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The rat model is an important resource in biomedical research due to its similarities to the human immune system and its use for functional studies. However, because of the preponderance of mouse models in foundational and mechanistic immunological studies, there is a relative lack of diverse, commercially available flow cytometry antibodies for immunological profiling in the rat model. Available antibodies are often conjugated to common fluorophores with similar peak emission wavelengths, making them hard to distinguish on conventional flow cytometers and restricting more comprehensive immune analysis. This can become a limitation when designing immunological studies in rat injury models to investigate the immune response to tissue injury. In addition, this lack of available antibodies limits the number of studies that can be done on the immune populations in lymphoid organs in other research areas. To address this critical unmet need, we designed a spectral flow cytometry panel for rat models. Spectral cytometry distinguishes between different fluorophores by capturing their full emission spectra instead of their peak emission wavelengths. This flow cytometry panel includes 24 distinct immune cell markers to analyze the innate and adaptive immune response. Importantly, this panel identifies different immune phenotypes, including tolerogenic, Type 1, and Type 2 immune responses. We show that this panel can identify unique immune populations and phenotypes in a rat muscle trauma model. We further validated that the panel can identify distinct adaptive and innate immune populations and their unique phenotypes in lymphoid organs. This panel expands the scope of previous rat panels providing a tool for scientists to examine the immune system in homeostasis and injury while pairing mechanistic immunological studies with functional studies.

Tissue clearing of whole intact organs has enhanced imaging by enabling the exploration of tissue structure at a subcellular level in three-dimensional space. Although clearing and imaging of the whole organ have been used to study tissue biology, the microenvironment in which cells evolve to adapt to biomaterial implants or allografts in the body is poorly understood. Obtaining high-resolution information from complex cell-biomaterial interactions with volumetric landscapes represents a key challenge in the fields of biomaterials and regenerative medicine. To provide a new approach to examine how tissue responds to biomaterial implants, we apply cleared tissue light-sheet microscopy and three-dimensional reconstruction to utilize the wealth of autofluorescence information for visualizing and contrasting anatomical structures. This study demonstrates the adaptability of the clearing and imaging technique to provide sub-cellular resolution (0.6μm isotropic) 3D maps of various tissue types, using samples from fully intact peritoneal organs to volumetric muscle loss injury specimens, with and without biomaterials implants. We further apply computational-driven image classification to analyze the autofluorescence spectrum at multiple emission wavelengths to categorize tissue types in the tissue-biomaterial microenvironment.

During wounding and material implantation there is a disturbance in tissue homeostasis and release of self-antigen, and regulation between tolerance and auto-inflammation in injury is not well understood. Here, we analyzed antigen-presenting cells in biomaterial-treated muscle injury and found that pro-regenerative materials enrich Batf3-dependent CD103+XCR1+CD301b+ dendritic cells associated with cross-presentation and self-tolerance. Muscle trauma was accompanied by CD8+ iTregs and expansion of CD103+XCR1+CD62L- adaptive immune cells. Up-regulation of E-Cadherin (the ligand for CD103) and XCL-1 in injured tissue suggests a mechanism for cell recruitment to trauma. Without cross-presenting cells T cell activation increases, pro-regenerative macrophage polarization decreases, and muscle healing is impaired. These data describe a regulatory communication network through CD103+XCR1+ immune cells resulting in downstream effects on tissue regeneration.

Hyaluronic acid (HA) is an abundant extracellular matrix (ECM) component in soft tissues throughout the body and has found wide adoption in tissue engineering. This study focuses on the optimization of methacrylated HA (MeHA) for three-dimensional (3D) bioprinting to create in vitro test beds that incorporate regeneration-promoting growth factors in neural repair processes. To evaluate MeHA as a potential bioink, rheological studies were performed with PC-12 cells to demonstrate shear thinning properties maintained when printing with and without cells. Next, an extrusion-based Cellink BIO X 3D printer was used to bioprint various MeHA solutions combined with collagen-I to determine which formulation was the most optimal for creating 3D features. Results indicated that MeHA (10 mg/mL) with collagen-I (3 mg/mL) was most suitable. As Schwann cells (SCs) are a critical component of neural repair and regeneration, SC adhesion assessment via integrin β1 immunostaining indicated that the bioink candidate adequately supported SC adhesion and migration when compared to Col-I, a highly cell-adhesive ECM component. MeHA/collagen-I bioink was adapted for neural specific applications by printing with the neural growth factor (NGF) and glial cell line-derived neurotrophic factor (GDNF). These test beds were conducive for SC infiltration and presented differential migration responses. Finally, a two-chamber in vitro test bed design was created to study competitive biochemical cues. Dorsal root ganglia were seeded in test beds and demonstrated directional neurite extension (measured by β-III tubulin and GAP43 immunostaining) in response to NGF and GDNF. Overall, the selected MeHA/collagen-I bioink was bioprintable, improved cell viability compared to molded controls, and was conducive for cell adhesion, growth factor sequestration, and neural cell infiltration. MeHA is a suitable bioink candidate for extrusion-based bioprinting and will be useful in future development of spatially complex test beds to advance in vitro models as an alternative to common in vivo tests for neural repair applications.

