Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Factors for Severe Hand-Foot-Mouth Disease in China: A Systematic Review and Meta-Analysis

Background: This study aimed to identify potential risk factors for severe hand-foot-mouth disease (HFMD). Methods: The PubMed, Embase, the Cochrane Library, Sinomed, WanFang, CNKI, and VIP databases were searched (up to August 2021). Results: Twenty-nine studies (9,241 and 927,355 patients with severe HFMD and controls, respectively; all from China) were included. EV71 was associated with higher odds of severe HFMD compared with other agents (OR = 4.44, 95%CI: 3.12–6.33, p < 0.001). Being home-raised (OR = 1.99, 95%CI: 1.59–2.50, p < 0.001), higher number of children in the family (OR = 2.09, 95%CI: 1.93–2.27, p < 0.001), poor hand hygiene (OR = 2.74, 95%CI: 1.78–4.23, p < 0.001), and no breastfeeding (OR = 2.01, 95%CI: 1.45–2.79, p < 0.001) were risk factors for severe HFMD. First consulting to a district-level or above hospital (OR = 0.34, 95%CI: 0.25–0.45, p < 0.001) and diagnosis of HFMD at baseline (OR = 0.17, 95%CI: 0.13–0.24, p < 0.001) were protective factors against severe HFMD. Fever, long fever duration, vomiting, lethargy, leukocytosis, tic, and convulsions were each associated with severe HFMD (all p < 0.05), while rash was not. Conclusions: EV71, lifestyle habits, frequent hospital visits, and symptoms are risk factors for severe HFMD in children in China, while early diagnosis and admission to higher-level hospitals are protective factors.
TERT Promoter Mutations and rs2853669 Polymorphism: Useful Markers for Clinical Outcome Stratification of Patients With Oral Cavity Squamous Cell Carcinoma

Objective To date, no useful prognostic biomarker exists for patients with oral squamous cell carcinoma (OCSCC), a tumour with uncertain biological behaviour and subsequent unpredictable clinical course. We aim to investigate the prognostic significance of two recurrent somatic mutations (-124 C>T and -146 C>T) within the promoter of telomerase reverse transcriptase (TERT) gene and the impact of TERT single nucleotide polymorphism (SNP) rs2853669 in patients surgically treated for OCSCC. Methods The genetic frequencies of rs2853669, -124 C>T and -146 C>T as well as the telomere length were investigated in 144 tumours and 57 normal adjacent mucosal (AM) specimens from OCSCC patients. Results Forty-five tumours harboured TERT promoter mutations (31.3%), with -124 C>T and -146 C>T accounting for 64.4% and 35.6% of the alterations respectively. Patients with -124 C>T TERT promoter mutated tumours had the shortest telomeres in the AM (p=0.016) and showed higher risk of local recurrence (hazard ratio [HR]:2.75, p=0.0143), death (HR:2.71, p=0.0079) and disease progression (HR:2.71, p=0.0024) with the effect being potentiated by the co-occurrence of T/T genotype of rs2853669. Conclusion -124 C>T TERT promoter mutation as well as the T/T genotype of the rs2853669 SNP are attractive independent prognostic biomarkers in patients surgically treated for OCSCC, with the coexistence of these genetic variants showing a synergistic impact on the aggressiveness of the disease.
S0033291720003505jra 2721..2729

