Effects of the frequency of ostomy management reinforcement education on self-care knowledge,self-efficacy,and ability of stoma appliance change among Korean hospitalised ostomates

Patients who undergo stoma surgery experience difficulties in adapting physically and psychologically. The priority is to support them in learning self-care for successful rehabilitation and psychosocial adaption to a new life. In order to do this, it is important to provide ostomates with repetitive reinforcement education on self-care in a continuous and individual manner, not just to increase knowledge or perform related skills. This study aims to evaluate the effects of ostomy management reinforcement education (OMRE) in ostomates and to identify the optimal frequency of reinforcement education using an equivalent control group post-test design. Participants were 60 ostomates admitted to a university hospital after ostomy formation surgery, and they were randomly assigned to a control and two experimental groups of this study. The OMRE was given to the control group (n = 20), experimental group 1 (n = 20), and experimental group 2 (n = 20) once, twice, and three times, respectively. Participants' self-care knowledge, self-efficacy, and ability of stoma appliance change were evaluated before and after the OMRE. Major results of this study were as follows: the self-care knowledge score of post-test was higher than the pretest in the control, experimental 1, and experimental two groups (P < 0.001). The self-efficacy score of post-test was higher than the pretest in the control, experimental 1, and experimental 2 groups (P < 0.001). The self-care knowledge score according to the frequency of OMRE did not differ among the control, experimental 1, and experimental 2 groups (F = 1.921, P = 0.156). The self-efficacy score according to the frequency of OMRE was significantly different between the control and experimental groups (F = 8.616, P = 0.001), but there was no difference between the experimental 1 and experimental 2 groups (Scheffe's post-hoc analysis: a < b, c). The ability of stoma appliance change score according to the frequency of OMRE was significantly different between the control and experimental groups (F = 49.546, P < 0.001), but there was no difference between the experimental 1 and experimental 2 groups (Scheffe's post-hoc analysis: a < b, c). Results of this study suggested that the OMRE was effective for promoting hospitalised ostomates' self-care knowledge, self-efficacy, and ability of stoma appliance change, and two sessions of the OMRE was the most effective. Findings of this study may be useful in planning education programmes designed to improve self-care ability for hospitalised ostomates.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

Human subjects exploit a cognitive map for credit assignment

An influential reinforcement learning framework proposes that behavior is jointly governed by model-free (MF) and model-based (MB) controllers. The former learns the values of actions directly from past encounters, and the latter exploits a cognitive map of the task to calculate these prospectively. Considerable attention has been paid to how these systems interact during choice, but how and whether knowledge of a cognitive map contributes to the way MF and MB controllers assign credit (i.e., to how they revaluate actions and states following the receipt of an outcome) remains underexplored. Here, we examine such sophisticated credit assignment using a dual-outcome bandit task. We provide evidence that knowledge of a cognitive map influences credit assignment in both MF and MB systems, mediating subtly different aspects of apparent relevance. Specifically, we show MF credit assignment is enhanced for those rewards that are related to a choice, and this contrasted with choice-unrelated rewards that reinforced subsequent choices negatively. This modulation is only possible based on knowledge of task structure. On the other hand, MB credit assignment was boosted for outcomes that impacted on differences in values between offered bandits. We consider mechanistic accounts and the normative status of these findings. We suggest the findings extend the scope and sophistication of cognitive map-based credit assignment during reinforcement learning, with implications for understanding behavioral control.

