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Introduction: Amyotrophic lateral sclerosis (ALS) can affect various eye movements, making eye tracking a potential means for disease monitoring. In this study, we evaluated the feasibility of ALS patients self-recording their eye movements using the “EyePhone,” a smartphone eye-tracking application. Methods: We prospectively enrolled ten participants and provided them with an iPhone equipped with the EyePhone app and a PowerPoint presentation with step-by-step recording instructions. The goal was for the participants to record their eye movements (saccades and smooth pursuit) without the help of the study team. Afterward, a trained physician administered the same tests using video-oculography (VOG) goggles and asked the participants to complete a questionnaire regarding their self-recording experience. Results: All participants successfully completed the self-recording process without assistance from the study team. Questionnaire data indicated that participants viewed self-recording with EyePhone favorably, considering it easy and comfortable. Moreover, 70% indicated that they prefer self-recording to being recorded by VOG goggles. Conclusion: With proper instruction, ALS patients can effectively use the EyePhone to record their eye movements, potentially even in a home environment. These results demonstrate the potential for smartphone eye-tracking technology as a viable and self-administered tool for monitoring disease progression in ALS, reducing the need for frequent clinic visits.

Abstract Background Driving is a complex behavior that may be affected by early changes in the cognition of older individuals. Early changes in driving behavior may include driving more slowly, making fewer and shorter trips, and errors related to inadequate anticipation of situations. Sensor systems installed in older drivers’ vehicles may detect these changes and may generate early warnings of possible changes in cognition. Method A naturalistic longitudinal design is employed to obtain continuous information on driving behavior that will be compared with the results of extensive cognitive testing conducted every 3 months for 3 years. A driver facing camera, forward facing camera, and telematics unit are installed in the vehicle and data downloaded every 3 months when the cognitive tests are administered. Results Data processing and analysis will proceed through a series of steps including data normalization, adding information on external factors (weather, traffic conditions), and identifying critical features (variables). Traditional prediction modeling results will be compared with Recurring Neural Network (RNN) approach to produce Driver Behavior Indices (DBIs), and algorithms to classify drivers within age, gender, ethnic group membership, and other potential group characteristics. Conclusion It is well established that individuals with progressive dementias are eventually unable to drive safely, yet many remain unaware of their cognitive decrements. Current screening and evaluation services can test only a small number of individuals with cognitive concerns, missing many who need to know if they require treatment. Given the increasing number of sensors being installed in passenger vehicles and pick-up trucks and their increasing acceptability, reconfigured in-vehicle sensing systems could provide widespread, low-cost early warnings of cognitive decline to the large number of older drivers on the road in the U.S. The proposed testing and evaluation of a readily and rapidly available, unobtrusive in-vehicle sensing system could provide the first step toward future widespread, low-cost early warnings of cognitive change for this large number of older drivers in the U.S. and elsewhere.

Objective: The objective of the study was to establish an AI-driven decision support system by identifying the most important features in the severity of disease for Intensive Care Unit (ICU) with Mechanical Ventilation (MV) requirement, ICU, and InterMediate Care Unit (IMCU) admission for hospitalized patients with COVID-19 in South Florida. The features implicated in the risk factors identified by the model interpretability can be used to forecast treatment plans faster before critical conditions exacerbate. Methods: We analyzed eHR data from 5371 patients diagnosed with COVID-19 from South Florida Memorial Healthcare Systems admitted between March 2020 and January 2021 to predict the need for ICU with MV, ICU, and IMCU admission. A Random Forest classifier was trained on patients’ data augmented by SMOTE, collected at hospital admission. We then compared the importance of features utilizing different model interpretability analyses, such as SHAP, MDI, and Permutation Importance. Results: The models for ICU with MV, ICU, and IMCU admission identified the following factors overlapping as the most important predictors among the three outcomes: age, race, sex, BMI, diarrhea, diabetes, hypertension, early stages of kidney disease, and pneumonia. It was observed that individuals over 65 years (‘older adults’), males, current smokers, and BMI classified as ‘overweight’ and ‘obese’ were at greater risk of severity of illness. The severity was intensified by the co-occurrence of two interacting features (e.g., diarrhea and diabetes). Conclusions: The top features identified by the models’ interpretability were from the ‘sociodemographic characteristics’, ‘pre-hospital comorbidities’, and ‘medications’ categories. However, ‘pre-hospital comorbidities’ played a vital role in different critical conditions. In addition to individual feature importance, the feature interactions also provide crucial information for predicting the most likely outcome of patients’ conditions when urgent treatment plans are needed during the surge of patients during the pandemic.

