Anti-PD-1/PD-L1 inhibitors provide a survival advantage over conventional therapies for treatment of advanced or metastatic cancer. However, the factors determining which patients benefit the most from anti-PD-1/PD-L1 inhibitors are unknown, making treatment-related decisions difficult. We performed a systematic review and meta-analysis of acquired data to assess the efficacy and toxicity of anti-PD-1/ PD-L1 inhibitors in advanced and metastatic cancer. A thorough search strategy was applied to identify randomised controlled trials (RCTs) in Pubmed, Embase, Cochrane, and major conferences. Studies meeting predefined selection criteria were selected, and two independent investigators performed data extraction; overall survival (OS), progression-free survival (PFS), and overall response rate were compared between anti-PD-1/PD-L1 inhibitors and control therapies. We calculated the pooled response rate and 95% CIs of all-grade and high-grade (≥3) adverse effects and evaluated the withinstudy heterogeneity using subgroup, sensitivity, and meta-regression analyses. In final, we included eligible 35 RCTs (21047 patients). The main estimated hazard ratios (HRs) for OS and PFS were 0.76 (0.71-0.82) and 0.81 (0.73-0.89) in a random-effects model. The anti-PD-1/PD-L1 inhibitor group had a significantly high risk for all-grade immune-related adverse events. Anti-PD-1/PD-L1 inhibitors were identified as a preferable treatment option for advanced or metastatic cancer patients who are male, aged < 65 years, current or former smokers, had no CNS or liver metastasis, had not EGFR mutation, and had high PD-L1 expression.Cancer is a common cause of death, accounting for more than 9.56 million deaths annually 1 . Over half of cancer patients have a poor prognosis due to locally advanced or systemic metastasis. For the majority of these cases, treatment with conventional therapies, such as chemotherapy and radiotherapy, does not improve their prognosis. Recently, several immune checkpoint inhibitors (ICIs), have been developed and approved for a wide range of tumour types and having shown potential for maintaining homeostasis and eliminating tumour cells. Immunotherapies targeting immune checkpoint pathways have shown potential for generating a durable response and for prolonging disease stabilisation in a significant proportion of inoperable, advanced, or recurrent cancers in patients with multiple cancer types, along with favourable tolerability. In addition to their use as a monotherapy, the general safety of immune checkpoint agents also allows for their use in the development of combined therapies for cancer treatment; combining ICIs with other conventional treatments or targeted therapies is expected to improve anti-tumour activity and increase ICI efficacy. However, although durable responses were reported in cancer patients treated with combination strategies involving ICIs, it is still necessary to optimise dose selection to minimise the adverse events (AEs) caused by combination regimens while maintaining stable clinical ef...

Conservation biologists have identified threats to the survival of about a quarter of the mammalian species; to identify patterns of rarity and commonness of mammals, we studied a global sample of 1212 species (about 28% of the mammals) using the ‘7 forms of rarity’ model (in which species are roughly divided into above and below the median for local population density, species’ range area, and number of habitat types). From a niche‐based hypothesis of abundance and distribution, we predicted that mammals would exhibit a bimodal pattern of rarity and commonness, with an overabundance of species in the relatively rarest and most common categories; and just such a significant bimodal pattern emerged, with over a quarter of the species classified as exceedingly rare and a further quarter very common, supporting the niche‐based hypothesis. Orders that include large mammals, including perissodactyls, primates, diprotodonts, and carnivores, exhibited significantly high proportions of relatively rare species; and tropical zoogeographic regions, especially Indomalaya, had relatively high proportions of species in the rarest category. Significant biases in the available data on mammals included under‐sampling of small species like rodents and bats, and a relative paucity of data on zoogeographic regions outside of North America and Australia. Mammalian species listed as of conservation concern by the IUCN occurred in all cells of the model, indicating that even relatively common species can be listed as threatened under some conditions; but we also found that sixty‐three species were relatively rare in all three criteria of the 7‐forms model but were not listed as threatened, indicating potential candidates for further study. Mammals may be a group of animals where rarity or commonness is a natural aspect of species biology, both confirming and perhaps partly explaining the large proportion of mammals assigned threatened status.

The identification of rare species is an important goal in conservation biology. Recent attempts to classify rare species have emphasized dichotomies in such characteristics as local population density, area of distribution, and degree of ecological specialization. In particular, Arita et al. (1990) dichotomized 100 Neotropical forest mammals according to local population density and area of distribution. Among these species of mammals, mean body mass was significantly associated with local population density and area of distribution. We argue that the effects of body mass should be removed before species are classified with respect to rarity. We re‐evaluated the data on Neotropical mammal species, using regression analyses to remove the effects of body mass on population density and area of distribution, followed by analysis of residuals. This new method resulted in substantial changes in the dichotomous classification of rare species. We combined the analysis of regression residuals with a ranking procedure that assumed that local population density and area of distribution were equally important in their effects on rarity. The new ranking technique produced another different classification of the rarity of the Neotropical forest mammal species. A graphical analysis showed that ranked species differed substantially in their degree of rarity, and in the importance of local population density, area of distribution, or both, to their degree of rarity. The ranking method allows the species of greatest concern to be singled out, it can be modified to include additional variables such as niche breadth, and it should be helpful for making conservation decisions.

