Managing cardiac arrest with refractory ventricular fibrillation in the emergency department: Conventional cardiopulmonary resuscitation versus extracorporeal cardiopulmonary resuscitation

AbstractAimRefractory ventricular fibrillation, resistant to conventional cardiopulmonary resuscitation (CPR), is a life threatening rhythm encountered in the emergency department. Although previous reports suggest the use of extracorporeal CPR can improve the clinical outcomes in patients with prolonged cardiac arrest, the effectiveness of this novel strategy for refractory ventricular fibrillation is not known. We aimed to compare the clinical outcomes of patients with refractory ventricular fibrillation managed with conventional CPR or extracorporeal CPR in our institution.MethodThis is a retrospective chart review study from an emergency department in a tertiary referral medical center. We identified 209 patients presenting with cardiac arrest due to ventricular fibrillation between September 2011 and September 2013. Of these, 60 patients were enrolled with ventricular fibrillation refractory to resuscitation for more than 10min. The clinical outcome of patients with ventricular fibrillation received either conventional CPR, including defibrillation, chest compression, and resuscitative medication (C-CPR, n=40) or CPR plus extracorporeal CPR (E-CPR, n=20) were compared.ResultsThe overall survival rate was 35%, and 18.3% of patients were discharged with good neurological function. The mean duration of CPR was longer in the E-CPR group than in the C-CPR group (69.90±49.6min vs 34.3±17.7min, p=0.0001). Patients receiving E-CPR had significantly higher rates of sustained return of spontaneous circulation (95.0% vs 47.5%, p=0.0009), and good neurological function at discharge (40.0% vs 7.5%, p=0.0067). The survival rate in the E-CPR group was higher (50% vs 27.5%, p=0.1512) at discharge and (50% vs 20%, p=0. 0998) at 1 year after discharge.ConclusionsThe management of refractory ventricular fibrillation in the emergency department remains challenging, as evidenced by an overall survival rate of 35% in this study. Patients with refractory ventricular fibrillation receiving E-CPR had a trend toward higher survival rates and significantly improved neurological outcomes than those receiving C-CPR

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Resorbable Beads Provide Extended Release of Antifungal Medication: In Vitro and In Vivo Analyses

Fungal osteomyelitis has been difficult to treat, with first-line treatments consisting of implant excision, radical debridement, and local release of high-dose antifungal agents. Locally impregnated antifungal beads are another popular treatment option. This study aimed to develop biodegradable antifungal-agent-loaded Poly(d,l-lactide-co-glycolide) (PLGA) beads and evaluate the in vitro/in vivo release patterns of amphotericin B and fluconazole from the beads. Beads of different sizes were formed using a compression-molding method, and their morphology was evaluated via scanning electron microscopy. Intrabead incorporation of antifungal agents was evaluated via Fourier-transform infrared spectroscopy, and in vitro fluconazole liberation curves of PLGA beads were inspected via high-performance liquid chromatography. When we implanted the drug-incorporated beads into the bone cavity of rabbits, we found that a high level of fluconazole (beyond the minimum therapeutic concentration [MTC]) was released for more than 49 d in vivo. Our results indicate that compression-molded PLGA/fluconazole beads have potential applications in treating bone infections

The Study on the Partial Least Squares Regression, Principal Component Regression, and Neural Networks to Analyze the Near-Infrared Spectrum Data