Background. This random-effects model meta-analysis of double-blind, randomized placebocontrolled trials compared recurrence rates in bipolar disorder (BD) patients between antipsychotic/mood stabilizer discontinuation and maintenance groups. Methods. We conducted systematic literature search of Embase, PubMed, and CENTRAL databases without language restriction from inception until 22 May 2020. Independent investigators assessed studies and extracted data. We calculated risk ratios (RRs) and numbers needed to benefit or harm (NNTB/NNTH). Primary outcome was the recurrence rate of any mood episode at 6 months. Secondary outcomes were recurrence rates of depressive episodes and manic/hypomanic/mixed episodes and all-cause discontinuation at 6 months. We also investigated these outcomes at 1, 3, 9, 12, 18, and 24 months. Results. We identified 22 studies (n = 5462) receiving aripiprazole, asenapine, divalproex, long-acting injectable (LAI)-aripiprazole, LAI-risperidone, lamotrigine, lithium, olanzapine, paliperidone, or quetiapine. Mean study duration was 64.50 ± 69.35 weeks. The maintenance group demonstrated lower recurrence rates of any mood episode, depressive episodes, and manic/hypomanic/mixed episodes as well as reduced all-cause discontinuation at every observational point. The RRs (95% confidence interval, NNTB/NNTH) of recurrence rate at 6 months were 0.61 (0.54–0.70, 5) for any mood episode, 0.72 (0.60–0.87, 13) for depressive episodes, and 0.45 (0.36–0.57, 6) for manic/hypomanic/mixed episodes. The RR for all-cause discontinuation at 6 months was 0.71 (0.61–0.82, 6). Conclusions.Maintaining drug treatment during clinically stable BD prevented recurrence for up to 24 months. Discontinuation of medications for ⩾1 month significantly increased recurrence risk. However, 47.3% of patients who discontinued drugs for 6 months did not experience recurrence.
High-Precision Slot-Die Coating Machine for Thin Films of Flexible Display

We developed a compact high-precision slot-die coating machine for thin-film deposition on a flexible substrate. For smooth and precise coating, air-bearing and linear motor system were employed to minimize velocity ripple. The gap control mechanism is specially designed to have repeatability of gap between nozzle and substrate under 1 μm. Due to extremely precise gap control, the machine can coat thin-films down to 50 nm with 200 mm × 100mm size. A thin film of Ag nano-particle ink is coated for demonstration.
LC‐tank injection‐locked frequency divider with variable division ratio

In this study, a multimodulus CMOS injection‐locked frequency divider (ILFD) fabricated in a 0.18‐μm CMOS process is designed and the operation principle of the ILFD is described, which was realized using a cross‐coupled PMOS LC‐tank oscillator as the load of a double‐balanced NMOS Gilbert mixer. The stacked structure allows entire mixer current to be reused by the VCO cross‐coupled pair. At the supply voltage of 1.8 V, the free‐running frequency is from 2.31 to 2.65 GHz. Division ratio of 2, 3, 4, and 6 can be found in the ILFD function, and the ILFD can also function as a first‐harmonic injection‐locked oscillator. At the incident power of 0 dBm, the locking range in the ÷2 mode is from the incident frequency 3.4 to 6.4 GHz. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 3232–3236, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23928
17.2: Invited Paper: Improved PVA Mode with High Transmittance and Aperture Ratio

In this paper, we propose a cell structure which has high aperture ratio and transmittance with chevron‐shaped electrode. As an exemplary cell, we selected PVA (Patterned Vertical Alignment) mode. Optical transmittance and aperture ratio were calculated with 3D‐FEM numerical solver, TechWiz LCD, which is commercially available. The simulation results exhibited 4.8% improvement in aperture ratio and 11.3 % improvement in optical transmission.
Implementing Remote-State Preparation on a Noisy-Intermediate Size Quantum Device

In this research project, we investigate the implementation of a protocol for remotely preparing quantum states, with applications to blind and veriﬁable delegated quantum computation. The remote-state preparation protocol relies on the use of cryptographic primitives known as Noisy Trapdoor Claw-Free (NTCF) functions. These functions are based on conjectured post-quantum secure problems, such as Learning with Errors (LWE). The goal of the project is to implement the evaluation of such functions in superposition on a Noisy-Intermediate Size Quantum (NISQ) Device, allowing us to assess the potential of NISQ devices for cryptographic applications. We designed and optimized quantum circuits for the implementation of the NTCF functions and analyzed their performance using the quantum programming languages Q#, Qiskit, and Cirq. We also considered diﬀerent post-quantum secure problems as the basis for these functions (such as Learning Parity with Noise , Learning with Rounding and Ring Learning with Errors ) to ﬁnd circuits that are optimal in terms of depth and required number of qubits.
CnCHunter: An MITM-approach to identify live CnC servers

