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Refractive error is the most common eye disorder and is the key cause behind correctable visual impairment, responsible for nearly 80% of the visual impairment in the US. Refractive error can be diagnosed using multiple methods, including subjective refraction, retinoscopy, and autorefractors. Although subjective refraction is the gold standard, it requires cooperation from the patient and hence is not suitable for infants, young children, and developmentally delayed adults. Retinoscopy is an objective refraction method that does not require any input from the patient. However, retinoscopy requires a lens kit and a trained examiner, which limits its use for mass screening. In this work, we automate retinoscopy by attaching a smartphone to a retinoscope and recording retinoscopic videos with the patient wearing a custom pair of paper frames. We develop a video processing pipeline that takes retinoscopic videos as input and estimates the net refractive error based on our proposed extension of the retinoscopy mathematical model. Our system alleviates the need for a lens kit and can be performed by an untrained examiner. In a clinical trial with 185 eyes, we achieved a sensitivity of 91.0% and specificity of 74.0% on refractive error diagnosis. Moreover, the mean absolute error of our approach was 0.75$\pm$0.67D on net refractive error estimation compared to subjective refraction measurements. Our results indicate that our approach has the potential to be used as a retinoscopy-based refractive error screening tool in real-world medical settings., This paper is accepted for publication in IMWUT 2022

Hate speech detection is a challenging natural language processing task that requires capturing linguistic and contextual nuances. Pre-trained language models (PLMs) offer rich semantic representations of text that can improve this task. However there is still limited knowledge about ways to effectively combine representations across PLMs and leverage their complementary strengths. In this work, we shed light on various combination techniques for several PLMs and comprehensively analyze their effectiveness. Our findings show that combining embeddings leads to slight improvements but at a high computational cost and the choice of combination has marginal effect on the final outcome. We also make our codebase public at https://github.com/aflah02/The-Art-of-Embedding-Fusion-Optimizing-Hate-Speech-Detection .

The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff), and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data has enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey data leakage, and unrealistic constraints on the task and the preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we have found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress towards to goal of wearable blood pressure measurement via PPG pulse wave analysis.

Funder: Finnish Foundation for Cardiovascular Research, Funder: Finnish Diabetes Research Foundation, Funder: University of Helsinki, Funder: Helsinki University Hospital Government Research Funds, Funder: University of Helsinki and Helsinki University Hospital Government Research, Funder: The Finnish Medical Foundation, Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 ± 3.1 weeks) leading to substantial fat mass loss of 52% (−7.9 ± 1.5 kg) and low body fat (12.7 ± 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 ± 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects.

The COVID-19 pandemic has brought out both the best and worst of language technology (LT). On one hand, conversational agents for information dissemination and basic diagnosis have seen widespread use, and arguably, had an important role in combating the pandemic. On the other hand, it has also become clear that such technologies are readily available for a handful of languages, and the vast majority of the global south is completely bereft of these benefits. What is the state of LT, especially conversational agents, for healthcare across the world's languages? And, what would it take to ensure global readiness of LT before the next pandemic? In this paper, we try to answer these questions through survey of existing literature and resources, as well as through a rapid chatbot building exercise for 15 Asian and African languages with varying amount of resource-availability. The study confirms the pitiful state of LT even for languages with large speaker bases, such as Sinhala and Hausa, and identifies the gaps that could help us prioritize research and investment strategies in LT for healthcare., Comment: Under Revision

Keratoconus is a severe eye disease affecting the cornea (the clear, dome-shaped outer surface of the eye), causing it to become thin and develop a conical bulge. The diagnosis of keratoconus requires sophisticated ophthalmic devices which are non-portable and very expensive. This makes early detection of keratoconus inaccessible to large populations in low- and middle-income countries, making it a leading cause for partial/complete blindness among such populations. We propose SmartKC, a low-cost, smartphone-based keratoconus diagnosis system comprising of a 3D-printed placido's disc attachment, an LED light strip, and an intelligent smartphone app to capture the reflection of the placido rings on the cornea. An image processing pipeline analyzes the corneal image and uses the smartphone's camera parameters, the placido rings' 3D location, the pixel location of the reflected placido rings and the setup's working distance to construct the corneal surface, via the Arc-Step method and Zernike polynomials based surface fitting. In a clinical study with 101 distinct eyes, we found that SmartKC achieves a sensitivity of 94.1% and a specificity of 100.0%. Moreover, the quantitative curvature estimates (sim-K) strongly correlate with a gold-standard medical device (Pearson correlation coefficient =0.78). Our results indicate that SmartKC has the potential to be used as a keratoconus screening tool under real-world medical settings., Change Log: + Fixed sim-K computation (updated Section 5.5.3); re-ran our pipeline with the updated sim-K values (updated Figure 7); + Conducted the comparative evaluation with doctors again (total 4 doctors), and got improved results (updated Section 7.2 and Table 2); [Note: This is an updated version of the paper that was accepted for publication in IMWUT 2021.]

