A newly evolved rice-specific gene JAUP1 regulates jasmonate biosynthesis and signalling to promote root development and multi-stress tolerance.

Root architecture and function are critical for plants to secure water and nutrient supply from the soil, but environmental stresses alter root development. The phytohormone jasmonic acid (JA) regulates plant growth and responses to wounding and other stresses, but its role in root development for adaptation to environmental challenges had not been well investigated. We discovered a novel JA Upregulated Protein 1 gene (JAUP1) that has recently evolved in rice and is specific to modern rice accessions. JAUP1 regulates a self-perpetuating feed-forward loop to activate the expression of genes involved in JA biosynthesis and signalling that confers tolerance to abiotic stresses and regulates auxin-dependent root development. Ectopic expression of JAUP1 alleviates abscisic acid- and salt-mediated suppression of lateral root (LR) growth. JAUP1 is primarily expressed in the root cap and epidermal cells (EPCs) that protect the meristematic stem cells and emerging LRs. Wound-activated JA/JAUP1 signalling promotes crosstalk between the root cap of LR and parental root EPCs, as well as induces cell wall remodelling in EPCs overlaying the emerging LR, thereby facilitating LR emergence even under ABA-suppressive conditions. Elevated expression of JAUP1 in transgenic rice or natural rice accessions enhances abiotic stress tolerance and reduces grain yield loss under a limited water supply. We reveal a hitherto unappreciated role for wound-induced JA in LR development under abiotic stress and suggest that JAUP1 can be used in biotechnology and as a molecular marker for breeding rice adapted to extreme environmental challenges and for the conservation of water resources.

Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review.

BACKGROUND: Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them. METHODS: A systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review. RESULTS: The literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users. CONCLUSION: This is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.

Accurately detecting AI text when ChatGPT is told to write like a chemist.

Large language models like ChatGPT can generate authentic-seeming text at lightning speed, but many journal publishers reject language models as authors on manuscripts. Thus, a means to accurately distinguish human-generated from artificial intelligence (AI)-generated text is immediately needed. We recently developed an accurate AI text detector for scientific journals and, herein, test its ability in a variety of challenging situations, including on human text from a wide variety of chemistry journals, on AI text from the most advanced publicly available language model (GPT-4), and, most important, on AI text generated using prompts designed to obfuscate AI use. In all cases, AI and human text was assigned with high accuracy. ChatGPT-generated text can be readily detected in chemistry journals; this advance is a fundamental prerequisite for understanding how automated text generation will impact scientific publishing from now into the future.

Benchmarking the diagnostic test accuracy of certified AI products for screening pulmonary tuberculosis in digital chest radiographs: Preliminary evidence from a rapid review and meta-analysis.

BACKGROUND AND OBJECTIVE: The global market for AI systems used in lung tuberculosis (TB) detection has expanded significantly in recent years. Verifying their performance across diverse settings is crucial before medical organisations can invest in them and pursue safe, wide-scale deployment. The goal of this research was to synthesise the clinical evidence for the diagnostic accuracy of certified AI products designed for screening TB in chest X-rays (CXRs) compared to a microbiological reference standard. METHODS: Four databases were searched between June to September 2022. Data concerning study methodology, system characteristics, and diagnostic accuracy metrics was extracted and summarised. Study bias was evaluated using QUADAS-2 and by examining sources of funding. Forest plots for diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC) curves were constructed for the AI products individually and collectively. RESULTS: 10 out of 3642 studies satisfied the review criteria however only 8 were subject to meta-analysis following bias assessment. Three AI products were evaluated with a 95 % confidence interval producing the following pooled estimates for accuracy rankings: qXR v2 (sensitivity of 0.944 [0.887-0.973], specificity of 0.692 [0.549-0.805], DOR of 3.63 [3.17-4.09], Lunit INSIGHT CXR v3.1 (sensitivity of 0.853 [0.787-0.901], specificity of 0.646 [0.627-0.665], DOR of 2.37 [1.96-2.78]), and CAD4TB v3.07 (sensitivity of 0.917 [0.848-0.956], specificity of 0.371 [0.336-0.408], DOR of 1.91 [1.4-2.47]). Overall, the products had a sensitivity of 0.903 (0.859-0.934), specificity of 0.526 (0.409-0.641), and DOR of 2.31 (1.78-2.84). CONCLUSION: Current publicly available evidence indicates considerable variability in the diagnostic accuracy of available AI products although overall they have high sensitivity and modest specificity which is improving with time. These preliminary results are limited by the small number of studies and poor coverage for low TB burden settings. More research is needed to expand the clinical evidence base for the performance of AI products.

Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools.

ChatGPT has enabled access to artificial intelligence (AI)-generated writing for the masses, initiating a culture shift in the way people work, learn, and write. The need to discriminate human writing from AI is now both critical and urgent. Addressing this need, we report a method for discriminating text generated by ChatGPT from (human) academic scientists, relying on prevalent and accessible supervised classification methods. The approach uses new features for discriminating (these) humans from AI; as examples, scientists write long paragraphs and have a penchant for equivocal language, frequently using words like "but," "however," and "although." With a set of 20 features, we built a model that assigns the author, as human or AI, at over 99% accuracy. This strategy could be further adapted and developed by others with basic skills in supervised classification, enabling access to many highly accurate and targeted models for detecting AI usage in academic writing and beyond.

Effect of seed biopriming with selected endophytes on the growth and chilling tolerance of rice plants.

The aim of this study was to develop an efficient bioinoculant for amelioration of adverse effects from chilling stress (10°C), which are frequently occurred during rice seedling stage. Seed germination bioassay under chilling condition with rice (Oryza sativa L.) cv. Tainan 11 was performed to screen for plant growth-promoting (PGP) bacteria among 41 chilling-tolerant rice endophytes. And several agronomic traits were used to evaluate the effects of bacterial inoculation on rice seedling, which were experienced for 7-d chilling stress in walk-in growth chamber. The field trials were further used to verify the performance of potential PGP endophytes on rice growth. A total of three endophytes with multiple PGP traits were obtained. It was demonstrated that Pseudomonas sp. CC-LS37 inoculation led to 18% increase of maximal efficiency of Photosystem II (PSII) after 7-d chilling stress and 7% increase of chlorophyll a content, and 64% decline of malondialdehyde content in shoot after 10-d recovery at normal temperature in walk-in growth chamber. In field trial, biopriming of seeds with strain CC-LS37 caused rice plants to increase shoot chlorophyll soil plant analysis development values (by 2.9% and 2.5%, respectively) and tiller number (both by 61%) under natural climate and chilling stress during the end of tillering stage, afterward 30% more grain yield was achieved. In conclusion, strain CC-LS37 exerted its function in increase of tiller number of chilling stress-treated rice seedlings via improvement of photosynthetic characteristics, which in turn increases the rice grain yield. This study also proposed multiple indices used in the screening of potential endophytes for conferring chilling tolerance of rice plants.

Advances, obstacles, and opportunities for machine learning in proteomics.

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

A unique self-truncation of bacterial GH5 endoglucanases leads to enhanced activity and thermostability.

BACKGROUND: ß-1,4-endoglucanase (EG) is one of the three types of cellulases used in cellulose saccharification during lignocellulosic biofuel/biomaterial production. GsCelA is an EG secreted by the thermophilic bacterium Geobacillus sp. 70PC53 isolated from rice straw compost in southern Taiwan. This enzyme belongs to glycoside hydrolase family 5 (GH5) with a TIM-barrel structure common among all members of this family. GsCelA exhibits excellent lignocellulolytic activity and thermostability. In the course of investigating the regulation of this enzyme, it was fortuitously discovered that GsCelA undergoes a novel self-truncation/activation process that appears to be common among GH5 enzymes. RESULTS: Three diverse Gram-positive bacterial GH5 EGs, but not a GH12 EG, undergo an unexpected self-truncation process by removing a part of their C-terminal region. This unique process has been studied in detail with GsCelA. The purified recombinant GsCelA was capable of removing a 53-amino-acid peptide from the C-terminus. Natural or engineered GsCelA truncated variants, with up to 60-amino-acid deletion from the C-terminus, exhibited higher specific activity and thermostability than the full-length enzyme. Interestingly, the C-terminal part that is removed in this self-truncation process is capable of binding to cellulosic substrates of EGs. The protein truncation, which is pH and temperature dependent, occurred between amino acids 315 and 316, but removal of these two amino acids did not stop the process. Furthermore, mutations of E142A and E231A, which are essential for EG activity, did not affect the protein self-truncation process. Conversely, two single amino acid substitution mutations affected the self-truncation activity without much impact on EG activities. In Geobacillus sp. 70PC53, the full-length GsCelA was first synthesized in the cell but progressively transformed into the truncated form and eventually secreted. The GsCelA self-truncation was not affected by standard protease inhibitors, but could be suppressed by EDTA and EGTA and enhanced by certain divalent ions, such as Ca2+, Mg2+, and Cu2+. CONCLUSIONS: This study reveals novel insights into the strategy of Gram-positive bacteria for directing their GH5 EGs to the substrate, and then releasing the catalytic part for enhanced activity via a spontaneous self-truncation process.