How can we identify active CnC servers? Answering this question is critical for containing and combating botnets. Finding CnC servers is not trivial because: CnC servers can change locations expressly to avoid detection, use proprietary communication protocols, and often use end-to-end encryption. Most prior efforts ﬁrst "learn" a malware communication protocol, and then, scan the Internet in search of live CnC servers. Although useful, this approach will not work with sophisticated malware that may use encryption or a communication protocol that is hard to reverse engineer. In this work, we propose CnCHunter, a systematic tool that discovers live CnC servers of IoT malware efﬁciently. The novelty of our approach is that it uses real "activated" malware to search for live CnC servers, with CnCHunter acting as a Man-In-The-Middle. As a result, our approach overcomes the limitations of prior efforts. For example, the malware binary knows how to communicate with its server even if in the presence of encryption. We randomly selected 100 IoT malware samples collected between 2017 and 2021, and found their CnC servers. CnCHunter could activate 90% of the malware and automatically ﬁnd the CnC servers with a 92% precision. Additionally, we demonstrate the potential of our system by activating old Gafgyt and Mirai malware samples and enabling them to communicate with live CnC servers for other recent samples of the same family. This proves that an old malware binary of a family can be used to scan the Internet and ﬁnd live Cnc servers for that malware family.
Research on Icing Status Forecasting of Wind Turbine Blades Based on Machine Learning

In order to respond to the problem of wind turbine blade icing disaster in a timely manner, preventive measures have been taken to prevent possible icing disaster and ensure the safe and efficient operation of wind plants. Based on monitoring data from wind plants supervisory control and data acquisition (SCADA), this paper proposes a wind turbine blade based on bidirectional long short-term memory (Bi-LSTM) and support vector machine (SVM) model to forecast the icing state. Firstly, the principal component analysis (PCA) is used to reduce the dimensionality of the monitoring data of the wind turbine blade icing state. The data features after screening are preprocessed. Secondly, the Bi-LSTM and SVM model are trained based on historical data, and the training results show that the model has good accuracy. Finally, the real data is input into the trained Bi-LSTM prediction model for data feature prediction, and then the prediction output result is input into the SVM model to determine whether the wind turbine blades will have an icing disaster. The analysis of examples shows that the proposed method can accurately predict the icing status of wind turbine blades.
Development of Thai Picture Description Task for Alzheimer’s Screening using Part-of-Speech Tagging

Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) are among the most common health conditions in elderly patients. Currently, methods to diagnose AD and MCI are lengthy, costly and require specialized staff to operate. A picture description task was developed to speed up the diagnosis. It was designed to be suitable and relatable to the Thai culture. In this paper, we will be presenting two picture description tasks named Thais-at-Home and Thai Temple Fair. The developed picture set was presented to 90 participants (30 normals, 30 MCI patients, and 30 AD patients). Then, the recording in the form of spontaneous speech is converted to text. A Part-of-Speech (PoS) tagger is used to categorize words into 7 types (noun, pronoun, adjective, verb, conjunction, preposition, and interjection) according to the Office of the Royal Society of Thailand. Six machine learning algorithms were applied to train with the PoS patterns and their performances were compared. Results showed that the PoS can be used to classify patients (MCI and AD) and healthy controls using multilayer perceptron with 90.00% sensitivity, 80.00% specificity, and 86.67% accuracy. Moreover, the findings showed that healthy controls used more conjunctions and verbs but fewer pronouns than the patients.Clinical relevance— The picture description tasks using part-of-speech (PoS) to showed promising results in screening Alzheimer’s patients.
Automated topic prediction of LitCovid using BioBERT

Background : The BioCreative VII-Track 5 challenge aims to increase the accuracy of automated topic prediction in the massively growing COVID-19 literature to help research community find effective diagnostics, drugs and vaccines for COVID-19. The challenge requires the participants to assign each of the 60k articles from LitCovid [1,2] database with upto seven topics such as Diagnosis, Treatment. Methods : We propose two different approaches for the multi label assignment task. In the following, we outline the different steps involved in these two models.
EXTENSION INTO CERAMICS: 3D PRINTING WITH PRECERAMIC POLYMERS

The work in this chapter is based the publication “Analysis of Multi-scale Mechanical Properties of Ceramic Trusses Prepared from Preceramic Polymers" by N.R. Brodnik, J. Schmidt, P. Colombo, and K.T. Faber, which is currently under review in Additive Manufacturing. N.R. Brodnik and J. Schmidt shared first authorship in this work, with N.R. Brodnik taking lead on sample and testing design, and J. Schmidt taking lead on sample fabrication. Both N.R. Brodnik and J. Schmidt contributed to mechanical testing of samples. The details of the print resin and fabrication process used in this study are discussed in reference [1].
Optimal Preprocessing of Raw Signals from Reflective Mode Photoplethysmography in Wearable Devices