Methane emissions from oil and gas production can occur throughout the value chain, but for many producers, one of the most significant sources is flaring. Understanding the influence of the operating conditions and the environmental factors the combustion efficiency and destruction and removal efficiency (CE/DRE) of flares is essential if their role in methane emissions, a potent but short-lived greenhouse gas, is to be better understood and mitigated. An industry-scale experimental study was focused on the emissions of un-assisted flares commonly encountered in upstream oil and gas production. This paper simulates two un-assisted flare tips combustions by using the commercial computational fluid dynamics (CFD) software package Fluent 21R2 to augment the physical experimental testing. Two three-dimensional (3D) flare tips models are built, and the k-omega SST turbulence model and flamelet generated manifold (FGM) combustion model are applied to simulate flaring combustion. The CFD model is first validated against full-scale industry flare tests that use extractive sampling of the combustion plume. CFD results are in good agreement with measured results when the vent gas net heating value (NHV) is greater than 300 BTU/SCF. Greater uncertainty exists for both CFD results and measured data if the NHV is less than 300 BTU/SCF. Then, the CFD model is extended to include high crosswind states up to 50 m/s that cannot be readily or safely examined empirically. The results emphasize the critical role of the vent gas net heating value (NHV) on flare combustion and crosswind in reducing the CE. The comparison helps pave the way for further use of CFD simulation to improve flare designs and modes of operation and supports the use of parametric models to track and report methane losses from flaring.

Magnetic reconnection and plasma turbulence are ubiquitous and key processes in the Universe. These two processes are suggested to be intrinsically related: magnetic reconnection can develop turbulence, and, in turn, turbulence can influence or excite magnetic reconnection. In this study, we report a rare and unique electron diffusion region (EDR) observed by the Magnetospheric Multiscale mission in the Earth’s magnetotail with significantly enhanced energetic particle fluxes. The EDR is in a region of strong turbulence within which the plasma density is dramatically depleted. We present three salient features. (1) Despite the turbulence, the EDR behaves nearly the same as that in 2D quasi-planar reconnection; the observations suggest that magnetic reconnection continues for several minutes. (2) The observed reconnection electric field and inferred energy transport are exceptionally large. However, the aspect ratio of the EDR (one definition of reconnection rate) is fairly typical. Instead, extraordinarily large-amplitude Hall electric fields appear to enable the strong energy transport. (3) We hypothesize that the high-energy transport rate, density depletion, and the strong particle acceleration are related to a near-runaway effect, which is due to the combination of low-plasma-density inflow (from lobes) and possible positive feedback between turbulence and reconnection. The detailed study on this EDR gives insight into the interplay between reconnection and turbulence, and the possible near-runaway effect, which may play an important role in other particle acceleration in astrophysical plasma.

Background There are ≈5 million annual dizziness visits to US emergency departments, of which vestibular strokes account for over 250 000. The head impulse, nystagmus, and test of skew eye examination can accurately distinguish vestibular strokes from peripheral dizziness. However, the eye‐movement signs are subtle, and lack of familiarity and difficulty with recognition of abnormal eye movements are significant barriers to widespread emergency department use. To break this barrier, we sought to assess the accuracy of EyePhone, our smartphone eye‐tracking application, for quantifying nystagmus. Methods and Results We prospectively enrolled healthy volunteers and recorded the velocity of induced nystagmus using a smartphone eye‐tracking application (EyePhone) and then compared the results with video oculography (VOG). Following a calibration protocol, the participants viewed optokinetic stimuli with incremental velocities (2–12 degrees/s) in 4 directions. We extracted slow phase velocities from EyePhone data in each direction and compared them with the corresponding slow phase velocities obtained by the VOG. Furthermore, we calculated the area under the receiver operating characteristic curve for nystagmus detection by EyePhone. We enrolled 10 volunteers (90% men) with an average age of 30.2±6 years. EyePhone‐recorded slow phase velocities highly correlated with the VOG recordings (r=0.98 for horizontal and r=0.94 for vertical). The calibration significantly increased the slope of linear regression for horizontal and vertical slow phase velocities. Evaluating the EyePhone's performance using VOG data with a 2 degrees/s threshold showed an area under the receiver operating characteristic curve of 0.87 for horizontal and vertical nystagmus detection. Conclusions We demonstrated that EyePhone could accurately detect and quantify optokinetic nystagmus, similar to the VOG goggles.

