Differences in activity and stability drive transposable element variation in tropical and temperate maize.

Much of the profound interspecific variation in genome content has been attributed to transposable elements (TEs). To explore the extent of TE variation within species, we developed an optimized open-source algorithm, panEDTA, to de novo annotate TEs in a pangenome context. We then generated a unified TE annotation for a maize pangenome derived from 26 reference-quality genomes, which reveals an excess of 35.1 Mb of TE sequences per genome in tropical maize relative to temperate maize. A small number (n = 216) of TE families, mainly LTR retrotransposons, drive these differences. Evidence from the methylome, transcriptome, LTR age distribution, and LTR insertional polymorphisms reveals that 64.7% of the variability is contributed by LTR families that are young, less methylated, and more expressed in tropical maize, whereas 18.5% is driven by LTR families with removal or loss in temperate maize. Additionally, we find enrichment for Young LTR families adjacent to nucleotide-binding and leucine-rich repeat (NLR) clusters of varying copy number across lines, suggesting TE activity may be associated with disease resistance in maize.

Burden of Typhoid and Paratyphoid Fever in India.

BACKGROUND: In 2017, more than half the cases of typhoid fever worldwide were projected to have occurred in India. In the absence of contemporary population-based data, it is unclear whether declining trends of hospitalization for typhoid in India reflect increased antibiotic treatment or a true reduction in infection. METHODS: From 2017 through 2020, we conducted weekly surveillance for acute febrile illness and measured the incidence of typhoid fever (as confirmed on blood culture) in a prospective cohort of children between the ages of 6 months and 14 years at three urban sites and one rural site in India. At an additional urban site and five rural sites, we combined blood-culture testing of hospitalized patients who had a fever with survey data regarding health care use to estimate incidence in the community. RESULTS: A total of 24,062 children who were enrolled in four cohorts contributed 46,959 child-years of observation. Among these children, 299 culture-confirmed typhoid cases were recorded, with an incidence per 100,000 child-years of 576 to 1173 cases in urban sites and 35 in rural Pune. The estimated incidence of typhoid fever from hospital surveillance ranged from 12 to 1622 cases per 100,000 child-years among children between the ages of 6 months and 14 years and from 108 to 970 cases per 100,000 person-years among those who were 15 years of age or older. Salmonella enterica serovar Paratyphi was isolated from 33 children, for an overall incidence of 68 cases per 100,000 child-years after adjustment for age. CONCLUSIONS: The incidence of typhoid fever in urban India remains high, with generally lower estimates of incidence in most rural areas. (Funded by the Bill and Melinda Gates Foundation; NSSEFI Clinical Trials Registry of India number, CTRI/2017/09/009719; ISRCTN registry number, ISRCTN72938224.).

Steering CO2 hydrogenation toward C-C coupling to hydrocarbons using porous organic polymer/metal interfaces.

The conversion of CO2 into fuels and chemicals is an attractive option for mitigating CO2 emissions. Controlling the selectivity of this process is beneficial to produce desirable liquid fuels, but C-C coupling is a limiting step in the reaction that requires high pressures. Here, we propose a strategy to favor C-C coupling on a supported Ru/TiO2 catalyst by encapsulating it within the polymer layers of an imine-based porous organic polymer that controls its selectivity. Such polymer confinement modifies the CO2 hydrogenation behavior of the Ru surface, significantly enhancing the C2+ production turnover frequency by 10-fold. We demonstrate that the polymer layers affect the adsorption of reactants and intermediates while being stable under the demanding reaction conditions. Our findings highlight the promising opportunity of using polymer/metal interfaces for the rational engineering of active sites and as a general tool for controlling selective transformations in supported catalyst systems.

The coordinated regulation of early meiotic stages is dominated by non-coding RNAs and stage-specific transcription in wheat.

