Synthesis and evaluation of pharmaceutical and fine chemicals processes for intensification and sustainability benefits

PhD ThesisIn the face of global competition and tighter safety and environmental regulations, the pharmaceutical industry is exploring new areas and technologies that could potentially bring about step change in process performance. Process intensification has the potential to improve early development by introducing new process options, which are capable of achieving green and sustainable benefits in production. In this thesis, the objective is to demonstrate the synthesis and evaluation of pharmaceutical processes for intensification and sustainability benefits. This is illustrated with two main processes – the amidation process and the ortho-lithiation process. Based on the experiences gained at the end of the case studies, a general framework that summarizes the approach to Process Intensification (PI) for pharmaceutical processes is developed. Firstly, the amidation process has been successfully intensified with the implementation of a number of PI options, which are proven feasible in lab-scale experiments. These options are represented in terms of three intensified cases - the intensified batch case, the continuous reaction case and the continuous process case, are compared to the batch base case. To compare their sustainability performance, the respective plants are designed at a hypothetical throughput of 3 tons per year. Overall, the intensified batch case provided the most benefits, with cost savings of up to 40%, and more than 70% improvements in total material efficiency and E-factor compared to the batch base case. This also indicates that batch mode operation in this particular process is more suitable than continuous mode. The second case study on the ortho-lithiation process consists of three parts. The first part investigates ortho-lithiation reaction in continuous flow reactors at ambient temperature. The findings demonstrated that the highest reaction yield of 99% was obtained in a T-reactor as a result of short residence time and good mixing. The Spinning Disc Reactor (SDR) also showed distinct advantage in handling this reaction with mild solid precipitation. The second part focuses on the comparison of the T-reactor, the SDR and the Stirred Tank Reactor (STR) based on the sustainability metrics. The results showed that the T-reactor process achieved 66% and 11% reduction in energy consumption and operating expenditure respectively as compared to the STR process. The last part of the ortho-lithiation process focuses on the study of the whole process including workup. To avoid dealing with inefficient separation process, consecutive reaction has been attempted by avoiding the isolation of ortho-lithiation crude product and directly transferring it into the next reactor for subsequent reaction. This is experimentally proven feasible and resulted in a greener process.GSK-EDB Singapor

Unleashing the Power of ChatGPT for Translation: An Empirical Study

The recently released ChatGPT has demonstrated surprising abilities in natural language understanding and natural language generation. Machine translation is an important and extensively studied task in the field of natural language processing, which heavily relies on the abilities of language understanding and generation. Thus, in this paper, we explore how to assist machine translation with ChatGPT. We adopt several translation prompts on a wide range of translations. Our experimental results show that ChatGPT with designed translation prompts can achieve comparable or better performance over professional translation systems for high-resource language translations but lags behind significantly on low-resource translations. We further evaluate the translation quality using multiple references, and ChatGPT achieves superior performance compared to the professional systems. We also conduct experiments on domain-specific translations, the final results show that ChatGPT is able to comprehend the provided domain keyword and adjust accordingly to output proper translations. At last, we perform few-shot prompts that show consistent improvement across different base prompts. Our work provides empirical evidence that ChatGPT still has great potential in translations

Improving Entity Linking through Semantic Reinforced Entity Embeddings

Entity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity embeddings are learned from canonical Wikipedia articles and local contexts surrounding target entities. Such entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality. FGS2EE first uses the embeddings of semantic type words to generate semantic embeddings, and then combines them with existing entity embeddings through linear aggregation. Extensive experiments show the effectiveness of such embeddings. Based on our entity embeddings, we achieved new sate-of-the-art performance on entity linking.Comment: 6 pages, 3 figures, ACL 202

Nonlinearity of root trait relationships and the root economics spectrum

The root economics spectrum (RES), a common hypothesis postulating a tradeoff between resource acquisition and conservation traits, is being challenged by conflicting relationships between root diameter, tissue density (RTD) and root nitrogen concentration (RN). Here, we analyze a global trait dataset of absorptive roots for over 800 plant species. For woody species (but not for non-woody species), we find nonlinear relationships between root diameter and RTD and RN, which stem from the allometric relationship between stele and cortical tissues. These nonlinear relationships explain how sampling bias from different ends of the nonlinear curves can result in conflicting trait relationships. Further, the shape of the relationships varies depending on evolutionary context and mycorrhizal affiliation. Importantly, the observed nonlinear trait relationships do not support the RES predictions. Allometry-based nonlinearity of root trait relationships improves our understanding of the ecology, physiology and evolution of absorptive roots

