An Exploration of Nature and Human Development in Young Adult Historical Fantasy

Traditional historical writing focuses on the cause and effect of human action, assuming that it is the historian’s responsibility to recount the ebbs and flows of human progress. In the process of laying hold of the past as a narrative of human action, historical writing has developed the tendency to marginalise nature and undermine its power to influence the historical narrative. My investigation explores the fantastic in historical fantasy as a means of resisting historical writing’s anthropocentrism. Historical fantasy uses fantastical elements to create counterfactual and alternative historical realities that have the potential to resist and undermine history’s anthropocentric norm. My thesis examines four contemporary young adult historical fantasy trilogies that reimagine key turning points in history such as industrialisation, the American frontier, European imperialism, and World War I. They share the theme of retrieving and subverting anthropocentric discourses in the history of human development and thereby creating space for nature's presence and agency. My study finds that the fantastic is an effective means of subverting historical writing’s anthropocentrism. But it also uncovers ambiguities and contradictions in historical fantasy's ecological revisionism, pointing to the idea that despite the fantastic’s capacity for subversion, historical representations of nature cannot be separated from considerations of human identity and survival

INTELLIGENT ENVIRONMENTAL SENSING WITH AN UNMANNED AERIAL SYSTEM IN A WIRELESS SENSOR NETWORK

This paper proposes a novel environmental monitoring mechanism to integrate recentlyestablished development of an Unmanned Aerial System (UAS) with WSNs for remote monitoring. The high mobility of UASs can solve the limitations associated with using WSNs in hazardous areas. In this paper, the WSN node, the Wireless Environmental Monitoring Station (WEMS), is based on ZigBee protocol for long-duration monitoring. Furthermore, to ensure the integrity of collected environmental data, an algorithm is designed in WEMS for verification. Finally, a detailed analysis of packet transmission efficiency based on ranges of flight distance is proposed to examine the effect of environmental monitoring

BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs

Recent studies have shown that Binary Graph Neural Networks (GNNs) are promising for saving computations of GNNs through binarized tensors. Prior work, however, mainly focused on algorithm designs or training techniques, leaving it open to how to materialize the performance potential on accelerator hardware fully. This work redesigns the binary GNN inference backend from the efficiency perspective. It fills the gap by proposing a series of abstractions and techniques to map binary GNNs and their computations best to fit the nature of bit manipulations on GPUs. Results on real-world graphs with GCNs, GraphSAGE, and GraphSAINT show that the proposed techniques outperform state-of-the-art binary GNN implementations by 8-22X with the same accuracy maintained. BitGNN code is publicly available.Comment: To appear in the International Conference on Supercomputing (ICS'23

Smoking, Alcohol, and Betel Quid and Oral Cancer: A Prospective Cohort Study

We aimed to investigate the association between smoking, alcoholic consumption, and betel quid chewing with oral cancer in a prospective manner. All male patients age ≥18 years who visited our clinic received an oral mucosa inspection. Basic data including personal habits were also obtained. A multivariate logistic regression model was utilized to determine relevant risk factors for developing oral cavity cancer. A total of 10,657 participants were enrolled in this study. Abnormal findings were found in 514 participants (4.8%). Three hundred forty-four participants received biopsy, and 230 patients were proven to have oral cancer. The results of multivariate logistic regression found that those who smoked, consumed alcohol, and chewed betel quid on a regular basis were most likely to develop cancer (odds ratio: 46.87, 95% confidence interval: 31.84–69.00). Therefore, habitual cigarette smokers, alcohol consumers, and betel quid chewers have a higher risk of contracting oral cancer and should receive oral screening regularly so potential oral cancer can be detected as early as possible

RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices

Mobile devices are becoming an important carrier for deep learning tasks, as they are being equipped with powerful, high-end mobile CPUs and GPUs. However, it is still a challenging task to execute 3D Convolutional Neural Networks (CNNs) targeting for real-time performance, besides high inference accuracy. The reason is more complex model structure and higher model dimensionality overwhelm the available computation/storage resources on mobile devices. A natural way may be turning to deep learning weight pruning techniques. However, the direct generalization of existing 2D CNN weight pruning methods to 3D CNNs is not ideal for fully exploiting mobile parallelism while achieving high inference accuracy. This paper proposes RT3D, a model compression and mobile acceleration framework for 3D CNNs, seamlessly integrating neural network weight pruning and compiler code generation techniques. We propose and investigate two structured sparsity schemes i.e., the vanilla structured sparsity and kernel group structured (KGS) sparsity that are mobile acceleration friendly. The vanilla sparsity removes whole kernel groups, while KGS sparsity is a more fine-grained structured sparsity that enjoys higher flexibility while exploiting full on-device parallelism. We propose a reweighted regularization pruning algorithm to achieve the proposed sparsity schemes. The inference time speedup due to sparsity is approaching the pruning rate of the whole model FLOPs (floating point operations). RT3D demonstrates up to 29.1$\times$ speedup in end-to-end inference time comparing with current mobile frameworks supporting 3D CNNs, with moderate 1%-1.5% accuracy loss. The end-to-end inference time for 16 video frames could be within 150 ms, when executing representative C3D and R(2+1)D models on a cellphone. For the first time, real-time execution of 3D CNNs is achieved on off-the-shelf mobiles.Comment: To appear in Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21

Pretreatment with a Heat-Killed Probiotic Modulates the NLRP3 Inflammasome and Attenuates Colitis-Associated Colorectal Cancer in Mice.