Reproductive success hinges on precisely coordinated meiosis, yet our understanding of how structural rearrangements of chromatin and phase transitions during meiosis are transcriptionally regulated is limited. In crop plants, detailed analysis of the meiotic transcriptome could identify regulatory genes and epigenetic regulators that can be targeted to increase recombination rates and broaden genetic variation, as well as provide a resource for comparison among eukaryotes of different taxa to answer outstanding questions about meiosis. We conducted a meiotic stage-specific analysis of messenger RNA (mRNA), small non-coding RNA (sncRNA), and long intervening/intergenic non-coding RNA (lincRNA) in wheat (Triticum aestivum L.) and revealed novel mechanisms of meiotic transcriptional regulation and meiosis-specific transcripts. Amidst general repression of mRNA expression, significant enrichment of ncRNAs was identified during prophase I relative to vegetative cells. The core meiotic transcriptome was comprised of 9309 meiosis-specific transcripts, 48 134 previously unannotated meiotic transcripts, and many known and novel ncRNAs differentially expressed at specific stages. The abundant meiotic sncRNAs controlled the reprogramming of central metabolic pathways by targeting genes involved in photosynthesis, glycolysis, hormone biosynthesis, and cellular homeostasis, and lincRNAs enhanced the expression of nearby genes. Alternative splicing was not evident in this polyploid species, but isoforms were switched at phase transitions. The novel, stage-specific regulatory controls uncovered here challenge the conventional understanding of this crucial biological process and provide a new resource of requisite knowledge for those aiming to directly modulate meiosis to improve crop plants. The wheat meiosis transcriptome dataset can be queried for genes of interest using an eFP browser located at https://bar.utoronto.ca/efp_wheat/cgi-bin/efpWeb.cgi?dataSource=Wheat_Meiosis.

Removal of Chromium and Arsenic from Water Using Polyol-Functionalized Porous Aromatic Frameworks.

Chromium and arsenic are two of the most problematic water pollutants due to their high toxicity and prevalence in various water streams. While adsorption and ion-exchange processes have been applied for the efficient removal of numerous toxic contaminants, including heavy metals, from water, these technologies display relatively low overall performances and stabilities for the remediation of chromium and arsenic oxyanions. This work presents the use of polyol-functionalized porous aromatic framework (PAF) adsorbent materials that use chelation, ion-exchange, redox activity, and hydrogen-bonding interactions for the highly selective capture of chromium and arsenic from water. The chromium and arsenic binding mechanisms within these materials are probed using an array of characterization techniques, including X-ray absorption and X-ray photoelectron spectroscopies. Adsorption studies reveal that the functionalized porous aromatic frameworks (PAFs) achieve selective, near-instantaneous (reaching equilibrium capacity within 10 s), and high-capacity (2.5 mmol/g) binding performances owing to their targeted chemistries, high porosities, and high functional group loadings. Cycling tests further demonstrate that the top-performing PAF material can be recycled using mild acid and base washes without any measurable performance loss over at least ten adsorption-desorption cycles. Finally, we establish chemical design principles enabling the selective removal of chromium, arsenic, and boron from water. To achieve this, we show that PAFs appended with analogous binding groups exhibit differences in adsorption behavior, revealing the importance of binding group length and chemical identity.

Extracellular vesicles from mouse trophoblast cells: Effects on neural progenitor cells and potential participants in the placenta-brain axis†.

The fetal brain of the mouse is thought to be dependent upon the placenta as a source of serotonin (5-hydroxytryptamine; 5-HT) and other factors. How factors reach the developing brain remains uncertain but are postulated here to be part of the cargo carried by placental extracellular vesicles (EV). We have analyzed the protein, catecholamine, and small RNA content of EV from mouse trophoblast stem cells (TSC) and TSC differentiated into parietal trophoblast giant cells (pTGC), potential primary purveyors of 5-HT. Current studies examined how exposure of mouse neural progenitor cells (NPC) to EV from either TSC or pTGC affect their transcriptome profiles. The EV from trophoblast cells contained relatively high amounts of 5-HT, as well as dopamine and norepinephrine, but there were no significant differences between EV derived from pTGC and from TSC. Content of miRNA and small nucleolar (sno)RNA, however, did differ according to EV source, and snoRNA were upregulated in EV from pTGC. The primary inferred targets of the microRNA (miRNA) from both pTGC and TSC were mRNA enriched in the fetal brain. NPC readily internalized EV, leading to changes in their transcriptome profiles. Transcripts regulated were mainly ones enriched in neural tissues. The transcripts in EV-treated NPC that demonstrated a likely complementarity with miRNA in EV were mainly up- rather than downregulated, with functions linked to neuronal processes. Our results are consistent with placenta-derived EV providing direct support for fetal brain development and being an integral part of the placenta-brain axis.

