Using an enzyme-linked immunosorbent assay (ELISA) and limited dilution methods to screen and clone antigen-specific hybridoma cells is extremely time-consuming and labor-intensive. This work ...features a simple and rapid cell surface fluorescence immunosorbent assay (CSFIA), designed for the detection and isolation of antigen-specific hybridoma clones. In this assay, antigens are first anchored to the hybridoma cell surface through a dual-functioning molecular Oleyl-PEG4000-NHS. Specific antibodies secreted from hybridoma cells are then captured by the antigens on the cell surface. Positive hybridoma cells are stained using a fluorescently labeled anti-mouse IgG-Fc antibody. After the addition of a methylcellulose semisolid medium, positive clones are easily picked using a pipet. These positive cell clones can be used to produce monoclonal antibodies after direct expansion. Using this method, positive hybridoma clones against both malachite green and porcine epidemic diarrhea virus are selected with high efficiency. Compared to the ELISA-based method, the CSFIA-based method achieved the capability of isolating >2-fold more hybridoma clones in <25% of the corresponding processing time. In brief, the CSFIA-based method is highly efficient and inexpensive with a simple and direct operation, which is an excellent candidate method for antigen-specific positive clone isolation in a monoclonal antibody preparation.
In this paper, we introduce an uplink transmission model with mobile users, where each user maximizes his/her utility to achieve the best performance. We propose a power allocation scheme for each ...mobile user when all channel information is available. Moreover, we illustrate that one user would expect to predict the aggregate interference to maximize the utility when the channel information is incomplete. It is shown that this approach forms a game with incomplete information. We demonstrate the prediction rules that can help the mobile users dynamically adjust predictions and apply the Kalman filter to tackle measurement noises. We also illustrate the theoretical bound for the difference between the utility with prediction and that with complete information. Moreover, applying dynamic programming from control theory, we give a dynamic power allocation scheme based on the predictions. Simulation results indicate that our power allocation scheme with complete information and dynamic power allocation with predictions give better performance compared with the scheme with even power allocation. In addition, results under our dynamic power allocation scheme are close to those under the power allocation with complete information.
Non-small cell lung cancer (NSCLC) has a poor prognosis despite conventional treatments of surgery, radiotherapy, and chemotherapy. Small-molecule tyrosine kinase inhibitors acting on epidermal ...growth factor receptor (EGFR) have shown high efficacy and low toxicity for NSCLC. In particular, combining erlotinib with the VEGF antibody bevacizumab has therapeutic value in NSCLC, but the drugs' separate effects as monotherapy and any adverse outcomes of combination therapy remain unclear.
To determine the efficacy and safety of erlotinib and bevacizumab for NSCLC, we conducted a meta-analysis and systematic review of randomized controlled trials.
PubMed, Embase, Web of Science, and Cochrane databases were searched using keywords and manual review.
We reviewed randomized controlled trials on the use of erlotinib combined with bevacizumab in adult patients with NSCLC, including data on outcome measures of overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events.
After quality assessment, datasets were evaluated for heterogeneity. In the event of significant heterogeneity, a random-effects model was used to assess the overall outcome measures as a result of treatments. Subgroup analysis was conducted to evaluate the source of heterogeneity on PFS.
Compared with erlotinib or bevacizumab alone, the combined treatment did not significantly prolong OS (95% confidence interval CI = 0.84-1.11; P = .62) or increase the ORR (95% CI = 0.91-1.20; P = .52), but significantly improved PFS (95% CI = 0.58-0.73; P < .001). This improvement was especially notable in patients with the following characteristics: Eastern Cooperative Oncology Group Performance Status score of 0 or 1, female, no smoking history, adenocarcinoma, and EGFR Exon19 deletion or Exon21 Leu858Arg mutation. Combination therapy significantly increased incidence of grade 1-2 hypertension (20.3% vs 6.3%, 95% CI 1.73-5.88; P < .01) and severe diarrhea (10% vs 3.2%, 95% CI 1.36-6.60; P = .01).
The low number of available randomized controlled trials could influence interpretation.
Compared with erlotinib or bevacizumab monotherapy, their combination effectively prolongs PFS but increases incidence of adverse events in NSCLC patients.
