Income Shocks and Gender Gaps in Education: Evidence from Uganda

This paper uses exogenous variation in rainfall across districts in Uganda to estimate the causal effects of household income shocks to in children’s enrollment and cognitive skills conditional on gender. I find negative income shocks to have large negative and highly significant effects on female enrollment in primary schools and the effect grows stronger for older girls. The effect on boys’ enrollment is smaller and only marginally significant. Moreover, I find that a negative income shock has an adverse effect on test scores in general and test scores of female students in particular. The results imply that households respond to income shocks by varying the quantity and quality of girls’ education while boys are to a larger extent sheltered – a finding consistent with a model where parents’ values of child labor differ across sexes.Rainfall; education; test scores; gender

Deep Predictive Policy Training using Reinforcement Learning

Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small sub-network with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems 2017 (IROS2017

Securely Launching Virtual Machines on Trustworthy Platforms in a Public Cloud

In this paper we consider the Infrastructure-as-a-Service (IaaS) cloud model which allows cloud users to run their own virtual machines (VMs) on available cloud computing resources. IaaS gives enterprises the possibility to outsource their process workloads with minimal effort and expense. However, one major problem with existing approaches of cloud leasing, is that the users can only get contractual guarantees regarding the integrity of the offered platforms. The fact that the IaaS user himself or herself cannot verify the provider promised cloud platform integrity, is a security risk which threatens to prevent the IaaS business in general. In this paper we address this issue and propose a novel secure VM launch protocol using Trusted Computing techniques. This protocol allows the cloud IaaS users to securely bind the VM to a trusted computer configuration such that the clear text VM only will run on a platform that has been booted into a trustworthy state. This capability builds user confidence and can serve as an important enabler for creating trust in public clouds. We evaluate the feasibility of our proposed protocol via a full scale system implementation and perform a system security analysis

Comparative pharmacokinetics of plasma- and albumin-free recombinant factor VIII in children and adults: the influence of blood sampling schedule on observed age-related differences and implications for dose tailoring

Background: Dose tailoring of coagulation factors requires reliably estimated and reproducible pharmacokinetics (PK) in the individual patient. Objectives: To investigate the contribution of both biological and methodological factors to the observed variability of factor VIII (FVIII) PK, with the focus on differences between children and adults, and to examine the implications for dosing. Patients: Data from 52 1–6-year-old and 100 10–65-year-old patients with hemophilia A (FVIII £ 2 IU dL)1) in three clinical studies were included. Results: In vivo recovery was lower, weight-adjusted clearance was higher and FVIII half-life was on average shorter in children than in adults. However, a reduced blood sampling schedule for children was estimated to account for up to one half of the total observed differences. Intrapatient variance in PK was smaller than interpatient variance in 10–65-year-olds. Age and ratio of actual to ideal weight only showed weak relationships with PK parameters. Variance in PK caused large variance in the calculated dose required to maintain a target FVIII trough level during prophylactic treatment. Conclusion: Differences in blood sampling schedules should be taken into account when results from different PK studies are compared. However, even with this consideration, PK cannot be predicted from observable patient characteristics but must be determined for the individual. Because the influence of reducing the blood samplingwas minor in comparison to the true variance between patients, a reduced blood sampling protocol can be used. Low intrapatient variability supports the use of PK measurements for dose tailoring of FVIII

Identification of Epigenetic Targets in Prostate Cancer for Therapeutic Development

Recurrent castration resistant prostate cancer remains a challenge for cancer therapies and novel treatment options in addition to current anti-androgen and mitosis inhibitors are needed. Aberrations in epigenetic enzymes and chromatin binding proteins have been linked to prostate cancer and they may form a novel class of drug targets in the future. In this thesis we systematically evaluated the epigenenome as a prostate cancer drug target. We functionally silenced 615 known and putative epigenetically active protein coding genes in prostate cancer cell lines using high throughput RNAi screening and evaluated the effects on cell proliferation, androgen receptor (AR) expression and histone patterns. Histone deacetylases (HDACs) were found to regulate AR expression. Furthermore, HDAC inhibitors reduced AR signaling and inhibited synergistically with androgen deprivation prostate cancer cell proliferation. In particular, TMPRSS2- EGR fusion gene positive prostate cancer cell lines were sensitive to combined HDAC and AR inhibition, which may partly be related to the dependency of a fusion gene induced epigenetic pathway. Histone demethylases (HDMs) were identified to regulate prostate cancer cell line proliferation. We discovered a novel histone JmjC-domain histone demethylase PHF8 to be highly expressed in high grade prostate cancers and mediate cell proliferation, migration and invasion in in vitro models. Additionally, we explored novel HDM inhibitor chemical structures using virtual screening methods. The structures best fitting to the active pocket of KDM4A were tested for enzyme inhibition and prostate cancer cell proliferation activity in vitro. In conclusion, our results show that prostate cancer may efficiently be targeted with combined AR and HDAC inhibition which is also currently being tested in clinical trials. HDMs were identified as another feasible novel drug target class. Future studies in representative animal models and development of specific inhibitors may reveal HDMs full potential in prostate cancer therapySiirretty Doriast