REFERRING TERMS DALAM SURAT LUKMAN

Penelitian referring terms dalam Surat Lukman bertujuan untuk mendeskripsikan referring terms dalam Al-Quran yang memfokuskan pada Surat Lukman pada bentuk definite/indefinite, explicit/inexplicit, speaker meaning, and the implicatur of referring terms pada ayat 12 dan 13. Penelitian ini menggunakan pendekatan Analisis Wacana Model Grasian yang termasuk dalam jenis penelitian kualitatif. Hasil analisis data dengan menggunakan Model Grasian menunjukkan bahwa ayat 12 dan 13 memiliki keragaman dalam bentuk definite/indefinite dan explicit/inexplicit, the referring terms pada first-mention adalah definite/inexplicit, dan indefinite/explicit, lalu pada next-mention adalah definite/inexplicit, definite/explicit, dan zero. Maksud pembicara (The speaker meaning) dideskripsikan pada ayat 13, sedangkan implikatur referring terms dideskripsikan pada ayat 12 dan 13 dalam Surat Al-Lukman.Penelitian referring terms dalam Surat Lukman bertujuan untuk mendeskripsikan referring terms dalam Al-Quran yang memfokuskan pada Surat Lukman pada bentuk definite/indefinite, explicit/inexplicit, speaker meaning, and the implicatur of referring terms pada ayat 12 dan 13. Penelitian ini menggunakan pendekatan Analisis Wacana Model Grasian yang termasuk dalam jenis penelitian kualitatif. Hasil analisis data dengan menggunakan Model Grasian menunjukkan bahwa ayat 12 dan 13 memiliki keragaman dalam bentuk definite/indefinite dan explicit/inexplicit, the referring terms pada first-mention adalah definite/inexplicit, dan indefinite/explicit, lalu pada next-mention adalah definite/inexplicit, definite/explicit, dan zero. Maksud pembicara (The speaker meaning) dideskripsikan pada ayat 13, sedangkan implikatur referring terms dideskripsikan pada ayat 12 dan 13 dalam Surat Al-Lukman

Real-time high-resolution CO2_2 geological storage prediction using nested Fourier neural operators

Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS deployment requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. However, such modeling is very challenging at scale due to the high computational costs of existing numerical methods. This challenge leads to significant uncertainties in evaluating storage opportunities, which can delay the pace of large-scale CCS deployment. We introduce Nested Fourier Neural Operator (FNO), a machine-learning framework for high-resolution dynamic 3D CO2 storage modeling at a basin scale. Nested FNO produces forecasts at different refinement levels using a hierarchy of FNOs and speeds up flow prediction nearly 700,000 times compared to existing methods. By learning the solution operator for the family of governing partial differential equations, Nested FNO creates a general-purpose numerical simulator alternative for CO2 storage with diverse reservoir conditions, geological heterogeneity, and injection schemes. Our framework enables unprecedented real-time modeling and probabilistic simulations that can support the scale-up of global CCS deployment

Persistence of Supplemented Bifidobacterium longum subsp. infantis EVC001 in Breastfed Infants.

