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Photodynamic therapy (PDT) has been considered a noninvasive and cost-effective modality for tumor treatment. However, the complexity of tumor microenvironments poses challenges to the implementation of traditional PDT. Here, we review recent advances in PDT to resolve the current problems. Major breakthroughs in PDTs are enabling significant progress in molecular medicine and are interconnected with innovative strategies based on smart bio/nanomaterials or therapeutic insights. We focus on newly developed PDT strategies designed by tailoring photosensitive reactive oxygen species generation, which include the use of proteinaceous photosensitizers, self-illumination, or oxygen-independent approaches. While these updated PDT platforms are expected to enable major advances in cancer treatment, addressing future challenges related to biosafety and target specificity is discussed throughout as a necessary goal to expand the usefulness of PDT. Photodynamic therapy: Bringing light into tumors to treat cancer Advancements in photosensitive proteins, nanomaterials, and luminescence are improving the ability of photodynamic therapy (PDT) to attack cancerous tumors. In PDT photosensitive drugs are introduced into tumors, which are then exposed to light, producing reactive oxygen species that kill cells. Young-Pil Kim and coworkers at Hanyang University in Seoul, South Korea, reviewed the challenges of PDT, including drug side effects and how to deliver light into tumors. They highlight advances in protein-based photosensitive drugs, which avoid the side effects of their non-protein counterparts, and could even be generated within the body through genetic manipulation. Bioluminescent and chemiluminescent chemicals have been incorporated into nanomaterials such as quantum dots, carrying light deep into tumors. The use of hybrid oxygen-carrying proteins can provide oxygen for PDT, even inside oxygen-poor tumors that have depleted the local blood supply.

Objectives The rapid growth of machine learning methods has led to an increase in the demand for data. For bearing fault diagnosis, the data acquisition is time-consuming with complicated processes. Existing datasets are only focused on only one type of bearing, which limits real-world applications. Therefore, the objective of this work is to propose a diverse dataset for ball bearing fault diagnosis based on vibration. Data description In this work, we introduce a practical dataset named HUST bearing , which provides a large set of vibration data on different ball bearings. This dataset contains 99 raw vibration signals of 6 types of defects (inner crack, outer crack, ball crack, and their 2-combinations) on 5 types of bearing (6204, 6205, 6206, 6207, and 6208) at 3 working conditions (0 W, 200 W, and 400 W). Each vibration signal is sampled at a rate of 51,200 samples per second for 10 s. The data acquisition system is elaborately designed with high reliability.

The high strength and hard-to-cut materials can be easily machined by using modern machining methods. It is important to introduce the efforts on enhancing the process for improving the machining quality. In the present investigation, an effort was made to analyze the effects of micro-size Al2O3 particles mixed into Kerosene as the dielectric under different powder concentrations on machining titanium alloy in Electrical Discharge Machining (EDM). Response Surface Methodology (RSM) based algorithm utilized to analyze the performance measures by considering machining time with Cost of Goods Manufactured (COGM) method in Powder Mixed Electro Discharge Machining (PMEDM) process. It was found that the micron size powders can significantly help to enhance the surface quality of the Ti-6Al-4V during machining in the PMEDM process. The presence of carbon, oxygen elements and the formation of surface oxides and carbides has been found due to the decomposition of dielectric fluid in the PMEDM process. The lower deep cavities and uniform of PMEDM’s surface have been produced by added Al2O3 powder into the EDM process owing to lower surface cracks density, conductivity.

The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152’s accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture.