Continuous flow intensification of ortho-lithiation at ambient conditions

Ortho-lithiation is an important class of reaction for the synthesis of regiospecifically substituted aromatics and it is an emerging method to prepare phthalides which are common pharmaceutically active compounds.1 This reaction is typically conducted in batch mode under cryogenic temperatures (-78 to -40 ℃)2 to tame the high reactivity of the organolithium intermediates. Scaling up batch cryogenic organolithiation chemistry has traditionally proven to be a significant challenge. This involves the need to handle large quantities of hazardous lithium reagents and excessive costs associated with cryogenic technology at scale. These challenges make ortho-lithiation reaction an ideal candidate in deploying continuous flow processing as a process intensification (PI) technique. Continuous flow processing offers several ‘green’ benefits in the case of ortho-lithiation reaction where the use of highly energy-intensive refrigeration to -78 ℃ may be avoided. This offers the prospect of considerable energy savings at industrial scale, leading to reduced greenhouse gas emissions. It can also achieve high purity product stream so the downstream processing steps may be simplified. This minimizes the amount of solvent used and increases productivity due to higher selectivity. In addition, the use of continuous flow processing lowers the risk of accidental releases arising from the lower inventories of hazardous material. Please click Additional Files below to see the full abstract

Neural Dependencies Emerging from Learning Massive Categories

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call \textbf{neural dependency}. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer. Code for this work will be made publicly available

Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization

The self-media era provides us tremendous high quality videos. Unfortunately, frequent video copyright infringements are now seriously damaging the interests and enthusiasm of video creators. Identifying infringing videos is therefore a compelling task. Current state-of-the-art methods tend to simply feed high-dimensional mixed video features into deep neural networks and count on the networks to extract useful representations. Despite its simplicity, this paradigm heavily relies on the original entangled features and lacks constraints guaranteeing that useful task-relevant semantics are extracted from the features. In this paper, we seek to tackle the above challenges from two aspects: (1) We propose to disentangle an original high-dimensional feature into multiple sub-features, explicitly disentangling the feature into exclusive lower-dimensional components. We expect the sub-features to encode non-overlapping semantics of the original feature and remove redundant information. (2) On top of the disentangled sub-features, we further learn an auxiliary feature to enhance the sub-features. We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature. Extensive experiments on two large-scale benchmark datasets (i.e., SVD and VCSL) demonstrate that our method achieves 90.1% TOP-100 mAP on the large-scale SVD dataset and also sets the new state-of-the-art on the VCSL benchmark dataset. Our code and model have been released at https://github.com/yyyooooo/DMI/, hoping to contribute to the community.Comment: This paper is accepted by ACM MM 202

Identification of Key Aroma Compounds in Fig Extract through Sensomics Approach

In this study, headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and gas chromatography-olfactory (GC-O) were applied to identify and analyze the volatile aroma compounds of the ethanol extract of figs. Its key characteristic flavor compounds were analyzed by odor activity (OAV) and aroma recombination and omission tests. The results showed that a total of 40 volatile aroma components were identified, of which 18 compounds, such as isobutyrate, γ-butyl lactone, aromatic camphor, nonaldehyde, vanillin and furfural, were important aroma components in the fig extract (OAV > 1). The aroma recombination experiments showed that the sensory properties of the recombined samples, which had typical aroma characteristics such as fruity, sweet, baked and burnt sweet with slight sour, ointment-like and milky, were similar to those of the fig extract. Furthermore, the aroma omission experiments identified gamma-hexanoate, ethyl palmitate, phenmethanol, aromatic camphor, vanillin, benzaldehyde, 4-hydroxy-2,5-dimethyl-3 (2H)-furan, 5-hydroxymyfuran, and methyl cyclopentanolone as key characteristic flavor compounds of the fig extract. The findings of this study provide a theoretical basis for the development and quality control of fig characteristic flavorings