A new technique based on beta functions is applied to compute the exact formula for the Riemann–Liouville fractional integral of the fractional-order generalized Chelyshkov wavelets. An approximation method based on the wavelets is proposed to effectively solve nonlinear fractional differential equations. Illustrative examples show that the proposed method gives solutions with less errors in comparison with the previous methods.

Objectives The objective is to investigate the primary factors that created experiences leading to moral injury in family medicine residents during the COVID-19 pandemic and also to identify any barriers keeping these residents from seeking or receiving help when they experienced moral injury. Method A DELPHI model study utilizing three rounds of surveys was conducted at four family medicine residency programs in the United States. Resident responses to Survey 1 generated factors perceived to be causing them moral injury or constituting barriers to their seeking help. Thematic analysis identified common themes which were presented to residents in Survey 2 for rating and justification. Results and feedback from Survey 2 were shared with residents in Survey 3, where residents were prompted to reevaluate their ratings for factors and barriers for the purpose of generating consensus among themselves. A ranked list of factors and barriers was thereby created for the participating sites. Results Residents shared several stories about the factors that most pressured them to violate their moral values. The most severe and frequent factors contributing to moral injury involved disruptions to doctor–patient relationships, patient–family relationships, and relationships with other healthcare professionals. Time was the major barrier to residents seeking help. Conclusion During times of crisis, moral injury among residents may be minimized by protecting and promoting important clinical and professional relationships with patients, colleagues, and other medical professionals. While residents report that lack of time was the most significant barrier to seeking help, it is unclear how this complicated and ubiquitous problem would be resolved or mitigated.

With few exceptions, humans are incapable of fully recovering from severe physical trauma. Due to these limitations, the field of regenerative medicine seeks to find clinically viable ways to repair permanently damaged tissue. There are two main approaches to regenerative medicine: promoting endogenous repair of the wound, or transplanting a material to replace the injured tissue. In recent years, these two methods have fused with the development of biomaterials that act as a scaffold and mobilize the body’s natural healing capabilities. This process involves not only promoting stem cell behavior, but by also inducing activity of the immune system. Through understanding the immune interactions with biomaterials, we can understand how the immune system participates in regeneration and wound healing. In this review, we will focus on biomaterials that promote endogenous tissue repair, with discussion on their interactions with the immune system.

The authors developed a wideband on-chip Wilkinson power divider (WPD) using lumped-element transmission lines (LE TLs) with 2-port negative capacitance (NCAP) circuits that provide the phase response with a positive slope and broadband port impedance. The bandwidth of the WPD was increased using the broadband quarter-wavelength ( λ /4) LE TLs with port-matched NCAP circuits. The WPD was implemented in the Global Foundry 8SF 130-nm RF CMOS IC technology. The size of the core chip is 1100 µm × 400 µm. The S-parameter measurements showed an absolute bandwidth ranging between 0.76 and 1.6 GHz, and a fractional bandwidth of 71.2%. In the frequency range of the bandwidth, the insertion loss was 0.4–3.4 dB, with the isolation between the two output ports was >18 dB, and the noise figures were 11–12 dB. The input-referred 1-dB gain compression point at the frequency of 1 GHz was ‒6 dBm.