An extensive body of psychological and neuroscientific literature on dual-system reinforcement learning (RL) indicates that behavior is governed by two distinct systems (1–17)—a rigid, retrospective model-free (MF) system (18, 19) and a flexible, prospective model-based (MB) system (18, 20). Unlike an MF system, which tends to repeat actions with a past history of success, an MB system relies on a cognitive map (CM) (21), that is, a model detailing the structure of a decision-making environment, including how states, actions, observations, and rewards are linked, to predict the impact of action choice on potential future rewards. Recent research highlights competitive and cooperative interactions between these systems, including speed accuracy trade-offs (22), reliability-based arbitration (1, 23), and a plan-to-habit strategy (24), with a focus on a prospective-planning role served by the MB system during choice. Recently, we demonstrated another influence of a CM (and thus, as we described it there, MB processes) in guiding credit assignment (CA) to MF action-values (i.e., affecting how MF values of actions and states are updated as reward-outcomes are received) (25). However, by design, this influence was limited to unraveling the resolution of state uncertainty for MF purposes, leaving broader aspects of the contribution of CM-based processes to CA unexplored.Here, we consider two potential complementary CM-based modulators of CA. Both concern the causal structure of the relationship between options and outcomes. One involves the “relatedness” of actual outcomes to an enacted choice, a retrospective effect of a CM on MF CA. The second involves the “importance” of potential outcomes during the deliberation process preceding a choice, a prospective effect of a CM on MB CA.“Relatedness” arises out of a complexity in assigning credit when information about streams of rewards is provided that depends only partly on the actions taken (unlike situations that involve simple lotteries, for instance, when an action is directly followed by the reward it occasions). An MF system, lacking structural causal knowledge, is disposed to assign credit naively to a choice based on the entire collection of ensuing outcomes, irrespective of whether these outcomes were caused by, or related to, an actual initiating action choice. By contrast, knowledge stored as a CM can guide MF CA to favor action-related outcomes.Take an example of a trader who deliberates purchasing one of two available mutual funds: X, which invests in companies A and B, or Y, which invests in companies A and C. Assume the trader opts for X and then later receives positive information about companies B and D. The trader might assign credit in an MF manner to her/his past action (“buy X”), updating the action’s cached value on the basis that positive consequences followed that choice. However, only one component of those positive consequences (that concerning company B) actually related to the choice of fund X. We propose that MF CA is modulated by a CM such that a change in the action’s value will be affected mostly by information about company B. More generally, relatedness depends on a causal attribution of rewards to actions (26).We consider a second modulator of CA, termed “importance,” as a form of attentional effect. When deliberating between several choice options, and taking into account their prospective outcomes, it is often the case that certain outcomes (which we dub “unimportant”) should not determine choice, as they are common to all choice options. In contrast, other (“important”) outcomes are distinctive to some choice options but not to others, and these should be the main determinants of choice. A CM will contain this type of information and direct attention to the latter alone. We consider the possibility that when the outcomes of the choice are observed, those that garnered more attention at choice are favored in learning.Consider our previous example where information about companies B and D triggers a CA process that leads to positive revaluation of these companies—a process useful for future MB financial decisions related to these companies. We propose this CA process can be biased by CM-based deliberations during choice. Notably, the values of companies B and C were “important” in the trader’s MB deliberation process (choosing a fund), as each is unique to one fund. The values of companies A and D, on the other hand, are less important, as these are either common to both choice options (A) or altogether absent (D). We hypothesize that representations of “important” components in a CM are activated more strongly during choice, leading to them being revalued more when information about choice outcomes are subsequently realized. Thus, ceteris paribus, the increase in the trader’s evaluation of company B will be higher than for company D, given the positive information. This evaluation is then exploited by MB planning processes for future choices.To test these hypotheses, we developed a variant of our previously described dual-outcome bandit task (25). Participants chose between pairs of bandits (i.e., lotteries) that led to different outcomes and received a stream of reward feedback pertaining to choice-related, choice-unrelated, important, and unimportant outcomes. Critically, there are two ways to value bandits in this task. An MF controller treats each bandit holistically, and, as described above, an MB controller predicts the values of the bandits from knowledge of the outcomes to which the bandits lead as provided by a putative CM. This distinction in the structure of evaluations can then be generalized to the apportioning of credit. We consider CA to a bandit to take the form of an MF credit assignment (MFCA; since the MF system makes decisions directly based on these values). Similarly, we consider CA to the outcomes associated with the bandits to be an MB credit assignment (MBCA). To put this another way, the main distinction between MFCA and MBCA in our task is that the former pertains to a revaluation of actions, while the latter pertains to a revaluation of latent causes for these actions (i.e., the ensuing outcomes).In support of our hypothesis that MFCA is guided by a CM, we found evidence that credit for choice-related and -unrelated outcomes is assigned to actions in a different manner. We show information about rewards actually related to chosen actions alone positively impact on the value of those actions. Information about rewards not related to chosen actions, on the other hand, have an opposite effect. Second, we found that MBCA was greater for choice outcomes that were “important” compared to “unimportant” during choice deliberations. We discuss mechanistic and normative accounts of these results.     

A novel fMRI paradigm to dissociate the behavioral and neural components of mixed‐strategy decision making from non‐strategic decisions in humans

During competitive interactions, such as predator–prey or team sports, the outcome of one's actions is dependent on both their own choices and those of their opponents. Success in these rivalries requires that individuals choose dynamically and unpredictably, often adopting a mixed strategy. Understanding the neural basis of strategic decision making is complicated by the fact that it recruits various cognitive processes that are often shared with non‐strategic forms of decision making, such as value estimation, working memory, response inhibition, response selection, and reward processes. Although researchers have explored neural activity within key brain regions during mixed‐strategy games, how brain activity differs in the context of strategic interactions versus non‐strategic choices is not well understood. We developed a novel behavioral paradigm to dissociate choice behavior during mixed‐strategy interactions from non‐strategic choices, and we used task‐based functional magnetic resonance imaging (fMRI) to contrast brain activation. In a block design, participants competed in the classic mixed‐strategy game, “matching pennies,” against a dynamic computer opponent designed to exploit predictability in players’ response patterns. Results were contrasted with a non‐strategic task that had comparable sensory input, motor output, and reward rate; thus, differences in behavior and brain activation reflect strategic processes. The mixed‐strategy game was associated with activation of a distributed cortico‐striatal network compared to the non‐strategic task. We propose that choosing in mixed‐strategy contexts requires additional cognitive demands present to a lesser degree during the control task, illustrating the strength of this design in probing function of cognitive systems beyond core sensory, motor, and reward processes.     

