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This paper discusses a case study of a 10-year-old Chinese-English bilingual boy, who has developmental dyslexia. The boy exhibits a discrepancy in his reading and writing abilities in both languages, which is believed to be due to the distinct orthographic characteristics and cognitive requirements of the two languages. The study investigated the reasons for his literacy skills profile from both orthographic and cognitive perspectives by evaluating the boy's working memory, literacy skills, receptive vocabulary, and cognitive abilities in both languages. Preliminary findings revealed that while the child's cognitive profile was consistent across both languages, his reading and writing accuracy in Chinese was lower compared to TD Chinese-English bilinguals, with greater difficulties in Chinese writing. This case study reinforces the cognitive account theory, suggesting that the varying cognitive demands needed for literacy skill development can result in differences in these skills, particularly regarding accuracy, in bilingual children (Sambai et al., 2022).

Diverse observational and simulation datasets are needed to understand and predict complex ecosystem behavior over seasonal to decadal and century time-scales. Integration of these datasets poses a major barrier towards advancing environmental science, particularly due to differences in the structure and formats of data provided by various sources. Here, we describe BASIN-3D (Broker for Assimilation, Synthesis and Integration of eNvironmental Diverse, Distributed Datasets), a data integration framework designed to dynamically retrieve and transform heterogeneous data from different sources into a common format to provide an integrated view. BASIN-3D enables users to adopt a standardized approach for data retrieval and avoid customizations for the data type or source. We demonstrate the value of BASIN-3D with two use cases that require integration of data from regional to watershed spatial scales. The first application uses the BASIN-3D Python library to integrate time-series hydrological and meteorological data to provide standardized inputs to analytical and machine learning codes in order to predict the impacts of hydrological disturbances on large river corridors of the United States. The second application uses the BASIN-3D Django framework to integrate diverse time-series data in a mountainous watershed in East River, Colorado, United States to enable scientific researchers to explore and download data through an interactive web portal. Thus, BASIN-3D can be used to support data integration for both web-based tools, as well as data analytics using Python scripting and extensions like Jupyter notebooks. The framework is expected to be transferable to and useful for many other field and modeling studies.

Human language offers a powerful window into our thoughts -- we tell stories, give explanations, and express our beliefs and goals through words. Abundant evidence also suggests that language plays a developmental role in structuring our learning. Here, we ask: how much of human-like thinking can be captured by learning statistical patterns in language alone? We first contribute a new challenge benchmark for comparing humans and distributional large language models (LLMs). Our benchmark contains two problem-solving domains (planning and explanation generation) and is designed to require generalization to new, out-of-distribution problems expressed in language. We find that humans are far more robust than LLMs on this benchmark. Next, we propose a hybrid Parse-and-Solve model, which augments distributional LLMs with a structured symbolic reasoning module. We find that this model shows more robust adaptation to out-of-distribution planning problems, demonstrating the promise of hybrid AI models for more human-like reasoning.

The Abstraction and Reasoning Challenge (ARC) is a set of tasks where one must induce a program from a few given input-output examples, and apply it to a new input. Although humans can easily solve most tasks, ARC is challenging for state-of-the-art algorithms. We hypothesize that humans use intuitive program induction, and interpret ARC tasks by constructing "natural programs'' in language.We experimentally study "natural programs'' by formulating a two-player game: A participant solves and then communicates the program to another participant using natural language; the second participant must solve the task using the description alone. We find at least 361 out of 400 tasks can be solved from a natural language description, demonstrating that natural language is sufficient in transmitting these natural programs. We compared the natural language programs to computer programs constructed using two separate state-of-the-art program synthesis approaches, and conducted a study on leveraging natural language annotations to improve performance of program-synthesis tools.

People think and learn abstractly and compositionally. These two key properties of human cognition are shared with natural language: we use a finite, composable vocabulary of nameable concepts to generate and understand a combinatorially large space of new sentence. In this paper, we present a domain of compositional reasoning tasks and an artificial language learning paradigm designed to probe the role language plays in bootstrapping learning. We discuss results from a language-guided program learning model suggesting that language can play an important role in bootstrapping learning by providing an important signal for search on individual problems, and a cue towards named, reusable abstractions across the domain as a whole. We evaluate adults on the same domain, comparing learning performance between those tasked with jointly learning language and solving reasoning tasks, and those who only approach the domain as a collection of inductive reasoning problems. We find that adults provided with abstract language prompts are better equipped to generalize and compose concepts learned across a domain than adults solving the same problems using reasoning alone.

