Publications

Achilles tendinopathy or rupture and anterior cruciate ligament (ACL) rupture are substantial injuries affecting athletes, associated with delayed recovery or inability to return to competition. To identify genetic markers that might be used to predict risk for these injuries, we performed genome-wide association screens for these injuries using data from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort consisting of 102,979 individuals. We did not find any single nucleotide polymorphisms (SNPs) associated with either of these injuries with a p-value that was genome-wide significant (p<5x10-8). We found, however, four and three polymorphisms with p-values that were borderline significant (p<10-6) for Achilles tendon injury and ACL rupture, respectively. We then tested SNPs previously reported to be associated with either Achilles tendon injury or ACL rupture. None showed an association in our cohort with a false discovery rate of less than 5%. We obtained, however, moderate to weak evidence for replication in one case; specifically, rs4919510 in MIR608 had a p-value of 5.1x10-3 for association with Achilles tendon injury, corresponding to a 7% chance of false replication. Finally, we tested 2855 SNPs in 90 candidate genes for musculoskeletal injury, but did not find any that showed a significant association below a false discovery rate of 5%. We provide data containing summary statistics for the entire genome, which will be useful for future genetic studies on these injuries.

The dorsal raphe (DR) constitutes a major serotonergic input to the forebrain and modulates diverse functions and brain states, including mood, anxiety, and sensory and motor functions. Most functional studies to date have treated DR serotonin neurons as a single population. Using viral-genetic methods, we found that subcortical- and cortical-projecting serotonin neurons have distinct cell-body distributions within the DR and differentially co-express a vesicular glutamate transporter. Further, amygdala- and frontal-cortex-projecting DR serotonin neurons have largely complementary whole-brain collateralization patterns, receive biased inputs from presynaptic partners, and exhibit opposite responses to aversive stimuli. Gain- and loss-of-function experiments suggest that amygdala-projecting DR serotonin neurons promote anxiety-like behavior, whereas frontal-cortex-projecting neurons promote active coping in the face of challenge. These results provide compelling evidence that the DR serotonin system contains parallel sub-systems that differ in input and output connectivity, physiological response properties, and behavioral functions.

Inhibition of the aromatase system, in particular with third-generation aromatase inhibitors and inactivators, appears to be associated with statistically significant improved survival of patients with advanced breast cancer compared with standard hormonal treatments.

Abstract Objective The development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability in specific populations. We sought to evaluate whether studies developing ML models from electronic health record (EHR) data report sufficient demographic data on the study populations to demonstrate representativeness and reproducibility. Materials and Methods We searched PubMed for articles applying ML models to improve clinical decision-making using EHR data. We limited our search to papers published between 2015 and 2019. Results Across the 164 studies reviewed, demographic variables were inconsistently reported and/or included as model inputs. Race/ethnicity was not reported in 64%; gender and age were not reported in 24% and 21% of studies, respectively. Socioeconomic status of the population was not reported in 92% of studies. Studies that mentioned these variables often did not report if they were included as model inputs. Few models (12%) were validated using external populations. Few studies (17%) open-sourced their code. Populations in the ML studies include higher proportions of White and Black yet fewer Hispanic subjects compared to the general US population. Discussion The demographic characteristics of study populations are poorly reported in the ML literature based on EHR data. Demographic representativeness in training data and model transparency is necessary to ensure that ML models are deployed in an equitable and reproducible manner. Wider adoption of reporting guidelines is warranted to improve representativeness and reproducibility.

Describes Tiny Tera: a small, high-bandwidth, single-stage switch. Tiny Tera has 32 ports switching fixed-size packets, each operating at over 10 Gbps (approximately the Sonet OC-192e rate, a telecom standard for system interconnects). The switch distinguishes four classes of traffic and includes efficient support for multicasting. We aim to demonstrate that it is possible to use currently available CMOS technology to build this compact switch with an aggregate bandwidth of approximately 1 terabit per second and a central hub no larger than a can of soda. Such a switch could serve as a core for an ATM switch or an Internet router. Tiny Tera is an input-buffered switch, which makes it the highest bandwidth switch possible given a particular CMOS and memory technology. The switch consists of three logical elements: ports, a central crossbar switch, and a central scheduler. It queues packets at a port on entry and optionally prior to exit. The scheduler, which has a map of each port's queue occupancy, determines the crossbar configuration every packet time slot. Input queueing, parallelism, and tight integration are the keys to such a high-bandwidth switch. Input queueing reduces the memory bandwidth requirements: When a switch queues packets at the input, the buffer memories need run no faster than the line rate. Thus, there is no need for the speedup required in output-queued switches.

Simulation is the primary method for evaluating computer systems during all phases of the design process. One significant problem with simulation is that it rarely models the system exactly, and quantifying the resulting simulator error can be difficult. More importantly, architects often assume without proof that although their simulator may make inaccurate absolute performance predictions, it will still accurately predict architectural trends.This paper studies the source and magnitude of error in a range of architectural simulators by comparing the simulated execution time of several applications and microbenchmarks to their execution time on the actual hardware being modeled. The existence of a hardware gold standard allows us to find, quantify, and fix simulator inaccuracies. We then use the simulators to predict architectural trends and analyze the sensitivity of the results to the simulator configuration. We find that most of our simulators predict trends accurately, as long as they model all of the important performance effects for the application in question. Unfortunately, it is difficult to know what these effects are without having a hardware reference, as they can be quite subtle. This calls into question the value, for architectural studies, of highly detailed simulators whose characteristics are not carefully validated against s real hardware design.

Abstract Large language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task‐specific LLMs have demonstrated potential across diverse applications. Science and medicine have many data types that are highly suitable for LLMs, such as scientific texts (publications, patents and textbooks), electronic medical records, large databases of DNA and protein sequences and chemical compounds. Carefully validated systems that can understand and reason across all these modalities may maximize benefits. Despite the inevitable limitations and caveats of any new technology and some uncertainties specific to LLMs, LLMs have the potential to be transformative in science and medicine.