Quantifiable Metrics Research Articles

Technical Analysis of Freshwater Use as Part of a Responsibly Sourced Gas ESG Strategy

The Unconventional Oil and Gas industry has seen growth over the last ten years that has drastically transformed the domestic energy outlook while bringing up increased concerns over climate and environmental issues. The rise of ESG and RSG can be seen as direct answers to these growing issues as communities and operators have both begun to demand better practices to limit the overall effects of UOG production. Few quantifiable metrics exist that holistically try to determine the overall effect UOG production has on local water resources. The FR2 metric/framework developed in this paper attempts to use commonly kept data such as water withdrawn and flowback volumes in conjunction with a new water stress index to quantify the effects operators are having on local water supplies. Testing this framework on a handful of operators from the Marcellus basin using open-source data revealed the value added by these methods as well as their use in a general RSG program.

Surgesture: A Novel Instrument Based on Surgical Actions for Objective Skill Assessment

Introduction: Due to varied surgical skill and lacking of an efficient rating system, we developed the Surgesture, based on the atomic functional gestrues performed by surgeons, which could serve as objective metrics to evaluate the surgical performance in laparoscopic cholecystectomy (LC). Method: We defined 14 LC basic Surgestures. Four surgeons annotated Surgestures among LC videos performed by experts and novices. The counts, durations, average action time, Dissection/Exposure ratio (D/E ratio) of Surgestures were compared. The phase of Mobilize the Hepatocystic Triangle (MHT) were extracted for skill assessment by 3 professors using a modified Global Operative Assessment of Laparosocpic Skills (mGOALS). Results: The operation time of novice were significantly longer than expert, especially in MHT phase. Novice had significantly more Surgestures than expert in both hand. Left hand and inefficient Surgesture of novice were dramatically more than that of expert. Expert demonstrated a significantly higher D/E ratio of time than novice (0.64±0.28 vs 2.84±1.98, P<0.001, Figure). The counts and time pattern map of LC Surgestures during MHT demonstrated that novice tended to complete LC with more types of Surgestures and cost more time on exposure of surgical scene. The performance metrics of LC Surgesture had significant but weak associations with each aspects of mGOALS. Conclusions: The newly constructed Surgestures could serve as accessible and quantifiable metrics demonstrating the operative pattern and distinguishing surgeons in different experience.The association between Surgestures and Global Rating Scale lay the foundation for bridging to automated objective surgical skill evaluation.

Bayesian networks as a novel tool to enhance interpretability and predictive power of ecological models

In today's world, it is becoming increasingly important to have the tools to understand, and ultimately to predict, the response of ecosystems to disturbance. However, understanding such dynamics is not simple. Ecosystems are a complex network of species interactions, and therefore any change to a population of one species will have some degree of community level effect. In recent years, the use of Bayesian networks (BNs) has seen successful applications in molecular biology and ecology, where they were able to recover plausible links in the respective systems they were applied to. The recovered network also comes with a quantifiable metric of interaction strength between variables. While the latter is an invaluable piece of information in ecology, an unexplored application of BNs would be using them as a novel variable selection tool in the training of predictive models. To this end, we evaluate the potential usefulness of BNs in two aspects: (1) we apply BN inference on species abundance data from a rocky shore ecosystem, a system with well documented links, to test the ecological validity of the revealed network; and (2) we evaluate BNs as a novel variable selection method to guide the training of an artificial neural network (ANN). Here, we demonstrate that not only was this approach able to recover meaningful species interactions networks from ecological data, but it also served as a meaningful tool to inform the training of predictive models, where there was an improvement in predictive performance in models with BN variable selection. Combining these results, we demonstrate the potential of this novel application of BNs in enhancing the interpretability and predictive power of ecological models; this has general applicability beyond the studied system, to ecosystems where existing relationships between species and other functional components are unknown.