IntroductionThe SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of COVID-19. This study aims to train an AI-driven decision support system that helps build a model to understand the most important features that predict the “mortality” of patients hospitalized with COVID-19.MethodsWe conducted a retrospective analysis of “5,371” patients hospitalized for COVID-19-related symptoms from the South Florida Memorial Health Care System between March 14th, 2020, and January 16th, 2021. A data set comprising patients’ sociodemographic characteristics, pre-existing health information, and medication was analyzed. We trained Random Forest classifier to predict “mortality” for patients hospitalized with COVID-19.ResultsBased on the interpretability of the model, age emerged as the primary predictor of “mortality”, followed by diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race in descending order of importance. Notably, individuals aged over 65 years (referred to as “older adults”), males, Whites, Hispanics, and current smokers were identified as being at higher risk of death. Additionally, BMI, specifically in the overweight and obese categories, significantly predicted “mortality”. These findings indicated that the model effectively learned from various categories, such as patients' sociodemographic characteristics, pre-hospital comorbidities, and medications, with a predominant focus on characterizing pre-hospital comorbidities. Consequently, the model demonstrated the ability to predict “mortality” with transparency and reliability.ConclusionAI can potentially provide healthcare workers with the ability to stratify patients and streamline optimal care solutions when time is of the essence and resources are limited. This work sets the platform for future work that forecasts patient responses to treatments at various levels of disease severity and assesses health disparities and patient conditions that promote improved health care in a broader context. This study contributed to one of the first predictive analyses applying AI/ML techniques to COVID-19 data using a vast sample from South Florida.

Accurately measuring the combustion and destruction removal efficiency of flaring is important when accounting for methane emissions from oil and gas production. Despite this, the amount of experimental data from full-size flares is limited, especially for flares built without air or steam assistance. The use of a single destruction value of 98% is commonly applied. In this paper, we present new empirical measurements of flare efficiency using three common flare designs employed in upstream applications. Combustion products were analyzed using an extractive sampling method. The results demonstrate that whilst destruction efficiencies in excess of 98% are achievable, if the gas composition falls below a critical heating value of ~300 BTU/scf, the efficiency deteriorates leading to elevated methane emissions. This is further complicated by accurately measuring the flow of combustible gas and the impact of crosswinds. In an operational setting, continuous tracking of flare conditions is therefore a key resource in reducing methane emissions but further work is required to standardize how continuous performance tracking is evaluated if such measurements are to attain full traceability.

A new method is described for calculating flare combustion efficiency (CE) and destruction and removal efficiency (DRE) using a numerical parametric model. The method combines key variables that affect flare performance including the flare vent gas net heating value (NHV), flare design, flow rate, exit velocity, and inert gas composition, alongside the environmental influence of crosswind speed. Each effect is characterized using a parametric model derived from experimental testing data and computational fluid dynamics (CFD). The inclusion of CFD allows the model to be extended into the high-wind conditions that cannot be adequately controlled for in empirical testing yet represent some of the most challenging conditions in which to maintain good combustion. This new parametric model method (PMM) is coupled with ultrasonic flowmeters from which the molecular weight and net heating value of the flare gas can be derived using the vent gas speed of sound measurement. In doing so, this method provides a reliable continuous flare combustion efficiency measure that can be deployed at scale with minimum hardware updates. The system was verified using an extractive sampling method with tests conducted on three full-scale industrial flares including non-assisted, single-arm pressure-assisted, and multi-arm pressure-assisted flare designs. A total of seventy valid test points were carried out with varying flow rate and flare gas heating value, covering a CE range from 46–100%. The uncertainty of the method was assessed using both traditional error propagation and Monte Carlo methodology. The results from the new method agree with the extractive method to within 0.8% in the ≥98% DRE region where flares are expected to operate to limit the impacts of flaring as a source of methane as a greenhouse gas. Uncertainty analysis revealed that the larger DRE discrepancy for DRE ≤ 98% correlates to the measurement uncertainties for both methods.

Abstract A 66‐year‐old lady presented with shortness of breath and a Wenckebach atrioventricular (AV) conduction pattern on the ECG. The electrophysiologic study showed split‐His potentials and intra‐Hisian Wenckebach. The case highlights the interesting finding of Wenckebach conduction in the His bundle.

