Structural insight into the functional regulation of Elongation factor Tu by reactive oxygen species in Synechococcus elongatus PCC 7942.

In cyanobacteria, Elongation factor Tu (EF-Tu) plays a crucial role in the repair of photosystem II (PSII), which is highly susceptible to oxidative stress induced by light exposure and regulated by reactive oxygen species (ROS). However, the specific molecular mechanism governing the functional regulation of EF-Tu by ROS remains unclear. Previous research has shown that a mutated EF-Tu, where C82 is substituted with a Ser residue, can alleviate photoinhibition, highlighting the important role of C82 in EF-Tu photosensitivity. In this study, we elucidated how ROS deactivate EF-Tu by examining the crystal structures of EF-Tu in both wild-type and mutated form (C82S) individually at resolutions of 1.7 Šand 2.0 Šin Synechococcus elongatus PCC 7942 complexed with GDP. Specifically, the GDP-bound form of EF-Tu adopts an open conformation with C82 located internally, making it resistant to oxidation. Coordinated conformational changes in switches I and II create a tunnel that positions C82 for ROS interaction, revealing the vulnerability of the closed conformation of EF-Tu to oxidation. An analysis of these two structures reveals that the precise spatial arrangement of C82 plays a crucial role in modulating EF-Tu's response to ROS, serving as a regulatory element that governs photosynthetic biosynthesis.

Power and reproducibility in the external validation of brain-phenotype predictions.

Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson's r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.

[High-temperature adaptation mechanisms and biotechnological potentials of thermophilic cyanobacteria].

Thermophilic cyanobacteria are prokaryotic organisms that possess exceptional heat-resistant characteristics. This group serves as an excellent model for investigating the heat tolerance of higher photosynthetic organisms, including higher plants, some protists (such as algae and euglena), and bacteria. Analyzing the mechanisms of high-temperature adaptation in thermophilic cyanobacteria can enhance our understanding of how photosynthetic organisms and microorganisms tolerate high temperatures at the molecular level. Additionally, these thermotolerant cyanobacteria have the potential to contribute to breeding heat-tolerant plants and developing microbial cell factories. This review summarizes current research on thermophilic cyanobacteria, focusing on their ecology, morphology, omics studies, and mechanisms of high-temperature tolerance. It offers insight into the potential biotechnological applications of thermophilic cyanobacteria and highlights future research opportunities. Specifically, attention is given to the photosynthetic physiology and metabolism of cyanobacteria, and the molecular basis of heat-tolerance mechanisms in thermophilic cyanobacteria is explored.

Thermal control over phosphorescence or thermally activated delayed fluorescence in a metal-organic framework.

By integrating a tailor-made donor-acceptor (D-A) ligand in a metal-organic framework (MOF), a material with unprecedented features emerges. The ligand combines a pair of cyano groups as acceptors with four sulfanylphenyls as donors, which expose each a carboxylic acid as coordination sites. Upon treatment with zinc nitrate in a solvothermal synthesis, the MOF is obtained. The new material combines temperature-assisted reverse intersystem crossing (RISC) and intersystem crossing (ISC). As these two mechanisms are active in different temperature windows, thermal switching between their characteristic emission wavelengths is observed for this material. The two mechanisms can be activated by both, one-photon absorption (OPA) and two-photon absorption (TPA) resulting in a large excitement window ranging from ultraviolet (UV) over visible light (VL) to near infrared (NIR). Furthermore, the emission features of the material are pH sensitive, such that its application potential is demonstrated in a first ammonia sensor.

Brain age prediction and deviations from normative trajectories in the neonatal connectome.

Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

MXene-Based Pressure Sensor with a Self-Healing Property for Joule Heating and Friction Sliding.

As a kind of flexible electronic device, flexible pressure sensor has attracted wide attention in medical monitoring and human-machine interaction. With the continuous deepening of research, high-sensitivity sensor is developing from single function to multi-function. However, Current multifunctional sensors lack the ability to integrate joule heating, detect sliding friction, and self-healing. Herein, a MXene/polyurethane (PU) flexible pressure sensor with a self-healing property for joule heating and friction sliding is fabricated. The MXene/PU sensitive layer with special spinosum structure is prepared by a simple spraying method. After face-to-face assembly of the sensitive layers, the MXene/PU flexible pressure sensor is obtained and showed excellent sensitivity (150.65 kPa-1), fast response/recovery speed (75.5/63.9 ms), and good stability (10 000 cycles). Based on the self-healing property of PU, the sensor also has the ability to heal after mechanical damage. In addition, the sensor realizes the joule heating function under low voltage, and has the real-time monitoring ability of sliding objects. Combined with low cost and simple manufacturing method, the multi-functional MXene/PU flexible sensor shows a wide range of application potential in human activity monitoring, thermal management, and slip recognition.

