Miracle-dz/UniFAR-FTUs huggingface dataset:
Compositional Learning-based Planning for Vision POMDPs

The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty. However, most current POMDP planners cannot effectively handle high-dimensional image observations prevalent in real world applications, and often require lengthy online training that requires interaction with the environment. In this work, we propose Visual Tree Search (VTS), a compositional learning and planning procedure that combines generative models learned offline with online model-based POMDP planning. The deep generative observation models evaluate the likelihood of and predict future image observations in a Monte Carlo tree search planner. We show that VTS is robust to different types of image noises that were not present during training and can adapt to different reward structures without the need to re-train. This new approach significantly and stably outperforms several baseline state-of-the-art vision POMDP algorithms while using a fraction of the training time.
The stochastic Fubini theorem revisited

In this paper, we present an elementary and self-contained proof of the stochastic Fubini theorem, which states that one can interchange a Lebesgue integral and a stochastic integral. The integrability conditions we use are weaker and more natural than the usual conditions in the literature. In particular, we do not need integrability in , and we use -integrability instead of -integrability in the additional parameter.
Exploring Sustainable Food Education as Multi-professional Collaboration between Home Economics and School Food Catering

This article explores microprocesses and transactions during 7 months of collaboration for sustainable food education involving two teachers of home economics and a school food manager in a Finnish secondary school. Data sources included interviews and multi-professional meetings, the professional reflections of participants and a researcher’s diary. Leaning on previous literature, we revisit and develop the concept of shared food sense (joint understanding, collective application, redefinition of co-action) as a tool for analysing the learning outcomes of collaborative multi-professional work. Accordingly, the outcomes were conceptualized as context-bound decisions and compromises that were collaboratively reached after the emergence of tensions in the process. The results highlight the need to acknowledge the complex relationships among systemic, institutional, interpersonal and intrapersonal tensions in multi-professional work, as well as to conduct a critical review of the work division, the professional motivations and the opportunities for mutual communication among participants in such alliances.
VioLinn: Proximity-aware Edge Placementwith Dynamic and Elastic Resource Provisioning

Deciding where to handle services and tasks, as well as provisioning an adequate amount of computing resources for this handling, is a main challenge of edge computing systems. Moreover, latency-sensitive services constrain the type and location of edge devices that can provide the needed resources. When available resources are scarce there is a possibility that some resource allocation requests are denied. In this work, we propose the VioLinn system to tackle the joint problems of task placement, service placement, and edge device provisioning. Dealing with latency-sensitive services is achieved through proximity-aware algorithms that ensure the tasks are handled close to the end-user. Moreover, the concept of spare edge device is introduced to handle sudden load variations in time and space without having to continuously overprovision. Several spare device selection algorithms are proposed with different cost/performance tradeoffs. Evaluations are performed both in a Kubernetes-based testbed and using simulations and show the benefit of using spare devices for handling localized load spikes with higher quality of service (QoS) and lower computing resource usage. The study of the different algorithms shows that it is possible to achieve this increase in QoS with different tradeoffs against cost and performance.
Discrete methods for fully nonlinear elliptic equations

This paper exhibits and proves the stability of discrete approximations for the solution of the Dirichlet problem for fully nonlinear, uniformly elliptic partial differential equations in situations where classical solutions are not known to exist. The resulting solutions are characterized in the viscosity sense of Crandall and Lions [Traps. Amer. Math. Soc. , 177 (1983) pp. 1–42] and our stability analysis depends on estimates for nonlinear difference equations of positive type which are consequences of earlier work on linear difference equations with random coefficients.
Optimal robust filtering with time-varying parameter uncertainty

Stochastic linear systems subject to time-varying parameter uncertainties affecting both system dynamics and noise statistics are considered. A linear filter is used to estimate a linear combination of the states of the system. When the filter is given, the stabilizing solution of a suitable Riccati equation is shown to yield an upper bound for the covariance of the estimator error. The main problem addressed in the paper is the design of an ‘optimal robust filter’ that minimizes such a covariance bound. Necessary conditions are given for the existence of an optimal reduced-order robust filter as well as necessary and sufficient conditions for the full-order case. The computation of the optimal filter calls for the solution of a Riccati equation that generalizes the standard Riccati equation for the Kalman filtering problem. A numerical example is provided in which the new filter is compared with both the Kalman and H ∞-filters.
Investigating graph neural network for RNA structural embedding