Two miners were fatally injured when a pillar bump occurred during retreat mining in a southern West Virginia coal mine. The mine was operating in the Eagle seam with overmining in the No. 2 Gas and Powellton seams. A coal bump is defined as a sudden and violent failure of coal caused by the release of stored strain energy in the pillar. While significant strides have been made by academia, industry, and regulatory agencies to better understand bump conditions and mitigation techniques, coal bumps represent a long standing, highly site-specific engineering problem in which the exact failure mechanism is not clearly understood. In this case history, a cut-by-cut analysis of retreat mining operations was conducted on the 4 East Main leading up to the pillar bump event. Numerical input parameters were derived from site-specific geologic information and mine geometry for the analysis of pillar stress conditions and energy release using LaModel. An overview of stress conditions in the panel was presented including a precursor event that occurred two crosscuts inby the bump site. The methodology presented in the paper for the evaluation of the fatal bump event can be used for the identification of bump prone conditions prior to development and retreat of a mining area.

The development of a multiple level underground mine is a complex task in which geology, engineering, ground control, and unit operations are integrated into a single mine design. The components are inter-dependent and must function cohesively to ensure a profitable underground mining operation. To optimize reserve recovery, mine planning should begin from the lowest level and progress up. This limits any misjudgments or oversights of a given level affecting the underlying levels and ensures the ability to maximize recovery from each level. Mine planning should start with the exploration and characterization of the geologic setting. Once the reserve geology and quality is well understood, then mine planning can begin with respect to the following: (1) orientation of mine works with respect to horizontal stress; (2) access to the reserve; (3) determination of opening widths; (4) selection of back, floor horizons and pillar centers; (5) selection of development and secondary mining heights; (6) appropriate inter-burden thicknesses; and (7) examining the stability of the multiple level mine through numerical modeling. The multiple level mine design process and decisions are presented through a case history example. The theme is that there is one opportunity to “get it right” and many chances to overlook a small aspect within the design that will plague the mine throughout all levels and through the entirety of its operating life. Keywords: Limestone, Mine planning, Geology, Multiple level, Rock mechanics

Due to the lack of new antibiotics with efficacy against the ESKAPE and other resistant microbes, coupled to the demise of major pharmaceutical company antibiotic discovery programs, due to a number of factors but mainly ROI calculations and the lack of efficacy of combinatorial chemistry as a substitute, the search for novel antibiotics may well have moved to the utilization of older structures with significant synthetic chemistry input. This short review demonstrates how modern synthetic chemistry, when applied to either modification of current resistant antibiotics such as glycopeptides, or production of novel peptidic agents based on natural product sourced antimicrobial peptides (AMPs) and other potential initial peptide-based agents from genomic searches and baiting techniques, have produced active agents of significant utility. In addition, synthetic chemistry practitioners have now shown that they can produce bioactive molecules of greater than 800 Daltons in kilogram quantities under cGMP conditions.

Magnetic reconnection is a fundamental plasma process that has been studied with analytical theory, numerical simulations, in situ observations, and laboratory experiments for decades. The models that have been established to describe magnetic reconnection often assume a reconnection plane normal to the current sheet in which an antiparallel magnetic field annihilates. The annihilation points, also known as the X-points, form an x -line, which is believed to be perpendicular to the reconnection plane. Recently, a new study using Magnetospheric Multiscale mission observations has challenged our understanding of magnetic reconnection by providing evidence that the x -line is not necessarily orthogonal to the reconnection plane. In this study we report a second nonorthogonal x -line event with similar features as that in the previous case study, supporting that the sheared x -line phenomenon is not an aberrant event. We employ a detailed directional derivative analysis to identify the x -line direction and show that the in-plane reconnection characteristics are well maintained even with a nonorthogonal x -line. In addition, we find the x -line tends to follow the magnetic field on one side of the current sheet, which suggests an asymmetry across the current sheet. We discuss the possibility that the nonorthogonal x -line arises from an interplay between the two aspects of reconnection: the macroscopic magnetic field topology and microscopic particle kinetics.

Physical activity is important for healthy aging, but few older adults achieve the goal of 150 min per week of moderate activity. The purpose of this study was to employ a robust statistical approach in the analysis of the factors related to physical activity in a diverse sample of older adults. A secondary analysis of factors associated with calculated MET-h/week was conducted in a sample of 601 African Americans, Afro-Caribbeans, European Americans, and Hispanic Americans age 59 to 96 living independently in the community. Age, education, social network, pain, and depression were the five variables that accounted for a statistically significant proportion of unique variance in the model. The strongest correlation to total MET-h/week was with depression. Directionality of the relationship between these variables and physical activity is complex: while pain and depression can reduce physical activity, activity may also help to reduce pain and depression. Additionally, of note is that many of these factors may be modified, calling for the design and testing of individual, group, and community level interventions to increase physical activity in the older population.