From simple and specific zymographic detections to the annotation of a fungus Daldinia caldariorum D263 that encodes a wide range of highly bioactive cellulolytic enzymes.

BACKGROUND: Lignocellulolytic enzymes are essential for agricultural waste disposal and production of renewable bioenergy. Many commercialized cellulase mixtures have been developed, mostly from saprophytic or endophytic fungal species. The cost of complete cellulose digestion is considerable because a wide range of cellulolytic enzymes is needed. However, most fungi can only produce limited range of highly bioactive cellulolytic enzymes. We aimed to investigate a simple yet specific method for discovering unique enzymes so that fungal species producing a diverse group of cellulolytic enzymes can be identified. RESULTS: The culture medium of an endophytic fungus, Daldinia caldariorum D263, contained a complete set of cellulolytic enzymes capable of effectively digesting cellulose residues into glucose. By taking advantage of the unique product inhibition property of ß-glucosidases, we have established an improved zymography method that can easily distinguish ß-glucosidase and exoglucanase activity. Our zymography method revealed that D263 can secrete a wide range of highly bioactive cellulases. Analyzing the assembled genome of D263, we found over 100 potential genes for cellulolytic enzymes that are distinct from those of the commercially used fungal species Trichoderma reesei and Aspergillus niger. We further identified several of these cellulolytic enzymes by mass spectrometry. CONCLUSIONS: The genome of Daldinia caldariorum D263 has been sequenced and annotated taking advantage of a simple yet specific zymography method followed by mass spectrometry analysis, and it appears to encode and secrete a wide range of bioactive cellulolytic enzymes. The genome and cellulolytic enzyme secretion of this unique endophytic fungus should be of value for identifying active cellulolytic enzymes that can facilitate conversion of agricultural wastes to fermentable sugars for the industrial production of biofuels.

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier.

Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several orders of magnitude in abundance. Machine learning tools that effectively leverage these data to accurately identify disease states are in high demand. While mass spectrometry data sets are rich with potentially useful information, using the data effectively can be challenging because of missing entries in the data sets and because the number of samples is typically much smaller than the number of features, two challenges that make machine learning difficult. To address this problem, we have modified a new supervised classification tool, the Aristotle Classifier, so that omics data sets can be better leveraged for identifying disease states. The optimized classifier, AC.2021, is benchmarked on multiple data sets against its predecessor and two leading supervised classification tools, Support Vector Machine (SVM) and XGBoost. The new classifier, AC.2021, outperformed existing tools on multiple tests using proteomics data. The underlying code for the classifier, provided herein, would be useful for researchers who desire improved classification accuracy when using their omics data sets to identify disease states.

The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data.

One unifying challenge when classifying biological samples with mass spectrometry data is overcoming the obstacle of sample-to-sample variability so that differences between groups, such as between a healthy set and a disease set, can be identified. Similarly, when the same sample is re-analyzed under identical conditions, instrument signals can fluctuate by more than 10%. This signal inconsistency imposes difficulties in identifying subtle differences across a set of samples, and it weakens the mass spectrometrist's ability to effectively leverage data in domains as diverse as proteomics, metabolomics, glycomics, and imaging. We selected challenging data sets in the fields of glycomics, mass spectrometry imaging, and bacterial typing to study the problem of within-group signal variability and adapted a 30-year-old statistical approach to address the problem. The solution, "local-balanced model," relies on using balanced subsets of training data to classify test samples. This analysis strategy was assessed on ESI-MS data of IgG-based glycopeptides and MALDI-MS imaging data of endogenous lipids, and MALDI-MS data of bacterial proteins. Two preliminary examples on non-mass spectrometry data sets are also included to show the potential generality of the method outside the field of MS analysis. We demonstrate that this approach is superior to simple normalization methods, generalizable to multiple mass spectrometry domains, and potentially appropriate in fields as diverse as physics and satellite imaging. In some cases, improvements in classification can be dramatic, with accuracy escalating from 60% with normalization alone to over 90% with the additional development described herein.