Getting 'ϕψχal' with proteins: minimum message length inference of joint distributions of backbone and sidechain dihedral angles.

The tendency of an amino acid to adopt certain configurations in folded proteins is treated here as a statistical estimation problem. We model the joint distribution of the observed mainchain and sidechain dihedral angles (〈ϕ,ψ,χ1,χ2,…〉) of any amino acid by a mixture of a product of von Mises probability distributions. This mixture model maps any vector of dihedral angles to a point on a multi-dimensional torus. The continuous space it uses to specify the dihedral angles provides an alternative to the commonly used rotamer libraries. These rotamer libraries discretize the space of dihedral angles into coarse angular bins, and cluster combinations of sidechain dihedral angles (〈χ1,χ2,…〉) as a function of backbone 〈ϕ,ψ〉 conformations. A 'good' model is one that is both concise and explains (compresses) observed data. Competing models can be compared directly and in particular our model is shown to outperform the Dunbrack rotamer library in terms of model complexity (by three orders of magnitude) and its fidelity (on average 20% more compression) when losslessly explaining the observed dihedral angle data across experimental resolutions of structures. Our method is unsupervised (with parameters estimated automatically) and uses information theory to determine the optimal complexity of the statistical model, thus avoiding under/over-fitting, a common pitfall in model selection problems. Our models are computationally inexpensive to sample from and are geared to support a number of downstream studies, ranging from experimental structure refinement, de novo protein design, and protein structure prediction. We call our collection of mixture models as PhiSiCal (ϕψχal). AVAILABILITY AND IMPLEMENTATION: PhiSiCal mixture models and programs to sample from them are available for download at http://lcb.infotech.monash.edu.au/phisical.

Antibiotic resistance: a global crisis, problems and solutions.

Healthy state is priority in today's world which can be achieved using effective medicines. But due to overuse and misuse of antibiotics, a menace of resistance has increased in pathogenic microbes. World Health Organization (WHO) has announced ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) as the top priority pathogens as these have developed resistance against certain antibiotics. To combat such a global issue, it is utmost important to identify novel therapeutic strategies/agents as an alternate to such antibiotics. To name certain antibiotic adjuvants including: inhibitors of beta-lactamase, efflux pumps and permeabilizers for outer membrane can potentially solve the antibiotic resistance problems. In this regard, inhibitors of lytic domain of lytic transglycosylases provide a novel way to not only act as an alternate to antibiotics but also capable of restoring the efficiency of previously resistant antibiotics. Further, use of bacteriophages is another promising strategy to deal with antibiotic resistant pathogens. Taking in consideration the alternatives of antibiotics, a green synthesis nanoparticle-based therapy exemplifies a good option to combat microbial resistance. As horizontal gene transfer (HGT) in bacteria facilitates the evolution of new resistance strains, therefore identifying the mechanism of resistance and development of inhibitors against it can be a novel approach to combat such problems. In our perspective, host-directed therapy (HDT) represents another promising strategy in combating antimicrobial resistance (AMR). This approach involves targeting specific factors within host cells that pathogens rely on for their survival, either through replication or persistence. As many new drugs are under clinical trials it is advisable that more clinical data and antimicrobial stewardship programs should be conducted to fully assess the clinical efficacy and safety of new therapeutic agents.

Fine mapping of major QTL qshgd1 for spontaneous haploid genome doubling in maize (Zea mays L.).

