Parkinson’s disease (PD) is a severe neurodegenerative disorder characterized by prominent motor and non-motor (e.g., cognitive) abnormalities. Notwithstanding Food and Drug Administration (FDA)-approved treatments (e.g., L-dopa), most persons with PD do not adequately benefit from the FDA-approved treatments and treatment emergent adverse events are often reasons for discontinuation. To date, no current therapy for PD is disease modifying or curative. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are central nervous system (CNS) penetrant and have shown to be neuroprotective against oxidative stress, neuroinflammation, and insulin resistance, as well as promoting neuroplasticity. Preclinical evidence suggests that GLP-1RAs also attenuate the accumulation of α-synuclein. The cellular and molecular effects of GLP-1RAs provide a basis to hypothesize putative therapeutic benefit in individuals with PD. Extant preclinical and clinical trial evidence in PD provide preliminary evidence of clinically meaningful benefit in the cardinal features of PD. Herein, we synthesize extant preclinical and early-phase clinical evidence, suggesting that GLP-1RAs may be beneficial as a treatment and/or illness progression modification therapeutic in PD.

In this paper, a super-twisting disturbance observer (STDO)-based adaptive reinforcement learning control scheme is proposed for the straight air compound missile system with aerodynamic uncertainties and unmodeled dynamics. Firstly, neural network (NN)-based adaptive reinforcement learning control scheme with actor-critic design is investigated to deal with the tracking problems for the straight gas compound system. The actor NN and the critic NN are utilised to cope with the unmodeled dynamics and approximate the cost function that are related to control input and tracking error, respectively. In other words, the actor NN is used to perform the tracking control behaviours, and the critic NN aims to evaluate the tracking performance and give feedback to actor NN. Moreover, with the aid of the STDO disturbance observer, the problem of the control signal fluctuation caused by the mismatched disturbance can be solved well. Based on the proposed adaptive law and the Lyapunov direct method, the eventually consistent boundedness of the straight gas compound system is proved. Finally, numerical simulations are carried out to demonstrate the feasibility and superiority of the proposed reinforcement learning-based STDO control algorithm.

Weapon target allocation (WTA) is an effective method to solve the battlefield fire optimisation problem, which plays an important role in intelligent automated decision-making. We researched the multitarget allocation problem to maximise the attack effectiveness when multiple interceptors cooperatively attack multiple ground targets. Firstly, an effective and reasonable fitness function is established, based on the situation between the interceptors and targets, by comprehensively considering the relative range, relative angle, speed, capture probability and radiation source matching performance and thoroughly evaluating them based on the advantage of the attack effectiveness. Secondly, the optimisation performance of the particle swarm optimisation (PSO) algorithm is adaptively improved. We propose an adaptive simulated annealing-particle swarm optimisation (SA-PSO) algorithm by introducing the simulated annealing algorithm into the adaptive PSO algorithm. The proposed algorithm can enhance the convergence speed and overcome the disadvantage of the PSO algorithm easily falling into a local extreme point. Finally, a simulation example is performed in a scenario where ten interceptors cooperate to attack eight ground targets; comparative experiments are conducted between the adaptive SA-PSO algorithm and PSO algorithm. The simulation results indicate that the proposed adaptive SA-PSO algorithm demonstrates great performance in convergence speed and global optimisation capabilities, and a maximised attack effectiveness can be guaranteed.

A new generation of high power laser facilities will provide laser pulses with extremely high powers of 10 petawatt (PW) and even 100 PW, capable of reaching intensities of $10^{23}~\text{W}/\text{cm}^{2}$ in the laser focus. These ultra-high intensities are nevertheless lower than the Schwinger intensity $I_{S}=2.3\times 10^{29}~\text{W}/\text{cm}^{2}$ at which the theory of quantum electrodynamics (QED) predicts that a large part of the energy of the laser photons will be transformed to hard Gamma-ray photons and even to matter, via electron–positron pair production. To enable the investigation of this physics at the intensities achievable with the next generation of high power laser facilities, an approach involving the interaction of two colliding PW laser pulses is being adopted. Theoretical simulations predict strong QED effects with colliding laser pulses of ${\geqslant}10~\text{PW}$ focused to intensities ${\geqslant}10^{22}~\text{W}/\text{cm}^{2}$.

Experiments on the National Ignition Facility show that multi-dimensional effects currently dominate the implosion performance. Low mode implosion symmetry and hydrodynamic instabilities seeded by capsule mounting features appear to be two key limiting factors for implosion performance. One reason these factors have a large impact on the performance of inertial confinement fusion implosions is the high convergence required to achieve high fusion gains. To tackle these problems, a predictable implosion platform is needed meaning experiments must trade-off high gain for performance. LANL has adopted three main approaches to develop a one-dimensional (1D) implosion platform where 1D means measured yield over the 1D clean calculation. A high adiabat, low convergence platform is being developed using beryllium capsules enabling larger case-to-capsule ratios to improve symmetry. The second approach is liquid fuel layers using wetted foam targets. With liquid fuel layers, the implosion convergence can be controlled via the initial vapor pressure set by the target fielding temperature. The last method is double shell targets. For double shells, the smaller inner shell houses the DT fuel and the convergence of this cavity is relatively small compared to hot spot ignition. However, double shell targets have a different set of trade-off versus advantages. Details for each of these approaches are described.

