Metabolomics-based analysis of the diatom Cheatoceros tenuissimus combining NMR and GC-MS techniques.

Metabolomics, a recent addition to omics sciences, studies small molecules across plants, animals, humans, and marine organisms. Nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS) are widely used in those studies, including microalgae metabolomics. NMR is non-destructive and highly reproducible but has limited sensitivity, which could be supplemented by joining GC-MS analysis. Extracting metabolites from macromolecules requires optimization for trustworthy results. Different extraction methods yield distinct profiles, emphasizing the need for optimization. The results indicated that the optimized extraction procedure successfully identified NMR and GC-MS-based metabolites in MeOH, CHCl3, and H2O extraction solvents. The findings represented the spectral information related to carbohydrates, organic molecules, and amino acids from the water-soluble metabolites fraction and a series of fatty acid chains, lipids, and sterols from the lipid fraction. Our study underscores the benefit of combining NMR and GC-MS techniques to comprehensively understand microalgae metabolomes, including high and low metabolite concentrations and abundances.•In this study, we focused on optimizing the extraction procedure and combining NMR and GC-MS techniques to overcome the low NMR sensitivity and the different detected range limits of NMR and GC-MS.•We explored metabolome diversity in a tropical strain of the small cells' diatom Cheatoceros tenuissimus.

Identification of primary metabolites in fungal species of Trichophyton mentagrophyte and Trichophyton rubrum by NMR spectroscopy.

BACKGROUND: Superficial mycoses are fungal infections limited to the outermost layers of the skin and its appendages. The chief causative agents of these mycoses are dermatophytes and yeasts. The diagnosis of dermatophytosis can be made by direct mycological examination with potassium hydroxide (10%-30%) of biological material obtained from patients with suspected mycosis, providing results more rapid than fungal cultures, which may take days or weeks. This information, together with clinical history and laboratory diagnosis, ensures that the appropriate treatment is initiated promptly. However, false negative results are obtained in 5%-15%, by conventional methods of diagnosis of dermatophytosis. OBJECTIVES: To study the metabolic profiles of the commonly occurring dermatophytes by NMR spectroscopy. PATIENTS/MATERIALS: We have used 1D and 2D Nuclear Magnetic Resonance (NMR) experiments along with Human Metabolome Database (HMDB) and Chenomx database search for identification of primary metabolites in the methanol extract of two fungal species: Trichophyton mentagrophyte (T. mentagrophyte) and Trichophyton rubrum (T. rubrum). Both standard strains and representative number of clinical isolates of these two species were investigated. Further, metabolic profiles obtained were analysed using multivariate analysis. RESULTS: We have identified 23 metabolites in the T. mentagrophyte and another 23 metabolites in T. rubrum. Many important metabolites like trehalose, proline, mannitol, acetate, GABA and several other amino acids were detected, which provide the necessary components for fungal growth and metabolism. Altered metabolites were defined between Trichophyton mentagrophyte and T. rubrum strains. CONCLUSION: We have detected many metabolites in the two fungal species T. mentagrophyte and T. rubrum by using NMR spectroscopy. NMR spectroscopy provides a holistic snapshot of the metabolome of an organism. Key metabolic differences were identified between the two fungal strains. We need to perform more studies on metabolite profiling of the samples from these species for their rapid diagnosis and prompt treatment.

Synthesis and characterization of bis-amide SSE1917 as a microtubule-stabilizing anticancer agent.

Microtubule dynamics are critical for spindle assembly and chromosome segregation during cell division. Pharmacological inhibition of microtubule dynamics in cells causes prolonged mitotic arrest, resulting in apoptosis, an approach extensively employed in treating different types of cancers. The present study reports the synthesis of thirty-two novel bis-amides (SSE1901-SSE1932) and the evaluation of their antiproliferative activities. N-(1-oxo-3-phenyl-1-(phenylamino)propan-2-yl)benzamide (SSE1917) exhibited the most potent activity with GI50 values of 0.331 ± 0.01 µM in HCT116 colorectal and 0.48 ± 0.27 µM in BT-549 breast cancer cells. SSE1917 stabilized microtubules in biochemical and cellular assays, bound to taxol site in docking studies, and caused aberrant mitosis and G2/M arrest in cells. Prolonged treatment of cells with the compound increased p53 expression and triggered apoptotic cell death. Furthermore, SSE1917 suppressed the growth of both mouse and patient-derived human colon cancer organoids, highlighting its potential therapeutic value as an anticancer agent.

Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study.

Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.

Effects of integrated plant nutrition systems with fertilizer deep placement on rice yields and nitrogen use efficiency under different irrigation regimes.

Improved fertilizer management, with a combination of organic and inorganic inputs, has the potential to enhance rice yield while maintaining soil health. However, studies on the effects of broadcast prilled urea (PU) and urea deep placement (UDP) applied in combination with organic inputs (poultry litter [PL] and vermicompost [VC]), as integrated plant nutrition systems (IPNSs), on rice yields and nitrogen use efficiency (NUE) under alternate wetting and drying (AWD) irrigation are limited. We conducted field experiments during the dry and wet seasons of 2018, 2019, and 2020 to investigate the effects of fertilizer treatments, including control (no nitrogen), UDP, PU, and IPNSs (PU + VC, PU + PL, and UDP + PL) on rice yield and NUE under two irrigation regimes - AWD and continuous flooding (CF). The results revealed that fertilizer treatment and irrigation regime had significant (p < 0.05) interaction effects on rice yield and the agronomic efficiency of N (AEN) during the dry season. UDP significantly (p < 0.05) boosted rice yield, total dry matter (TDM), and NUE as compared to broadcast PU in both wet and dry seasons. Similarly, the IPNS treatment of UDP with PL significantly (p < 0.05) boosted rice yield, TDM, and NUE in comparison to broadcast PU. Under AWD irrigation, UDP alone produced higher rice yields than other treatments, while UDP, and UDP with PL produced similar yields under CF irrigation. During the dry season, AWD irrigation significantly (p < 0.05) increased rice yield, TDM, and AEN when compared to CF conditions, but during the wet season, AWD irrigation demonstrated a rice yield and NUE equivalent to CF. This research implies that using a UDP alone or in combination with PL as an IPNS could be a good way to boost crop productivity while also maintaining soil fertility.

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies.

INTRODUCTION: Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra. OBJECTIVES: The aim of this study is to tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals. METHODS: We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS). RESULTS: The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites. CONCLUSION: This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.

Untargeted metabolomics analysis of four date palm (Phoenix dactylifera L.) cultivars using MS and NMR.

Since ancient times, the inhabitants of dry areas have depended on the date palm (Phoenix dactylifera L.) as a staple food and means of economic security. For example, dates have been a staple diet for the inhabitants of the Arabian Peninsula and Sahara Desert in North Africa for millennia and the local culture is rich in knowledge and experience with the benefits of dates, suggesting that dates contain many substances essential for the human body. Madinah dates are considered one of the most important types of dates in the Arabian Peninsula, with Ajwa being one of the most famous types and grown only in Madinah, Saudi Arabia. Date seeds are traditionally used for animal feed, seed oil production, cosmetics, and as a coffee substitute. Phytochemical compounds that have been detected in date fruits and date seeds include phenolic acids, carotenoids, and flavonoids. Phenolic acids are the most prevalent bioactive constituents that contribute to the antioxidant activity of date fruits. The bioactive properties of these phytochemicals are believed to promote human health by reducing the risk of diseases such as chronic inflammation. Ajwa dates especially are thought to have superior bioactivity properties. To investigate these claims, in this study, we compare the metabolic profiles of Ajwa with different types of dates collected from Saudi Arabia and Tunisia. We show by UHPLC-MS that date seeds contain several classes of flavonoids, phenolic acids, and amino acid derivatives, including citric acid, malic acid, lactic acid, and hydroxyadipic acid. Additionally, GC-MS profiling showed that date seeds are richer in metabolite classes, such as hydrocinnamic acids (caffeic, ferulic and sinapic acids), than flesh samples. Deglet N fruit extract (minimum inhibitory concentration: 27 MIC/µM) and Sukkari fruit extract (IC50: 479 ± 0.58µg /mL) have higher levels of antibacterial and antioxidative activity than Ajwa fruits. However, the seed analysis showed that seed extracts have better bioactivity effects than fruit extracts. Specifically, Ajwa extract showed the best MIC and strongest ABTS radical-scavenging activity among examined seed extracts (minimum inhibitory concentration: 20 µM; IC50: 54 ± 3.61µg /mL). Our assays are a starting point for more advanced in vitro antibacterial models and investigation into the specific molecules that are responsible for the antioxidative and anti-bacterial activities of dates.