Translational Science Education Through Citizen Science

Guided by the six elements of Translational Ecology (TE; i.e., decision-framing, collaboration, engagement, commitment, process, and communication), we showcase the first explicit example of a Translational Science Education (TSE) effort in the coastal redwood ecosystem of Humboldt County, CA. Using iNaturalist, a flexible and free citizen science/crowdsourcing app, we worked with students from grade school through college, and their teachers and community, to generate species lists for comparison among 19 school and non-profit locations spanning a range of urbanization. Importantly, this TSE effort resulted in both learning and data generation, highlighting the ability of a TSE framework to connect and benefit both students and researchers. Our data showed that, regardless of the age of the observers, holding organized BioBlitzes added substantially more species to local biodiversity lists than would have been generated without them. In support of current ecological theory, these data showed an urbanization gradient among sites, with rural sites containing fewer non-native species than urban ones. On the education side, qualitative assessments revealed students and educators remained engaged throughout the project. Future projects would also benefit by establishing quantifiable metrics for assessing student learning from project conception. Throughout the project, the fundamentals of TE were followed with repeated interactions and shared objectives developed over time within trusted community relationships. Such positive human interactions can lead new naturalists to think of themselves as champions of their local biodiversity (i.e., as land stewards). We anticipate that such newly empowered and locally expert naturalists will remain committed to land stewardship in perpetuity and that other scientists and educators are inspired to conduct similar work.

Daily Phonatory Activity of Individuals With Parkinson's Disease

This study evaluated the amount of phonatory activity of Persons with Parkinson disease (PwPD) compared to adults without Parkinson's disease measured over 3 days. The relationship between the amount of phonatory activity and Voice Handicap Index (VHI) total score was assessed as were differences in voicing activity across 3 days of data collection. Fifteen PwPD receiving dopaminergic medication and fifteen age and sex matched adults without Parkinson's disease completed the VHI and then wore a VocaLog vocal monitor (VM) for 3 consecutive days. From the VM data, the number of 1-second windows with dB sound pressure level > 0 were summed as a measure of phonatory activity (PA) and reported relative to the time the VM was worn (%PA). The percentage of time the VM was worn did not differ between groups or across days. The PwPD had statistically significantly fewer minutes of PA per day than controls (F = 21.782, P < 0.001) by 54 minutes on average. The %PA also differed significantly (F = 31.825, P < 0.001) with a mean of 11.1% for PwPD and 18.6% for controls. Neither PA nor %PA differed across the 3 days of vocal monitoring. VHI total score was significantly correlated with PA (r = -0.436, P = 0.016) and %PA (r = -0.534, P = 0.002) for all participants. The results indicate that PwPD engaged in less verbal communication in their daily environment compared to adults without Parkinson's disease. The findings support reports in the literature indicating that PwPD often have reduced communication participation. Measures such as %PA could serve as a quantifiable metric in future studies assessing communication changes in PwPD as a function of disease progression or therapeutic interventions.

LINKING LEARNING TO PERFORMANCE—A SALES CASE STUDY: PART 1

In this two-part case study, Part 1 provides the overall context and the performance analysis component of this transformational project to tie learning to quantifiable metrics of performance improvement. The co-authors were involved throughout the total project life cycle, with Jaime serving as the client's Commercial Learning Manager and Paul providing thought leadership, performance analysis, and architecture expertise for the vendor team. Part 2 will describe the second major component of this case study—translating the performance analysis results into an accomplishment-based training curriculum along with additional tools to support high performance. Throughout this two-part case study, you will encounter 'On the Shoulders of Giants’ call-out boxes that tie the work back to founders in the field of human performance. As we approach the 60th anniversary of the founding of ISPI, this is an effort to recognize the rich heritage that allowed this specific project to succeed.