KEY MESSAGE: A large-effect QTL was fine mapped, which revealed 79 gene models, with 10 promising candidate genes, along with a novel inversion. In commercial maize breeding, doubled haploid (DH) technology is arguably the most efficient resource for rapidly developing novel, completely homozygous lines. However, the DH strategy, using in vivo haploid induction, currently requires the use of mutagenic agents which can be not only hazardous, but laborious. This study focuses on an alternative approach to develop DH lines-spontaneous haploid genome duplication (SHGD) via naturally restored haploid male fertility (HMF). Inbred lines A427 and Wf9, the former with high HMF and the latter with low HMF, were selected to fine-map a large-effect QTL associated with SHGD-qshgd1. SHGD alleles were derived from A427, with novel haploid recombinant groups having varying levels of the A427 chromosomal region recovered. The chromosomal region of interest is composed of 45 megabases (Mb) of genetic information on chromosome 5. Significant differences between haploid recombinant groups for HMF were identified, signaling the possibility of mapping the QTL more closely. Due to suppression of recombination from the proximity of the centromere, and a newly discovered inversion region, the associated QTL was only confined to a 25 Mb region, within which only a single recombinant was observed among ca. 9,000 BC1 individuals. Nevertheless, 79 gene models were identified within this 25 Mb region. Additionally, 10 promising candidate genes, based on RNA-seq data, are described for future evaluation, while the narrowed down genome region is accessible for straightforward introgression into elite germplasm by BC methods.

Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study.

Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy - the control energy required to maintain brain activation states - during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings provide a new perspective on the distributed emotion processing network and the effect of GABAergic modulation on this network, in addition to identifying an association between schizophrenia risk and abnormal GABAergic effects on persistence energy during threat processing.

A mathematical model of competition between fiber and mucin degraders in the gut provides a possible explanation for mucus thinning.

The human gut microbiota relies on complex carbohydrates (glycans) for energy and growth, primarily dietary fiber and host-derived mucins. We introduce a mathematical model of a glycan generalist and a mucin specialist in a two-compartment chemostat model of the human colon. Our objective is to characterize the influence of dietary fiber and mucin supply on the abundance of mucin-degrading species within the gut ecosystem. Current mathematical gut reactor models that include the enzymatic degradation of glycans do not differentiate between glycan types and their degraders. The model we present distinguishes between a generalist that can degrade both dietary fiber and mucin, and a specialist species that can only degrade mucin. The integrity of the colonic mucus barrier is essential for overall human health and well-being, with the mucin specialist Akkermanisa muciniphila being associated with a healthy mucus layer. Competition, particularly between the specialist and generalists like Bacteroides thetaiotaomicron, may lead to mucus layer erosion, especially during periods of dietary fiber deprivation. Our model treats the colon as a gut reactor system, dividing it into two compartments that represent the lumen and the mucus of the gut, resulting in a complex system of ordinary differential equations with a large and uncertain parameter space. To understand the influence of model parameters on long-term behavior, we employ a random forest classifier, a supervised machine learning method. Additionally, a variance-based sensitivity analysis is utilized to determine the sensitivity of steady-state values to changes in model parameter inputs. By constructing this model, we can investigate the underlying mechanisms that control gut microbiota composition and function, free from confounding factors.

Sarcoma in patients with Lynch syndrome and response to immunotherapy.

BACKGROUND: Lynch syndrome (LS) is an autosomal dominant genetic predisposition to multiple malignancies and is characterized by deficient DNA mismatch repair. Increased incidence of sarcomas is not formally ascribed to LS; however, increasing evidence suggests a preponderance of these malignancies in affected families. Sarcomas typically possess a low tumor mutational burden and incite a poor immune infiltrate, thereby rendering them poorly responsive to immunotherapy. METHODS: We searched the University of California, Los Angeles (UCLA) sarcoma program database for patients with a diagnosis of sarcoma and LS from 2016 to 2023. Three such patients were identified and all three were treated with PD1 blockade. RESULTS: We present three cases of LS-associated sarcomas (two soft tissue sarcoma and one osteosarcoma) with increased tumor mutational burdens. These patients were each treated with an anti-PD1 antibody and experienced a response far superior to that reported for non-LS-associated sarcomas. CONCLUSIONS: Increased mutational burden and immune infiltrate are observed for sarcomas associated with LS. Although unselected patients with sarcoma have demonstrated poor response rates to immunotherapy, our findings suggest that patients with Lynch-associated sarcomas are more likely to respond to treatment with anti-PD1. These patients should be given consideration for immunotherapy.

