bluuebunny/crossref_metadata_2025_split huggingface dataset:
The influence of printing accuracy on the performance of additively manufactured AlSi10Mg phononic crystals
Authors: Wang Y F , Guo J C , Zhang Z 
Published: November 2022

Abstract
The printing accuracy is one of the key factors affecting the final additively manufactured structures. As metamaterials for wave controlling, the printing accuracy caused in additive manufacturing can affect the band gap structures and the wave transmissions in phononic crystals (PnCs). Selective laser melting (SLM) was used to fabricate the designed PnCs. AlSi10Mg was used as the material for the fabrication. The distorted geometry of the additively manufactured PnCs by selective laser melting (SLM) was experimentally measured and then was re-built as finite element model. The band gap structures and the wave transmissions were calculated based on finite element method for the comparison of the structural performances of the theoretically designed and fabricated PnCs. Result indicated that the fabricated PnCs were thermally distorted from 0.298 mm to 1.664 mm depending on the designed geometries. The thermal distortion can lead to a deviation of central frequency in the range of −6.2%∼3.7% in comparison with the theoretical design. Compared with the theoretical design, the band gap widths of the distorted PnCs vary from −7.4% to 5.6% due to the frequencies change of the eigenmodes at the upper and lower band edges which are directly affected by the thermal distortion. The further analysis shows that the change of the band structures of the distorted PnCs is due to the increase of the inertia moment of the distorted PnCs caused by the printing accuracy.
Skyrmion based energy-efficient straintronic physical reservoir computing
Authors: Rajib Md Mahadi , Misba Walid Al , Chowdhury Md Fahim F , Alam Muhammad Sabbir , Atulasimha Jayasimha 
Published: November 2022

Abstract
Physical Reservoir Computing (PRC) is an unconventional computing paradigm that exploits the nonlinear dynamics of reservoir blocks to perform temporal data classification and prediction tasks. Here, we show with simulations that patterned thin films hosting skyrmion can implement energy-efficient straintronic reservoir computing (RC) in the presence of room-temperature thermal perturbation. This RC block is based on strain-induced nonlinear breathing dynamics of skyrmions, which are coupled to each other through dipole and spin-wave interaction. The nonlinear and coupled magnetization dynamics were exploited to perform temporal data classification and prediction. Two performance metrics, namely Short-Term Memory (STM) and Parity Check (PC) capacity are studied and shown to be promising (4.39 and 4.62 respectively), in addition to showing it can classify sine and square waves with 100% accuracy. These demonstrate the potential of such skyrmion based PRC. Furthermore, our study shows that nonlinear magnetization dynamics and interaction through spin-wave and dipole coupling have a strong influence on STM and PC capacity, thus explaining the role of physical interaction in a dynamical system on its ability to perform RC.
A construction of a 𝜆-Poisson generic sequence
Authors: Becher Verónica , Sac Himelfarb Gabriel 
Published: November 2022

Years ago Zeev Rudnick defined the

λ
\lambda

-Poisson generic sequences as the infinite sequences of symbols in a finite alphabet where the number of occurrences of long words in the initial segments follow the Poisson distribution with parameter

λ
\lambda

. Although almost all sequences, with respect to the uniform measure, are Poisson generic, no explicit instance has yet been given. In this note we give a construction of an explicit

λ
\lambda

-Poisson generic sequence over any alphabet and any positive

λ
\lambda

, except for the case of the two-symbol alphabet, in which it is required that

λ
\lambda

be less than or equal to the natural logarithm of

2
2

. Since

λ
\lambda

-Poisson genericity implies Borel normality, the constructed sequences are Borel normal. The same construction provides explicit instances of Borel normal sequences that are not

λ
\lambda

-Poisson generic.
Engineering antibody and protein therapeutics to cross the blood–brain barrier
Authors: Zhao Peng , Zhang Ningyan , An Zhiqiang 
Published: November 2022

Abstract
Diseases in the central nervous system (CNS) are often difficult to treat. Antibody- and protein-based therapeutics hold huge promises in CNS disease treatment. However, proteins are restricted from entering the CNS by the blood–brain barrier (BBB). To achieve enhanced BBB crossing, antibody-based carriers have been developed by utilizing the endogenous macromolecule transportation pathway, known as receptor-mediated transcytosis. In this report, we first provided an overall review on key CNS diseases and the most promising antibody- or protein-based therapeutics approved or in clinical trials. We then reviewed the platforms that are being explored to increase the macromolecule brain entry to combat CNS diseases. Finally, we have analyzed the lessons learned from past experiences and have provided a perspective on the future engineering of novel delivery vehicles for antibody- and protein-based therapies for CNS diseases.
The role of cohabitation on adaptive and innate immune cell profiles in the Health and Retirement Study
Authors: Ramasubramanian Ramya , Meier Helen , Crimmins Eileen , Faul Jessica , Thyagarajan Bharat 
Published: November 2022