Experimental data are presented showing maximum carbon C6+ ion energies obtained from nm-scaled targets in the relativistic transparent regime for laser intensities between 9 × 1019 and 2 × 1021 W/cm2. When combined with two-dimensional particle-in-cell simulations, these results show a steep linear scaling for carbon ions with the normalized laser amplitude a0 ($a_0 \propto \sqrt ( I)$). The results are in good agreement with a semi-analytic model that allows one to calculate the optimum thickness and the maximum ion energies as functions of a0 and the laser pulse duration τλ for ion acceleration in the relativistic-induced transparency regime. Following our results, ion energies exceeding 100 MeV/amu may be accessible with currently available laser systems.

In endangered and local pig breeds of small population sizes, production has to focus on alternative niche markets with an emphasis on specific product and meat quality traits to achieve economic competiveness. For designing breeding strategies on meat quality, an adequate performance testing scheme focussing on phenotyped selection candidates is required. For the endangered German pig breed ‘Bunte Bentheimer’ (BB), no breeding program has been designed until now, and no performance testing scheme has been implemented. For local breeds, mainly reared in small-scale production systems, a performance test based on in vivo indicator traits might be a promising alternative in order to increase genetic gain for meat quality traits. Hence, the main objective of this study was to design and evaluate breeding strategies for the improvement of meat quality within the BB breed using in vivo indicator traits and genetic markers. The in vivo indicator trait was backfat thickness measured by ultrasound (BFiv), and genetic markers were allele variants at the ryanodine receptor 1 (RYR1) locus. In total, 1116 records of production and meat quality traits were collected, including 613 in vivo ultrasound measurements and 713 carcass and meat quality records. Additionally, 700 pigs were genotyped at the RYR1 locus. Data were used (1) to estimate genetic (co)variance components for production and meat quality traits, (2) to estimate allele substitution effects at the RYR1 locus using a selective genotyping approach and (3) to evaluate breeding strategies on meat quality by combining results from quantitative-genetic and molecular-genetic approaches. Heritability for the production trait BFiv was 0.27, and 0.48 for backfat thickness measured on carcass. Estimated heritabilities for meat quality traits ranged from 0.14 for meat brightness to 0.78 for the intramuscular fat content (IMF). Genetic correlations between BFiv and IMF were higher than estimates based on carcass backfat measurements (0.39 v. 0.25). The presence of the unfavorable n allele was associated with increased electric conductivity, paler meat and higher drip loss. The allele substitution effect on IMF was unfavorable, indicating lower IMF when the n allele is present. A breeding strategy including the phenotype (BFiv) combined with genetic marker information at the RYR1 locus from the selection candidate, resulted in a 20% increase in accuracy and selection response when compared with a breeding strategy without genetic marker information.

This study was conducted to determine the effects of soy isoflavones on changes in body and tissue weight and on insulin-like factor I (IGF-I) and peroxisome proliferator-activated receptor-γ (PPARγ) gene and protein expression in muscle and adipose tissues in Chinese Guangxi minipig, as a model for studying human nutrition. A total of 72 male Chinese Guangxi minipigs were fed basal diet (control, Con), low dose of soy isoflavones and high dose of soy isoflavones (HSI). The results showed that HSI increased the body weight (BW) gain and fat percentage of minipigs (P < 0.05). In addition, the serum concentrations of IGF-I and interleukin-6 were increased by high levels of soy isoflavones (P < 0.05). Furthermore, a diet containing soy isoflavones enhanced IGF-I mRNA expression levels in longissimus muscle, but decreased these levels in perirenal fat. However, the mRNA and protein expression levels of PPARγ in longissimus muscle and subcutaneous adipose tissue were both increased when compared with the Con. The data indicated that soy isoflavones regulated the BW gain and fat percentage of Chinese Guangxi minipigs, which also showed changes in IGF-I system and PPARγ. However, further research is required to clarify the causative relationship.

Experiments demonstrate the ~77× amplification of 0.5 to 3.5-ps pulses of seed light by interaction with Langmuir waves in a low density (1.2 × 1019 cm−3) plasma produced by a 1-ns, 230-J, 1054-nm pump beam with 1.2 × 1014 W/cm2 intensity. The waves are strongly damped (kλD = 0.38, Te = 244 eV) and grow over a ~ 1 mm length, similar to what is experienced by scattered light when it interacts with crossing beams as it exits an ignition target. The amplification reduces when the seed intensity increases above ~1 × 1011 W/cm2, indicating that saturation of the plasma waves on the electron kinetic time scale (<0.5 ps) limits the scatter to ~1% of the available pump energy. The observations are in agreement with 2D PIC simulations in this case.

We obtained a list of all reported cases of haemorrhagic fever with renal syndrome (HFRS) in Shenyang, China, during 1990–2003, and used GIS-based scan statistics to determine the distribution of HFRS cases and to identify key areas and periods for future risk-factor research. Spatial cluster analysis suggested three areas were at increased risk for HFRS. Temporal cluster analysis suggested one period was at increased risk for HFRS. Space–time cluster analysis suggested six areas from 1995 to 1996 and four areas from 1998 to 2003 were at increased risk for HFRS. We also discussed the likely reasons for these clusters. We conclude that GIS-based scan statistics may provide an opportunity to classify the epidemic situation of HFRS, and we can pursue future investigations to study the likely factors responsible for the increased disease risk based on the classification.