近年來利用近紅外光分析儀等光譜儀器，來測定物質中的各種化學成分含量，有越來越普遍的趨勢，而分析這類型的資料，需要藉助多變數檢量的技巧，本研究利用主成分迴歸（principal component regression, PCR）、淨最小平方迴歸（partial least squares regression, PLSR）以及類神經網路（neural networks , NN）三種檢量模式，以及利用Gnanadesikan（1997）的概念，改良主成分分析及淨最小平方法，並藉著所得到的加性主成分（additive principal components），以及線性二次淨最小平方法（linear-quadratic partial least squares），針對糙米粒粗蛋白質含量的近紅外線光譜資料，進行檢量模式的建立，結果Gnanadesikan（1997）的擴充 矩陣概念，確實能改善PCR模式，不過當與PLSR相比時，PLSR模式的表現仍較佳。 除此之外，本文也探討眾多非線性檢量技巧之一的類神經網路，並分別以原始351個波段、主成分分析之成分以及淨最小平方法的得點 ，當作輸入的變數，但是得到的NN模式表現並不理想，因此再利用PCR、PLSR模式的迴歸係數，取代NN輸入層與隱藏層之間初始的加權值，來改良NN模式，最後得到的結果，以PLSR模式中的迴歸係數為輸入層與隱藏層之間初始的加權值之NN表現最好，並且也能加快網路的學習速度，因此以迴歸係數來改良NN，的確能夠改善強NN建立模式的能力。Near-infrared reflectance spectroscopy (NIRS) is widespreadly used for quantitative applications of chemometrics in recent years. In this work, calibration methods including principal component regression (PCR), partial least squares regression (PLSR), additive principal component regression (APCR), linear-quadratic partial least squares regression (LQ-PLSR) and neural networks (NN) were used in conjunction with the near-infrared reflectance spectroscopy technique to determine the protein content of brown rice. Some calibration methods are insensitive to the effects of non-linearity. Such is the case with the model developed by Gnanadesikan (1977), which expands the X matrix with the squares of the variables. The projection of the additive principal component analysis (APCs) and linear-quadratic partial least squares (LQ-PLS) components on a surface in the expanded space corresponds to that of the original X matrices in a quadratic space. The LQ-PLS regression preserves a linear internal relationship between the scores of the X and Y matrices. Based on the results, the additive principal component regression performed better than principal component regression and the performance of partial least squares regression was the best. In addition, the result of applying the scores of the principal component analysis and partial least squares to the neural networks was compared. It was found that the neural networks approach was not effective. Hence the regression coefficient of PCR and PLSR were used as the initial weights between the input and hidden layer in neural networks model. It was shown that the neural networks model based on the regression coefficient of PLSR performed most effective, so it was the best choice of calibration modeling.中文摘要………………………………………………… I 英文摘要………………………………………………… II 第壹章 前言………………………………………………1 第貳章 前人研究…………………………………………4 一、線性檢量方法……………………………………… 4 （一）主成分迴歸……………………………………… 4 （二）淨最小平方法…………………………………… 6 二、非線性檢量方法…………………………………… 12 （一）類神經網路……………………………………… 13 1.類神經網路的原理…………………………………… 13 2.倒傳遞網路…………………………………………… 16 (1).倒傳遞網路演算法………………………………… 19 (2).倒傳遞網路的參數設定…………………………… 23 三、預測模式的驗證…………………………………… 25 （一）內部驗證法……………………………………… 27 （二）外部驗證法……………………………………… 28 第參章 實例研究..………………………………………29 一、試驗資料分析……………………………………… 29 二、實例研究一………………………………………… 32 （一）目的與方法……………………………………… 32 （二）結果與…………………………………………… 33 三、實例研究二………………………………………… 44 （一）目的與方法……………………………………… 44 （二）結果與討論……………………………………… 45 四、實例研究三………………………………………… 57 （一）目的與方法……………………………………… 57 （二）結果與討論……………………………………… 58 第肆章 綜合討論…………………………………………78 參考文獻………………………………………………… 81 附錄 SAS/IML POGRAM……………………………………84 A.1 PCR……………………………………………………84 A.2 APCR………………………………………………… 85 A.3 PLSR………………………………………………… 86 A.4 LQ-PLSR………………………………………………87 A.5 NN-1………………………………………………… 88 A.6 NN-2………………………………………………… 92 A.7 NN-3………………………………………………… 96 A.8 PC-NN……………………………………………… 100 A.9 PLS-NN………………………………………………10

Maize Pollen Dispersal Model: Using Nonlinear, Quasi-mechanistic Models and Neural Networks to Evaluate the Recommended Isolation Distance for Coexistence between GM and Non-GM Crops in Taiwan

