Background & objectives: One of the biggest concerns about the increasing development of the mining industries, especially in the dry and tropical regions, is their high water consumption and, consequently, the serious threat to the local water resources as well as the impacts of these industrieschr('39') pollution on the environment and other downstream water resources. Therefore, it is necess...

In Caenorhabditis elegans, polycistronic pre-mRNAs are processed by cleavage and polyadenylation at the 3' ends of the upstream genes and trans splicing, generally to the specialized spliced leader SL2, at the 5' ends of the downstream genes. Previous studies have indicated a relationship between these two events in the processing of a heat shock-induced gpd-2-gpd-3 polycistronic pre-mRNA. Here...

Downstream protein processing in biochemical production plants can be improved significantly with the use of peptide purification tags: comparatively short sequences of amino acids fused onto the product protein, which modify the physical properties of the desired product in a way that enhances its separation from contaminants. A two-step MINLP framework that integrates the selection of optimal...

4-Androstenedione (AD), a 19-carbon steroid hormone is an oxidation product of phytosterol fermentation by Mycobacterium sp. The isolation of the product from the fermentation broth is a challenging task. Downstream processing is an important aspect of all biotechnological processes and has significant implications on quality and yield of the final product. In the present study, the statistical...

Intracellular products such as recombinant insulin, which are typically produced in microbial host cells, demand a product release step to remove them from the cell. How this is performed determines the quantity of released contaminants, the particle size distribution of cell debris and the physical properties of the resultant process stream, which all impact on the performance of the downstrea...

Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We perform an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typologically different language pairs. Our evaluation setup spans...

Distributed word representations have a rising interest in NLP community. Most of existing models assume only one vector for each individual word, which ignores polysemy and thus degrades their effectiveness for downstream tasks. To address this problem, some recent work adopts multiprototype models to learn multiple embeddings per word type. In this paper, we distinguish the different senses o...

WordNet has facilitated important research in natural language processing but its usefulness is somewhat limited by its relatively small lexical coverage. The Paraphrase Database (PPDB) covers 650 times more words, but lacks the semantic structure of WordNet that would make it more directly useful for downstream tasks. We present a method for mapping words from PPDB to WordNet synsets with 89% ...

Unsupervisedly learned word vectors have proven to provide exceptionally effective features in many NLP tasks. Most common intrinsic evaluations of vector quality measure correlation with similarity judgments. However, these often correlate poorly with how well the learned representations perform as features in downstream evaluation tasks. We present QVEC—a computationally inexpensive intrinsic...