Abstract
Immune cells distribution is shaped by numerous factors including environmental factors, age, and genetics. Cohabitation has been associated with similar microbiomes, possibly due to dietary patterns and exposure to similar pathogens but has not been studied in the context of adaptive and innate immune systems previously. We used immunophenotyping data of 2283 households with participants living in the same household and compared it to 2283 randomly generated pairs of participants from the Health and Retirement study. The adaptive immune cells (subsets of T-cells and B-cells), and innate immune cells (monocytes, natural killer cells, and neutrophils) were compressed to two coordinates using multidimensional scaling. The Euclidean distances between participants in the same household were compared to the distances between the random pairs of participants using two sample independent t-tests. The mean distances of the immune coordinate points for adaptive immune cells between participants in the same household were lower than the randomly paired participants (p-value &lt; 0.0001) and the variability of intra-household distances was lower than the random pairs (IQR: 7.18 vs 8.99). For the innate immune cells, the mean distances between participants in the same household were slightly lower than the randomly paired participants (p-value = 0.03) but the variability of the intra-household distances was higher than the random pairs (IQR: 4.08 vs 3.65). Variability in the adaptive immune system among participants living in the same household were substantially lower indicating the influence of shared environmental conditions in determining the adaptive immune profiles.
RecutClub 2.0: Deployment of a Trainee-Led, Didactic-Centered Whole Slide Imaging Platform
Authors: Hsu Jim , Christensen Paul , Long Scott 
Published: November 2022

Abstract

Objectives
The Houston Methodist Pathology training program encourages trainee participation in quality improvement (QI) projects for improving the didactics experience. One of these initiatives is RecutClub, a whole slide imaging (WSI) web-based platform for presenting surgical pathology unknown conference cases. With the evolving nature of pathology didactics in an ever-changing world where remote work plays an increasingly important role, several refinements were needed to ensure that the platform could continue to provide an unparalleled WSI experience for incoming trainees. Additionally, the burden of updating site content needed to be distributed from the site administrators to the end-users.

Methods
RecutClub is operated through Google Cloud via a Google App Engine instance. It connects to Google Cloud Databases to store persistent tabular data and Google Cloud Storage for pyramidal JPEG files used to display whole slide images in a web-browser. In the next generation platform (RecutClub 2.0), the largest change was to migrate case uploading from a developer-controlled solution to a decentralized, trainee-driven web portal. To ensure a superior user experience, the entire site as well as the upload form was migrated to Bootstrap 4. An AJAX-native, asynchronous uploader was built to interface the existing Google Cloud platform to a new upload portal that allows authorized trainees to build cases from scratch. Additionally, a tagging solution was implemented to allow for server-side persistent storage of trainee-selected cases, such as “interesting” cases or cases for further review. This tagging complements the existing region of interest (ROI) and case search functionalities.

Results
The deployment of RecutClub 2.0 involved multiple rounds of resident-led testing and feedback. Several regular testers offered ongoing user feedback and suggestions for visual layout and usability. The response from the general trainee population after deployment was overwhelming positive, with many trainees commenting on the “improved” visual aesthetics as well as the convenience of the upload functionality. Administrator involvement in case-upload time decreased from 2 hours/month to 0 hours/month due to enabling trainee-driven uploading.

Conclusions
The deployment of RecutClub 2.0 fulfilled the dual primary objectives of enabling straightforward trainee-led content updates, as well as decreasing administrator involvement in case-upload time. These and other changes ensure that RecutClub can continue to provide an unparalleled WSI experience for pathology trainees at Houston Methodist, while also providing fresh content in a landscape with several competing WSI platforms.
A multimodal genetic testing approach to a diagnosis of Roberts-SC phocomelia syndrome, an ESCO2 spectrum disorder
Authors: Killeen Trevor , Bower Matt , Berry Susan , Hirsch Betsy 
Published: November 2022

