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The health of the trees in the forest affects the ecological environment, so timely detection of Standing Dead Trees (SDTs) plays an important role in forest management. However, due to the large spatial scope of forests, it is difficult to find SDTs through conventional approaches such as field inventories. In recent years, the development of deep learning and Unmanned Aerial Vehicle (UAV) has provided technical support for low-cost real-time monitoring of SDTs, but the inability to fully utilize global features and the difficulty of small-scale SDTs detection have brought challenges to the detection of SDTs in visible light images. Therefore, this paper proposes a multi-scale attention mechanism detection method for identifying SDTs in UAV RGB images. This method takes Faster-RCNN as the basic framework and uses Swin-Transformer as the backbone network for feature extraction, which can effectively obtain global information. Then, features of different scales are extracted through the feature pyramid structure and feature balance enhancement module. Finally, dynamic training is used to improve the quality of the model. The experimental results show that the algorithm proposed in this paper can effectively identify the SDTs in the visible light image of the UAV with an accuracy of 95.9%. This method of SDTs identification can not only improve the efficiency of SDTs exploration, but also help relevant departments to explore other forest species in the future. [ABSTRACT FROM AUTHOR]

Polyamines are critical metabolites involved in various cellular processes and often dysregulated in cancers. Kaposi's sarcoma-associated Herpesvirus (KSHV), a defined human oncogenic virus, leads to profound alterations of host metabolic landscape to favor development of KSHV-associated malignancies. In our studies, we identified that polyamine biosynthesis and eIF5A hypusination are dynamically regulated by KSHV infection through modulation of key enzymes (ODC1 and DHPS) of these pathways. During KSHV latency, ODC1 and DHPS are upregulated along with increase of hypusinated eIF5A (hyp-eIF5A), while hyp-eIF5A is further induced along with reduction of ODC1 and intracellular polyamines during KSHV lytic reactivation. In return these metabolic pathways are required for both KSHV lytic reactivation and de novo infection. Further analysis unraveled that synthesis of critical KSHV latent and lytic proteins (LANA, RTA) depends on hypusinated-eIF5A. We also demonstrated that KSHV infection can be efficiently and specifically suppressed by inhibitors targeting these pathways. Collectively, our results illustrated that the dynamic and profound interaction of a DNA tumor virus (KSHV) with host polyamine biosynthesis and eIF5A hypusination pathways promote viral propagation, thus defining new therapeutic targets to treat KSHV-associated malignancies. Author summary: Understanding virus-host interactions is crucial to develop and improve therapies. Kaposi's sarcoma associated Herpesvirus (KSHV) is a human gamma-herpesvirus which deeply modulates the host metabolism and is associated with various cancers of endothelial and lymphoid origin. Polyamines are critical metabolites often dysregulated in cancers. In this study we demonstrated KSHV dynamically modulates polyamine metabolism to favor eIF5A hypusination and translation of critical KSHV latent and lytic proteins (LANA, RTA). Consequently, we found KSHV lytic switch from latency and de novo infection were dependent on polyamines and hypusination and pharmacological inhibition efficiently and specifically restricted KSHV infection. Our study provides new insights into KSHV alteration of the host metabolism and describe new therapeutic targets to treat KSHV-associated malignancies. [ABSTRACT FROM AUTHOR]

Influenza is active during the winter and spring in the city of Beijing, which has a typical temperate climate with four clear distinct seasons. The clinical and laboratory surveillance data for influenza have been used to construct critical indicators for influenza activities in the community, and previous studies have reported varying degrees of association between laboratory-confirmed influenza specimens and outpatient consultation rates of influenza-like illness in subtropical cities. However, few studies have reported on this issue for cities in temperate regions, especially in developing countries. Furthermore, the mechanism behind age-specific seasonal epidemics remains unresolved, although it has been widely discussed. We utilized a wavelet analysis method to monitor the coherence of weekly percentage of laboratory-confirmed influenza specimens with the weekly outpatient consultation rates of influenza-like illness in Beijing, China. We first examined the seasonal pattern of laboratory-confirmed cases of influenza A (subtyped into seasonal A(H1N1) and A(H3N2) and pandemic virus A(H1N1) pdm09) and influenza B separately within the period from 2008–2015; then, we detected the coherence of clinical and laboratory surveillance data in this district, specially examining weekly time series of age-specific epidemics of influenza-like illnesses in the whole study period for three age categories (age 0–5, 5–15 and 25–60). We found that influenza A and B were both active in winter but were not always seasonally synchronous in Beijing. Synchronization between age ranges was found in most epidemic peaks from 2008–2015. Our findings suggested that peaks of influenza-like illness in individuals aged 0–5 and 5–15 years consistently appeared ahead of those of adults, implying the possibility that schoolchildren may lead epidemic fluctuations. [ABSTRACT FROM AUTHOR]

