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The exponential spread of COVID‐19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID‐19, it is not easy for front‐line doctors to categorise severity levels of clinical COVID‐19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID‐19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network‐based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1‐score, and 100% consistency using a two‐way patient classification of severe/critical to general. For severe/critical cases, F1‐score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini‐second‐level computational cost (in contrast to minute‐level manual). Based on available COVID‐19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID‐19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.

Accumulating evidence demonstrates that ferroptosis may be important in the pathophysiological process of traumatic brain injury (TBI). As a major hormone of the pineal gland, melatonin exerts many beneficial effects on TBI, but there is no information regarding the effects of melatonin on ferroptosis after TBI. As expected, TBI resulted in the time-course changes of ferroptosis-related molecules expression and iron accumulation in the ipsilateral cortex. Importantly, we found that treating with melatonin potently rescued TBI induced the changes mentioned above and improved functional deficits versus vehicle. Similar results were obtained with a ferroptosis inhibitor, liproxstatin-1. Moreover, the protective effect of melatonin is likely dependent on melatonin receptor 1B (MT2). Although ferritin plays a vital role in iron metabolism by storing excess cellular iron, its precise function in the brain, and whether it involves melatonin's neuroprotection remain unexplored. Considering ferritin H (Fth) is expressed predominantly in the neurons and global loss of Fth in mice induces early embryonic lethality, we then generated neuron-specific Fth conditional knockout (Fth-KO) mice, which are viable and fertile but have altered iron metabolism. In addition, Fth-KO mice were more susceptible to ferroptosis after TBI, and the neuroprotection by melatonin was largely abolished in Fth-KO mice. In vitro siFth experiments further confirmed the results mentioned above. Taken together, these data indicate that melatonin produces cerebroprotection, at least partly by inhibiting neuronal Fth-mediated ferroptosis following TBI, supporting the notion that melatonin is an excellent ferroptosis inhibitor and its anti-ferroptosis provides a potential therapeutic target for treating TBI.

We present a record 20‐W continuous‐wave output power membrane‐external‐cavity surface‐emitting laser (MECSEL) double‐bonded to silicon carbide (SiC) heat spreaders emitting around 1178 nm. We achieved a maximum slope efficiency of 27.7% and an optical‐to‐optical efficiency of 26.7%. By utilising an intracavity birefringent filtre, the wavelength was tuned over 75 nm and maximum output power of 12.7 W was recorded at 1178 nm. This study shows the potential of MECSELs to achieve high‐performance and cost‐effective optically pumped semiconductor disk lasers targeting applications in sodium guide stars.

The development of new antibacterial agents that can efficiently eradicate biofilms is of crucial importance to combat persistent and chronic bacterial infections. Herein, the fabrication of photoresponsive vesicles capable of the sequential release of nitric oxide (NO) and gentamicin sulfate (GS) is reported, which can not only efficiently disperse Pseudomonas aeruginosa (P. aeruginosa) PAO1 biofilm but also kill the planktonic bacteria. Well-defined amphiphilic diblockcopolymers of poly(ethylene oxide)-b-poly(4-((2-nitrobenzyl)(nitroso)amino)benzyl methacrylate) (PNO) is first synthesized through atom transfer radical polymerization (ATRP). The PNO diblock copolymer self-assembled into vesicles in aqueous solution, and a hydrophilic antibiotic of GS is subsequently encapsulated into the aqueous lumens of vesicles. The vesicles undergo visible light-mediated N-NO cleavage, releasing NO and disintegrating the vesicles with the release of the GS payload. The sequential release of NO and GS efficiently eradicate P. aeruginosa PAO1 biofilm and kill the liberated bacteria, showing a better antibiofilm effect than that of NO or GS alone.

Carbon monoxide (CO) has emerged as a potential therapeutic agent for the treatment of many diseases. However, the therapeutic outcome is highly dependent on the dosages and administration sites. Hence, there is mounting interest in the development of CO-releasing materials to accomplish site-specific and dose-controlled delivery of CO. Herein, a micellar nanoparticle platform for the photo-mediated release of CO by using amphiphilic triblock copolymers bearing CO-releasing moieties of 3-hydroxylflavone (3-HF) derivatives within the middle blocks is developed. These micelles are relatively stable without CO leakage but undergo visible light-mediated CO release and simultaneous main chain scission. Moreover, these micellar nanoparticles are cytocompatible regardless of light irradiation, which shows unique anti-inflammatory performance only after light irradiation as a result of photo-triggered CO release. This work may represent the first example of main-chain degradable micellar nanocarriers with controlled CO-releasing performance for potential anti-inflammatory applications.

Starting from elemental powder mixture, ternary chromium chalcogenide CuCrSe2 has been synthesized facilely via the route of mechanical alloying and subsequent annealing. Effect of indium doping on the thermoelectric performance of CuCrSe2 has been studied detailedly. With the increase in indium doping content, the electrical resistivity decreases firstly until 0.25 at.% due to the electronegativity difference between In and Cr, and then increases due to the dissolution of indium and subsequent supersaturation precipitation of CuInSe2, and the Seebeck coefficient shows a similar trend with the electrical resistivity. The thermal conductivity increases firstly and then decreases with the increase in indium doping content due to the increasing phonon scattering by the CuInSe2 and CuCr2Se4. As a result, a maximum ZT of 0.65 at 673 K was achieved in the sample with 1.0 at.% In doping, which is enhanced almost 54% in compared with the In-free sample.

Uniformly dispersed TiC nanoparticle strengthened In4Se2.65 composites have been fabricated by a combined process of mechanical alloying (MA) and hot pressing (HP) successfully. Due to the good electrical conductivity and the extra phonon scattering effect of the TiC nanoinclusions, the electrical resistivity and thermal conductivity decrease with the TiC content up to 0.8 wt%, and a maximum ZT of 0.98 at 723 K was achieved in the sample with 0.8 wt% TiC. Taking account of the measurement uncertainly, the enhancement of ZT value by TiC nanoinclusions is less obvious. On the other hand, the mechanical performance of In4Se2.65 can be effectively improved by TiC nanoinclusions due to the dispersive strengthening effect of the nanoinclusions, and the flexural strength of the sample with 0.8 wt% TiC is improved to 73 MPa, which is over 40% higher than that of the pristine sample.

This study investigates a spatially explicit, individual-based model for simulating the spread of invasive smooth cordgrass (Spartina alterniflora) in Yancheng coastal wetlands from 1995 to 2010. The model, which considers the landscape heterogeneity and changes detected by remote sensing, also reveals the relative importance of sexual and asexual reproduction in the spread by global sensitivity analysis. The model was verified as suitable for simulating the range expansion of S. alterniflora. The results show that: (1) although seedling recruitment is low, it significantly contributes to the range expansion of S. alterniflora. Removing sexual propagation greatly reduces the expansion rate. Rapid expansion requires both sexual and asexual reproduction; (2) in the global sensitivity analysis, the most significant affecters of S. alterniflora invasion were seed dispersal distance, adult survival rate and asexual recruitment survival rate. Sexual propagation contributes much more significantly to quick range expansion than asexual reproduction, but asexual reproduction is the main source of recruitment. Invasion control strategies should target a single reproduction mode. Here, limiting the germination and dispersal of seeds is suggested as a realistic strategy for controlling and managing invasion by this species.