Benefits of near-zero freight: The air quality and health impacts of low-NOx compressed natural gas trucks

ABSTRACT The use of low-NOx compressed natural gas (CNG) medium-duty vehicles (MDVs) and heavy-duty vehicles (HDVs) has the potential to significantly reduce NOx emissions and yield improvements in regional air quality. However, the extent of air quality improvement depends on many factors including future levels of vehicle deployment, the evolution of emissions from other sources, and meteorology. An analysis of the impacts requires modeling the atmosphere to account for both primary and secondary air pollutants, and the use of health impact assessment tools to map air quality changes into quantifiable metrics of human health. Here, we quantify and compare the air quality and health impacts associated with the deployment of low-NOx CNG engines to power future MDV and HDV fleets in California relative to both a business-as-usual and a more advanced fleet composition. The results project that reductions in summer ground-level ozone could reach 13 ppb when compared to a baseline fleet of diesel and gasoline HDV and MDV and could reach 6 ppb when compared to a cleaner fleet that includes some zero-emission vehicles and fuels. Similarly, for all CNG cases considered reductions in PM2.5 are predicted to range from 1.2 ug/m3 to 2.7 ug/m3 for a summer episode and from 3.1 ug/m3 to approximately 7.8 ug/m3 for a winter episode. These improvements yield short-term health benefits equivalent to $47 to $56 million in summer and $38 to $43 million in winter during episodes conducive to poor air quality. Additionally, the use of zero emission vehicle options such as battery electric and hydrogen fuel cell trucks could achieve approximately 25% to 31% higher benefits for an equivalent fleet penetration level due to the additional emission reductions achieved. Implications: The paper provides a quantitative estimate of the air quality and human health benefits that can be achieved through the use of novel compressed natural gas engines (i.e., low-NOx CNG) in medium- and heavy-duty vehicles and provide a comparison with zero emission vehicles. Thus, our findings will provide support for policy development seeking to transform the trucking sector to meet clean air and climate goals given the current struggle policymakers have with selecting between alternative truck technologies due to variance in factors like cost and technical maturity.

Establishing an Acceptance Criteria for Assessing Fatigue of Additive Repair Processes

Abstract Criteria for assessing high cycle fatigue (HCF) capability is important for transitioning additive repair technologies to turbine engine applications. By studying the fatigue results of two laser directed energy deposition additive manufacturing repairs on airfoil representative Titanium 6Al-4V coupons, acceptable HCF results for repairs can be determined by observing the confidence in predicted values and the reliability of the empirical data compared to baseline results. The following document details the steps and philosophies behind fabricating coupons that capture the repair capability. Furthermore, regression analysis as well as investigations of dataset distribution, fractography, and microscopy are necessary for instilling confidence in the understanding of repair fatigue behavior. The findings in this work provide a decision gate with quantifiable metrics for advancing novel repair techniques.

Incorporating the six aims for quality in the analysis of trauma care

ABSTRACT The Institute of Medicine proposed six aims for healthcare quality improvement. Nevertheless, trauma care quality research still focuses on one aim at a time. This research investigates how to incorporate all aims into trauma care quality assessments using data from the Michigan Trauma Quality Improvement Program. Through a literature review, we identified quantifiable metrics for most aims, except for equity and patient-centeredness. We proposed two approaches to build composite scores accounting for equity via an adjustment procedure based on observed disparities. The single- and multi-aim approaches were compared through correlation, concordance of trauma centre categorisations, and hypothetical incentives. The differences in the approaches stemmed mainly from the weights allocated to the different aims. Results indicated the potential value of multi-aim quality assessment and provided insights about implementation challenges and opportunities. The methods are applicable to the preferred metrics; nevertheless, further research is needed in measuring patient-centeredness.

