What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review.

Introduction: Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling. Methods: MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation. Results: In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups. Conclusion: The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.

Core Set of Patient-Reported Outcome Measures for Measuring Quality of Life in Clinical Obesity Care.

PURPOSE: The focus of measuring success in obesity treatment is shifting from weight loss to patients' health and quality of life. The objective of this study was to select a core set of patient-reported outcomes and patient-reported outcome measures to be used in clinical obesity care. MATERIALS AND METHODS: The Standardizing Quality of Life in Obesity Treatment III, face-to-face hybrid consensus meeting, including people living with obesity as well as healthcare providers, was held in Maastricht, the Netherlands, in 2022. It was preceded by two prior multinational consensus meetings and a systematic review. RESULTS: The meeting was attended by 27 participants, representing twelve countries from five continents. The participants included healthcare providers, such as surgeons, endocrinologists, dietitians, psychologists, researchers, and people living with obesity, most of whom were involved in patient representative networks. Three patient-reported outcome measures (patient-reported outcomes) were selected: the Impact of Weight on Quality of Life-Lite (self-esteem) measure, the BODY-Q (physical function, physical symptoms, psychological function, social function, eating behavior, and body image), and the Quality of Life for Obesity Surgery questionnaire (excess skin). No patient-reported outcome measure was selected for stigma. CONCLUSION: A core set of patient-reported outcomes and patient-reported outcome measures for measuring quality of life in clinical obesity care is established incorporating patients' and experts' opinions. This set should be used as a minimum for measuring quality of life in routine clinical practice. It is essential that individual patient-reported outcome measure scores are shared with people living with obesity in order to enhance patient engagement and shared decision-making.

Transcriptomic investigations of polymyxins and colistin/sulbactam combination against carbapenem-resistant Acinetobacter baumannii.

Carbapenem-resistant Acinetobacter baumannii (CRAB) is a Priority 1 (Critical) pathogen urgently requiring new antibiotics. Polymyxins are a last-line option against CRAB-associated infections. This transcriptomic study utilized a CRAB strain to investigate mechanisms of bacterial killing with polymyxin B, colistin, colistin B, and colistin/sulbactam combination therapy. After 4 h of 2 mg/L polymyxin monotherapy, all polymyxins exhibited common transcriptomic responses which primarily involved disruption to amino acid and fatty acid metabolism. Of the three monotherapies, polymyxin B induced the greatest number of differentially expressed genes (DEGs), including for genes involved with fatty acid metabolism. Gene disturbances with colistin and colistin B were highly similar (89 % common genes for colistin B), though effects on gene expression were generally lower (0-1.5-fold in most cases) with colistin B. Colistin alone (2 mg/L) or combined with sulbactam (64 mg/L) resulted in rapid membrane disruption as early as 1 h. Transcriptomic analysis of this combination revealed that the effects were driven by colistin, which included disturbances in fatty acid synthesis and catabolism, and inhibition of nutrient uptake. Combination therapy produced substantially higher fold changes in 72 % of DEGs shared with monotherapy, leading to substantially greater reductions in fatty acid biosynthesis and increases in biofilm, cell wall, and phospholipid synthesis. This indicates synergistic bacterial killing with the colistin/sulbactam combination results from a systematic increase in perturbation of many genes associated with bacterial metabolism. These mechanistic insights enhance our understanding of bacterial responses to polymyxin mono- and combination therapy and will assist to optimize polymyxin use in patients.

Transdermal Hydrogen Sulfide Delivery Enabled by Open-Metal-Site Metal-Organic Frameworks.