Foster thy young: enhanced prediction of orphan genes in assembled genomes.

Proteins encoded by newly-emerged genes ('orphan genes') share no sequence similarity with proteins in any other species. They provide organisms with a reservoir of genetic elements to quickly respond to changing selection pressures. Here, we systematically assess the ability of five gene prediction pipelines to accurately predict genes in genomes according to phylostratal origin. BRAKER and MAKER are existing, popular ab initio tools that infer gene structures by machine learning. Direct Inference is an evidence-based pipeline we developed to predict gene structures from alignments of RNA-Seq data. The BIND pipeline integrates ab initio predictions of BRAKER and Direct inference; MIND combines Direct Inference and MAKER predictions. We use highly-curated Arabidopsis and yeast annotations as gold-standard benchmarks, and cross-validate in rice. Each pipeline under-predicts orphan genes (as few as 11 percent, under one prediction scenario). Increasing RNA-Seq diversity greatly improves prediction efficacy. The combined methods (BIND and MIND) yield best predictions overall, BIND identifying 68% of annotated orphan genes, 99% of ancient genes, and give the highest sensitivity score regardless dataset in Arabidopsis. We provide a light weight, flexible, reproducible, and well-documented solution to improve gene prediction.

Improvement in the quality of frozen dough for fried flour products by electrostatic field-assisted freezing.

BACKGROUND: With the development of the food industry, frozen dough technology has gradually become an indispensable part of dough processing but its quality is often reduced due to freezing during the production process. Electrostatic field-assisted freezing (EF) technology, a key research project in recent years, reduces the physical damage to food materials by reducing or changing the size of ice crystals in frozen products. RESULTS: In this study, different intensities of electrostatic fields were used to assist in the repeated freezing and thawing of dough. The effects of electrostatic fields on the freezing nucleation process were evaluated by measuring dough freezing curves, low field nuclear magnetic resonance, and melting enthalpy. It was found that the freezing time of frozen dough added with electrostatic field-assisted freezing processing was shortened, the rate at which hardness, viscosity, and elasticity decreased was reduced, and the indicators of water distribution and protein secondary structure components were closer to those of fresh dough. CONCLUSION: This experiment used electrostatic field-assisted freezing to reduce the damage to the dough structure during the freezing process, improve the quality of frozen dough and fried products, and improve the freezing efficiency of frozen dough. It provides a new idea for the study of frozen dough. © 2023 Society of Chemical Industry.

3D/4D printed super reconstructed foods: Characteristics, research progress, and prospects.

Super reconstructed foods (SRFs) have characteristics beyond those of real system in terms of nutrition, texture, appearance, and other properties. As 3D/4D food printing technology continues to be improved in recent years, this layered manufacturing/additive manufacturing preparation technology based on food reconstruction has made it possible to continuously develop large-scale manufacture of SRFs. Compared with the traditional reconstructed foods, SRFs prepared using 3D/4D printing technologies are discussed comprehensively in this review. To meet the requirements of customers in terms of nutrition or other characteristics, multi-processing technologies are being combined with 3D/4D printing. Aspects of printing inks, product quality parameters, and recent progress in SRFs based on 3D/4D printing are assessed systematically and discussed critically. The potential for 3D/4D printed SRFs and the need for further research and developments in this area are presented and discussed critically. In addition to the natural materials which were initially suitable for 3D/4D printing, other derivative components have already been applied, which include hydrogels, polysaccharide-based materials, protein-based materials, and smart materials with distinctive characteristics. SRFs based on 3D/4D printing can retain the characteristics of deconstruction and reconstruction while also exhibiting quality parameters beyond those of the original material systems, such as variable rheological properties, on-demand texture, essential printability, improved microstructure, improved nutrition, and more appealing appearance. SRFs with 3D/4D printing are already widely used in foods such as simulated foods, staple foods, fermented foods, foods for people with special dietary needs, and foods made from food processingbyproducts.

