BACKGROUND Insulin therapy plays a crucial role in managing diabetes.Regulatory guidelines mandate assessing the pharmacokinetics(PK)and pharmacodynamics(PD)of new insulin formulations with euglycemic clamp techniques...BACKGROUND Insulin therapy plays a crucial role in managing diabetes.Regulatory guidelines mandate assessing the pharmacokinetics(PK)and pharmacodynamics(PD)of new insulin formulations with euglycemic clamp techniques before entry into the market.Typically,blood glucose(BG)levels are maintained at 5%below baseline to suppress endogenous insulin secretion in healthy volunteers.However,in scenarios where BG baseline is relatively low,maintaining it at 5%below baseline can increase hypoglycemic risk.Consequently,we adjusted to maintain it at 2.5%below a baseline of<4.00 mmol/L.It remains uncertain whether this adjustment impacts endogenous insulin inhibition or the PD of study insulin.AIM To evaluate and compare the PD and C-peptide status using two different target BG setting methods.METHODS Data came from euglycemic clamp trials assessing the PK/PD of insulin aspart(IAsp)in healthy participants.Target BG was set at 2.5%below baseline for those with a basal BG of<4.00 mmol/L(group A),and at 5%below baseline for others(group B).The area under the curve(AUC)of IAsp(AUC_(IAsp,0-8 h))and GIR from 0 to 8 hours(AUCGIR,0-8 h)was used to characterize the PK and PD of IAsp,respectively.The C-peptide reduction and PK/PD of IAsp were compared between the two groups.RESULTS Out of 135 subjects,15 were assigned to group A and 120 to group B;however,group B exhibited higher basal Cpeptide(1.59±0.36 vs 1.32±0.42 ng/mL,P=0.006).Following propensity score matching to adjust for basal Cpeptide differences,71 subjects(15 in group A and 56 in group B)were analyzed.No significant differences were observed in demographics,IAsp dosage,or clamp quality.Group B showed significantly higher baseline(4.35±0.21 vs 3.91±0.09 mmol/L,P<0.001),target(4.13±0.20 vs 3.81±0.08 mmol/L,P<0.001),and clamped(4.10±0.17 vs 3.80±0.06 mmol/L,P<0.001)BG levels.Both groups exhibited comparable C-peptide suppression(32.5%±10.0%vs 35.6%±12.1%,P=0.370)and similar IAsp activity(AUCGIR,0-8 h:1433±400 vs 1440±397 mg/kg,P=0.952)under nearly equivalent IAsp exposure(AUC_(IAsp,0-8 h):566±51 vs 571±85 ng/mL×h,P=0.840).CONCLUSION Maintaining BG at 2.5%below a baseline of<4.00 mmol/L did not compromise the endogenous insulin suppression nor alter the observed pharmacodynamic effects of the study insulin.展开更多
Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In ...Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.展开更多
It is understood that the forward-backward probability hypothesis density （PHD） smoothing algorithms proposed recently can significantly improve state estimation of targets. However, our analyses in this paper show ...It is understood that the forward-backward probability hypothesis density （PHD） smoothing algorithms proposed recently can significantly improve state estimation of targets. However, our analyses in this paper show that they cannot give a good cardinality （i.e., the number of targets） estimate. This is because backward smoothing ignores the effect of temporary track drop- ping caused by forward filtering and/or anomalous smoothing resulted from deaths of targets. To cope with such a problem, a novel PHD smoothing algorithm, called the variable-lag PHD smoother, in which a detection process used to identify whether the filtered cardinality varies within the smooth lag is added before backward smoothing, is developed here. The analytical results show that the proposed smoother can almost eliminate the influences of temporary track dropping and anomalous smoothing, while both the cardinality and the state estimations can significantly be improved. Simulation results on two multi-target tracking scenarios verify the effectiveness of the proposed smoother.展开更多
The Chinese Academy of Sci-ences (CAS)has designatedguaranteeing food safety forthe future population peak of 1.6 bil-lion as its first and foremost targettaking into account the stress thatwill place on China’s exis...The Chinese Academy of Sci-ences (CAS)has designatedguaranteeing food safety forthe future population peak of 1.6 bil-lion as its first and foremost targettaking into account the stress thatwill place on China’s existingresources, said a CAS official at theacademy’s annual work展开更多
At the opening of full annual session of the lawmaking National People’s Congress on March 5, Premier Wen Jiabao delivered the government work report. The report consists of three parts: Review of National Economic a...At the opening of full annual session of the lawmaking National People’s Congress on March 5, Premier Wen Jiabao delivered the government work report. The report consists of three parts: Review of National Economic and Social Development During the 11th Five-Year Plan Period, Major Objectives and Tasks for the 12th Five-Year Plan Period and Work for 2011. When outlining this year’s work, Wen said the government would focus on improving people’s livelihoods. Excerpts follow:展开更多
This paper considers a first passage model for discounted semi-Markov decision processes with denumerable states and nonnegative costs. The criterion to be optimized is the expected discounted cost incurred during a f...This paper considers a first passage model for discounted semi-Markov decision processes with denumerable states and nonnegative costs. The criterion to be optimized is the expected discounted cost incurred during a first passage time to a given target set. We first construct a semi-Markov decision process under a given semi-Markov decision kernel and a policy. Then, we prove that the value function satisfies the optimality equation and there exists an optimal （or ε-optimal） stationary policy under suitable conditions by using a minimum nonnegative solution approach. Further we give some properties of optimal policies. In addition, a value iteration algorithm for computing the value function and optimal policies is developed and an example is given. Finally, it is showed that our model is an extension of the first passage models for both discrete-time and continuous-time Markov decision processes.展开更多
基金This retrospective analysis incorporated data from two clinical trials(CTR20220854 and CTR20222843)sponsored by Chongqing Chenan Biopharmaceutical Co.,Ltd.and Jiangsu Hengrui Pharmaceuticals Co.,Ltd.However,these sponsors did not partake in the study design,data interpretation,or manuscript preparation.