The objective of this study was to compare different methods of evaluating intramuscular fat (IMF) in pork and test the accuracy of using a computer vision system (CVS) on different locations of the loin. Whole pork loins (n = 1,400) were obtained from 6 pork processing plants. Subjective marbling scores and CVS IMF percentage (CVS IMF%) were assessed on the ventral lean surface of the whole loin and the 3rd (A) and 10th (B) rib chops. Additionally, the A and B chops were evaluated for crude fat percentage (CF%) using ether extract. The CF% of the whole loin was represented by using the average CF% of A and B chops. A combination of the bootstrap method and stepwise regression models was used to increase prediction and robustness for CF% prediction. To better understand whether plants played an effect, models for individual plants and using all plants together were built, tested, and compared. Results were that subjective marbling score had stronger correlations with CF% compared to CVS IMF% for the whole loin (0.70 vs. 0.58), A chop (0.79 vs.0.62), and B chop (0.74 vs. 0.61). When using the stepwise regression models to predict CF%, B chop (71.8%) had the highest prediction accuracy (estimates within 0.5% residual compared to CF% were considered accurate) followed by A chop (58.1%) and whole loin (48.2%). When comparing individual plant models and overall models, the overall accuracy improved; however, this improvement in accuracy was not consistent through every single plant. In conclusion, CVS has shown potential to estimate pork IMF on all locations, especially the posterior pork chop.

OBJECTIVES/GOALS: Leveraging Patient’s Experience to improve Diagnosis (LEAPED) is our proposed method of measuring diagnostic error through seeking patient feedback on their understanding of their diagnosis and health status following emergency department discharge. To pilot test LEAPED’s feasibility, we deployed and determined patient uptake of LEAPED. METHODS/STUDY POPULATION: To test LEAPED, we employed a longitudinal cohort study design at emergency departments across one academic health system in the Mid-Atlantic region. Patients consented to complete questionnaires regarding their understanding of their diagnosis and/or follow-up steps and their health status at 2 weeks, 1 month, and 3 months following emergency department discharge. People aged 18 and older who were seen at the emergency department within the past 7 days with at least one chronic condition (hypertension, diabetes, history of stroke, arthritis, cancer, heart disease, osteoporosis, depression, and/or chronic obstructive lung disease) and one or more of the following common chief complaints: chest pain, upper back pain, abdominal pain, shortness of breath/cough, dizziness, and headache were eligible to join the study. RESULTS/ANTICIPATED RESULTS: Of those enrolled (n = 59), 95% (n = 53) responded to the two week post-ED discharge questionnaire (1 and 3-month ongoing). Of the 6 non-responders, 1 had died and 3 were hospitalized at two weeks. The average age was 50 years (SD 16) and 64% were female. Over half of participants (53%) were white and 41% were black. Almost one-third (27%) reported they were not given an explanation of their health problem on leaving the ED, and of those, a third did not have an understanding of what steps to take after leaving the ED. Participants reported a new health problem was identified after ED discharge (19%), worsening health status (12%), and health status stayed the same (16%). DISCUSSION/SIGNIFICANCE OF IMPACT: Patient uptake of LEAPED was high, which suggests that patient-report is a feasible method of evaluating diagnostic decision making and delivery to patients and yields insightful information beyond administrative data. The next steps are to validate the accuracy of patient-reported diagnostic error by comparing with administrative data.

Over 80% of adults in the US fail to meet the ≥150 min weekly physical activity guideline; 40% age ≥ 75 are entirely inactive. The study purpose was to understand the reasons why community-dwelling older adults (age ≥ 60) from diverse backgrounds increase, sustain, or decline in their physical activity levels over time. Sixty-two older adults were interviewed. Two-thirds of the African Americans, 57% of the Afro-Caribbeans, and 50% of the European Americans reported being less active than 2–3 years ago. Reasons for activity decline included health issues (e.g., pain, shortness of breath), lack of time, interest, or motivation. Reasons for sustaining or increasing activity levels included meeting personal goals, having a purpose for remaining active, or feeling better when active (e.g., it is important to keep moving, good for the joints, going on a cruise). Themes identified were pride in maintaining activity, goal-driven activity, pushing oneself to get past pain or fatigue, and some confusion between social and physical activity in participant reports. The results indicate widespread acceptance that activity is beneficial, but that knowledge alone was insufficient to maintain activity levels over time unless individuals had a goal or purpose (“means to an end”) and could overcome their physical and psychological barriers to physical activity.