How does rice cope with too little oxygen during its early life?

Most crops cannot germinate underwater. Rice exhibits certain degrees of tolerance to oxygen deficiency for anaerobic germination (AG) and anaerobic seedling development (ASD). Direct rice seeding, whereby seeds are sown into soil rather than transplanting seedlings from the nursery, becomes an increasingly popular cultivation method due to labor shortages and opportunities for sustainable cultivation. Flooding is common under direct seeding, but most rice varieties have poor capability of AG/ASD, which is a major obstacle to broad adoption of direct seeding. A better understanding of the physiological basis and molecular mechanisms regulating AG/ASD should facilitate rice breeding for enhanced seedling vigor under flooding. This review highlights recent advances on molecular and physiological mechanisms and future breeding strategies of rice AG/ASD.

How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab.

As the field of mass spectrometry imaging continues to grow, so too do its needs for optimal methods of data analysis. One general need in image analysis is the ability to classify the underlying regions within an image, as healthy or diseased, for example. Classification, as a general problem, is often best accomplished by supervised machine learning strategies; unfortunately, conducting supervised machine learning on MS imaging files is not typically done by mass spectrometrists because a high degree of specialized knowledge is needed. To address this problem, we developed a fully open-source approach that facilitates supervised machine learning on MS imaging files, and we demonstrated its implementation on sets of cancer spheroids that either had or had not undergone chemotherapy treatment. These supervised machine learning studies demonstrated that metabolic changes induced by the monoclonal antibody, Cetuximab, are detectable but modest at 24 h, and by 72 h, the drug induces a larger and more diverse metabolic response.

An Intrinsically Disordered Protein Interacts with the Cytoskeleton for Adaptive Root Growth under Stress.

Intrinsically disordered proteins function as flexible stress modulators in vivo through largely unknown mechanisms. Here, we elucidated the mechanistic role of an intrinsically disordered protein, REPETITIVE PRO-RICH PROTEIN (RePRP), in regulating rice (Oryza sativa) root growth under water deficit. With nearly 40% Pro, RePRP is induced by water deficit and abscisic acid (ABA) in the root elongation zone. RePRP is sufficient and necessary for repression of root development by water deficit or ABA. We showed that RePRP interacts with the highly ordered cytoskeleton components actin and tubulin both in vivo and in vitro. Binding of RePRP reduces the abundance of actin filaments, thus diminishing noncellulosic polysaccharide transport to the cell wall and increasing the enzyme activity of Suc synthase. RePRP also reorients the microtubule network, which leads to disordered cellulose microfibril organization in the cell wall. The cell wall modification suppresses root cell elongation, thereby generating short roots, whereas increased Suc synthase activity triggers starch accumulation in "heavy" roots. Intrinsically disordered proteins control cell elongation and carbon reserves via an order-by-disorder mechanism, regulating the highly ordered cytoskeleton for development of "short-but-heavy" roots as an adaptive response to water deficit in rice.

Rice Big Grain 1 promotes cell division to enhance organ development, stress tolerance and grain yield.