Interfacial evaluation of experimentally weakened roots restored with adhesive materials and fibre posts: an SEM analysis

OBJECTIVES: To evaluate the bonding interface in experimentally weakened roots reinforced with adhesive restorative materials and quartz fibre posts, varying the light-exposure time of the composite resin used for root reinforcement. METHODS: Twelve extracted human maxillary incisors teeth were used. The crowns were removed and the roots were endodontically treated. After post space preparation, the roots were assigned to four groups. The thickness of the root dentine was reduced and adhesively restored with composite resin light-activated through a translucent fibre post for either 40s (group 1), 80s (group 2) or 120s (group 3). In the case of control (group 4), the roots were not weakened. One day after post cementation, the specimens were sectioned transversally in three slices and processed for scanning electron microscopic analysis to observe bonding interface formation, quality of the hybrid layer and density of resin tags using a four-step scale method. RESULTS: Formation of a hybrid layer and resin tags were evident in all groups. There was no statistically (p>0.05) significant difference between the regions analysed in each group (Friedman test) and between groups in each section depth (Kruskal-Wallis test). Furthermore, comparison of the flared/reinforced groups showed that the different times used for composite resin cure did not affect the results significantly (Kruskal-Wallis test, p=0.2139). CONCLUSIONS: Different light-exposure times used for composite resin polymerisation during root canal reinforcement did not affect significantly the formation and quality of the dentine/adhesive/composite resin bonding interface.     

The effect of the addition of surface treated chopped and continuous poly (methyl methacrylate) fibres on some properties of acrylic resin

Poly (methyl methacrylate), the material most commonly used in the construction of dentures, is not without limitations particularly in terms of mechanical strength, such as flexural and fatigue strength. The Dental Practice Board for England statistics show that many dentures fail as a result of fracture. This study investigated the self-reinforcement of acrylic resin through the addition of surface treated poly (methyl methacrylate) fibres in chopped and continuous form. The effect of the addition of fibres on transverse and impact strengths was investigated. The addition of surface treated chopped or continuous fibres to acrylic resin did not improve the transverse or impact strengths. The addition of surface treated poly (methyl methacrylate), in the present form, to acrylic resin cannot be recommended as a method of reinforcement.     

An experimental analysis of stresses in simulated flared root canals subjected to various post-core applications

Cervical root fracture is a major clinical problem in flared root canals treated with different post and core applications. This in vitro study evaluated the stress transfer of different post and core systems to the cervical part of the artificially created flared root canals, by using strain gauges. The post-core systems investigated were: (a) cast post-core system without resin reinforcement, (b) cast post-core system with resin reinforcement, (c) pre-fabricated post and resin core with resin reinforcement. The post-core systems which were cemented on simulated roots were subjected to a load applied at an angle of 45 degrees to the long axis of the simulated roots. The strain gauges which were cemented to the cervical part of simulated roots were connected to the data acquisition module to measure and record the changes in strain data. Specimens restored with resin reinforcement either with cast post-core or pre-fabricated post and resin core transferred the stress to the cervical part of the artificial roots at a rate lower than conventional cast post-core system (P = 0.001; anova and Tukey's post hoc test). It was concluded that the resin reinforcement of root canals before post-core applications reduces the stresses at the cervical part of the root surfaces.     

利用生物材料行腹股沟后壁加强重建技术

利用生物材料行腹股沟后壁加强重建（tARB）技术并非传统意义上的无张力疝修补术,其通过重叠缝合腹横筋膜,加强腹股沟管后壁,尽管增加了一定的张力,但其充分发挥了生物补片的最大优势,即可降解性及组织再生功能。通过tARB技术能够使中青年病人早日恢复正常的工作学习,从而产生比较大的社会效益。此外,tARB技术还能够有效降低病人术后感染和慢性疼痛的风险。鉴于其上述优势,有必要开展多中心的临床研究进一步验证其治疗效果。     

后巩膜加固术在高度近视性黄斑病变手术中的应用

近视性黄斑病变致盲率高,常用治疗手段为玻璃体切割术,然而单纯玻璃体切割术对于已出现严重后巩膜葡萄肿的超高度近视患者疗效欠佳。近年来临床应用玻璃体切割术联合后巩膜加固术治疗高度近视继发眼底病变,特别在黄斑劈裂及黄斑裂孔病例中,疗效肯定,前景可期。我们针对近年来后巩膜加固术的材料与术式变迁作一全面综述,并进一步讨论其应用于眼底手术中的发展远景。