Synthesis of the Hexasaccharide Related to the Exopolysaccharide from Lactobacillus mucosae VG1 through Regioselective Glycosylation.

Chemical synthesis of the hexasaccharide repeating unit associated with the exopolysaccharide of Lactobacillus mucosae VG1 is reported. The total synthesis is accomplished through a convergent [2 + 2 + 2] strategy using rationally protected monosaccharide derivatives. Chemoselective activation of the glycosyl donors and regioselective nucleophilicity of the acceptors were successfully employed throughout the chemical synthesis.

Concise synthesis of the tetrasaccharide repeating unit of the O-antigen from Escherichia coli O131 containing N-acetyl neuraminic acid.

Synthesis of the tetrasaccharide repeating unit of the O-antigen from E. coli O131 was accomplished through a linear strategy involving rationally protected monosaccharide units derived from commercially available sugars. The challenging α-glycosylation of N-acetyl neuraminic acid was achieved through activation of thioglycoside using NIS-mediated glycosylation strategy. The target tetrasaccharide in the form of its 3-aminopropyl glycoside may be used for further glycoconjugate formation without hampering the anomeric stereochemistry.

Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique.

The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company's performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN, due to its lower rate of false negatives. XAI techniques, such as SHAP and LIME, revealed the feature contribution of the words used towards positive and negative sentiments, which were used to validate the model.

Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review.

During the COVID-19 crisis, customers' preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important source of information about the company's performance. FDS organisations aim to gather complaints from customer feedback and effectively use the data to determine the areas for improvement to enhance customer satisfaction. This work aimed to review machine learning (ML) and deep learning (DL) models and explainable artificial intelligence (XAI) methods to predict customer sentiments in the FDS domain. A literature review revealed the wide usage of lexicon-based and ML techniques for predicting sentiments through customer reviews in FDS. However, limited studies applying DL techniques were found due to the lack of the model interpretability and explainability of the decisions made. The key findings of this systematic review are as follows: 77% of the models are non-interpretable in nature, and organisations can argue for the explainability and trust in the system. DL models in other domains perform well in terms of accuracy but lack explainability, which can be achieved with XAI implementation. Future research should focus on implementing DL models for sentiment analysis in the FDS domain and incorporating XAI techniques to bring out the explainability of the models.

Concise chemical synthesis of the pentasaccharide repeating unit of the O-antigen from Escherichia albertii O2.

Total chemical synthesis of the pentasaccharide repeating unit of the O-antigen from Escherichia albertii O2 is accomplished by following a [3 + 2] strategy. The target pentasaccharide in the form of its 2-aminoethyl glycoside is particularly attractive as the free amine end can be coupled with suitable aglycon to make further glycoconjugate without affecting the anomeric stereochemistry. Phthalimido derivatives were used successfully as the precursor of the desired acetamido glucose moieties and ensured the 1,2-trans linkages.

Chemical synthesis of the 4-amino-4,6-dideoxy-d-glucose containing pentasaccharide repeating unit of the O-specific polysaccharide from Aeromonas hydrophila strain K691 in the form of its 2-aminoethyl glycoside.

Chemical synthesis of the pentasaccharide repeating unit of the O-specific polysaccharide from Aeromonas hydrophilastrain K691 is reported. Synthesis of the pentasaccharide is accomplished by using a common disaccharide in sequence and finally attaching the rare sugar unit. The target structure was made in the form of its 2-aminoethyl glycoside which is essential for further glycconjugate formation. Stereoselective glycosylations were achieved by the activation of thioglycosides in the presence of H2SO4-silica in conjunction with N-iodosuccinimide.

Distinct Mechanoresponsive Luminescence, Thermochromism, Vapochromism, and Chlorine Gas Sensing by a Solid-State Organic Emitter.

In this study, we report a synthetically simple donor-acceptor (D-A)-type organic solid-state emitter 1 that displays unique fluorescence switching under mechanical stimuli. Orange and yellow emissive crystals of 1 (1O, 1Y) exhibit an unusual "back and forth" fluorescence response to mechanical force. Gentle crushing (mild pressure) of the orange or yellow emissive crystal results in hypsochromic shift to cyan emissive fragments (λem = 498-501 nm) with a large wavelength shift Δλem = -71 to -96 nm, while further grinding results in bathochromic swing to green emissive powder λem = 540-550 nm, Δλem = +40 to 58 nm. Single-crystal X-ray diffraction study reveals that molecules are packed by weak interactions, such as C-H···π, C-H···N, and C-H···F, which facilitate intermolecular charge transfer in the crystal. With the aid of structural, spectroscopic, and morphological studies, we established the interplay between intermolecular and intramolecular charge-transfer interaction that is responsible for this elusive mechanochromic luminescence. Moreover, we have also demonstrated the application of this organic material for chlorine gas sensing in solid state.