New solid solution of Na 0.5 Bi 0.5 TiO 3 with BaFeO 3− δ materials were fabricated by sol–gel method. Analysis of X-ray diffraction patterns indicated that BaFeO 3− δ materials existed as a well solid solution and resulted in distortion the structure of host Na 0.5 Bi 0.5 TiO 3 materials. The randomly incorporated Fe and Ba cations in the host Na 0.5 Bi 0.5 TiO 3 crystal decreased the optical band gap from 3.11 to 2.48 eV, and induced the room-temperature ferromagnetism. Our density-functional theory calculations further suggested that both Ba for Bi/Na-site and Fe dopant, regardless of the substitutional sites, in Na 0.5 Bi 0.5 TiO 3 lead to the induced magnetism, which is illustrated in terms of the exchange splitting between spin subbands through the crystal field theory and Jahn–Teller distortion effects. Our work proposes a simple method for fabricating lead-free ferroelectric materials with ferromagnetism property for multifunctional applications in smart electronic devices.

Endothelial cells adopt tissue-specific characteristics to instruct organ development and regeneration 1 , 2 . This adaptability is lost in cultured adult endothelial cells, which do not vascularize tissues in an organotypic manner. Here, we show that transient reactivation of the embryonic-restricted ETS variant transcription factor 2 (ETV2) 3 in mature human endothelial cells cultured in a serum-free three-dimensional matrix composed of a mixture of laminin, entactin and type-IV collagen (LEC matrix) ‘resets’ these endothelial cells to adaptable, vasculogenic cells, which form perfusable and plastic vascular plexi. Through chromatin remodelling, ETV2 induces tubulogenic pathways, including the activation of RAP1, which promotes the formation of durable lumens 4 , 5 . In three-dimensional matrices—which do not have the constraints of bioprinted scaffolds—the ‘reset’ vascular endothelial cells (R-VECs) self-assemble into stable, multilayered and branching vascular networks within scalable microfluidic chambers, which are capable of transporting human blood. In vivo, R-VECs implanted subcutaneously in mice self-organize into durable pericyte-coated vessels that functionally anastomose to the host circulation and exhibit long-lasting patterning, with no evidence of malformations or angiomas. R-VECs directly interact with cells within three-dimensional co-cultured organoids, removing the need for the restrictive synthetic semipermeable membranes that are required for organ-on-chip systems, therefore providing a physiological platform for vascularization, which we call ‘Organ-On-VascularNet’. R-VECs enable perfusion of glucose-responsive insulin-secreting human pancreatic islets, vascularize decellularized rat intestines and arborize healthy or cancerous human colon organoids. Using single-cell RNA sequencing and epigenetic profiling, we demonstrate that R-VECs establish an adaptive vascular niche that differentially adjusts and conforms to organoids and tumoroids in a tissue-specific manner. Our Organ-On-VascularNet model will permit metabolic, immunological and physiochemical studies and screens to decipher the crosstalk between organotypic endothelial cells and parenchymal cells for identification of determinants of endothelial cell heterogeneity, and could lead to advances in therapeutic organ repair and tumour targeting. The transient reactivation of ETV2 in adult human endothelial cells reprograms these cells to become adaptable vasculogenic endothelia that in three-dimensional matrices self-assemble into vascular networks that can transport blood and physiologically arborize organoids and decellularized tissues.

This study investigates the potential benefits for incidental vocabulary acquisition of implementing a particular sequence of input-output-input activities. More specifically, learners of English as a foreign language (EFL; n = 32) were asked to watch a TED Talk video, orally sum up its content in English, and then watch the video once more. A comparison group (n = 32) also watched the TED Talk video twice but were not required to sum it up in between. Immediate and delayed posttests showed significantly better word-meaning recall in the former condition. An analysis of the oral summaries showed that it was especially words that learners attempted to use that stood a good chance of being recalled later. These findings are interpreted with reference to Swain's (1995) output hypothesis, Laufer and Hulstijn's (2001) involvement load hypothesis, and Nation and Webb's (2011) technique feature analysis. What makes the text-based output task in this experiment fundamentally different from many previous studies that have investigated the merits of text-based output activities is that it was at no point stipulated for the participants that they should use particular words from the input text. The study also illustrates the potential of TED Talks as a source of authentic audiovisual input in EFL classrooms.

Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.

Since February 2020, over 2.5 million Texans have been diagnosed with COVID-19, and 20% are young adults at risk for SARS-CoV-2 exposure at work, academic, and social settings. This study investigated demographic and clinical risk factors for severe disease and readmission among young adults 18-29 years old, who were diagnosed at a hospital encounter in Houston, Texas, USA. A retrospective registry-based chart review was conducted investigating demographic and clinical risk factors for severe COVID-19 among patients aged 18-29 with positive SARS-CoV-2 tests within a large metropolitan healthcare system in Houston, Texas, USA. In the cohort of 1,853 young adult patients diagnosed with COVID-19 infection at a hospital encounter, including 226 pregnant women, 1,438 (78%) scored 0 on the Charlson Comorbidity Index, and 833 (45%) were obese ([greater than or equal to]30 kg/m.sup.2). Within 30 days of their diagnostic encounter, 316 (17%) patients were diagnosed with pneumonia, 148 (8%) received other severe disease diagnoses, and 268 (14%) returned to the hospital after being discharged home. In multivariable logistic regression analyses, increasing age (adjusted odds ratio [aOR] 1.1, 95% confidence interval [CI] 1.1-1.2, p<0.001), male gender (aOR 1.8, 95% CI 1.2-2.7, p = 0.002), Hispanic ethnicity (aOR 1.9, 95% CI 1.2-3.1, p = 0.01), obesity (3.1, 95% CI 1.9-5.1, p<0.001), asthma history (aOR 2.3, 95% CI 1.3-4.0, p = 0.003), congestive heart failure (aOR 6.0, 95% CI 1.5-25.1, p = 0.01), cerebrovascular disease (aOR 4.9, 95% CI 1.7-14.7, p = 0.004), and diabetes (aOR 3.4, 95% CI 1.9-6.2, p<0.001) were predictive of severe disease diagnoses within 30 days. Non-Hispanic Black race (aOR 1.6, 95% CI 1.0-2.4, p = 0.04), obesity (aOR 1.7, 95% CI 1.0-2.9, p = 0.046), asthma history (aOR 1.7, 95% CI 1.0-2.7, p = 0.03), myocardial infarction history (aOR 6.2, 95% CI 1.7-23.3, p = 0.01), and household exposure (aOR 1.5, 95% CI 1.1-2.2, p = 0.02) were predictive of 30-day readmission. This investigation demonstrated the significant risk of severe disease and readmission among young adult populations, especially marginalized communities and people with comorbidities, including obesity, asthma, cardiovascular disease, and diabetes. Health authorities must emphasize COVID-19 awareness and prevention in young adults and continue investigating risk factors for severe disease, readmission and long-term sequalae.

Cells derived from pluripotent sources in vitro must resemble those found in vivo as closely as possible at both transcriptional and functional levels in order to be a useful tool for studying diseases and developing therapeutics. Recently, differentiation of human pluripotent stem cells (hPSCs) into brain microvascular endothelial cells (ECs) with blood–brain barrier (BBB)-like properties has been reported. These cells have since been used as a robust in vitro BBB model for drug delivery and mechanistic understanding of neurological diseases. However, the precise cellular identity of these induced brain microvascular endothelial cells (iBMECs) has not been well described. Employing a comprehensive transcriptomic metaanalysis of previously published hPSC-derived cells validated by physiological assays, we demonstrate that iBMECs lack functional attributes of ECs since they are deficient in vascular lineage genes while expressing clusters of genes related to the neuroectodermal epithelial lineage (Epi-iBMEC). Overexpression of key endothelial ETS transcription factors (ETV2, ERG, and FLI1) reprograms Epi-iBMECs into authentic endothelial cells that are congruent with bona fide endothelium at both transcriptomic as well as some functional levels. This approach could eventually be used to develop a robust human BBB model in vitro that resembles the human brain EC in vivo for functional studies and drug discovery.