An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model's generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.For the extraction of information on building damage from remote sensing images, previous studies have investigated numerous methods, which can currently be divided into multi-and single-temporal evaluation methods. The multi-temporal evaluation method is mainly based on detecting changes to evaluate the information on building damage. Gong et al. [6] used high-resolution remote sensing images from before and after the 2010 Yushu earthquake as examples for the extraction of information on building damage based on the object-oriented change detection, pixel-based change detection, and principal component analysis-based change detection methods. The results showed that the object-oriented change detection method had the highest accuracy for extracting information on building damage. However, due to effects from data acquisition, such as revisit cycles, shooting angle, time, and other factors, the application of the multi-temporal evaluation method is difficult in practice [7]. For the single-temporal evaluation method, data acquired via remote sensing after an earthquake has less constraints, such that it has become an effective technical means that can be directly used to extract and evaluate information on building damage [8]. Janalipour et al. [9] used high spatial resolution remote sensing images as backg...

In this paper, the iterative learning fault-tolerant control problem for multiphase batch processes with uncertainty and actuator faults is studied. First, making full use of the characteristics of the two-time dimension (2D) feature and repetitiveness in batch processes and introducing the state error and output error between the adjacent batches, the established model is transformed into an equivalent 2D-Roesser switched system with different dimensions. Under the framework of the 2D system theory and by means of the average dwell time method, sufficient conditions ensuring the system to be 2D robustly stable along the time and batch directions and the minimum running time lower bound in each phase are given. Simultaneously, the designed updating law is derived. In order to examine the control performance of the proposed method, the traditional reliable control method is also investigated in this paper. The batch process is regarded as a continuous system, in which only the fault-tolerant control along the time direction is considered. Finally, the injection modeling process is taken as an example, where the main parameters, namely the injection velocity and packing-holding pressure, are controlled in the filling and packing-holding phases. The simulation results show that the proposed iterative learning fault-tolerant control method is a better choice for the multiphase batch processes with actuator faults.

In this paper, a T-S model-based fuzzy delay-range-dependent iterative learning control (ILC) scheme is developed for highly nonlinear batch processes with interval time-varying delays. The two-dimensional (2D) T-S time-delay model is constructed to remedy the disadvantage that the overall linear model cannot sufficiently describe the nonlinear batch process. Then, exploiting the repetitive nature of batch processes, a 2D fuzzy delay-range-dependent iterative learning control is designed. The delay-range-dependent stabilization problem and H∞ control are studied by using 2D Lyapunov function under a 2D system framework. At the same time, the controller gain design is given and its gain can be obtained in terms of LMIs. A water tank is taken as a simulation case to demonstrate the effectiveness of the proposed fuzzy iterative learning control scheme.

The outbreak caused by COVID-19 is causing a major challenge to clinical management and a worldwide threat to public health. So far, there is no specific anti-coronavirus therapy approved for the treatment of COVID-19. Recently, as the efficacy and safety of traditional Chinese medicine (TCM) is widely acknowledged, it has been brought to a crucial status by the public, governments, and World Health Organization (WHO). For a better popularization of TCM, governments have made several advances in regulations and policies for treatment and measures of novel coronavirus pneumonia (NCP). Therefore, on the basis of epidemiology and virology information, we reviewed relevant meta-analysis and clinical studies of anti-coronavirus therapeutics by TCM, in the aspect of mortality, symptom improvement, duration and dosage of corticosteroid, incidence of complications and the like. In addition, we also summarized preclinical rationale for anti-coronavirus activity by TCM in terms of virion assembly and release, as well as viral entry and replication, which could be a useful contribution for figuring out effective Chinese herbal medicine (CHM) for coronavirus, including ingredients from single monomeric compounds, Chinese herbs, Chinese herb extracts and Chinese herb formulas, or potential targets for medicine. We would like to see these relevant studies, ranging from basic researches to clinical application, could provide some idea on effects of CHM to combat COVID-19 or other coronaviruses, and also offer new thinking for the exploration of therapeutic strategies under the guidance of TCM.

Concerning multiphase batch processes with delays, disturbances, and actuator faults, the design of 2D robust hybrid composite iterative learning fault-tolerant guaranteed cost controller is put forward. First, a hybrid iterative learning control law is introduced and the multiphase batch process with interval timevarying delays is converted to an equivalent 2D-FM switched system with actuator faults by the introduction of system output tracking errors and state errors and consideration of fault effects. Next, a sufficient condition for satisfying asymptotical stability that the closed-loop switched system has the upper bound of minimum performance index is established by application of the theoretical framework of 2D system and selection of 2D Lyapunov−Krasovskii function on the basis of an average dwell time method. Then, the optimal design algorithm of the 2D hybrid robust iterative learning fault-tolerant guaranteed cost control is presented. In the meantime, in view of the effects of delay terms on system stability, the constraint condition that the closed-loop system makes steady operation and has the optimal control performance where the system encounters actuator failure faults within the allowed fault coverage is constructed, and the design scheme of the control law is provided. In the end, key variables in the injection and packing phases in the injection molding process are controlled to further verify the effectiveness of the proposed method.