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Enormous amounts of data are generated and analyzed in the latest semiconductor industry. Established yield prediction studies have dealt with one type of data or a dataset from one procedure. However, semiconductor device fabrication comprises hundreds of processes, and various factors affect device yields. This challenge is addressed in this study by using an expandable input data-based framework to include divergent factors in the prediction and by adapting explainable artificial intelligence (XAI), which utilizes model interpretation to modify fabrication conditions. After preprocessing the data, the procedure of optimizing and comparing several machine learning models is followed to select the best performing model for the dataset, which is a random forest (RF) regression with a root mean square error (RMSE) value of 0.648. The prediction results enhance production management, and the explanations of the model deepen the understanding of yield-related factors with Shapley additive explanation (SHAP) values. This work provides evidence with an empirical case study of device production data. The framework improves prediction accuracy, and the relationships between yield and features are illustrated with the SHAP value. The proposed approach can potentially analyze expandable fields of fabrication conditions to interpret multifaceted semiconductor manufacturing.

A polariton is a fundamental quasiparticle that arises from strong light-matter interaction and as such has attracted wide scientific and practical interest. When light is strongly coupled to the crystal lattice, it gives rise to phonon-polaritons (PPs), which have been proven useful in the dynamical manipulation of quantum materials and the advancement of terahertz technologies. Yet, current detection and characterization methods of polaritons are still limited. Traditional techniques such as Raman or transient grating either rely on fine-tuning of external parameters or complex phase extraction techniques. To overcome these inherent limitations, we propose and demonstrate a technique based on a time-of-flight measurement of PPs. We resonantly launch broadband PPs with intense terahertz fields and measure the time-of-flight of each spectral component with time-resolved second harmonic generation. The time-of-flight information, combined with the PP attenuation, enables us to resolve the real and imaginary parts of the PP dispersion relation. We demonstrate this technique in the van der Waals magnets NiI 2 and MnPS 3 and reveal a hidden magnon-phonon interaction. We believe that this approach will unlock new opportunities for studying polaritons across diverse material systems and enhance our understanding of strong light-matter interaction. Polaritons, light-matter hybridized quasiparticles, are the fundamental excitation of strong coupling systems and are widely applicable in information technologies. Here the authors applied the concept of time-of-flight measurement in terahertz induced second harmonic generation experiments in various systems to comprehensively study the dispersion relation of phonon-polaritons and reveal potential spin-lattice couplings.

ObjectivesTo develop a machine learning (ML)-based predictive model to determine the key predictors of dissatisfaction after occupational injury (OI).DesignA retrospective cohort study.SettingNationwide 5-year panel data (2018–2022) from the Panel Study of Workers’ Compensation Insurance in South Korea.ParticipantsA total of 2298 workers who completed compensation-related medical care in 2017.MethodsPredictive modelling was conducted with extreme gradient (XG) Boost, light gradient boosting machine (GBM), CatBoost and random forest. SHapley Additive Explanations (SHAPs) analysis was conducted to interpret the feature importance. Further, logistic regression was conducted for comparison.Primary outcome measuresThis study evaluated postinjury satisfaction among workers using survey items associated with satisfaction levels. We adopted a 5-year follow-up period.ResultsOf the 2298 participants, 570 were dissatisfied. The logistic regression model indicated that dissatisfaction was significantly associated with unemployment (adjusted OR (aOR) 1.701; 95% CI: 1.296 to 2.233), lack of private health insurance (aOR 1.347; 95% CI 1.042 to 1.741) and lower perceived socioeconomic status (aOR 2.097; 95% CI 1.109 to 3.965). Among the ML models, light GBM exhibited the highest area under the receiver operating characteristics curve (0.770 (95% CI 0.718 to 0.819)), followed by CatBoost (0.768 (95% CI 0.718 to 0.815)), random forest (0.766 (95% CI 0.715 to 0.814)) and XGBoost (0.765 (95% CI 0.717 to 0.811)). The SHAP analysis demonstrated the total number of household members, extent of pain interference with daily life, perceived health status before injury and financial factors as the strongest predictors.ConclusionThis study developed and demonstrated robust predictive performance of an ML-based model for determining dissatisfaction after OI. The key features included employment status, financial stability, chronic pain and cognitive function, highlighting the multifaceted nature of worker satisfaction.

