A Monocarboxylate Permease of Rhizobium leguminosarum Is the First Member of a New Subfamily of Transporters

Amino acid transport by Rhizobium leguminosarum is dominated by two ABC transporters, the general amino acid permease (Aap) and the branched-chain amino acid permease (Bra). However, mutation of these transporters does not prevent this organism from utilizing alanine for growth. An R. leguminosarum permease (MctP) has been identified which is required for optimal growth on alanine as a sole carbon and nitrogen source. Characterization of MctP confirmed that it transports alanine (K(m) = 0.56 mM) and other monocarboxylates such as lactate and pyruvate (K(m) = 4.4 and 3.8 micro M, respectively). Uptake inhibition studies indicate that propionate, butyrate, alpha-hydroxybutyrate, and acetate are also transported by MctP, with the apparent affinity for solutes demonstrating a preference for C3-monocarboxylates. MctP has significant sequence similarity to members of the sodium/solute symporter family. However, sequence comparisons suggest that it is the first characterized permease of a new subfamily of transporters. While transport via MctP was inhibited by CCCP, it was not apparently affected by the concentration of sodium. In contrast, glutamate uptake in R. leguminosarum by the Escherichia coli GltS system did require sodium, which suggests that MctP may be proton coupled. Uncharacterized members of this new subfamily have been identified in a broad taxonomic range of species, including proteobacteria of the beta-subdivision, gram-positive bacteria, and archaea. A two-component sensor-regulator (MctSR), encoded by genes adjacent to mctP, is required for activation of mctP expression

Exceptions, instantiations, and overgeneralization:Insights into how language models process generics

Large language models (LLMs) have garnered a great deal of attention for their exceptional generative performance on commonsense and reasoning tasks. In this work, we investigate LLMs’ capabilities for generalization using a particularly challenging type of statement: generics. Generics express generalizations (e.g., birds can fly) but do so without explicit quantification. They are notable because they generalize over their instantiations (e.g., sparrows can fly) yet hold true even in the presence of exceptions (e.g., penguins do not). For humans, these generic generalization play a fundamental role in cognition, concept acquisition, and intuitive reasoning. We investigate how LLMs respond to and reason about generics. To this end, we first propose a framework grounded in pragmatics to automatically generate both exceptions and instantiations – collectively exemplars. We make use of focus – a pragmatic phenomenon that highlights meaning-bearing elements in a sentence – to capture the full range of interpretations of generics across different contexts of use. This allows us to derive precise logical definitions for exemplars and operationalize them to automatically generate exemplars from LLMs. Using our system, we generate a dataset of ∼370k exemplars across ∼17k generics and conduct a human validation of a sample of the generated data. We use our final generated dataset to investigate how LLMs’ reason about generics. Humans have a documented tendency to conflate universally quantified statements (e.g., all birds can fly) with generics. Therefore, we probe whether LLMs exhibit similar overgeneralization behavior in terms of quantification and in property inheritance. We find that LLMs do show evidence of overgeneralization, although they sometimes struggle to reason about exceptions. Furthermore, we find that LLMs may exhibit similar non-logical behavior to humans when considering property inheritance from generics

Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions

Generics express generalizations about the world (e.g., birds can fly) that are not universally true (e.g., newborn birds and penguins cannot fly). Commonsense knowledge bases, used extensively in NLP, encode some generic knowledge but rarely enumerate such exceptions and knowing when a generic statement holds or does not hold true is crucial for developing a comprehensive understanding of generics. We present a novel framework informed by linguistic theory to generate exemplars -- specific cases when a generic holds true or false. We generate ~19k exemplars for ~650 generics and show that our framework outperforms a strong GPT-3 baseline by 12.8 precision points. Our analysis highlights the importance of linguistic theory-based controllability for generating exemplars, the insufficiency of knowledge bases as a source of exemplars, and the challenges exemplars pose for the task of natural language inference.Comment: EACL 202

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Background: Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods: This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results: BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions: Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation

Can Acute Galactic Cosmic Radiation-Induced Bone Loss Be Mitigated By Dietary Modulation Of Inflammatory Cytokines?