文摘BACKGROUND Insulin therapy plays a crucial role in managing diabetes.Regulatory guidelines mandate assessing the pharmacokinetics(PK)and pharmacodynamics(PD)of new insulin formulations with euglycemic clamp techniques before entry into the market.Typically,blood glucose(BG)levels are maintained at 5%below baseline to suppress endogenous insulin secretion in healthy volunteers.However,in scenarios where BG baseline is relatively low,maintaining it at 5%below baseline can increase hypoglycemic risk.Consequently,we adjusted to maintain it at 2.5%below a baseline of<4.00 mmol/L.It remains uncertain whether this adjustment impacts endogenous insulin inhibition or the PD of study insulin.AIM To evaluate and compare the PD and C-peptide status using two different target BG setting methods.METHODS Data came from euglycemic clamp trials assessing the PK/PD of insulin aspart(IAsp)in healthy participants.Target BG was set at 2.5%below baseline for those with a basal BG of<4.00 mmol/L(group A),and at 5%below baseline for others(group B).The area under the curve(AUC)of IAsp(AUC_(IAsp,0-8 h))and GIR from 0 to 8 hours(AUCGIR,0-8 h)was used to characterize the PK and PD of IAsp,respectively.The C-peptide reduction and PK/PD of IAsp were compared between the two groups.RESULTS Out of 135 subjects,15 were assigned to group A and 120 to group B;however,group B exhibited higher basal Cpeptide(1.59±0.36 vs 1.32±0.42 ng/mL,P=0.006).Following propensity score matching to adjust for basal Cpeptide differences,71 subjects(15 in group A and 56 in group B)were analyzed.No significant differences were observed in demographics,IAsp dosage,or clamp quality.Group B showed significantly higher baseline(4.35±0.21 vs 3.91±0.09 mmol/L,P<0.001),target(4.13±0.20 vs 3.81±0.08 mmol/L,P<0.001),and clamped(4.10±0.17 vs 3.80±0.06 mmol/L,P<0.001)BG levels.Both groups exhibited comparable C-peptide suppression(32.5%±10.0%vs 35.6%±12.1%,P=0.370)and similar IAsp activity(AUCGIR,0-8 h:1433±400 vs 1440±397 mg/kg,P=0.952)under nearly equivalent IAsp exposure(AUC_(IAsp,0-8 h):566±51 vs 571±85 ng/mL×h,P=0.840).CONCLUSION Maintaining BG at 2.5%below a baseline of<4.00 mmol/L did not compromise the endogenous insulin suppression nor alter the observed pharmacodynamic effects of the study insulin.
基金supported by the National Natural Science Foundation of China(No.62276204)Open Foundation of Science and Technology on Electronic Information Control Laboratory,Natural Science Basic Research Program of Shanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470).
文摘Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.
基金co-supported by the National Natural Science Foundation of China(No.61171127)NSF of China(No.60972024)NSTMP of China(No.2011ZX03003-001-02 and No.2012ZX03001007-003)
文摘It is understood that the forward-backward probability hypothesis density （PHD） smoothing algorithms proposed recently can significantly improve state estimation of targets. However, our analyses in this paper show that they cannot give a good cardinality （i.e., the number of targets） estimate. This is because backward smoothing ignores the effect of temporary track drop- ping caused by forward filtering and/or anomalous smoothing resulted from deaths of targets. To cope with such a problem, a novel PHD smoothing algorithm, called the variable-lag PHD smoother, in which a detection process used to identify whether the filtered cardinality varies within the smooth lag is added before backward smoothing, is developed here. The analytical results show that the proposed smoother can almost eliminate the influences of temporary track dropping and anomalous smoothing, while both the cardinality and the state estimations can significantly be improved. Simulation results on two multi-target tracking scenarios verify the effectiveness of the proposed smoother.
文摘The Chinese Academy of Sci-ences (CAS)has designatedguaranteeing food safety forthe future population peak of 1.6 bil-lion as its first and foremost targettaking into account the stress thatwill place on China’s existingresources, said a CAS official at theacademy’s annual work
文摘At the opening of full annual session of the lawmaking National People’s Congress on March 5, Premier Wen Jiabao delivered the government work report. The report consists of three parts: Review of National Economic and Social Development During the 11th Five-Year Plan Period, Major Objectives and Tasks for the 12th Five-Year Plan Period and Work for 2011. When outlining this year’s work, Wen said the government would focus on improving people’s livelihoods. Excerpts follow:
基金Supported by the Natural Science Foundation of China(No.60874004,60736028)Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2010)
文摘This paper considers a first passage model for discounted semi-Markov decision processes with denumerable states and nonnegative costs. The criterion to be optimized is the expected discounted cost incurred during a first passage time to a given target set. We first construct a semi-Markov decision process under a given semi-Markov decision kernel and a policy. Then, we prove that the value function satisfies the optimality equation and there exists an optimal （or ε-optimal） stationary policy under suitable conditions by using a minimum nonnegative solution approach. Further we give some properties of optimal policies. In addition, a value iteration algorithm for computing the value function and optimal policies is developed and an example is given. Finally, it is showed that our model is an extension of the first passage models for both discrete-time and continuous-time Markov decision processes.
