Average Pearson Correlation Research Articles

A bioimpedance spectroscopy technique to monitor bioprocess involving complex growth micro-organisms

Although analytical methods (often offline) are considered as references (gold standards) for monitoring industrial bioprocesses, real-time monitoring (online) is always preferable for maximizing such processes. However, many of the real-time methods that measure total cell concentration cannot distinguish living micro-organisms from the dead ones or even other materials. Moreover, some real-time methods also lead to possible wrong results when the micro-organism involved in the bioprocess exhibits significant metabolic, physiological, and kinetic changes as it grows. This study presents a real-time technique intended to monitor this class of micro-organisms and that is based on an alternative method of bioimpedance spectroscopy. The feasibility of the proposed technique was evaluated during bacterial growth experiments using the bacteria Bacillus thuringiensis var. israelensis (Bti) with initial inocula of 5%, 10%, and 15% (v/v on 220 ml). Each growth experiment was monitored every hour over 18 h by the proposed technique and by a classical offline method (McFarland method). The results of monitoring the studied bioprocess by both methods exhibited an average Pearson correlation coefficient (r̄) of above 0.9 and good agreement when the Bland–Altman method was used to compare them.

Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain.

Gene-gene relationships are commonly measured via the co-variation of gene expression across samples, also known as gene co-expression. Because shared expression patterns are thought to reflect shared function, co-expression networks describe functional relationships between genes, including co-regulation. However, the heterogeneity of cell types in bulk RNA-seq samples creates connections in co-expression networks that potentially obscure co-regulatory modules. The brain initiative cell census network (BICCN) single-cell RNA sequencing (scRNA-seq) datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. Comparison of the BICCN data (500,000 cells/nuclei across 7 BICCN datasets) with that of bulk RNA-seq networks (2,000 mouse brain samples across 52 studies) reveals a consistent topology reflecting a shared co-regulatory signal. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.

Inferring End-to-End Latency in Live Videos

This paper provides an end-to-end latency model of real-time generated live videos composed of sub-models of capturing, encoding, network, decoding, rendering, and display refreshing. We build the model by deeply analyzing each sub part’s latency with parameters like video’s Spatial (frame scale <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> ), Temporal (frame rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> ), Amplitude (quantization step-size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> ) Resolutions (STAR), Compressed Frame Bit Numbers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n_{F}$ </tex-math></inline-formula> ), and other necessary parameters. The latency of 640 combinations of STAR for eight representative video sequences is measured to fit accurate models. We primarily focus on processing procedures, whose latency is becoming the principal affection, as network latency decreases due to the its development. We then introduce a baseline video sequence set to reduce the complexity when fitting the encoding and decoding sub-models by predicting the model parameters on new hardware from parameters of a known one, and improve models’ robustness crossing different scenarios. These sub-models correlate well with the realistic latency data, with the average Pearson Correlation Coefficient (PCC) of 0.99 and the average Spearman’s Rank Correlation Coefficient (SRCC) of 0.98, according to an independent validation test. Furthermore, we validate our end-to-end latency model in a simulated end-to-end live video transmission from generating to displaying, introducing the latency as a strict condition in adaptive bit-rate selection. The simulation shows that our model could significantly increase time during which the latency can satisfy the given tolerance at a low cost.

A multi-institutional analysis of a general pelvis continuous Hounsfield unit synthetic CT software for radiotherapy.

