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BackgroundRetinal detachment (RD) is a common sight-threatening condition in the emergency department. Early postural intervention based on detachment regions can improve visual prognosis.MethodsWe developed a weakly supervised model with 24,208 ultra-widefield fundus images to localize and coarsely outline the anatomical RD regions. The customized preoperative postural guidance was generated for patients accordingly. The localization performance was then compared with the baseline model and an ophthalmologist according to the reference standard established by the retina experts.ResultsIn the 48-partition lesion detection, our proposed model reached an 86.42% (95% confidence interval (CI): 85.81–87.01%) precision and an 83.27% (95%CI: 82.62–83.90%) recall with an average precision (PA) of 0.9132. In contrast, the baseline model achieved a 92.67% (95%CI: 92.11–93.19%) precision and limited recall of 68.07% (95%CI: 67.25–68.88%). Our holistic lesion localization performance was comparable to the ophthalmologist’s 89.16% (95%CI: 88.75–89.55%) precision and 83.38% (95%CI: 82.91–83.84%) recall. As to the performance of four-zone anatomical localization, compared with the ground truth, the un-weighted Cohen’s κ coefficients were 0.710(95%CI: 0.659–0.761) and 0.753(95%CI: 0.702–0.804) for the weakly-supervised model and the general ophthalmologist, respectively.ConclusionThe proposed weakly-supervised deep learning model showed outstanding performance comparable to that of the general ophthalmologist in localizing and outlining the RD regions. Hopefully, it would greatly facilitate managing RD patients, especially for medical referral and patient education.

Fluoride, iron and manganese simultaneous exceedance of standard can be observed in groundwater in northeastern China. This work aims to apply a highly efficient method combining adsorption and oxidation for the synchronous removal of the inorganic ions. An innovative adsorbent (manganese-supported activated alumina) was synthesized by the impregnation method and showed a significant adsorption capacity better than that of fresh activated alumina. The characterization (scanning electron microscope; Brunauer, Emmett and Teller; X-ray diffraction and Fourier transform infrared spectroscopy) results verified the successful introduction of MnOOH and MnO2, and the improvement of surface microstructure enhanced the removal ability. The effect of single factors, such as pH value, reaction time or dosage on the removal performance has been verified. The maximum removal efficiencies of fluoride, iron and manganese were optimized via Response surface methodology considering the independent factors in the range of MO@AA dosage (5–9 g/L), pH (4–6) and contact time (4–12 h). Noted that compared with control, MO@AA exhibited 59.4% of improved fluoride performance. At pH of 5.79, contacting time of 12 h and 8.21 g/L of MO@AA, fluoride, iron and manganese removal were found to be 91, 100 and 23%, respectively. Herein, MO@AA was distinguished as good applicability for the treatment of fluoride-, iron- and manganese-containing groundwater. HIGHLIGHTS The modified activated alumina were prepared by impregnation method.; Response surface methodology model is used in this research to find out the relationships among the factors.; The modified activated alumina can be applied for the removal of fluoride, manganese and iron.;