Abstract
Diagnosis of a genetic syndrome has traditionally relied upon the identification of characteristic phenotypic findings, followed by targeted laboratory testing. Shared clinical manifestations between genetically distinct syndromes and variable expressivity of findings within a single syndrome can complicate the diagnosis. Multiple testing modalities may be necessary, even when the putative gene(s) is known. We describe the multi-pronged genetic testing applied to a patient with a suspected diagnosis of Roberts- SC phocomelia syndrome (RBS). RBS is an autosomal recessive developmental disorder (OMIM 268300) caused by pathogenic variants in the “establishment of sister chromatid cohesion N-acetyltransferase” (ESCO2 ) gene mapped to chromosome 8p21.1. The ESCO2 enzyme product is a conserved protein with acetyltransferase activity required for formation of the cohesin complex, which, among other activities, maintains sister chromatid association during S-phase of the cell cycle. Because of the defective cohesin complex, RBS joins other syndromes referred to as cohesinopathies. The patient, a newborn male, was small for gestational age, with hypertelorism, cleft lip and palate, shortened forearms, absence of the radius and thumbs, and bilateral clubbed feet. All of these findings are established characteristics of RBS. Targeted Next Generation Sequencing revealed a single variant in exon 10 of ESCO2 (c.1654C&gt;T, p.Arg552*) predicted to result in a loss of function. Chromosomal microarray was also performed and showed an estimated 15 Mb region of homozygosity (ROH) extending from 8p11.21 to 8p21.2, which spans ESCO2. The finding of this ROH supported the interpretation of homozygosity for the variant, as did a family history of consanguinity. Additional cytogenetic analysis documented premature sister chromatid separation and repulsion of heterochromatin chromosomal regions characteristic of RBS. These chromosomal findings served as a functional assay supporting pathogenicity of the ESCO2 variant. Interestingly, the only report of an identical variant within exon 10 of ESCO2 was described for two brothers diagnosed with Juberg-Hayward syndrome. Juberg-Hayward was considered allelic to, but a distinct entity from RBS. In contrast, our findings support the conclusion that RBS and Juberg-Hayward are not distinct syndromes, but rather represent variability within an ESCO2 syndrome spectrum. Such unification of diagnoses based on genomic findings has influenced numerous other clinical disorders. Further, our findings demonstrate the contributions of each of the genetic testing modalities, which may be applied, selectively or collectively, based on integration with clinical and family history data.
Machine Learning Models Accurately Predict PTHrP Results But Fail To Predict Physician Behavior
Authors: Spies Nicholas , Jackups Ron , Zaydman Mark 
Published: November 2022

Abstract
Quantification of circulating parathyroid hormone-related peptide (PTHrP) aids in the diagnosis of humoral hypercalcemia of malignancy. However, this test is often ordered in settings of low pre-test probability or mistakenly ordered when parathyroid hormone (PTH) was desired. To improve utilization, all PTHrP orders at our institutions are reviewed by a laboratory medicine resident. If the order appears to be inappropriate, the ordering physician is contacted to ask for permission to cancel. In this work, we attempted to automate this labor-intensive review process by developing machine learning models to predict the orders the physician is willing to cancel. We collected 2171 PTHrP orders that were subjected to manual review over the past 10 years. We removed any repeats, leaving 1649 first-time orders. For each order, we assigned a class label of ‘canceled’ or ‘completed’ based upon the notes in the resident’s documentation logs. For each order, we aggregated all data for the patient existing within the laboratory information system at the time of the first order (n = 40 million). Various strategies were applied to impute missing data, including leaving missingness as a feature, fill by medians, k-nearest neighbors, bagged trees, and linear regression. Class imbalances were adjusted using synthetic minority upsampling technique (SMOTE) and adaptive synthetic upsampling (ADASYN) for a final ratio of one:one. The dataset was partitioned into a 70:30 split between training and testing sets with five-fold cross-validation. Several machine learning algorithms were trained, including logistic regression, naive Bayes, random forest, and XGBoost. After training and cross-validation, the models were applied to the held-out test set, and performance was evaluated using the area under the receiver operating characteristic curve (AUC). XGBoost was the best performer at predicting the provider’s likelihood to cancel the test, but with an AUC of only 0.63. Surprised by this poor performance, we devised a second classification task of predicting the PTHrP results (normal vs abnormal, threshold = 4.2 pmol/L) for the subset of orders that were completed (n=1371). Using the same machine learning pipeline described above, we again observed that XGBoost was the best performer, but this time with an AUC of 0.89. The striking performance difference between the models trained on the two different targets suggests that the physician’s willingness to assent to our intervention may be unrelated to existing laboratory data or underlying biology. Likely explanations may include that our intervention is reaching a provider who is not the primary medical decision-maker or the correct provider at the wrong time when they are too busy to revisit details of prior work. In either case, a reflex order set that defines specific criteria for performing PHTrP upfront may be a more effective way to improve utilization.
Trends in Viral Respiratory Culture: Impacts of the SARS-CoV-2 Pandemic
Authors: Bradley Benjamin , Hymas Weston , Barker Adam 
Published: November 2022

Abstract

Introduction
Due to the increasing ease and availability of molecular assays, viral culture is rarely employed for the diagnosis of respiratory illnesses. Despite lower sensitivity than molecular techniques, viral culture may detect a wider array of viral pathogens at a lower cost relative to multiplex molecular panels. In this study, we examined the effects of the SARS-CoV-2 pandemic on viral respiratory culture ordering and trends in the rates of pathogen detection.