Progress of plant-derived non-starch polysaccharides and their challenges and applications in future foods.

The development of future food is devoted not only to obtaining a sustainable food supply but also to providing high-quality foods for humans. Plant-derived non-starch polysaccharides (PNPs) are widely available, biocompatible, and nontoxic and have been largely applied to the food industry owing to their mechanical properties and biological activities. PNPs are considered excellent biomaterials and food ingredients contributing to future food development. However, a comprehensive review of the potential applications of PNPs in future food has not been reported. This review summarized the physicochemical and biological activities of PNPs and then discussed the structure-activity relationships of PNPs. Latest studies of PNPs on future foods including cell-cultured meat, food for special medical purposes (FSMPs), and three-dimensional-printed foods were reviewed. The challenges and prospects of PNPs applied to future food were critically proposed. PNPs with strong thermal stability are considered good thickeners, emulsifiers, and gelatinizers that greatly improve the processing adaptability of foods. The mechanical properties of PNPs and decellularized plant-based PNPs make them desirable scaffolds for cultured meat manufacturing. In addition, the biological activities of PNPs exhibit multiple health-promoting effects; therefore, PNPs can act as food ingredients producing FSMP to promote human health. Three-dimensional printing technology enhances food structures and biological activities of functional foods, which is in favor of expanding the application scopes of PNPs in future food. PNPs are promising in future food manufacturing, and more efforts need to be made to realize their commercial applications.

Potential of 3D printing in development of foods for special medical purpose: A review.

Nutritional management has emerged as an effective strategy to mitigate the risks of malnutrition and disease-related mortality among patients. The emergence of novel food types, particularly foods for special medical purposes (FSMPs), has garnered increasing attention from researchers and businesses. 3D printing (3DP) technology, alternatively known as food additive manufacturing, has gained popularity among novel food developers due to its distinct capabilities in tailoring nutrition, appearance, texture, and enhancing overall edible quality. This review examines current market trends, product forms, and unique characteristics of FSMPs, highlighting the progress made in applying 3DP to the development of functional foods and drugs. Despite its potential medical benefits, there are limited instances of direct utilization of 3DP in the production of such specialized food type. Currently, the FSMP market faces several challenges, including limited product diversity, inadequate formula design, and a lack of product appeal. 3DP offers significant advantages in catering to the unique needs of special patients, encompassing both physiological medical benefits and enhanced sensory as well as psychological eating experiences. It holds great promise in promoting precision medicine and personalized home-based FSMPs preparations. This review will delve into the development strategies and feasibility of 3DP in creating specialized medical food for patients with unique conditions and across different age groups. Additionally, it explores the potential challenges of applying 3DP to the FSMP sector, such as regulatory frameworks, patient acceptance, cost of 3D-printed FSMPs, and the improvement of 3DP.

AI-based R&D for frozen and thawed meat: Research progress and future prospects.

Frozen and thawed meat plays an important role in stabilizing the meat supply chain and extending the shelf life of meat. However, traditional methods of research and development (R&D) struggle to meet rising demands for quality, nutritional value, innovation, safety, production efficiency, and sustainability. Frozen and thawed meat faces specific challenges, including quality degradation during thawing. Artificial intelligence (AI) has emerged as a promising solution to tackle these challenges in R&D of frozen and thawed meat. AI's capabilities in perception, judgment, and execution demonstrate significant potential in problem-solving and task execution. This review outlines the architecture of applying AI technology to the R&D of frozen and thawed meat, aiming to make AI better implement and deliver solutions. In comparison to traditional R&D methods, the current research progress and promising application prospects of AI in this field are comprehensively summarized, focusing on its role in addressing key challenges such as rapid optimization of thawing process. AI has already demonstrated success in areas such as product development, production optimization, risk management, and quality control for frozen and thawed meat. In the future, AI-based R&D for frozen and thawed meat will also play an important role in promoting personalization, intelligent production, and sustainable development. However, challenges remain, including the need for high-quality data, complex implementation, volatile processes, and environmental considerations. To realize the full potential of AI that can be integrated into R&D of frozen and thawed meat, further research is needed to develop more robust and reliable AI solutions, such as general AI, explainable AI, and green AI.