Seattle Children’s Virtual Research Training Program: Pivoting to Remote Science Education for High School Students

Seattle Children’s Research Institute has built a series of in-person education programs to inspire and empower students to explore futures in biomedical research and healthcare. The COVID-19 pandemic prevented us from offering an in-person laboratory program, forcing a rapid pivot to an online format. The Virtual Research Training Program (VRTP) was a one-week summer experience for high school students, including students from groups that are under-represented in STEM. The curriculum introduced topics such as biochemistry, immunology and immunotherapy, and global and public health. Also included were laboratory demonstrations to emphasize cutting-edge applications for healthcare and discussions regarding college and career preparation. Key challenges included converting the in-person curriculum into a digestible virtual format, becoming proficient with the technology to provide for a seamless end-user experience with equitable access, and establishing quantifiable metrics for evaluation. Students reported statistically significant gains with large effect sizes in knowledge about science concepts and laboratory procedures, and in preparation for college and future STEM careers. Students were also engaged by asking questions, indicating their active participation despite the online environment. This article discusses the adaptation of an in-person laboratory program into a virtual program as a potential model for increasing remote access to science education.

Counterterrorism and Modern White Supremacy

Too often the terrorist threat is judged by lives lost, but less quantifiable metrics like political impact and strategic plausibility are also vital for understanding the danger. The history of the modern white supremacist movement in the United States illustrates different dimensions of how terrorism and political violence affect a country. Changing politics led to a devastating U.S. government crackdown on various Klan and like-minded groups in the mid and late 1960s. In the years that followed, violent white supremacist groups decentralized and followed new strategies, and the internet also transformed the movement. As a result, the threat white supremacists pose changed: they no longer enforce a structurally racist political and social system in the South, but they target a wider range of enemies, conduct bloodier attacks, and embrace sedition.

Inflection points in community-level homeless rates

Statistical models of community-level homeless rates typically assume a linear relationship to covariates. This linear model assumption precludes the possibility of inflection points in homeless rates—thresholds in quantifiable metrics of a community that, once breached, are associated with large increases in homelessness. In this paper we identify points of structural change in the relationship between homeless rates and community-level measures of housing affordability and extreme poverty. We utilize the Ewens–Pitman attraction (EPA) distribution to develop a Bayesian nonparametric regression model in which clusters of communities with similar covariates share common patterns of variation in homeless rates. A main finding of the study is that the expected homeless rate in a community begins to quickly increase once median rental costs exceed 30% of median income, providing a statistical link between homelessness and the U.S. government’s definition of a housing cost burden. Our analysis also identifies clusters of communities that exhibit distinct geographic patterns and yields insight into the homelessness and housing affordability crisis unfolding on both coasts of the United States.

Design subspace learning: Structural design space exploration using performance-conditioned generative modeling

Designers increasingly rely on parametric design studies to explore and improve structural concepts based on quantifiable metrics, generally either by generating design variations manually or using optimization methods. Unfortunately, both of these approaches have important shortcomings: effectively searching a large design space manually is infeasible, and design optimization overlooks qualitative aspects important in architectural and structural design. There is a need for methods that take advantage of computing intelligence to augment a designer's creativity while guiding—not forcing—their search for better-performing solutions. This research addresses this need by integrating conditional variational autoencoders in a performance-driven design exploration framework. First, a sampling algorithm generates a dataset of meaningful design options from an unwieldy design space. Second, a performance-conditioned variational autoencoder with a low-dimensional latent space is trained using the collected data. This latent space is intuitive to explore by designers even as it offers a diversity of high-performing design options.

Deep Learning for Visual Analytics of the Spread of COVID-19 Infection in Crowded Urban Environments

The novel SARS-CoV-2 coronavirus caused a global pandemic in 2020 with millions of diagnosed cases and a staggering number of deaths. As a preventive measure, many governments issued social distancing and shelter-in-place mandates to limit human contact and slow the rate of infection. The large extent and duration of the crisis is poised to transform the health sector and alter current practices in retail, business, manufacturing, and construction. While medical researchers are working on antidote and vaccine solutions, contact tracing and self-isolation are deemed effective methods to control community spread. This paper presents a visual analysis approach that uses convolutional neural networks (CNNs) to generate quantifiable metrics of contact tracing. In particular, the YOLO-v3 architecture was trained on an annotated video dataset containing pedestrians. Network pruning and non-maximum suppression were applied to optimize model performance, resulting in 69.41% average precision. The fully trained model was then tested on sample crosswalk video data from Xiamen, China, recorded during the COVID-19 pandemic, followed by projecting detected pedestrians onto an orthogonal map for contact tracing by tracking movement trajectories and simulating the spread of droplets among the healthy population. Results demonstrate that the proposed technique is capable of tracing and documenting infection sources, times, and locations.

