Search depth, knowledge characteristics, and innovation performance

This study takes a contingent perspective regarding the relationships among a firm's technological search depth, the characteristics of its knowledge, and its product innovation performance. While a firm's search patterns directly influence innovative output, their effectiveness is moderated by the internal context of knowledge: knowledge depth, knowledge scope, and related technological opportunities. Findings from the US electrical medical device industry (1990 to 2000) provide general support for these arguments

Inverse Design of Nanoparticles Using Multi‐Target Machine Learning

In this study a new approach to inverse design is presented that draws on the multi-functionality of nanomaterials and uses sets of properties to predict a unique nanoparticle structure. This approach involves multi-target regression and uses a precursory forward structure/property prediction to focus the model on the most important characteristics before inverting the problem and simultaneously predicting multiple structural features of a single nanoparticle. The workflow is general, as demonstrated on two nanoparticle data sets, and can rapidly predict property/structure relationships to guide further research and development without the need for additional optimization or high-throughput sampling.Computational resources for this project have been supplied by the National Computing Infrastructure (NCI) national facility under partner Grant p00

Causal Paths Allowing Simultaneous Control of Multiple Nanoparticle Properties Using Multi‐Target Bayesian Inference

Machine learning can extract complex structure/property relationships but is often insufficient to explain how to control or tune the properties of materials, particularly when they are multi-functional. This study demonstrates the value of combining multi-target regression and multi-target causal graphs to address the need to simultaneously control multiple properties of nanomaterials, and the need to translate these relationships into actionable insights. Using nanodiamonds as an exemplar, recursive feature elimination is first used to identify nine structural features that allow simultaneous prediction of their electron charge transfer properties and thermochemical stability to high accuracy by an interpretable random forest regressor. A multi-target Bayesian network with domain knowledge incorporated via interactive learning using a hill-climbing algorithm then determines how these important structural features of nanodiamonds relate to their functional properties, proposing causal paths that can be used to inform experimental design.The authors acknowledge Prof. Andrew Wood from the Research School of Finance, Actuarial Studies, and Statistics at the Australian National University for his useful comments on the paper drafts. J.Y.C.T. acknowledges the financial support from the Australian National University under the University Research Scholarshi

Research advances and trends in the surgical treatment of carpal tunnel syndrome from 2003 to 2022: A CiteSpace-based bibliometric analysis

BackgroundCarpal Tunnel Syndrome (CTS) is one of the most common peripheral neuropathies. The typical symptoms are tingling and numbness in the median nerve distribution of the hand. Current treatment for CTS includes general conservative treatment and surgical treatment. Surgical treatment plays a crucial role in the management of CTS, but little bibliometric analysis has been conducted on it. Therefore, this study aimed to map the literature co-citation network using CiteSpace (6.1 R4) software. Research frontiers and trends were identified by retrieving subject headings with significant changing word frequency trends, which can be used to predict future research advances in the surgical treatment of CTS.MethodsPublications on the surgical treatment of CTS in the Web of Science database were collected between 2003 and 2022. CiteSpace software was applied to visualize and analyze publications, countries, institutions, journals, authors, references, and keywords.ResultsA total of 336 articles were collected, with the USA being the major publishing power in all countries/regions. JOURNAL OF HAND SURGERY AMERICAN VOLUME was the journal with the most published and co-cited articles. Based on keyword and reference co-citation analysis, keywords such as CTS, surgery, release, median nerve, and diagnosis were the focus of the study.ConclusionThe results of this bibliometric study provide clinical research advances and trends in the surgical treatment of patients with CTS over the past 20 years, which may help researchers to identify hot topics and explore new directions for future research in the field

Transcriptome analysis showed that tomato-rootstock enhanced salt tolerance of grafted seedlings was accompanied by multiple metabolic processes and gene differences

