Lab-based Data Research Articles

Computational design of experimentally validated multi-epitopes vaccine against hepatitis E virus: An immunological approach

Hepatitis E virus (HEV) is one of the leading acute liver infections triggered by viral hepatitis. Patients infected with HEV usually recover and the annual death rate is negligible. Currently, there is no HEV licensed vaccine available globally. This study was carried out to design a multi-epitope HEV peptide-based vaccine by retrieving already experimentally validated epitopes from ViPR database leading to epitope prioritization. Epitopes selected as potential vaccine candidates were non-allergen, immunogenic, soluble, non-toxic and IFN gamma positive. The epitopes were linked together by AAY linkers and the linker EAAAK was used to join adjuvant with epitopes. Toll-like receptor (TLR)-4 agonist was used as an adjuvant to boost efficacy of the vaccine. Furthermore, codon optimization followed by disulfide engineering was performed to analyse the designed vaccine’s structural stability. Computational modeling of the immune simulation was done to examine the immune response against the vaccine. The designed vaccine construct was docked with TLR-3 receptor for their interactions and then subjected to molecular dynamic simulations. The vaccine model was examined computationally towards the capability of inducing immune responses which showed the induction of both humoral and cell mediated immunity. Taken together, our study suggests an In-silico designed HEV based multi-epitope peptide-based vaccine (MEPV) that needs to be examined in the wet lab-based data that can help to develop a potential vaccine against HEV.

In Situ Radioactivity Maps and Trace Metal Concentrations in Beach Sands of a Mining Coastal Area at North Aegean, Greece

In recent years, the environmental effects of both active and legacy mining activity have motivated many research groups worldwide through the use of a variety of methods that have been conducted among diverse environments. In this study, we measured radionuclide concentrations at two coastline locations of the Northern Aegean Sea: Stratoni and Ierissos. We deployed KATERINA II, an in situ gamma ray spectrometer. Our results indicate that the activity concentration for 238U progenies (214Bi), 232Th progenies (208Tl and 228Ac) and 40K vary by up to (33 ± 4) Bq kg−1, (19 ± 3) Bq kg−1, and (420 ± 30) Bq kg−1, respectively. The activity concentration of the 137Cs in Stratoni and Ierissos beach sands were (8.1 ± 2.2) and (3.9 ± 1.2) Bq kg−1, respectively. Lab-based measurements were also collected prior to the in situ data collection for the determination of radionuclide and metal concentrations. The lab-based data were found to be (800 ± 40) μg g−1 and (12 ± 1) μg g−1, for As, (1200 ± 60) μg g−1 and (33.3 ± 0.3) μg g−1 for Pb, (100 ± 6) μg g−1 and (6.0± 0.3) μg g−1 for Cu and (2000 ± 60) μg g−1 and (8.0 ± 0.4) μg g−1 for Zn, respectively. We used the R language and environment for statistical computing to produce radiological maps of the subject beach sands. We used the Enrichment Factor (EF) to estimate assessment indices for the target area and compared them to internationally recommended values. The in situ maps will be discussed since the beach area of the load-out pier area of Stratoni was undergoing the first phase of active remediation. We conclude that the temporal aspect of this dataset can be of significant reference value against future comparative studies after the remediation of the Stratoni beach with potentially denser spatial and temporal data coverage.

Web-based psychoacoustics: Hearing screening, infrastructure, and validation

Anonymous web-based experiments are increasingly used in many domains of behavioral research. However, online studies of auditory perception, especially of psychoacoustic phenomena pertaining to low-level sensory processing, are challenging because of limited available control of the acoustics, and the inability to perform audiometry to confirm normal-hearing status of participants. Here, we outline our approach to mitigate these challenges and validate our procedures by comparing web-based measurements to lab-based data on a range of classic psychoacoustic tasks. Individual tasks were created using jsPsych, an open-source JavaScript front-end library. Dynamic sequences of psychoacoustic tasks were implemented using Django, an open-source library for web applications, and combined with consent pages, questionnaires, and debriefing pages. Subjects were recruited via Prolific, a subject recruitment platform for web-based studies. Guided by a meta-analysis of lab-based data, we developed and validated a screening procedure to select participants for (putative) normal-hearing status based on their responses in a suprathreshold task and a survey. Headphone use was standardized by supplementing procedures from prior literature with a binaural hearing task. Individuals meeting all criteria were re-invited to complete a range of classic psychoacoustic tasks. For the re-invited participants, absolute thresholds were in excellent agreement with lab-based data for fundamental frequency discrimination, gap detection, and sensitivity to interaural time delay and level difference. Furthermore, word identification scores, consonant confusion patterns, and co-modulation masking release effect also matched lab-based studies. Our results suggest that web-based psychoacoustics is a viable complement to lab-based research. Source code for our infrastructure is provided.

