Causal Inference Using Variation In Treatment Over Time

This thesis and related research is motivated by my interest in understanding the use of time-varying treatments in causal inference from complex longitudinal data, which play a prominent role in public health, economics, and epidemiology, as well as in biological and medical sciences. Longitudinal data allow the direct study of temporal changes within individuals and across populations, therefore give us the edge to utilize time this important factor to explore causal relationships than static data. There are also a variety challenges that arise in analyzing longitudinal data. By the very nature of repeated measurements, longitudinal data are multivariate in various dimensions and have completed random-error structures, which make many conventional causal assumptions and related statistical methods are not directly applicable. Therefore, new methodologies, most likely data-driven, are always encouraged and sometimes necessary in longitudinal causal inference, as will be seen throughout this thesis As a result of the various topics explored, this thesis is split into four parts corresponding to three dierent patterns of variation in treatment. The rst pattern is the one-directional change of a binary treatment assignment, meaning that each study participant is only allowed to experience the change from untreated to treated at the staggered time. Such pattern is observed in a novel cluster-randomized design called the stepped-wedge. The second pattern is the arbitrary switching of a binary treatment caused by changes in person-specic characteristics and general time trend. The patterns is the most common thing one would observe in longitudinal data and we develop a method utilizing trends in treatment to account for unmeasured confounding. The third pattern is that the underlying treatment, outcome, covariates are time-continuous, yet are only observed at discrete time points. Instead of modeling cross-sectional and pooled longitudinal data, we take a mechanistic view by modeling reactions among variables using stochastic dierential equations and investigate whether it is possible to draw sensible causal conclusions from discrete measurements

Parallel In Vivo and In Vitro Melanoma RNAi Dropout Screens Reveal Synthetic Lethality between Hypoxia and DNA Damage Response Inhibition

SummaryTo identify factors preferentially necessary for driving tumor expansion, we performed parallel in vitro and in vivo negative-selection short hairpin RNA (shRNA) screens. Melanoma cells harboring shRNAs targeting several DNA damage response (DDR) kinases had a greater selective disadvantage in vivo than in vitro, indicating an essential contribution of these factors during tumor expansion. In growing tumors, DDR kinases were activated following hypoxia. Correspondingly, depletion or pharmacologic inhibition of DDR kinases was toxic to melanoma cells, including those that were resistant to BRAF inhibitor, and this could be enhanced by angiogenesis blockade. These results reveal that hypoxia sensitizes melanomas to targeted inhibition of the DDR and illustrate the utility of in vivo shRNA dropout screens for the identification of pharmacologically tractable targets

A Long Short-Term Memory Network for Plasma Diagnosis from Langmuir Probe Data

Electrostatic probe diagnosis is the main method of plasma diagnosis. However, the traditional diagnosis theory is affected by many factors, and it is difficult to obtain accurate diagnosis results. In this study, a long short-term memory (LSTM) approach is used for plasma probe diagnosis to derive electron density (Ne) and temperature (Te) more accurately and quickly. The LSTM network uses the data collected by Langmuir probes as input to eliminate the influence of the discharge device on the diagnosis that can be applied to a variety of discharge environments and even space ionospheric diagnosis. In the high-vacuum gas discharge environment, the Langmuir probe is used to obtain current–voltage (I–V) characteristic curves under different Ne and Te. A part of the data input network is selected for training, the other part of the data is used as the test set to test the network, and the parameters are adjusted to make the network obtain better prediction results. Two indexes, namely, mean squared error (MSE) and mean absolute percentage error (MAPE), are evaluated to calculate the prediction accuracy. The results show that using LSTM to diagnose plasma can reduce the impact of probe surface contamination on the traditional diagnosis methods and can accurately diagnose the underdense plasma. In addition, compared with Te, the Ne diagnosis result output by LSTM is more accurate

An Analysis of Conductor Surface Roughness Effects on Signal Propagation for Stripline Interconnects

