On the Complexity of Counterfactual Reasoning

We study the computational complexity of counterfactual reasoning in relation to the complexity of associational and interventional reasoning on structural causal models (SCMs). We show that counterfactual reasoning is no harder than associational or interventional reasoning on fully specified SCMs in the context of two computational frameworks. The first framework is based on the notion of treewidth and includes the classical variable elimination and jointree algorithms. The second framework is based on the more recent and refined notion of causal treewidth which is directed towards models with functional dependencies such as SCMs. Our results are constructive and based on bounding the (causal) treewidth of twin networks -- used in standard counterfactual reasoning that contemplates two worlds, real and imaginary -- to the (causal) treewidth of the underlying SCM structure. In particular, we show that the latter (causal) treewidth is no more than twice the former plus one. Hence, if associational or interventional reasoning is tractable on a fully specified SCM then counterfactual reasoning is tractable too. We extend our results to general counterfactual reasoning that requires contemplating more than two worlds and discuss applications of our results to counterfactual reasoning with a partially specified SCM that is coupled with data. We finally present empirical results that measure the gap between the complexities of counterfactual reasoning and associational/interventional reasoning on random SCMs.Comment: An earlier version of this paper appeared in NeurIPS 2022 workshop, "A causal view on dynamical systems.

An average queue weight parameterization in a network supporting TCP flows with RED

In this paper we use a previously developed RED (random early detection) model to analyze and develop a quantitative approach of defining one of the RED parameters, average queue weight, while the network load level varies. First, we introduce the linear control system and the pre-developed RED model. Based on this model, we next develop a proposition aiming at the RED parameter, average queue weight, and the load level only, to ensure the system stay stable. Our research is intended to provide a quantitative basis for parameterizing RED while load level varies. We present ns simulations to support our analysis

A compact ka-band active integrated antenna with a GaAs amplifier in a ceramic package

This letter presents the design of a Ka-band active integrated antenna in package (AIAiP). A monolithic microwave integrated circuit amplifier based on the GaAs process and a compact patch antenna based on the printed circuit board process are implemented, respectively. Then, the amplifier and antenna are assembled together in a specified package using the wire-bond process. Thus, compared to the traditional solutions, the transmission loss and the size of the proposed AIAiP are significantly reduced. Furthermore, the influence of the bonding wire and the package is taken into account in the design of the amplifier and the antenna, respectively. A good agreement between the simulation and measurement results can be observed. The proposed AIAiP occupies a compact size of 7 × 7 mm2. Meanwhile, it achieves -10-dB impedance bandwidth from 33.4 to 37.2 GHz and a peak gain of 18.9 dBi at 35 GHz. Additionally, the impact of the package size on the antenna performance has been demonstrated for future AIA designers

Evaluating protein cross-linking as a therapeutic strategy to stabilize SOD1 variants in a mouse model of familial ALS

Mutations in the gene encoding Cu-Zn superoxide dismutase 1 (SOD1) cause a subset of familial amyotrophic lateral sclerosis (fALS) cases. A shared effect of these mutations is that SOD1, which is normally a stable dimer, dissociates into toxic monomers that seed toxic aggregates. Considerable research effort has been devoted to developing compounds that stabilize the dimer of fALS SOD1 variants, but unfortunately, this has not yet resulted in a treatment. We hypothesized that cyclic thiosulfinate cross-linkers, which selectively target a rare, 2 cysteine-containing motif, can stabilize fALS-causing SOD1 variants in vivo. We created a library of chemically diverse cyclic thiosulfinates and determined structure-cross-linking-activity relationships. A pre-lead compound, “S-XL6,” was selected based upon its cross-linking rate and drug-like properties. Co-crystallographic structure clearly establishes the binding of S-XL6 at Cys 111 bridging the monomers and stabilizing the SOD1 dimer. Biophysical studies reveal that the degree of stabilization afforded by S-XL6 (up to 24°C) is unprecedented for fALS, and to our knowledge, for any protein target of any kinetic stabilizer. Gene silencing and protein degrading therapeutic approaches require careful dose titration to balance the benefit of diminished fALS SOD1 expression with the toxic loss-of-enzymatic function. We show that S-XL6 does not share this liability because it rescues the activity of fALS SOD1 variants. No pharmacological agent has been proven to bind to SOD1 in vivo. Here, using a fALS mouse model, we demonstrate oral bioavailability; rapid engagement of SOD1G93A by S-XL6 that increases SOD1G93A’s in vivo half-life; and that S-XL6 crosses the blood–brain barrier. S-XL6 demonstrated a degree of selectivity by avoiding off-target binding to plasma proteins. Taken together, our results indicate that cyclic thiosulfinate-mediated SOD1 stabilization should receive further attention as a potential therapeutic approach for fALS

Data from: Haplotype-based genome-wide association study identifies loci and candidate genes for milk yield in Holsteins

Since milk yield is a highly important economic trait in dairy cattle, the genome-wide association study (GWAS) is vital to explain the genetic architecture underlying milk yield and to perform marker-assisted selection (MAS). In this study, we adopted a haplotype-based empirical Bayesian GWAS to identify the loci and candidate genes for milk yield. A total of 1 092 Holstein cows were sequenced by using the genotyping by genome reducing and sequencing (GGRS) method. After filtering, 164 312 high-confidence SNPs and 13 476 haplotype blocks were identified to use for GWAS. The results indicated that 17 blocks were significantly associated with milk yield. We further identified the nearest gene of each haplotype block and annotated the genes with milk-associated quantitative trait locus (QTL) intervals and ingenuity pathway analysis (IPA) networks. Our analysis showed that four genes, DLGAP1, AP2B1, ITPR2 and THBS4, have relationships with milk yield, while another three, ARHGEF4, TDRD1 and KIF19, were inferred to have potential relationships. Additionally, a network derived from the IPA containing one inferred (ARHGEF4) and all four confirmed genes likely regulates milk yield. Our findings add to the understanding of identifying the causal genes underlying milk production traits and could guide follow up studies for further confirmation of the associated genes, pathways and biological networks

An Average Queue Weight Parameterization in a Network Supporting TCP Flows with RED

In this paper we use a previously developed RED (random early detection) model to analyze and develop a quantitative approach of defining one of the RED parameters, average queue weight, while the network load level varies. First, we introduce the linear control system and the pre-developed RED model. Based on this model, we next develop a proposition aiming at the RED parameter, average queue weight, and the load level only, to ensure the system stay stable. Our research is intended to provide a quantitative basis for parameterizing RED while load level varies. We present ns simulations to support our analysis

Diversity and Abundance of Bacterial and Fungal Communities Inhabiting <i>Camellia sinensis</i> Leaf, Rhizospheric Soil, and Gut of <i>Agriophara rhombata</i>

Agriophara rhombata is a tea leaf moth that is considered one of the most destructive pests of Camellia sinensis (tea plant). Several recent studies have shown that many insects acquire part of the microbiome from their host and soil, but the pattern and diversity of their microbiome have not been clearly demonstrated. The present study aimed to investigate the bacterial and fungal communities present in the rhizospheric soil and leaf of tea plant compared to the gut of tea moth at different developmental stages (larvae, pupae, adult female and male) using Illumina MiSeq technology. Alpha diversity (Shannon index) showed higher (p p > 0.05) between larvae, pupae, female, and male guts. Beta diversity also revealed more distinct bacterial and fungal communities in soil and leaf samples compared with tea moth gut samples, which had a more similar microbiome. Furthermore, Proteobacteria, Firmicutes, and Tenericutes were detected as the dominant bacterial phyla, while Ascomycota, Basidiomycota, and Mortierellomycota were the most abundant fungal phyla among all groups, but their relative abundance was comparatively higher (p Klebsiella, Streptophyta, and Enterococcus were the top three bacterial genera, while Candida, Aureobasidium, and Strelitziana were the top three fungal genera, and their relative abundance varied significantly (p p < 0.5) enrichment of the functional pathways of bacterial communities in soil and leaf samples than in tea moth gut samples. Our study concluded that the bacterial and fungal communities of soil and tea leaves were more diverse and were significantly different from the tea moth gut microbiome at different developmental stages. Our findings contribute to our understanding of the gut microbiota of the tea moth and its potential application in the development of pest management techniques