Physicochemical and bioactive constituents, microbial counts, and color components of spray-dried Syzygium cumini L. pulp powder stored in different packaging materials under two controlled environmental conditions.

Currently, the demand for functional food items that impart health benefits has been rising. Blackberry (Syzygium cumini L.) fruit has high anthocyanin content and other functional attributes. However, this seasonal fruit is highly perishable, and a large proportion of it goes unharvested and wasted worldwide. Spray drying of the fruit pulp can impart improved shelf life, ensuring long-term availability for consumers to exploit its health benefits. The storage quality varies according to the type of packaging material and the storage environment. Therefore, in this study, the shelf life span of the spray-dried Syzygium cumini L. pulp powder (SSCPP) was investigated during 6 months of storage under three types of packaging materials (i.e., polystyrene, metalized polyester, and 4-ply laminates) in a low-temperature environmental (LTE) and at ambient environmental conditions. The physicochemical stability of bioactive principles (TPC and TAC), microbial counts, and color components were analyzed at 0, 2, 4, and 6 months of storage. There was a significant gradual loss of dispersibility and solubility with an increase in flowability, bulk density, and wettability during the entire storage period for all three packaging materials. The TSS, pH, TPC, TAC, and microbial counts decreased in the SSCPP both at ambient and LTE conditions during the study. Among all the packaging materials, the 4-ply laminate was found to be the most appropriate and safe for storage of spray-dried SCPP at LTE conditions.

Fishery, growth, mortality, and stock assessment of endangered Tor putitora from Tehri dam reservoir, Uttarakhand, Himalayan foothills of India in relation to environmental variables.

The current study determined Tor putitora (Hamilton, 1822) fishery, growth, mortality, and population characteristics using length-frequency data assembled monthly from the Tehri dam reservoir in Uttarakhand from January to December 2022. The estimation data was separated into 40-mm class intervals, and population parameters were investigated and computed using the FiSAT-II software tool. W = 0.0101 L2.996, where a = 0.0101 and b = 2.99, were determined as the length-weight relationships, and the growth performance index (ϕ) was computed to be 5.40. Tor putitora commercial catches in the Tehri dam reservoir were dominated by length groups of 360-399 and 320-359 mm. Different growth parameters were estimated using length-frequency data as L∞ = 987.00 mm, K = 0.26 yr-1, and t0 = -0.0003 years. Z, M, and F mortality coefficients were estimated to be 1.01, 0.27, and 0.73, respectively. At the end of the first, second, third, fourth, fifth, sixth, and seventh years, the fish measured 226, 400, 535, 638, 718, and 780, and 827, respectively. The estimated value of the exploitation rate (E) was 0.73 using the length-converted catch curve approach, which was determined to be somewhat higher than the optimum value (0.50). Tor putitora recruitment patterns from the Tehri dam reservoir reveal that the species only has one recruitment pattern every year, and that solely occurs from June to September. The current exploitation level (0.73) has already exceeded the maximum fishing pressure (Emax = 0.508), indicating that there is a decline in the catch at the current fishing pressure, and a further increase in fishing efforts may lead to a decline in the stock, which may be detrimental to the sustainable fishery of Tor putitora in the Tehri dam reservoir, Uttarakhand, India.

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses.

