Novel risperidone orally disintegrating minitablets for pediatric use: patient acceptance and dose adjustment

OBJECTIVE: Risperidone is a potent psychotropic agent has been approved for symptomatic treatment of irritability in children and adolescents with autism spectrum disorders. However, its bitter taste and dose adjustment of liquid dosage forms are a main hurdle for patient acceptance. SIGNIFICANCE: Thus, this recent study investigate the formulation of taste masked risperidone orally disintegration minitablets (ODMT) as a way of enhancing patient acceptance. METHODS: Taste masked risperidone hydrogenated castor oil or Cetyl alcohol based granules were prepared using a simple melt granulation technique in different drug to lipid ratios; drug release, bitterness score of the prepared granular formulations were evaluated. DSC was also performed to detect the possible drug lipid interaction. The selected lipid-based granules were further compressed into ODMT formulations. Bitterness score was assessed by gustatory sensation test and results were compared to marketed liquid formulations of risperidone. RESULTS: All the prepared ODMT formulations showed high content uniformity, with minimum dose fluctuation compared to marketed oral liquid preparations. CONCLUSION: In conclusion, risperidone lipid-based granules could be formulated in different ratios by simple techniques and commonly used excipients into taste masked risperidone ODMT with accurate and flexible doses suitable for pediatric use with high taste preference and acceptability

SpaceNet: Make Free Space For Continual Learning

The continual learning (CL) paradigm aims to enable neural networks to learn tasks continually in a sequential fashion. The fundamental challenge in this learning paradigm is catastrophic forgetting previously learned tasks when the model is optimized for a new task, especially when their data is not accessible. Current architectural-based methods aim at alleviating the catastrophic forgetting problem but at the expense of expanding the capacity of the model. Regularization-based methods maintain a fixed model capacity; however, previous studies showed the huge performance degradation of these methods when the task identity is not available during inference (e.g. class incremental learning scenario). In this work, we propose a novel architectural-based method referred as SpaceNet for class incremental learning scenario where we utilize the available fixed capacity of the model intelligently. SpaceNet trains sparse deep neural networks from scratch in an adaptive way that compresses the sparse connections of each task in a compact number of neurons. The adaptive training of the sparse connections results in sparse representations that reduce the interference between the tasks. Experimental results show the robustness of our proposed method against catastrophic forgetting old tasks and the efficiency of SpaceNet in utilizing the available capacity of the model, leaving space for more tasks to be learned. In particular, when SpaceNet is tested on the well-known benchmarks for CL: split MNIST, split Fashion-MNIST, and CIFAR-10/100, it outperforms regularization-based methods by a big performance gap. Moreover, it achieves better performance than architectural-based methods without model expansion and achieved comparable results with rehearsal-based methods, while offering a huge memory reduction.Comment: Accepted in Neurocomputing Journa

Self-Attention Meta-Learner for Continual Learning

Continual learning aims to provide intelligent agents capable of learning multiple tasks sequentially with neural networks. One of its main challenging, catastrophic forgetting, is caused by the neural networks non-optimal ability to learn in non-stationary distributions. In most settings of the current approaches, the agent starts from randomly initialized parameters and is optimized to master the current task regardless of the usefulness of the learned representation for future tasks. Moreover, each of the future tasks uses all the previously learned knowledge although parts of this knowledge might not be helpful for its learning. These cause interference among tasks, especially when the data of previous tasks is not accessible. In this paper, we propose a new method, named Self-Attention Meta-Learner (SAM), which learns a prior knowledge for continual learning that permits learning a sequence of tasks, while avoiding catastrophic forgetting. SAM incorporates an attention mechanism that learns to select the particular relevant representation for each future task. Each task builds a specific representation branch on top of the selected knowledge, avoiding the interference between tasks. We evaluate the proposed method on the Split CIFAR-10/100 and Split MNIST benchmarks in the task agnostic inference. We empirically show that we can achieve a better performance than several state-of-the-art methods for continual learning by building on the top of selected representation learned by SAM. We also show the role of the meta-attention mechanism in boosting informative features corresponding to the input data and identifying the correct target in the task agnostic inference. Finally, we demonstrate that popular existing continual learning methods gain a performance boost when they adopt SAM as a starting point

Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks

Using task-specific components within a neural network in continual learning (CL) is a compelling strategy to address the stability-plasticity dilemma in fixed-capacity models without access to past data. Current methods focus only on selecting a sub-network for a new task that reduces forgetting of past tasks. However, this selection could limit the forward transfer of relevant past knowledge that helps in future learning. Our study reveals that satisfying both objectives jointly is more challenging when a unified classifier is used for all classes of seen tasks-class-Incremental Learning (class-IL)-as it is prone to ambiguities between classes across tasks. Moreover, the challenge increases when the semantic similarity of classes across tasks increases. To address this challenge, we propose a new CL method, named AFAF, that aims to Avoid Forgetting and Allow Forward transfer in class-IL using fix-capacity models. AFAF allocates a sub-network that enables selective transfer of relevant knowledge to a new task while preserving past knowledge, reusing some of the previously allocated components to utilize the fixed-capacity, and addressing class-ambiguities when similarities exist. The experiments show the effectiveness of AFAF in providing models with multiple CL desirable properties, while outperforming state-of-the-art methods on various challenging benchmarks with different semantic similarities.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022