在過去的二十年，許多基因改造 (genetically modified, GM) 作物已被成功開發上市，並進入商業化的田間生產。種植在開放田間的基因改造作物，可能與野生近緣種或傳統非基改品種的作物發生雜交，導致基因流和基因污染。由於花粉傳播是影響玉米基因流的主要因素，瞭解在田野中發生異交 (cross-pollination,CP) 的程度，可以幫助建立基因改造與非基因改造作物間，共存的隔離距離和田區種植的配置。本研究於 2009 至 2011 年間，在台灣本土進行實際玉米田間試驗，考慮七種不同的場景佈局，調查田間種植時玉米花粉流的程度。試驗田區設置位於台中霧豐的農業試驗所 (Taiwan Agricultural Research Institute, TARI) (24°1'N,120°41'E)，及位於嘉義朴子的台南區農業改良場 (Tainan District Agricultural Research and Extension Station , TNDAIS) 朴子分場 (23°47'N, 120°26'E)。利用此本土田間試驗的異交率資料，以及數種非線性和擬機械模式，建立花粉接受親與花粉貢獻親間之距離與異交程度的關係。此外，在模式建立同時，也採用重複抽樣的技術，進行模式的評估及驗證。在第三章中，NIG (normal inverse Gaussian)模式在所使用的二維與一維模式中，擁有最好的建模表現。根據保守的結果，田區模擬的異交汙染率在 95%信心水準，小於 0.9%的情況下 (kernel-number-based量測法)，其所需的距離分別為 NIG 模式的 25 公尺。此外，在 95%信心水準小於 3%與 5%的情況下 (kernel-number-based 量測方法)，其所需的距離為 NIG 模式的 15 與 10 公尺。在第四章中，我們使用三種具有不同的輸入的類神經網絡 (artificial neural network, ANN）模型建立花粉飄散模式：原始的輸入神經網絡 (neural network,NN)、主成分神經網絡 (principal components neural network, PCNN)、淨最小平方 神經網絡 （partial least squared neural network, PLSNN）模型。根據 ANN 模型田區 模 擬 結 果 ， 異 交 汙 染 率 在 95% 信 心 水 準 小 於 0.9% 的 情 況 下(kernel-number-based 量測法)，其所需的距離分別為 NN 模型的 40 公尺、PCNN模型與 PLSNN 模型的 35 公尺。在小於 3%的情況下 (kernel-number-based 量測 法)，其所需的距離分別為在 NN 模式與 PCNN 模式的 30 公尺、以及 PLSNN 模式的 35 公尺。在 95%信心水準小於 5%的情況下 (kernel-number-based 量測法)，其所需的距離分別為在 NN 模式與 PLSNN 模式的 30 公尺、以及 PCNN 模式的25 公尺。此外，我們亦發展了一套方法，可應用於解釋 ANN 模型的黑盒子，根據本研究的所建立的 3 種 ANN 模型，其模型訓練後的記憶特徵可藉由因素分析(factor analysis) 的結果進行說明與解釋。綜合各項結果，同時考慮模型效率和保守的預測隔離距離，本研究建議在大多數的情況下，在 95%信心水準下，30 公尺、35 公尺、40 公尺的隔離距離即分別足以維持非基因改造玉米田區收穫物，基改基因污染率小於 5%、3%與 0.9%的門檻。In the past two decades, numerous genetically modified (GM) crops have been successfully developed and released into commercial production. GM crops planted in an open field can outcross with feral or conventional crops, leading to gene flow and gene contamination. Because pollen dispersal is the major factor that affects gene flow in maize, the exploration of cross-pollination (CP) in the field may facilitate the establishment of the isolation distance and field arrangements to allow the coexistence of GM and non-GM crops. In this study, we conducted local field experiments to measure pollen flow in maize by using seven scenarios of diverse field-layout patterns during 2009 and 2011. The experimental fields were located at Wufeng (24°1'N, 120°41'E) managed by the Taiwan Agricultural Research Institute (TARI) and at Puzih (23°47'N, 120°26'E) belonging to the Potzu Branch Station of the Tainan District Agricultural Research and Extension Station (TNDAIS). The CP rates collected from local maize field trials were used to establish the relationship between the extent of CP in recipient and distances from the pollen source by using several nonlinear and quasi-mechanistic models. In addition, the resampling method was used for both evaluating and verifying the models. The normal inverse Gaussian (NIG) model had a better performance among the 2D and 1D model. According to the conservative result, the 95% upper confidence limits of simulated CP rates were below 0.9% (kernel-number-based approach) at the isolation distance of 25 m on the basis of the NIG model. Additionally, the simulated CP rates were <3% and <5% beyond 15 m and 10 m for NIG model at a 95% confidence level, respectively. In chapter 4, we used three artificial neural network (ANN) models featuring distinct inputs to establish the pollen dispersal models: the neural network (NN), principal components neural network (PCNN), and partial least squared neural network (PLSNN) models. Our analyses revealed that the 95% upper confidence limits of the CP rate (as per the kernel-number-based approach) in field simulations were <0.9% at the isolation distances of 40 m, 35 m, and 35 m predicted using the NN, PCNN, and PLSNN models, respectively. The simulated CP rates were <3% at the isolation distances of 30 m, 30 m, and 35 m predicted by NN, PCNN, and PLSNN, respectively. The simulated CP rates predicted using the NN, PCNN, and PLSNN models were <5% at a 95% confidence level at distances greater than 30 m, 25 m, and 30 m, respectively. Furthermore, we developed a technique to interpret the black box of ANN model, and the result showed that various characteristics of the data can be explained by using factor analysis in 3 ANN models. In conclusion, to consider both model performance and conservative isolation distance in most cases, a recommended isolation distances of 30 m, 35 m, and 40 m are adequate for maintaining the GM contamination rate of the total harvest of a field at <5%, <3%, and <0.9% at a 95% confidence level, respectively.CHAPTER 1 COEXISTENCE BETWEEN GM AND NON-GM MAIZE ......1 1.1 INTRODUCTION........................1 1.2 FIELD EXPERIMENTAL STUDIES .....................2 1.3 MODELING APPROACH ..............................4 1.4 MEASUREMENT UNITS FOR GENETICALLY MODIFIED ORGANISMS.............................................5 1.5 COEXISTENCE MEASURES AND MANAGEMENT...................5 1.6 MAIZE IN TAIWAN .........................8 CHAPTER 2 FIELD EXPERIMENT ON CROSS-POLLINATION OF MAIZE IN TAIWAN ......................... 