Molecular imaging allows clinicians to visualize the progression of tumours and obtain relevant information for patient diagnosis and treatment 1 . Owing to their intrinsic optical, electrical and magnetic properties, nanoparticles are promising contrast agents for imaging dynamic molecular and cellular processes such as protein–protein interactions, enzyme activity or gene expression 2 . Until now, nanoparticles have been engineered with targeting ligands such as antibodies and peptides to improve tumour specificity and uptake. However, excessive loading of ligands can reduce the targeting capabilities of the ligand 3 , 4 , 5 and reduce the ability of the nanoparticle to bind to a finite number of receptors on cells 6 . Increasing the number of nanoparticles delivered to cells by each targeting molecule would lead to higher signal-to-noise ratios and would improve image contrast. Here, we show that M13 filamentous bacteriophage can be used as a scaffold to display targeting ligands and multiple nanoparticles for magnetic resonance imaging of cancer cells and tumours in mice. Monodisperse iron oxide magnetic nanoparticles assemble along the M13 coat, and its distal end is engineered to display a peptide that targets SPARC glycoprotein, which is overexpressed in various cancers. Compared with nanoparticles that are directly functionalized with targeting peptides, our approach improves contrast because each SPARC-targeting molecule delivers a large number of nanoparticles into the cells. Moreover, the targeting ligand and nanoparticles could be easily exchanged for others, making this platform attractive for in vivo high-throughput screening and molecular detection. The M13 filamentous virus can be used to deliver large numbers of magnetic nanoparticles with a minimum number of targeting ligands for improved molecular imaging.

T helper type 17 (TH17) cells are highly proinflammatory effector T cells that are characterized by the production of high amounts of IL-17A, IL-17F, IL-21, and IL-22. Furthermore, TH17 cells have been associated with a number of autoimmune diseases. However, it is not clear whether TH17 cells can also serve as effective helper cells. Here we show that TH17 cells can function as B-cell helpers in that they not only induce a strong proliferative response of B cells in vitro but also trigger antibody production with class switch recombination in vivo. Transfer of TH17 cells into WT or T-cell receptor α–deficient mice, which lack endogenous T cells, induces a pronounced antibody response with preferential isotype class switching to IgG1, IgG2a, IgG2b, and IgG3, as well as the formation of germinal centers. Conversely, blockade of IL-17 signaling results in a significant reduction in both number and size of germinal centers. Whereas IL-21 is known to help B cells, IL-17 on its own drives B cells to undergo preferential isotype class switching to IgG2a and IgG3 subtypes. These observations provide insights into the unappreciated role of TH17 cells and their signature cytokines in mediating B-cell differentiation and class switch recombination.

Hyperuricemia is a metabolic disorder caused by increased uric acid (UA) synthesis or decreased UA excretion. Changes in eating habits have led to an increase in the consumption of purine-rich foods, which is closely related to hyperuricemia. Therefore, decreased purine absorption, increased UA excretion, and decreased UA synthesis are the main strategies to ameliorate hyperuricemia. This study aimed to screen the lactic acid bacteria (LAB) with purine degrading ability and examine the serum UA-lowering effect in a hyperuricemia mouse model. As a result, Lacticaseibacillus paracasei MJM60396 was selected from 22 LAB isolated from fermented foods for 100% assimilation of inosine and guanosine. MJM60396 showed probiotic characteristics and safety properties. In the animal study, the serum uric acid was significantly reduced to a normal level after oral administration of MJM60396 for 3 weeks. The amount of xanthine oxidase, which catalyzes the formation of uric acid, decreased by 81%, and the transporters for excretion of urate were upregulated. Histopathological analysis showed that the damaged glomerulus, Bowman’s capsule, and tubules of the kidney caused by hyperuricemia was relieved. In addition, the impaired intestinal barrier was recovered and the expression of tight junction proteins, ZO-1 and occludin, was increased. Analysis of the microbiome showed that the relative abundance of Muribaculaceae and Lachnospiraceae bacteria, which were related to the intestinal barrier integrity, was increased in the MJM60396 group. Therefore, these results demonstrated that L. paracasei MJM60396 can prevent hyperuricemia in multiple ways by absorbing purines, decreasing UA synthesis by suppressing xanthine oxidase, and increasing UA excretion by regulating urate transporters.

We have previously reported the molecular signature of murine pathogenic T H 17 cells that induce experimental autoimmune encephalomyelitis (EAE) in animals. Here we show that human peripheral blood IFN-γ + IL-17 + (T H 1/17) and IFN-γ − IL-17 + (T H 17) CD4 + T cells display distinct transcriptional profiles in high-throughput transcription analyses. Compared to T H 17 cells, T H 1/17 cells have gene signatures with marked similarity to mouse pathogenic T H 17 cells. Assessing 15 representative signature genes in patients with multiple sclerosis, we find that T H 1/17 cells have elevated expression of CXCR3 and reduced expression of IFNG , CCL3 , CLL4 , GZMB , and IL10 compared to healthy controls. Moreover, higher expression of IL10 in T H 17 cells is found in clinically stable vs. active patients. Our results define the molecular signature of human pro-inflammatory T H 17 cells, which can be used to both identify pathogenic T H 17 cells and to measure the effect of treatment on T H 17 cells in human autoimmune diseases. CD4 + T cells secreting interleukin-17 (T H 17) have diverse functions in modulating autoimmune diseases. Here the authors show via transcriptome analyses that a subset of human T H 17 co-expressing interferon-γ (T H 1/17) has a molecular signature similar to “pathogenic” mouse T H 17 but distinct from “non-pathogenic” mouse T H 17.

