280 research outputs found
Tracking the 6-DOF Flight Trajectory of Windborne Debris using Stereophotogrammetry
Numerous post-windstorm investigations have reported that windborne debris can cause costly damage to the envelope of buildings in urban areas under strong winds (e.g., during hurricanes or tornados). Thus, understanding the physics of debris flight is of critical importance. Previously developed numerical models describing debris flight physics have not been validated for the complex urban flow environment; such a validation requires experimentally measuring the debris flight trajectory in wind tunnel tests. In this context, this paper proposes a debris measurement algorithm using stereophotogrammetry. This algorithm aims to determine the six-degree-of-freedom (6-DOF) trajectory and velocity of flying debris, addressing the research gap, i.e., the lack of an algorithm/software for measuring three-rotational-DOF using stereophotogrammetry. This is a civil engineering problem, but computer graphics is the foundation to solve it. This paper focuses on the theoretical development of the algorithm. The developed algorithm can be readily implemented in modern wind tunnel experiments
System Identification for Time-Varying Systems under Non-White Excitations
Accurate identification of structural properties such as natural frequency and damping under extreme wind conditions is critical for assessing the structural performances. Full-scale monitoring has witnessed that dynamic properties of structures may change over time under extreme winds. System identification (SI) is non-trivial in this case. While wavelet and short time Fourier transforms have been used in tracking time-varying frequencies, they have seldom been used to identify the time-varying damping ratio. This is because the short window (required to capture the temporal information) will amplify the bandwidth significantly and lead to considerably overestimated damping ratios. To address this challenge, this paper proposes a novel non-stationary SI approach to identify time-varying systems under general non-white excitations by extending an earlier approach proposed by the authors. To solve this problem, this study innovatively adapted theoretical frequency response functions (FRF) of systems for marginal spectra of the wavelet transform by adding short window effects explicitly. In this way, both the natural frequency and damping ratio at each time instant can be identified accurately. However, the initially proposed method assumed the excitation to be white in the vicinity of the structural natural frequencies. This assumption might not be strictly valid in some cases. For example, for wind-excited structures, the spectrum of wind force is a function of frequency derived from the turbulent wind spec-trum and aerodynamic admittance function. To better handle these non-white excitations, the method proposed in this paper directly models the non-white spectrum of excitation in the frequency domain. Then both the parameters of the force spectrum and the system properties are identified using the out-put data. The uncertainties of the SI results are evaluated. The performance of the proposed method is demonstrated by numerical examples considering structures under extreme wind conditions
Implications of Ape1 in reactive oxygen signaling response following cisplatin treatment of dorsal root ganglion neurons
Peripheral neuropathy is one of the major side-effects of the anticancer drug, cisplatin. Although previous work suggests that this neuropathy correlates with formation of DNA adducts in sensory neurons, growing evidence suggests that cisplatin also increases the generation of reactive oxygen species (ROS), which could cause DNA damage. Apurinic/apyrimidinic endonuclease/redox factor-1 (Ape1/Ref-1) is a multifunctional protein involved in DNA base excision repair (BER) of oxidative DNA damage and in redox regulation of a number of transcription factors. Therefore, we asked whether altering Ape1 functions would influence cisplatin induced neurotoxicity. Sensory neurons in culture were exposed to cisplatin for 24 hrs and several endpoints of toxicity were measured including production of ROS, cell death, apoptosis, and release of the immunoreactive calcitonin gene-related peptide (iCGRP). Reducing expression of Ape1 in neuronal cultures using siRNA enhances cisplatin-induced cell killing, apoptosis, ROS generation and the cisplatin-induced reduction in iCGRP release. Overexpressing wild-type (WT)-Ape1 attenuates all the toxic effects of cisplatin in cells containing normal endogenous levels of Ape1 and in cells with reduced Ape1 levels following Ape1siRNA treatment. Overexpressing the redox deficient/repair competent C65-Ape1 provides partial rescue, while the repair deficient Ape1 (N226A+R177A) does not protect neurons from cisplatin toxicity. We also observe an increase in phosphorylation of p53 following a decrease in Ape1 levels in sensory neuronal cultures. These results strongly support the notion that Ape1 is a potential translational target such that protecting Ape1 levels and particularly its DNA repair function could reduce peripheral neuropathy in patients undergoing cisplatin treatment
Murine Trophoblast Stem Cells and Their Differentiated Cells Attenuate Zika Virus In Vitro By Reducing Glycosylation of the Viral Envelope Protein
Zika virus (ZIKV) infection during pregnancy can cause devastating fetal neuropathological abnormalities, including microcephaly. Most studies of ZIKV infection in pregnancy have focused on post-implantation stage embryos. Currently, we have limited knowledge about how a pre-implantation stage embryo deals with a viral infection. This study investigates ZIKV infection on mouse trophoblast stem cells (TSCs) and their in vitro differentiated TSCs (DTSCs), which resemble the cellular components of the trophectoderm layer of the blastocyst that later develops into the placenta. We demonstrate that TSCs and DTSCs are permissive to ZIKV infection; however, ZIKV propagated in TSCs and DTSCs exhibit substantially lower infectivity, as shown in vitro and in a mouse model compared to ZIKV that was generated in Vero cells or mouse embryonic fibroblasts (MEFs). We further show that the low infectivity of ZIKV propagated in TSCs and DTSCs is associated with a reduced level of glycosylation on the viral envelope (E) proteins, which are essential for ZIKV to establish initial attachment by binding to cell surface glycosaminoglycans (GAGs). The decreased level of glycosylation on ZIKV E is, at least, partially due to the low-level expression of a glycosylation-related gene, Hexa, in TSCs and DTSCs. Furthermore, this finding is not limited to ZIKV since similar observations have been made as to the chikungunya virus (CHIKV) and West Nile virus (WNV) propagated in TSCs and DTSCs. In conclusion, our results reveal a novel phenomenon suggesting that murine TSCs and their differentiated cells may have adapted a cellular glycosylation system that can limit viral infectivity by altering the glycosylation of viral envelope proteins, therefore serving as a unique, innate anti-viral mechanism in the pre-implantation stage embryo
Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion
User post-click conversion prediction is of high interest to researchers and
developers. Recent studies employ multi-task learning to tackle the selection
bias and data sparsity problem, two severe challenges in post-click behavior
prediction, by incorporating click data. However, prior works mainly focused on
pointwise learning and the orders of labels (i.e., click and post-click) are
not well explored, which naturally poses a listwise learning problem. Inspired
by recent advances on differentiable sorting, in this paper, we propose a novel
multi-task framework that leverages orders of user behaviors to predict user
post-click conversion in an end-to-end approach. Specifically, we define an
aggregation operator to combine predicted outputs of different tasks to a
unified score, then we use the computed scores to model the label relations via
differentiable sorting. Extensive experiments on public and industrial datasets
show the superiority of our proposed model against competitive baselines.Comment: The paper is accepted as a short research paper by SIGIR 202
Tourist shopping behavior : a case of Shanghai outbound tourists / Yingzhi Guo...[et al.]
Shopping is an important tourist activity and its contribution to the economy is significant. For many visitors no trip is complete without having spent time shopping and tourists often feel they cannot return home without buying “something”. This research explored the basic shopping behaviour model of Shanghai outbound tourists. Data from 65 respondents were collected using the convenient sampling method. The motivations of Shanghai outbound tourists’ shopping behaviour were presented in seven dimensions including function, gift giving, affection, curiosity, scarcity, memory and cultural orientations. Quality, brand, shortage of specific goods in China’s domestic market, the lower price of goods in the outbound market and the convenient payments in the destinations are important attributes that spark their shopping behaviours. This outcome has at least had significant implications for tour operators and marketers to understand the needs of Chinese outbound tourists
A Comprehensive RNA Expression Signature for Cervical Squamous Cell Carcinoma Prognosis
Clinicopathological characteristics alone are not enough to predict the survival of patients with cervical squamous cell carcinoma (CESC) due to clinical heterogeneity. In recent years, many genes and non-coding RNAs have been shown to be oncogenes or tumor-suppressors in CESC cells. This study aimed to develop a comprehensive transcriptomic signature for CESC patient prognosis. Univariate, multivariate, and Least Absolute Shrinkage and Selection Operator penalized Cox regression were used to identify prognostic signatures for CESC patients from transcriptomic data of The Cancer Genome Atlas. A normalized prognostic index (NPI) was formulated as a synthetical index for CESC prognosis. Time-dependent receiver operating characteristic curve analysis was used to compare prognostic signatures. A prognostic transcriptomic signature was identified, including 1 microRNA, 1 long non-coding RNA, and 6 messenger RNAs. Decreased survival was associated with CESC patients being in the high-risk group stratified by NPI. The NPI was an independent predictor for CESC patient prognosis and it outperformed the known clinicopathological characteristics, microRNA-only signature, gene-only signature, and previously identified microRNA and gene signatures. Function and pathway enrichment analysis revealed that the identified prognostic RNAs were mainly involved in angiogenesis. In conclusion, we proposed a transcriptomic signature for CESC prognosis and it may be useful for effective clinical risk management of CESC patients. Moreover, RNAs in the transcriptomic signature provided clues for downstream experimental validation and mechanism exploration
Towards an Understanding of Large Language Models in Software Engineering Tasks
Large Language Models (LLMs) have drawn widespread attention and research due
to their astounding performance in tasks such as text generation and reasoning.
Derivative products, like ChatGPT, have been extensively deployed and highly
sought after. Meanwhile, the evaluation and optimization of LLMs in software
engineering tasks, such as code generation, have become a research focus.
However, there is still a lack of systematic research on the application and
evaluation of LLMs in the field of software engineering. Therefore, this paper
is the first to comprehensively investigate and collate the research and
products combining LLMs with software engineering, aiming to answer two
questions: (1) What are the current integrations of LLMs with software
engineering? (2) Can LLMs effectively handle software engineering tasks? To
find the answers, we have collected related literature as extensively as
possible from seven mainstream databases, and selected 123 papers for analysis.
We have categorized these papers in detail and reviewed the current research
status of LLMs from the perspective of seven major software engineering tasks,
hoping this will help researchers better grasp the research trends and address
the issues when applying LLMs. Meanwhile, we have also organized and presented
papers with evaluation content to reveal the performance and effectiveness of
LLMs in various software engineering tasks, providing guidance for researchers
and developers to optimize
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