When Mommy or Daddy is Gay: Developing Constitutional Standards for Custody Decisions

Divorce can be one of the most traumatic and stressful experiences a person will undergo in his or her lifetime. When the trauma of divorce is intensified by a battle over custody of one\u27s children, the process becomes even more difficult. When someone involved in that process is at the same time dealing with issues of same-sex orientation, perhaps for the first time, it is easy to see why gay, lesbian, or bisexual individuals involved in a dissolution of marriage may experience extreme pressure, since the issue of their sexual orientation could become a critical issue in the court proceedings. When gay and lesbian parents are forced to make the choice between their children and their partners, or when courts make that choice for them by imposing draconian restrictions based on myths about how homosexuality impacts child-rearing, the impact of divorce is particularly heartbreaking

When Mommy or Daddy is Gay: Developing Constitutional Standards for Custody Decisions

Divorce can be one of the most traumatic and stressful experiences a person will undergo in his or her lifetime. When the trauma of divorce is intensified by a battle over custody of one\u27s children, the process becomes even more difficult. When someone involved in that process is at the same time dealing with issues of same-sex orientation, perhaps for the first time, it is easy to see why gay, lesbian, or bisexual individuals involved in a dissolution of marriage may experience extreme pressure, since the issue of their sexual orientation could become a critical issue in the court proceedings. When gay and lesbian parents are forced to make the choice between their children and their partners, or when courts make that choice for them by imposing draconian restrictions based on myths about how homosexuality impacts child-rearing, the impact of divorce is particularly heartbreaking

An effective scalable SQL engine for NoSQL databases

NoSQL databases were initially devised to support a few concrete extreme scale applications. Since the specificity and scale of the target systems justified the investment of manually crafting application code their limited query and indexing capabilities were not a major im- pediment. However, with a considerable number of mature alternatives now available there is an increasing willingness to use NoSQL databases in a wider and more diverse spectrum of applications and, to most of them, hand-crafted query code is not an enticing trade-off. In this paper we address this shortcoming of current NoSQL databases with an effective approach for executing SQL queries while preserving their scalability and schema flexibility. We show how a full-fledged SQL engine can be integrated atop of HBase leading to an ANSI SQL compli- ant database. Under a standard TPC-C workload our prototype scales linearly with the number of nodes in the system and outperforms a NoSQL TPC-C implementation optimized for HBase.(undefined

An approach to less climate-impactful conferences

Air travelling has a large and rapidly increasing impact on our global climate; as frequent conference goers, we all share an uncomfortable responsibility. We describe a modest attempt at reducing conference-related air travel while remaining true to the core benets of scientific conferencing

TGF-β Sustains Tumor Progression through Biochemical and Mechanical Signal Transduction.

Transforming growth factor β (TGF-β) signaling transduces immunosuppressive biochemical and mechanical signals in the tumor microenvironment. In addition to canonical SMAD transcription factor signaling, TGF-β can promote tumor growth and survival by inhibiting proinflammatory signaling and extracellular matrix (ECM) remodeling. In this article, we review how TGF-β activated kinase 1 (TAK1) activation lies at the intersection of proinflammatory signaling by immune receptors and anti-inflammatory signaling by TGF-β receptors. Additionally, we discuss the role of TGF-β in the mechanobiology of cancer. Understanding how TGF-β dampens proinflammatory responses and induces pro-survival mechanical signals throughout cancer development is critical for designing therapeutics that inhibit tumor progression while bolstering the immune response

Comparing Machine Learning Methods for Estimating Heterogeneous Treatment Effects by Combining Data from Multiple Randomized Controlled Trials

Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to improve the power to estimate heterogeneous treatment effects. This paper discusses several non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder

Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity

Estimating treatment effects conditional on observed covariates can improve the ability to tailor treatments to particular individuals. Doing so effectively requires dealing with potential confounding, and also enough data to adequately estimate effect moderation. A recent influx of work has looked into estimating treatment effect heterogeneity using data from multiple randomized controlled trials and/or observational datasets. With many new methods available for assessing treatment effect heterogeneity using multiple studies, it is important to understand which methods are best used in which setting, how the methods compare to one another, and what needs to be done to continue progress in this field. This paper reviews these methods broken down by data setting: aggregate-level data, federated learning, and individual participant-level data. We define the conditional average treatment effect and discuss differences between parametric and nonparametric estimators, and we list key assumptions, both those that are required within a single study and those that are necessary for data combination. After describing existing approaches, we compare and contrast them and reveal open areas for future research. This review demonstrates that there are many possible approaches for estimating treatment effect heterogeneity through the combination of datasets, but that there is substantial work to be done to compare these methods through case studies and simulations, extend them to different settings, and refine them to account for various challenges present in real data