (Sr) isotope ratios, respectively. In addition, we show graphically the strengths and weaknesses of each dataset in respect to class prediction and how the integration of these datasets strengthens the overall model

Module networks revisited: computational assessment and prioritization of model predictions

The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. We revisit the approach of Segal et al. (2003) to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.Comment: 8 pages REVTeX, 6 figure

Performance and safety of femoral central venous catheters in pediatric autologous peripheral blood stem cell collection

IntroductionAutologous peripheral blood hematopoietic progenitor cell collection (A‐HPCC) in children typically requires placement of a central venous catheter (CVC) for venous access. There is scant published data regarding the performance and safety of femoral CVCs in pediatric A‐HPCC.MethodsSeven‐year, retrospective study of A‐HPCC in pediatric patients collected between 2009 and January 2017. Inclusion criteria were an age ≤ 21 years and A‐HPCC using a femoral CVC for venous access. Femoral CVC performance was examined by CD34 collection rate, inlet rate, collection efficiency (MNC‐FE, CD34‐FE), bleeding, flow‐related adverse events (AE), CVC removal, and product sterility testing. Statistical analysis and graphing were performed with commercial software.ResultsA total of 75/119 (63%) pediatric patients (median age 3 years) met study criteria. Only 16% of children required a CVC for ≥ 3 days. The CD34 collect rate and CD34‐FE was stable over time whereas MNC‐FE decreased after day 4 in 80% of patients. CD34‐FE and MNC‐FE showed inter‐ and intra‐patient variability over time and appeared sensitive to plerixafor administration. Femoral CVC showed fewer flow‐related AE compared to thoracic CVC, especially in pediatric patients (6.7% vs. 37%, P = 0.0005; OR = 0.12 (95%CI: 0.03‐0.45). CVC removal was uneventful in 73/75 (97%) patients with hemostasis achieved after 20–30 min of pressure. In a 10‐year period, there were no instances of product contamination associated with femoral CVC colonization.ConclusionFemoral CVC are safe and effective for A‐HPCC in young pediatric patients. Femoral CVC performance was maintained over several days with few flow‐related alarms when compared to thoracic CVCs.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139987/1/jca21548.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139987/2/jca21548_am.pd

Procedure‐related complications and adverse events associated with pediatric autologous peripheral blood stem cell collection

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135698/1/jca21465.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135698/2/jca21465_am.pd

Physicochemical property distributions for accurate and rapid pairwise protein homology detection

<p>Abstract</p> <p>Background</p> <p>The challenge of remote homology detection is that many evolutionarily related sequences have very little similarity at the amino acid level. Kernel-based discriminative methods, such as support vector machines (SVMs), that use vector representations of sequences derived from sequence properties have been shown to have superior accuracy when compared to traditional approaches for the task of remote homology detection.</p> <p>Results</p> <p>We introduce a new method for feature vector representation based on the physicochemical properties of the primary protein sequence. A distribution of physicochemical property scores are assembled from 4-mers of the sequence and normalized based on the null distribution of the property over all possible 4-mers. With this approach there is little computational cost associated with the transformation of the protein into feature space, and overall performance in terms of remote homology detection is comparable with current state-of-the-art methods. We demonstrate that the features can be used for the task of pairwise remote homology detection with improved accuracy versus sequence-based methods such as BLAST and other feature-based methods of similar computational cost.</p> <p>Conclusions</p> <p>A protein feature method based on physicochemical properties is a viable approach for extracting features in a computationally inexpensive manner while retaining the sensitivity of SVM protein homology detection. Furthermore, identifying features that can be used for generic pairwise homology detection in lieu of family-based homology detection is important for applications such as large database searches and comparative genomics.</p

Plasma Biomarkers for Detecting Hodgkin's Lymphoma in HIV Patients

The lifespan of people with human immunodeficiency virus (HIV) infection has increased as a result of effective antiretroviral therapy, and the incidences of the AIDS-defining cancers, non-Hodgkin's lymphoma and Kaposi sarcoma, have declined. Even so, HIV-infected individuals are now at greater risk of other cancers, including Hodgkin's lymphoma (HL). To identify candidate biomarkers for the early detection of HL, we undertook an accurate mass and elution time tag proteomics analysis of individual plasma samples from either HIV-infected patients without HL (controls; n = 14) and from HIV-infected patient samples with HL (n = 22). This analysis identified 60 proteins that were statistically (p<0.05) altered and at least 1.5-fold different between the two groups. At least three of these proteins have previously been reported to be altered in the blood of HL patients that were not known to be HIV positive, suggesting that these markers may be broadly useful for detecting HL. Ingenuity Pathway Analysis software identified “inflammatory response” and “cancer” as the top two biological functions associated with these proteins. Overall, this study validated three plasma proteins as candidate biomarkers for detecting HL, and identified 57 novel candidate biomarkers that remain to be validated. The relationship of these novel candidate biomarkers with cancer and inflammation suggests that they are truly associated with HL and therefore may be useful for the early detection of this cancer in susceptible populations

Computational Proteomics: High-throughput Analysis for Systems Biology

High-throughput (HTP) proteomics is a rapidly developing field that offers the global profiling of proteins from a biological system. The HTP technological advances are fueling a revolution in biology, enabling analyses at the scales of entire systems (e.g., whole cells, tumors, or environmental communities). However, simply identifying the proteins in a cell is insufficient for understanding the underlying complexity and operating mechanisms of the overall system. Systems level investigations are relying more and more on computational analyses, especially in the field of proteomics generating large-scale global data

Challenges on the interaction of models and policy for pandemic control.

The COVID-19 pandemic has seen infectious disease modelling at the forefront of government decision-making. Models have been widely used throughout the pandemic to estimate pathogen spread and explore the potential impact of different intervention strategies. Infectious disease modellers and policymakers have worked effectively together, but there are many avenues for progress on this interface. In this paper, we identify and discuss seven broad challenges on the interaction of models and policy for pandemic control. We then conclude with suggestions and recommendations for the future