Towards a new understanding of the concept of quietness

Sound is an inseparable part of the living environment and the liveability of the city, whilst environmental noise is a major stressor affecting the urban environment. Several approaches have been applied for its mitigation and the goal of several strategy plans regards the creation of quietness. “Quietness” as a term retains an ambiguity and it can be described, so far, as the lack of noise that is portrayed by means of intensity. The concept of quietness in the urban environment is revisited, in an attempt to reveal a new understanding of the specific phenomena. In this research, the focus is on setting aside all indicators either measuring intensity or contextual ones and use quantifiable metrics regarding the acoustic environment, thus introducing a new composite index called the Composite Urban Quietness Index (CUQI). CUQI comprises two sub-indicators, the Acoustic Complexity Index and the Normalized Difference Soundscape Index. CUQI was tested in order to verify the results of previous research regarding the identification of Quiet Areas in the city of Mytilene. It was concluded that CUQI is effective even in this early stage of development.

Relationships between psoriatic arthritis composite measures of disease activity with patient-reported outcomes in phase 3 studies of tofacitinib

BackgroundIn psoriatic arthritis (PsA), further understanding of the relationships between clinical measures and patient-reported outcomes (PROs) is needed. This post hoc analysis evaluated associations between minimal disease activity (MDA) as a continuous outcome (termed ScoreMDA) or Psoriatic Arthritis Disease Activity Score (PASDAS) with selected PROs not included in the composite measures.MethodsData from two phase 3 studies of tofacitinib in PsA (OPAL Broaden [NCT01877668; N = 422]; OPAL Beyond [NCT01882439; N = 394]) were included. MDA (binary outcome) was defined as meeting ≥5/7 criteria. For ScoreMDA, each criterion was assigned a value (1 = true; 0 = false; score range, 0–7; scores ≥5 indicated MDA). For PASDAS (score range, 0–10), higher scores indicated worse disease activity. PROs analyzed included Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), Patient’s Assessment of Arthritis Pain visual analog scale (Pain VAS), and EuroQoL-Five Dimensions-Three Level Health Questionnaire visual analog scale (EQ-5D-3L VAS) and utility index. Relationships were evaluated using repeated measures regression models.ResultsSimilar, approximately linear relationships were confirmed between PASDAS or ScoreMDA and PROs in both studies. In OPAL Broaden and OPAL Beyond, a one-point difference in PASDAS was associated with clinically relevant differences in PROs, including EQ-5D-3L VAS (− 6.7 mm, − 6.9 mm), Pain VAS (9.9 mm, 10.7 mm), and FACIT-F (− 2.8, − 3.3). A one-point difference in ScoreMDA was associated with clinically relevant differences in PROs, including EQ-5D-3L VAS (5.0 mm, 5.5 mm) and FACIT-F (1.9, 2.7) in OPAL Broaden and OPAL Beyond, respectively.ConclusionsLinear associations between PASDAS or ScoreMDA and PROs provide interpretable and quantifiable metrics between composite clinical measures and PROs, highlighting the importance of these measures in understanding the relevance of treat-to-target goals in PsA.Trial registrationClinicalTrials.gov, NCT01877668. Registered on June 12, 2013. ClinicalTrials.gov, NCT01882439. Registered on June 18, 2013