ABSTRACT IMPACT: We conducted a study to understand how a patient’s report of a new diagnosis compares with what was documented in the electronic medical record, since it is critical to the diagnostic process that the patient both understands and agrees with a new diagnosis. OBJECTIVES/GOALS: We sought feedback on patient’s understanding of their diagnosis and health status follow Emergency Department discharge. We compared patient report of a new diagnosis to documentation in the electronic medical record. METHODS/STUDY POPULATION: To compare patient reported diagnoses to documented diagnoses, we employed a longitudinal cohort study design at 3 of emergency departments in an academic health system in the Mid-Atlantic. Patients consented to complete questionnaires regarding their understanding of their diagnosis and/or follow-up steps and their health status at 2 weeks, 1 month, and 3 months following emergency department discharge. Inclusion criteria: adult ED patients aged 18 and older seen within the last 7 days with one or more of the following common chief complaints: chest pain, upper back pain, abdominal pain, shortness of breath/cough, dizziness, and headache. We compared patient report of a new diagnosis following discharge to documentation in the electronic medical record. RESULTS/ANTICIPATED RESULTS: Of the sample recruited (n=137), the majority were women (66%, n=91), the average age was 42 (SD 16). A third (n=45) were black and 56% (n=76) were white. The majority of participants (84%, n=115) reported that they either understood the diagnosis they received on ED discharge, or were not given a diagnosis but they understood follow-up steps. At two weeks following discharge, 25% of participants (n=36) had a new diagnosis identified after discharge and 33% (n=45) reported that their health status stayed the same or worsened. There was 85% agreement (kappa 0.49) between patient report of a new diagnosis and a new diagnosis identified in the electronic medical record. Only one of the participants who reported a new diagnosis also reported seeking healthcare outside of the health system. DISCUSSION/SIGNIFICANCE OF FINDINGS: Patient report of a new diagnosis following emergency department discharge had moderate agreement with new diagnoses identified in the electronic medical record, and differences in agreement were not explained by outside healthcare visits.

Concrete and asphalt are the primary materials used to construct roadways for motor vehicles, paths for pedestrians and bicyclists, and runways for aircraft. Solar Roadways®, Inc. (SR) proposed a novel solar pavement technology (i.e., solar road panels (SRP)) as an alternative material and energy source. SR performed load, traction, and impact testing to use SRPs in non-critical applications like parking lots. To use SRP in public roads, engineering tests including freeze/thaw, moisture absorption, heavy vehicle, and shear testing were accomplished on “SR3” prototypes. Testing was performed at Marquette University in the Engineering Materials and Structural Testing Laboratory and the SR Pilot Project area. Moisture absorption and freeze/thaw tests showed “SR3” resistant to extreme weather and moisture environments. Heavy vehicle testing revealed no physical damage to the “SR3” after approximately 989,457 equivalent single axle loads were continuously rolled over a prototype pavement. Shear testing was conducted to investigate “SR3” laminate structure properties. In all cases, electrical failure was defined when “SR3” photovoltaic voltage dropped to zero volts. The maximum shear stress and applied torque for “SR3”’ (S/N’s Paver 1, 002B, 007C, and 004B) were 1756 kPa, 1835 kPa, 1643 kPa, 2023 kPa; and 121.2 kN·m, 131.3 kN·m, 117.6 kN·m, 144.8 kN·m, respectively. In addition, the “SR3” “heartbeat” light emitting diode (LED) remained operational (i.e., indicates computer bus traffic) in all phases of shear testing. Overall, the results show “SR3” prototypes to be robust, resilient, and functional when subjected to “real-world” test conditions.

The objective of this study was to investigate the ability of image color features to predict subjective pork color scores. Subjective and instrumental color were assessed on the bloomed, cross-sectional surface of pork longissimus thoracis et lumborum chops. Images of pork chop samples were acquired using a computer vision system, and 18 image color features (mean and standard deviation of R, G, B, H, S, I, L*, a*, b*) were extracted for inclusion in partial least squares (PLS) and support vector machine (SVM) regression models. For color scores 2, 3, 4, and 5, the accuracies were 50.4, 75.9, 72.4, and 47.3% classified correctly by PLS, respectively, and 70.7, 72.8, 76.7, and 69.7% by SVM, respectively. The overall prediction accuracies of 2 models for pork color scores were 68.3% for PLS and 73.4% for SVM. There was minimal major misclassification of samples (< 0.5%). Image color features isolated through the development of PLS and SVM models, particularly SVM, show potential as a method to predict pork color scores.