Core knowledge concepts such as object behavior principles provide a rich inventory of primitives for thinking and learning in the natural world. However, it remains unexplored how these concepts are reused for problem-solving and communication in highly abstract domains.We analyze a large-scale natural language study drawing on the Abstraction and Reasoning Corpus (ARC), a set of highly abstract visual tasks where solvers construct outputs from input grids according to an inferred pattern. ARC explicitly incorporates core knowledge principles without any real-world objects. In the study, subjects solved and communicated the inferred patterns of ARC tasks via written explanations for other subjects attempting to solve tasks using only the explanations.We examine how subjects solve, communicate, and interpret these explanations, and we show that subjects use fundamentally abstract core knowledge properties–object cohesion and contact causality–to reason about, understand, and communicate the inference tasks with language.

Currently, coronavirus disease 2019 (COVID-19) is still spreading in a global scale, exerting a massive health and socioeconomic crisis. Deep insights into the molecular functions of the viral proteins and the pathogenesis of this infectious disease are urgently needed. In this review, we comprehensively describe the proteome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and summarize their protein interaction map with host cells. In the protein interaction network between the virus and the host, a total of 787 host prey proteins that appeared in at least two studies or were verified by co-immunoprecipitation experiments. Together with 29 viral proteins, a network of 1762 proximal interactions were observed. We also review the proteomics results of COVID-19 patients and proved that SARS-CoV-2 hijacked the host’s translation system, post-translation modification system, and energy supply system via viral proteins, resulting in various immune disorders, multiple cardiomyopathies, and cholesterol metabolism diseases.

Scavenger receptor class B, member 2 (SCARB2) is linked to Gaucher disease and Parkinson's disease. Deficiency in the SCARB2 gene causes progressive myoclonus epilepsy (PME), a rare group of inherited neurodegenerative diseases characterized by myoclonus. We found that Scarb2 deficiency in mice leads to age-dependent dietary lipid malabsorption, accompanied with vitamin E deficiency. Our investigation revealed that Scarb2 deficiency is associated with gut dysbiosis and an altered bile acid pool, leading to hyperactivation of FXR in intestine. Hyperactivation of FXR impairs epithelium renewal and lipid absorption. Patients with SCARB2 mutations have a severe reduction in their vitamin E levels and cannot absorb dietary vitamin E. Finally, inhibiting FXR or supplementing vitamin E ameliorates the neuromotor impairment and neuropathy in Scarb2 knockout mice. These data indicate that gastrointestinal dysfunction is associated with SCARB2 deficiency-related neurodegeneration, and SCARB2-associated neurodegeneration can be improved by addressing the nutrition deficits and gastrointestinal issues. [ABSTRACT FROM AUTHOR]

The European Union (EU) regulations mandate 10% of all food packaging to be reusable by 2030. United States (U.S.) exporters of specialty crops face new challenges in ensuring microbiological food safety using reusable packaging. A novel antimicrobial formulation consisting of ammonium carboxylate salt of capric acid and L-arginine (GS-2) was recently developed as a spray coating chemical for food packaging materials. In this study, we evaluated the antimicrobial efficacy of GS-2 against microbial strains representing three foodborne bacterial pathogens (Escherichia coli O157:H7, Listeria monocytogenes, Salmonella enterica), one fungal spoilage organism (Aspergillus niger), and one surrogate viral pathogen (murine norovirus) on three reusable plastic materials (acrylonitrile butadiene styrene, high-density polyethylene, and polypropylene) and one cardboard packaging material, respectively. Different chemical concentrations, exposure times, and storage conditions were individually evaluated for the relative antimicrobial efficacies of GS-2 against these microorganisms. Our results showed that GS-2 was highly effective for inactivating bacterial pathogens on both plastic and cardboard surfaces. For instance, 3% GS-2 achieved a >5 log CFU/in2 reduction in E. coli O157:H7, L. monocytogenes, and S. enterica on tested plastic surfaces at an exposure time of 60 min. However, its efficacy against A. niger and murine norovirus was less optimal, resulting in a ≤1 log CFU/in2 reduction on all tested surfaces. Based on our study, GS-2 demonstrated a strong potential as an antibacterial coating reagent for reusable food packaging materials to minimize pathogen contamination and ensure the safety of the specialty crops. [ABSTRACT FROM AUTHOR]

Studies of human expertise suggest that experts and novices “see“ problems differently. Experts not only acquire a bodyof domain-specific strategies and knowledge, but also learn to quickly identify when those concepts apply to problemswithin the domain. We propose modeling these elements as an iterative process of domain-specific language (DSL)learning, while jointly training a neural network to recognize when learned concepts apply to new problems. We showthat the algorithm solves problems more accurately and quickly than either a neural network alone, or a model that simplyacquires new concepts without learning when to use them. We also examine the implicit problem representations learnedby the neural network recognition model, and find that they increasingly come to reflect abstract relationships betweenproblems, rather than surface features, as the model acquires domain expertise. A full paper and additional details areavailable at: https://sites.google.com/view/neurally-guided-expertise-mit

Extensive pre-mRNA back-splicing generates numerous circular RNAs (circRNAs) in human transcriptome. However, the biological functions of these circRNAs remain largely unclear. Here we report that N6-methyladenosine (m6A), the most abundant base modification of RNA, promotes efficient initiation of protein translation from circRNAs in human cells. We discover that consensus m6A motifs are enriched in circRNAs and a single m6A site is sufficient to drive translation initiation. This m6A-driven translation requires initiation factor eIF4G2 and m6A reader YTHDF3, and is enhanced by methyltransferase METTL3/14, inhibited by demethylase FTO, and upregulated upon heat shock. Further analyses through polysome profiling, computational prediction and mass spectrometry reveal that m6A-driven translation of circRNAs is widespread, with hundreds of endogenous circRNAs having translation potential. Our study expands the coding landscape of human transcriptome, and suggests a role of circRNA-derived proteins in cellular responses to environmental stress.