NMR-based metabolomics approaches have been used in a wide range of applications, for example, with medical, plant, and marine samples. One-dimensional (1D) 1H NMR is routinely used to find out biomarkers in biofluids such as urine, blood plasma, and serum. To mimic biological conditions, most NMR studies have been carried out in an aqueous solution where the high intensity of the water peak is a major problem in obtaining a meaningful spectrum. Different methods have been used to suppress the water signal, including 1D Carr-Purcell-Meiboom-Gill (CPMG) presat, consisting of a T2 filter to suppress macromolecule signals and reduce the humped curve in the spectrum. 1D nuclear Overhauser enhancement spectroscopy (NOESY) is another method for water suppression that is used routinely in plant samples with fewer macromolecules than in biofluid samples. Other common 1D 1H NMR methods such as 1D 1H presat and 1D 1H ES have simple pulse sequences; their acquisition parameters can be set easily. The proton with presat has just one pulse and the presat block causes water suppression, while other 1D 1H NMR methods including those mentioned above have more pulses. However, it is not well known in metabolomics studies because it is used only occasionally and in a few types of samples by metabolomics experts. Another effective method is excitation sculpting to suppress water. Herein, we evaluate the effect of method selection on signal intensities of commonly detected metabolites. Different classes of samples including biofluid, plant, and marine samples were investigated, and recommendations on the advantages and limitations of each method are presented.

Association of clinical factors with socio-occupational functioning among individuals with schizophrenia.

Background: Schizophrenia is assumed to be developing into a marked disability affecting performances in educational and vocational fields for both males and females. It is a psychiatric disorder that has been proven to be associated with poor occupational skills and functioning. This research aimed at studying the clinical correlates of socio-occupational functioning skills of persons with schizophrenia as per the ICD-10, DCR criteria. Materials and Methods: The is a cross-sectional descriptive study that included 200 participants diagnosed with schizophrenia, in which sociode mographic and clinical data sheet and Socio-occupational Functioning Scale (SOFS) by Saraswat et al. were used. Results: Mean age of the participants was 34 years, with onset of illness being 24 years of age. The results indicated significant correlation at 0.05 level between the total score of SOFS and age of onset and duration of illness. Conclusion: It can be concluded that for a better treatment outcome in a disorder like schizophrenia, bio-psychosocial model of treatment is necessary.

Nature-inspired stochastic hybrid technique for joint and individual inversion of DC and MT data.

Here, a new naturally-inspired stochastic nonlinear joint and individual inversion technique for integrating direct current (DC) and magnetotelluric (MT) data interpretation-based simulation of a swarm intelligence combo with specific capabilities for exploitation of the variable weight particle swarm optimizer (vPSO) and exploration of the grey wolf optimizer (GWO), vPSOGWO, is used. They are particularly notable for their capacity for information exchange while hunting for food. Through synthetic MT and DC data contaminated with various sets of random noise, the applicability of the anticipated vPSOGWO algorithm based joint and individual inversion algorithm was assessed. The field examples, collected from diversified different geological terrains, including Digha (West Bengal), India; Sundar Pahari (Jharkhand), India; Puga Valley (Ladakh), India; New Brunswick, Canada; and South Central Australia, have shown the practical application of the proposed algorithm. Further, a Bayesian probability density function (bpdf) for estimating a mean global model and uncertainty assessment in posterior; and a histogram for model resolution assessment have also been created using 1000 inverted models. We examined the inverted outcomes and compared them with results from other cutting-edge methodologies, including the GWO, PSO, genetic algorithm (GA), Levenberg-Marquardt (LM), and ridge-regression (RR). Our findings showed that the current methodology is more effective than the GWO, PSO, GA, LM, and RR techniques at consistently improving the convergence of the global minimum. In contrast to earlier approaches, the current cutting-edge strategy vPSOGWO offers an improved resolution of an additional significant crustal thickness of about 65.68 ± 1.96 km over the Puga Valley, in which the inverted crustal thickness determined by vPSOGWO agrees well with the published crustal thickness over the Puga Valley. The new technology brings simulations closer to genuine models by significantly reducing uncertainty and enhancing model resolution.

Resolving the phylogenetic relationship of Himalayan snow trout Schizothorax plagiostomus with other species of Schizothoracine using mitochondrial CO-I and Cyt b genes.