Barcode attached on product is to transfer information to users. In practice, many barcodes are degraded over time and they are difficult for users to recognize product information. Therefore, barcode image restoration plays an important role due to clearly showing product information for users. This paper proposed a restoration approach of barcode- EAN 13 images with different degraded characteristics such as vertical lines, blurring, dashed lines. In particular, the degraded barcode images are pre-processed for restoring before recognition, in which a radon method is applied for rotating barcode image and an Otsu segmentation method is employed to split the barcode image from an original image. Therefore, bars in each barcode image are determined for recognition of the correct barcode. Barcode image datasets are collected from different practical products with different quality for restoration before recognizing them. Experimental results show to illustrate the proposed approach for the barcode recognition is the effectiveness

In comparison to the general patient population, trauma patients show higher level detections of bloodborne infectious diseases, such as Hepatitis and Human Immunodeficiency Virus. In comparison to bloodborne pathogens, the prevalence of respiratory infections such as SARS-CoV-2 and how that relates with other variables, such as drug usage and trauma type, is currently unknown in trauma populations. Here, we evaluated SARS-CoV-2 seropositivity and antibody isotype profile in 2,542 trauma patients from six Level-1 trauma centers between April and October of 2020 during the first wave of the COVID-19 pandemic. We found that the seroprevalence in trauma victims 18-44 years old (9.79%, 95% confidence interval/CI: 8.33 11.47) was much higher in comparison to older patients (45-69 years old: 6.03%, 4.59-5.88; 70+ years old: 4.33%, 2.54 – 7.20). Black/African American (9.54%, 7.77 – 11.65) and Hispanic/Latino patients (14.95%, 11.80 – 18.75) also had higher seroprevalence in comparison, respectively, to White (5.72%, 4.62 7.05) and Non-Latino patients (6.55%, 5.57 – 7.69). More than half (55.54%) of those tested for drug toxicology had at least one drug present in their system. Those that tested positive for narcotics or sedatives had a significant negative correlation with seropositivity, while those on anti-depressants trended positive. These findings represent an important consideration for both the patients and first responders that treat trauma patients facing potential risk of respiratory infectious diseases like SARS-CoV-2.

Asymptomatic SARS-CoV-2 infection and delayed implementation of diagnostics have led to poorly defined viral prevalence rates. To address this, we analyzed seropositivity in US adults who have not previously been diagnosed with COVID-19. Individuals with characteristics that reflect the US population (n = 11,382) and who had not previously been diagnosed with COVID-19 were selected by quota sampling from 241,424 volunteers (ClinicalTrials.gov NCT04334954). Enrolled participants provided medical, geographic, demographic, and socioeconomic information and 9,028 blood samples. The majority (88.7%) of samples were collected between May 10(th) and July 31(st), 2020. Samples were analyzed via ELISA for anti-Spike and anti-RBD antibodies. Estimation of seroprevalence was performed by using a weighted analysis to reflect the US population. We detected an undiagnosed seropositivity rate of 4.6% (95% CI: 2.6 – 6.5%). There was distinct regional variability, with heightened seropositivity in locations of early outbreaks. Subgroup analysis demonstrated that the highest estimated undiagnosed seropositivity within groups was detected in younger participants (ages 18-45, 5.9%), females (5.5%), Black/African American (14.2%), Hispanic (6.1%), and Urban residents (5.3%), and lower undiagnosed seropositivity in those with chronic diseases. During the first wave of infection over the spring/summer of 2020 an estimate of 4.6% of adults had a prior undiagnosed SARS-CoV-2 infection. These data indicate that there were 4.8 (95% CI: 2.8-6.8) undiagnosed cases for every diagnosed case of COVID-19 during this same time period in the United States, and an estimated 16.8 million undiagnosed cases by mid-July 2020.

We utilize the Clemson supercomputer to generate a massive workload for testing the performance of Microsoft Azure IoT Hub. The workload emulates sensor data from a large manufacturing facility. We study the effects of message frequency, distribution, and size on round-trip latency for different IoT Hub configurations. Significant variation in latency occurs when the system exceeds IoT Hub specifications. The results are predictable and well-behaved for a well-engineered system and can meet soft real-time deadlines.