Summary: Background: Clinical application of artificial intelligence is limited due to the lack of interpretability and expandability in complex clinical settings. We aimed to develop an eye diseases screening system with improved interpretability and expandability based on a lesion-level dissection and tested the clinical expandability and auxiliary ability of the system. Methods: The four-hierarchical interpretable eye diseases screening system (IEDSS) based on a novel structural pattern named lesion atlas was developed to identify 30 eye diseases and conditions using a total of 32,026 ultra-wide field images collected from the Second Affiliated Hospital of Zhejiang University, School of Medicine (SAHZU), the First Affiliated Hospital of University of Science and Technology of China (FAHUSTC), and the Affiliated People's Hospital of Ningbo University (APHNU) in China between November 1, 2016 to February 28, 2022. The performance of IEDSS was compared with ophthalmologists and classic models trained with image-level labels. We further evaluated IEDSS in two external datasets, and tested it in a real-world scenario and an extended dataset with new phenotypes beyond the training categories. The accuracy (ACC), F1 score and confusion matrix were calculated to assess the performance of IEDSS. Findings: IEDSS reached average ACCs (aACC) of 0·9781 (95%CI 0·9739-0·9824), 0·9660 (95%CI 0·9591-0·9730) and 0·9709 (95%CI 0·9655-0·9763), frequency-weighted average F1 scores of 0·9042 (95%CI 0·8957-0·9127), 0·8837 (95%CI 0·8714-0·8960) and 0·8874 (95%CI 0·8772-0·8972) in datasets of SAHZU, APHNU and FAHUSTC, respectively. IEDSS reached a higher aACC (0·9781, 95%CI 0·9739-0·9824) compared with a multi-class image-level model (0·9398, 95%CI 0·9329-0·9467), a classic multi-label image-level model (0·9278, 95%CI 0·9189-0·9366), a novel multi-label image-level model (0·9241, 95%CI 0·9151-0·9331) and a lesion-level model without Adaboost (0·9381, 95%CI 0·9299-0·9463). In the real-world scenario, the aACC of IEDSS (0·9872, 95%CI 0·9828-0·9915) was higher than that of the senior ophthalmologist (SO) (0·9413, 95%CI 0·9321-0·9504, p = 0·000) and the junior ophthalmologist (JO) (0·8846, 95%CI 0·8722-0·8971, p = 0·000). IEDSS remained strong performance (ACC = 0·8560, 95%CI 0·8252-0·8868) compared with JO (ACC = 0·784, 95%CI 0·7479-0·8201, p= 0·003) and SO (ACC = 0·8500, 95%CI 0·8187-0·8813, p = 0·789) in the extended dataset. Interpretation: IEDSS showed excellent and stable performance in identifying common eye conditions and conditions beyond the training categories. The transparency and expandability of IEDSS could tremendously increase the clinical application range and the practical clinical value of it. It would enhance the efficiency and reliability of clinical practice, especially in remote areas with a lack of experienced specialists. Funding: National Natural Science Foundation Regional Innovation and Development Joint Fund (U20A20386), Key research and development program of Zhejiang Province (2019C03020), Clinical Medical Research Centre for Eye Diseases of Zhejiang Province (2021E50007).

Abstract High concentrations of environmental ammonia can cause reduced immunity and death in fish, causing enormous economic losses. Air‐breathing fish usually have a high ammonia tolerance and are very suitable for high‐density fish farming. However, research on the effects of environmental ammonia on air‐breathing fish immunity is lacking. Therefore, this study investigated the effects of environmental ammonia on the immunity of large‐scale loach (Paramisgurnus dabryanus) by exposing fish to 30 mmol/L NH4Cl solution and subsequently analyzing the changes in serum and liver immune indicators, including total protein, albumin, globulin, immunoglobulin (Ig) M, lysozyme, complement component (C) 3 and C4, heat shock protein (HSP) 70, HSP90, tumor necrosis factor (TNF)‐α, interleukin (IL)‐1β, IL‐6, and IL‐12. Results revealed that ammonia exposure significantly affected the total protein, albumin, globulin, IgM, complement C3 and C4, HSP70, HSP90, and inflammatory cytokine contents in the body, indicating that ammonia exposure induced a significant immune response and lowered bodily immunity. However, most of the immune indicators significantly decreased in the later stages of the experiment, suggesting a weakened immune response, which may be due to the species‐specific ammonia detoxification ability of large‐scale loach that reduces ammonia toxicity in the body.

In this paper, a visual servoing robotic control scheme of fast convergence and calibration-free is proposed. The objective of this paper is to achieve a better convergence around the equilibrium for uncalibrated visual servoing systems. Moreover, there are threefold unknown parameters, the visual kinematic parameters, the dynamic parameters and the parameters of the feature points, considered in the design of the proposed control scheme. In order to achieve the above control objective, the finite-time tracking controller and three adaptive laws are proposed. The adaptability to the unknown parameters is guaranteed by the online adaptive laws. The finite-time convergence is achieved by the continuously non-smooth fractional order function in the controller. The rigorously mathematic proof of stability is given by homogeneous theory and the Lyapunov function formalism. Three real-time experiments are conducted to demonstrate the practical performance of the proposed scheme.

