• 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • The period of status survived before transition and its


    The Tideglusib of status survived before transition and its censorship were required to be identified. Referring to the extracted data set in Table 1, the system started to operate at 10:30 with the status 1111 and then changed to the status 1112 at 12:00. The period for the transitional status 1111→1112 was 90 min (i.e., the time the status 1111 lasted for) and it belonged to the censored data because of no previous change from 1111. Regarding the second transitional status 1112→1111 at 13:00, the period was 60 min (i.e., the time the status 1112 lasted for) and the data set was classified as non-censored. The period and censorship classification between successive transitional statuses were ascertained similarly. The period from one status to another was from 15 min to 735 min based on the whole set of data. The models for different transitional statuses were developed by using the survival package (version 2.42–6) (Therneau, 2018) under the statistical platform R (version 3.4.3) (R Project for Statistical Computing (V3.4.3) 2017). The selection of important independent variables was carried out by using the Akaike Information Criterion (AIC). When building the Cox regression models in the statistical platform R, there was a model-related option to control the handling of ties in the transition period, other than typical convergence criteria. In Cox regression, it was often assumed that all the transition periods were different. However, ties were observed in the transition periods in the real datasets. The default feature—Efron procedure (Efron, 1977, Borucka, 2014)—in the statistical platform R was used to correct the ties.
    Results and discussion
    Conclusions A total of 3 ambient variables and 6 flow- and temperature-related operating variables were considered to develop Cox regression models for the four transitional statuses and their degree of significance was examined by their hazard ratios. The temperature of water entering the condensers was the most significant variable in the statuses 1111→1110 and 1112→1111 in which the transition involved turning off one cooling tower. For the status 1111→1112, the transition of switching one extra cooling tower depended strongly on the dry bulb temperature. The temperature of water leaving the condensers was the most significant variable affecting the transition period of the status 1110→1111. Survival curves of the most significant variables suggest how their throttling ranges served to identify transitional statuses with COP fluctuation. The setpoint of the temperature of water entering the condensers should have a throttling range of at least 1.56°C from the set point. The pair-up operation should be maintained at a dry bulb temperature of above 23.42°C or a temperature of water leaving the condensers of above 31.43°C. The future work is to explore the use of multi-task learning formulation for survival analysis (Li et al., 2016) to enhance accuracy in predicting the survival time of transitional statuses.
    Globally, cervical cancer remains the most common gynecologic malignancy. In 2012, more than one-half a million women were estimated to have been diagnosed with this disease. Because nearly one-third of the patients succumb to their disease within the first 5 years from diagnosis, improvement in survival remains the ultimate treatment goal in the clinical setting. To this end, accurate prediction of survival is critical in precision medicine. Individual survival predictions are also important because they may provide clinicians a way to gauge treatment outcomes.
    Introduction Ever since the discovery of prostaglandin-endoperoxide synthase (prostaglandin G/H synthase and cyclooxygenase) also known as cyclooxygenase or COX PTGS (COX), has ushered in new era into understanding inflammation and its related biological pathways. It is encoded by PTGS gene and are basically two types of isozymes COX-1 and COX-2 in human beings. “Cyclooxygenase-2 (COX-2) is a key enzyme in the conversion of arachidonic acid to prostanoids which in turn lead to carcinogenesis as well as inflammation was discovered by the Daniel Simmons [1] in 1991”. Pharmaceutical inhibition of these enzymes can lead to treatment of symptoms associated with inflammation and pain. Although COX-2 are primarily responsible for inflammation and pain, studies have shown that COX-2 appear to be related to cancers mainly breast cancer [2] and schizophrenia [3]. Proverbially speaking, study of these enzymes has been crucial as they happen to “have fingers in many pies”. Clacitriol (active form of vitamin-D) has been known as the natural inhibitor of COX-2, along with certain flavonoids. Non-steroidal anti-inflammatory drugs (NSAID) are anti-inflammatory and analgesics drugsthat act as selective COX-2 inhibitors by specifically targeting COX-2. Ibuprofen, Etoricoxib, Celecoxib and Rofecoxib are some of the NSAID drugs that are commercially available (Fig. 1). Although several COX-2 inhibiting drugs have been approved for commercial use, clinical trials revealed that COX-2 inhibitors caused a significant increase in heart attacks and strokes, with some drugs having worse risks than others.