17 2.1 INTRODUCTION..................17 2.2 MATERIALS AND METHODS ...........................17 2.2.1 Experimental fields .............................17 2.2.2 Meteorological data.............................18 2.2.3 Sampling method........................... 19 2.2.3.1 Scenario W1................................19 2.2.3.2 Scenario W2.................................19 2.2.3.3 Scenarios W3 and W4 .............................20 2.2.3.4 Scenarios P1, P2, and P3.........................20 2.2.4 CP rate and distance definition..............20 2.3 RESULTS AND DISCUSSION ..............................21 2.3.1 Experimental scenarios.....................21 2.3.2 Extent of observed CP rate.........................26 2.4 CONCLUSION ...............................28 CHAPTER 3 USING EMPIRICAL AND QUASI-MECHANISTIC MODELS TO ASSESS THE CROSS-POLLINATION OF MAIZE BY POLLEN-MEDIATED GENE FLOW AT A FIELD SCALE...................................60 3.1 INTRODUCTION.........................60 3.2 MATERIALS AND METHODS ...........................60 3.2.1 Fit models ..................................60 3.2.2 Model evaluation..................................65 3.2.3 Evaluation criteria .........................66 3.3 RESULTS AND DISCUSSION .............................68 3.3.1 Meteorological data and experimental sites .....................................68 3.3.2 Parameter estimation ...........................69 3.3.3 Model selection...............................72 3.3.4 Field simulation of the NIG models ................74 3.4 CONCLUSION ....................................76 CHAPTER 4 DETERMINING AND INTERPRETING THE EXTENT OF MAIZE CROSS-POLLINATION AT A FIELD SCALE BY USING ARTIFICIAL NEURAL NETWORKS ................................... 97 4.1 INTRODUCTION.....................97 4.2 MATERIALS AND METHODS ...........................98 4.2.1 Artificial neural network models and input variables.........................................98 4.2.2 Resampling simulation..........................101 4.2.2.1 Model evaluation ........................... 101 4.2.2.2 Model interpretation ...........................101 4.2.3 Interpretation of input variables in artificial neural network models ............................ 101 4.2.3.1 Sensitivity analysis of input variables..........102 4.2.3.2 The black box of weight and bias ...............102 4.2.4 Simulation analysis ........................... 104 4.2.4.1 Single recipient point ..........................104 4.2.4.2 Total harvest of recipient field ...............105 4.3 RESULTS AND DISCUSSION ..............................105 4.3.1 Model selection....................106 4.3.2 Factor analysis and interpretation of artificial neural network models......................... 108 4.3.2.1 Neural network model ...........................110 4.3.2.2 Principal components neural network model .......112 4.3.2.3 Partial least squares neural network model ......113 4.3.3 Field simulation ..............................116 4.3.3.1 Single recipient point.................... 116 4.3.3.2 Whole recipient field ...........................119 4.4 CONCLUSION .......................................122 CHAPTER 5 CONCLUDING REMARKS AND FUTURE WORKS ....................161 5.1 CONCLUDING REMARKS.......................... 161 5.1.1 Filed experiment .................................161 5.1.2 Nonlinear and quasi-mechanistic models ............162 5.1.3 Artificial neural network models ..............162 5.2 COEXISTENCE BETWEEN GM AND NON-GM MAIZE IN TAIWAN............................................. 164 5.3 FUTURE WORKS.....................166 REFERENCES ................................168 APPENDIX A.............................180 APPENDIX B........................... 189 APPENDIX C .............................21