Manganese dioxide is regarded as a promising energy functional material due to its open tunnel structure with enormous applications in energy storage and catalysis. In this paper, α-MnO2 with a 2 × 2 tunnel structure and β-MnO2 with a 1 × 1 tunnel structure were hydrothermally synthesized, which possess characteristic tunnel structures formed by the interconnected unit structure of [MnO6] octahedrons. With regards to their different tunnel dimensions, the specific mechanism of ion intercalation in these two phases and the effect on their performance as aqueous Zn-MnO2 battery cathodes are explored and compared. Comprehensive analyses illustrate that both α-MnO2 and β-MnO2 provide decent capacity in the aqueous battery system, but their intrinsic stability is poor due to the structural instability upon cycling. At the same time, experiments show that α-MnO2 has a better rate performance than β-MnO2 under larger currents, thus implying that the former has a broader application in this aqueous battery system.

Activated alumina, with low cost to implement and consistent performance, has been widely used as a defluoridation adsorbent in contaminated groundwater. However, its application was hampered by an undesirable adsorption capacity. In the recent research, an innovative adsorbent (manganese modified activated alumina, MAA) was synthesized by impregnation method and showed a more significant adsorption capacity than that of freshly activated alumina. The scanning electron microscope, Brunauer, Emmett and Teller, X-ray diffraction spectroscopy, X-ray photoelectron spectroscopy, and Fourier transform infrared verified introduction of manganese oxides (MnOOH and MnO2) successfully, improvement of surface microstructure and nature of single-layer adsorption, which enhanced the adsorption ability. In a short period of adsorption, the MAA maximum capacity increased from 38% to 67%. Then, batch-scale kinetic and thermodynamic adsorption investigations were performed. The high correlation coefficients R2 (close to 1) of the quasi-second-order model, Langmuir kinetic model, and Langmuir isotherm model confirmed a better fit to the adsorption experimental data, further indicating that the diffusion of adsorbate was primarily governed by adsorption onto the active sites and the adsorption of fluoride on MAA was single-layer physical adsorption. This paper provides an approach to modifying activated alumina as a groundwater fluoride adsorbent.

Fluoride pollution frequently occurs in many underground drinking water sources due to discrepancies in the geological environment. To address this problem, a manganese-supported activated alumina (MnOOH-supported AA) adsorbent was proposed in the present study. The adsorbent was prepared with an impregnation method, then the morphology and microstructure were systematically characterized. Further, the adsorption kinetics and thermodynamics were systematically explored through static experiments to confirm the adsorption mechanism. The results showed that MnOOH was successfully loaded on the activated alumina (AA), and irregular and convex spinous structures were formed on the surface of particles. Compared with the AA, MnOOH-supported AA exhibited a significantly higher defluoridation rate, which has been doubled. The kinetic behavior of fluoride adsorption on MnOOH-supported AA was governed by the quasi-second-order kinetics model with regression coefficients of 0.9862, 0.9978 and 0.9956, respectively. The adsorption rate was mainly ascribed to the intra-particle diffusion. Additionally, the Freundlich isotherm equation fitted the adsorption thermodynamic process reasonably well compared with the Langmuir adsorption model. Specifically, the correlation coefficients were 0.9614, 0.9383 and 0.9852 at 25 °C, 35 °C and 45 °C, respectively. The adsorption–desorption isotherm plot was similar to the Type V isotherm. The whole fluoride adsorption was a spontaneous endothermic reaction, and controlled by chemical adsorption. These results demonstrated that MnOOH-supported AA as an alternative to the conventional AA showed promising potential for defluoridation in drinking water treatment.