In recent years, organ-on-chip (OoC) systems have provoked increasing interest among researchers from different disciplines. OoCs enable the recreation of in vivo-like microenvironments and the generation of a wide range of different tissues or organs in a miniaturized way. Most commonly, OoC platforms are based on microfluidic modules made of polydimethylsiloxane (PDMS). While advantageous in terms of biocompatibility, oxygen permeability, and fast prototyping amenability, PDMS features a major limitation as it absorbs small hydrophobic molecules, including many types of test compounds, hormones, and cytokines. Another common feature of OoC systems is the integration of membranes (i) to separate different tissue compartments, (ii) to confine convective perfusion to media channels, and/or (iii) to provide mechanical support for cell monolayers. Typically, porous polymer membranes are microstructured using track-etching (e.g., polyethylene terephthalate; PET) or lithography (e.g., PDMS). Although membranes of different biomechanical properties (rigid PET to elastic PDMS) have been utilized, the membrane structure and material remain mostly artificial and do not resemble in vivo conditions (extracellular matrix). Here, we report a method for the reliable fabrication and integration of electrospun membranes in OoC modules, which are made of laser-structured poly(methyl methacrylate) (PMMA). The choice of PMMA as base material provides optical parameters and biocompatibility similar to PDMS while avoiding the absorption problem. Using electrospinning for the generation of 3D membranes, microenvironments resembling the native extracellular matrix (ECM) can be generated. We tested two different kinds of electrospun membranes and established processes for a tight integration into PMMA modules. Human (microvasculature) endothelial as well as (retinal pigment) epithelial cell layers could be successfully cultured inside the systems for up to 7 days, while being either directly exposed to (endothelial cells) or protected (epithelial cells) from the shear flow. Our novel method enables the versatile fabrication of OoC platforms that can be tailored to the native environment of tissues of interest and at the same time are applicable for the testing of compounds or chemicals without constraints.

16.8 million SARS-CoV-2 infections in the US went undiagnosed in the first 6 months of the pandemic compared to 3.5 million diagnosed infections., Elucidating seroprevalence in COVID-19 Symptoms of SARS-CoV-2 infection range from completely asymptomatic, to those of a common cold, to a drop in oxygen saturation and lung function, and death in some patients. To evaluate the proportion of the U.S. population who had an undiagnosed infection during the first wave of the COVID-19 pandemic, we measured antibody prevalence in study participants who had not previously been diagnosed with a SARS-CoV-2 infection. By mid-July of 2020, 16.8 million people had an undiagnosed SARS-CoV-2 infection, almost five times the rate of diagnosed infections., Asymptomatic SARS-CoV-2 infection and delayed implementation of diagnostics have led to poorly defined viral prevalence rates in the United States and elsewhere. To address this, we analyzed seropositivity in 9089 adults in the United States who had not been diagnosed previously with COVID-19. Individuals with characteristics that reflected the U.S. population (n = 27,716) were selected by quota sampling from 462,949 volunteers. Enrolled participants (n = 11,382) provided medical, geographic, demographic, and socioeconomic information and dried blood samples. Survey questions coincident with the Behavioral Risk Factor Surveillance System survey, a large probability-based national survey, were used to adjust for selection bias. Most blood samples (88.7%) were collected between 10 May and 31 July 2020 and were processed using ELISA to measure seropositivity (IgG and IgM antibodies against SARS-CoV-2 spike protein and the spike protein receptor binding domain). The overall weighted undiagnosed seropositivity estimate was 4.6% (95% CI, 2.6 to 6.5%), with race, age, sex, ethnicity, and urban/rural subgroup estimates ranging from 1.1% to 14.2%. The highest seropositivity estimates were in African American participants; younger, female, and Hispanic participants; and residents of urban centers. These data indicate that there were 4.8 undiagnosed SARS-CoV-2 infections for every diagnosed case of COVID-19, and an estimated 16.8 million infections were undiagnosed by mid-July 2020 in the United States.

We investigated patient and clinician impressions of cognitive impairment and whether they correlated with objective measures of cognitive impairment. Cognitive categorization, neuropsychological assessment scores, and Montreal Cognitive Assessment scores were documented at baseline, 3 years, and 7 years for 388 PD patients in the Parkinson’s Progression Markers Initiative (PPMI). We found that both patient and clinician impressions of cognitive decline were significantly associated with gold-standard criteria for cognitive impairment to a similar degree. Both patient and clinician perspectives should be considered in determining cognitive status and should be followed up with diagnostic testing.