Hydrogen sulfide (H2S) is an endogenously produced gasotransmitter involved in many physiological processes that are integral to proper cellular functioning. Due to its profound anti-inflammatory and antioxidant properties, H2S plays important roles in preventing inflammatory skin disorders and improving wound healing. Transdermal H2S delivery is a therapeutically viable option for the management of such disorders. However, current small-molecule H2S donors are not optimally suited for transdermal delivery and typically generate electrophilic byproducts that may lead to undesired toxicity. Here, we demonstrate that H2S release from metal-organic frameworks (MOFs) bearing coordinatively unsaturated metal centers is a promising alternative for controlled transdermal delivery of H2S. Gas sorption measurements and powder X-ray diffraction (PXRD) studies of 11 MOFs support that the Mg-based framework Mg2(dobdc) (dobdc4- = 2,5-dioxidobenzene-1,4-dicarboxylate) is uniquely well-suited for transdermal H2S delivery due to its strong yet reversible binding of H2S, high capacity (14.7 mmol/g at 1 bar and 25 °C), and lack of toxicity. In addition, Rietveld refinement of synchrotron PXRD data from H2S-dosed Mg2(dobdc) supports that the high H2S capacity of this framework arises due to the presence of three distinct binding sites. Last, we demonstrate that transdermal delivery of H2S from Mg2(dobdc) is sustained over a 24 h period through porcine skin. Not only is this significantly longer than sodium sulfide but this represents the first example of controlled transdermal delivery of pure H2S gas. Overall, H2S-loaded Mg2(dobdc) is an easily accessible, solid-state source of H2S, enabling safe storage and transdermal delivery of this therapeutically relevant gas.

Under pressure: A systematic review of the association between blood pressure variability with depression and anxiety.

Blood pressure variability (BPV) impacts brain health by influencing brain structure and cerebrovascular pathologies, though the mechanisms are poorly understood. Changes in the cerebrovasculature may lead to late-onset depression, cognitive impairment, and dementia, however the relationship between BPV with depression and anxiety remains unclear, due to methodological differences and inconsistencies in past research. This review aims to clarify the association between BPV with depression and anxiety in adults to inform understandings of the mechanisms implicating BPV in cognitive health. A systematic search from inception through to January 2024 was performed on Embase, PubMed, PsycINFO, and Web of Science. Studies that assessed BPV quantified by beat-to-beat, 24-hour, or visit-to-visit were eligible if the standardised assessment of depression and/or anxiety were reported as a linear association, or mean differences across control and affect groups. A total of 14 articles reporting on 13 samples and N = 5055 persons met the inclusion criteria (median female proportion = 61 %, range 0 % - 76 %). A meta-analysis was not possible due to methodological heterogeneity in BPV measurements and metrics across studies. Mixed results were observed across depression studies with inconsistencies and variation in the direction, strength of association, and BPV metric. There was weak evidence from only three studies to support a linear association between systolic coefficient of variation and anxiety. Collectively, the findings contribute to understanding the association between BPV and brain health, suggesting that any relationship between BPV and brain structures critical for cognitive function are independent of depression and only modestly implicate anxiety.

Using Flashcards and Descriptive Feedback to Teach Industrial Kitchen Equipment to a Student with Intellectual Disabilities in a Postsecondary Education Program.

With postsecondary education opportunities for adult students with intellectual and developmental disabilities (IDD) on the rise, it is important to find socially validated research-based methods that are appropriate for the university or other community-based postsecondary instructional settings. The present research examines the effects of using flashcards with descriptive feedback and opportunities to respond, to teach one student with intellectual disabilities, enrolled in a postsecondary education-training program, commonly used industrial kitchen equipment. Results showed that discrete trail instruction, which included an error correction strategy of descriptive feedback plus opportunities to correctly respond was highly effective in mastery and maintenance of kitchen equipment identification, and generalization when asked to locate those items in the university kitchen lab.

Outcomes and Performance Following Posteromedial Elbow Debridement in Major League Baseball Players.