Methods
Viral respiratory culture results from Jan 1st, 2017 to February 28th, 2022 were analyzed for changes in the number of monthly orders, positivity rate, and incidence of individual pathogens. To determine changes in seasonal incidence and viral etiology, a comparison was made between winter (Dec-Feb) and summer (June-Aug) months as well as acute (Influenza A/B, RSV) and chronic (HSV, CMV) infections. Given SARS-CoV-2’s classification as a BSL-3 pathogen, our viral culture assay was not designed to detect this virus. As a surrogate method to measure rates of SARS-CoV-2 in viral culture specimens, we performed NAAT testing with the ThermoFisher TaqPath COVID-19, Flu A/B, RSV assay on negative specimens.

Results
Following the pandemic, testing volume decreased by 46.7% with the overall positivity rate decreasing from 6.67% to 4.85%. Among the 46 states for which more than ten orders were placed, monthly testing decreased in 38 states. Of the eight states with increased average monthly testing, the greatest increases were seen in Rhode Island, Nevada, and Montana. During the pre-pandemic timeframe, acute respiratory pathogens demonstrated a typical winter peak with low summer detection. Post-pandemic, there was an atypical increase of acute respiratory pathogens, driven primarily by RSV. The positivity rate for chronic viral infections increased from 3.43% pre-pandemic to 4.09% post-pandemic. Following the pandemic, HSV has replaced influenza as the most commonly detected pathogen during winter months. Molecular studies of 229 negative viral culture specimens identified 36 (15.7%) samples positive for SARS-CoV-2, 9 (3.9%) for RSV, and none for influenza A or B. Median cycle threshold values for SARS-CoV-2 samples were 21.3 (range: 9.1-36.5).

Conclusions
Following the SARS-CoV-2 pandemic, the number of respiratory virus cultures ordered significantly decreased. There has also been a statistically significant decrease in the positivity rate driven by the absence of acute viral respiratory pathogens, including influenza A/B and RSV. We also observed an offseason increase of RSV during the summer months of 2021. Detection rates of chronic viral pathogens including CMV and HSV have remained relative stable. The presence of SARS-CoV-2 in negative specimens raises concerns for inappropriate test utilization. While less sensitive than molecular methods, viral culture has the potential to offer a lower cost alternative for monitoring a broad range of viral pathogens.
Machine Learning Models Can Accurately Detect Crystalloid Contamination of Basic Metabolic Panels
Authors: Spies Nicholas , Zhang Ray , Farnsworth Christopher , Jackups Ron , Zaydman Mark 
Published: November 2022

Abstract
A common cause of erroneous laboratory results is preanalytical contamination of samples with intravenous crystalloids. Rules-based approaches, such as delta checks or feasibility cut-offs, can detect these errors, but rely on prior laboratory results and lack sensitivity. In this study, we aimed to improve the detection of crystalloid contamination using two machine learning workflows for the detection of contamination by the four most common crystalloids; normal saline (NS), lactated Ringer’s (LR), and their dextrose-containing counterparts (D5NS and D5LR). First, we employed a semi-supervised approach using manifold approximation and contrastive learning. To do this, we aggregated five years' worth of basic metabolic panel results (n = 67 million), then simulated contaminated samples by mixing random subsets of samples in silico with the crystalloid solutions at different ratios. The dataset was partitioned into a 70:30 split between training and testing sets with five-fold cross-validation. An autoencoder was trained to create an embedding of the data by minimizing the triplet loss. This embedding was then projected onto a two-dimensional manifold. When the four crystalloid solutions and the independent test set were mapped to the manifold, decision boundaries were drawn based on the threshold that maximized the F1 statistic, and labels were assigned. This method detected the 10% in silico mixtures with an area under the receiver operating characteristic curve (AUC) of 0.97, 0.94, 0.99, and 0.99 for NS, LR, D5NS, and D5LR respectively. Next, we evaluated a fully-supervised approach by training and validating Random Forest and XGBoost models using the same training and testing sets as above. Both models performed well, with XGBoost slightly outperforming Random Forest. The XGBoost model demonstrated AUCs of 0.992, 0.990, 0.998, and 0.996 for the NS, LR, D5NS, and D5LR mixtures. This high-level performance was maintained even as the proportion of mixed samples in the dataset was decreased from 50% to 1% to more accurately reflect real-world contamination rates. For NS and LR, the variables with the highest relative importance were calcium and chloride. For their D5 counterparts, glucose and calcium were most important. Creatinine and blood urea nitrogen were the least important variables in both approaches. Overall, the fully-supervised learning approach outperformed that of the contrastive embeddings for the specific task of identifying contamination from pre-defined crystalloids. One advantage of the semi-supervised approach is that it could be adapted to identify outliers that are not pre-defined. However, considering the relative ease of translating tree-based models into practice, we conclude that our XGBoost model offers the most promising solution and plan to prospectively validate this model for real-time detection of sample contamination.