PurposeTo validate a synthetic computed tomography (sCT) software with continuous HUs and large field‐of‐view (FOV) coverage for magnetic resonance imaging (MRI)‐only workflow of general pelvis anatomy in radiotherapy (RT).MethodsAn sCT software for general pelvis anatomy (prostate, rectum, and female pelvis) has been developed by Philips Healthcare and includes continuous HUs assignment along with large FOV coverage. General pelvis sCTs were generated using a two‐stack T1‐weighted mDixon fast‐field echo (FFE) sequence with a superior‐inferior coverage of 36 cm. Seventy‐seven prostate, 43 rectum, and 27 gynecological cases were scanned by three different institutions. mDixon image quality and sCTs were evaluated for soft tissue contrast by using a confidence level scale from 1 to 5 for bladder, prostate/rectum interface, mesorectum, and fiducial maker visibility. Dosimetric comparison was performed by recalculating the RT plans on the sCT after rigid registration. For 12 randomly selected cases, the mean absolute error (MAE) between sCT and CT was calculated to evaluate HU similarity, and the Pearson correlation coefficients (PCC) between the CT‐ and sCT‐generated digitally reconstructed radiographs (DRRs) were obtained for quantitative comparison. To examine geometric accuracy of sCT as a reference for cone beam CT (CBCT), the difference between bone‐based alignment of CBCT to CT and CBCT to sCT was obtained for 19 online‐acquired CBCTs from three patients.ResultsTwo‐stack mDixon scans with large FOV did not show any image inhomogeneity or fat‐water swap artifact. Fiducials, Foley catheter, and even rectal spacer were visible as dark signal on the sCT. Average visibility confidence level (average ± standard deviation) on the sCT was 5.0 ± 0.0, 4.6 ± 0.5, 3.8 ± 0.4, and 4.0 ± 1.1 for bladder, prostate/rectum interface, mesorectum and fiducial markers. Dosimetric accuracy showed on average < 1% difference with the CT‐based plans for target and normal structures. The MAE of bone and soft tissue between the sCT and CT are 120.9 ± 15.4 HU, 33.4 ± 4.1 HU, respectively. Average PCC of all evaluated DRR pairs was 0.975. The average offset between CT and sCT as reference was (LR, AP, SI) = (0.19 ± 0.35, 0.14 ± 0.60, 0.44 ± 0.54) mm.ConclusionsThe continuous HU sCT software‐generated realistic sCTs and DRRs to enable MRI‐only planning for general pelvis anatomy.

Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer.

Few studies have reported the reproducibility and stability of ultrasound (US) images based radiomics features obtained from automatic segmentation in oncology. The purpose of this study is to study the accuracy of automatic segmentation algorithms based on multiple U-net models and their effects on radiomics features from US images for patients with ovarian cancer. A total of 469 US images from 127 patients were collected and randomly divided into three groups: training sets (353 images), validation sets (23 images), and test sets (93 images) for automatic segmentation models building. Manual segmentation of target volumes was delineated as ground truth. Automatic segmentations were conducted with U-net, U-net++, U-net with Resnet as the backbone (U-net with Resnet), and CE-Net. A python 3.7.0 and package Pyradiomics 2.2.0 were used to extract radiomic features from the segmented target volumes. The accuracy of automatic segmentations was evaluated by Jaccard similarity coefficient (JSC), dice similarity coefficient (DSC), and average surface distance (ASD). The reliability of radiomics features were evaluated by Pearson correlation and intraclass correlation coefficients (ICC). CE-Net and U-net with Resnet outperformed U-net and U-net++ in accuracy performance by achieving a DSC, JSC, and ASD of 0.87, 0.79, 8.54, and 0.86, 0.78, 10.00, respectively. A total of 97 features were extracted from the delineated target volumes. The average Pearson correlation was 0.86 (95% CI, 0.83–0.89), 0.87 (95% CI, 0.84–0.90), 0.88 (95% CI, 0.86–0.91), and 0.90 (95% CI, 0.88–0.92) for U-net++, U-net, U-net with Resnet, and CE-Net, respectively. The average ICC was 0.84 (95% CI, 0.81–0.87), 0.85 (95% CI, 0.82–0.88), 0.88 (95% CI, 0.85–0.90), and 0.89 (95% CI, 0.86–0.91) for U-net++, U-net, U-net with Resnet, and CE-Net, respectively. CE-Net based segmentation achieved the best radiomics reliability. In conclusion, U-net based automatic segmentation was accurate enough to delineate the target volumes on US images for patients with ovarian cancer. Radiomics features extracted from automatic segmented targets showed good reproducibility and for reliability further radiomics investigations.

Recording Intrinsic Nerve Activity at the Sinoatrial Node in Normal Dogs With High-Density Mapping.