The optical measurement principle photoplethysmography has emerged in today’s wearable devices as the standard to monitor the wearer’s heart rate in everyday life. This cost-effective and easy-to-integrate technique has transformed from the original transmission mode pulse oximetry for clinical settings to the reflective mode of modern ambulatory, wrist-worn devices. Numerous proposed algorithms aim at the efficient heart rate measurement and accurate detection of the consecutive pulses for the derivation of secondary features from the heart rate variability. Most, however, have been evaluated either on own, closed recordings or on public datasets that often stem from clinical pulse oximeters in transmission instead of wearables’ reflective mode. Signals tend furthermore to be preprocessed with filters, which are rarely documented and unintentionally fitted to the available and applied signals. We investigate the influence of preprocessing on the peak positions and present the benchmark of two cutting-edge pulse detection algorithms on actual raw measurements from reflective mode photoplethysmography. Based on 21806 pulse labels, our evaluation shows that the most suitable but still universal filter passband is located at 0.5 to 15.0Hz since it preserves the required harmonics to shape the peak positions.
Comparing Weather Data in Display Environments to Measure Impact of Quality of Experience: Virtual Reality Versus Desktop

Over the past decades, the data quantity and complexity has increased rapidly. Without the right tools to present and analyse the large quantity of data, the data might become incomprehensible. To utilise the data in the best possible way, we need to explore new methods and tools for data analysis and how they impact the Quality of Experience (QoE) of the end-users. The QoE is important to investigate in order to gain knowledge about new analysing methods and tools because it measures the end-to-end performance and the users’ overall experience of the service. In this research, we investigate new methods of exploring data through Immersive Analytics (IA) by using immersive multimedia technologies such as Virtual Reality (VR) as an analytical tool. IA can be used to analyse and visualise data when the standard desktop setup is insufficient. It allows the user to be immersed in the data and can provide a new perspective. However, understanding to what extent IA and the use of VR as an analytical tool impact the users’ QoE and level of understanding compared to the standard desktop for 3-Dimensional (3D) data visualisations remains unclear. The primary purpose of this study is to determine to what extent does a Virtual Reality Environment (VRE) affect the users’ QoE, and to what extent does a VRE affect the users’ understanding of the data? In this thesis, we design, develop, and compare an interactive prototype for 3D weather data analytics in two analysis environments: a Desktop Environment (DE) and a VRE. 12 participants (6 for each environment) were presented with 4 tasks to explore and analyse the displayed weather data. The participants interacted with the prototype to analyse the presented data in order to find the correct answers. The number of incorrect answers for each task provided objective measures regarding the users’ level of understanding. Subjective measures, including qualitative data, of the experience, were provided through a post-hoc survey. The results report that participants felt that the visual aspect of the VRE was more involving than the DE, and they were less conscious of being in the real world. This show a minimal increase in QoE for the VRE. For the users’ level of understanding, the subjective measures report that the DE led to a higher level of understanding. In contrast, the objective measures report less incorrect answers for the VRE. However, the results fail to report any significant difference confirming a difference in the users’ level of understanding between the analysis environments. We conclude that VRE provides the user with a comparable level of understanding to the DE when analysing data, but it does not necessarily increase the users’ level of understanding. However, we conclude that IA and VR increase the users’ immersion when analysing weather data, resulting in a minimal impact and improvement in QoE.
Closure of a large nasal dorsum defect using a bilateral crescent advancement flap from the malar region to the nose

The closure of large defects on the nasal dorsum is a challenge for dermatologic surgeons. The alternatives to repair the defect are a skin graft and some forms of skin flaps. One particular defect closure technique is the crescentic advancement flap, which uses crescent incisions at the nasolabial fold to accommodate the excess tissue. This flap is best used if the defect is in the lateral nose, alar, and nasolabial fold. We report a large defect of the nasal dorsum in a 65-year-old woman that was successfully reconstructed using bilateral cheek-to-nose crescentic advancement flap. The patient showed excellent cosmetic and outcome.
Retrieving Freeze/Thaw Surface State From CYGNSS Measurements

