Gun Violence, Policing, and Young Communities of Color

Young people of color are leading the response to recent instances of gun violence. Young people do not all experience gun violence at the same rate nor do they feel its consequences evenly. Our research on young adults between the ages of 18 and 29 years old highlights the very different experiences young people have with guns, gun violence, and policing across racial and ethnic groups

Tree-level electron-photon interactions in graphene

Graphene's low-energy electronic excitations obey a 2+1 dimensional Dirac Hamiltonian. After extending this Hamiltonian to include interactions with a quantized electromagnetic field, we calculate the amplitude associated with the simplest, tree-level Feynman diagram: the vertex connecting a photon with two electrons. This amplitude leads to analytic expressions for the 3D angular dependence of photon emission, the photon-mediated electron-hole recombination rate, and corrections to graphene's opacity $\pi \alpha$ and dynamic conductivity $\pi e^2/2 h$ for situations away from thermal equilibrium, as would occur in a graphene laser. We find that Ohmic dissipation in perfect graphene can be attributed to spontaneous emission.Comment: 5 pages, 3 figure

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.

BackgroundAutism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.MethodsWe introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.LimitationsWhile this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.ResultsOur models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.ConclusionThis study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism

Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.

BackgroundAutism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied.MethodsWe introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs.LimitationsWhile this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism.ResultsOur models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity.ConclusionThis study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism

Anterior cruciate ligament reconstruction with bone-patellar tendon-bone autograft versus allograft in young patients

Objectives: Traditionally, bone-patella tendon-bone (BTB) autograft has been the gold standard graft choice for younger, athletic patients requiring ACL reconstruction. However, donor site morbidity, post-operative patella fracture, and increased operative time have led many surgeons to choose BTB allograft for their reconstructions. Opponents of allografts feel that slower healing time, higher rate of graft failure, and potential for disease transmission makes them undesirable graft choices in athletic patients. The purpose of this study is to evaluate the clinical outcomes, both subjective and objective, of young patients that who have undergone either BTB autograft or allograft reconstructions with a minimum of 2-year follow-up. Methods: One hundred and twenty patients (60 autograft, 60 allograft), age 25 and below at time of surgery, were contacted after being retrospectively identified as patients having an ACL reconstruction with either a BTB allograft or autograft by one senior surgeon. Patients were administered the Lysholm Knee Scoring Scale and IKDC Subjective Knee Evaluation questionnaires. Fifty (25 BTB autograft and 25 BTB allograft) of the 120 returned for physical examination as well as completion of a single leg hop test and laxity evaluation using a KT-1000 arthrometer evaluation. Of the 120 patients contacted, there were a total of 7 failures (5.8%) requiring revision, 6 in the allograft group (86%) and 1 in the autograft group (14%). Results: The average Lysholm scores were 89.0 and 89.56 and the average IKDC scores were 90.8 and 92.1 in the autograft and allograft groups respectively. The differences in the Lysholm scores and the IKDC scores were not significant. The single leg hop and KT-1000 scores were also not significantly different. One autograft patient had a minor motion deficit. Three allograft patients had a grade 1 Lachman and pivot glide. One autograft patient and two allograft patients had mild patellafemoral crepitus. There was no significant difference in anterior knee pain between the two groups Conclusion: There is no significant difference in patient-rated outcome between ACL reconstructions using BTB autografts versus allografts. However, the overall study group did reveal an increased failure rate requiring revision in the allograft group. © The Author(s) 2015

MEMS 411: Mini-Golf Robot

The following report details the design and construction of a mini-golf robot overthree months. The robot was designed under the standards provided by theASME Fall 2023 Design Challenge. The robot would need to traverse the mini-golfcourse, completing it in under 10 minutes with the assistance of an attached strikermechanism. It was necessary that the robot could position itself precisely withrespect to the goofball, while also staying stationary while striking the object. Therobot design timeline followed the guidelines of the engineering design process, withconcept generation, concept selection, prototype embodiment and design refinementall playing important roles in ensuring the design of the vehicle was best suited tothe goals it was supposed to complete. The final prototype performance goals ofthe vehicle, determined by our customer, Dr. J. Jackson Potter, were for the deviceto a) climb over a long wooden board that is 3.5” tall and 1.5” thick, b) climb onto,over, and back down from a sheet of 1/2”-thick plywood whose bottom surfaceis 3.5” above the ground, and c) position itself next to three golf balls (withoutdisturbing them) and ”aim” in a specified direction before removing the ball andcontinuing to the next ball in ¡ 1 minute, all while carrying extra weight in theshape of a wooden striker template. The group was able to complete prototypegoal number 3 successfully but failed to complete prototype goals 1 or 2. Thisreport outlines the full process of the creation and assembly of the vehicle

Education in America: The Views of Millennials

This GenForward report presents the views of young adults between the ages of 18 and 34 on education in the United States. We asked our nationally representative and diverse sample of young adults to provide their evaluations of their own school system, their thoughts about what makes a good school, their preferences regarding proposals for reforming education, their perceptions about issues of equity in schools, and their beliefs about the promise and challenges of higher education in America today. Who better to assess the strengths and weaknesses of our educational systems than those most proximate to the American educational experience? Many Millennials recently graduated from high school, while some are currently pursuing higher educational opportunities and/or navigating educational systems for their children

B Physics on the Lattice: Present and Future

Recent experimental measurements and lattice QCD calculations are now reaching the precision (and accuracy) needed to over-constrain the CKM parameters $\bar\rho$ and $\bar\eta$. In this brief review, I discuss the current status of lattice QCD calculations needed to connect the experimental measurements of $B$ meson properties to quark flavor-changing parameters. Special attention is given to $B\to\pi\ell\nu$, which is becoming a competitive way to determine $|V_{ub}|$, and to $B^0-\bar{B^0}$ mixings, which now include reliable extrapolation to the physical light quark mass. The combination of the recent measurement of the $B_s$ mass difference and current lattice calculations dramatically reduces the uncertainty in $|V_{td}|$. I present an outlook for reducing dominant lattice QCD uncertainties entering CKM fits, and I remark on lattice calculations for other decay channels.Comment: Invited brief review for Mod. Phys. Lett. A. 15 pages. v2: typos corrected, references adde