Attempts to alter intestinal dysbiosis via administration of probiotics have consistently shown that colonization with the administered microbes is transient. This study sought to determine whether provision of an initial course of Bifidobacterium longum subsp. infantis (B. infantis) would lead to persistent colonization of the probiotic organism in breastfed infants. Mothers intending to breastfeed were recruited and provided with lactation support. One group of mothers fed B. infantis EVC001 to their infants from day 7 to day 28 of life (n = 34), and the second group did not administer any probiotic (n = 32). Fecal samples were collected during the first 60 postnatal days in both groups. Fecal samples were assessed by 16S rRNA gene sequencing, quantitative PCR, mass spectrometry, and endotoxin measurement. B. infantis-fed infants had significantly higher populations of fecal Bifidobacteriaceae, in particular B. infantis, while EVC001 was fed, and this difference persisted more than 30 days after EVC001 supplementation ceased. Fecal milk oligosaccharides were significantly lower in B. infantis EVC001-fed infants, demonstrating higher consumption of human milk oligosaccharides by B. infantis EVC001. Concentrations of acetate and lactate were significantly higher and fecal pH was significantly lower in infants fed EVC001, demonstrating alterations in intestinal fermentation. Infants colonized by Bifidobacteriaceae at high levels had 4-fold-lower fecal endotoxin levels, consistent with observed lower levels of Gram-negative Proteobacteria and Bacteroidetes. IMPORTANCE The gut microbiome in early life plays an important role for long-term health and is shaped in large part by diet. Probiotics may contribute to improvements in health, but they have not been shown to alter the community composition of the gut microbiome. Here, we found that breastfed infants could be stably colonized at high levels by provision of B. infantis EVC001, with significant changes to the overall microbiome composition persisting more than a month later, whether the infants were born vaginally or by caesarean section. This observation is consistent with previous studies demonstrating the capacity of this subspecies to utilize human milk glycans as a nutrient and underscores the importance of pairing a probiotic organism with a specific substrate. Colonization by B. infantis EVC001 resulted in significant changes to fecal microbiome composition and was associated with improvements in fecal biochemistry. The combination of human milk and an infant-associated Bifidobacterium sp. shows, for the first time, that durable changes to the human gut microbiome are possible and are associated with improved gut function

Retrievals of the main phytoplankton groups at Lake Constance using OLCI, DESIS, and evaluated with field observations

Phytoplankton play an important role in the aquatic biogeochemical cycling such as for the formation of organic matter by photosynthetic processes through the fixation of carbon dioxide, and assimilation of macro- and micronutrients depending on their metabolic needs. These processes are common to all phytoplankton, however some phytoplankton groups have specific needs and thus play different functional roles in the biogeochemical cycle, which are used to classify phytoplankton into different phytoplankton functional types (PFTs). Information on the phytoplankton groups can be obtained from satellite observations such as the Ocean and Land Colour Instrument (OLCI) onboard of ISS and Sentinel-3. PFTs global ocean abundance can be estimated based on the OC-PFT algorithm (Hirata et al. 2011 and related updates to it) which is based on the assumption that a marker pigment for a specific PFT varies in dependence to the chlorophyll-a concentration. In this study, OC-PFT retrieval has been developed and adapted for estimation of PFT from Lake Constance by using a large collection of in-situ HPLC data set measured since 2000 at the largest German inland water by the regional authority and further analysed to derive PFT using the diagnostic pigment analysis following Vidussi et al. (2001) with adapted coefficients for Lake Constance. The PFT retrieved from OLCI are validated using independent in situ data derived from HPLC pigment measurements from 4 field campaigns performed in 2019 and 2020 at Lake Constance. Concentrations for five phytoplankton groups (diatoms, dinoflagellates, cryptophytes, green algae, and prokaryotes) are retrieved for Lake Constance, being the dominants diatoms and cryptophytes and at lesser degree green algae. In addition, evaluation of synergistic PFT products are presented to enlarge the capabilities of PFT data in inland and coastal waters analytically retrieved from high spectral and high spatial data such as DESIS, EnMAP or PRISMA by synergistic use with OLCI OC-PFT data sets is discussed

GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring

The CEOS Feasibility Study for an aquatic ecosystem imaging spectrometer

The Committee on Earth Observation Satellites (CEOS) response to the Group on Earth Observations System of Systems (GEOSS) Water Strategy developed under the auspices of the Water Strategy Implementation Study Team was endorsed by CEOS at the 2015 Plenary. As one of the actions, CSIRO has taken the lead on recommendation C.10: A feasibility assessment to determine the benefits and technological difficulties of designing a hyperspectral satellite mission focused on water quality measurements. More specifically this report is a highlevel feasibility assessment of the benefits and technological difficulties of designing a hyperspectral satellite mission focused on biogeochemistry of inland, estuarine, deltaic and near coastal waters as well as mapping macrophytes, macroalgae , seagrasses and coral reefs at significantly higher spatial resolution than 250 m, which is the maximum spatial resolution of dedicated current aquatic sensors such as Sentinel3 and future planned aquatic sensors such as the Coastal Ocean Color Imager (COCI – 100 m res). Further, the GEO Community of Practice Aquawatch suggested that alternative approaches, involving augmenting designs of spaceborne sensors for terrestrial and ocean colour applications to allow improved inland, near coastal waters and benthic applications, could offer an alternative pathway to addressing the same underlying science questions. Accordingly, this study also analizes the benefits and technological difficulties of this option as part of the highlevel feasibility study