The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n=7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status. Gut microbiome composition has a role in health and disease. Here the authors show that microbiome signatures related to the Enterobacteriaceae family are associated with cause-specific mortality risk in a well phenotyped Finish population over a 15-year follow-up.

Additional file 1. Eligibility and Monitoring Criteria.

Elderly-onset rheumatoid arthritis (EORA) and polymyalgia rheumatica (PMR) are common rheumatic diseases in older adults. Oxylipins are bioactive lipids derived from omega-6 (n-6) and omega-3 (n-3) polyunsaturated fatty acids (PUFAs) that serve as activators or suppressors of systemic inflammation. We hypothesized that arthritis symptoms in older adults were related to oxylipin-related perturbations. Arthritis in older adults (ARTIEL) is an observational prospective cohort with 64 patients older than 60 years of age with newly diagnosed arthritis. Patients' blood samples at baseline and 3 months posttreatment were compared with 18 controls. A thorough clinical examination was conducted. Serum oxylipins were determined by mass spectrometry. Data processing and statistical analysis were performed in R. Forty-four patients were diagnosed with EORA and 20 with PMR. At diagnosis, EORA patients had a mean DAS28CRP (Disease Activity Score 28 using C-reactive protein) of 5.77 (SD 1.02). One hundred percent of PMR patients reported shoulder pain and 90% reported pelvic pain. Several n-6- and n-3-derived oxylipin species were significantly different between controls and arthritis patients. The ratio of n-3/n-6 PUFA was significantly downregulated in EORA but not in PMR patients as compared to controls. The top two candidates as biomarkers for differentiating PMR from EORA were 4-HDoHE, a hydroxydocosahexaenoic acid, and 8,15-dihydroxy-eicosatrienoic acid (8,15-diHETE). The levels of n-3-derived anti-inflammatory species increased in EORA after treatment. These results suggest that certain oxylipins may be key effectors in arthrtis in older adults and that the imbalance between n-6- and n-3-derived oxylipins might be related to pathobiology in this population.

Mitochondrial energetics is responsive to the one-carbon metabolic network., Induction of the one-carbon cycle is an early hallmark of mitochondrial dysfunction and cancer metabolism. Vital intermediary steps are localized to mitochondria, but it remains unclear how one-carbon availability connects to mitochondrial function. Here, we show that the one-carbon metabolite and methyl group donor S-adenosylmethionine (SAM) is pivotal for energy metabolism. A gradual decline in mitochondrial SAM (mitoSAM) causes hierarchical defects in fly and mouse, comprising loss of mitoSAM-dependent metabolites and impaired assembly of the oxidative phosphorylation system. Complex I stability and iron-sulfur cluster biosynthesis are directly controlled by mitoSAM levels, while other protein targets are predominantly methylated outside of the organelle before import. The mitoSAM pool follows its cytosolic production, establishing mitochondria as responsive receivers of one-carbon units. Thus, we demonstrate that cellular methylation potential is required for energy metabolism, with direct relevance for pathophysiology, aging, and cancer.

Benign hepatosteatosis, affected by lipid uptake, de novo lipogenesis and fatty acid (FA) oxidation, progresses to non-alcoholic steatohepatitis (NASH) on stress and inflammation. A key macronutrient proposed to increase hepatosteatosis and NASH risk is fructose. Excessive intake of fructose causes intestinal-barrier deterioration and endotoxaemia. However, how fructose triggers these alterations and their roles in hepatosteatosis and NASH pathogenesis remain unknown. Here we show, using mice, that microbiota-derived Toll-like receptor (TLR) agonists promote hepatosteatosis without affecting fructose-1-phosphate (F1P) and cytosolic acetyl-CoA. Activation of mucosal-regenerative gp130 signalling, administration of the YAP-induced matricellular protein CCN1 or expression of the antimicrobial peptide Reg3b (beta) peptide counteract fructose-induced barrier deterioration, which depends on endoplasmic-reticulum stress and subsequent endotoxaemia. Endotoxin engages TLR4 to trigger TNF production by liver macrophages, thereby inducing lipogenic enzymes that convert F1P and acetyl-CoA to FA in both mouse and human hepatocytes.