BACKGROUND: Overhead athletes are particularly susceptible to elbow valgus extension overload syndrome and development of pathologic changes in the posteromedial elbow. Though arthroscopic débridement/osteophyte resection is frequently performed, few studies have analyzed the outcomes of this procedure, and none have specifically addressed professional level athletes. HYPOTHESIS/PURPOSE: We hypothesized that following posteromedial elbow débridement, Major League Baseball (MLB) pitchers would exhibit a higher rate of ulnar collateral ligament (UCL) reconstruction than baseline incidence in the existing literature, along with a decline in pitching performance. METHODS: Using publicly accessible websites, MLB athletes undergoing posteromedial elbow débridement from 2007 to 2022 were identified. Demographic information, procedure details, return to play (RTP) information, return to the disabled/injured list (DL/IL), subsequent UCL reconstruction, and pitching statistics were analyzed. Pitching performance metrics included Earned Runs Average (ERA), Walks Plus Hits Per Innings Pitched (WHIP), innings pitched, and fastball velocity. RESULTS: A total of 39 MLB players, including 26 pitchers, were included. Within the first three seasons after surgery, 82.1% (n=32) of players returned to play at the MLB level at a mean time of 176.1 ± 69 days. Pitchers exhibited a return to play (RTP) rate of 76.9% (n=20) at 175.8 ± 16 days. A total of 38.5% (n=10) of pitchers returned to the DL/IL for elbow-related issues within three seasons. Subsequent UCL reconstruction was seen only in pitchers, with a frequency of 19.2% (n=5). No statistically significant differences between single season pre/postoperative pitching metrics were identified. A small but significant (p<0.05) decrease in fastball velocity (94.4 vs 92.84; p=0.02) was observed over a three-season comparison. CONCLUSION: Contrary to our hypothesis, this study demonstrates that posteromedial elbow débridement is a viable surgery in MLB athletes, with RTP rate of 82.1% and no increase in rate of UCL reconstruction. Furthermore, there was no significant difference in single season pre- and postoperative statistical pitching performance. However, over three years postoperatively, there was a 38.5% rate of return to the DL/IL for ongoing elbow ailment and a significant decrease in pitch velocity, raising some concern over the longevity of postoperative improvements.

Elucidating the Mechanism of Efficient Eu(III) and Yb(III) Sensitisation from a Re(I) Tetrazolato Triangular Assembly.

The reaction of Re(CO)5Br with deprotonated 1H-(5-(2,2':6',2''-terpyridine)pyrid-2-yl)tetrazole yields a triangular assembly formed by tricarbonyl Re(I) vertices. Photophysical measurements reveal blue-green emission with a maximum at 520 nm, 32% quantum yield, and 2430 ns long-lived excited state decay lifetime in deaerated dichloromethane solution. Coordination of lanthanoid ions to the terpyridine units red-shifts the emission to 570 nm and also reveals efficient (90%) and fast sensitisation to both Eu(III) and Yb(III) at room temperature, with a similar rate constant kET of the order of 107 s-1. Efficient sensitisation of Eu(III) from Re(I) is unprecedented, especially when considering the close proximity in energy between the donor and acceptor excited states. On the other hand, comparative measurements at 77 K reveal that energy transfer to Yb(III) is two orders of magnitude slower than that to Eu(III). A two-step mechanism of sensitisation is therefore proposed, whereby the rate-determining step is a thermally activated energy transfer step between the Re(I) centre and the terpyridine functionality, followed by rapid energy transfer to the respective Ln(III) excited states. At 77 K, the direct Re(I) to Eu(III) energy transfer seems to proceed via a ligand-mediated superexchange Dexter-type mechanism.

Human cytokine and coronavirus nucleocapsid protein interactivity using large-scale virtual screens.

Understanding the interactions between SARS-CoV-2 and the human immune system is paramount to the characterization of novel variants as the virus co-evolves with the human host. In this study, we employed state-of-the-art molecular docking tools to conduct large-scale virtual screens, predicting the binding affinities between 64 human cytokines against 17 nucleocapsid proteins from six betacoronaviruses. Our comprehensive in silico analyses reveal specific changes in cytokine-nucleocapsid protein interactions, shedding light on potential modulators of the host immune response during infection. These findings offer valuable insights into the molecular mechanisms underlying viral pathogenesis and may guide the future development of targeted interventions. This manuscript serves as insight into the comparison of deep learning based AlphaFold2-Multimer and the semi-physicochemical based HADDOCK for protein-protein docking. We show the two methods are complementary in their predictive capabilities. We also introduce a novel algorithm for rapidly assessing the binding interface of protein-protein docks using graph edit distance: graph-based interface residue assessment function (GIRAF). The high-performance computational framework presented here will not only aid in accelerating the discovery of effective interventions against emerging viral threats, but extend to other applications of high throughput protein-protein screens.