Impact of flowering temperature on litchi yield under climate change: A case study in Taiwan

Litchi is a subtropical fruit tree that undergoes flower bud differentiation under low-temperature conditions. However, climate change has affected litchi production in Taiwan, causing litchi farmers to experience economic losses. This study explored the influence of flowering temperature on litchi yield under climate change in Taiwan by analyzing litchi production data from 2001 to 2020 and observation data from meteorological stations in litchi-producing areas. Historical observed data were used to construct several regression models relating temperature to yield, with the performance of the models used to determine critical temperature thresholds for litchi flower bud differentiation. Analytical climate data (CMIP5) were used to project yield changes in Taiwan’s litchi-producing regions under anticipated low-temperature conditions for the mid- (2036–2065) and late- (2071–2100) 21st century. The variable that exhibited the highest correlation with yield changes was the number of days with an average flowering temperature below 16 °C. The production yield, in terms of yield variation per hectare, is expected to decrease by 12 % to 35 % by the end of the 21st century (2071–2100). Given the projected decline in the number of cooler days due to climate change, existing litchi cultivars may become unsuitable for cultivation in production areas in southern Taiwan. Practical Implications: Some fruit trees require a period of low temperature before their flowering stage. Climate change is expected to cause warming of winter temperatures in Taiwan, which is likely to lead to reduced litchi flowering. The current study assessed the potential effects of climate change on litchi flowering in the future.Historical observed data were used to establish models, and critical temperature thresholds for litchi flowering were determined on the basis of model performance. Days with average temperatures below 16 °C exhibited the highest correlation with litchi yield among the tested thresholds. According to our results, farmers can use this 16 °C threshold to evaluate the potential effects of future climate change at their current farm locations and to identify other areas with similar or more favorable conditions for litchi cultivation. For agricultural researchers, this temperature threshold could provide a target for new litchi variety breeding and a reference basis for research on optimal cultivation methods.Notably, because climate change projection data have a high degree of uncertainty, the results of this study may differ from those of studies using different databases. In this study, we used an ensemble of CMIP5 projections incorporating data from models from various research centers around the world, which can provide more robust results based on an ensemble mean than those obtainable from a single model or a few models. In addition, rainfall is a crucial factor during the flowering growth stage. Future studies should consider the effects of rainfall and temperature on yield and should consider using a model with yield being considered a function of both of these variables to improve model accuracy.To conclude, the present study provides researchers, policymakers, and other stakeholders with insights into the primary effects of climate change on litchi production. It also lays the groundwork for future climate adaptation strategies in Taiwan’s litchi industry

New fixation approach for transverse metacarpal neck fracture: a biomechanical study

Abstract Background Fifth metacarpal neck fracture, also known as boxer’s fracture, is the most common metacarpal fracture. Percutaneous Kirschner-wire (K-wire) pinning has been shown to produce favorable clinical results. However, the fixation power of K-wires is a major concern. Plate fixation is also a surgical option, but it has the disadvantages of tendon adhesion, requirement of secondary surgery for removal of the implant, and postoperative joint stiffness. A fixation method that causes little soft tissue damage and provides high biomechanical stability is required for patients with fifth metacarpal neck fracture for whom surgical intervention is indicated. The present study proposed fixation using K-wires and a cerclage wire to treat fifth metacarpal neck fracture. The fixation power of this new method was compared with that of K-wires alone and plates. Methods We used a saw blade to create transverse metacarpal neck fractures in 16 artificial metacarpal bone specimens, which were then treated with four types of fixation as follows: (1) locking plate with five locking bicortical screws (LP group), (2) regular plate with five bicortical screws (RP group), (3) two K-wires (K group), and (4) two K-wires and a figure-of-eight cerclage wire (KW group). The specimens were tested by using cantilever bending testing on a material testing system. The stiffness of the four fixation types was determined by observing force–displacement curves. Finally, the Kruskal–Wallis test was adopted to process the data, and the Mann–Whitney exact test was performed to conduct paired comparison between the fixation types. Results The fixation strength levels of the four fixation approaches for treating fifth metacarpal neck fracture were ranked in a descending order of LP group (24.6 ± 5.1 N/mm, median ± interquartile range) > RP group (22.2 ± 5.8 N/mm) ≅ KW group (20.1 ± 3.2 N/mm) > K group (16.9 ± 3.0 N/mm). Conclusion The fixation strength of two K-wires was significantly higher when reinforcement was provided using a figure-of-eight cerclage wire. The strength of the proposed approach is similar to that of a regular plate with five bicortical screws but weaker than that of a locking plate with the same amount of bicortical screws. Cerclage wire-integrated K-wires can be an alternative method that avoids the excessive soft tissue dissection required for plating in open reduction internal fixation for fifth metacarpal neck fracture