It is known that autonomic nerve activity controls the sinus rate. However, the coupling between local nerve activity and electrical activation at the sinoatrial node (SAN) remains unclear. We hypothesized that we would be able to record nerve activity at the SAN to investigate if right stellate ganglion (RSG) activation can increase the local intrinsic nerve activity, accelerate sinus rate, and change the earliest activation sites. High-density mapping of the epicardial surface of the right atrium including the SAN was performed in 6 dogs during stimulation of the RSG and after RSG stellectomy. A radio transmitter was implanted into 3 additional dogs to record RSG and local nerve activity at the SAN. Heart rate accelerated from 108±4 bpm at baseline to 125±7 bpm after RSG stimulation (P=0.001), and to 132±7 bpm after apamin injection (P<0.001). Both electrical RSG stimulation and apamin injection induced local nerve activity at the SAN with the average amplitudes of 3.60±0.72 and 3.86±0.56 μV, respectively. RSG stellectomy eliminated the local nerve activity and decreased the heart rate. In ambulatory dogs, local nerve activity at the SAN had a significantly higher average Pearson correlation to heart rate (0.72±0.02, P=0.001) than RSG nerve activity to HR (0.45±0.04, P=0.001). Local intrinsic nerve activity can be recorded at the SAN. Short bursts of these local nerve activities are present before each atrial activation during heart rate acceleration induced by stimulation of the RSG.

The methylome of a human polar body reflects that of its sibling oocyte and its aberrance may indicate poor embryo development

Is it possible to evaluate the methylome of individual oocytes to investigate the DNA methylome alterations in metaphase II (MII) oocytes with reduced embryo developmental potential? The DNA methylome of each human first polar body (PB1) closely mirrored that of its sibling MII oocyte; hypermethylated long interspersed nuclear element (LINE) and long terminal repeats (LTRs) and methylation aberrations in PB1 promoter regions may indicate poor embryo development. The developmental potential of an embryo is determined by the oocyte's developmental competence, and the PB1 is a good substitute to examine the chromosomal status of the corresponding oocyte. However, DNA methylation, a key epigenetic modification, also regulates gene expression and embryo development. Twelve pairs of PB1s and sibling MII oocytes were biopsied and sequenced to compare their methylomes. To further investigate the methylome of PB1s and the potential epigenetic factors that may affect oocyte quality, MII oocytes (n = 74) were fertilized through ICSI, while PB1s were biopsied and profiled to measure DNA methylation. The corresponding embryos were further cultured to track their development potential. The oocytes and sperm samples used in this study were donated by healthy volunteers with signed informed consent. Single-cell methylome sequencing was applied to obtain the DNA methylation profiles of PB1s and oocytes. The DNA methylome of PB1s was compared between the respective group of oocytes that progressed to blastocysts and the group of oocytes that failed to develop. DNA methylation levels of corresponding regions and differentially methylated regions were calculated using customized Perl and R scripts. RNA-seq data were downloaded from a previously published paper and reanalysed. The results from PB1-MII oocyte pair validated that PB1 contains nearly the same methylome (average Pearson correlation is 0.92) with sibling MII oocyte. LINE and LTR expression increased markedly after fertilization. Moreover, the DNA methylation levels in LINE (including LINE1 and LINE2) and LTR were significantly higher in the PB1s of embryos that could not reach the blastocyst stage (Wilcoxon-Matt-Whitney test, P < 0.05). DNA methylation in PB1 promoters correlated negatively with gene expression of MII oocyte. Regarding the methylation status of the promoter regions, 66 genes were hypermethylated in the developmental arrested group, with their related functions (significantly enriched in several Gene Ontology terms) including transcription, positive regulation of adenylate cyclase activity, mitogen-activated protein kinase (MAPK) cascade and intracellular oestrogen receptor signalling pathway. N/A. Data analysis performed in this study focused on the competence of human oocytes and compared them with maternal genetic and epigenetic profiles. Therefore, data regarding the potential regulatory roles of paternal genomes in embryo development are lacking. The results from PB1-oocyte pairs demonstrated that PB1s shared similar methylomes with their sibling oocytes. The selection of the good embryos for transfer should not only rely on morphology but also consider the DNA methylation of the corresponding PB1 and therefore MII oocyte. The application of early-stage analysis of PB1 offers an option for high-quality oocyte and embryo selection, which provides an additional tool for elective single embryo transfer in assisted reproduction. This study was supported by the National Key Research and Development Program of China (2018YFC1004003, 2017YFA0103801), the National Natural Science Foundation of China (81730038, 3187144, 81521002) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16020703). The authors have no conflicts of interest to declare.