Freeze/Thaw (F/T) surface state retrieval is important to further understand hydrological patterns and climate change. This article investigates the use of Earth-reflected Global Positioning System (GPS) L-band signals as collected by the National Aeronautics and Space Administration NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission for F/T surface state retrieval over a target area in South America, covering the Andes Mountains and the Argentinian Pampas. In the study, CYGNSS responsiveness to changes in surface permittivity is leveraged to detect transitions of F/T surface state, at an improved spatio-temporal sampling as compared to traditional Remote Sensing missions. A Seasonal-Threshold Algorithm (STA) is developed and validated using surface temperature data as provided by the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5-Land numerical reanalysis model. Then, the monthly evolution of CYGNSS-derived F/T surface state maps is evaluated and an inter-comparison with the Soil Moisture Active Passive (SMAP) F/T data product is performed.
AAS 20-682 MULTIVARIABLE ROBUST CONTROL FOR THE POWERED DESCENT OF A MULTIBODY LUNAR LANDING SYSTEM

We present a novel approach to linear multivariable feedback control design, using Youla Parameterization, to enable robust trajectory tracking for the powereddescent and safe-landing of a lunar lander with a gimbaled main engine. The extended Kane’s equations for variable-mass systems are used to analytically model the nonlinear multibody dynamics of the lunar landing system. Propellant-optimal guidance state trajectories are generated by employing lossless convexification in a convex optimization framework. The procedure to design an internally stabilizing, computationally efficient, multiple-input multiple-output (MIMO) robust feedback control system is described. The closed-loop system is shown to possess guaranteed robustness to bounded uncertainties.
PGPM_A_328285 1403..1413

Department of Otorhinolaryngology, Head and Neck Surgery, University of Chinese Academy of Sciences, Shenzhen Hospital, Shenzhen, 518106, People’s Republic of China; Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Xinhua Hospital of Hainan Medical Colleage, Haikou, 570311, People’s Republic of China; Department of Otolaryngology, Head and Neck Surgery, Affiliated General Hospital of Shenzhen University, Shenzhen, 518106, People’s Republic of China; Wuhan Medical Science Research Institution, Wuhan, 430013, People’s Republic of China; Department of Throat, Head and Neck Surgery, Affiliated Otolaryngological Hospital, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People’s Republic of China; Department of Epidemiology, Public Health College of Xiamen University, Xiamen, 361005, People’s Republic of China; Clinical Laboratory, Affiliated Xinhua Hospital of Hainan Medical Colleage, Haikou, 570311, People’s Republic of China; Department of Endocrine Oncology, University of Chinese Academy of Sciences, Shenzhen Hospital, Shenzhen, 518106, People’s Republic of China; Department of Medical Imaging Centre, The First Affiliated Hospital, Jinan University, Guangzhou, 510630, People’s Republic of China
TALON: a universal unmanned ground vehicle platform, enabling the mission to be the focus

Foster-Miller's unmanned ground vehicle, TALON, was originally developed under DARPA's Tactical Mobile Robotics (TMR) program. TALON has evolved over the years and has proven to be a robust, mobile, universal platform. As a result of the advances made in the evolution of TALON, new and far-reaching opportunities have been realized for unmanned ground vehicles. In recent conflicts such as in Afghanistan and Iraq, unmanned systems have played an important role and have extended the reach and capabilities of the War fighter. Technological advances have transformed unmanned vehicles in to useful tools and in some cases are used in lieu of sending in a soldier. Unmanned ground vehicles have seen recent and persistent success, as shown in theater, in the explosive ordinance disposal (EOD) and improvised ordinance disposal (IED) missions. Foster-Miller's TALON has experienced over ten thousand EOD and IED missions in Iraq alone. The success of the unmanned system has resulted in the doctrine "Send the robot in first". Foster-Miller has taken the role of the unmanned vehicle in yet another direction. Foster-Miller has transformed the TALON from a "practical" to "tactical" system. Through the combined efforts of Foster-Miller and the US Army, TALON has been involved in a weaponization program. To date, Foster-Miller has outfitted the TALON with 11 systems. As one can see, the unmanned ground vehicle is much more than a mobility platform.