Bacteria possess an array of defenses against foreign invaders, including a broadly distributed bacteriophage defense system termed CBASS (cyclic oligonucleotide-based anti-phage signaling system). In CBASS systems, a cGAS/DncV-like nucleotidyltransferase synthesizes cyclic di- or tri-nucleotide second messengers in response to infection, and these molecules activate diverse effectors to mediate bacteriophage immunity via abortive infection. Here, we show that the CBASS effector NucC is related to restriction enzymes but uniquely assembles into a homotrimer. Binding of NucC trimers to a cyclic tri-adenylate second messenger promotes assembly of a NucC homohexamer competent for non-specific double-strand DNA cleavage. In infected cells, NucC activation leads to complete destruction of the bacterial chromosome, causing cell death prior to completion of phage replication. In addition to CBASS systems, we identify NucC homologs in over 30 type III CRISPR/Cas systems, where they likely function as accessory nucleases activated by cyclic oligoadenylate second messengers synthesized by these systems' effector complexes.

Autism often remains undiagnosed in adolescents and adults. Prior research has indicated that an autistic individual often shows atypical fixation and gaze patterns. In this short paper, we demonstrate that by monitoring a user's gaze as they watch commonplace (i.e., not specialized, structured or coded) video, we can identify individuals with autism spectrum disorder. We recruited 35 autistic and 25 non-autistic individuals, and captured their gaze using an off-the-shelf eye tracker connected to a laptop. Within 15 seconds, our approach was 92.5% accurate at identifying individuals with an autism diagnosis. We envision such automatic detection being applied during e.g., the consumption of web media, which could allow for passive screening and adaptation of user interfaces.

The apolipoprotein ε4 (APOE4) is the most prevalent genetic risk factor for late-onset Alzheimer’s Disease (AD). Here we assessed the metabolomic profile differences between APOE4 carriers who develop AD vs. who do not in a sample of 142 participants, aged 65-99 years in the Long Life Family Study (LLFS). Of 7,321 metabolites, we applied a generalized estimating equation model and identified 137 metabolites significantly associated with AD. Subsequent multivariate analyses were performed for prediction and clustering recognition. Among annotated metabolites, 8 metabolites in the eicosanoids and docosanoids group, 3 metabolites in the fatty acids group, and arabitol were associated with elevated risks of AD (OR: 1.6-2.3). On the other hand, a different set of metabolites were associated with reduced risks of AD (OR: 0.34-0.64). These metabolomic profile differences can be used to help with early diagnosis in the population of older APOE4 carriers in the pre-clinical stage.

BACKGROUND: Dietary intake of choline has been linked to systemic inflammation through the microbial production of two metabolites trimethylamine (TMA) and trimethylamine-N-oxide (TMAO). Herein we explore the association between choline metabolites and inflammation in psoriatic arthritis (PsA) patients. MATERIAL AND METHODS: Thirty-eight patients with PsA, all of whom satisfied the CASPAR classification criteria for PsA, were studied. Outcomes reflecting the activity of peripheral arthritis as well as skin psoriasis, Disease Activity Score (DAS)28, Clinical Disease Index (CDAI) and Body Surface Area (BSA) were assessed. Serum concentration of choline metabolites (choline, TMA, TMAO, betaine and carnitine) were determined by LC-MS, and metabolite levels associated with disease scores. RESULTS: Among the 38 PsA patients included the mean of DAS28PCR was 2.74 ± 1.29. Twenty-seven patients had active skin disease, with an average BSA of 7.2 ± 16.22. TMAO, but not TMA or choline, significantly correlated with measures of disease activity for both skin and peripheral joints. CONCLUSION: In our cohort, only TMAO, but not TMA, choline, betaine or carnitine, was associated with inflammation in PsA patients, establishing a mechanistic link between TMAO and PsA phenotypes. Future studies will explore modulation of TMAO and disease severity in PsA.

Eicosanoids and related species are critical, small bioactive mediators of human physiology and inflammation. While ~1100 distinct eicosanoids have been predicted to exist, to date, less than 150 of these molecules have been measured in humans, limiting our understanding of eicosanoids and their role in human biology. Using a directed non-targeted mass spectrometry approach in conjunction with computational chemical networking of spectral fragmentation patterns, we find over 500 discrete chemical signals highly consistent with known and putative eicosanoids in human plasma, including 46 putative novel molecules not previously described, thereby greatly expanding the breath of prior analytical strategies. In plasma samples from 1500 individuals, we find members of this expanded eicosanoid library hold close association with markers of inflammation, as well as clinical characteristics linked with inflammation, including advancing age and obesity. These experimental and computational approaches enable discovery of new chemical entities and will shed important insight into the role of bioactive molecules in human disease.

