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\"Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor. Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. It covers the fundamentals, algorithms, and applications of MSL. Emphasizing essential concepts and system-level perspectives, the authors provide a foundation for solving many of today's most interesting and challenging problems in big multidimensional data processing. They trace the history of MSL, detail recent advances, and explore future developments and emerging applications.The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Implementation tips help practitioners in further development, evaluation, and application. The book also provides researchers with useful theoretical information on big multidimensional data in machine learning and pattern recognition. MATLAB source code, data, and other materials are available at www.comp.hkbu.edu.hk/haiping/MSL.html\"-- Provided by publisher

Metals with a high density of nanometre-scale twins have demonstrated simultaneous high strength and good ductility, attributed to the interaction between lattice dislocations and twin boundaries. Maximum strength was observed at a critical twin lamella spacing (∼15 nm) by mechanical testing; hence, an explanation of how twin lamella spacing influences dislocation behaviours is desired. Here, we report a transition of dislocation nucleation from steps on the twin boundaries to twin boundary/grain boundary junctions at a critical twin lamella spacing (12–37 nm), observed with in situ transmission electron microscopy. The local stress concentrations vary significantly with twin lamella spacing, thus resulting in a critical twin lamella spacing (∼18 nm) for the transition of dislocation nucleation. This agrees quantitatively with the mechanical test. These results demonstrate that by quantitatively analysing local stress concentrations, a direct relationship can be resolved between the microscopic dislocation activities and macroscopic mechanical properties of nanotwinned metals. Metallic materials with a nanometre-scaled lamella structure can have properties that are very different from their coarser-grained counterparts. Here, the authors demonstrate how dislocations in such a material—nanotwinned copper—can nucleate in two distinctly different mechanisms depending on local stress

Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%. Laser wakefield accelerators are compact sources of ultra-relativistic electrons which are highly sensitive to many control parameters. Here the authors present an automated machine learning based method for the efficient multi-dimensional optimization of these plasma-based particle accelerators.

A plant factory with artificial light is an effective system producing food to satisfy specific demands on yield, morphology, taste and nutrient accumulation in plants. All environmental factors inside a plant factory can be controlled without climate and location limitation. Light is one of the most important factors affecting plant growth and quality. By regulating light aspects, such as light intensity, light period, light quality, lighting position, and daily light integral, the growth and quality of the plants grown in a plant factory can be largely enhanced. As known, the initial and operating cost for a plant factory with artificial light is high, particularly the cost of electrical energy related to lighting. Identifying the optimal light environment that promotes plant growth and quality is critical for commercialization of plant factories. Recent researches have paid great attentions to the effects of light environment on the growth and morphology of leafy vegetables. On the other hand, the demand on functional plants that contain high concentration of bioactive compounds is increasing rapidly. Bioactive compounds in plants have been intensively studied to evaluate their effects on human health and many of them are proved to be clinically active against various types of diseases (e.g. anti-cancer effects). More and more people prefer to take health product derived from natural plants for disease prevention. Solutions to realize sustainable production of high quality functional/medicinal plants can be provided by developing environmental control technologies, such as light recipe, in plant factories. Aromatic herbs such as coriander; medicinal plants such as perilla and water spinach are subjected to different light conditions and root zone environments. Some bioactive compounds e.g. perillaldehyde and rosmarinic acid in perilla leaves; phenolic compounds and flavonoids, especially rutin and chlorogenic acid in coriander can be enhanced. The effects of each light aspect on plant growth vary with plant species and other environmental conditions, however, there are also some general trends that can be used to guide commercial application. This presentation introduces the basic of light and its effects on plant growth in plant factories, demonstrates research results that have been published in scientific journals, reports the current study on herbs and medicinal plants, and summarizes the general application of light in plant production.

Synchrotron microbeam radiotherapy (MRT), which has entered the clinical transfer phase, requires the development of appropriate quality assurance (QA) tools due to very high dose rates and spatial hyperfractionation. A microstrip plastic scintillating detector system with associated modules was proposed in the context of real-time MRT QA. A prototype of such a system with 105 scintillating microstrips was developed and tested under MRT conditions. The signal obtained from each microstrip when irradiated was reproducible, linear with the dose, and independent of both the dose rate and the beam energy. The detector prototype was capable of measuring an entire 52-microbeam field in real time and exhibited outstanding radiation hardness. It could withstand more than 100 kGy absorbed dose, which is at least ten times higher than the doses reported in the literature for plastic scintillators before deterioration. The potential of this detector system in MRT QA was demonstrated in this study.