Using remote sensing to assess peatland resilience by estimating soil surface moisture and drought recovery

Peatland areas provide a range of ecosystem services, including biodiversity, carbon storage, clean water, and flood mitigation, but many areas of peatland in the UK have been degraded through human land use including drainage. Here, we explore whether remote sensing can be used to monitor peatland resilience to drought. We take resilience to mean the rate at which a system recovers from perturbation; here measured literally as a recovery timescale of a soil surface moisture proxy from drought lowering. Our objectives were (1) to assess the reliability of Sentinel-1 Synthetic Aperture Radar (SAR) backscatter as a proxy for water table depth (WTD); (2) to develop a method using SAR to estimate below-ground (hydrological) resilience of peatlands; and (3) to apply the developed method to different sites and consider the links between resilience and land management. Our inferences of WTD from Sentinel-1 SAR data gave results with an average Pearson's correlation of 0.77 when compared to measured WTD values. The 2018 summer drought was used to assess resilience across three different UK peatland areas (Dartmoor, the Peak District, and the Flow Country) by considering the timescale of the soil moisture proxy recovery. Results show clear areas of lower resilience within all three study sites, which often correspond to areas of high drainage and may be particularly vulnerable to increasing drought severity/events under climate change. This method is applicable to monitoring peatland resilience elsewhere over larger scales, and could be used to target restoration work towards the most vulnerable areas.

Wearable Optical Fiber Sensor Based on a Bend Singlemode-Multimode-Singlemode Fiber Structure for Respiration Monitoring

Respiration rate (RR) is an important information related to human physiological health. A wearable optical fiber sensor for respiration monitoring based on a bend singlemode-multimode-singlemode (SMS) fiber structure, which is highly sensitive to bend, is firstly proposed and experimentally demonstrated. The sensor fastened by an elastic belt on the abdomen of a person will acquire the respiration signal when the person breaths, which will introduce front and back movement of the abdomen, and thus bend of SMS fiber structure. Short-time Fourier transform (STFT) method is employed for signal processing to extract characteristic information of both the time and frequency domain of the measured waveform, which provides accurate RR measurement. Six different SMS fiber sensors have been tested by six individuals and the experimental results demonstrated that the RR signals can be effectively monitored among different individuals, where an average Pearson Correlation Coefficient of 0.88 of the respiration signal has been achieved, which agrees very well with that of commercial belt respiration sensor. The proposed technique can provide a new wearable and portable solution for monitoring of respiratory with advantage of easy fabrication and robust to environment.

Circulating miRNAs as Biomarkers for CYP2B6 Enzyme Activity.

The CYP2B6 gene is highly polymorphic and its activity shows wide interindividual variability. However, substantial variability in CYP2B6 activity remains unexplained by the known CYP2B6 genetic variations. Circulating, cell-free micro RNAs (miRNAs) may serve as biomarkers of hepatic enzyme activity. CYP2B6 activity in 72 healthy volunteers was determined using the disposition of efavirenz as a probe drug. Circulating miRNA expression was quantified from baseline plasma samples. A linear model consisting of the effects of miRNA expression, genotype-determined metabolizer status, and demographic information was developed to predict CYP2B6 activity. Expression of 2,510 miRNAs were quantified out of which 7 miRNAs, together with the CYP2B6-genotypic metabolizer status and demographics, was shown to be predictive markers for CYP2B6 activity. The reproducibility of the model was evaluated by cross-validation. The average Pearson's correlation (R) between the predicted and observed maximum plasma concentration (Cmax ) ratios of efavirenz and its metabolite-8-OH efavirenz using the linear model with all features (7 miRNA+metabolizer status+age+sex+race) was 0.6702. Similar results were also observed using area under the curve (AUC) ratios (Pearson correlation's R=0.6035). Thus, at least 36% (R2 ) of the variability of in vivo CYP2B6 activity was explained using this model. This is a significant improvement over the models using only the genotype-based metabolizer status or the demographic information, which explained only 6% or less of the variability of in vivo CYP2B6 activity. Our results, therefore, demonstrate that circulating plasma miRNAs can be valuable biomarkers for in vivo CYP2B6 activity.