Transformational Modulation of Fluorescence to Room Temperature Phosphorescence in Metal-Organic Frameworks with Flexible C-S-C Bonds.

Photoluminescent metal-organic frameworks (MOFs) have been a subject of considerable interest for many years. However, the regulation of excited states of MOFs at the single crystal level remains restricted due to a lack of control methods. The singlet-triplet emissive property can be significantly influenced by crystal conformational distortions. This review introduces an intelligent responsive MOF material, denoted as LIFM-SHL-3a, characterized by flexible C-S-C bonds. LIFM-SHL-3a integrates elastic structural dynamics with fluorescence and room temperature phosphorescence (RTP) modulation under heating conditions. The deformable carbon-sulfur bond essentially propels the distortion of molecular conformation and alters the stacking mode, as illustrated by single-crystal-to-single-crystal transformation detection. The deformation of flexible C-S-C bonds leads to different noncovalent interactions in the crystal system, thereby achieving modulation of the fluorescence (F) and RTP bands. In the final state structure, the ratio of fluorescence is 66.7%, and the ratio of RTP is 32.6%. This stands as a successful demonstration of modulating F/RTP within the dynamic MOF, unlocking potential applications in optical sensing and beyond. Especially, a PL thermometer with a relative sensitivity of 0.096-0.104%·K-1 in the range of 300-380 K and a H2S probe with a remarkably low LOD of 125.80 nM can be obtained using this responsive MOF material of LIFM-SHL-3a.

Artemether regulates liver glycogen and lipid utilization through mitochondrial pyruvate oxidation in db/db mice.

OBJECTIVES: Diabetes is an important global health problem. The occurrence and development of type 2 diabetes (T2D) involves multiple organs, among which the liver is an important organ. Artemether is a methyl ether derivative of artemisinin and has displayed significant antidiabetic effects. However, its regulation of glucose metabolism is not clearly elucidated. This study explored the effect of artemether on liver mitochondrial pyruvate metabolism. METHODS: T2D db/db mice were used and grouped into db/db and db/db+Art groups. Lean wild type mice served as control. After artemether intervention for 12 weeks, the respiratory exchange ratio (RER), redox state, relevant serum lipid content, liver glycogen and lipid content, liver insulin and insulin-like growth factor 1 (IGF-1) signal transduction, mitochondrial pyruvate oxidation pathway, fatty acid and glycogen metabolic pathways were evaluated. RESULTS: This experiment demonstrated that artemether raised RER and enhanced liver mitochondrial pyruvate metabolism in db/db mice. Artemether also reduced serum and urinary lipid peroxidation products and regulated the redox status in liver. The accumulation of liver glycogen in diabetic mice was attenuated, the proportion of lipid content in serum and liver was changed by artemether. The signal pathway associated with liver glycogen metabolism was also regulated by artemether. In addition, artemether increased serum insulin and regulated insulin/IGF-1 signal pathway in liver. CONCLUSIONS: The present study confirmed that artemether can regulate liver glycogen and lipid utilization in T2D mice, its biological mechanisms were associated with mitochondrial pyruvate oxidation in the liver.

Autophagy dysfunction contributes to NLRP1 inflammasome-linked depressive-like behaviors in mice.