Financial Toxicity Among Patients With Metastatic Prostate Cancer: A Mixed Methods Approach to Identify Effective Interventions.

INTRODUCTION: Financial toxicity associated with treatments for metastatic prostate cancer remains poorly defined. We sought to understand aspects of financial toxicity not captured in a commonly employed financial toxicity questionnaire and identify potential interventions to help alleviate financial toxicity through a convergent mixed methods approach. METHODS: Patients seen at our institution's advanced prostate cancer clinic were approached for completion of the Comprehensive Score for Financial Toxicity (COST-FACIT) questionnaire (quantitative analysis). A maximal variation purposive sample was chosen to participate in focus group discussions (qualitative analysis). Conventional content analysis was performed using an inductive approach. COST-FACIT scores were compared between patients experiencing high and low financial toxicity using Wilcoxon rank sum test. RESULTS: Three themes were identified through qualitative analysis: (1) workload, (2) coping strategies, and (3) communication. We found alignment with the existing theory of financial capacity across our findings. Two unique aspects of financial toxicity emerged that were not assessed quantitatively and deemed to be significant. Specifically, cost transparency (including health care teams knowledgeable about and willing to discuss costs) and inclusion of informal caregivers in financial toxicity screening and decision-making may guide future interventions aimed at limiting financial toxicity in this population. CONCLUSIONS: Prolonged treatment courses involving multiple lines of treatment with varying costs result in distinct financial toxicity components for patients with metastatic prostate cancer that are not assessed with COST-FACIT. Improving cost transparency, health care team knowledge and engagement, and providing resources to support informal caregivers may have a significant impact on the financial toxicity experienced by these patients.

Dynamin 1xA interacts with Endophilin A1 via its spliced long C-terminus for ultrafast endocytosis.

Dynamin 1 mediates fission of endocytic synaptic vesicles in the brain and has two major splice variants, Dyn1xA and Dyn1xB, which are nearly identical apart from the extended C-terminal region of Dyn1xA. Despite a similar set of binding partners, only Dyn1xA is enriched at endocytic zones and accelerates vesicle fission during ultrafast endocytosis. Here, we report that Dyn1xA achieves this localization by preferentially binding to Endophilin A1 through a newly defined binding site within its long C-terminal tail extension. Endophilin A1 binds this site at higher affinity than the previously reported site, and the affinity is determined by amino acids within the Dyn1xA tail but outside the binding site. This interaction is regulated by the phosphorylation state of two serine residues specific to the Dyn1xA variant. Dyn1xA and Endophilin A1 colocalize in patches near the active zone, and mutations disrupting Endophilin A binding to the long tail cause Dyn1xA mislocalization and stalled endocytic pits on the plasma membrane during ultrafast endocytosis. Together, these data suggest that the specificity for ultrafast endocytosis is defined by the phosphorylation-regulated interaction of Endophilin A1 with the C-terminal extension of Dyn1xA.

Contrast Sensitivity, Visual Field, Color Vision, Motion Perception, and Cognitive Impairment: A Systematic Review.

OBJECTIVES: To examine relationships between visual function (ie, contrast sensitivity, visual field, color vision, and motion perception) and cognitive impairment, including any definition of "cognitive impairment," mild cognitive impairment, or dementia. DESIGN: Systematic review and meta-analyses. SETTING AND PARTICIPANTS: Any settings; participants with (cases) or without (controls) cognitive impairment. METHODS: We searched 4 databases (to January 2024) and included published studies that compared visual function between cases and controls. Standardized mean differences (SMD) with 95% CIs were calculated where data were available. Data were sufficient for meta-analyses when cases were people with dementia. The Joanna Briggs Institute checklists were used for quality assessment. RESULTS: Fifty-one studies/69 reports were included. Cross-sectional evidence shows that people with dementia had worse contrast sensitivity function and color vision than controls: measured by contrast sensitivity (log units) on letter charts, SMD -1.22 (95% CI -1.98, -0.47), or at varied spatial frequencies, -0.92 (-1.28, -0.57); and by pseudoisochromatic plates, -1.04 (-1.59, -0.49); color arrangement, -1.30 (-2.31, -0.29); or matching tests, -0.51 (-0.78, -0.24). They also performed more poorly on tests of motion perception, -1.20 (-1.73, -0.67), and visual field: mean deviation, -0.87 (-1.29, -0.46), and pattern standard deviation, -0.69 (-1.24, -0.15). Results were similar when cases were limited to participants with clinically diagnosed Alzheimer disease. Sources of bias included lack of clarity on study populations or settings and definitions of cognitive impairment. The 2 included longitudinal studies with follow-ups of approximately 10 years were of good quality but reported inconsistent results. CONCLUSIONS AND IMPLICATIONS: In the lack of longitudinal data, cross-sectional studies indicate that individuals with cognitive impairment have poorer visual function than those with normal cognition. Additional longitudinal data are needed to understand whether poor visual function precedes cognitive impairment and the most relevant aspects of visual function, dementia pathologies, and domains of cognition.