Biomedical knowledge discovery based on Sentence‐BERT

AbstractIn the advent of the big data era, the volume of biomedical information grows exponentially, which means that the researchers have to spend a large amount of time in biomedical information mining. Effective mining of biomedical information and building the knowledge graph in the biomedical field are of high importance for biomedical knowledge discovery. In this study, datasets were constructed by using the text similarity corpus of CDD. The average Pearson's correlation coefficient was calculated by using the Sentence‐BERT model with sentence embeddings. Then a comparison was performed among the wordnet‐based similarity method (WBSM), UMLS‐based similarity method (UBSM), and the combination of these two methods. The results showed that the average Pearson correlation r calculated by the Sentence‐BERT model was the highest, the value of which is 74.37%. Triplets consisting of diseases, genes and proteins were extracted by using the Sentence‐BERT model. On this basis, the biomedical knowledge graph was constructed. It was found that this knowledge graph had a good effect on the testing dataset.

A deconvolution method and its application in analyzing the cellular fractions in acute myeloid leukemia samples

BackgroundThe identification of cell type-specific genes (markers) is an essential step for the deconvolution of the cellular fractions, primarily, from the gene expression data of a bulk sample. However, the genes with significant changes identified by pair-wise comparisons cannot indeed represent the specificity of gene expression across multiple conditions. In addition, the knowledge about the identification of gene expression markers across multiple conditions is still paucity.ResultsHerein, we developed a hybrid tool, LinDeconSeq, which consists of 1) identifying marker genes using specificity scoring and mutual linearity strategies across any number of cell types, and 2) predicting cellular fractions of bulk samples using weighted robust linear regression with the marker genes identified in the first stage. On multiple publicly available datasets, the marker genes identified by LinDeconSeq demonstrated better accuracy and reproducibility compared to MGFM and RNentropy. Among deconvolution methods, LinDeconSeq showed low average deviations (≤0.0958) and high average Pearson correlations (≥0.8792) between the predicted and actual fractions on the benchmark datasets. Importantly, the cellular fractions predicted by LinDeconSeq appear to be relevant in the diagnosis of acute myeloid leukemia (AML). The distinct cellular fractions in granulocyte-monocyte progenitor (GMP), lymphoid-primed multipotent progenitor (LMPP) and monocytes (MONO) were found to be closely associated with AML compared to the healthy samples. Moreover, the heterogeneity of cellular fractions in AML patients divided these patients into two subgroups, differing in both prognosis and mutation patterns. GMP fraction was the most pronounced between these two subgroups, particularly, in SubgroupA, which was strongly associated with the better AML prognosis and the younger population. Totally, the identification of marker genes by LinDeconSeq represents the improved feature for deconvolution. The data processing strategy with regard to the cellular fractions used in this study also showed potential for the diagnosis and prognosis of diseases.ConclusionsTaken together, we developed a freely-available and open-source tool LinDeconSeq (https://github.com/lihuamei/LinDeconSeq), which includes marker identification and deconvolution procedures. LinDeconSeq is comparable to other current methods in terms of accuracy when applied to benchmark datasets and has broad application in clinical outcome and disease-specific molecular mechanisms.

Abstract 5881: Assessment of two whole transcriptome RNA sequencing library preparation Illumina workflows in FFPE human specimens with suboptimal quality