Conductors with a roughened surface have significant effects on high-speed signal propagation on backplane traces designed for a 10+ Gb/s network. An accurate approach to evaluate these effects, including the signal attenuation and the phase delay, is proposed in this paper. A differential extrapolation roughness measurement technique is first used to extract the dielectric properties of the substrate used for lamination, and then a periodic model is used to calculate an equivalent roughened conductor surface impedance, which is then used to modify the transmission line per-unit-length parameters R and L. The results indicate that the conductor surface roughness increases the conductor loss significantly as well as noticeably increasing the effective dielectric constant. This approach is validated using both a full-wave simulation tool and measurements, and is shown to be able to provide robust results for the attenuation constant within ±0.2 Np/m up to 20 GHz

A Wearable Lower Limb Exoskeleton: Reducing the Energy Cost of Human Movement

Human body enhancement is an interesting branch of robotics. It focuses on wearable robots in order to improve the performance of human body, reduce energy consumption and delay fatigue, as well as increase body speed. Robot-assisted equipment, such as wearable exoskeletons, are wearable robot systems that integrate human intelligence and robot power. After careful design and adaptation, the human body has energy-saving sports, but it is an arduous task for the exoskeleton to achieve considerable reduction in metabolic rate. Therefore, it is necessary to understand the biomechanics of human sports, the body, and its weaknesses. In this study, a lower limb exoskeleton was classified according to the power source, and the working principle, design idea, wearing mode, material and performance of different types of lower limb exoskeletons were compared and analyzed. The study shows that the unpowered exoskeleton robot has inherent advantages in endurance, mass, volume, and cost, which is a new development direction of robot exoskeletons. This paper not only summarizes the existing research but also points out its shortcomings through the comparative analysis of different lower limb wearable exoskeletons. Furthermore, improvement measures suitable for practical application have been provided

Anther extrusion and plant height are associated with Type I resistance to Fusarium head blight in bread wheat line ‘Shanghai-3/Catbird’

acceptedVersio

Data-Driven Operation of Flexible Distribution Networks with Charging Loads

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging stations. However, the absence of accurate parameters poses challenges to model-based methods. This paper proposes a data-driven operation method of FDNs with charging loads. First, a data-driven model-free adaptive predictive control (MFAPC) approach is proposed to fully involve charging loads in the control of FDN without accurate network parameters. Then, a multi-timescale coordination control model of an SOP with charging loads is established to satisfy the demand of charging loads and improve the control performance. The effectiveness of the proposed method is numerically demonstrated on the modified IEEE 33-node distribution network. The results indicate that the proposed method can effectively reduce the electricity purchasing cost of charging stations and improve the operational performance of FDNs

Humic acids alleviate aflatoxin B1-induced hepatic injury by reprogramming gut microbiota and absorbing toxin

Aflatoxin B1 (AFB1) is a hepatotoxic fungal metabolite that is widely present in food and can cause liver cancer. As a potential detoxifier, naturally occurring humic acids (HAs) may be able to reduce inflammation and restructure the gut microbiota composition; however, little is known about the mechanism of HAs detoxification as applied to liver cells. In this study, HAs treatment alleviated AFB1-induced liver cell swelling and the infiltration of inflammatory cells. HAs treatment also reinstated various enzyme levels in the liver disturbed by AFB1 and substantially alleviated AFB1-caused oxidative stress and inflammatory responses by enhancing immune functions in mice. Moreover, HAs increased the length of the small intestinal and villus height to restore intestinal permeability, which is impaired by AFB1. In addition, HAs reconstructed the gut microbiota, increasing the relative abundance of Desulfovibrio, Odoribacter, and Alistipes. In vitro and in vivo assays demonstrated that HAs could efficiently remove AFB1 by absorbing the toxin. Therefore, HAs treatment can ameliorate AFB1-induced hepatic injury by enhancing gut barrier function, regulating gut microbiota, and adsorbing toxin