CEFEs: A CNN Explainable Framework for ECG Signals

In the healthcare domain, trust, confidence, and functional understanding are critical for decision support systems, therefore, presenting challenges in the prevalent use of black-box deep learning (DL) models. With recent advances in deep learning methods for classification tasks, there is an increased use of deep learning in healthcare decision support systems, such as detection and classification of abnormal Electrocardiogram (ECG) signals. Domain experts seek to understand the functional mechanism of black-box models with an emphasis on understanding how these models arrive at specific classification of patient medical data. In this paper, we focus on ECG data as the healthcare data signal to be analyzed. Since ECG is a one-dimensional time-series data, we target 1D-CNN (Convolutional Neural Networks) as the candidate DL model. Majority of existing interpretation and explanations research has been on 2D-CNN models in non-medical domain leaving a gap in terms of explanation of CNN models used on medical time-series data. Hence, we propose a modular framework, CNN Explanations Framework for ECG Signals (CEFEs), for interpretable explanations. Each module of CEFEs provides users with the functional understanding of the underlying CNN models in terms of data descriptive statistics, feature visualization, feature detection, and feature mapping. The modules evaluate a model’s capacity while inherently accounting for correlation between learned features and raw signals which translates to correlation between model’s capacity to classify and it’s learned features. Explainable models such as CEFEs could be evaluated in different ways: training one deep learning architecture on different volumes/amounts of the same dataset, training different architectures on the same data set or a combination of different CNN architectures and datasets. In this paper, we choose to evaluate CEFEs extensively by training on different volumes of datasets with the same CNN architecture. The CEFEs’ interpretations, in terms of quantifiable metrics, feature visualization, provide explanation as to the quality of the deep learning model where traditional performance metrics (such as precision, recall, accuracy, etc.) do not suffice.

Crafting Virtual Community of Indigenous Entrepreneurs: Case in Social Entrepreneurship

Facebook has voyaged a significant distance since its launch in 2004 as a web-based social media connector. What initially started off as a way to socially associate people at an individual level has in its journey become a platform for doing business easily. It is currently a huge ring to associate and network across the globe. Sonia Konjeti Agarwal has weaved a comparable success story utilizing Facebook. Her story, like that of Facebook, started with a motive to socially connect people of a geographical area. She created a Facebook group for women in Pune and christened it PULA (Pune Ladies Association), which has now become a vibrant business platform. Research Questions: i. Social entrepreneurs are change agents. In the context of social entrepreneurship which is most important: initiation of an idea, transmission of idea or creating a sustainable impact? ii. Social media is a powerful tool for networking. How did PULA strategically leverage social media for real time interactions and entrepreneurial ventures? Link to Theory: This case draws linkage to Social Cognitive Theory. Social Cognitive theory depicts bidirectional character of causation where behavior, cognitive, emotional factors and environment persistently and jointly persuade each other. Role of the protagonist in creating an exciting platform for indigenous entrepreneurs through the path of social entrepreneurship is linked here. Phenomenon Studied: Social entrepreneurship is a promising field with diverse interpretations. As a social entrepreneur, Sonia’s efforts are directed towards strengthening the identity of indigenous entrepreneurs. She initiated small venture creations within social network. Growth of indigenous entrepreneurs ignites self dependency and magnetizes new investments. Success of the virtual community to hone entrepreneurial skills is studied. Case context - As a social entrepreneur, the protagonist used Facebook to create virtual community of indigenous (women) entrepreneurs. PULA enhanced visibility, reach, network and prominence of indigenous entrepreneurs at low cost. Opportunity to interact with buyers directly expanded vendor registration in the group. It also created level playing field for first generation entrepreneurs. Findings: This case depicts pragmatism of the social entrepreneur. PULA unified the virtual community and resources fostering entrepreneurship. This virtual community signifies uniqueness of domain, novel practices and policies. PULA was initially constituted for social networking. Gradually members discovered business opportunities. Efficacy of social media for virtual engagement and small venture creation resulted in financial independence for women. Status of women transitioned to indigenous entrepreneur from homemaker. Growth of indigenous entrepreneurs being contagious, a wave of first generation entrepreneurs was born. Discussions: PULA encouraged individuals hitherto in the veil of self imposed life situations to become entrepreneurs. Sonia was zealous about empowering women, to create opportunities for them to succeed and advance in lives. As a social influencer her actions are directed towards social well being. Her primary objective is to leverage the trust shown by two hundred forty-five thousand plus members and six thousand five hundred plus verified sellers. Non existence of tangible and quantifiable metrics for measuring the performance of the social entrepreneur creates a challenge. Sonia is exploring new social media engagement strategies for creating a sustainable virtual community.

