Monitoring Waste to Minimize Waste at the University of Massachusetts Amherst

The University of Massachusetts Amherst is committed to sustainability, however, the campus could further reduce its costs and save energy by optimizing the current method of waste removal. The Intergovernmental Panel on Climate Change predicts that by the end of the century, Earth’s average temperature will rise by 11 degrees Fahrenheit unless society takes action to reduce greenhouse gas emissions. According to the EPA, about one-third of carbon emissions in the U.S. come from transportation. Campus garbage bins are collected by carbon-emitting trucks daily, and large truckable waste compactors are collected about three times per week. The amount of harmful carbon emissions released by trucking all of the compactors to their disposal sites totals 9,600 pounds of CO2 (the weight of 12 grand pianos) every week. In this analysis, the current waste removal system is investigated and a method is proposed to save UMass money and energy by reducing the number of waste collections. Initial research focused on how traditional bins could be replaced with solar-powered compactors from Bigbelly Solar Inc. to reduce pickup frequency and generate revenue from separating waste. Findings indicate that solar compactors alone would not have a worthwhile impact on the energy consumption of the UMass campus. Alternatively, a monitoring system that reduces how frequently waste compactors are hauled from campus would have greater impact, saving $1,000 every two weeks, reducing harmful carbon emissions, and using less diesel fuel. Due to the current environmental crisis, UMass should take action to reduce its carbon footprint through this economically favorable system

Post-Traumatic Epilepsy Associations with Mental Health Outcomes in the First Two Years after Moderate to Severe TBI: A TBI Model Systems Analysis

Purpose Research suggests that there are reciprocal relationships between mental health (MH) disorders and epilepsy risk. However, MH relationships to post-traumatic epilepsy (PTE) have not been explored. Thus, the objective of this study was to assess associations between PTE and frequency of depression and/or anxiety in a cohort of individuals with moderate-to-severe TBI who received acute inpatient rehabilitation. Methods Multivariate regression models were developed using a recent (2010–2012) cohort (n = 867 unique participants) from the TBI Model Systems (TBIMS) National Database, a time frame during which self-reported seizures, depression [Patient Health Questionnaire (PHQ)-9], and anxiety [Generalized Anxiety Disorder (GAD-7)] follow-up measures were concurrently collected at year-1 and year-2 after injury. Results PTE did not significantly contribute to depression status in either the year-1 or year-2 cohort, nor did it contribute significantly to anxiety status in the year-1 cohort, after controlling for other known depression and anxiety predictors. However, those with PTE in year-2 had 3.34 times the odds (p = .002) of having clinically significant anxiety, even after accounting for other relevant predictors. In this model, participants who self-identified as Black were also more likely to report clinical symptoms of anxiety than those who identified as White. PTE was the only significant predictor of comorbid depression and anxiety at year-2 (Odds Ratio 2.71; p = 0.049). Conclusions Our data suggest that PTE is associated with MH outcomes 2 years after TBI, findings whose significance may reflect reciprocal, biological, psychological, and/or experiential factors contributing to and resulting from both PTE and MH status post-TBI. Future work should consider temporal and reciprocal relationships between PTE and MH as well as if/how treatment of each condition influences biosusceptibility to the other condition

Optimizing management of invasions in an uncertain world using dynamic spatial models

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions

Incidence and risk factors of posttraumatic seizures following traumatic brain injury: A Traumatic Brain Injury Model Systems Study

Objective Determine incidence of posttraumatic seizure (PTS) following traumatic brain injury (TBI) among individuals with moderate-to-severe TBI requiring rehabilitation and surviving at least 5 years. Methods Using the prospective TBI Model Systems National Database, we calculated PTS incidence during acute hospitalization, and at years 1, 2, and 5 postinjury in a continuously followed cohort enrolled from 1989 to 2000 (n = 795). Incidence rates were stratified by risk factors, and adjusted relative risk (RR) was calculated. Late PTS associations with immediate (7 day) versus no seizure prior to discharge from acute hospitalization was also examined. Results PTS incidence during acute hospitalization was highest immediately (<24 h) post-TBI (8.9%). New onset PTS incidence was greatest between discharge from inpatient rehabilitation and year 1 (9.2%). Late PTS cumulative incidence from injury to year 1 was 11.9%, and reached 20.5% by year 5. Immediate/early PTS RR (2.04) was increased for those undergoing surgical evacuation procedures. Late PTS RR was significantly greater for individuals who self-identified as a race other than black/white (year 1 RR = 2.22), and for black individuals (year 5 RR = 3.02) versus white individuals. Late PTS was greater for individuals with subarachnoid hemorrhage (year 1 RR = 2.06) and individuals age 23–32 (year 5 RR = 2.43) and 33–44 (year 5 RR = 3.02). Late PTS RR years 1 and 5 was significantly higher for those undergoing surgical evacuation procedures (RR: 3.05 and 2.72, respectively). Significance In this prospective, longitudinal, observational study, PTS incidence was similar to that in studies published previously. Individuals with immediate/late seizures during acute hospitalization have increased late PTS risk. Race, intracranial pathologies, and neurosurgical procedures also influenced PTS RR. Further studies are needed to examine the impact of seizure prophylaxis in high-risk subgroups and to delineate contributors to race/age associations on long-term seizure outcomes