EfpA, the first major facilitator superfamily (MFS) protein identified in Mycobacterium tuberculosis (Mtb), is an essential efflux pump implicated in resistance to multiple drugs. EfpA-inhibitors have been developed to kill drug-tolerant Mtb. However, the biological function of EfpA has not yet been elucidated. Here, we present the cryo-EM structures of EfpA complexed with lipids or the inhibitor BRD-8000.3 at resolutions of 2.9 Å and 3.4 Å, respectively. Unexpectedly, EfpA forms an antiparallel dimer. Functional studies reveal that EfpA is a lipid transporter and BRD-8000.3 inhibits its lipid transport activity. Intriguingly, the mutation V319F, known to confer resistance to BRD-8000.3, alters the expression level and oligomeric state of EfpA. Based on our results and the observation of other antiparallel dimers in the MFS family, we propose an antiparallel-function model of EfpA. Collectively, our work provides structural and functional insights into EfpA's role in lipid transport and drug resistance, which would accelerate the development of antibiotics against this promising drug target. [ABSTRACT FROM AUTHOR]

Preimplantation genetic testing (PGT) can minimize the risk of birth defects. However, the accuracy and applicability of routine PGT is confounded by uneven genome coverage and high allele drop‐out rate from existing single‐cell whole genome amplification methods. Here, a method to diagnose genetic mutations and concurrently evaluate embryo competence by leveraging the abundant mRNA transcript copies present in trophectoderm cells is developed. The feasibility of the method is confirmed with 19 donated blastocysts. Next, the method is applied to 82 embryos from 26 families with monogenic defects for simultaneous mutation detection and competence assessment. The accuracy rate of direct mutation detection is up to 95%, which is significantly higher than DNA‐based method. Meanwhile, this approach correctly predicted seven out of eight (87.5%) embryos that failed to implant. Of six embryos that are predicted to implant successfully, four met such expectations (66.7%). Notably, this method is superior at conditions for mutation detection that are challenging when using DNA‐based PGT, such as when detecting pathogenic genes with a high de novo rate, multiple pseudogenes, or an abnormal expansion of CAG trinucleotide repeats. Taken together, this study establishes the feasibility of an RNA‐based PGT that is also informative for assessing implantation competence. [ABSTRACT FROM AUTHOR]

The evolutionarily conserved target of rapamycin (TOR) kinase acts as a master regulator that coordinates cell proliferation and growth by integrating nutrient, energy, hormone and stress signals in all eukaryotes.sup.1,2. Research has focused mainly on TOR-regulated translation, but how TOR orchestrates the global transcriptional network remains unclear. Here we identify ethylene-insensitive protein 2 (EIN2), a central integrator.sup.3-5 that shuttles between the cytoplasm and the nucleus, as a direct substrate of TOR in Arabidopsis thaliana. Glucose-activated TOR kinase directly phosphorylates EIN2 to prevent its nuclear localization. Notably, the rapid global transcriptional reprogramming that is directed by glucose-TOR signalling is largely compromised in the ein2-5 mutant, and EIN2 negatively regulates the expression of a wide range of target genes of glucose-activated TOR that are involved in DNA replication, cell wall and lipid synthesis and various secondary metabolic pathways. Chemical, cellular and genetic analyses reveal that cell elongation and proliferation processes that are controlled by the glucose-TOR-EIN2 axis are decoupled from canonical ethylene-CTR1-EIN2 signalling, and mediated by different phosphorylation sites. Our findings reveal a molecular mechanism by which a central signalling hub is shared but differentially modulated by diverse signalling pathways using distinct phosphorylation codes that can be specified by upstream protein kinases. In Arabidopsis, phosphorylation of EIN2 by TOR kinase in the presence of glucose prevents the nuclear localization of EIN2, showing that the glucose-TOR-EIN2 axis regulates the transcriptome independently of ethylene signalling pathways., Author(s): Liwen Fu [sup.1] [sup.2] , Yanlin Liu [sup.2] , Guochen Qin [sup.1] , Ping Wu [sup.3] , Hailing Zi [sup.1] , Zhongtian Xu [sup.1] , Xiaodi Zhao [sup.2] , [...]