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  1. Abstract Higher-order exceptional points have attracted increased attention in recent years due to their enhanced sensitivity and distinct topological features. Here, we show that non-local acoustic metagratings enabling precise and simultaneous control over their multiple orders of diffraction can serve as a robust platform for investigating higher-order exceptional points in free space. The proposed metagratings, not only could advance the fundamental research of arbitrary order exceptional points, but could also empower unconventional free-space wave manipulation for applications related to sensing and extremely asymmetrical wave control.
  2. We consider the problem of synthesizing good-enough (GE)-strategies for linear temporal logic (LTL) over finite traces or LTLf for short.The problem of synthesizing GE-strategies for an LTL formula φ over infinite traces reduces to the problem of synthesizing winning strategies for the formula (∃Oφ)⇒φ where O is the set of propositions controlled by the system.We first prove that this reduction does not work for LTLf formulas.Then we show how to synthesize GE-strategies for LTLf formulas via the Good-Enough (GE)-synthesis of LTL formulas.Unfortunately, this requires to construct deterministic parity automata on infinite words, which is computationally expensive.We then show how to synthesize GE-strategies for LTLf formulas by a reduction to solving games played on deterministic Büchi automata, based on an easier construction of deterministic automata on finite words.We show empirically that our specialized synthesis algorithm for GE-strategies outperforms the algorithms going through GE-synthesis of LTL formulas by orders of magnitude.

  3. Abstract

    Bacterial natural product biosynthetic genes, canonically clustered, have been increasingly found to rely on hidden enzymes encoded elsewhere in the genome for completion of biosynthesis. The study and application of lanthipeptides are frequently hindered by unclustered protease genes required for final maturation. Here, we establish a global correlation network bridging the gap between lanthipeptide precursors and hidden proteases. Applying our analysis to 161,954 bacterial genomes, we establish 5209 correlations between precursors and hidden proteases, with 91 prioritized. We use network predictions and co-expression analysis to reveal a previously missing protease for the maturation of class I lanthipeptide paenilan. We further discover widely distributed bacterial M16B metallopeptidases of previously unclear biological function as a new family of lanthipeptide proteases. We show the involvement of a pair of bifunctional M16B proteases in the production of previously unreported class III lanthipeptides with high substrate specificity. Together, these results demonstrate the strength of our correlational networking approach to the discovery of hidden lanthipeptide proteases and potentially other missing enzymes for natural products biosynthesis.

  4. The superoxide dismutases (SODs) play vital roles in controlling cellular reactive oxygen species (ROS) that are generated both under optimal as well as stress conditions in plants. The rice genome harbors seven SOD genes (CSD1, CSD2, CSD3, CSD4, FSD1, FSD2, and MSD) that encode seven constitutive transcripts. Of these, five (CSD2, CSD3, CSD4, FSD1, and MSD) utilizes an alternative splicing (AS) strategy and generate seven additional splice variants (SVs) or mRNA variants, i.e., three for CSD3, and one each for CSD2, CSD4, FSD1, and MSD. The exon-intron organization of these SVs revealed variations in the number and length of exons and/or untranslated regions (UTRs). We determined the expression patterns of SVs along with their constitutive forms of SODs in rice seedlings exposed to salt, osmotic, cold, heavy metal (Cu+2) stresses, as well as copper-deprivation. The results revealed that all seven SVs were transcriptionally active in both roots and shoots. When compared to their corresponding constitutive transcripts, the profiles of five SVs were almost similar, while two specific SVs (CSD3-SV4 and MSD-SV2) differed significantly, and the differences were also apparent between shoots and roots suggesting that the specific SVs are likely to play important roles in a tissue-specific and stress-specific manner.more »Overall, the present study has provided a comprehensive analysis of the SVs of SODs and their responses to stress conditions in shoots and roots of rice seedlings.« less
  5. Can health conditions be inferred from an individual's mobility pattern? Existing research has discussed the relationship between individual physical activity/mobility and well-being, yet no systematic study has been done to investigate the predictability of fine-grained health conditions from mobility, largely due to the unavailability of data and unsatisfactory modelling techniques. Here, we present a large-scale longitudinal study, where we collect the health conditions of 747 individuals who visit a hospital and tracked their mobility for 2 months in Beijing, China. To facilitate fine-grained individual health condition sensing, we propose HealthWalks, an interpretable machine learning model that takes user location traces, the associated points of interest, and user social demographics as input, at the core of which a Deterministic Finite Automaton (DFA) model is proposed to auto-generate explainable features to capture useful signals. We evaluate the effectiveness of our proposed model, which achieves 40.29% in micro-F1 and 31.63% in Macro-F1 for the 8-class disease category prediction, and outperforms the best baseline by 22.84% in Micro-F1 and 31.79% in Macro-F1. In addition, deeper analysis based on the SHapley Additive exPlanations (SHAP) showcases that HealthWalks can derive meaningful insights with regard to the correlation between mobility and health conditions, which provide important researchmore »insights and design implications for mobile sensing and health informatics.« less
  6. Abstract

    A precise fear memory encoding a traumatic event enables an individual to avoid danger and identify safety. An impaired fear memory (contextual amnesia), however, puts the individual at risk of developing posttraumatic stress disorder (PTSD) due to the inability to identify a safe context when encountering trauma-associated cues later in life. Although it is gaining attention that contextual amnesia is a critical etiologic factor for PTSD, there is no treatment currently available that can reverse contextual amnesia, and whether such treatment can prevent the development of PTSD is unknown. Here, we report that (I) a single dose of transcranial photobiomodulation (PBM) applied immediately after tone fear conditioning can reverse contextual amnesia. PBM treatment preserved an appropriately high level of contextual fear memory in rats revisiting the “dangerous” context, while control rats displayed memory impairment. (II) A single dose of PBM applied after memory recall can reduce contextual fear during both contextual and cued memory testing. (III) In a model of complex PTSD with repeated trauma, rats given early PBM interventions efficiently discriminated safety from danger during cued memory testing and, importantly, these rats did not develop PTSD-like symptoms and comorbidities. (IV) Finally, we report that fear extinction was facilitatedmore »when PBM was applied in the early intervention window of memory consolidation. Our results demonstrate that PBM treatment applied immediately after a traumatic event or its memory recall can protect contextual fear memory and prevent the development of PTSD-like psychopathological fear in rats.

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  7. Abstract With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.
    Free, publicly-accessible full text available November 18, 2023