It becomes a key technology to measure the concentration of the vehicle exhaust components with the transmission spectra. But in the conventional methods for noise reduction and baseline correction, such as wavelet transform, derivative, interpolation, polynomial fitting, and so forth, the basic functions of these algorithms, the number of decomposition layers, and the way to reconstruct the signal have to be adjusted according to the characteristics of different components in the transmission spectra. The parameter settings of the algorithms above are not transcendental, so with them, it is difficult to achieve the best noise reduction effect for the vehicle exhaust spectra which are sharp and drastic in the waveform. In this paper, an adaptive ensemble empirical mode decomposition (EEMD) denoising model based on a special normalized index optimization is proposed and used in the spectral noise reduction of vehicle exhaust NOX. It is shown with the experimental results that the method can effectively improve the accuracy of the spectral noise reduction and simplify the denoising process and its operation difficulty.

Optical remote sensing systems (RSSs) for monitoring vehicle emissions can be installed on any road and provide non-contact on-road measurements, that allow law enforcement departments to monitor emissions of a large number of on-road vehicles. Although many studies in different research fields have been performed using RSSs, there has been little research on the automatic recognition of on-road high-emitting vehicles. In general, high-emitting vehicles and low-emitting vehicles are classified by fixed emission concentration cut-points, that lack a strict scientific basis, and the actual cut-points are sensitive to environmental factors, such as wind speed and direction, outdoor temperature, relative humidity, atmospheric pressure, and so on. Besides this issue, single instantaneous monitoring results from RSSs are easily affected by systematic and random errors, leading to unreliable results. This paper proposes a method to solve the above problems. The automatic and fast-recognition method for on-road high-emitting vehicles (AFR-OHV) is the first application of machine learning, combined with big data analysis for remote sensing monitoring of on-road high-emitting vehicles. The method constructs adaptively updates a clustering database using real-time collections of emission datasets from an RSS. Then, new vehicles, that pass through the RSS, are recognized rapidly by the nearest neighbor classifier, which is guided by a real-time updated clustering database. Experimental results, based on real data, including the Davies-Bouldin Index (DBI) and Dunn Validity Index (DVI), show that AFR-OHV provides faster convergence speed and better performance. Furthermore, it is not easily disturbed by outliers. Our classifier obtains high scores for Precision (PRE), Recall (REC), the Receiver Operator Characteristic (ROC), and the Area Under the Curve (AUC). The rates of different classifications of excessive emissions and self-adaptive cut-points are calculated automatically in order to provide references for law enforcement departments to establish evaluation criterion for on-road high-emitting vehicles, detected by the RSS.

We report a case of a ruptured triple hormone-secreting adrenal mass with hyperaldosteronism, hypercortisolism, and elevated normetanephrine levels, diagnosed as adrenal cortical carcinoma (ACC) by histology. A 53-year-old male patient who initially presented with abdominal pain was referred to our hospital for angiocoagulation of an adrenal mass rupture. Abdominal computed tomography revealed a heterogeneous 19×11×15 cm right adrenal mass with invasion into the right lobe of the liver, inferior vena cava, retrocaval lymph nodes, and aortocaval lymph nodes. Angiocoagulation was performed. Laboratory evaluation revealed excess cortisol via a positive 1-mg overnight dexamethasone suppression test, primary hyperaldosteronism via a positive saline infusion test, and plasma normetanephrine levels three times higher than normal. An adrenal mass biopsy was performed for pathological confirmation to commence palliative chemotherapy because surgical management was not deemed appropriate considering the extent of the tumor. Pathological examination revealed stage T4N1M1 ACC. The patient started the first cycle of adjuvant mitotane therapy along with adjuvant treatment with doxorubicin, cisplatin, and etoposide, and was discharged. Clinical cases of dual cortisol- and aldosterone-secreting ACCs or ACCs presenting as pheochromocytomas have occasionally been reported; however, both are rare. Moreover, to the best of our knowledge, a triple hormone-secreting ACC has not yet been reported. Here, we report a rare case and its management. This case report underscores the necessity of performing comprehensive clinical and biochemical hormone evaluations in patients with adrenal masses because ACC can present with multiple hormone elevations.