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...