A systematic analysis of results available from in vitro, in vivo and clinical trials on the effects of biocompatible calcium phosphate (CaP) coatings is presented. An overview of the most frequently ...used methods to prepare CaP-based coatings was conducted. Dense, homogeneous, highly adherent and biocompatible CaP or hybrid organic/inorganic CaP coatings with tailored properties can be deposited. It has been demonstrated that CaP coatings have a significant effect on the bone regeneration process. In vitro experiments using different cells (e.g. SaOS-2, human mesenchymal stem cells and osteoblast-like cells) have revealed that CaP coatings enhance cellular adhesion, proliferation and differentiation to promote bone regeneration. However, in vivo, the exact mechanism of osteogenesis in response to CaP coatings is unclear; indeed, there are conflicting reports of the effectiveness of CaP coatings, with results ranging from highly effective to no significant or even negative effects. This review therefore highlights progress in CaP coatings for orthopaedic implants and discusses the future research and use of these devices. Currently, an exciting area of research is in bioactive hybrid composite CaP-based coatings containing both inorganic (CaP coating) and organic (collagen, bone morphogenetic proteins, arginylglycylaspartic acid etc.) components with the aim of promoting tissue ingrowth and vascularization. Further investigations are necessary to reveal the relative influences of implant design, surgical procedure, and coating characteristics (thickness, structure, topography, porosity, wettability etc.) on the long-term clinical effects of hybrid CaP coatings. In addition to commercially available plasma spraying, other effective routes for the fabrication of hybrid CaP coatings for clinical use still need to be determined and current progress is discussed.
Language behaviour is complex, but neuroscientific evidence disentangles it into distinct components supported by dedicated brain areas or networks. In this Review, we describe the 'core' language ...network, which includes left-hemisphere frontal and temporal areas, and show that it is strongly interconnected, independent of input and output modalities, causally important for language and language-selective. We discuss evidence that this language network plausibly stores language knowledge and supports core linguistic computations related to accessing words and constructions from memory and combining them to interpret (decode) or generate (encode) linguistic messages. We emphasize that the language network works closely with, but is distinct from, both lower-level - perceptual and motor - mechanisms and higher-level systems of knowledge and reasoning. The perceptual and motor mechanisms process linguistic signals, but, in contrast to the language network, are sensitive only to these signals' surface properties, not their meanings; the systems of knowledge and reasoning (such as the system that supports social reasoning) are sometimes engaged during language use but are not language-selective. This Review lays a foundation both for in-depth investigations of these different components of the language processing pipeline and for probing inter-component interactions.
Formal linguistic competence (getting the form of language right) and functional linguistic competence (using language to accomplish goals in the world) are distinct cognitive skills.The human brain ...contains a network of areas that selectively support language processing (formal linguistic competence), but not other domains like logical or social reasoning (functional linguistic competence).In the late 2010s, large language models trained on word prediction tasks began achieving unprecedented success in formal linguistic competence, showing impressive performance on linguistic tasks that likely require hierarchy and abstraction.Consistent performance on tasks requiring functional linguistic competence is harder to achieve for large language models and often involves augmentations beyond next word prediction.Evidence from cognitive science and neuroscience can illuminate the capabilities and limitations of large language models and pave the way toward better, human-like models of both language and thought.
Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of separate mechanisms specialized for formal versus functional linguistic competence.
Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of separate mechanisms specialized for formal versus functional linguistic competence.
Porous inorganic nanostructured materials are widely used nowadays as drug delivery carriers due to their adventurous features: suitable architecture, large surface area and stability in the ...biological fluids. Among the different types of inorganic porous materials, silica, calcium carbonate, and calcium phosphate have received significant attention in the last decade. The use of porous inorganic materials as drug carriers for cancer therapy, gene delivery etc. has the potential to improve the life expectancy of the patients affected by the disease. The main goal of this review is to provide general information on the current state of the art of synthesis of the inorganic porous particles based on silica, calcium carbonate and calcium phosphate. Special focus is dedicated to the loading capacity, controllable release of drugs under internal biological stimuli (e.g., pH, redox, enzymes) and external noninvasive stimuli (e.g., light, magnetic field, and ultrasound). Moreover, the diverse compounds to deliver with silica, calcium carbonate and calcium phosphate particles, ranging from the commercial drugs to genetic materials are also discussed.
ARL13B is a regulatory GTPase highly enriched in cilia. Complete loss of
disrupts cilia architecture, protein trafficking and Sonic hedgehog signaling. To determine whether ARL13B is required within ...cilia, we knocked in a cilia-excluded variant of ARL13B (V358A) and showed it retains all known biochemical function. We found that ARL13B
protein was expressed but could not be detected in cilia, even when retrograde ciliary transport was blocked. We showed
mice are viable and fertile with normal Shh signal transduction. However, in contrast to wild type cilia,
cells displayed short cilia and lacked ciliary ARL3 and INPP5E. These data indicate that ARL13B's role within cilia can be uncoupled from its function outside of cilia. Furthermore, these data imply that the cilia defects upon complete absence of ARL13B do not underlie the alterations in Shh transduction, which is unexpected given the requirement of cilia for Shh transduction.