The biological function of natural non-coding RNAs (ncRNA) is tightly bound to their molecular structure. Sequence analyses such as multiple sequence alignments (MSA) are the bread and butter of bio-molecules functional analysis; however, analyzing sequence and structure simultaneously is a difficult task. In this work, we propose CARNAGE (Clustering/Alignment of RNA with Graph-network Embedding), which leverages a graph neural network encoder to imprint structural information into a sequence-like embedding; therefore, downstream sequence analyses now account implicitly for structural constraints. In contrast to the traditional “supervised” alignment approaches, we trained our network on a masking problem, independent from the alignment or clustering problem. Our method is very versatile and has shown good performances in 1) designing RNAs sequences, 2) clustering sequences, and 3) aligning multiple sequences only using the simplest Needleman and Wunsch’s algorithm. Not only can this approach be readily extended to RNA tridimensional structures, but it can also be applied to proteins.
Elevated plasma and urinary erythritol is a biomarker of excess simple carbohydrate intake in mice

Background Elevated serum erythritol is a predictive biomarker of diabetes and cardiovascular incidence and complications. Erythritol is synthesized endogenously from glucose, but little is known regarding the origin of elevated circulating erythritol in vivo. Objective In vitro evidence indicates that intracellular erythritol is elevated by high-glucose cell culture conditions and that final step of erythritol synthesis is catalyzed by the enzymes SORD and ADH1. The purpose of this study was to determine if dietary intake and/or diet-induced obesity (DIO) affect erythritol synthesis in mice, and if this relationship is modified by loss of the enzymes SORD or ADH1. Methods First, 8-week-old, male Sord+/+, Sord-/-, Adh1+/+, and Adh1-/- mice were fed either low-fat diet (LFD) with 10% fat-derived calories or DIO high-fat diet (HFD) with 60% fat-derived calories for 8 weeks. Plasma and tissue erythritol were measured using GC-MS. Second, wild-type 8-week-old C57BL/6J mice were fed LFD or HFD with plain drinking water or 30% sucrose water for 8 weeks. Blood glucose and plasma and urinary erythritol were measured in non-fasted and fasted samples. Tissue erythritol was measured following sacrifice. Finally, Sord+/+ and Sord-/- mice were fed LFD with 30% sucrose water for two weeks, then non-fasted plasma, urine, and tissue erythritol were quantified. Results Plasma and tissue erythritol were not impacted by loss of Sord or Adh1 on LFD or HFD. In wild-type mice, consumption of 30% sucrose water significantly elevated plasma and urinary erythritol on both LFD and HFD compared to plain water. Sord genotype did not affect plasma or urinary erythritol in response to sucrose feeding, but Sord-/- mice had reduced kidney erythritol content compared to wildtype littermates in response to sucrose. Conclusions Sucrose intake, not high-fat diet, elevates erythritol synthesis and excretion in mice. Loss of ADH1 or SORD does not significantly impact erythritol levels in mice.
Extensive RNA editing in tomato nuclear genes

5 College of Horticulture, Academy for Advanced Interdisciplinary Studies, Nanjing 6 Agricultural University, Nanjing 210095, China 7 State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, 8 Nanjing University, Nanjing, 210023, China 9 College of Tropical Crops, Sanya Nanfan Research Institute, Hainan University, 10 Haikou 570228, China 11 Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China 12 Department of Plant Biotechnology and Bioinformatics, Ghent University, 13 VIB-UGent Center for Plant Systems Biology, B-9052 Ghent, Belgium 14 Department of Biochemistry, Genetics and Microbiology, University of Pretoria, 15 Pretoria 0028, South Africa 16
Characterisation of neonatal Staphylococcus capitis NRCS-A isolates compared with non NRCS-A Staphylococcus capitis from neonates and adults

Staphylococcus capitis is a frequent cause of Late-Onset Sepsis (LOS) in neonates admitted to Neonatal Intensive Care Units (NICU). The NRCS-A clone of S. capitis has been isolated from NICUs globally although the reasons for the global success of this clone are not understood. We analysed a collection of S. capitis colonising babies admitted to two NICUs, one in the UK and one in Germany as well as corresponding pathological clinical isolates. Genome analysis identified 3 groups; non-NRCS-A isolates, NRCS-A isolates, and a group of ‘proto NRCS-A’ - isolates closely related to NRCS-A but not associated with neonatal infection. All bloodstream isolates belonged to the NRCS-A group and were indistinguishable from strains carried on the skin or in the gut. NRCS-A isolates showed increased tolerance to chlorhexidine and antibiotics relative to the other S. capitis as well as enhanced ability to grow at higher pH values. Analysis of 138 pangenomes of the clades identified characteristic nsr and tarJ genes in the NRCS-A and proto groups with a CRISPR-cas system only seen in NRCS-A isolates which also showed enrichment of genes for metal acquisition and transport. We found evidence for transmission of S. capitis NRCS-A within NICU, with related isolates shared between babies and multiple acquisitions by some babies. Our data show NRCS-A strains commonly colonise uninfected babies in NICU representing a potential reservoir for potential infection. This work provides more evidence that adaptation to survive in the gut and on skin facilitates spread of NRCS-A, and that metal acquisition and tolerance may be important to the biology of NRCS-A. Understanding how NRCS-A survives in NICUs can help develop infection control procedures against this clone.
Excimer laser-induced temperature field in melting and resolidification of silicon thin films