Grain/seed yield and plant stress tolerance are two major traits that determine the yield potential of many crops. In cereals, grain size is one of the key factors affecting grain yield. Here, we identify and characterize a newly discovered gene Rice Big Grain 1 (RBG1) that regulates grain and organ development, as well as abiotic stress tolerance. Ectopic expression of RBG1 leads to significant increases in the size of not only grains but also other major organs such as roots, shoots and panicles. Increased grain size is primarily due to elevated cell numbers rather than cell enlargement. RBG1 is preferentially expressed in meristematic and proliferating tissues. Ectopic expression of RBG1 promotes cell division, and RBG1 co-localizes with microtubules known to be involved in cell division, which may account for the increase in organ size. Ectopic expression of RBG1 also increases auxin accumulation and sensitivity, which facilitates root development, particularly crown roots. Moreover, overexpression of RBG1 up-regulated a large number of heat-shock proteins, leading to enhanced tolerance to heat, osmotic and salt stresses, as well as rapid recovery from water-deficit stress. Ectopic expression of RBG1 regulated by a specific constitutive promoter, GOS2, enhanced harvest index and grain yield in rice. Taken together, we have discovered that RBG1 regulates two distinct and important traits in rice, namely grain yield and stress tolerance, via its effects on cell division, auxin and stress protein induction.

Adaption of the Aristotle Classifier for Accurately Identifying Highly Similar Bacteria Analyzed by MALDI-TOF MS.

MALDI-TOF MS has shown great utility for rapidly identifying microbial species. It can be used to successfully type bacteria and fungi from a variety of sources more rapidly and cost-effectively than traditional methods. One area where improvements are necessary is in the typing of highly similar samples, such as those samples from the same genus but different species or samples from within a single species but from different strains. One promising way to address this current limitation is by using advanced machine learning techniques. In this work, we adapt a newly developed machine learning tool, the Aristotle Classifier, to bacterial classification of MALDI-TOF MS data. This tool was originally developed for classifying glycomics and glycoproteomics data, so we modified it to be well-suited for assigning mass spectral data from bacterial proteins. The classifier exceeds existing benchmarks in classifying bacteria, and it shows particularly strong performance when the samples to be identified are highly similar. The combination of mass spectrometry data and tools like the Aristotle Classifier could ameliorate the ambiguities associated with challenging bacterial classification problems.

Chaetomella raphigera ß-glucosidase D2-BGL has intriguing structural features and a high substrate affinity that renders it an efficient cellulase supplement for lignocellulosic biomass hydrolysis.

BACKGROUND: To produce second-generation biofuels, enzymatic catalysis is required to convert cellulose from lignocellulosic biomass into fermentable sugars. ß-Glucosidases finalize the process by hydrolyzing cellobiose into glucose, so the efficiency of cellulose hydrolysis largely depends on the quantity and quality of these enzymes used during saccharification. Accordingly, to reduce biofuel production costs, new microbial strains are needed that can produce highly efficient enzymes on a large scale. RESULTS: We heterologously expressed the fungal ß-glucosidase D2-BGL from a Taiwanese indigenous fungus Chaetomella raphigera in Pichia pastoris for constitutive production by fermentation. Recombinant D2-BGL presented significantly higher substrate affinity than the commercial ß-glucosidase Novozyme 188 (N188; K m = 0.2 vs 2.14 mM for p-nitrophenyl ß-d-glucopyranoside and 0.96 vs 2.38 mM for cellobiose). When combined with RUT-C30 cellulases, it hydrolyzed acid-pretreated lignocellulosic biomasses more efficiently than the commercial cellulase mixture CTec3. The extent of conversion from cellulose to glucose was 83% for sugarcane bagasse and 63% for rice straws. Compared to N188, use of D2-BGL halved the time necessary to produce maximal levels of ethanol by a semi-simultaneous saccharification and fermentation process. We upscaled production of recombinant D2-BGL to 33.6 U/mL within 15 days using a 1-ton bioreactor. Crystal structure analysis revealed that D2-BGL belongs to glycoside hydrolase (GH) family 3. Removing the N-glycosylation N68 or O-glycosylation T431 residues by site-directed mutagenesis negatively affected enzyme production in P. pastoris. The F256 substrate-binding residue in D2-BGL is located in a shorter loop surrounding the active site pocket relative to that of Aspergillus ß-glucosidases, and this short loop is responsible for its high substrate affinity toward cellobiose. CONCLUSIONS: D2-BGL is an efficient supplement for lignocellulosic biomass saccharification, and we upscaled production of this enzyme using a 1-ton bioreactor. Enzyme production could be further improved using optimized fermentation, which could reduce biofuel production costs. Our structure analysis of D2-BGL offers new insights into GH3 ß-glucosidases, which will be useful for strain improvements via a structure-based mutagenesis approach.