A dual strategy to improve psychotic patients\u27 compliance using sustained release quetiapine oral disintegrating tablets

Quetiapine (QT) is a short acting atypical antipsychotic drug effective in schizophrenia and bipolar disorder. This study aims at designing a novel dosage form of sustained release taste-masked QT orally disintegrating tablets (ODTs) based on solid lipid micro-pellets (SLMPs). QT SLMPs were prepared using the hot melt extrusion technique utilizing three lipid carriers: Compritol, Precirol and white beeswax either alone or in mixtures. They showed sustained QT release and a taste masking effect. The selected QT SLMP was further blended with an aqueous solution containing polyvinylpyrollidone (2.5%), croscarmellose sodium (2%) and mannitol (50%); it was then lyophilized into ODT in a mass ratio of 1:2 respectively. ODTs containing QT SLMPs showed: average wetting time (40.92 s), average oral disintegration time (21.49 s), average hardness (16.85 N) and also imparted suitable viscosity to suspend pellets during lyophilization process. In conclusion, lyophilization is a promising technique for the formulation of multiparticulate systems into ODTs

The Dormant Neuron Phenomenon in Deep Reinforcement Learning

In this work we identify the dormant neuron phenomenon in deep reinforcement learning, where an agent's network suffers from an increasing number of inactive neurons, thereby affecting network expressivity. We demonstrate the presence of this phenomenon across a variety of algorithms and environments, and highlight its effect on learning. To address this issue, we propose a simple and effective method (ReDo) that Recycles Dormant neurons throughout training. Our experiments demonstrate that ReDo maintains the expressive power of networks by reducing the number of dormant neurons and results in improved performance.Comment: Oral at ICML 202

Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates

Continual learning (CL) refers to the ability of an intelligent system to sequentially acquire and retain knowledge from a stream of data with as little computational overhead as possible. To this end; regularization, replay, architecture, and parameter isolation approaches were introduced to the literature. Parameter isolation using a sparse network which enables to allocate distinct parts of the neural network to different tasks and also allows to share of parameters between tasks if they are similar. Dynamic Sparse Training (DST) is a prominent way to find these sparse networks and isolate them for each task. This paper is the first empirical study investigating the effect of different DST components under the CL paradigm to fill a critical research gap and shed light on the optimal configuration of DST for CL if it exists. Therefore, we perform a comprehensive study in which we investigate various DST components to find the best topology per task on well-known CIFAR100 and miniImageNet benchmarks in a task-incremental CL setup since our primary focus is to evaluate the performance of various DST criteria, rather than the process of mask selection. We found that, at a low sparsity level, Erdos-Renyi Kernel (ERK) initialization utilizes the backbone more efficiently and allows to effectively learn increments of tasks. At a high sparsity level, however, uniform initialization demonstrates more reliable and robust performance. In terms of growth strategy; performance is dependent on the defined initialization strategy, and the extent of sparsity. Finally, adaptivity within DST components is a promising way for better continual learners

Analgesic and anti-inflammatory effects of hydroalcoholic extract of Astragalus ibrahimianus

This study aims to investigate the analgesic, anti-inflammatory, and antioxidant effects of the hydroalcoholic extract of Astragalus ibrahimianus, an endemic plant of Morocco. The analgesic effect was tested using the hot plate, writhing, and formalin tests. The anti-inflammatory activity was assessed by carrageenan and xylene-induced paw/ear edema respectively. The antioxidant activity was evaluated by three tests, namely DPPH, reducing power, and iron chelation. A. ibrahimianus treatment resulted in a significant reduction in carrageenan-induced paw edema and xylene-induced ear edema. Acute pretreatment with the extract reduced the number of abdominal cramps induced by acetic acid injection, increased paw withdrawal latency in the hot plate test, and suppressed both phases of the formalin test. The extract showed significant antioxidant activity regarding the standard molecules. In conclusion, A. ibrahimianus has analgesic, anti-inflammatory, and antioxidant properties

Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning

Tomorrow's robots will need to distinguish useful information from noise when performing different tasks. A household robot for instance may continuously receive a plethora of information about the home, but needs to focus on just a small subset to successfully execute its current chore. Filtering distracting inputs that contain irrelevant data has received little attention in the reinforcement learning literature. To start resolving this, we formulate a problem setting in reinforcement learning called the $\textit{extremely noisy environment}$ (ENE), where up to $99\%$ of the input features are pure noise. Agents need to detect which features provide task-relevant information about the state of the environment. Consequently, we propose a new method termed $\textit{Automatic Noise Filtering}$ (ANF), which uses the principles of dynamic sparse training in synergy with various deep reinforcement learning algorithms. The sparse input layer learns to focus its connectivity on task-relevant features, such that ANF-SAC and ANF-TD3 outperform standard SAC and TD3 by a large margin, while using up to $95\%$ fewer weights. Furthermore, we devise a transfer learning setting for ENEs, by permuting all features of the environment after 1M timesteps to simulate the fact that other information sources can become relevant as the world evolves. Again, ANF surpasses the baselines in final performance and sample complexity. Our code is available at https://github.com/bramgrooten/automatic-noise-filterin