Prognostic models for predicting posttraumatic seizures during acute hospitalization, and at 1 and 2 years following traumatic brain injury

Objective Posttraumatic seizures (PTS) are well-recognized acute and chronic complications of traumatic brain injury (TBI). Risk factors have been identified, but considerable variability in who develops PTS remains. Existing PTS prognostic models are not widely adopted for clinical use and do not reflect current trends in injury, diagnosis, or care. We aimed to develop and internally validate preliminary prognostic regression models to predict PTS during acute care hospitalization, and at year 1 and year 2 postinjury. Methods Prognostic models predicting PTS during acute care hospitalization and year 1 and year 2 post-injury were developed using a recent (2011–2014) cohort from the TBI Model Systems National Database. Potential PTS predictors were selected based on previous literature and biologic plausibility. Bivariable logistic regression identified variables with a p-value < 0.20 that were used to fit initial prognostic models. Multivariable logistic regression modeling with backward-stepwise elimination was used to determine reduced prognostic models and to internally validate using 1,000 bootstrap samples. Fit statistics were calculated, correcting for overfitting (optimism). Results The prognostic models identified sex, craniotomy, contusion load, and pre-injury limitation in learning/remembering/concentrating as significant PTS predictors during acute hospitalization. Significant predictors of PTS at year 1 were subdural hematoma (SDH), contusion load, craniotomy, craniectomy, seizure during acute hospitalization, duration of posttraumatic amnesia, preinjury mental health treatment/psychiatric hospitalization, and preinjury incarceration. Year 2 significant predictors were similar to those of year 1: SDH, intraparenchymal fragment, craniotomy, craniectomy, seizure during acute hospitalization, and preinjury incarceration. Corrected concordance (C) statistics were 0.599, 0.747, and 0.716 for acute hospitalization, year 1, and year 2 models, respectively. Significance The prognostic model for PTS during acute hospitalization did not discriminate well. Year 1 and year 2 models showed fair to good predictive validity for PTS. Cranial surgery, although medically necessary, requires ongoing research regarding potential benefits of increased monitoring for signs of epileptogenesis, PTS prophylaxis, and/or rehabilitation/social support. Future studies should externally validate models and determine clinical utility

RB but not R-HCVAD is a feasible induction regimen prior to auto-HCT in frontline MCL: results of SWOG Study S1106

Aggressive induction chemotherapy followed by autologous haematopoietic stem cell transplant (auto-HCT) is effective for younger patients with mantle cell lymphoma (MCL). However, the optimal induction regimen is widely debated. The Southwesterm Oncology Group S1106 trial was designed to assess rituximab plushyperCVAD/MTX/ARAC (hyperfractionated cyclophosphamide, vincristine, doxorubicin and dexamethasone, alternating with high dose cytarabine and methotrexate) (RH) versus rituximab plus bendamustine (RB) in a randomized phase II trial to select a pre-transplant induction regimen for future development. Patients had previously untreated stage III, IV, or bulky stage II MCL and received either 4 cycles of RH or 6 cycles of RB, followed by auto-HCT. Fifty-three of a planned 160 patients were accrued; an unacceptably high mobilization failure rate (29%) on the RH arm prompted premature study closure. The estimated 2-year progression-free survival (PFS) was 81% vs. 82% and overall survival (OS) was 87% vs. 88% for RB and RH, respectively. RH is not an ideal platform for future multi-centre transplant trials in MCL. RB achieved a 2-year PFS of 81% and a 78% MRD negative rate. Premature closure of the study limited the sample size and the precision of PFS estimates and MRD rates. However, RB can achieve a deep remission and could be a platform for future trials in MCL

RB but not R‐HCVAD is a feasible induction regimen prior to auto‐HCT in frontline MCL: results of SWOG Study S1106

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136294/1/bjh14480_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136294/2/bjh14480.pd