Effects of Exogenous Melatonin on Body Mass Regulation and Hormone Concentrations in Eothenomys miletus

By regulating the pineal hormone, photoperiods affect many physiological characteristics in small mammals. Thus, melatonin might take part in the thermoregulation of seasonal variations in small mammals. This study determined the influence of melatonin treatment on thermogenic pattern, we measured body mass, thermogenic activities and hormone concentrations of Eothenomys miletus were given exogenous melatonin (MLT) for 28 days. The results shown that body mass was reduced significantly, whereas resting metabolic rate (RMR) and nonshivering thermogenesis (NST) increased at 28 days in MLT group compared to control group as well as the oxidative capacities of mitochondria in liver and brown adipose tissue (BAT) were enhanced; the contents of total and mitochodrial protein increased markedly. Melatonin treatment significantly increased the State 3, State 4 respiration of liver mitochondria, and the activity of cytochrome C oxidase (COX) in liver; but the α-glerocephasphate oxidase (α-PGO) capacity showed no differences during the acclimation in liver. Furthermore, the State 4 respiration, the activities of COX and α-PGO in BAT increased, respectively. The activity of thyroxin 5’-deiodinase ( T45’-DII) in BAT increased remarkably. The serum content of thyroxine (T 4) decreased, and that of tri-iodothyronine (T 3) increased. Moreover, serum leptin levels showed no significant differences in MLT group compared to control group. Together, these data indicate that melatonin enhances thermogenic capacity in E. miletus. Our results suggested that melatonin is potentially involved in the regulation of body mass, adaptive thermogenic capacity and hormone concentrations in E. miletus

Ask One More Time: Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios

Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality. To address this shortcoming, ensemble-optimization tries to obtain multiple reasoning paths to get the final answer assembly. However, current ensemble-optimization methods either simply employ rule-based post-processing such as \textit{self-consistency}, or train an additional model based on several task-related human annotations to select the best one among multiple reasoning paths, yet fail to generalize to realistic settings where the type of input questions is unknown or the answer format of reasoning paths is unknown. To avoid their limitations, we propose \textbf{self-agreement}, a generalizable ensemble-optimization method applying in almost all scenarios where the type of input questions and the answer format of reasoning paths may be known or unknown. Self-agreement firstly samples from language model's decoder to generate a \textit{diverse} set of reasoning paths, and subsequently prompts the language model \textit{one more time} to determine the optimal answer by selecting the most \textit{agreed} answer among the sampled reasoning paths. Self-agreement simultaneously achieves remarkable performance on six public reasoning benchmarks and superior generalization capabilities.Comment: Work in progres

Examination of overall treatment effect and the proportion attributable to contextual effect in osteoarthritis: meta-analysis of randomised controlled trials

Objective: To examine the overall treatment effect and the proportion attributable to contextual effect (PCE) in randomised controlled trials (RCTs) of diverse treatments for osteoarthritis (OA). Methods: We searched MEDLINE, EMBASE, CENTRAL, Science Citation Index, AMED, CINAHL through October 2014, supplemented with manual search of reference lists, published meta-analyses and systematic reviews. Included were RCTs in OA comparing placebo with representative complementary, pharmacological, non-pharmacological and surgical treatments. The primary outcome was pain. Secondary outcomes were function and stiffness. The overall treatment effect was defined as the improvement from baseline in the treatment group. The contextual effect was defined as that of the placebo group. The PCE was calculated by dividing the contextual effect over the overall treatment effect. The effect size (ES) of overall treatment effect and the PCE were pooled using random-effects model. Subgroup analysis and meta-regression were conducted to examine determinants of the PCE. Results: In total, 215 trials (41,392 participants) were included. The overall treatment effect for pain-reduction ranged from the smallest with lavage (ES=0.46, 95%CI: 0.24, 0.68) to the largest with topical NSAIDs (ES=1.37, 95%CI 1.19, 1.55). On average, 75% (PCE=0.75, 95%CI 0.72, 0.79) of pain reduction was attributable to contextual effect. It varied by treatment from 47% (PCE=0.47, 95%CI: 0.32, 0.70) for intra-articular corticosteroid to 91% (PCE=0.91, 95%CI: 0.60, 1.37) for joint lavage. Similar results were observed for function and stiffness. Treatment delivered by needle/injection and other means but oral medication, longer duration of treatment, larger sample size (≥100 per arm) and public funding source were associated with increased PCE for pain-reduction. Conclusions: The majority (75%) of the overall treatment effect in OA RCTs is attributable to contextual effects, rather than the specific effect of treatments. Reporting overall treatment effect and PCE, in addition to traditional ES over placebo, permits a more balanced, clinically meaningful interpretation of RCT results. This would help dispel the frequent discordance between conclusions from RCT evidence and clinical experience - the “efficacy paradox”

Computational Analysis of Propulsion Performance of Modified Pitching Motion Airfoils in Laminar Flow

The thrust generation performance of airfoils with modified pitching motion was investigated by computational fluid dynamics (CFD) modeling two-dimensional laminar flow at Reynolds number of 104. The effect of shift distance of the pitch axis outside the chord line (R), reduced frequency (k), pitching amplitude (θ), pitching profile, and airfoil shape (airfoil thickness and camber) on the thrust generated and efficiency were studied. The results reveal that the increase in R and k leads to an enhancement in thrust generation and a decrease in propulsive efficiency. Besides, there exists an optimal range of θ for the maximum thrust and the increasing θ induces a rapid decrease in propulsive efficiency. Six adjustable parameters (K) were employed to realize various nonsinusoidal pitching profiles. An increase in K results in more thrust generated at the cost of decreased propulsive efficiency. The investigation of the airfoil shape effect reveals that there exists an optimal range of airfoil thickness for the best propulsion performance and that the vortex structure is strongly influenced by the airfoil thickness, while varying the camber or camber location of airfoil sections offers no benefit in thrust generation over symmetric airfoil sections

Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising

Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.Comment: In The 27th ACM International Conference on Information and Knowledge Management (CIKM 18), October 22-26, 2018, Torino, Italy. ACM, New York, NY, USA, 9 page

Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models will be available at https://github.com/jy0205/LaVIT