IntroductionGrafting is a commonly used cultural practice to counteract salt stress and is especially important for vegetable production. However, it is not clear which metabolic processes and genes are involved in the response of tomato rootstocks to salt stress.MethodsTo elucidate the regulatory mechanism through which grafting enhances salt tolerance, we first evaluated the salt damage index, electrolyte permeability and Na+ accumulation in tomato (Solanum lycopersicum L.) leaves of grafted seedlings (GSs) and nongrafted seedlings (NGSs) subjected to 175 mmol·L− 1 NaCl for 0-96 h, covering the front, middle and rear ranges.ResultsCompared with the NGS, the GSs were more salt tolerant, and the Na+ content in the leaves decreased significantly. Through transcriptome sequencing data analysis of 36 samples, we found that GSs exhibited more stable gene expression patterns, with a lower number of DEGs. WRKY and PosF21 transcription factors were significantly upregulated in the GSs compared to the NGSs. Moreover, the GSs presented more amino acids, a higher photosynthetic index and a higher content of growth-promoting hormones. The main differences between GSs and NGSs were in the expression levels of genes involved in the BR signaling pathway, with significant upregulation of XTHs. The above results show that the metabolic pathways of “photosynthetic antenna protein”, “amino acid biosynthesis” and “plant hormone signal transduction” participate in the salt tolerance response of grafted seedlings at different stages of salt stress, maintaining the stability of the photosynthetic system and increasing the contents of amino acids and growth-promoting hormones (especially BRs). In this process, the transcription factors WRKYs, PosF21 and XTHs might play an important role at the molecular level.DiscussionThe results of this study demonstrates that grafting on salt tolerant rootstocks can bring different metabolic processes and transcription levels changes to scion leaves, thereby the scion leaves show stronger salt tolerance. This information provides new insight into the mechanism underlying tolerance to salt stress regulation and provides useful molecular biological basis for improving plant salt resistance

Analysis of cognitive function and its related factors after treatment in Meniere’s disease

A growing body of research recently suggested the association between vestibular dysfunction and cognitive impairment. Meniere’s disease (MD), a common clinical vestibular disorder, is usually accompanied by hearing loss and emotional stress, both of which may mediate the relationship between vestibule dysfunction and cognition. It is currently unknown whether the cognitive decline in MD patients could improve through treatment and how it relates to multiple clinical characteristics, particularly the severity of vertigo. Therefore, in the present study, the MD patients were followed up for 3, 6, and 12 months after treatment, and the cognitive functions, vertigo symptoms, and related physical, functional, and emotional effects of the patients were assessed using the Montreal Cognitive Assessment (MoCA) and Dizziness Handicap Inventory (DHI), aiming to explore the change in cognition before and after therapy and the correlation with various clinical features. It was found that cognitive decline in MD patients compared to healthy controls before therapy. Importantly, this cognitive impairment could improve after effective therapy, which was related to the severity of vertigo, especially in functional and physical impacts. Our results support the view that vestibular dysfunction is a potentially modifiable risk factor for cognitive decline

QSAR Studies on Andrographolide Derivatives as α-Glucosidase Inhibitors

Andrographolide derivatives were shown to inhibit α-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by 2D and 3D QSAR methods. The cross-validation r2 (0.731) and standard error (0.225) illustrated that the 2D-QSAR model was able to identify the important molecular fragments and the cross-validation r2 (0.794) and standard error (0.127) demonstrated that the 3D-QSAR model was capable of exploring the spatial distribution of important fragments. The obtained results suggested that proposed combination of 2D and 3D QSAR models could be useful in predicting the α-glucosidase inhibiting activity of andrographolide derivatives

Variance Tolerance Factors For Interpreting ALL Neural Networks

Black box models only provide results for deep learning tasks, and lack informative details about how these results were obtained. Knowing how input variables are related to outputs, in addition to why they are related, can be critical to translating predictions into laboratory experiments, or defending a model prediction under scrutiny. In this paper, we propose a general theory that defines a variance tolerance factor (VTF) inspired by influence function, to interpret features in the context of black box neural networks by ranking the importance of features, and construct a novel architecture consisting of a base model and feature model to explore the feature importance in a Rashomon set that contains all well-performing neural networks. Two feature importance ranking methods in the Rashomon set and a feature selection method based on the VTF are created and explored. A thorough evaluation on synthetic and benchmark datasets is provided, and the method is applied to two real world examples predicting the formation of noncrystalline gold nanoparticles and the chemical toxicity 1793 aromatic compounds exposed to a protozoan ciliate for 40 hours

Inverse Design of MXenes for High-Capacity Energy Storage Materials Using Multi-Target Machine Learning

There is significant interest in discovering high-capacity battery materials, prompting the investigation of the electrochemical energy storage potential of the two-dimensional early transition metal carbides known as MXenes. Predicting the relationship between the composition of a MXene and electrochemical properties is a focus of considerable research. In this paper we classify the specific MXene chemical formula using a new categorical descriptor and simultaneously predict multiple target electrochemical properties. We then invert the design challenge and predict the formula for MXenes based on a set of battery performance criteria. This approach involves a workflow that includes multi-target regression and multi-target classification, focusing on the physicochemical features most pertinent to battery design. The final inverse model recommends Li2M2C and Mg2M2C (M = Sc, Ti, Cr) as candidates for more focused research, based on desirable ranges of gravimetric capacity, voltage, and induced charge