BACKGROUND: The classification of the sub-family Schizothoracinae has been debatable due to the overlap in morphological characters. There are discrepancies between classical taxonomy and molecular taxonomy, as well. In the present study, mitochondrial genes CO-I and Cyt b were sequenced to elucidate the phylogenetic status of three species of the genus Schizothorax. METHODS AND RESULTS: In total, 29 samples of three species viz., S. plagiostomus, S. progastus, and S. richardsonii, were collected from rivers of Uttarakhand, India. For phylogenetic analyses, 40 sequences of CO-I and 41 sequences of Cyt b of Schizothoracinae species were downloaded from NCBI. The highest genetic divergence based on CO-I (16.08%) is between S. plagiostomus and Ptychobarbus dipogon, while the lowest divergence (0.00%) is between 10 pairs of species. The highest divergence based on Cyt b (19.43%), is between S. niger and Gymnocypris eckloni, while the lowest divergence (0.00%) is between four pairs of species. The divergence (0.00% for CO-I and 2.38% for Cyt b) between S. chongi and S. kozlovi, seems a case of convergent molecular evolution of the CO-I gene and in this case, CO-I alone cannot be used to differentiate these two species. CONCLUSION: The simultaneous use of two molecular markers along with morphomeristic data is a better strategy for the classification of the sub-family Schizothoracinae. These results will be a resource dataset for determining the taxonomical status of Schizothoracine species and will help in the conservation and commercial production of these commercially important fish species.

Towards improving e-commerce customer review analysis for sentiment detection.

According to a report published by Business Wire, the market value of e-commerce reached US$ 13 trillion and is expected to reach US$ 55.6 trillion by 2027. In this rapidly growing market, product and service reviews can influence our purchasing decisions. It is challenging to manually evaluate reviews to make decisions and examine business models. However, users can examine and automate this process with Natural Language Processing (NLP). NLP is a well-known technique for evaluating and extracting information from written or audible texts. NLP research investigates the social architecture of societies. This article analyses the Amazon dataset using various combinations of voice components and deep learning. The suggested module focuses on identifying sentences as 'Positive', 'Neutral', 'Negative', or 'Indifferent'. It analyses the data and labels the 'better' and 'worse' assumptions as positive and negative, respectively. With the expansion of the internet and e-commerce websites over the past decade, consumers now have a vast selection of products within the same domain, and NLP plays a vital part in classifying products based on evaluations. It is possible to predict sponsored and unpaid reviews using NLP with Machine Learning. This article examined various Machine Learning algorithms for predicting the sentiment of e-commerce website reviews. The automation achieves a maximum validation accuracy of 79.83% when using Fast Text as word embedding and the Multi-channel Convolution Neural Network.

Dusquetide modulates innate immune response through binding to p62.

SQSTM1/p62 is an autophagic receptor that plays a major role in mediating stress and innate immune responses. Preclinical studies identified p62 as a target of the prototype innate defense regulator (IDR); however, the molecular mechanism of this process remains unclear. Here, we describe the structural basis and biological consequences of the interaction of p62 with the next generation of IDRs, dusquetide. Both electrostatic and hydrophobic contacts drive the formation of the complex between dusquetide and the ZZ domain of p62. We show that dusquetide penetrates the cell membrane and associates with p62 in vivo. Dusquetide binding modulates the p62-RIP1 complex, increases p38 phosphorylation, and enhances CEBP/B expression without activating autophagy. Our findings provide molecular details underlying the IDR action that may help in the development of new strategies to pharmacologically target p62.

Chitosan nanomaterials: A prelim of next-generation fertilizers; existing and future prospects.

Global agriculture is urgently seeking ways to mitigate the detrimental effects of conventional chemical fertilizers on the environment. Biodegradable, eco-friendly, renewable energy-sourced next-generation fertilizers could be an answer, allowing for improved nutrient use efficiency and a lower environmental footprint. During the last decade, agricultural research on chitosan nanomaterials (NMs) has expanded, demonstrating their usefulness in enhancing agricultural output not only as plant immune boosters but also via slow, controlled and target delivery of nutrients to plants. Chitosan NMs natively act as an abundant nutrient source of C (54.4-47.9 wt%), O (42.3-30.19 wt%), N (7.6-5.8 wt%), and P (6.1-3.4 wt%) to plants. Moreover, chitosan NMs can further functionalized by more nutrients payloads through its functional groups. The current review investigates the technical features of chitosan NMs as prospective next-generation fertilizers based on rationales. The review offers crucial insights into future directions, sources, production capacity of chitosan-based next-generation nanofertilizers for industrial-scale manufacturing.

