Experimental and Numerical Study of Tsunami Wave Propagation and Run-Up on Sloping Beaches

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Clinical application of tumor volume in advanced nasopharyngeal carcinoma to predict outcome

<p>Abstract</p> <p>Background</p> <p>Current staging systems have limited ability to adjust optimal therapy in advanced nasopharyngeal carcinoma (NPC). This study aimed to delineate the correlation between tumor volume, treatment outcome and chemotherapy cycles in advanced NPC.</p> <p>Methods</p> <p>A retrospective review of 110 patients with stage III-IV NPC was performed. All patients were treated first with neoadjuvant chemotherapy, then concurrent chemoradiation, and followed by adjuvant chemotherapy as being the definitive therapy. Gross tumor volume of primary tumor plus retropharyngeal nodes (GTVprn) was calculated to be an index of treatment outcome.</p> <p>Results</p> <p>GTVprn had a close relationship with survival and recurrence in advanced NPC. Large GTVprn (≧13 ml) was associated with a significantly poorer local control, lower distant metastasis-free rate, and poorer survival. In patients with GTVprn ≧ 13 ml, overall survival was better after ≧4 cycles of chemotherapy than after less than 4 cycles.</p> <p>Conclusions</p> <p>The incorporation of GTVprn can provide more information to adjust treatment strategy.</p

Radio Occultation Retrieval of Atmospheric Profiles from the FORMOSAT-3/COSMIC Mission: Early Results

Six identical micro-satellites of the FORMOSAT-3/COSMIC (Formosa Satellite #3 and Constellation Observing System for Meteorology, Ionosphere and Climate: FS-3/C) mission were successfully launched on 14 April 2006. The FS-3/C mission provides the first satellite constellation for monitoring global weather using the Global Positioning System (GPS) radio occultation (RO) technique. The mission¡¦s primary scientificific goal is to obtain near-real time profiles of the bending angle and refractivity in the neutral atmosphere and in the ionosphere. In April, 2007 the FS-3/C mission provide about RO soundings of 2000 atmospheric vertical profiles per day in a nearly uniform distribution around the globe. The lowest altitude penetration for more than 80% of RO soundings reached below 1 kmin altitude. Most soundings have penetration below 800m altitude in the equatorial region and below 200 m altitude in polar regions. The quality and accuracy of the RO sounding profiles are in good agreement with the CHAMP(CHAllenging Minisatellite Payload) RO soundings and direct measurements using dropsondes. The FS-3/C RO sounding observations are used to support operational global weather prediction, climate monitoring and research, space weather forecasting, and ionosphere and gravity research

Clinical and pathological correlates of severity classifications in trigger fingers based on computer-aided image analysis

BACKGROUND: The treatment of trigger finger so far has heavily relied on clinicians’ evaluations for the severity of patients’ symptoms and the functionality of affected fingers. However, there is still a lack of pathological evidence supporting the criteria of clinical evaluations. This study’s aim was to correlate clinical classification and pathological changes for trigger finger based on the tissue abnormality observed from microscopic images. METHODS: Tissue samples were acquired, and microscopic images were randomly selected and then graded by three pathologists and two physicians, respectively. Moreover, the acquired images were automatically analyzed to derive two quantitative parameters, the size ratio of the abnormal tissue region and the number ratio of the abnormal nuclei, which can reflect tissue abnormality caused by trigger finger. A self-developed image analysis system was used to avoid human subjectivity during the quantification process. Finally, correlations between the quantitative image parameters, pathological grading, and clinical severity classification were assessed. RESULTS: One-way ANOVA tests revealed significant correlations between the image quantification and pathological grading as well as between the image quantification and clinical severity classification. The Cohen’s kappa coefficient test also depicted good consistency between pathological grading and clinical severity classification. CONCLUSIONS: The criteria of clinical classification were found to be highly associated with the pathological changes of affected tissues. The correlations serve as explicit evidence supporting clinicians in making a treatment strategy of trigger finger. In addition, our proposed computer-aided image analysis system was considered to be a promising and objective approach to determining trigger finger severity at the microscopic level

Organic Electrochemical Transistors/SERS-Active Hybrid Biosensors Featuring Gold Nanoparticles Immobilized on Thiol-Functionalized PEDOT Films

In this study we immobilized gold nanoparticles (AuNPs) onto thiol-functionalized poly(3,4-ethylenedioxythiophene) (PEDOT) films as bioelectronic interfaces (BEIs) to be integrated into organic electrochemical transistors (OECTs) for effective detection of dopamine (DA) and also as surface-enhanced Raman scattering (SERS)—active substrates for the selective detection of p-cresol (PC) in the presence of multiple interferers. This novel PEDOT-based BEI device platform combined (i) an underlying layer of polystyrenesulfonate-doped PEDOT (PEDOT:PSS), which greatly enhanced the transconductance and sensitivity of OECTs for electrochemical sensing of DA in the presence of other ascorbic acid and uric acid metabolites, as well as amperometric response toward DA with a detection limit (S/N = 3) of 37 nM in the linear range from 50 nM to 100 μM; with (ii) a top interfacial layer of AuNP-immobilized three-dimensional (3D) thiol-functionalized PEDOT, which not only improved the performance of OECTs for detecting DA, due to the signal amplification effect of the AuNPs with high catalytic activity, but also enabled downstream analysis (SERS detection) of PC on the same chip. We demonstrate that PEDOT-based 3D OECT devices decorated with a high-density of AuNPs can display new versatility for the design of next-generation biosensors for point-of-care diagnostics

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

EEG-based Brain-computer interfaces (BCI) are facing grant challenges in their real-world applications. The technical difficulties in developing truly wearable multi-modal BCI systems that are capable of making reliable real-time prediction of users’ cognitive states under dynamic real-life situations may appear at times almost insurmountable. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report our attempt to develop a pervasive on-line BCI system by employing state-of-art technologies such as multi-tier fog and cloud computing, semantic Linked Data search and adaptive prediction/classification models. To verify our approach, we implement a pilot system using wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end fog servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end cloud servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch and the UCSD Movement Disorder Center to use our system in real-life personal stress and in-home Parkinson’s disease patient monitoring experiments. We shall proceed to develop a necessary BCI ontology and add automatic semantic annotation and progressive model refinement capability to our system

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.Comment: 48 pages, 8 figures. To be submitte