Repeated intravenous cardiosphere-derived cell therapy in late-stage Duchenne muscular dystrophy (HOPE-2): a multicentre, randomised, double-blind, placebo-controlled, phase 2 trial.

BACKGROUND: Cardiosphere-derived cells (CDCs) ameliorate skeletal and cardiac muscle deterioration in experimental models of Duchenne muscular dystrophy. The HOPE-2 trial examined the safety and efficacy of sequential intravenous infusions of human allogeneic CDCs in late-stage Duchenne muscular dystrophy. METHODS: In this multicentre, randomised, double-blind, placebo-controlled, phase 2 trial, patients with Duchenne muscular dystrophy, aged 10 years or older with moderate upper limb impairment, were enrolled at seven centres in the USA. Patients were randomly assigned (1:1) using stratified permuted blocks to receive CAP-1002 (1·5 × 108 CDCs) or placebo intravenously every 3 months for a total of four infusions. Clinicians, caregivers, patients, and clinical operations personnel were fully masked to treatment groups. The primary outcome was the change in mid-level elbow Performance of Upper Limb version 1.2 (PUL 1.2) score at 12 months, assessed in the intention-to-treat population. Safety was assessed in all individuals who received an investigational product. This trial is registered with ClinicalTrials.gov, NCT03406780. FINDINGS: Between March 1, 2018, and March 31, 2020, 26 male patients with Duchenne muscular dystrophy were enrolled, of whom eight were randomly assigned to the CAP-1002 group and 12 to the placebo group (six were not randomised due to screening failure). In patients who had a post-treatment PUL 1.2 assessment (eight in the CAP-1002 group and 11 in the placebo group), the mean 12-month change from baseline in mid-level elbow PUL1.2 favoured CAP-1002 over placebo (percentile difference 36·2, 95% CI 12·7-59·7; difference of 2·6 points; p=0·014). Infusion-related hypersensitivity reactions without long-term sequelae were observed in three patients, with one patient discontinuing therapy due to a severe allergic reaction. No other major adverse reactions were noted, and no deaths occurred. INTERPRETATION: CAP-1002 cell therapy appears to be safe and effective in reducing deterioration of upper limb function in patients with late-stage Duchenne muscular dystrophy. Various measures of cardiac function and structure were also improved in the CAP-1002 group compared with the placebo group. Longer-term extension studies are needed to confirm the therapeutic durability and safety of CAP-1002 beyond 12 months for the treatment of skeletal myopathy and cardiomyopathy in Duchenne muscular dystrophy. FUNDING: Capricor Therapeutics.

Inferring the Nominal Molecular Mass of an Analyte from Its Electron Ionization Mass Spectrum.

The performance of three algorithms for predicting nominal molecular mass from an analyte's electron ionization mass spectrum is presented. The Peak Interpretation Method (PIM) attempts to quantify the likelihood that a molecular ion peak is contained in the mass spectrum, whereas the Simple Search Hitlist Method (SS-HM) and iterative Hybrid Search Hitlist Method (iHS-HM) leverage results from mass spectral library searching. These predictions can be employed in combination (recommended) or independently. The methods were tested on two sets of query mass spectra searched against libraries that did not contain the reference mass spectra of the same compounds: 19,074 spectra of various organic molecules searched against the NIST17 mass spectral library and 162 spectra of small molecule drugs searched against SWGDRUG version 3.3. Individually, each molecular mass prediction method had computed precisions (the fraction of positive predictions that were correct) of 91, 89, and 74%, respectively. The methods become more valuable when predictions are taken together. When all three predictions were identical, which occurred in 33% of the test cases, the predicted molecular mass was almost always correct (>99%).