Academic Performance Improves with Emotional Intelligence Awareness and Physical Exercise among Medical Students

Background and objective: The importance of exercise and knowledge of emotional intelligence (EI) is gaining pace in academia, which is a positive development. Only a few single-blind research studies on the benefits of exercise and Emotional intelligence have indicated their effects on educational attainment among primary school children, but there are very few reports from young adult students. This longitudinal study aims to develop an intervention protocol to investigate the differential association between induced moderate physical exercise combined with emotional intelligence awareness on academic performance among 18-24-year-old private medical university students. Methods: Students are voluntarily enrolled in two groups: TEST (n = 180) and CONTROL (n = 180) based on inclusion criteria along with applicable consent’s and ethical clearance. The groups' baseline data mapping was completed in three phases. For the TEST group, baseline data was obtained in three stages during Phase-1; -, which is the pre-intervention phase: Anthropometric data, haemoglobin concentration, and cardiorespiratory measurements were all measured in Stage 1. (Lab-based data). Stage 2: two questionnaires were used, one to measure EI awareness and the other to assess learning styles (VARK). Stage 3 entailed the simultaneous recording of pre-intervention academic grades, of a said end block examination for both TEST and CONTROL groups. Phase 2: This is the intervention phase, which included only two components for the TEST group: the first was a moderate exercise regime (BRISK WALKING) of 2-3 sessions per week for about 30 minutes per session, and the second was addressing EI awareness. Which refers to knowing the current status of EI level, and fostering EI knowledge through various teaching-learning methods. Following the end of Phase -1 baseline data mapping, the two intervention components for the TEST group run concurrently for about 6months/24weeks, for the TEST group. Phase-3: is the post-intervention phase which included repeating of Phase-1; Stages 1, 2, and 3 for the TEST group, as well as a concurrent record and analysis of post-intervention academic grades, of the final professional examination for both TEST and CONTROL groups was done after Phase-2. The outcomes of Phase-1 and Phase-3 differential association compared. Results: The Statistical Package for Social Science (SPSS) Version 21.0 software was used to analyse all data from phases 1 and 3. A statistically significant correlation was found between the TEST group (p = 0.0001) and the CONTROL group (p = 0.406) when the pre-intervention period of multifactorial components subjected to intervention was compared to the post-intervention period. Conclusion: Intervention protocol with combined effect of emotional intelligence awareness and induced moderate physical exercise (BRISK WALKING) has shown significant improvement in academic grades.

Equal Quality for Online and Lab Data: A Direct Comparison from Two Dual-Task Paradigms

Abstract Conducting behavioral experiments online has become more prevalent recently. Still, there is reluctance to embrace the possibilities this technology has to offer. So far, only simple tasks have been replicated in an online setting. In order to investigate whether collecting online also leads to high quality data in demanding tasks, we directly compared data collected in the lab with data collected online from a demanding dual-task paradigm and a psychological refractory period paradigm. In Experiment 1, we recruited from local pools, online and offline; in Experiment 2, we collected lab data from our local pool and online data from a remote commercial participant platform. We found that all relevant effects were replicated in the lab and online settings; effect sizes were similar. Additionally, most response time distributions were even statistically equivalent when comparing online and lab data. Thus, online effect sizes and variances can be comparable to lab-based data. Online studies are time-efficient and recruiting an online sample instead or on top of a laboratory sample should be considered for basic behavioral research. This can serve an important role in the generalizability and replicability of findings in the cognitive and behavioral sciences.

In Silico, In Vitro, and In Vivo Analysis Identifies Endometrial Circadian Clock Genes in Recurrent Implantation Failure.