Purpose Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets. Methods ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2′-deoxyuridine (EdU) assay, and xenograft tumors. Results Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA ( p  < 0.01) and protein level in LNM patient tissues ( p  < 0.001). And differed in PTC tissues with different pathologic typing ( p  < 0.001). Further, EdU ( p  < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase ( p  < 0.001). Conclusions This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

Evidence demonstrates that brain-derived neurotrophic factor (BDNF) has a pivotal role in the pathogenesis of major depressive disorder (MDD). Precursor-BDNF (proBDNF) and mature BDNF (mBDNF) have opposing biological effects in neuroplasticity, and the tissue-type plasminogen activator (tPA)/plasmin system is crucial in the cleavage processing of proBDNF to mBDNF. However, very little is known about the role of the tPA–BDNF pathway in MDD. We examined serum protein concentrations in the tPA–BDNF pathway, including tPA, BDNF, tropomyosin receptor kinase B (TrkB), proBDNF and p75NTR, obtained from 35 drug-free depressed patients before and after 8 weeks of escitalopram (mean 12.5 mg per day) or duloxetine (mean 64 mg per day) treatment and 35 healthy controls using sandwich ELISA (enzyme-linked immunosorbent assay) methods. Serum tPA and BDNF and the ratio of BDNF/proBDNF were significantly lower in the MDD patients than in controls, whereas TrkB, proBDNF and its receptor p75NTR were higher. After 8 weeks of treatment, tPA, BDNF and proBDNF and the BDNF/proBDNF ratio were reversed, but p75NTR was higher than baseline, and TrkB was not significantly changed. tPA, BDNF, TrkB, proBDNF and p75NTR all yielded fairly good or excellent diagnostic performance (area under the receiver operating characteristic curve (AUC) >0.8 or 0.9). Combination of these five proteins demonstrated much better diagnostic effectiveness (AUC: 0.977) and adequate sensitivity and specificity of 88.1% and 92.7%, respectively. Our results suggest that the tPA–BDNF lysis pathway may be implicated in the pathogenesis of MDD and the mechanisms underlying antidepressant therapeutic action. The combination of tPA, BDNF, TrkB, proBDNF and p75NTR may provide a diagnostic biomarker panel for MDD.

Small-and-medium-sized enterprises (SME) often need to draw on the knowledge of their supply chain partners to remain innovative and competitive in the market place. In the context of global value chains (GVC), this study examines the factors enabling the learning of SMEs from their GVC dependence by applying the logic of power and the logic of embeddedness. Specifically, we identify the technical adaptation of SMEs in the GVC as a response to their interdependence on the GVC following the logic of power, and an action that heightens information exchange and interorganizational learning at the dyad level following the logic of embeddedness. Linking these logics, we hypothesize that the technical adaptation of an SME mediates the relationship between its GVC dependence and its learning outcome from the GVC, namely the knowledge transfer it receives. Furthermore, this mediating role is stronger when the SME has a longer history of transactional relationship with its GVC partners which amplifies the logic of power, and when it possesses a higher level of financial slack which strengthens the logic of embeddedness. Using multisourced survey data from 292 Thai manufacturing SMEs, we find substantial support for the hypothesized relationships. Our findings offer theoretical and practical implications in terms of enabling and supporting the learning pathway of SMEs participating in the GVC.

Cancer is mainly caused by somatic genome alterations (SGAs). Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. We developed a novel tumor-specific computational framework that finds the likely causative SGAs in an individual tumor and estimates their impact on oncogenic processes, which suggests the disease mechanisms that are acting in that tumor. This information can be used to guide precision oncology. We report a tumor-specific causal inference (TCI) framework, which estimates causative SGAs by modeling causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. Overall, TCI identified 634 SGAs that are predicted to cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both the well-known and the novel candidate drivers that are predicted by TCI. TCI provides a unified framework that integrates multiple types of SGAs and molecular phenotypes to estimate which genome perturbations are causally influencing one or more molecular/cellular phenotypes in an individual tumor. By identifying major candidate drivers and revealing their functional impact in an individual tumor, TCI sheds light on the disease mechanisms of that tumor, which can serve to advance our basic knowledge of cancer biology and to support precision oncology that provides tailored treatment of individual tumors.