Abstract Purpose High analytical sensitivity and a broad dynamic range make RNA sequencing (RNA-Seq) an appealing platform for mRNA expression analyses for clinical exploratory biomarker development and identification. However, the reliability and accuracy of RNA-Seq data rely on the input RNA template quality and quantity. Additionally, cDNA library preparation methods can significantly influence the results, especially in samples with suboptimal quality such as those originating from FFPE specimens. Here we compared two Illumina library preparation methods for RNA-Seq analysis using several FFPE samples from human cancer indications at two independent vendors. Study Procedure Twenty-five FFPE samples from 5 indications (NSCLC, CRC, RC, BC and HCC) were used, covering a wide range of sample storage (3-25 years-old), sample qualities, and specimen types (resection vs. core needle biopsy). Each sample was processed independently by both vendors. Total RNA was isolated using the Qiagen miRNeasy FFPE kit followed by library construction using either TruSeq Stranded Total RNA library preparation kit with Ribo-Zero Gold, or TruSeq RNA Access library preparation kit. Libraries were normalized to 20pM and sequenced on an Illumina HiSeq 2500 using V3 chemistry, in paired-end mode with a read length of 2 × 50bp. The data were processed through a standard RNASeq pipeline to produce counts and transcripts per millions (TPMs) for each gene in each sample to compare two library kits at two different vendors. Data Summary QC data for FFPE RNA extraction, library preparation and sequencing generated by the two vendors are comparable, and sequencing results show high concordance as well. With the TruSeq Total kit, the average spearman correlation between vendors was 0.87 and the average Pearson correlation was 0.76. With the Access kit, the average spearman correlation between vendors was 0.89 and the average Pearson correlation was 0.73. Illumina TruSeq RNA Access Library generates more reads from protein coding genes than the Illumina TruSeq Stranded kit and generated acceptable sequencing results at both vendors in all usable samples (15/20), based on post alignment QC mapping to a reference genome (&amp;gt;90%). Conclusion Our analyses provide evidence to guide selection of sequencing methods for studies in which the sample quality is severely compromised. The comparison results demonstrate that the Illumina RNA Access library protocol more efficiently targets protein coding genes than TruSeq Stranded Total RNA library protocol on highly degraded FFPE samples and can be an attractive alternative for clinical trial studies in which the FFPE quality is severely compromised. However, since the probe selection step in the RNA Access protocol may bias results compared to other platforms, further exploration may be needed. Citation Format: Danyi Wang, Alex Rolfe, Alice Huang, Dennis O'Rourke, Juergen Scheuenpflug, Zheng Feng. Assessment of two whole transcriptome RNA sequencing library preparation Illumina workflows in FFPE human specimens with suboptimal quality [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5881.

A wireless, real-time respiratory effort and body position monitoring system for sleep

The number of people suffering from a sleep disorder is growing considerably, with obstructive sleep apnea syndrome (OSAS) being the most common one. Unfortunately, it is estimated that at least 75% of OSAS cases are currently undiagnosed and without treatment, mainly due to the lack of availability and high costs of the polysomnography (PSG) test required. OSAS is known to increase the risk for many severe health complications such as hypertension and neurodegenerative conditions, to mention a few, making early diagnosis important. The monitoring of respiratory effort (RE) and body position (BP) during sleep is essential for OSAS detection. Compared to existing commercial systems for respiratory effort monitoring using piezoelectric sensors wired to a central processing system, we present an embedded system solution that is wireless, runs standalone for up to 12 h, and implements on board an extended Kalman filter (EKF) for performing data fusion. An inertial measurement unit (3D acceleration, angular rate, and orientation sensors) is used for real-time measurement of both the respiratory effort and the body position. The experimental tests, when we compare the collected data with a reference signal, acquired by a commercial belt, indicate a general average Pearson correlation coefficient of ρ=0.963 with results ranging from 0.928 to 0.981, improving over previous works. Low-cost technologies, such as the one presented here, can help to reduce the number of undiagnosed cases of OSAS, and sleep disorders in general, and improve the health of those patients.

A Comparison of Three Trapezoid Models Using Optical and Thermal Satellite Imagery for Water Table Depth Monitoring in Estonian Bogs

This study explored the potential of optical and thermal satellite imagery to monitor temporal and spatial changes in the position of the water table depth (WTD) in the peat layer of northern bogs. We evaluated three different trapezoid models that are proposed in the literature for soil moisture monitoring in regions with mineral soils. Due to the tight capillary connection between water table and surface soil moisture, we hypothesized that the soil moisture indices retrieved from these models would be correlated with WTD measured in situ. Two trapezoid models were based on optical and thermal imagery, also known as Thermal-Optical TRApezoid Models (TOTRAM), and one was based on optical imagery alone, also known as the OPtical TRApezoid Model (OPTRAM). The models were applied to Landsat imagery from 2008 to 2019 and the derived soil moisture indices were compared with in-situ WTD from eight locations in two Estonian bogs. Our results show that only the OPTRAM index was significantly (p-value &lt; 0.05) correlated in time with WTD (average Pearson correlation coefficient of 0.41 and 0.37, for original and anomaly time series, respectively), while the two tested TOTRAM indices were not. The highest temporal correlation coefficients (up to 0.8) were observed for OPTRAM over treeless parts of the bogs. An assessment of the spatial correlation between soil moisture indices and WTD indicated that all three models did not capture the spatial variation in water table depth. Instead, the spatial patterns of the indices were primarily attributable to vegetation patterns.