The regulatory GTPase Arl13b localizes to primary cilia, where it regulates Sonic hedgehog (Shh) signaling. Missense mutations in ARL13B can cause the ciliopathy Joubert syndrome, while the mouse ...null allele is embryonic lethal. We used mouse embryonic fibroblasts as a system to determine the effects of Arl13b mutations on Shh signaling. We tested a total of seven different mutants, three JS-causing variants, two point mutants predicted to alter guanine nucleotide handling, one that disrupts cilia localization, and one that prevents palmitoylation and thus membrane binding, in assays of transcriptional and non-transcriptional Shh signaling. We found that mutations disrupting Arl13b's palmitoylation site, cilia localization signal, or GTPase handling altered the Shh response in distinct assays of transcriptional or non-transcriptional signaling. In contrast, JS-causing mutations in Arl13b did not affect Shh signaling in these same assays, suggesting these mutations result in more subtle defects, likely affecting only a subset of signaling outputs. Finally, we show that restricting Arl13b from cilia interferes with its ability to regulate Shh-stimulated chemotaxis, despite previous evidence that cilia themselves are not required for this non-transcriptional Shh response. This points to a more complex relationship between the ciliary and non-ciliary roles of this regulatory GTPase than previously envisioned.
Word co‐occurrence patterns in language corpora contain a surprising amount of conceptual knowledge. Large language models (LLMs), trained to predict words in context, leverage these patterns to ...achieve impressive performance on diverse semantic tasks requiring world knowledge. An important but understudied question about LLMs’ semantic abilities is whether they acquire generalized knowledge of common events. Here, we test whether five pretrained LLMs (from 2018's BERT to 2023's MPT) assign a higher likelihood to plausible descriptions of agent−patient interactions than to minimally different implausible versions of the same event. Using three curated sets of minimal sentence pairs (total n = 1215), we found that pretrained LLMs possess substantial event knowledge, outperforming other distributional language models. In particular, they almost always assign a higher likelihood to possible versus impossible events (The teacher bought the laptop vs. The laptop bought the teacher). However, LLMs show less consistent preferences for likely versus unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow‐up analyses, we show that (i) LLM scores are driven by both plausibility and surface‐level sentence features, (ii) LLM scores generalize well across syntactic variants (active vs. passive constructions) but less well across semantic variants (synonymous sentences), (iii) some LLM errors mirror human judgment ambiguity, and (iv) sentence plausibility serves as an organizing dimension in internal LLM representations. Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events.
Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to ...investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.
Designing studies for lipid-metabolism-related biomarker discovery is challenging because of the high prevalence of various statin and fibrate usage for lipid-lowering therapies. When the statin and ...fibrate use is determined based on self-reports, patient adherence to the prescribed statin dose regimen remains unknown. A potentially more accurate way to verify a patient's medication adherence is by direct analytical measurements. Current analytical methods are prohibitive because of the limited panel of drugs per test and large sample volume requirement that is not available from archived samples. A 4-min-long method was developed for the detection of seven statins and three fibrates using 10 µL of plasma analyzed via reverse-phase liquid chromatography and tandem mass spectrometry. The method was applied to the analysis of 941 archived plasma samples collected from patients before cardiac catheterization. When statin use was self-reported, statins were detected in 78.6% of the samples. In the case of self-reported atorvastatin use, the agreement with detection was 90.2%. However, when no statin use was reported, 42.4% of the samples had detectable levels of statins, with a similar range of concentrations as the samples from the self-reported statin users. The method is highly applicable in population studies designed for biomarker discovery or diet and lifestyle intervention studies, where the accuracy of statin or fibrate use may strongly affect the statistical evaluation of the biomarker data.
Abstract
The relationship between language and thought is the subject of long-standing debate. One claim states that language facilitates categorization of objects based on a certain feature (e.g. ...color) through the use of category labels that reduce interference from other, irrelevant features. Therefore, language impairment is expected to affect categorization of items grouped by a single feature (low-dimensional categories, e.g. “Yellow Things”) more than categorization of items that share many features (high-dimensional categories, e.g. “Animals”). To test this account, we conducted two behavioral studies with individuals with aphasia and an fMRI experiment with healthy adults. The aphasia studies showed that selective low-dimensional categorization impairment was present in some, but not all, individuals with severe anomia and was not characteristic of aphasia in general. fMRI results revealed little activity in language-responsive brain regions during both low- and high-dimensional categorization; instead, categorization recruited the domain-general multiple-demand network (involved in wide-ranging cognitive tasks). Combined, results demonstrate that the language system is not implicated in object categorization. Instead, selective low-dimensional categorization impairment might be caused by damage to brain regions responsible for cognitive control. Our work adds to the growing evidence of the dissociation between the language system and many cognitive tasks in adults.
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