Background:Both the ACR[1] and EULAR[2] guidelines endorse the use of colchicine or NSAIDs for gout flare prophylaxis when initiating urate-lowering therapy for gout care; however, a recent UK study[3] found 1.6- and 1.9-fold higher risk of myocardial infarction (MI) associated with colchicine and NSAID prophylaxis (compared to no active comparator), respectively, when starting allopurinol for gout. These findings are surprising and concerning,[4] as gout patients are already at high risk of major adverse cardiovascular (CV) events (MACE).[5] Furthermore, this increased risk associated with colchicine contradicts the randomized trial results[6] and FDA approval for its cardiovascular benefit.Objectives:To emulate target trials to determine the risk of MI and MACE associated with colchicine or NSAID prophylaxis when initiating allopurinol for gout care.Methods:Using a Canadian general population database, we designed and conducted our primary target trial emulation (TTE) to compare colchicine vs. NSAID (active comparator) for the risks of MI and MACE among patients with gout starting allopurinol for gout care. Randomization was emulated using propensity score matching based on 60 pre-exposure MI/MACE-related covariates, including demographics, comorbidities, medication use, and healthcare utilization. Our primary TTE followed individuals for 3 months or until the first occurrence of MI (or MACE) or end of colchicine or NSAID treatment, whichever came first (3 month-as treated [AT]). Sensitivity analyses followed individuals for 6 months or until the first occurrence of MI (or MACE) or end of colchicine or NSAID treatment (6 month-AT), and regardless of treatment cessation (intention-to-treat, ITT) for 3 and 6 months (3 month-ITT and 6 month-ITT, respectively). Our secondary TTEs compared 1) colchicine vs. no prophylaxis and 2) NSAID vs. no prophylaxis (i.e., no active comparator, as done in the recent UK study,3 where the causal contrast was limited to 6 month-AT).Results:In all emulated target trials, the baseline characteristics of patients (including CV diseases and CKD) in each treatment group were well-balanced after 1:1 propensity score matching (all standardized differences < 0.1). We identified 13,506 individuals who started allopurinol with colchicine prophylaxis and 13,506 individuals who started allopurinol with NSAID prophylaxis. After propensity score matching, the incidence rates for MI for the primary analysis were 27.3 per 1000 person-years (PYs) for colchicine and 39.7 per 1000 PY for NSAID, resulting in a hazard ratio (HR) of 0.69 (95% CI; 0.51, 0.93) (Table 1). The incidence rates for MACE were 55.7 per 1000 PY for colchicine and 69.5 per 1000 PY for NSAID, resulting in a HR of 0.80 (95% CI; 0.65, 0.99) (Table 1). These findings persisted in all sensitivity analyses with varying causal contrasts and durations (Table 1). Colchicine was not associated with MI or MACE risk compared to no prophylaxis, except for MACE in the 6 month-AT analysis, the approach employed by the UK study3 that resulted in a major imbalance in follow-up times (as the no prophylaxis group had no treatment to stop for 6 months, unlike colchicine group) (Table 2). Conversely, in all TTEs, NSAIDs were associated with a higher risk of MI and MACE compared with no prophylaxis, most prominently in the 6 month-AT analysis (as expected for the same reason above) (Table 2).Conclusion:In these target trial analyses emulating pragmatic trials of gout patients starting allopurinol in the general population, colchicine prophylaxis was associated with a lower risk of MI and MACE than NSAID, whereas the risk was unaltered compared with no prophylaxis. These TTE results agree with the known and expected cardiovascular effects of colchicine and NSAID, supporting colchicine as the preferred flare prophylaxis, particularly among gout patients with CV disease or risk factors.REFERENCES:[1] FitzGerald et al., PMID: 32391934.[2] Richette et al., PMID: 27457514.[3] Roddy et al., PMID: 37788904.[4] Yokose et al., https://ard.bmj.com/content/82/12/1618.responses.[5] Choi et al., PMID: 17698728.[6] Nidorf et al., PMID: 32865380.Acknowledgements:NIL.Disclosure of Interests:Chio Yokose: None declared, Natalie McCormick: None declared, Na Lu: None declared, Abhishek Abhishek: None declared, Angelo Gaffo SOBI, PK Med, Yuqing Zhang: None declared, Hyon Choi Ani, LG, Horizon, Shanton, and Protalix, Horizon.