Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?

The Potential Outcome Framework (POF) plays a prominent role in the field of causal inference. Most causal inference models based on the POF (CIMs-POF) are designed for eliminating confounding bias and default to an underlying assumption of Confounding Covariates. This assumption posits that the covariates consist solely of confounders. However, the assumption of Confounding Covariates is challenging to maintain in practice, particularly when dealing with high-dimensional covariates. While certain methods have been proposed to differentiate the distinct components of covariates prior to conducting causal inference, the consequences of treating non-confounding covariates as confounders remain unclear. This ambiguity poses a potential risk when conducting causal inference in practical scenarios. In this paper, we present a unified graphical framework for the CIMs-POF, which greatly enhances the comprehension of these models' underlying principles. Using this graphical framework, we quantitatively analyze the extent to which the inference performance of CIMs-POF is influenced when incorporating various types of non-confounding covariates, such as instrumental variables, mediators, colliders, and adjustment variables. The key findings are: in the task of eliminating confounding bias, the optimal scenario is for the covariates to exclusively encompass confounders; in the subsequent task of inferring counterfactual outcomes, the adjustment variables contribute to more accurate inferences. Furthermore, extensive experiments conducted on synthetic datasets consistently validate these theoretical conclusions.Comment: 12 pages, 4 figure

Fu et al, Self-Exciting Threshold Auto-Regressive

Abstract: Some periodicity was noticed in records of water usage for rice irrigation, which was analyzed using the Self-Exciting Threshold Auto-Regressive (SETAR) model. Using auto-correlation analysis, values of rice evapotranspiration for different growth phases were found to depend on each other. Weather and other factors cause the rice evapotranspiration to change periodically. So the SETAR model was prepared for analysis of groundwater usage to irrigate rice in the Sanjiang Plain. Nine parameters were used to describe the periodicity of weather factors. By comparison with practical values, the precision is high. So, the model can be used to help layout and manage an irrigation area. At the same time, it can be applied to optimizing an irrigation system

Recent Advances on Sorting Methods of High-Throughput Droplet-Based Microfluidics in Enzyme Directed Evolution

Droplet-based microfluidics has been widely applied in enzyme directed evolution (DE), in either cell or cell-free system, due to its low cost and high throughput. As the isolation principles are based on the labeled or label-free characteristics in the droplets, sorting method contributes mostly to the efficiency of the whole system. Fluorescence-activated droplet sorting (FADS) is the mostly applied labeled method but faces challenges of target enzyme scope. Label-free sorting methods show potential to greatly broaden the microfluidic application range. Here, we review the developments of droplet sorting methods through a comprehensive literature survey, including labeled detections [FADS and absorbance-activated droplet sorting (AADS)] and label-free detections [electrochemical-based droplet sorting (ECDS), mass-activated droplet sorting (MADS), Raman-activated droplet sorting (RADS), and nuclear magnetic resonance-based droplet sorting (NMR-DS)]. We highlight recent cases in the last 5 years in which novel enzymes or highly efficient variants are generated by microfluidic DE. In addition, the advantages and challenges of different sorting methods are briefly discussed to provide an outlook for future applications in enzyme DE

Decoy State Quantum Key Distribution With Modified Coherent State

To beat PNS attack, decoy state quantum key distribution (QKD) based on coherent state has been studied widely. We present a decoy state QKD protocol with modified coherent state (MCS). By destruction quantum interference, MCS with fewer multi-photon events can be get, which may improve key bit rate and security distance of QKD. Through numerical simulation, we show about 2-dB increment on security distance for BB84 protocol.Comment: 4 pages, 4 figure

ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected. Results: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools. Conclusion: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input