BACKGROUND: Major depressive disorder (MDD) is a common but severe psychiatric illness characterized by depressive mood and diminished interest. Both nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain-containing 1 (NLRP1) inflammasome and autophagy have been reported to implicate in the pathological processes of depression. However, the mechanistic interplay between NLRP1 inflammasome, autophagy, and depression is still poorly known. METHODS: Animal model of depression was established by chronic social defeat stress (CSDS). Depressive-like behaviors were determined by social interaction test (SIT), sucrose preference test (SPT), open field test (OFT), forced swim test (FST), and tail-suspension test (TST). The protein expression levels of NLRP1 inflammasome complexes, pro-inflammatory cytokines, phosphorylated-phosphatidylinositol 3-kinase (p-PI3K)/PI3K, phosphorylated-AKT (p-AKT)/AKT, phosphorylated-mechanistic target of rapamycin (p-mTOR)/mTOR, brain-derived neurotrophic factor (BDNF), phosphorylated-tyrosine kinase receptor B (p-TrkB)/TrkB, Bcl-2-associated X protein (Bax)/B-cell lymphoma-2 (Bcl2) and cleaved cysteinyl aspartate-specific proteinase-3 (caspase-3) were examined by western blotting. The mRNA expression levels of pro-inflammatory cytokines were tested by quantitative real-time PCR. The interaction between proteins was detected by immunofluorescence and coimmunoprecipitation. Neuronal injury was assessed by Nissl staining. The autophagosomes were visualized by transmission electron microscopy. Nlrp1a knockdown was performed using an adeno-associated virus (AAV) vector containing Nlrp1a-shRNA-eGFP infusion. RESULTS: CSDS exposure caused a bidirectional change in hippocampal autophagy function, which was activated in the initial period but impaired at the later stage. In addition, CSDS exposure increased the expression levels of hippocampal NLRP1 inflammasome complexes, pro-inflammatory cytokines, p-PI3K, p-AKT and p-mTOR in a time-dependent manner. Interestingly, NLRP1 is immunoprecipitated with mTOR but not PI3K/AKT and CSDS exposure facilitated the immunoprecipitation between them. Hippocampal Nlrp1a knockdown inhibited the activity of PI3K/AKT/mTOR signaling, rescued the impaired autophagy and ameliorated depressive-like behavior induced by CSDS. In addition, rapamycin, an autophagy inducer, abolished NLRP1 inflammasome-driven inflammatory reactions, alleviated depressive-like behavior and exerted a neuroprotective effect. CONCLUSIONS: Autophagy dysfunction contributes to NLRP1 inflammasome-linked depressive-like behavior in mice and the regulation of autophagy could be a valuable therapeutic strategy for the management of depression.

Culture-free identification of fast-growing cyanobacteria cells by Raman-activated gravity-driven encapsulation and sequencing.

By directly converting solar energy and carbon dioxide into biobased products, cyanobacteria are promising chassis for photosynthetic biosynthesis. To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales, exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance. To simplify and accelerate the screening for fast-growing cyanobacteria strains, a method called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS) was established. We show that the 13C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations, thus greatly accelerating the process screening for fastest-growing cells. The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing. Therefore, this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories.

Engineering Cyanobacterial Cell Factories for Photosynthetic Production of Fructose.

Fructose is an important monosaccharide product widely applied in the food, medicine, and chemical industries. Currently, fructose is mainly manufactured with plant biomass-sourced polysaccharides through multiple steps of digestion, conversion, separation, and purification. The development of cyanobacterial metabolic engineering provides an attractive alternative route for the one-step direct production of fructose utilizing carbon dioxide and solar energy. In this work, we developed a paradigm for engineering cyanobacterial chassis cells into efficient cell factories for the photosynthetic production of fructose. In a representative cyanobacterial strain, Synechococcus elongatus PCC 7942, knockout of fructokinase effectively activated the synthesis and secretion of fructose in hypersaline conditions, independent of any heterologous transporters. The native sucrose synthesis pathway was identified as playing a primary role in fructose synthesis. Through combinatory optimizations on the levels of metabolism, physiology, and cultivation, the fructose yield of the Synechococcus cell factories was stepwise improved to 3.9 g/L. Such a paradigm was also adopted to engineer another Synechococcus strain, the marine species Synechococcus sp. PCC 7002, and facilitated an even higher fructose yield of over 6 g/L. Finally, the fructose synthesized and secreted by the cyanobacterial photosynthetic cell factories was successfully extracted and prepared from the culture broth in the form of products with 86% purity through multistep separation-purification operations. This work demonstrated a paradigm for systematically engineering cyanobacteria for photosynthetic production of desired metabolites, and it also confirmed the feasibility and potential of cyanobacterial photosynthetic biomanufacturing as a simple and efficient route for fructose production.

Network controllability of structural connectomes in the neonatal brain.

White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant's brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth.

Tuna trimmings (Thunnas albacares) hydrolysate alleviates immune stress and intestinal mucosal injury during chemotherapy on mice and identification of potentially active peptides.