With the advent of very rapid and cheap genome analyses and the linkage of these plus microbial metabolomics to potential compound structures came the realization that there was an immense sea of novel agents to be mined and tested. In addition, it is now recognized that there is significant microbial involvement in many natural products isolated from “nominally non-microbial sources”. This short review covers the current screening methods that have evolved and one might even be tempted to say “devolved” in light of the realization that target-based screens had problems when the products entered clinical testing, with off-target effects being the major ones. Modern systems include, but are not limited to, screening in cell lines utilizing very modern techniques (a high content screen) that are designed to show interactions within cells when treated with an “agent”. The underlying principle(s) used in such systems dated back to unpublished attempts in the very early 1980s by the pharmaceutical industry to show toxic interactions within animal cells by using automated light microscopy. Though somewhat successful, the technology was not adequate for any significant commercialization. Somewhat later, mammalian cell lines that were “genetically modified” to alter signal transduction cascades, either up or down, and frequently linked to luciferase readouts, were then employed in a 96-well format. In the case of microbes, specific resistance parameters were induced in isogenic cell lines from approximately the mid-1970s. In the latter two cases, comparisons against parent and sibling cell lines were used in order that a rapid determination of potential natural product “hits” could be made. Obviously, all of these assay systems could also be, and were, used for synthetic molecules. These methods and their results have led to a change in what the term “screening for bioactivity” means. In practice, versions of phenotypic screening are returning, but in a dramatically different scientific environment from the 1970s, as I hope to demonstrate in the short article that follows.

Ventricular safety pacing (VSP) is used to avoid cross talk by delivering ventricular stimulus shortly after an atrial-paced event if ventricular-sensed event occurs. Although VSP is a protective feature that exists for decades in different pacing devices, there are some reports of unfavorable outcomes of this algorithm. More so, health care providers sometimes face difficulties in interpreting and dealing with VSP strips. This case report discusses an important pacemaker algorithm and encourages further attention to possible pitfalls and hence avoids unnecessary interventions.

The objective of this study was to investigate the effects of seasonal environment, transport conditions, and time in lairage on pork quality and serum cortisol concentrations. Market hogs were slaughtered during winter (n = 535), spring (n = 645), summer (n = 644), and fall (n = 488). Within season, hogs were randomly assigned to treatments in a 2 × 2 × 2 factorial arrangement, with 2 deck locations (top vs. bottom) and 2 transport and lairage durations (3 h vs. 6 h). Blood samples were collected at exsanguination for analysis of cortisol concentration. Loins were collected at 24 h postmortem for pork quality assessment. Season and deck did not have a main effect on cortisol concentrations or pork quality. Hogs transported 6 h had increased cortisol concentrations (103.0 vs. 95.5 ng/mL; P < 0.001) and decreased L* (52.49 vs. 52.69; P = 0.09), b* (6.28 vs. 6.36; P = 0.03), and hue angle (20.70 vs. 20.95; P = 0.03) compared to hogs transported 3 h. Hogs subjected to 6 h of lairage had increased 24-h pH (5.69 vs. 5.66; P = 0.005), a* (16.64 vs. 16.48; P < 0.0001), b* (6.42 vs. 6.22; P < 0.0001), saturation (17.85 vs. 17.64; P < 0.0001), and hue angle (21.01 vs. 20.65; P = 0.002) and decreased L* (52.49 vs. 52.69; P = 0.07) when compared to hogs subjected to 3 h of lairage.

Deficiency of mitochondrial complex I is encountered in both rare and common diseases, but we have limited therapeutic options to treat this lesion to the oxidative phosphorylation system (OXPHOS). Idebenone and menadione are redox-active molecules capable of rescuing OXPHOS activity by engaging complex I-independent pathways of entry, often referred to as "complex I bypass." In the present study, we created a cellular model of complex I deficiency by using CRISPR genome editing to knock out Ndufa9 in mouse myoblasts, and utilized this cell line to develop a high-throughput screening platform for novel complex I bypass factors. We screened a library of ~40,000 natural product extracts and performed bioassay-guided fractionation on a subset of the top scoring hits. We isolated four plant-derived 1,4-naphthoquinone complex I bypass factors with structural similarity to menadione: chimaphilin and 3-chloro-chimaphilin from Chimaphila umbellata and dehydro-α-lapachone and dehydroiso-α-lapachone from Stereospermum euphoroides. We also tested a small number of structurally related naphthoquinones from commercial sources and identified two additional compounds with complex I bypass activity: 2-methoxy-1,4-naphthoquinone and 2-methoxy-3-methyl-1,4,-naphthoquinone. The six novel complex I bypass factors reported here expand this class of molecules and will be useful as tool compounds for investigating complex I disease biology.