Understanding how dietary patterns influence chronic kidney disease (CKD) development is crucial for effective prevention strategies. This study identified distinct dietary patterns among Korean adults and investigated their association with CKD development. This retrospective cohort study used data from the Korean Genome and Epidemiology Study health examinee study database of community-dwelling adults aged ≥ 40 years in South Korea (2004–2016). Then, dietary patterns were identified using K-means clustering analysis based on the quantity (weights) of 106 foods and intakes of energy and 22 nutrients. The dependent variable for Cox regression analyses was the development of new-onset CKD. A total of 57,213 participants were classified into three dietary clusters. Cluster C, characterized by lower overall food, energy, and nutrient intakes and higher carbohydrate intake, was independently associated with increased CKD risk (adjusted hazard ratio, 1.59; 95% confidence interval, 1.04–2.41; P  = 0.031) compared to Cluster A, characterized by higher intake of vegetables and fish/shellfish. Subgroup analyses revealed that Cluster C still had a significantly high risk for CKD development in age ≥ 65 years, male sex, previous cardiovascular disease, systolic blood pressure ≥ 130 mm Hg, and body mass index ≥ 25 kg/m 2 . Both the quantity and quality of food intake may influence CKD development in Korean adults. Maintaining a balanced, nutrient-rich diet could be key for CKD prevention, especially in high-risk subgroups.

Background Nontuberculous mycobacterial lung disease (NTM-LD) and Mycobacterium tuberculosis lung disease (MTB-LD) have similar clinical characteristics. Therefore, NTM-LD is sometimes incorrectly diagnosed with MTB-LD and treated incorrectly. To solve these difficulties, we aimed to distinguish the two diseases in chest X-ray images using deep learning technology, which has been used in various fields recently. Methods We retrospectively collected chest X-ray images from 3314 patients infected with Mycobacterium tuberculosis (MTB) or nontuberculosis mycobacterium (NTM). After selecting the data according to the diagnostic criteria, various experiments were conducted to create the optimal deep learning model. A performance comparison was performed with the radiologist. Additionally, the model performance was verified using newly collected MTB-LD and NTM-LD patient data. Results Among the implemented deep learning models, the ensemble model combining EfficientNet B4 and ResNet 50 performed the best in the test data. Also, the ensemble model outperformed the radiologist on all evaluation metrics. In addition, the accuracy of the ensemble model was 0.85 for MTB-LD and 0.78 for NTM-LD on an additional validation dataset consisting of newly collected patients. Conclusions In previous studies, it was known that it was difficult to distinguish between MTB-LD and NTM-LD in chest X-ray images, but we have successfully distinguished the two diseases using deep learning methods. This study has the potential to aid clinical decisions if the two diseases need to be differentiated.

Dominant mutations in p97/VCP (valosin-containing protein) cause a rare multisystem degenerative disease with varied phenotypes that include inclusion body myopathy, Paget’s disease of bone, frontotemporal dementia, and amyotrophic lateral sclerosis. p97 disease mutants have altered N-domain conformations, elevated ATPase activity, and altered cofactor association. We have now discovered a previously unidentified disease-relevant functional property of p97 by identifying how the cofactors p37 and p47 regulate p97 ATPase activity. We define p37 as, to our knowledge, the first known p97-activating cofactor, which enhances the catalytic efficiency ( k cₐₜ/ K ₘ) of p97 by 11-fold. Whereas both p37 and p47 decrease the K ₘ of ATP in p97, p37 increases the k cₐₜ of p97. In contrast, regulation by p47 is biphasic, with decreased k cₐₜ at low levels but increased k cₐₜ at higher levels. By deleting a region of p47 that lacks homology to p37 (amino acids 69–92), we changed p47 from an inhibitory cofactor to an activating cofactor, similar to p37. Our data suggest that cofactors regulate p97 ATPase activity by binding to the N domain. Induced conformation changes affect ADP/ATP binding at the D1 domain, which in turn controls ATPase cycling. Most importantly, we found that the D2 domain of disease mutants failed to be activated by p37 or p47. Our results show that cofactors play a critical role in controlling p97 ATPase activity, and suggest that lack of cofactor-regulated communication may contribute to p97-associated disease pathogenesis. Significance Age-associated degenerative diseases have similar pathogenic mechanisms related to defects in protein homeostasis. p97/VCP (valosin-containing protein) is essential for coordinating protein degradation and is mutated in a multisystem degenerative disease that affects the central nervous system, muscle, and bone. p97/VCP is an enzyme in the AAA ATPases (ATPases associated with diverse cellular activities) family, which takes apart ATP and uses this energy to perform pivotal functions. We found that p97/VCP cofactors control its enzymatic activity. p97/VCP disease mutants behave abnormally due to lack of appropriate control by these cofactors. Correcting the function of the disease-associated proteins may be a desirable approach to developing safe treatment for fatal degenerative diseases. The next steps are to screen and characterize large panels of compounds to identify potential drugs that may correct the malfunction.