LabVIEW is a versatile tool with various inbuilt toolkits to perform various measurement and control tasks. Hence, it is used in almost every field of engineering. However, it does not provide enough contribution in the field of optimization which is the major concern. It has only one optimizer based on differential evolution (DE) algorithm. Even though DE is a very effective global optimization technique, but its performance highly depends on parametric settings. DE contains high number of user-defined parameters; therefore, it becomes cumbersome for user to obtain best parametric settings for a given optimization problem. Recently, several nature-inspired algorithms are developed with reduced number of parametric settings to obtain the optimum solutions while solving complex black box optimization problems. Hence, to update the LabVIEW in the field of optimization, there exists a need of continuous development of other efficient global optimizers. Multi-verse optimizer (MVO) is considered as one of the latest but effective nature-inspired optimization algorithm with only two user-defined parameters. In this paper, MVO toolkit is developed for LabVIEW platflorm and the efficiency of the proposed toolkit is validated on a test bed of five standard benchmark functions. The statistical analysis of results shows that the MVO is far better in solving optimization problems as compared to DE.

Introduction: Plasters have been frequently associated with known complications such as infection, and compartment syndrome or deep vein thrombosis. However, life-threatening complication of necrotizing fasciitis (NF) has not been frequently attributed to plaster. Case Report: We had a case of a 62-year male developing a lethal NF triggered by a below knee plaster for undisplaced fracture medial malleolus after twisting injury. He had no history suggestive of diabetes, renal impairment, and predisposing allergic factors or any comorbidity. Despite early diagnosis and aggressive management with above knee amputation, death occurs due to septic shock on the 20th day. A similar case of reported lethal NF triggered by plaster has also been reviewed in this report. Conclusion: This case highlights a life-threatening rare complication of plaster and author recommends thorough clinical history taking, precleaning of limb, use of sterile water and use of adequate wrap around skin for gypsum plasters as prevention apart from high index of suspicion for early diagnosis, and rapid management.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity and mechanisms underlying human health and disease. Large-scale metabolomics data, generated using targeted or nontargeted platforms, are increasingly more common. Appropriate statistical analysis of these complex high-dimensional data is critical for extracting meaningful results from such large-scale human metabolomics studies. Herein, we consider the main statistical analytical approaches that have been employed in human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we propose a step-by-step framework for pursuing statistical analyses of human metabolomics data. We discuss the range of options and potential approaches that may be employed at each stage of data management, analysis, and interpretation, and offer guidance on analytical considerations that are important for implementing an analysis workflow. Certain pervasive analytical challenges facing human metabolomics warrant ongoing research. Addressing these challenges will allow for more standardization in the field and lead to analytical advances in metabolomics investigations with the potential to elucidate novel mechanisms underlying human health and disease.

Atherosclerosis is a multifaceted inflammatory disease involving cells in the vascular wall (eg, endothelial cells [ECs]), as well as circulating and resident immunogenic cells (eg, monocytes/macrophages). Acting as a ligand for liver X receptor (LXR), but an inhibitor of SREBP2 (sterol regulatory element-binding protein 2), 25-hydroxycholesterol, and its catalyzing enzyme cholesterol-25-hydroxylase (Ch25h) are important in regulating cellular inflammatory status and cholesterol biosynthesis in both ECs and monocytes/macrophages.Bioinformatic analyses were used to investigate RNA-sequencing data to identify cholesterol oxidation and efflux genes regulated by Krüppel-like factor 4 (KLF4). In vitro experiments involving cultured ECs and macrophages and in vivo methods involving mice with Ch25h ablation were then used to explore the atheroprotective role of KLF4-Ch25h/LXR.Vasoprotective stimuli increased the expression of Ch25h and LXR via KLF4. The KLF4-Ch25h/LXR homeostatic axis functions through suppressing inflammation, evidenced by the reduction of inflammasome activity in ECs and the promotion of M1 to M2 phenotypic transition in macrophages. The increased atherosclerosis in apolipoprotein EKLF4 transactivates Ch25h and LXR, thereby promoting the synergistic effects between ECs and macrophages to protect against atherosclerosis susceptibility.