The liquid/solid interface motion and temperature history during excimer laser annealing of 50-nm-thick Si films on fused quartz substrates are investigated by in situ nanosecond time-resolved electrical conductance, optical reflectance, and transmittance at visible and near-IR wavelengths, combined with thermal emission measurements. The temperature response, melt propagation and evolution of the recrystallization process are fundamentally different in the partial-melting and the complete-melting regimes. Because it is necessary to balance the latent heat across the propagating phase-change interface, the maximum induced temperature in the partial-melting regime remains close to the melting point of amorphous Si. The peak temperature rises in the complete-melting regime, but the nonparticipating nature of the liquid Si/fused quartz interface allows substantial supercooling (>200 K), followed by spontaneous nucleation into fine-grained material. These phase transformations are consistent with the recrysta.
Single-molecule visualization of stalled replication-fork rescue by the Escherichia coli Rep helicase

Genome duplication occurs while the template DNA is bound by numerous DNA-binding proteins. Each of these proteins act as potential roadblocks to the replication fork and can have deleterious effects on cells. In Escherichia coli, these roadblocks are displaced by the accessory helicase Rep, a DNA translocase and helicase that interacts with the replisome. The mechanistic details underlying the coordination with replication and roadblock removal by Rep remain poorly understood. Through real-time fluorescence imaging of the DNA produced by individual E. coli replisomes and the simultaneous visualization of fluorescently-labeled Rep, we show that Rep continually surveils elongating replisomes. We found that this association of Rep with the replisome is stochastic and occurs independently of whether the fork is stalled or not. Further, we visualize the efficient rescue of stalled replication forks by directly imaging individual Rep molecules as they remove a model protein roadblock, dCas9, from the template DNA. Using roadblocks of varying DNA-binding stabilities, we conclude that replication restart is the rate-limiting step of stalled replication rescue.
Editorial: Insights in plant abiotic stress: 2021

COPYRIGHT © 2022 Sandalio. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 06 December 2022 DOI 10.3389/fpls.2022. 1085150
Editorial: Lipid metabolism and membrane structure in plant biotic interactions

COPYRIGHT © 2022 Goggin, Shah and Gillaspy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 06 December 2022 DOI 10.3389/fpls.2022. 1096268
Spatial distribution and source identification of metal contaminants in the surface soil of Matehuala, Mexico based on positive matrix factorization model and GIS techniques

The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil samples. After implementing PMF to analyze the soils, it was possible to differentiate the variations in factors linked to the contaminants, farming impacts, and the reference soil geochemistry. The soil in the two studied locations included high concentrations of As, Ca, Mg, K, Sr, Mn, and Fe, including variations in their spatial compositions, which were caused by direct mining activities, the movement and deposition of smelting waste, and the extensive use of irrigated contaminated groundwater for irrigation. The four possible factors were identified for soil pollution including industrial, transportation, agricultural, and naturogenic based on the PMF and geostatistical analysis. The spatial distribution of metal concentrations in the soil was also presented using a geographical information system (GIS) interpolation technique. The identification of metal sources and contamination risk mapping presents a significant role in minimizing pollution sources, and it may be performed in regions with high levels of soil contamination risk.
Boosting lignan-precursor synthesis in yeast cell factories through co-factor supply optimization