The space environment includes weightlessness and galactic cosmic radiation (GCR), both of which can have a negative impact on bone parameters. In particular, acute exposures to space-relevant doses (2 Gy or less) of simulated GCR lead to a rapid acceleration of bone resorption activity and suppression of bone forming osteoblasts, resulting in diminished bone mineral density (BMD), strength and altered microarchitecture. A key mechanism driving these changes may be a radiation-induced increase in pro-inflammatory cytokines, such as TNF-α. Consuming a diet rich in omega-3 fatty acids has been associated with attenuated reductions in bone parameters in astronauts, mice and elderly humans with corresponding reductions in circulating inflammatory cytokines. PURPOSE: To test the hypothesis thata diet high in omega-3 fatty acids will mitigate radiation-induced bone loss and reduce inflammatory cytokines in bone osteocytes and serum. METHODS: Adult (30- to 50-week-old) female Lgr5-EGFP C57BL/6 mice (n=4-6 per group) were acclimated to a corn oil/cellulose (COC) or fish oil/pectin (FOP) diet for 3 weeks. Animals were subsequently randomized to total body low dose high-energy radiation (0.1, 0.25, 0.5 Gy of 1000 MeV/n 56Fe at 25 cGy/min at Brookhaven National Lab) or non-irradiated control (sham) and euthanized 8 weeks later. MicroCT (ScanCo, Switzerland) analyses were performed to assess bone geometry and microarchitecture at the mid-shaft and distal end of the femur. Significance was assessed using an αof 0.10. RESULTS:There was a significant main effect of diet on mid-shaft femur periosteal diameter (Peri.Dm) (p=0.001) and endocortical diameter (Endo. Dm.) (p\u3c0.001). The FOP diet led to larger Peri.Dm. (p\u3c0.051 for all) and Endo.Dm. (p\u3c0.41 for all) than did the COC diet at all doses. We could not detect an impact of 56Fe on cortical area or cancellous bone volume at the distal femur. Irradiation with 0.25 and 0.5 Gy in the FOP mice showed significant increases in distal femur volumetric BMD (p=0.014, p=0.063) and trabecular thickness (p=0.058, p=0.028), as compared with sham FOP mice. CONCLUSION: Though we did not detect a significant impact of radiation on bone parameters, these early data analyses suggest some modest benefits from a diet high in omega-3 fatty acids on cortical and cancellous bone parameters

Identification of a putative LPS-associated cation exporter from Rhizobium leguminosarum bv. Viciae

A gene, cpaA, with similarity to calcium proton antiporters has been identified adjacent to lpcAB in Rhizobiumleguminosarum. LpcA is a galactosyl transferase while LpcB is a 2-keto-3-deoxyoctonate transferase, both of which are required to form the lipopolysaccharide (LPS) core in R. leguminosarum. Mutations in lpcAB result in a rough LPS phenotype with a requirement for elevated calcium concentrations to allow growth, suggesting that truncation of the LPS core exposes a highly negatively charged molecule. This is consistent with the LPS core being one of the main sites for binding calcium in the Gram-negative outer membrane. Strain RU1109 (cpaA::Tn5-lacZ) has a normal LPS layer, as measured by silver staining and Western blotting. This indicates that cpaA mutants are not grossly affected in their LPS layer. LacZ fusion analysis indicates that cpaA is constitutively expressed and is not directly regulated by the calcium concentration. Over-expression of cpaA increased the concentration of calcium required for growth, consistent with CpaA mediating calcium export from the cytosol. The location of lpcA, lpcB and cpaA as well as the phenotype of lpcB mutants suggests that CpaA might provide a specific export pathway for calcium to the LPS core

Modeling the adoption of innovations in the presence of geographic and media influences

While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reflecting real geography or influence from mass media forces. In this article, we show these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homopholy both amongst individuals with similar propensities to adopt a technology and geographic location are critical to reproduce features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves

Suitability of Dried Blood Spots for Accelerating Veterinary Biobank Collections and Identifying Metabolomics Biomarkers With Minimal Resources

Biomarker discovery using biobank samples collected from veterinary clinics would deliver insights into the diverse population of pets and accelerate diagnostic development. The acquisition, preparation, processing, and storage of biofluid samples in sufficient volumes and at a quality suitable for later analysis with most suitable discovery methods remain challenging. Metabolomics analysis is a valuable approach to detect health/disease phenotypes. Pre-processing changes during preparation of plasma/serum samples may induce variability that may be overcome using dried blood spots (DBSs). We report a proof of principle study by metabolite fingerprinting applying UHPLC-MS of plasma and DBSs acquired from healthy adult dogs and cats (age range 1-9 years), representing each of 4 dog breeds (Labrador retriever, Beagle, Petit Basset Griffon Vendeen, and Norfolk terrier) and the British domestic shorthair cat (n = 10 per group). Blood samples (20 and 40 μL) for DBSs were loaded onto filter paper, air-dried at room temperature (3 h), and sealed and stored (4°C for ~72 h) prior to storage at -80°C. Plasma from the same blood draw (250 μL) was prepared and stored at -80°C within 1 h of sampling. Metabolite fingerprinting of the DBSs and plasma produced similar numbers of metabolite features that had similar abilities to discriminate between biological classes and correctly assign blinded samples. These provide evidence that DBSs, sampled in a manner amenable to application in in-clinic/in-field processing, are a suitable sample for biomarker discovery using UHPLC-MS metabolomics. Further, given appropriate owner consent, the volumes tested (20-40 μL) make the acquisition of remnant blood from blood samples drawn for other reasons available for biobanking and other research activities. Together, this makes possible large-scale biobanking of veterinary samples, gaining sufficient material sooner and enabling quicker identification of biomarkers of interest