Mechanisms behind altered pulsatile intracranial pressure in idiopathic normal pressure hydrocephalus: role of vascular pulsatility and systemic hemodynamic variables

BackgroundThe dementia subtype idiopathic normal pressure hydrocephalus (iNPH) has unknown etiology, but one characteristic is elevated intracranial pressure (ICP) wave amplitudes in those individuals who respond with clinical improvement following cerebrospinal fluid (CSF) diversion. To explore the mechanisms behind altered ICP wave amplitudes, we correlated central aortic blood pressure (BP) and ICP waveform amplitudes (intracranial aortic amplitude correlation) and examined how this correlation relates to ICP wave amplitude levels and systemic hemodynamic parameters.MethodsThe study included 29 patients with probable iNPH who underwent continuous multi-hour measurement of ICP, radial artery BP, and systemic hemodynamic parameters. The radial artery BP waveforms were used to estimate central aortic BP waveforms, and the intracranial aortic amplitude correlation was determined over consecutive 4-min periods.ResultsThe average intracranial aortic amplitude correlation was 0.28 ± 0.16 at the group level. In the majority of iNPH patients, the intracranial aortic amplitude correlation was low, while in about 1/5 patients, the correlation was rather high (average Pearson correlation coefficient > 0.4). The degree of correlation was hardly influenced by systemic hemodynamic parameters.ConclusionsIn about 1/5 iNPH patients of this study, the intracranial aortic amplitude correlation (IAACAORTIC) was rather high (average Pearson correlation coefficient > 0.4), suggesting that cerebrovascular factors to some extent may affect the ICP wave amplitudes in a subset of patients. However, in 14/19 (74%) iNPH patients with elevated ICP wave amplitudes, the intracranial aortic amplitude correlation was low, indicating that the ICP pulse amplitude in most iNPH patients is independent of central vascular excitation, ergo it is modulated by local cerebrospinal physiology. In support of this assumption, the intracranial aortic amplitude correlation was not related to most systemic hemodynamic variables. An exception was found for a subgroup of the patients with high systemic vascular resistance, where there was a correlation.

Shallow whole-genome sequencing of plasma cell-free DNA accurately differentiates small from non-small cell lung carcinoma

BackgroundAccurate lung cancer classification is crucial to guide therapeutic decisions. However, histological subtyping by pathologists requires tumor tissue—a necessity that is often intrinsically associated with procedural difficulties. The analysis of circulating tumor DNA present in minimal-invasive blood samples, referred to as liquid biopsies, could therefore emerge as an attractive alternative.MethodsConcerning adenocarcinoma, squamous cell carcinoma, and small cell carcinoma, our proof of concept study investigates the potential of liquid biopsy-derived copy number alterations, derived from single-end shallow whole-genome sequencing (coverage 0.1–0.5×), across 51 advanced stage lung cancer patients.ResultsGenomic abnormality testing reveals anomalies in 86.3% of the liquid biopsies (16/20 for adenocarcinoma, 13/16 for squamous cell, and 15/15 for small cell carcinoma). We demonstrate that copy number profiles from formalin-fixed paraffin-embedded tumor biopsies are well represented by their liquid equivalent. This is especially valid within the small cell carcinoma group, where paired profiles have an average Pearson correlation of 0.86 (95% CI 0.79–0.93). A predictive model trained with public data, derived from 843 tissue biopsies, shows that liquid biopsies exhibit multiple deviations that reflect histological classification. Most notably, distinguishing small from non-small cell lung cancer is characterized by an area under the curve of 0.98 during receiver operating characteristic analysis. Additionally, we investigated how deeper paired-end sequencing, which will eventually become feasible for routine diagnosis, empowers tumor read enrichment by insert size filtering: for all of the 29 resequenced liquid biopsies, the tumor fraction could be increased in silico, thereby “rescuing” three out of five cases with previously undetectable alterations.ConclusionsCopy number profiling of cell-free DNA enables histological classification. Since shallow whole-genome sequencing is inexpensive and often fully operational at routine molecular laboratories, this finding has current diagnostic potential, especially for patients with lesions that are difficult to reach.