In this study, Tuna trimmings (Thunnas albacares) protein hydrolysate (TPA) was produced by alcalase. The anti-tumor synergistic effect and intestinal mucosa protective effect of TPA on S180 tumor-bearing mice treated with 5-fluorouracil (5-FU) chemotherapy were investigated. The results showed that TPA can enhance the anti-tumor effect of 5-FU chemotherapy, as evident by a significant reduction in tumor volume observed in the medium and high dose TPA+5-FU groups compared to the 5-FU group (p < 0.001). Moreover, TPA significantly elevated the content of total protein and albumin in all TPA dose groups (p < 0.01, p < 0.001), indicating its ability to regulate the nutritional status of the mice. Furthermore, histopathological studies revealed a significant increase in the height of small intestinal villi, crypt depth, mucosal thickness, and villi area in the TPA+5-FU groups compared to the 5-FU group (p < 0.05), suggesting that TPA has a protective effect on the intestinal mucosa. Amino acid analysis revealed that TPA had a total amino acid content of 66.30 g/100 g, with essential amino acids accounting for 30.36 g/100 g. Peptide molecular weight distribution analysis of TPA indicated that peptides ranging from 0.25 to 1 kDa constituted 64.54%. LC-MS/MS analysis identified 109 peptide sequences, which were predicted to possess anti-cancer and anti-inflammatory activities through database prediction. Therefore, TPA has the potential to enhance the antitumor effects of 5-FU, mitigate immune depression and intestinal mucosal damage induced by 5-FU. Thus, TPA could be serve as an adjuvant nutritional support for malnourished patients undergoing chemotherapy.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available.

Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).

Artemether ameliorates adriamycin induced cardiac atrophy in mice.

Adriamycin is a widely used and effective antitumor drug; however, its application is limited by various side effects, including irreversible cardiotoxicity. The central role of cardiac atrophy in Adriamycin­induced cardiotoxicity has been revealed; however, the underlying mechanism of this process remains unclear. Artemether is a well­known Chinese herbal medicine, and its pharmacological action is related to the regulation of mitochondrial function and redox status. The present study determined the effects of artemether on Adriamycin­induced cardiotoxicity and investigated the underlying mechanisms. After mouse model establishment and artemether intervention, experimental methods including pathological staining, immunohistochemistry, immunofluorescence, immunoblotting, ELISA and reverse transcription­quantitative PCR were used to evaluate the therapeutic effect. The results demonstrated that artemether prevented Adriamycin­induced cardiac atrophy and recovered the intercombination of connexin 43 and N­cadherin at the intercalated discs. Artemether also regulated the autophagy pathway and restored the unbalanced ratio of Bax and Bcl­2 in myocardial cells. In addition, the increased serum H2O2 levels after Adriamycin exposure were significantly decreased by artemether, and the mitochondrial alterations and redox imbalance in myocardial cells were also improved to varying degrees. In summary, the findings of the present study provide reliable evidence that artemether could ameliorate cardiac atrophy induced by Adriamycin. This therapeutic approach may be translated to the clinic for preventing drug­induced heart diseases.

Unlocking the potentials of cyanobacterial photosynthesis for directly converting carbon dioxide into glucose.

Glucose is the most abundant monosaccharide, serving as an essential energy source for cells in all domains of life and as an important feedstock for the biorefinery industry. The plant-biomass-sugar route dominates the current glucose supply, while the direct conversion of carbon dioxide into glucose through photosynthesis is not well studied. Here, we show that the potential of Synechococcus elongatus PCC 7942 for photosynthetic glucose production can be unlocked by preventing native glucokinase activity. Knocking out two glucokinase genes causes intracellular accumulation of glucose and promotes the formation of a spontaneous mutation in the genome, which eventually leads to glucose secretion. Without heterologous catalysis or transportation genes, glucokinase deficiency and spontaneous genomic mutation lead to a glucose secretion of 1.5 g/L, which is further increased to 5 g/L through metabolic and cultivation engineering. These findings underline the cyanobacterial metabolism plasticities and demonstrate their applications for supporting the direct photosynthetic production of glucose.

Altered Brain Dynamics Across Bipolar Disorder and Schizophrenia During Rest and Task Switching Revealed by Overlapping Brain States.