: A widespread application of integrin αvβ3 imaging has been emerging in both pre-clinical and clinical studies. But few studies reported its value as compared with 18F-FDG PET, especially for differentiated thyroid cancer (DTC). In this study, we compared the tracer uptake of 18F-AIF-NOTA-PRGD2 and 18F-FDG in lymph node metastasis of DTC to evaluate 18F-AIF-NOTA-PRGD2 as compared with 18F-FDG. Methods: 20 DTC patients with presumptive lymph node metastasis were examined with 18F-AIF-NOTA-PRGD2 and 18F-FDG PET/CT. 16 patients undergoing fine needle aspiration biopsy (FNAB) were evaluated by cytology results. For lesions without FNAB, the findings of clinical staging procedures served as the standard of reference (including neck ultrasound and serum thyroglobulin). Results: A total of 39 presumptive lymph node metastases were visualized on PET/CT images. 35 lesions were confirmed as malignant by FNAB and other clinical findings. The mean 18F-AIF-NOTA-PRGD2 in radioactive iodine-refractory (RAIR) lesions and benign lesions were 2.5±0.9 and 2.8±0.9 respectively. The mean SUV for 18F-FDG in all malignant lesions was 4.5±1.6 while in benign lesions it was 3.3±1.2. For all malignant lesions, the mean SUV for 18F-FDG was significantly higher than that for 18F-AIF-NOTA-PRGD2 (P<0.05). No significant correlation was found between the SUVs of 18F-AIF-NOTA-PRGD2 and 18F-FDG for 35 lesions (r = 0.114, P = 0.515). Moreover, 15 lesions of which the diameter larger than 1.5cm had higher 18F-AIF-NOTA-PRGD2 uptake as compared with the lesions smaller than 1.5cm. Conclusion: Although most lymph node metastases of DTC showed abnormal uptake of 18F-AIF-NOTA-PRGD2, its diagnostic value was inferior to 18F-FDG. No correlation was found between the uptake of 18F-AIF-NOTA-PRGD2 and 18F-FDG, which may suggest the two tracers provide complementary information in DTC lesions. [ABSTRACT FROM AUTHOR]

Student Dropout Prediction (SDP) is pivotal in mitigating withdrawals in Massive Open Online Courses. Previous studies generally modeled the SDP problem as a binary classification task, providing a single prediction outcome. Accordingly, some attempts introduce survival analysis methods to achieve continuous and consistent predictions over time. However, the volatility and sparsity of data always weaken the models' performance. Prevailing solutions rely heavily on data pre-processing independent of predictive models, which are labor-intensive and may contaminate authentic data. This paper proposes a Survival Analysis based Volatility and Sparsity Modeling Network (SAVSNet) to address these issues in an end-to-end deep learning framework. Specifically, SAVSNet smooths the volatile time series by convolution network while preserving the original data information using Long-Short Term Memory Network (LSTM). Furthermore, we propose a Time-Missing-Aware LSTM unit to mitigate the impact of data sparsity by integrating informative missingness patterns into the model. A survival analysis loss function is adopted for parameter estimation, and the model outputs monotonically decreasing survival probabilities. In the experiments, we compare the proposed method with state-of-the-art methods in two real-world MOOC datasets, and the experiment results show the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

Both Kaposi's sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV) establish the persistent, life-long infection primarily at the latent status, and associate with certain types of tumors, such as B cell lymphomas, especially in immuno-compromised individuals including people living with HIV (PLWH). Lytic reactivation of these viruses can be employed to kill tumor cells harboring latently infected viral episomes through the viral cytopathic effects and the subsequent antiviral immune responses. In this study, we identified that polo-like kinase 1 (PLK1) is induced by KSHV de novo infection as well as lytic switch from KSHV latency. We further demonstrated that PLK1 depletion or inhibition facilitates KSHV reactivation and promotes cell death of KSHV-infected lymphoma cells. Mechanistically, PLK1 regulates Myc that is critical to both maintenance of KSHV latency and support of cell survival, and preferentially affects the level of H3K27me3 inactive mark both globally and at certain loci of KSHV viral episomes. Furthremore, we recognized that PLK1 inhibition synergizes with STAT3 inhibition to efficiently induce KSHV reactivation. We also confirmed that PLK1 depletion or inhibition yields the similar effect on EBV lytic reactivation and cell death of EBV-infected lymphoma cells. Lastly, we noticed that PLK1 in B cells is elevated in the context of HIV infection and caused by HIV Nef protein to favor KSHV/EBV latency. Author summary: KSHV is a large double-stranded DNA virus belonging to human gamma-herpesviruses and capable of establishing the long-term persistent infection, which causes cancers under immunocompromised states, such as acquired immunodeficiency syndrome (AIDS). There are currently still no effective therapies to specifically treat KSHV-associated tumors, nor eliminate its persistent infection. In this study, we illustrated that PLK1 is upregulated by KSHV, which in return suppresses KSHV lytic infection. We further demonstrated that PLK1 is a novel host target that can be inhibited pharmacologically to benefit the viral oncolysis for promotion of KSHV lytic reactivation and elimination of KSHV-positive lymphoma cells, particularly for people living with HIV (PLWH). PLK1 inhibitors also exerted the similar effect on EBV that is the other member of human gamma-herpesvirus. [ABSTRACT FROM AUTHOR]