Sugar starvation-regulated MYBS2 and 14-3-3 protein interactions enhance plant growth, stress tolerance, and grain weight in rice.

Autotrophic plants have evolved distinctive mechanisms for maintaining a range of homeostatic states for sugars. The on/off switch of reversible gene expression by sugar starvation/provision represents one of the major mechanisms by which sugar levels are maintained, but the details remain unclear. α-Amylase (αAmy) is the key enzyme for hydrolyzing starch into sugars for plant growth, and it is induced by sugar starvation and repressed by sugar provision. αAmy can also be induced by various other stresses, but the physiological significance is unclear. Here, we reveal that the on/off switch of αAmy expression is regulated by 2 MYB transcription factors competing for the same promoter element. MYBS1 promotes αAmy expression under sugar starvation, whereas MYBS2 represses it. Sugar starvation promotes nuclear import of MYBS1 and nuclear export of MYBS2, whereas sugar provision has the opposite effects. Phosphorylation of MYBS2 at distinct serine residues plays important roles in regulating its sugar-dependent nucleocytoplasmic shuttling and maintenance in cytoplasm by 14-3-3 proteins. Moreover, dehydration, heat, and osmotic stress repress MYBS2 expression, thereby inducing αAmy3 Importantly, activation of αAmy3 and suppression of MYBS2 enhances plant growth, stress tolerance, and total grain weight per plant in rice. Our findings reveal insights into a unique regulatory mechanism for an on/off switch of reversible gene expression in maintaining sugar homeostatic states, which tightly regulates plant growth and development, and also highlight MYBS2 and αAmy3 as potential targets for crop improvement.

The Aristotle Classifier: Using the Whole Glycomic Profile To Indicate a Disease State.

"The totality is not, as it were, a mere heap, but the whole is something besides the parts."-Aristotle. We built a classifier that uses the totality of the glycomic profile, not restricted to a few glycoforms, to differentiate samples from two different sources. This approach, which relies on using thousands of features, is a radical departure from current strategies, where most of the glycomic profile is ignored in favor of selecting a few features, or even a single feature, meant to capture the differences in sample types. The classifier can be used to differentiate the source of the material; applicable sources may be different species of animals, different protein production methods, or, most importantly, different biological states (disease vs healthy). The classifier can be used on glycomic data in any form, including derivatized monosaccharides, intact glycans, or glycopeptides. It takes advantage of the fact that changing the source material can cause a change in the glycomic profile in many subtle ways: some glycoforms can be upregulated, some downregulated, some may appear unchanged, yet their proportion-with respect to other forms present-can be altered to a detectable degree. By classifying samples using the entirety of their glycan abundances, along with the glycans' relative proportions to each other, the "Aristotle Classifier" is more effective at capturing the underlying trends than standard classification procedures used in glycomics, including PCA (principal components analysis). It also outperforms workflows where a single, representative glycomic-based biomarker is used to classify samples. We describe the Aristotle Classifier and provide several examples of its utility for biomarker studies and other classification problems using glycomic data from several sources.

Enhancement of laccase activity by pre-incubation with organic solvents.

Laccases that are tolerant to organic solvents are powerful bio-catalysts with broad applications in biotechnology. Most of these uses must be accomplished at high concentration of organic solvents, during which proteins undergo unfolding, thereby losing enzyme activity. Here we show that organic-solvent pre-incubation provides effective and reversible 1.5- to 4.0-fold enhancement of enzyme activity of fungal laccases. Several organic solvents, including acetone, methanol, ethanol, DMSO, and DMF had an enhancement effect among all laccases studied. The enhancement was not substrate-specific and could be observed by using both phenolic and non-phenolic substrates. Laccase preincubated with organic solvents was sensitive to high temperature but remained stable at 25 °C, for an advantage for long-term storage. The acetone-pre-incubated 3-D structure of DLac, a high-efficiency fungal laccase, was determined and confirmed that the DLac protein structure remains intact and stable at a high concentration of organic solvent. Moreover, the turnover rates of fungal laccases were improved after organic-solvent pre-incubation, with DLac showing the highest enhancement among the fungal laccases examined. Our investigation sheds light on improving fungal laccase usage under extreme conditions and extends opportunities for bioremediation, decolorization, and organic synthesis.