It is almost twenty years since Paasi and Newman published a framework paper for understanding borders in an era of globalization in Progress in Human Geography, entitled Fences and Neighbours in a Postmodern World. In the interim period, border studies have undergone a major renaissance and expansion, drawing in scholars from across the disciplinary borders. Much of the research has focused on case study material relating to the border crossing experience as borders have become more porous and easier to transverse. Theoretical questions have been limited in their nature, with a focus on the dynamics of the bordering process, as contrasted with the border and its demarcation per se. The globalization narrative which focuses on the process through which borders are opened, and in some cases may be totally removed, is now paralleled by the post 9/11 securitization discourse which focuses on the reclosing and resealing of borders, even though the actual border dynamics may be taking place in different border spaces than the physical border itself. The next generation of border scholars need to posit a new set of challenges and questions which will help bridge the scholarly and practitioner understandings of border dynamics and change.

Individuals affected with tuberous sclerosis complex (TSC) develop cortical tubers characterized by disorganized cytoarchitecture and morphologically abnormal cell types, such as dysplastic neurons (DNs) and giant cells (GCs). As part of ongoing cDNA array analysis to study the molecular pathogenesis of tuber formation, we detected increased expression of intercellular adhesion molecule-1 (ICAM-1) mRNA, a cell adhesion molecule (CAM) that functions in cytokine signaling, in tubers. Western and immunohistochemical analyses revealed that ICAM-1 protein was selectively expressed in tubers, but was only minimally expressed in control cortex, adjacent nontuberal cortex, or in non-TSC focal cortical dysplasia. Increased expression of ICAM-1 was found in mice in which the Tsc1 gene was conditionally inactivated in astrocytes. Expression of molecules involved in ICAM-1 activation and cytokine signaling were increased in tubers, including tumor necrosis factor alpha (TNF-α), mitogen activated protein kinase (MAPK), and nuclear factor kappa B (NF-κB). Numerous CD68-immunoreactive macrophages were observed clustered around GCs further supporting an inflammatory response in tubers. Expression of caspase 8 and Fas support cytokine activation and detection of TUNEL reactivity suggests ongoing cell death in tubers. Specific alterations in ICAM-1, TNF-α, NF-κB1, and MAPK expression coupled with the detection of numerous CD68-immunoreactive macrophages suggests activation of proinflammatory cytokine signaling pathways in tubers that may culminate in cell death.

Context. Severe hypercalcemia is a life-threatening condition. Atypical parathyroid adenoma and parathyroid carcinomas are uncommon causes which can be difficult to differentiate. Objective. We report a case of a 36-year-old male with very high serum calcium due to a possible atypical parathyroid adenoma versus parathyroid carcinoma. Case Illustration. A serum calcium level of 23.2 mg/dl was noted on admission. He was initially treated with IV hydration, pamidronate, and salmon calcitonin to lower his calcium levels. He also underwent a surgical en bloc resection of parathyroid mass. Pathology showed a mixed picture consistent with possible atypical adenoma versus parathyroid carcinoma. However, due to the possible involvement of the recurrent laryngeal nerve, parathyroid carcinoma was more likely. Also after operation the patient developed hungry bones syndrome and his calcium was replaced vigorously. He continues to be on calcium, vitamin D, and calcitriol supplementation. Results. A review of the literature was conducted to identify previous studies pertaining to parathyroid adenomas and parathyroid cancer. Conclusion. We thereby conclude that hypercalcemia requires very careful monitoring especially after operation. Also it can be very difficult to distinguish between atypical parathyroid adenomas and parathyroid carcinomas as in our case and no clear cut guidelines yet exist to differentiate the two based on histology.

We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents.We used a corpus of 2.15 million recent (2004-2008) records from MEDLINE, and generated nine different document-document similarity matrices from information extracted from their bibliographic records, including titles, abstracts and subject headings. The nine approaches were comprised of five different analytical techniques with two data sources. The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models--BM25 and PMRA (PubMed Related Articles). The two data sources were a) MeSH subject headings, and b) words from titles and abstracts. Each similarity matrix was filtered to keep the top-n highest similarities per document and then clustered using a combination of graph layout and average-link clustering. Cluster results from the nine similarity approaches were compared using (1) within-cluster textual coherence based on the Jensen-Shannon divergence, and (2) two concentration measures based on grant-to-article linkages indexed in MEDLINE.PubMed's own related article approach (PMRA) generated the most coherent and most concentrated cluster solution of the nine text-based similarity approaches tested, followed closely by the BM25 approach using titles and abstracts. Approaches using only MeSH subject headings were not competitive with those based on titles and abstracts.