Arginine catabolism is essential to polymyxin dependence in Acinetobacter baumannii.

Polymyxins are often the only effective antibiotics against the "Critical" pathogen Acinetobacter baumannii. Worryingly, highly polymyxin-resistant A. baumannii displaying dependence on polymyxins has emerged in the clinic, leading to diagnosis and treatment failures. Here, we report that arginine metabolism is essential for polymyxin-dependent A. baumannii. Specifically, the arginine degradation pathway was significantly altered in polymyxin-dependent strains compared to wild-type strains, with critical metabolites (e.g., L-arginine and L-glutamate) severely depleted and expression of the astABCDE operon significantly increased. Supplementation of arginine increased bacterial metabolic activity and suppressed polymyxin dependence. Deletion of astA, the first gene in the arginine degradation pathway, decreased phosphatidylglycerol and increased phosphatidylethanolamine levels in the outer membrane, thereby reducing the interaction with polymyxins. This study elucidates the molecular mechanism by which arginine metabolism impacts polymyxin dependence in A. baumannii, underscoring its critical role in improving diagnosis and treatment of life-threatening infections caused by "undetectable" polymyxin-dependent A. baumannii.

Deep learning with mixup augmentation for improved pore detection during additive manufacturing.

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM's intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

Inhibition clathrin mediated endocytosis: Pitstop 1 and Pitstop 2 chimeras.

Twenty-five chimera compounds of Pitstop® 1 and 2 were synthesised and screened for their ability to block the clathrin terminal domain-amphiphysin protein-protein interaction (NTD-PPI using an ELISA) and clathrin mediated endocytosis (CME) in cells.  Library 1 was based on Pitstop 2, but no notable clathrin PPI or in-cell activity was observed.  With the Pitstop 1, 16 analogues were produced with 1,8-naphthalic imide core as a foundation.  Analogues with methylene spaced linkers and simple amides showed a modest to good range of PPI inhibition (7.6 to 42.5 mM, naphthyl 39 and 4-nitrophenyl 40 respectively) activity.  These data reveal the importance of the naphthalene sulfonate moiety, with no des-SO3 analogue displaying PPI inhibition.  This was consistent with the observed analogue docked poses within the clathrin terminal domain Site 1 binding pocket.  Further modifications targeted the naphthalene imide moiety, with the installation of 5-Br (45a), 5-OH (45c) and 5-propyl ether (45d) moieties.  Among them, the OH 45c and propyl ether 45d retained PPI inhibition, with propyl ether 45d being the most active with a PPI inhibition IC50 = 7.3 mM.  This is 2x more potent than Pitstop® 2 and 3x more potent than Pitstop 1.

A Community Mental Health Integrated Disaster Preparedness Intervention for Bushfire Recovery in Rural Australian Communities: Protocol for a Multimethods Feasibility and Acceptability Pilot Study.

