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Activated carbon prepared from waste coffee was utilized as a potential low-cost adsorbent to remove Rhodamine B from aqueous solution. A series of physical characterizations verify that the obtained activated carbon possesses a layered and ordered hexagonal structure with a wrinkled and rough surface. In addition, high specific surface area, appropriate pore distribution, and desired surface functional groups are revealed, which promote the adsorption properties. Various adsorption experiments were conducted to investigate the effect on the absorption capacity (e.g., of initial dye concentration, temperature and solution pH) of the material. The results showed that the waste-coffee-derived activated carbon with a large surface area of approximately 952.7 m2 g−1 showed a maximum uptake capacity of 83.4 mg g−1 at the pH of 7 with the initial dye concentration of 100 mg L−1 under 50°C. The higher adsorption capacity can be attributed to the strong electrostatic attraction between the negatively charged functional groups in activated carbon and the positively charged functional groups in RB. The kinetic data and the corresponding kinetic parameters were simulated to evaluate the mechanism of the adsorption process, which can fit well with the highest R2. The adsorption results confirmed the promising potential of the as-prepared waste-coffee-derived activated carbon as a dye adsorbent.

Many biopolymers, including polysaccharides, must be translocated across at least one membrane to reach their site of biological function. Cellulose is a linear glucose polymer synthesized and secreted by a membrane-integrated cellulose synthase. Here, in crystallo enzymology with the catalytically active bacterial cellulose synthase BcsA–BcsB complex reveals structural snapshots of a complete cellulose biosynthesis cycle, from substrate binding to polymer translocation. Substrate- and product-bound structures of BcsA provide the basis for substrate recognition and demonstrate the stepwise elongation of cellulose. Furthermore, the structural snapshots show that BcsA translocates cellulose via a ratcheting mechanism involving a ‘finger helix’ that contacts the polymer’s terminal glucose. Cooperating with BcsA’s gating loop, the finger helix moves ‘up’ and ‘down’ in response to substrate binding and polymer elongation, respectively, thereby pushing the elongated polymer into BcsA’s transmembrane channel. This mechanism is validated experimentally by tethering BcsA’s finger helix, which inhibits polymer translocation but not elongation. Here the authors use in crystallo enzymology to obtain structural snapshots of a complete cellulose biosynthesis cycle and reveal the mechanism by which the bacterial cellulose synthase BcsA–BcsB translocates the nascent cellulose polymer. Scenes from cellulose biosynthesis Cellulose is a long, linear polysaccharide made from D-glucose molecules. It is an important component of plant cell walls and a starting material for the production of many potential biofuels. These authors have used in crystallo enzymology to obtain structural snapshots of several steps in the cellulose biosynthesis cycle, including structures of the substrate-bound and product-bound states of the bacterial cellulose synthase BcsA–BcsB complex. And by incubating cellulose-bound BcsA–BcsB with UDP-glucose, they demonstrate polymer elongatation in the crystal. The structures suggest that cellulose is translocated via a ratcheting mechanism involving the up/down movement of a key 'finger helix'.

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x10 3 / μL )], fever (≥38°C), low platelet counts [< 100 (x10 3 / μL )], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96–6.76], 3.17 [95%CI: 2.74–3.66], 3.10 [95%CI: 2.44–3.94], and 1.77 [95%CI: 1.50–2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.

Glioblastoma, formerly known as glioblastoma multiforme (GBM), is refractory to existing adjuvant chemotherapy and radiotherapy. We successfully synthesized a complex, Au–OMV, with two specific nanoparticles: gold nanoparticles (AuNPs) and outer-membrane vesicles (OMVs) from E. coli. Au–OMV, when combined with radiotherapy, produced radiosensitizing and immuno-modulatory effects that successfully suppressed tumor growth in both subcutaneous G261 tumor-bearing and in situ (brain) tumor-bearing C57BL/6 mice. Longer survival was also noted with in situ tumor-bearing mice treated with Au–OMV and radiotherapy. The mechanisms for the successful treatment were evaluated. Intracellular reactive oxygen species (ROS) greatly increased in response to Au–OMV in combination with radiotherapy in G261 glioma cells. Furthermore, with a co-culture of G261 glioma cells and RAW 264.7 macrophages, we found that GL261 cell viability was related to chemotaxis of macrophages and TNF-α production.

Chronic inflammation is known to be associated with both rotator cuff tears (RCTs) and depression. However, no epidemiological studies with a longitudinal follow-up have been performed to prove this association. We aimed to investigate whether depressed patients had an elevated risk of RCT and subsequent repair surgery compared with those without depression. This retrospective cohort study comprised of patients diagnosed with depression between 2000 and 2010 (depression cohort) and patients without depression (non-depression cohort, 1:2 age and sex matched). The risk of RCT and rotator cuff repair surgery were determined during a 13-year follow-up (2000-2013) between these two cohorts. This study included 26,868 patients with depression and 53,736 patients without depression. The incidence of RCT was 648 and 438 per 100,000 person-years in the depression and non-depression cohorts, respectively. The adjusted hazard ratio (HR) was 1.46 (95% confidence interval [CI], 1.36-1.57) for depressed patients. The incidence of rotator cuff repair surgery was 28 and 18 per 100,000 person-years in the depression and non-depression cohorts, respectively. Depressed patients also had a significantly increased risk of subsequent rotator cuff repair surgery (adjusted HR = 1.46; 95% CI, 1.04-2.06). The present study showed that depression was associated with an increased risk of rotator cuff tear and rotator cuff repair surgery.