Objectives Nail workers are exposed to many hazardous chemicals. Despite many warnings about health problems among nail workers in other countries, data concerning exposure to chemical hazards among nail workers is still limited in Vietnam. In this study, we aimed to identify exposure to volatile organic compounds and their relationship with occupational symptoms among Vietnamese female nail salon workers. Methods A cross‐sectional study was conducted in Danang, Vietnam, from January 2019 to September 2019. Total 42 personal passive samplers were collected to evaluate 12 substances from 21 nail workers (15 salons) twice a week. We chose one representative worker from each of the nine salons with less than six workers and two representative workers from each of the six salons with over five workers for personal sampling based on the principle of similar exposure groups. We interviewed a total of 100 nail workers in 15 salons and 100 office workers in offices adjacent to the salons to compare occupational symptoms among them. Results The commonly detected compounds in nail salons were acetone (97.6%), butyl acetate (83.3%), and ethyl acetate and ethyl methacrylate (78%). The concentration of total target VOCs was related to the number of serviced customers, the concentration of CO2, and general ventilation used. The subjective symptoms were significantly higher for the nail workers than for the comparison subjects, that is, headache, nausea, nose irritation, skin irritation, shortness of breath, and confusion. Among 100 nail workers, nose irritation was significantly higher for nail workers who were exposed to acetone at levels exceeding the Vietnam occupational exposure limit (VOEL) adjusted with the Brief‐Scala model. Conclusions Exposure to VOCs such as acetone in nail salons results in occupational symptoms among workers.

A complex and vast amount of data will be collected from on-board sensors of operational connected vehicles (CVs), infrastructure data sources such as roadway sensors and traffic signals, mobile data sources such as cell phones, social media sources such as Twitter, and news and weather data services. Unfortunately, these data will create a bottleneck at data centers for processing and retrievals of collected data, and will require the deployment of additional message transfer infrastructure between data producers and consumers to support diverse CV applications. In this paper, we present a strategy for creating an efficient and low-latency distributed message delivery system for CV applications using a distributed message delivery platform. This strategy enables large-scale ingestion, curation, and transformation of unstructured data (roadway traffic-related and roadway non-traffic-related data) into labeled and customized topics for a large number of subscribers or consumers, such as CVs, mobile devices, and data centers. We evaluate the performance of this strategy by developing a prototype infrastructure using Apache Kafka, an open source message delivery system, and compared its performance with the latency requirements of CV applications. We present experimental results of the message delivery infrastructure on two different distributed computing testbeds at Clemson University: the Holocron cluster and the Palmetto cluster. Experiments were performed to measure the latency of the message delivery system for a variety of testing scenarios. These experiments reveal that measured latencies are less than the U.S. Department of Transportation recommended latency requirements for CV applications, which prove the efficacy of the system for CV related data distribution and management tasks.

A majority of the literature on the foreign body response (FBR) to biomaterial implants and medical devices has focused on the role of the innate immune system in mediating the rejection or tolerance of implants. However, the immune system is a complex interconnected network that also involves adaptive immune cells which must be included to form a complete picture of the immune response to biomaterials and medical devices. In this review, we explore recent advances in knowledge about the role of the adaptive immune system –specifically T cells– in modulating the immune response to medical devices. The immune response to implants follows a delicate balance between tissue fibrosis and repair that is mainly regulated by three types of T cell responses –Th1, Th2, and Th17– and their crosstalk with innate immune cells. Interestingly, many T cell response mechanisms to biomaterial implants overlap with the process of fibrosis or repair in different tissues. This review explores this fibrotic and regenerative T cell biology and draws parallels to T cell responses to biomaterials. Additionally, we also explore the biomedical engineering advancements in particle, scaffold and cell-based systems designed to modulate T cell activity for therapeutics and devices. The deliberate engineering design of physical and chemical material properties and the direct genetic modulation of T cells not only offer insights to T cell biology but also present different platforms to develop immunomodulatory biomaterials. Thus, an in-depth understanding of the role of T cells can help to map out biomaterial-immune interactions and reconsider the long-lasting adaptive immune response to implants, which, in the end, contribute to the design of immunomodulatory medical devices that can advance the next generation of regenerative therapy.