The cryoprotective effect of collagen hydrolysates from squid skin (CH-SS) on shrimp muscle was investigated during the freeze-thaw cycles, and the antifreeze peptides (AFPs) in CH-SS were separated ...and identified. The results showed that CH-SS generated by acid protease had the highest antifreeze activity. It was further found that CH-SS showed an inhibitory effect on the denaturation and structural changes of myofibrillar protein during the freeze-thaw cycles, and partially retained the ability to bind water. SEM analysis indicated that CH-SS could effectively reduce the mechanical injury caused by ice crystals to shrimp muscle. The fraction with the highest antifreeze activity from CH-SS was obtained by Sephadex G-25 chromatography, and its main amino acid sequence was identified as DVRGAEGSAGL by the UPLC-MS analysis. This study provides a theoretical basis for the intensive processing of squid skin and the development of new antifreeze agents.
•CH-SS generated by acid protease had a high antifreeze activity.•CH-SS had a better cryoprotective effect on shrimp muscle than that of TSPP.•Antifreeze activity of F2 was significantly higher than that of CH-SS.•Amino acid sequence of F2 was identified as DVRGAEGSAGL.
Relayed transmission is an interesting topic in cooperative diversity. Some researchers propose power allocation schemes for relayed transmission by only considering transmission through a fixed ...relay node. In our paper, we analyze a relayed transmission through a mobile relay node. We build up a transmission system model and determine the long term distribution characteristics of the movement of the mobile node. We also calculate the time-varying data rate for transmission through the mobile relay node and demonstrate the improvements due to mobility. Moreover, we illustrate the optimal power allocation for relayed transmission through the mobile relay node. Our results show that under some practical data rate requirements, we have 3 dB gain compared with transmission under even power allocation.
Spectrum sharing and power allocation are interesting issues in cognitive radio networks. In our paper, we introduce a pricing scheme to demonstrate the spectrum sharing scenario between primary ...users and secondary users. We demonstrate the joint optimal allocation scheme of both spectrum and power for secondary users buying spectrum from multiple primary users. The joint optimal allocation scheme maximizes secondary users' utilities while primary users' utilities meet certain requirements. Moreover, we illustrate a distributed resource allocation scheme to jointly optimize usage of power and spectrum when each secondary user does not know others' buying strategies. Our simulation results show that under some utility requirements, the joint optimized resource allocation scheme has 3dB gain over that under even resource allocation.
•We report a novel dual-functional lateral-flow sensor for monitoring small molecule analytes (clenbuterol, CL) based on fluorescence-quenching effect of gold nanoparticles.•This sensor performed as ...“turn-on” LFSs under excitation light and turn-off LFSs under natural light.•Under natural light, this lateral-flow sensor performance was as a common AuNPs lateral-flow sensor.
Recently, more sensitive methods for analyzing small molecule analytes were required. Lateral-flow sensors (LFSs) have been used as reliable, rapid and cost-effective on-site screening technique to detect small molecule analytes. However, available LFSs for small molecule analytes were competitive and performed in “turn-off” mode, allowing low-sensitivity detection by the naked eye. In this study, we used dual-functional LFSs for monitoring small molecule analytes (clenbuterol CL), which based a fluorescence-quenching effect of gold nanoparticles (AuNPs). The most important feature of this sensor is its dual function as “turn-on” LFSs under excitation light and turn-off LFSs under natural light. The sensitivity of “turn-on” LFSs reached 0.04ng/m under excitation light; however, under natural light they performed as common “turn-off” AuNPs LFSs with 5.0ng/mL sensitivity. This novel LFS can be used in either traditional or highly sensitive tools to detect a variety of small analytes.