BACKGROUND: Natural hazards are increasing in frequency and intensity due to climate change. Many of these natural disasters cannot be prevented; what may be reduced is the extent of the risk and negative impact on people and property. Research indicates that the 2019-2020 bushfires in Australia (also known as the "Black Summer Bushfires") resulted in significant psychological distress among Australians both directly and indirectly exposed to the fires. Previous intervention research suggests that communities impacted by natural hazards (eg, earthquakes, hurricanes, and floods) can benefit from interventions that integrate mental health and social support components within disaster preparedness frameworks. Research suggests that disaster-affected communities often prefer the support of community leaders, local services, and preexisting relationships over external supports, highlighting that community-based interventions, where knowledge stays within the local community, are highly beneficial. The Community-Based Disaster Mental Health Intervention (CBDMHI) is an evidence-based approach that aims to increase disaster preparedness, resilience, social cohesion, and social support (disaster-related help-seeking), and decrease mental health symptoms, such as depression and anxiety. OBJECTIVE: This research aims to gain insight into rural Australian's recovery needs post natural hazards, and to enhance community resilience in advance of future fires. Specifically, this research aims to adapt the CBDMHI for the rural Australian context and for bushfires and second, to assess the acceptability and feasibility of the adapted CBDMHI in a rural Australian community. METHODS: Phase 1 consists of qualitative interviews (individual or dyads) with members of the target bushfire-affected rural community. Analysis of these data will include identifying themes related to disaster preparedness, social cohesion, and mental health, which will inform the adaptation. An initial consultation phase is a key component of the adaptation process and, therefore, phase 2 will involve additional discussion with key stakeholders and members of the community to further guide adaptation of the CBDMHI to specific community needs, building on phase 1 inputs. Phase 3 includes identifying and training local community leaders in the adapted intervention. Following this, leaders will co-deliver the intervention. The acceptability and feasibility of the adapted CBDMHI within the community will be evaluated by questionnaires and semistructured interviews. Effectiveness will be evaluated by quantifying psychological distress, resilience, community cohesion, psychological preparedness, and help-seeking intentions. RESULTS: This study has received institutional review board approval and commenced phase 1 recruitment in October 2022. CONCLUSIONS: The study will identify if the adapted CBDMHI is viable and acceptable within a village in the Northern Tablelands of New South Wales, Australia. These findings will inform future scale-up in the broader rural Australian context. If this intervention is well received, the CBDMHI may be valuable for future disaster recovery and preparedness efforts in rural Australia. These findings may inform future scale-up in the broader rural Australian context. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53454.

Interactions Between 3D Surface Shape and Material Perception.

Our visual systems are remarkably adept at deriving the shape and material properties of surfaces even when only one image of a surface is available. This ability implies that a single image of a surface contains potent information about both surface shape and material. However, from a computational perspective, the problem of deriving surface shape and material is formally ill posed. Any given image could be due to many combinations of shape, material, and illumination. Early computational models required prior knowledge about two of the three scene variables to derive the third. However, such models are biologically implausible because our visual systems are tasked with extracting all relevant scene variables from images simultaneously. This review describes recent progress in understanding how the visual system solves this problem by identifying complex forms of image structure that support its ability to simultaneously derive the shape and material properties of surfaces from images.

Capturing carbon dioxide from air with charged-sorbents.

Emissions reduction and greenhouse gas removal from the atmosphere are both necessary to achieve net-zero emissions and limit climate change1. There is thus a need for improved sorbents for the capture of carbon dioxide from the atmosphere, a process known as direct air capture. In particular, low-cost materials that can be regenerated at low temperatures would overcome the limitations of current technologies. In this work, we introduce a new class of designer sorbent materials known as 'charged-sorbents'. These materials are prepared through a battery-like charging process that accumulates ions in the pores of low-cost activated carbons, with the inserted ions then serving as sites for carbon dioxide adsorption. We use our charging process to accumulate reactive hydroxide ions in the pores of a carbon electrode, and find that the resulting sorbent material can rapidly capture carbon dioxide from ambient air by means of (bi)carbonate formation. Unlike traditional bulk carbonates, charged-sorbent regeneration can be achieved at low temperatures (90-100 °C) and the sorbent's conductive nature permits direct Joule heating regeneration2,3 using renewable electricity. Given their highly tailorable pore environments and low cost, we anticipate that charged-sorbents will find numerous potential applications in chemical separations, catalysis and beyond.

Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.

The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs ("dark matter") for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best-in-class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance.