Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization

We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment information in training data. In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs. We further develop an adversarial training strategy to jointly optimize these two properties for casual subgraph discovery with theoretical guarantees. Extensive experiments and analysis show that LECI significantly outperforms prior methods on both synthetic and real-world datasets, establishing LECI as a practical and effective solution for graph OOD generalization

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science

Study on the use and effectiveness of malaria preventive measures reported by employees of chinese construction companies in Western Africa in 2021

Abstract Background As malaria continues to be a significant global public health concern, especially in Sub-Saharan Africa, Chinese workers in Africa are at increased risk of malaria. The effectiveness of malaria prevention measures implemented by Chinese companies and workers is a question that may correlate with the malaria infection rate in this population. This study explored the use and effectiveness of malaria prevention measures for Chinese employees in West Africa to provide a reference for companies and individuals on improving malaria prevention and control. Methods Using a cross-sectional approach, we surveyed 256 participants in 2021, mainly from Nigeria, Mali, Côte d’Ivoire, Ghana, Guinea, Sierra Leone, and Senegal in West Africa. The survey duration is from July to the end of September 2021. We selected two companies from the 2020 ENR "World’s Largest 250 International Contractors" list, which featured 6 Chinese companies, all of which are state-owned and have a 61.9% market share in Africa. The participants were Chinese workers with more than a year of work experience in construction companies in Africa. A 20-minute WeChat-based structured online questionnaire was used to obtain information on malaria infection status and malaria prevention measures. Descriptive statistical analysis, chi-square test, principal components analysis, and ordinal logistic regression analysis are used to analyze the data obtained. The difference in Statistical significance was set at P  0.05), while standardized use of mosquito nets (P = 0.016) and pesticide spraying (P = 0.047) contributed significantly to fewer malaria infections at the individual level, but the removal of vegetation around houses (P = 0.028) at the individual level related to higher malaria infection. Conclusions In our sample of Chinese construction workers going to Africa, some individual preventive measures had a stronger association with malaria prevention than a variety of public environmental measures. Furthermore, individual and public preventive measures were not associated with each other. Both of these findings are surprising and require further investigation in larger and more diverse samples. This- study provides important clues about the challenges that risk reduction programs face for migrant workers from China and elsewhere

Nucleotide polymorphisms of <i>ORF18</i> and <i>ORF22</i> among three watermelon genomes.

<p>No nucleotide polymorphisms were identified in the cDNA sequences of <i>ORF18</i> among three genomes. Two deletions (27-bp and 24-bp) were found in the second exon of <i>ORF22</i> among three genomes. The homeodomain (HD) domain and leucine zipper (LZ) motif of <i>ORF22</i> were predicted by the software Pfam (<a href="http://pfam.xfam.org/" target="_blank">http://pfam.xfam.org/</a>).</p

List of all primers for 12 markers used in genetic mapping of <i>ClLL1</i>.

<p>List of all primers for 12 markers used in genetic mapping of <i>ClLL1</i>.</p

Predicted genes between markers W08314 and W07061.

<p>Predicted genes between markers W08314 and W07061.</p

Prediction and relative expression level of candidate genes in the <i>ClLL1</i> region.

<p>(<b>a)</b> 23 putative ORFs were predicted in a 127.6-kb region between makers W08314 and W07061. (<b>b)</b> The relative expression level of candidate genes in both lobed and non-lobed leaf plants. The data are presented as average values of three replicates (mean value ± SD). “*, **” represent significant differences at <i>p</i> < 0.05 and <i>p</i> < 0.01, respectively, according to the Student’s t-test. <i>Actin</i> was used as an internal control.</p

Genetic mapping of the <i>LOBED LEAF 1 (ClLL1)</i> gene to a 127.6-kb region in watermelon (<i>Citrullus lanatus</i> L.)

<div><p>The lobed leaf character is a unique morphologic trait in crops, featuring many potential advantages for agricultural productivity. Although the majority of watermelon varieties feature lobed leaves, the genetic factors responsible for lobed leaf formation remain elusive. The F_2:3 leaf shape segregating population offers the opportunity to study the underlying mechanism of lobed leaf formation in watermelon. Genetic analysis revealed that a single dominant allele (designated <i>ClLL1</i>) controlled the lobed leaf trait. A large-sized F_3:4 population derived from F_2:3 individuals was used to map <i>ClLL1</i>. A total of 5,966 reliable SNPs and indels were identified genome-wide via a combination of BSA and RNA-seq. Using the validated SNP and indel markers, the location of <i>ClLL1</i> was narrowed down to a 127.6-kb region between markers W08314 and W07061, containing 23 putative ORFs. Expression analysis via qRT-PCR revealed differential expression patterns (fold-changes above 2-fold or below 0.5-fold) of three ORFs (<i>ORF3</i>, <i>ORF11</i>, and <i>ORF18</i>) between lobed and non-lobed leaf plants. Based on gene annotation and expression analysis, <i>ORF18</i> (encoding an uncharacterized protein) and <i>ORF22</i> (encoding a homeobox-leucine zipper-like protein) were considered as most likely candidate genes. Furthermore, sequence analysis revealed no polymorphisms in cDNA sequences of <i>ORF18</i>; however, two notable deletions were identified in <i>ORF22</i>. This study is the first report to map a leaf shape gene in watermelon and will facilitate cloning and functional characterization of <i>ClLL1</i> in future studies.</p></div

Detailed information of RNA-seq data of lobed and non-lobed leaf bulks.

<p>Detailed information of RNA-seq data of lobed and non-lobed leaf bulks.</p