Revisiting the Concept of Quietness in the Urban Environment-Towards Ecosystems' Health and Human Well-Being.

There is plenty of proof that environmental noise is a major pollutant in the urban environment. Several approaches were successfully applied for its calculation, visualization, prediction and mitigation. The goal of all strategy plans regards its reduction and the creation of quietness. This study aims to revisit the concept of quietness in the urban environment and attempts to portray a new understanding of the specific phenomena. “Quietness” as a term retains an ambiguity, and so far, it can be described as the lack of something, meaning the lack of noise that is portrayed by means of intensity. Several studies describe quietness as the combination of perceptual soundscape elements and contextual factors that can be quantified, combined, weighed and used as indicators of healthy soundscapes. In this research, the focus is on setting aside all indicators, either measuring the intensity or contextual ones and use solely quantifiable metrics regarding the acoustic environment, thus introducing a new composite index called the composite urban quietness index (CUQI). After testing the CUQI, in order to verify the results of previous research regarding the identification of quiet Areas in the city of Mytilene (Lesbos Island, Greece), the study concludes that CUQI is efficiently functioning even in this early stage of development.

Cerebrospinal fluid NCAM-1 concentration is associated with neurodevelopmental outcome in post-hemorrhagic hydrocephalus of prematurity.

ObjectiveEfforts directed at mitigating neurological disability in preterm infants with intraventricular hemorrhage (IVH) and post hemorrhagic hydrocephalus (PHH) are limited by a dearth of quantifiable metrics capable of predicting long-term outcome. The objective of this study was to examine the relationships between candidate cerebrospinal fluid (CSF) biomarkers of PHH and neurodevelopmental outcomes in infants undergoing neurosurgical treatment for PHH.Study designPreterm infants with PHH were enrolled across the Hydrocephalus Clinical Research Network. CSF samples were collected at the time of temporizing neurosurgical procedure (n = 98). Amyloid precursor protein (APP), L1CAM, NCAM-1, and total protein (TP) were compared in PHH versus control CSF. Fifty-four of these PHH subjects underwent Bayley Scales of Infant Development-III (Bayley-III) testing at 15–30 months corrected age. Controlling for false discovery rate (FDR) and adjusting for post-menstrual age (PMA) and IVH grade, Pearson’s partial correlation coefficients were used to examine relationships between CSF proteins and Bayley-III composite cognitive, language, and motor scores.ResultsCSF APP, L1CAM, NCAM-1, and TP were elevated in PHH over control at temporizing surgery. CSF NCAM-1 was associated with Bayley-III motor score (R = -0.422, p = 0.007, FDR Q = 0.089), with modest relationships noted with cognition (R = -0.335, p = 0.030, FDR Q = 0.182) and language (R = -0.314, p = 0.048, FDR Q = 0.194) scores. No relationships were observed between CSF APP, L1CAM, or TP and Bayley-III scores. FOHR at the time of temporization did not correlate with Bayley-III scores, though trends were observed with Bayley-III motor (p = 0.0647 and R = -0.2912) and cognitive scores (p = 0.0506 and R = -0.2966).ConclusionCSF NCAM-1 was associated with neurodevelopment in this multi-institutional PHH cohort. This is the first report relating a specific CSF protein, NCAM-1, to neurodevelopment in PHH. Future work will further investigate a possible role for NCAM-1 as a biomarker of PHH-associated neurological disability.