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Inflammation contributes to cancer development and inflammatory bowel disease is an important risk factor for CRC. The aim of this study is to assess whether a widely used probiotic Enterococcus faecalis can modulate the NLRP3 inflammasome and protect against colitis and colitis-associated CRC. We studied the effect of heat-killed cells of E. faecalis on NLRP3 inflammasome activation in THP-1-derived macrophages. Pretreatment of E. faecalis or NLRP3 siRNA can inhibit NLRP3 inflammasome activation in macrophages in response to fecal content or commensal microbes, P. mirabilis or E. coli, according to the reduction of caspase-1 activation and IL-1β maturation. Mechanistically, E. faecalis attenuates the phagocytosis that is required for the full activation of the NLRP3 inflammasome. In in vivo mouse experiments, E. faecalis can ameliorate the severity of intestinal inflammation and thereby protect mice from dextran sodium sulfate (DSS)-induced colitis and the formation of CRC in wild type mice. On the other hand, E. faecalis cannot prevent DSS-induced colitis in NLRP3 knockout mice. Our findings indicate that application of the inactivated probiotic, E. faecalis, may be a useful and safe strategy for attenuation of NLRP3-mediated colitis and inflammation-associated colon carcinogenesis

Bevacizumab Dose Affects the Severity of Adverse Events in Gynecologic Malignancies

In this retrospective study, we investigated adverse events and outcomes in patients treated with bevacizumab for ovarian, fallopian tube, or primary peritoneal cancers at a single hospital. We determined the cumulative incidences of various bevacizumab-related adverse events and the correlation between dose and adverse event incidences. We analyzed data from 154 patients that received 251 rounds of bevacizumab as first-line, first salvage, >2 salvage treatments. Adverse events of any grade were observed in 121 (78.6%) patients; at least one grade 3 or 4 adverse event occurred in 32 (20.8%) patients. The two most common events were proteinuria (38.3%) and hypertension (33.8%). The first-line treatment group displayed significantly higher frequencies of hypertension (52.7% vs. 18.9% vs. 15.5%, p < 0.001), wound complications (9.1% vs. 0% vs. 1.2%, p = 0.010), arthralgia (29.1% vs. 11.3% vs. 8.3%, p = 0.003), and reduced range of joint motion (14.5% vs. 5.7% vs. 3.6%, p = 0.046), compared to those in the first and >2 lines salvage groups, respectively (Kruskal–Wallis test). The cumulative incidences of all grades and grades 3/4 of hypertension cumulative incidence plateaued at around 30% for all grades and 10% for grades 3 and 4, at bevacizumab doses above 8080 and 3510 mg, respectively. The proteinuria cumulative incidence plateaued at around 35% for all grades and 3% for grades 3 and 4, at bevacizumab doses above 11,190 and 4530 mg, respectively. We concluded that, in this realistic clinical population, different kinds and higher cumulative incidences of adverse events were observed compared to those reported in previous clinical trials. Moreover, bevacizumab doses showed cumulative toxicity and plateau effects on hypertension and proteinuria

P53 and Pten control neural and glioma stem/progenitor cell renewal and differentiation

Glioblastoma (GBM) is a highly lethal brain tumor presenting as one of two subtypes with distinct clinical histories and molecular profiles. The primary GBM subtype presents acutely as high-grade disease that typically harbors EGFR, Pten and Ink4a/Arf mutations, and the secondary GBM subtype evolves from the slow progression of low-grade disease that classically possesses PDGF and p53 events1–3. Here, we show that concomitant CNS-specific deletion of p53 and Pten in the mouse CNS generates a penetrant acute-onset high-grade malignant glioma phenotype with striking clinical, pathological and molecular resemblance to primary GBM in humans. This genetic observation prompted p53 and Pten mutational analysis in human primary GBM, demonstrating unexpectedly frequent inactivating mutations of p53 as well the expected Pten mutations. Integrated transcriptomic profiling, in silico promoter analysis and functional studies of murine neural stem cells (NSCs) established that dual, but not singular, inactivation of p53 and Pten promotes an undifferentiated state with high renewal potential and drives elevated c-Myc levels and its associated signature. Functional studies validated increased c-Myc activity as a potent contributor to the impaired differentiation and enhanced renewal of p53-Pten null NSCs as well as tumor neurospheres (TNSs) derived from this model. c-Myc also serves to maintain robust tumorigenic potential of p53-Pten null TNSs. These murine modeling studies, together with confirmatory transcriptomic/promoter studies in human primary GBM, validate a pathogenetic role of a common tumor suppressor mutation profile in human primary GBM and establish c-Myc as a key target for cooperative actions of p53 and Pten in the regulation of normal and malignant stem/progenitor cell differentiation, self-renewal and tumorigenic potential