Plants constitute one of the largest sources of natural products (NPs) whose biological activities have been exploited by humans for agronomic, cosmetic or pharmacological purposes. For instance, many of these NPs became essential components of the therapeutic arsenal deployed against cancers since many years, such as the lignan-derived podophyllotoxin produced by the mayapple Podophyllum hexandrum (Buss et al., 1995). However, NPs are usually accumulated in minute amounts in plants and their complex structures hinder total chemical synthesis at least at an industrial scale. While extraction and purification procedures have been improved and combined to semi-synthetic approaches, many plant drugs still exhibit high production costs and a supply mostly based on the exploitation of the natural resources. This engenders a huge pressure on the NP producing plants, many of which suffer from overexploitation, causing a recurring shortage of drugs currently used in the clinic. In order to secure NP supply, new strategies of production have emerged such as those relying on the transfer of genes in heterologous organisms and the metabolic engineering (Carqueijeiro et al., 2020). Based on the pioneering production of the semi-synthetic production of the antimalarial artemisinin (Paddon and Keasling, 2014), metabolic engineering approaches relying on the creation of microbial cell factories have been continuously developed over the last 10 years. Through the transfer of plant genes into diverse heterologous organisms such as yeast, successful productions of valuables NPs have been achieved including resveratrol, a grape wine polyphenol displaying antioxidant properties (Li et al., 2015), ginsenosides Rh2 and R3 with promising properties against cancers (Wang et al., 2015), cannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) from hemp known for their beneficial psychoactive effects on human health (Gülck et al., 2020), andmonoterpene indole alkaloids (MIAs) from the Madagascar periwinkle whose prominent members vincristine and vinblastine exhibit anticancer activities (Brown et al., 2015). While most of these NPs results from de novo synthesis, bioconversion approaches, relying on the feeding of the cell factories with an easily accessible precursor, has been also explored with success to produce vindoline used OPEN ACCESS
Channa striatus in inflammatory conditions: A systematic review

Channa striatus (CS), or snakehead murrel, is an obligate air-breathing freshwater fish. Besides its wound healing properties, CS has also been reported to exhibit anti-inflammatory effects in multiple studies. While there are anti-inflammatory medications such as nonsteroidal anti-inflammatory drugs (NSAIDs), their long-term use is associated with an increased risk of peptic ulcers, acute renal failure, stroke, and myocardial infarction. Thus, it is essential to look at natural methods such as CS extract. While there is an abundant number of investigative studies on the inflammatory properties of CS, the quality of these studies has not been evaluated effectively. Thus, this review aims to summarise, evaluate, and critically appraise currently available literature regarding the anti-inflammatory properties of CS extract. This is done by performing a search using four databases, namely Google Scholar, Embase via Elsevier, Scopus, and Web of Science, with the following terms: Channa striatus AND inflammation. From our review, CS has been experimentally shown to positively affect inflammatory conditions such as gastric ulcers, dermatitis, osteoarthritis, and allergic rhinitis. Beneficial effects were also found on inflammation in the presence of tuberculosis and in situations that involve inflammation, such as wound healing. While CS clearly has potential for treating inflammatory conditions, much work needs to be done on identifying and isolating the active constituents before exact mechanisms of action can be worked out to develop future anti-inflammatory medications.
Multi-omics profiles refine L-dopa decarboxylase (DDC) as a reliable biomarker for prognosis and immune microenvironment of clear cell renal cell carcinoma

Background Increasing evidence indicates that L-dopa decarboxylase (DDC), which mediates aberrant amino acid metabolism, is significantly associated with tumor progression. However, the impacts of DDC are not elucidated clearly in clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate DDC prognostic value and potential mechanisms for ccRCC patients. Methods Transcriptomic and proteomic expressions of and clinical data including 532 patients with ccRCC (The Cancer Genome Atlas RNA-seq data), 226 ccRCC samples (Gene Expression Omnibus), 101 ccRCC patients from the E-MTAB-1980 cohort, and 232 patients with ccRCC with proteogenomic data (Fudan University Shanghai Cancer Center) were downloaded and analyzed to investigate the prognostic implications of DDC expression. Cox regression analyses were implemented to explore the effect of DDC expression on the prognosis of pan-cancer. The "limma" package identified the differentially expressed genes (DEGs) between high DDC subgroups and low DDC groups. Functional enrichments were performed based DEGs between DDC subgroups. The differences of immune cell infiltrations and immune checkpoint genes between DDC subgroups were analyzed to identify potential influence on immune microenvironment. Results We found significantly decreased DDC expression in ccRCC tissues compared with normal tissues from multiple independent cohorts based on multi-omics data. We also found that DDC expression was correlated with tumor grades and stages.The following findings revealed that lower DDC expression levels significantly correlated with shorter overall survival (P <0.001) of patients with ccRCC. Moreover, we found that DDC expression significantly correlated with an immunosuppressive tumor microenvironment, higher intra-tumoral heterogeneity, elevated expression of immune checkpoint CD274, and possibly mediated malignant behaviors of ccRCC cells via the PI3k/Akt signaling pathway. Conclusion The present study is the first to our knowledge to indicate that decreased DDC expression is significantly associated with poor survival and an immune-suppressive tumor microenvironment in ccRCC. These findings suggest that DDC could serve as a biomarker for guiding molecular diagnosis and facilitating the development of novel individual therapeutic strategies for patients with advanced ccRCC.