Building heating, ventilation, and air conditioning (HVAC) systems consume large amounts of energy, and precise energy prediction is necessary for developing various energy-efficiency strategies. ...Energy prediction using data-driven models has received increasing attention in recent years. Typically, two types of driven models are used for building energy prediction: sequential and parallel predictive models. The latter uses the historical energy of the target building as training data to predict future energy consumption. However, for newly built buildings or buildings without historical data records, the energy can be estimated using the parallel model, which employs the energy data of similar buildings as training data. The second predictive model is seldom studied because the model input feature is difficult to identify and collect. Herein, we propose a novel key-variable-based parallel HVAC energy predictive model. This model has informative input features (including meteorological data, occupancy activity, and key variables representing building and system characteristics) and a simple architecture. A general key-variable screening toolkit which was more versatile and flexible than present parametric analysis tools was developed to facilitate the selection of key variables for the parallel HVAC energy predictive model. A case study is conducted to screen the key variables of hotel buildings in eastern China, based on which a parallel chiller energy predictive model is trained and tested. The average cross-test error measured in terms of the coefficient of variation of the root mean square error (CV-RMSE) and normalized mean bias error (NMBE) of the parallel chiller energy predictive model is approximately 16% and 8.3%, which is acceptable for energy prediction without using historical energy data of the target building.
Resource allocation for secondary users is an important issue in cognitive radio networks. In our article, we introduce a resource allocation scheme for secondary users to share spectrum in a ...cognitive radio network. Secondary users can exploit the spectrum owned by primary links when the interference level does not exceed certain requirements. Uncertainties of channel gains pose a great impact on the allocation scheme. Since there are uncertainties about the channel states, we apply chance constraints to represent the interference level requirements with uncertainties. Secondary users can exceed the interference level with a predefined small probability level. Since chance constraints are generally difficult to solve and full information about the uncertain variables is not available due to the fading effects of wireless channels, we reformulate the constraints into stochastic expectation constraints. With sample average approximation method, we propose stochastic distributed learning algorithms to help secondary users satisfy the constraints with the feedback information from primary links when maximizing the utilities.
Two main issues of resource allocation in wireless networks with incomplete information are addressed in this thesis. Transmission node is not fixed in the wireless system and uncertainties of the ...channel states would also affect the choices of resource allocation, since full information cannot be provided or may not be exact under these scenarios. For incomplete information in wireless networks, mobility of the users and uncertainties of channel gains are two main issues that would be considered in this thesis. The first part of this thesis is concerning the resource allocation problems with mobile transmission nodes. We consider mobile users in an up-link system. We analyze the mobile system where each user would try to maximize his or her own utility to achieve the best performance. Besides, we propose a power allocation scheme for the mobile users when all channel information is available. We show that our model can form a game. Moreover, we illustrate that each user would expect to predict the aggregate interference to maximize the utility when channel information is incomplete. It can be shown that this forms a game with incomplete information. We demonstrate the prediction rules which help predict the aggregate interference dynamically. We apply the Kalman filter to tackle measurement noises. We also illustrate the bound on the difference between the utility with prediction and that with complete information. Moreover, applying dynamic programming, we give a dynamic power allocation scheme based on the predictions. The second part discusses the issue of uncertain programming in resource allocation when information about channel gains is incomplete. We mainly consider the model of cognitive radio networks. We introduce a resource allocation scheme for secondary users with spectrum sharing in a cognitive radio network. Secondary users can exploit the spectrum owned by primary links when their interference level does not exceed certain requirements. We first model the interference constraints as robust constraints such that secondary users would satisfy the interference constraints even under the worst cases, which would help him or her to avoid the unfeasible solutions. We then extend our consideration of the interference constraints as chance constraints to represent uncertainties. Since chance constraints are generally difficult to solve and full information about the uncertain variables is not available due to the fading effects of wireless channels, we reformulate the constraints into stochastic expectation constraints. With sample average approximation method, we propose stochastic distributed learning algorithms to help secondary users satisfy the constraints with the feedback information from primary links when maximizing the utilities. Moreover, we introduce a resource allocation scheme for secondary users to share spectrum and optimize usage of power with pricing. Secondary users need to buy spectrum from primary users. In the process, secondary users also enhance the utilization of the unused bandwidth by primary users. We first demonstrate the resource allocation scheme when full information about channel gains is available. When there are uncertainties of channel gains, secondary users would like to maximize the expected value of the utilities to pursue the best benefits on average with relatively stable buying strategies. It can be shown that it is a stochastic optimization problem with saddle points. We demonstrate a Distributed Stochastic Algorithm to help secondary users update their resource allocation strategies. For some practical scenarios, to reduce computation complexity and make implementation easy, we illustrate an Iterate Average from Distributed Stochastic Algorithm for secondary users.
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