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study.

BackgroundAs the process of producing official health statistics for lifestyle diseases is slow, researchers have explored using Web search data as a proxy for lifestyle disease surveillance. Existing studies, however, are prone to at least one of the following issues: ad-hoc keyword selection, overfitting, insufficient predictive evaluation, lack of generalization, and failure to compare against trivial baselines.ObjectiveThe aims of this study were to (1) employ a corrective approach improving previous methods; (2) study the key limitations in using Google Trends for lifestyle disease surveillance; and (3) test the generalizability of our methodology to other countries beyond the United States.MethodsFor each of the target variables (diabetes, obesity, and exercise), prevalence rates were collected. After a rigorous keyword selection process, data from Google Trends were collected. These data were denormalized to form spatio-temporal indices. L1-regularized regression models were trained to predict prevalence rates from denormalized Google Trends indices. Models were tested on a held-out set and compared against baselines from the literature as well as a trivial last year equals this year baseline. A similar analysis was done using a multivariate spatio-temporal model where the previous year’s prevalence was included as a covariate. This model was modified to create a time-lagged regression analysis framework. Finally, a hierarchical time-lagged multivariate spatio-temporal model was created to account for subnational trends in the data. The model trained on US data was, then, applied in a transfer learning framework to Canada.ResultsIn the US context, our proposed models beat the performances of the prior work, as well as the trivial baselines. In terms of the mean absolute error (MAE), the best of our proposed models yields 24% improvement (0.72-0.55; P<.001) for diabetes; 18% improvement (1.20-0.99; P=.001) for obesity, and 34% improvement (2.89-1.95; P<.001) for exercise. Our proposed across-country transfer learning framework also shows promising results with an average Spearman and Pearson correlation of 0.70 for diabetes and 0.90 and 0.91 for obesity, respectively.ConclusionsAlthough our proposed models beat the baselines, we find the modeling of lifestyle diseases to be a challenging problem, one that requires an abundance of data as well as creative modeling strategies. In doing so, this study shows a low-to-moderate validity of Google Trends in the context of lifestyle disease surveillance, even when applying novel corrective approaches, including a proposed denormalization scheme. We envision qualitative analyses to be a more practical use of Google Trends in the context of lifestyle disease surveillance. For the quantitative analyses, the highest utility of using Google Trends is in the context of transfer learning where low-resource countries could benefit from high-resource countries by using proxy models.

High-Throughput Quantitative Top-Down Proteomics: Histone H4.

Proteins physiologically exist as "proteoforms" that arise from one gene and acquire additional function by post-translational modifications (PTM). When multiple PTMs coexist on single protein molecules, top-down proteomics becomes the only feasible method of characterization; however, most top-down methods have limited quantitative capacity and insufficient throughput to truly address proteoform biology. Here we demonstrate that top-down proteomics can be quantitative, reproducible, sensitive, and high throughput. The proteoforms of histone H4 are well studied both as a challenging proteoform identification problem and due to their essential role in the regulation of all eukaryotic DNA-templated processes. Much of histone H4's function is obfuscated from prevailing methods due to combinatorial mechanisms. Starting from cells or tissues, after an optimized protein purification process, the H4 proteoforms are physically separated by on-line C3 chromatography, narrowly isolated in MS1 and sequenced with ETD fragmentation. We achieve more than 30 replicates from a single 35-mm tissue culture dish by loading 55 ng of H4 on column. Parallelization and automation yield a sustained throughput of 12 replicates per day. We achieve reproducible quantitation (average biological Pearson correlations of 0.89) of hundreds of proteoforms (about 200-300) over almost six orders of magnitude and an estimated LLoQ of 0.001% abundance. We demonstrate the capacity of the method to precisely measure well-established changes with sodium butyrate treatment of SUM159 cells. We show that the data produced by a quantitative top-down method can be amenable to parametric statistical comparisons and is capable of delineating relevant biological changes at the full proteoform level.