BACKGROUND: Individuals with bipolar disorder (BD) and schizophrenia (SCZ) show aberrant brain dynamics (i.e., altered recruitment or traversal through different brain states over time). Existing investigations of brain dynamics typically assume that one dominant brain state characterizes each time point. However, as multiple brain states likely are engaged at any given moment, this approach can obscure alterations in less prominent but critical brain states. Here, we examined brain dynamics in BD and SCZ by implementing a novel framework that simultaneously assessed the engagement of multiple brain states. METHODS: Four recurring brain states were identified by applying nonlinear manifold learning and k-means clustering to the Human Connectome Project task-based functional magnetic resonance imaging data. We then assessed moment-to-moment state engagement in 2 independent samples of healthy control participants and patients with BD or SCZ using resting-state (N = 336) or task-based (N = 217) functional magnetic resonance imaging data. Relative state engagement and state engagement variability were extracted and compared across groups using multivariate analysis of covariance, controlling for site, medication, age, and sex. RESULTS: Our framework identified dynamic alterations in BD and SCZ, while a state discretization approach revealed no significant group differences. Participants with BD or SCZ showed reduced state engagement variability, but not relative state engagement, across multiple brain states during resting-state and task-based functional magnetic resonance imaging. We found decreased state engagement variability in older participants and preliminary evidence suggesting an association with avolition. CONCLUSIONS: Assessing multiple brain states simultaneously can reflect the complexity of aberrant brain dynamics in BD and SCZ, providing a more comprehensive understanding of the neural mechanisms underpinning these conditions.

Associations of physical frailty with health outcomes and brain structure in 483 033 middle-aged and older adults: a population-based study from the UK Biobank.

BACKGROUND: Physical frailty is a state of increased vulnerability to stressors and is associated with serious health issues. However, how frailty affects and is affected by numerous other factors, including mental health and brain structure, remains underexplored. We aimed to investigate the mutual effects of frailty and health using large, multidimensional data. METHODS: For this population-based study, we used data from the UK Biobank to examine the pattern and direction of association between physical frailty and 325 health-related measures across multiple domains, using linear mixed-effect models and adjusting for numerous confounders. Participants were included if complete data were available for all five indicators of frailty, all covariates, and at least one health measure. We further examined the association between frailty and brain structure and the role of this association in mediating the relationship between frailty and health outcomes. FINDINGS: 483 033 participants aged 38-73 years were included in the study at baseline (between Dec 19, 2006, and Oct 1, 2010); at a median follow-up of 9 years (IQR 8-10), behavioural data were available for 46 501 participants and neuroimaging data for 40 210 participants. The severity of physical frailty was significantly associated with decreased cognitive performance (Cohen's d=0·025-0·162), increased early-life risks (d=0·026-0·111), unhealthy lifestyle (d=0·013-0·394), poor physical fitness (d=0·007-0·668), increased symptoms of poor mental health (d=0·032-0·607), severe environmental pollution (d=0·013-0·064), and adverse biochemical markers (d=0·025-0·198). Some associations were bidirectional, with the strongest effects on mental health measures. The severity of frailty correlated with increased total white matter hyperintensity and lower grey matter volume, particularly in subcortical regions (d=0·027-0·082), which significantly mediated the association between frailty and health-related outcomes, although the mediated effects were small. INTERPRETATION: Physical frailty is associated with diverse unfavourable health-related outcomes, which can be mediated by differences in brain structure. Our findings offer a framework for guiding preventative strategies targeting both frailty and psychiatric disorders. FUNDING: National Institute of Mental Health, National Science Foundation.

Engineered hypermutation adapts cyanobacterial photosynthesis to combined high light and high temperature stress.

Photosynthesis can be impaired by combined high light and high temperature (HLHT) stress. Obtaining HLHT tolerant photoautotrophs is laborious and time-consuming, and in most cases the underlying molecular mechanisms remain unclear. Here, we increase the mutation rates of cyanobacterium Synechococcus elongatus PCC 7942 by three orders of magnitude through combinatory perturbations of the genetic fidelity machinery and cultivation environment. Utilizing the hypermutation system, we isolate Synechococcus mutants with improved HLHT tolerance and identify genome mutations contributing to the adaptation process. A specific mutation located in the upstream non-coding region of the gene encoding a shikimate kinase results in enhanced expression of this gene. Overexpression of the shikimate kinase encoding gene in both Synechococcus and Synechocystis leads to improved HLHT tolerance. Transcriptome analysis indicates that the mutation remodels the photosynthetic chain and metabolism network in Synechococcus. Thus, mutations identified by the hypermutation system are useful for engineering cyanobacteria with improved HLHT tolerance.

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.