Challenges and opportunities in producing high-quality edible mushrooms from lignocellulosic biomass in a small scale.

Mushrooms are high-value products that can be produced from lignocellulosic biomass. Mushrooms are the fruiting body of fungi and are domestically cultivated using lignocellulosic biomass obtained from agricultural byproducts and woody biomass. A handful of edible mushroom species are commercially cultivated at small, medium, and large scales for culinary and medicinal use. Details about different lignocellulosic biomass and their composition that are commonly used to produce mushrooms are outlined in this review. In addition, discussions on four major processing steps (i) producing solid and liquid spawn, (ii) conventional and mechanized processing lignocellulosic biomass substrates to produce mushroom beds, (iii) maintaining growth conditions in climate-controlled rooms, and (iv) energy requirements and managements to produce mushrooms are also provided. The new processing methods and technology outlined in this review may allow mushrooms to be economically and sustainably produced at a small scale to satisfy the growing food needs and create rural jobs. KEY POINTS: • Some of the challenges faced by small-scale mushroom growers are presented. This review is expected to stimulate more research to address the challenges.

High-performance computing for SARS-CoV-2 RNAs clustering: a data science‒based genomics approach.

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain.

BACKGROUND AND OBJECTIVE: We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic health records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives. METHODS: This review surveys the methods used in three important area: modeling and representing of time, medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations. RESULTS: The main findings of this review are revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations. CONCLUSIONS: Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.