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel 'rain plot' approach to display the results of these analyses. The 'rain plot' combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.

Background. Emerging technologies now allow for mass spectrometry based profiling of up to thousands of small molecule metabolites (metabolomics) in an increasing number of biosamples. While offering great promise for revealing insight into the pathogenesis of human disease, standard approaches have yet to be established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes including disease outcomes. To determine optimal statistical approaches for metabolomics analysis, we sought to formally compare traditional statistical as well as newer statistical learning methods across a range of metabolomics dataset types. Results. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observed that with an increasing number of study subjects, univariate compared to multivariate methods resulted in a higher false discovery rate due to substantial correlations among metabolites. In scenarios wherein the number of assayed metabolites increases, as in the application of nontargeted versus targeted metabolomics measures, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. Conclusion. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small sized cohorts, sparse multivariate models exhibited the most robust statistical power with more consistent results. These findings have important implications for the analysis of metabolomics studies of human disease.

In recent times, many protocols have been proposed to provide security for various information and communication systems. Such protocols must be tested for their functional correctness before they are used in practice. Application of formal methods for verification of security protocols would enhance their reliability thereby, increasing the usability of systems that employ them. Thus, formal verification of security protocols has become a key issue in computer and communications security. In this paper we present, analyze and compare some prevalent approaches towards verification of secure systems. We follow the notion of - same goal through different approaches - as we formally analyze the Needham Schroeder Public Key protocol for Lowe's attack using each of our presented approaches.

Transnitrosylation and denitrosylation are emerging as key post-translational modification events in regulating both normal physiology and a wide spectrum of human diseases. Thioredoxin 1 (Trx1) is a conserved antioxidant that functions as a classic disulfide reductase. It also catalyzes the transnitrosylation or denitrosylation of caspase 3 (Casp3), underscoring its central role in determining Casp3 nitrosylation specificity. However, the mechanisms that regulate Trx1 transnitrosylation and denitrosylation of specific targets are unresolved. Here we used an optimized mass spectrometric method to demonstrate that Trx1 is itself nitrosylated by S-nitrosoglutathione at Cys(73) only after the formation of a Cys(32)-Cys(35) disulfide bond upon which the disulfide reductase and denitrosylase activities of Trx1 are attenuated. Following nitrosylation, Trx1 subsequently transnitrosylates Casp3. Overexpression of Trx1(C32S/C35S) (a mutant Trx1 with both Cys(32) and Cys(35) replaced by serine to mimic the disulfide reductase-inactive Trx1) in HeLa cells promoted the nitrosylation of specific target proteins. Using a global proteomics approach, we identified 47 novel Trx1 transnitrosylation target protein candidates. From further bioinformatics analysis of this set of nitrosylated peptides, we identified consensus motifs that are likely to be the determinants of Trx1-mediated transnitrosylation specificity. Among these proteins, we confirmed that Trx1 directly transnitrosylates peroxiredoxin 1 at Cys(173) and Cys(83) and protects it from H2O2-induced overoxidation. Functionally, we found that Cys(73)-mediated Trx1 transnitrosylation of target proteins is important for protecting HeLa cells from apoptosis. These data demonstrate that the ability of Trx1 to transnitrosylate target proteins is regulated by a crucial stepwise oxidative and nitrosative modification of specific cysteines, suggesting that Trx1, as a master regulator of redox signaling, can modulate target proteins via alternating modalities of reduction and nitrosylation. Molecular & Cellular Proteomics 9:2262-2275, 2010.

NOTE: Text or symbols not renderable in plain ASCII are indicated by [...]. Abstract is included in .pdf document. The mechanical behavior of ultrafine grain Fe-28A1-2Cr and 304 stainless steel was examined by conducting conventional mechanical testing and pre- and post-deformation microstructural characterization on bulk samples. Shock wave consolidation was used to produce a fully-dense nanophase Fe-28A1-2Cr (grain size = 80 nm) intermetallic compound. In tension, the nanophase intermetallic failed in a brittle fashion with failure strength comparable to the coarse grain intermetallic of similar composition. However, the nanophase intermetallic yielded at 2.1 GPa during quasi-static compressive deformation and deformed to true strains greater than 1.4 without work hardening. The elastic-perfectly plastic behavior of nanophase Fe-28A1-2Cr is significantly different from that of coarse-grained intermetallic of the same composition, which yielded at 0.25 GPa and works hardened to 1.5 GPa before failure (at true strain of about 0.37). Microstructural examination before and after compressive deformation revealed that a significant portion of the microstructure refined to 10 nm grains surrounded by amorphous material. A similar grain refinement process was observed in 80 urn Fe- 28A1-2Cr produced by ingot metallurgical technique. A novel thermo-mechanical processing technique was developed for the production of a ultrafine grain 304 stainless steel (grain size = 200 nm). The key steps to this processing technique involved (1) formation of ultrafine dislocation cell structure, and (2) the conversion of dislocation cells into grains with medium to high misorientation by initiating grain boundary sliding in the microstructure. The ultrafine grain steel (grain size = 200 nm) was about six times stronger ([...] = 1700 MPa) than coarse-grained steel of the same composition. Grain size hardening behavior of 304 stainless steel was also investigated over a broad range of grain sizes (200 nm to 200[...]).