STUDY QUESTION What is the prevalence of cannabis use and the perceptions of its impact on fertility among infertility patients? SUMMARY ANSWER A total of 13% of infertility patients used cannabis within the last year, and current usage is associated with patient perceptions of negative effects of cannabis on fertility and pregnancy. WHAT IS KNOWN ALREADY Cannabis use is increasing among the general population and pregnant women, particularly in places where cannabis use is legal despite having known and potential negative effects on fertility and pregnancy. STUDY DESIGN, SIZE, DURATION A cross-sectional patient survey study was performed between July 2017 and September 2017. Patients attending a university-affiliated hospital-based fertility clinic (n = 290) were invited to complete a written survey. Inclusion criteria were limited to the ability to read English. There were no exclusion criteria. PARTICIPANTS/MATERIALS, SETTING, METHODS Of the 290 patients approached, 270 (93%) agreed to participate. The questions covered demographics, cannabis usage, perceptions of the effect of cannabis on fertility and pregnancy, cessation of use due to infertility and personal history of disclosing cannabis use to healthcare providers (HCP). MAIN RESULTS AND THE ROLE OF CHANCE The results showed that 13% of respondents disclosed use of cannabis in the past year (past year users) and 38% had not used cannabis in the past year but had previously used cannabis (>1 year users) while 49% had never used cannabis (never users). Baseline demographics were similar for the three groups, but across four measures of fertility and pregnancy health, past-year users perceived less of a negative effect compared to >1 year users, and never users (P values of 0.02, 0.03, 0.01, LIMITATIONS, REASONS FOR CAUTION Self-reported patient surveys are subject to reporting bias and may not reflect actual use and perceptions. WIDER IMPLICATIONS OF THE FINDINGS This study suggests that cannabis use is common among infertility patients. Given the known negative impacts of cannabis on pregnancy, the authors would have expected informed infertility patients to cease cannabis use as part of their efforts to conceive. As the prevalence of cannabis use in the last year among infertility patients is similar to that in the general Canadian population, it is unclear whether the prevalence of cannabis use in the sample population merely reflects the average usage in society or, after taking into account those who reduced their usage to improve their fertility, is a factor contributing to infertility and thus prompting fertility referral. Given concern about the potential negative impact of cannabis use on fertility, and that only 9% of past year users had been instructed by an HCP to cease cannabis use, HCPs should consider the benefits of counselling about cannabis cessation for patients who are attempting to conceive. Future research should focus on analysing the effects of cannabis use on female fertility and determining whether a reduction in use among patients with infertility can improve conception rates. STUDY FUNDING/COMPETING INTEREST(S) Michelle Shin, Clinical Research Associate, is supported by the University of Toronto GREI Fellowship Fund, which is sponsored by unrestricted research grants from EMD Serono, Merck Canada and Ferring Pharmaceuticals. The authors have no potential conflicts of interest to disclose.

Forward flux sampling (FFS) is an established scientific method for sampling rare events in molecular simulations. However, as the difficulty of the scientific problem increases, the amount of data and the number of tasks required for FFS is challenging to manage with traditional scripting tools and languages for high performance computing. The SAFFIRE software framework has been developed to address these challenges. SAFFIRE utilizes Hadoop to manage a large number of tasks and data for large scale FFS simulations. The framework is shown to be highly scalable and able to support large scale FFS simulations. This enables studies of rare events in complex molecular systems on commodity cluster computing systems.

This study details the progress in transportation data analysis with a novel computing framework in keeping with the continuous evolution of the computing technology. The computing framework combines the Labeled Latent Dirichlet Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with the supporting computing strategy on publicly available Twitter data in determining transportation-related events to provide reliable information to travelers. The analytical approach includes analyzing tweets using text classification and geocoding locations based on string similarity. A case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach. Approximately 700,010 tweets are analyzed to extract relevant transportation-related information for one week. The SVM classifier achieves >85% accuracy in identifying transportation-related tweets from structured data. To further categorize the transportation-related tweets into sub-classes: incident, congestion, construction, special events, and other events, three supervised classifiers are used: L-LDA, SVM, and L-LDA incorporated SVM. Findings from this study demonstrate that the analytical framework, which uses the L-LDA incorporated SVM, can classify roadway transportation-related data from Twitter with over 98.3% accuracy, which is significantly higher than the accuracies achieved by standalone L-LDA and SVM.

Advances in scientific software and computing infrastructure have enabled researchers across disciplines to simulate and model highly complex systems. At the same time, these increases in simulation duration and scale have led to significant growths in the sizes of output data, which can be as much as hundreds of gigabytes or more. While there exist solutions to assist with most standard post-simulation analytics, researchers must develop their own code to support customized analytical tasks. Given the nature of these output data, most naive in-house sequential codes end up being inefficient, and in most cases, time-consuming. In this paper, we propose a solution to this issue by transparently combining the strengths of a high-performance computing cluster and a big data infrastructure to support an end-to-end scientific workflow. More specifically, we present a case study around the design of a research computing environment at Clemson University where these two computing systems are integrated and accessible from one another. This environment allows simulation data to be automatically transferred across systems and complex analytical tasks on these data to be developed using the Hadoop/Spark frameworks. Results show that a hybrid workflow for molecular dynamics simulation can provide significant performance improvements over a traditional workflow. Furthermore, code complexity of Hadoop/Spark solutions is shown to be less than that of a traditional solution.