Directional asymmetry indicates a unidirectional deviation from perfect bilateral symmetry, which was rarely examined in the inner organs of the teleost (Teleostei) compared to external traits. This study examines the directional asymmetry in the gonad length of 20 species of moray eels (Muraenidae) and two outgroup species with 2959 individuals. We tested three hypotheses: (1) moray eel species did not exhibit directional asymmetry in the gonad length; (2) the directional asymmetry pattern was the same for all selected species; (3) the directional asymmetry was not related to the major habitat types, depth and size classes, and taxonomic closeness of the species. Moray eels were generally “right-gonadal”, the right gonad length being constantly and significantly longer than the left one in all studied Muraenidae species. The degree of asymmetry varied among species and was not significantly related to taxonomic closeness. The habitat types, depth, and size classes had intermingled effects on observed asymmetry without a clear correspondence. The directional asymmetry in the gonad length is a unique and widely occurring phenomenon in the Family Muraenidae, which was likely a by-product in the evolutionary history without significant disadvantage in survival.

Aqueous Zn‐ion batteries (AZIBs) and Zn‐ion hybrid supercapacitors (AZHSCs) are considered promising energy‐storage alternatives to Li‐ion batteries due to the attractive merits of low‐price and high‐safety. However, the lack of suitable cathode materials always hinders their large‐scale application. Herein, amorphous K‐buserite microspheres (denoted as K‐MnOx) are reported as cathode materials for both AZIBs and AZHSCs, and the energy‐storage mechanism is systematically revealed. It is found that K‐MnOx is composed of rich amorphous K‐buserite units, which can irreversibly be transformed into amorphous Zn‐buserite units in the first discharge cycle. Innovatively, the transformed Zn‐buserite acts as active materials in the following cycles and is highly active/stable for fast Zn‐diffusion and superhigh pseudocapacitance, enabling the achievement of high‐efficiency energy storage. In the AZIBs, K‐MnOx delivers 306 mAh g−1 after 100 cycles at 0.1 A g−1 with 102% capacity retention, while in the AZHSCs, it shows 515.0/116.0 F g−1 at 0.15/20.0 A g−1 with 92.9% capacitance retention at 5.0 A g−1 after 20 000 cycles. Besides, the power/energy density of AZHSCs device can reach up to 16.94 kW kg−1 (at 20 A g−1)/206.7 Wh kg−1 (at 0.15 A g−1). This work may provide some references for designing next‐generation aqueous energy‐storage devices with high energy/power density. Herein, an amorphous K‐buserite microspheres (K‐MnOx) cathode material is developed for aqueous Zn‐ion batteries and Zn‐ion hybrid supercapacitors. The K‐MnOx is composed of numerous amorphous K‐buserite units, which are irreversibly transformed into amorphous Zn‐buserite units in the first discharge cycle. The Zn‐buserite is highly active/stable for high‐efficiency energy‐storage.

Glioblastoma is a highly aggressive brain tumor with universally poor outcomes. Recent progress in immune checkpoint inhibitors has led to increased interest in their application in glioblastoma. Nonetheless, the unique immune milieu in the brain has posed remarkable challenges to the efficacy of immunotherapy. We aimed to leverage the radiation-induced immunogenic cell death to overcome the immunosuppressive network in glioblastoma. We developed a novel approach using the gold-core silica-shell nanoparticles (Au@SiO NPs) in combination with low-dose radiation to enhance the therapeutic efficacy of the immune checkpoint inhibitor (atezolizumab) in brain tumors. The biocompatibility, immune cell recruitment, and antitumor ability of the combinatorial strategy were determined using in vitro assays and in vivo models. Our approach successfully induced the migration of macrophages towards brain tumors and promoted cancer cell apoptosis. Subcutaneous tumor models demonstrated favorable safety profiles and significantly enhanced anticancer effects. In orthotopic brain tumor models, the multimodal therapy yielded substantial prognostic benefits over any individual modalities, achieving an impressive 40% survival rate. In summary, the combination of Au@SiO NPs and low-dose radiation holds the potential to improve the clinical efficacy of immune checkpoint inhibitors. The synergetic strategy modulates tumor microenvironments and enhances systemic antitumor immunity, paving a novel way for glioblastoma treatment.

From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.

This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater’s power consumption and the hot-pressing furnace’s fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97.