Mitigating greenhouse gas emissions from irrigated rice cultivation through improved fertilizer and water management.

Greenhouse gas (GHG) emissions from agriculture sector play an important role for global warming and climate change. Thus, it is necessary to find out GHG emissions mitigation strategies from rice cultivation. The efficient management of nitrogen fertilizer using urea deep placement (UDP) and the use of the water-saving alternate wetting and drying (AWD) irrigation could mitigate greenhouse gas (GHG) emissions and reduce environmental pollution. However, there is a dearth of studies on the impacts of UDP and the integrated plant nutrient system (IPNS) which combines poultry manure and prilled urea (PU) with different irrigation regimes on GHG emissions, nitrogen use efficiency (NUE) and rice yields. We conducted field experiments during the dry seasons of 2018, 2019, and 2020 to compare the effects of four fertilizer treatments including control (no N), PU, UDP, and IPNS in combination with two irrigation systems- (AWD and continuous flooding, CF) on GHG emissions, NUE and rice yield. Fertilizer treatments had significant (p < 0.05) interaction effects with irrigation regimes on methane (CH4) and nitrous oxide (N2O) emissions. PU reduced CH4 and N2O emissions by 6% and 20% compared to IPNS treatment, respectively under AWD irrigation, but produced similar emissions under CF irrigation. Similarly, UDP reduced cumulative CH4 emissions by 9% and 15% under AWD irrigation, and 9% and 11% under CF condition compared to PU and IPNS treatments, respectively. Across the year and fertilizer treatments, AWD irrigation significantly (p < 0.05) reduced cumulative CH4 emissions and GHG intensity by 28%, and 26%, respectively without significant yield loss compared to CF condition. Although AWD irrigation increased cumulative N2O emissions by 73%, it reduced the total global warming potential by 27% compared to CF irrigation. The CH4 emission factor for AWD was lower (1.67 kg ha-1 day-1) compared to CF (2.33 kg ha-1 day-1). Across the irrigation regimes, UDP increased rice yield by 21% and N recovery efficiency by 58% compared to PU. These results suggest that both UDP and AWD irrigation might be considered as a carbon-friendly technology.

A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Protocol to identify drug-binding sites in proteins using solution NMR spectroscopy.

Dusquetide is a next-generation IDR (innate defense regulator) targeting the major autophagy receptor protein SQSTM1/p62 and modulating the innate immune response. Here, we describe a protocol for determining dusquetide-binding sites of p62 by solution NMR spectroscopy. Step-by-step technique details were provided, including sample preparation, NMR experiment setup, data processing, and binding site analysis. This protocol could be applied to characterize other small molecules targeting the ZZ domain of p62 (9 kDa) or other proteins containing ZZ domains. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022).

Influence of Transglutaminase Crosslinking on Casein Protein Fractionation during Low Temperature Microfiltration.

Low temperature microfiltration (MF) is applied in dairy processing to achieve higher protein and microbiological quality ingredients and to support ingredient innovation; however, low temperature reduces hydrophobic interactions between casein proteins and increases the solubility of colloidal calcium phosphate, promoting reversible dissociation of micellar ß-casein into the serum phase, and thus into permeate, during MF. Crosslinking of casein proteins using transglutaminase was studied as an approach to reduce the permeation of casein monomers, which typically results in reduced yield of protein in the retentate fraction. Two treatments (a) 5 °C/24 h (TA) and (b) 40 °C/90 min (TB), were applied to the feed before filtration at 5 °C, with a 0.1 µm membrane. Flux was high for TA treatment possibly due to the stabilising effect of transglutaminase on casein micelles. It is likely that formation of isopeptide bonds within and on the surface of micelles results in the micelles being less readily available for protein-protein and protein-membrane interactions, resulting in less resistance to membrane pores and flow passage, thereby conferring higher permeate flux. The results also showed that permeation of casein monomers into the permeate was significantly reduced after both enzymatic treatments as compared to control feed due to the reduced molecular mobility of soluble casein, mainly ß-casein, caused by transglutaminase crosslinking.