What’s Behind Recent Transit Ridership Trends in the Bay Area? Volume I: Overview and Analysis of Underlying Factors

Public transit ridership has been falling nationally and in California since 2014. The San Francisco Bay Area, with the state’s highest rates of transit use, had until recently resisted those trends, especially compared to Greater Los Angeles. However, in 2017 and 2018 the region lost over five percent (>27 million) of its annual riders, despite a booming economy and service increases. This report examines Bay Area transit ridership to understand the dimensions of changing transit use, its possible causes, and potential solutions. We find that: 1) the steepest ridership losses have come on buses, at off-peak times, on weekends, in non-commute directions, on outlying lines, and on operators that do not serve the region’s core employment clusters; 2) transit trips in the region are increasingly commute-focused, particularly into and out of downtown San Francisco; 3) transit commuters are increasingly non-traditional transit users, such as those with higher incomes and automobile access; 4) the growing job-housing imbalance in the Bay Area is related to rising housing costs and likely depressing transit ridership as more residents live less transit-friendly parts of the region; and 5) ridehail is substituting for some transit trips, particularly in the off-peak. Arresting falling transit use will likely require action both by transit operators (to address peak capacity constraints; improve off-peak service; ease fare payments; adopt fare structures that attract off-peak riders; and better integrate transit with new mobility options) and public policymakers in other realms (to better meter and manage private vehicle use and to increase the supply and affordability of housing near job centers)

Adaptation-Based Programming in Haskell

We present an embedded DSL to support adaptation-based programming (ABP) in Haskell. ABP is an abstract model for defining adaptive values, called adaptives, which adapt in response to some associated feedback. We show how our design choices in Haskell motivate higher-level combinators and constructs and help us derive more complicated compositional adaptives. We also show an important specialization of ABP is in support of reinforcement learning constructs, which optimize adaptive values based on a programmer-specified objective function. This permits ABP users to easily define adaptive values that express uncertainty anywhere in their programs. Over repeated executions, these adaptive values adjust to more efficient ones and enable the user's programs to self optimize. The design of our DSL depends significantly on the use of type classes. We will illustrate, along with presenting our DSL, how the use of type classes can support the gradual evolution of DSLs.Comment: In Proceedings DSL 2011, arXiv:1109.032

Human SP-D acts as an innate immune surveillance molecule against androgen-responsive and androgen-resistant prostate cancer cells

Surfactant Protein D (SP-D), a pattern recognition innate immune molecule, has been implicated in the immune surveillance against cancer. A recent report showed an association of decreased SP-D expression in human prostate adenocarcinoma with an increased Gleason score and severity. In the present study, the SP-D expression was evaluated in primary prostate epithelial cells (PrEC) and prostate cancer cell lines. LNCaP, an androgen dependent prostate cancer cell line, exhibited significantly lower mRNA and protein levels of SP-D than PrEC and the androgen independent cell lines (PC3 and DU145). A recombinant fragment of human SP-D, rfhSP-D, showed a dose and time dependent binding to prostate cancer cells via its carbohydrate recognition domain. This study, for the first time, provides evidence of significant and specific cell death of tumor cells in rfhSP-D treated explants as well as primary tumor cells isolated from tissue biopsies of metatstatic prostate cancer patients. Viability of PrEC was not altered by rfhSP-D. Treated LNCaP (p53+/+) and PC3 (p53 −/−) cells exhibited reduced cell viability in a dose and time dependent manner and were arrested in G2/M and G1/G0 phase of the cell cycle, respectively. rfhSP-D treated LNCaP cells showed a significant upregulation of p53 whereas a significant downregulation of pAkt was observed in both PC3 and LNCaP cell lines. The rfhSP-D-induced apoptosis signaling cascade involved upregulation of Bax:Bcl2 ratio, cytochrome c and cleaved products of caspase 7. The study concludes that rfhSP-D induces apoptosis in prostate tumor explants as well as in androgen dependent and independent prostate cancer cells via p53 and pAkt pathways.ICMR-NIRR

Sperm DNA fragmentation: A new guideline for clinicians

Sperm DNA integrity is crucial for fertilization and development of healthy offspring. The spermatozoon undergoes extensive molecular remodeling of its nucleus during later phases of spermatogenesis, which imparts compaction and protects the genetic content. Testicular (defective maturation and abortive apoptosis) and post-testicular (oxidative stress) mechanisms are implicated in the etiology of sperm DNA fragmentation (SDF), which affects both natural and assisted reproduction. Several clinical and environmental factors are known to negatively impact sperm DNA integrity. An increasing number of reports emphasizes the direct relationship between sperm DNA damage and male infertility. Currently, several assays are available to assess sperm DNA damage, however, routine assessment of SDF in clinical practice is not recommended by professional organizations

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock

A computational approach to the study of social interaction

For scientists, explanations of natural phenomenon based on optimality principles are critical tools for understanding the phenomena that shape the solutions the brain devises for the complex perceptual and motor problems of daily life. The neuroscientist David Marr called this type of analysis the "computational approach''. While the computational approach has been applied with a great deal of success to phenomena such as neural coding and human motor control, the success of the computational approach for studying interactive behavior, particularly social behavior, has been more modest. The purpose of this dissertation is threefold: to make the case for the study of social interaction from the computational perspective; to understand the challenges involved in this study and provide computational tools to address these challenges; and to apply the computational approach to the study of social behavior in the real world. Our principle contributions are : (1) developing a framework for both analyzing and synthesizing behaviors in continuous state, action, and time from the perspective of the intentions that these behaviors appear to be realizing (our approach is well-suited for many motor-control and social-interaction problems), and (2) carrying out two computational studies of early infant social behavior that shed light on the computational forces that shape development. Our empirical results provide a new view of early infant social behavior as intentional, with the surprising intention of maximizing time spent with mother smiling at infant and the infant not smiling hersel

Dude, Where's My Robot?: A Localization Challenge for Undergraduate Robotics

I present a robotics localization challenge based on the inexpensive Neato XV robotic vacuum cleaner platform. The challenge teaches skills such as computational modeling, probabilistic inference, efficiency vs. accuracy tradeoffs, debugging, parameter tuning, and benchmarking of algorithmic performance. Rather than allowing students to pursue any localization algorithm of their choosing, here, I propose a challenge structured around the particle filter family of algorithms. This additional scaffolding allows students at all levels to successfully implement one approach to the challenge, while providing enough flexibility and richness to enable students to pursue their own creative ideas. Additionally, I provide infrastructure for automatic evaluation of systems through the collection of ground truth robot location data via ceiling-mounted location tags that are automatically scanned using an upward facing camera attached to the robot. The robot and supporting hardware can be purchased for under $400 dollars, and the challenge can even be run without any robots at all using a set of recorded sensor traces

Automatic cry detection in early childhood education settings

Abstract—We present results on a novel machine learning approach for learning auditory moods in natural environments [1]. Here, we apply the approach for problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2]. I

Online Multi-Task Learning via Sparse Dictionary Optimization

This paper develops an efficient online algorithm for learning multiple consecutive tasks based on the K-SVD algorithm for sparse dictionary optimization. We first derive a batch multi-task learning method that builds upon K-SVD, and then extend the batch algorithm to train models online in a lifelong learning setting. The resulting method has lower computational complexity than other current lifelong learning algorithms while maintaining nearly identical model performance. Additionally, the proposed method offers an alternate formulation for lifelong learning that supports both task and feature similarity matrices