Integrative inference of gene-regulatory networks in Escherichia coli using information theoretic concepts and sequence analysis

<p>Abstract</p> <p>Background</p> <p>Although <it>Escherichia coli </it>is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. There exist many approaches to reconstruct regulatory interaction networks from gene expression experiments. Mutual information based approaches are most useful for large-scale network inference.</p> <p>Results</p> <p>We used a three-step approach in which we combined gene regulatory network inference based on directed information (DTI) and sequence analysis. DTI values were calculated on a set of gene expression profiles from 19 time course experiments extracted from the Many Microbes Microarray Database. Focusing on influences between pairs of genes in which one partner encodes a transcription factor (TF) we derived a network which contains 878 TF - gene interactions of which 166 are known according to RegulonDB. Afterward, we selected a subset of 109 interactions that could be confirmed by the presence of a phylogenetically conserved binding site of the respective regulator. By this second step, the fraction of known interactions increased from 19% to 60%. In the last step, we checked the 44 of the 109 interactions not yet included in RegulonDB for functional relationships between the regulator and the target and, thus, obtained ten TF - target gene interactions. Five of them concern the regulator LexA and have already been reported in the literature. The remaining five influences describe regulations by Fis (with two novel targets), PhdR, PhoP, and KdgR. For the validation of our approach, one of them, the regulation of lipoate synthase (LipA) by the pyruvate-sensing pyruvate dehydrogenate repressor (PdhR), was experimentally checked and confirmed.</p> <p>Conclusions</p> <p>We predicted a set of five novel TF - target gene interactions in <it>E. coli</it>. One of them, the regulation of <it>lipA </it>by the transcriptional regulator PdhR was validated experimentally. Furthermore, we developed DTInfer, a new R-package for the inference of gene-regulatory networks from microarrays using directed information.</p

Discovery and Clinical Proof-of-Concept of RLY-2608, a First-in-Class Mutant-Selective Allosteric PI3Kα Inhibitor That Decouples Antitumor Activity from Hyperinsulinemia

PIK3CA (PI3Kα) is a lipid kinase commonly mutated in cancer, including ∼40% of hormone receptor–positive breast cancer. The most frequently observed mutants occur in the kinase and helical domains. Orthosteric PI3Kα inhibitors suffer from poor selectivity leading to undesirable side effects, most prominently hyperglycemia due to inhibition of wild-type (WT) PI3Kα. Here, we used molecular dynamics simulations and cryo-electron microscopy to identify an allosteric network that provides an explanation for how mutations favor PI3Kα activation. A DNA-encoded library screen leveraging electron microscopy-optimized constructs, differential enrichment, and an orthosteric-blocking compound led to the identification of RLY-2608, a first-in-class allosteric mutant-selective inhibitor of PI3Kα. RLY-2608 inhibited tumor growth in PIK3CA-mutant xenograft models with minimal impact on insulin, a marker of dysregulated glucose homeostasis. RLY-2608 elicited objective tumor responses in two patients diagnosed with advanced hormone receptor–positive breast cancer with kinase or helical domain PIK3CA mutations, with no observed WT PI3Kα-related toxicities. Significance: Treatments for PIK3CA-mutant cancers are limited by toxicities associated with the inhibition of WT PI3Kα. Molecular dynamics, cryo-electron microscopy, and DNA-encoded libraries were used to develop RLY-2608, a first-in-class inhibitor that demonstrates mutant selectivity in patients. This marks the advance of clinical mutant-selective inhibition that overcomes limitations of orthosteric PI3Kα inhibitors