Previous work has demonstrated the role of the circadian clock in ovarian steroid hormone synthesis and attributed embryo implantation failure associated with arrhythmic circadian clock genes to insufficient ovarian-derived progesterone synthesis. Research on expression of core circadian clock genes in the endometrium itself and possible roles in compromised endometrial receptivity and recurrent implantation failure (RIF) are limited. We aimed to assess the core circadian clock gene profiling in human endometrium across the menstrual cycle and the possible gene interaction networks in the endometrial receptivity of window of implantation (WOI) as well as RIF. The study was initially an in silico study, with confirmatory lab-based data from primary human endometrial stromal cells (hESCs) as well as endometrial biopsies obtained from 60 women undergoing gynecological surgery in a clinical research center. The study included 30 RIF women and 30 age-matched and body mass index-matched controls. Initial data mining and bioinformatics analysis of human endometrial microarray datasets across the menstrual cycle and between RIF women versus controls demonstrated the varied expression of core circadian clock genes across menstrual cycle, including the key role of PER2 in WOI and RIF. A PER2-centered network was investigated in the regulation of endometrial receptivity. We also confirmed the evidently increased mRNA expression of SHTN1, RXFP1, KLF5, and STEAP4 in the endometrium of RIF women, displaying the same trend as PER2 did, without any changes in MT1E and FKBP5. Treatment of PER2 siRNA in hESCs verified the positive regulation of PER2 to SHTN1, KLF5, and STEAP4. Aberrant expression of endometrial PER2 might contribute to impaired endometrial receptivity and development of RIF via regulating SHTN1, KLF5, and STEAP4.

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.

There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable sensors enable non-invasive monitoring of human movement in applied situations, current commercial wearables do not estimate tissue-level loading on structures inside the body. Here we explore the feasibility of using wearable sensors to estimate tibial bone force during running. First, we used lab-based data and musculoskeletal modeling to estimate tibial force for ten participants running across a range of speeds and slopes. Next, we converted lab-based data to signals feasibly measured with wearables (inertial measurement units on the foot and shank, and pressure-sensing insoles) and used these data to develop two multi-sensor algorithms for estimating peak tibial force: one physics-based and one machine learning. Additionally, to reflect current running wearables that utilize running impact metrics to infer musculoskeletal loading or injury risk, we estimated tibial force using a commonly measured impact metric, the ground reaction force vertical average loading rate (VALR). Using VALR to estimate peak tibial force resulted in a mean absolute percent error of 9.9%, which was no more accurate than a theoretical step counter that assumed the same peak force for every running stride. Our physics-based algorithm reduced error to 5.2%, and our machine learning algorithm reduced error to 2.6%. Further, to gain insights into how force estimation accuracy relates to overuse injury risk, we computed bone damage expected due to a given loading cycle. We found that modest errors in tibial force translated into large errors in bone damage estimates. For example, a 9.9% error in tibial force using VALR translated into 104% error in estimated bone damage. Encouragingly, the physics-based and machine learning algorithms reduced damage errors to 41% and 18%, respectively. This study highlights the exciting potential to combine wearables, musculoskeletal biomechanics and machine learning to develop more accurate tools for monitoring musculoskeletal loading in applied situations.

Evaluation of the trophic state predicted from lab and Landsat data of western coastal water of Suez Bay, Egypt

This study aimed to compare the trophic status assessments of the western coastal waters of Suez Bay, Gulf of Suez, Egypt. The Trophic Index (TRIX) that depends on the lab-based water quality measurements of nutrients, chlorophyll-a (Chl-a) and dissolved oxygen, was compared with the Carlson trophic state index that integrates lab-based data such as NH4 or PO4 concentration with Landsat data and geographic information system (GIS) techniques. The physicochemical properties were determined, which indicated that Suez Bay was in an eutrophication state limited by both N and Si. It was suggested that the most influential activities be the industrial and domestic drains that raised the TRIX value to 5.59 with a mesotrophic state on average. Likewise, phosphorus trophic status mapping applying Carlson trophic state index showed high activity nearby drain outlets, especially at the Cabanon drain. The regression statistical model indicated a relationship between lab data of nutrients and band values extracted from Landsat images. Accordingly, the integration of remote sensing and GIS techniques with laboratory data is useful for predicting the state of water quality in the coastal areas with a single lab-based water quality parameter.

Conducting Web-Based Experiments for Numerical Cognition Research.

It is becoming increasingly popular and straightforward to collect data in cognitive psychology through web-based studies. In this paper, I review issues around web-based data collection for the purpose of numerical cognition research. Provided that the desired type of data can be collected through a web-browser, such online studies offer numerous advantages over traditional forms of physical lab-based data collection, such as gathering data from larger sample sizes in shorter time-windows and easier access to non-local populations. I then present results of two replication studies that employ classical paradigms in numerical cognition research: the number-size congruity paradigm and comparison to a given standard, which also included a masked priming manipulation. In both replications, reaction times and error rates were comparable to original, physical lab-based studies. Consistent with the results of original studies, a distance effect, a congruity effect, and a priming effect were observed. Data collected online thus offers a level of reliability comparable to data collected in a physical lab when it comes to questions in numerical cognition.

Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors

Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU).Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants’ free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time.The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant’s field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants’ estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users’ field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.

Data Science Summer Academy for Chicago Public School Students

In this article, we describe DePaul University's summer data science academy for Chicago Public School students which was, in part, funded through the SIGKDD Impact Award program in 2018. The goal of the academy was to increase awareness about data science among high school students. The program specifically aimed to broaden participation of underrepresented groups in computing by targeting economically disadvantaged, African American, Hispanic, and female students. A cohort of 15 high school students from the Chicago Public School system participated in this week-long lab-based data science program learning about a variety of data science methods and their applications, including data visualization, distance-based methods, classification, clustering and others. The group comprised of 75% African American and Hispanic students, 58.3% of whom were female.

Epidemiology of Influenza: A Diagnostic Lab based observational study

Background: Influenza is a highly infectious respiratory disease of viral origin. It's a public health concern worldwide as well in India. The annual attack rate of influenza is estimated to be 5-10% in adults and 20-30% in children. Worldwide annual epidemics of influenza results in 3-5 million cases of severe illness and 250,000-500,000 deaths. The objective of this study is to understand the recent epidemiological behaviour of influenza virus by using data from diagnostic labs across states of Maharashtra and Delhi (India). Methods & Materials: A diagnostic lab-based data of 5950 subjects who were tested for influenza between 1st January 2015 to 5th October 2017 across Delhi, Mumbai, and suburbs of Mumbai (Thane, Goa, and Pune) was analysed. It's a descriptive observational retrospective study. The confidentiality of subjects is maintained, and no personal identifying information is used in the analysis. Results: Sample population has almost equal male and female proportion (49.4%) with 34.9 years mean age. Overall positivity for type A was 40.2% while for H1N1 was 26%. For type B positivity was 3%. The quarterly trends show third quarter of the year (July-Sept.) had the highest positivity except during 2017, where 2nd quarter has shown the highest rate of influenza (A and B collectively) positivity. Even during 2017, the third quarter has recorded high positivity rate (45.0). The third quarter in India corresponds to the rainy season, while second quarter to summer. Chadha et al study has shown peaks during monsoon season (July-sept) in Delhi and Pune. Area wise trends of Influenza shows less positivity for type A and for type B (0.6%) in Delhi. Navi Mumbai has shown overall high positivity for A, B and H1N1. On the comparison between Mumbai and Delhi, overall positivity (42.1%), for type A (40.5%), B (3.2%) and H1N1 (26.8%) was higher than Delhi. Conclusion: Big data-based research studies can be very helpful in understanding the epidemiological behaviour of diseases like influenza. A well-connected network of diagnostic labs can be a smart surveillance system to alert health care system timely in case of epidemics and pandemics of influenza.

Data analysis for CO2-EOR in shale-oil reservoirs based on a laboratory database

Unconventional resources have played a significant role in changing oil industry plans recently. Shale formations in North America such as Bakken, Niobrara, and Eagle Ford have huge oil in place, 100–900 Billion barrels of recoverable oil in Bakken only. However, the primary oil recovery is still low as 5–10%. EOR methods are currently considered as a new concept in unconventional reservoirs due to the immature information about these plays. Injecting carbon dioxide (CO2) might be the most potential strategy to improve oil recovery in such complex plays (Alfarge et al., 2017a,b).Jin et al. (2016) conducted an experimental study to investigate improving oil recovery by CO2 injection in 21 natural preserved core samples from Bakken Petroleum System (BPS). Their experimental results indicated that CO2 injection has a significant potential to improve oil transportability in these Nano-pores formations. In this study, data analysis for feasibility of CO2-EOR has been conducted on 95 cases of natural preserved cores collected from different formations including 44 cases from Middle Bakken, 26 cases from Lower Bakken, 17 from Upper Bakken, 4 cases from Three Forks, and 4 cases from unknown formation/formations. The relationship between the improved oil recovery by the injected CO2 and 6 rock properties including porosity, permeability, mean pore throat radius, total organic carbon content (TOC), water saturation, and oil saturation has been separately determined and physically discussed. Furthermore, the relationships between the improved oil recovery obtained by the injected CO2 and 4 operating parameters including CO2 bath pressure, CO2 bath temperature, and core sample bulk size, and exposing time have been also separately investigated. A Proxy model to associate the functionality of the improved oil recovery by CO2 injection and these 10 parameters have been constructed. Moreover, statistical methods for Design of Experiments (DOE) have been used to rank the most important parameters affecting CO2-EOR performance in the microscale level (lab scale) of these unconventional reservoirs. This study provides an important set of lab-based data obtained from natural preserved cores to find out the applicability of CO2-EOR in these unconventional reservoirs. Also, this research demonstrates some key points which could help in understanding CO2-EOR mechanisms in shale plays since they are much complex and very different from conventional formations.

Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG.

Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.

Missouri Mothers and Their Children: A Family Study of the Effects of Genetics and the Prenatal Environment.

The Missouri Mothers and Their Children Study (MO-MATCH) was specifically designed to critically investigate prenatal environmental influences on child attention problems and associated learning and cognitive deficits. The project began as a pilot study in 2004 and was formally launched in 2008. Participants in the study were initially identified via the Department of Vital Statistics birth record (BR) database. Interview and lab-based data were obtained from: (1) mothers of Missouri-born children (born 1998-2005), who smoked during one pregnancy but not during another pregnancy; (2) biological fathers when available; and (3) the children (i.e., full sibling pairs discordant for exposure to maternal smoking during pregnancy (SDP). This within-mother, between-pregnancy contrast provides the best possible methodological control for many stable maternal and familial confounding factors (e.g., heritable and socio-demographic characteristics of the mother that predict increased probability of SDP). It also controls for differences between mothers who do and do not smoke during pregnancy, and their partners, that might otherwise artifactually create, or alternatively mask, associations between SDP and child outcomes. Such a design will therefore provide opportunities to determine less biased effect sizes while also allowing us to investigate (on a preliminary basis) the possible contribution of paternal or other second-hand smoke exposure during the pre, peri, and postnatal periods to offspring outcome. This protocol has developed a cohort that can be followed longitudinally through periods typically associated with increased externalizing symptoms and substance used initiation.

Facial expression recognition experiments with data from television broadcasts and the World Wide Web

Facial expression recognition (FER) systems must ultimately work on real data in uncontrolled environments although most research studies have been conducted on lab-based data with posed or evoked facial expressions obtained in pre-set laboratory environments. It is very difficult to obtain data in real-world situations because privacy laws prevent unauthorized capture and use of video from events such as funerals, birthday parties, marriages etc. It is a challenge to acquire such data on a scale large enough for benchmarking algorithms. Although video obtained from TV or movies or postings on the World Wide Web may also contain ‘acted’ emotions and facial expressions, they may be more ‘realistic’ than lab-based data currently used by most researchers. Or is it? One way of testing this is to compare feature distributions and FER performance. This paper describes a database that has been collected from television broadcasts and the World Wide Web containing a range of environmental and facial variations expected in real conditions and uses it to answer this question. A fully automatic system that uses a fusion based approach for FER on such data is introduced for performance evaluation. Performance improvements arising from the fusion of point-based texture and geometry features, and the robustness to image scale variations are experimentally evaluated on this image and video dataset. Differences in FER performance between lab-based and realistic data, between different feature sets, and between different train-test data splits are investigated.

Comment on “A snapshot of climate variability at Tahiti at 9.5 ka using a fossil coral from IODP Expedition 310” by Kristine L. DeLong, Terrence M. Quinn, Chuan‐Chou Shen, and Ke Lin

[1] The Integrated Ocean Drilling Program (IODP) Expedition 310 recovered drill cores from the drowned reefs around the island of Tahiti (17°40′S, 149°30′W), many of which contained samples of massive corals from the genus Porites. Herein we report on one well-preserved fossil coral sample: a 13.6 cm long Porites sp. dated by uranium series techniques at 9523 ± 33 years. Monthly δ18O and Sr/Ca determinations reveal nine clear and robust annual cycles. Coral δ18O and Sr/Ca determinations estimate a mean temperature of ∼24.3°C (∼3.2°C colder than modern) for Tahiti at 9.5 ka; however, this estimate is viewed with caution since potential sources of cold bias in coral geochemistry remain to be resolved. The interannual variability in coral δ18O is similar between the 9.5 ka coral record and a modern record from nearby Moorea. The seasonal cycle in coral Sr/Ca is approximately the same or greater in the 9.5 ka coral record than in modern coral records from Tahiti. Paired analysis of coral δ18O and Sr/Ca indicates cold/wet (warm/dry) interannual anomalies, opposite from those observed in the modern instrumental record.

SQA: a software tool for integrated sediment quality evaluation based on the Weight-Of-Evidence procedure

A new software tool for integrative sediment quality assessment is developed. The program is based on WOE methodologies which together may be used for assessing degree of contamination in marine sediment. Statistical analysis of field-collected and lab-based data is performed by means of commercial statistical software package Statistica 6.0. The designed tool provides user-friendly interface, is extremely easy in use, greatly simplifies statistical analysis process, interprets the results in text and graphical format.