Nitrogen utilization analysis reveals the synergetic effect of arginine and urea in promoting fucoxanthin biosynthesis in the mixotrophic marine diatom Phaeodactylum tricornutum

Fucoxanthin is a new dietary ingredient applied in healthy foods with specific benefits of body weight loss and liver fat reduction. The marine diatom Phaeodactylum tricornutum is a highly suitable species for fucoxanthin production. In the present study, aiming to promote fucoxanthin biosynthesis in mixotrophic P. tricornutum , NaNO 3 , tryptone, and urea were evaluated as nitrogen sources with 0.10 mol L −1 of glycerol as the organic carbon source for mixotrophic growth in shake flasks. Compared to NaNO 3 , the mixture of tryptone and urea (referred to as T+U, 1:1, mol N:mol N) as organic nitrogen sources could induce a higher biomass and fucoxanthin production. Through nitrogen utilization analysis, leucine, arginine, lysine, and phenylalanine in the T+U medium were identified as the amino acids that primarily support cell growth. Among those amino acids, arginine causes the highest rate of nitrogen utilization and cell growth promotion. After 12 days of cultivation, the highest biomass concentration (3.18 g L −1 ), fucoxanthin content (12.17 mg g −1 ), and productivity (2.68 mg L −1 day −1 ) were achieved using 25 mmol N L −1 of arginine and 5 mmol N L −1 of urea as nitrogen sources, indicating that arginine and urea performed synergistically on enhancing biomass and pigment production. This study provides new insights into the promotion of fucoxanthin biosynthesis by nitrogen utilization analysis and verifies the synergetic effect of arginine and urea on facilitating the development of a promising strategy for efficient enhancement of fucoxanthin production through mixotrophic cultivation of P. tricornutum

Evaluation of Effectiveness of Speed Reduction Markings on Driving Speed in Highway Tunnel Entrance and Exit Areas

Tunnels are critical areas for highway safety because the severity of crashes in tunnels tends to be more serious. Controlling vehicle speed is regarded as a feasible measure to reduce the accident rate in the tunnel entrance and exit areas. This paper aims to evaluate the effectiveness of three types of speed reduction markings (SRMs) in tunnel entrance and exit zones by conducting a driving simulation experiment. For this study, 25 drivers completed the driving tasks in the day and night scenarios. The vehicle speed and acceleration data were collected for analysing and the relative speed contrast, time mean speed and acceleration were adopted as indices to evaluate the effectiveness of SRMs. The repeated ANOVA test results revealed that SRMs have a significant effect in reducing vehicle speed, especially in the exit zone. Colour Anti-skid Markings (CASMs) produced a more obvious deceleration in the entrance zone. In the entrance zone, a similar downward trend was performed in the situation of NSRMs and SRMs, but a lower speed occurred in case of SRMs. Besides, CASMs work better and cause an obvious gap of 10 km/h in daytime and 5 km/h at night compared to the speed without SRMs. In the exit zone, the present study supports the conclusion that the drivers are prone to accelerate. Our results showed that the drivers accelerated in case of NSRMs, while they slowed down in case of SRMs. Thus, SRMs are necessarily implemented in the highway tunnel entrance and exit zones. Our study also indicates that though CASMs result in lower speed at night, the Transverse Speed Reduction Markings(TSRMs) have a better performance than CASMs in daytime. The investigation provides essential information for developing a new marking design criterion and intelligent driver support systems in the highway tunnel zones.</p

Cost-Effectiveness of Poly ADP-Ribose Polymerase Inhibitors in Cancer Treatment: A Systematic Review

Background: PARP inhibitors have shown significant improvement in progression-free survival, but their costs cast a considerable financial burden. In line with value-based oncology, it is important to evaluate whether drug prices justify the outcomes. / Objectives: The aim of the study was to systematically evaluate PARP inhibitors on 1) cost-effectiveness against the standard care, 2) impact on cost-effectiveness upon stratification for genetic characteristics, and 3) identify factors determining their cost-effectiveness, in four cancer types. / Methods: We systematically searched PubMed, EMBASE, Web of Science, and Cochrane Library using designated search terms, updated to 31 August 2021. Trial-based or modeling cost-effectiveness analyses of four FDA-approved PARP inhibitors were eligible. Other studies known to authors were included. Reference lists of selected articles were screened. Eligible studies were assessed for methodological and reporting quality before review. / Results: A total of 20 original articles proceeded to final review. PARP inhibitors were not cost-effective as recurrence maintenance in advanced ovarian cancer despite improved performance upon genetic stratification. Cost-effectiveness was achieved when moved to upfront maintenance in a new diagnosis setting. Limited evidence indicated non–cost-effectiveness in metastatic breast cancer, mixed conclusions in metastatic pancreatic cancer, and cost-effectiveness in metastatic prostate cancer. Stratification by genetic testing displayed an effect on cost-effectiveness, given the plummeting ICER values when compared to the “treat-all” strategy. Drug cost was a strong determinant for cost-effectiveness in most models. / Conclusions: In advanced ovarian cancer, drug use should be prioritized for upfront maintenance and for patients with BRCA mutation or BRCAness at recurrence. Additional economic evaluations are anticipated for novel indications

in situ Monitoring of Lithium Electrodeposition using Transient Grating Spectroscopy

The mechanisms of lithium electrodeposition, which overwhelmingly affect lithium metal battery performance and safety, remain insufficiently understood due to its electrochemical complexity. Novel, non-destructive and in situ techniques to probe electrochemical interfaces during lithium electrodeposition are highly desirable. In this work, we demonstrate the capability of transient grating spectroscopy to monitor lithium electrodeposition at the micrometer scale by generating and detecting surface acoustic waves that sensitively interact with the deposited lithium. Specifically, we show that the evolution of the frequency, velocity and damping rate of the surface acoustic waves strongly correlate with the lithium nucleation and growth process. Our work illustrates the sensitivity of high-frequency surface acoustic waves to micrometer scale changes in electrochemical cells and establishes transient grating spectroscopy as a versatile platform for future in situ investigation of electrochemical int

Dual-Mode Learning for Multi-Dataset X-Ray Security Image Detection

With the recent advance of deep learning, a large number of methods have been developed for prohibited item detection in X-ray security images. Generally, these methods train models on a single X-ray image dataset that may contain only limited categories of prohibited items. To detect more prohibited items, it is desirable to train a model on the multi-dataset that is constructed by combining multiple datasets. However, directly applying existing methods to the multi-dataset cannot guarantee good performance because of the large domain discrepancy between datasets and the occlusion in images. To address the above problems, we propose a novel Dual-Mode Learning Network (DML-Net) to effectively detect all the prohibited items in the multi-dataset. In particular, we develop an enhanced RetinaNet as the architecture of DML-Net, where we introduce a lattice appearance enhanced sub-net to enhance appearance representations. Such a way benefits the detection of occluded prohibited items. Based on the enhanced RetinaNet, the learning process of DML-Net involves both common mode learning (detecting the common prohibited items across datasets) and unique mode learning (detecting the unique prohibited items in each dataset). For common mode learning, we introduce an adversarial prototype alignment module to align the feature prototypes from different datasets in the domain-invariant feature space. For unique mode learning, we take advantage of feature distillation to enforce the student model to mimic the features extracted by multiple pre-trained teacher models. By tightly combining and jointly training the dual modes, our DML-Net method successfully eliminates the domain discrepancy and exhibits superior model capacity on the multi-dataset. Extensive experimental results on several combined X-ray image datasets demonstrate the effectiveness of our method against several state-of-the-art methods. Our code is available at https://github.com/vampirename/dmlnet