Meat and bone meal stimulates microbial diversity and suppresses plant pathogens in asparagus straw composting

Meat and bone meal (MBM), as slaughterhouse waste, is a potential biostimulating agent, but its efficiency and reliability in composting are largely unknown. To access the MBM application to the composting process of asparagus straw rice, we followed the composting process for 60 days in 220-L composters and another 180 days in 20-L buckets in treatments applied with MBM or urea. The microbial succession was investigated by high-throughput sequencing. Compared with urea treatments, MBM addition stabilized pH and extended the thermophilic phase for 7 days. The germination index of MBM treatments was 24.76% higher than that of urea treatments. MBM also promoted higher microbial diversity and shifted community compositions. Organic matter and pH were the most significant factors that influence the bacterial and fungal community structure. At the genus level, MBM enriched relative abundances of organic matter-degrading bacteria (Alterococcus) and lignocellulose-degrading fungi (Trichoderma), as well as lignocellulolytic enzyme activities. Notably, MBM addition decreased sum abundances of plant pathogenic fungi of Phaeoacremonium, Acremonium, and Geosmithia from 17.27 to 0.11%. This study demonstrated the potential of MBM as an effective additive in asparagus straw composting, thus providing insights into the development of new industrial aerobic fermentation.Peer reviewe

Diversity of Endophytic and Pathogenic Fungi of Saffron (Crocus sativus) Plants from Cultivation Sites in Italy

Crocus sativus is an important crop for the production of saffron and bioactive compounds. Plant endophytic fungi are a source of secondary metabolites additional to those produced by the plant itself. We analysed the biodiversity of endophytic fungi present in corms, stems, leaves, tepals, and stigmas of C. sativus from ten Italian sites; furthermore, we isolated putative pathogenic fungi from rotten plants. We used an in vitro isolation approach followed by molecular analysis of the internal transcribed spacer (ITS rDNA) region. We obtained 165 strains belonging to 39 OTUs, spreading over 26 genera and 29 species. Dark septate endophytes of the genus Cadophora and the species Talaromyces pinophilus dominated in corms, while Alternaria alternata, Epicoccum spp., T. pinophilus, Mucor fragilis, and Stemphylium vesicarium dominated in other tissues. The most frequently isolated pathogens were Fusarium oxysporum and Rhizopus oryzae. Endophytic communities significantly differed among tissues and life stages, whereas differences among cultivation sites were not statistically supported. Several endophytes were hypothesized to have changing trophic modes and/or to be latent pathogens in C. sativus. All strains were conserved ex-situ for future bioactivity tests and production of metabolites

Machine learning in the real world with multiple objectives

Machine learning (ML) is ubiquitous in many real-world applications. Existing ML systems are based on optimizing a single quality metric such as prediction accuracy. These metrics typically do not fully align with real-world design constraints such as computation, latency, fairness, and acquisition costs that we encounter in real-world applications. In this thesis, we develop ML methods for optimizing prediction accuracy while accounting for such real-world constraints. In particular, we introduce multi-objective learning in two different setups: resource-efficient prediction and algorithmic fairness in language models. First, we focus on decreasing the test-time computational costs of prediction systems. Budget constraints arise in many machine learning problems. Computational costs limit the usage of many models on small devices such as IoT or mobile phones and increase the energy consumption in cloud computing. We design systems that allow on-the-fly modification of the prediction model for each input sample. These sample-adaptive systems allow us to leverage wide variability in sample complexity where we learn policies for selecting cheap models for low complexity instances and using descriptive models only for complex ones. We utilize multiple--objective approach where one minimizes the system cost while preserving predictive accuracy. We demonstrate significant speed-ups in the fields of computer vision, structured prediction, natural language processing, and deep learning. In the context of fairness, we first demonstrate that a naive application of ML methods runs the risk of amplifying social biases present in data. This danger is particularly acute for methods based on word embeddings, which are increasingly gaining importance in many natural language processing applications of ML. We show that word embeddings trained on Google News articles exhibit female/male gender stereotypes. We demonstrate that geometrically, gender bias is captured by unique directions in the word embedding vector space. To remove bias we formulate a empirical risk objective with fairness constraints to remove stereotypes from embeddings while maintaining desired associations. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduces gender bias in embeddings, while preserving its useful properties such as the ability to cluster related concepts

Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging

Spectral learning algorithms are becoming increasingly popular in data-rich domains, driven in part by recent advances in large scale randomized SVD, and in spectral estimation of Hidden Markov Models. Extensions of these methods lead to statistical estimation algorithms which are not only fast, scalable, and useful on real data sets, but are also provably correct. Following this line of research, we make two contributions. First, we propose a set of spectral algorithms for text analysis and natural language processing. In particular, we propose fast and scalable spectral algorithms for learning word embeddings -- low dimensional real vectors (called Eigenwords) that capture the “meaning” of words from their context. Second, we show how similar spectral methods can be applied to analyzing brain images. State-of-the-art approaches to learning word embeddings are slow to train or lack theoretical grounding; We propose three spectral algorithms that overcome these limitations. All three algorithms harness the multi-view nature of text data i.e. the left and right context of each word, and share three characteristics: 1). They are fast to train and are scalable. 2). They have strong theoretical properties. 3). They can induce context-specific embeddings i.e. different embedding for “river bank” or “Bank of America”. \end{enumerate} They also have lower sample complexity and hence higher statistical power for rare words. We provide theory which establishes relationships between these algorithms and optimality criteria for the estimates they provide. We also perform thorough qualitative and quantitative evaluation of Eigenwords and demonstrate their superior performance over state-of-the-art approaches. Next, we turn to the task of using spectral learning methods for brain imaging data. Methods like Sparse Principal Component Analysis (SPCA), Non-negative Matrix Factorization (NMF) and Independent Component Analysis (ICA) have been used to obtain state-of-the-art accuracies in a variety of problems in machine learning. However, their usage in brain imaging, though increasing, is limited by the fact that they are used as out-of-the-box techniques and are seldom tailored to the domain specific constraints and knowledge pertaining to medical imaging, which leads to difficulties in interpretation of results. In order to address the above shortcomings, we propose Eigenanatomy (EANAT), a general framework for sparse matrix factorization. Its goal is to statistically learn the boundaries of and connections between brain regions by weighing both the data and prior neuroanatomical knowledge. Although EANAT incorporates some neuroanatomical prior knowledge in the form of connectedness and smoothness constraints, it can still be difficult for clinicians to interpret the results in specific domains where network-specific hypotheses exist. We thus extend EANAT and present a novel framework for prior-constrained sparse decomposition of matrices derived from brain imaging data, called Prior Based Eigenanatomy (p-Eigen). We formulate our solution in terms of a prior-constrained l1 penalized (sparse) principal component analysis. Experimental evaluation confirms that p-Eigen extracts biologically-relevant, patient-specific functional parcels and that it significantly aids classification of Mild Cognitive Impairment when compared to state-of-the-art competing approaches

Andrographis paniculata transcriptome provides molecular insights into tissue-specific accumulation of medicinal diterpenes

A summary of SSRs identified in leaf and root transcriptomes. (DOCX 11 kb