The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status.

This study aims to investigate the potential of ethanol-based proliposomes in generating paclitaxel-loaded liposome delivery in vitro, by employing various phospholipid compositions.\ud \ud Liposomes prepared using ethanol-based proliposome method successfully generated multilamellar vesicles. Three different lipid phases: SPC:Chol, HSPC:Chol or DPPC:Chol in 1:1 mole ratio were used in each liposomal formulation to compare their size, size distribution, zeta potential, pH and morphology. The size of the liposomes was then reduced into nanometre size range. \ud \ud DPPC-liposomes entrapped 70-85% of the available paclitaxel compared to only 46-75% and 26-67% entrapped by liposomes made from SPC and HSPC respectively, using a range of paclitaxel concentration. The entrapment efficiency of liposomes was dependent on the lipid bilayer properties and ability of paclitaxel to modify surface charge. \ud \ud In vitro studies revealed that paclitaxel alone was more toxic to U87-MG as well as SVG-P12 cell lines than liposome formulations. The cytotoxicity of liposomes was dependent on their entrapment efficiency and sustained drug release. Thus, DPPC-liposomes had a more cytotoxic effect on the cells than SPC and HSPC liposomes. However, Drug-free liposomes proved to be non-toxic to the cells, indicating that liposomes might enhance the efficacy of the entrapped drug. The properties of different liposome formulations were essential in understanding their drug delivery mechanism.

Additional file 5. Fig. S1-8

Additional file 25. Review history.

Funder: Academy of Finland (Suomen Akatemia); doi: https://doi.org/10.13039/501100002341, Funder: Suomen Lääketieteen Säätiö (Finnish Medical Foundation); doi: https://doi.org/10.13039/100008723, Funder: Paavo Nurmen Säätiö (Paavo Nurmi Foundation); doi: https://doi.org/10.13039/501100008484, The collection of fecal material and developments in sequencing technologies have enabled standardised and non-invasive gut microbiome profiling. Microbiome composition from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterised due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. Here, we analyse the long-term association between gut microbiome variation and mortality in a well-phenotyped and representative population cohort from Finland (n = 7211). We report robust taxonomic and functional microbiome signatures related to the Enterobacteriaceae family that are associated with mortality risk during a 15-year follow-up. Our results extend previous cross-sectional studies, and help to establish the basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status.

Additional file 5. Fig. S1-8

Additional file 25. Review history.

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the chi(2)-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development. Funding Agencies|Swedish Foundation for Strategic Research [FFL12-0220, IMT17-0245]; Swedish Research Council [2018-05418, 2018-03319]; Karolinska Institutet; CENIIT [15.09]; SciLifeLab National COVID-19 Research Program - Knut and Alice Wallenberg Foundation [2020.0182]; H2020 project PRECISE4Q grant [777107]; VINNOVA grants VisualSweden [2020-04711]; Swedish Fund for Research without Animal Experiments [F2019-0010]; ELLIIT [2020A12]

Funder: Emil Aaltosen Säätiö, Funder: Emerald Foundation, Funder: Sydäntutkimussäätiö, Funder: Suomen Lääketieteen Säätiö, Funder: Suomen Lääketieteen Säätiö (Finnish Medical Foundation), Funder: Sydäntutkimussäätiö (Finnish Foundation for Cardiovascular Research), Funder: Emil Aaltosen Säätiö (Emil Aaltonen Foundation), We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.

Importance Given that hypertension remains a leading risk factor for chronic disease globally, there are substantial ongoing efforts to define the optimal range of blood pressure (BP). Objective To identify a common threshold level above which BP rise tends to accelerate in progression toward hypertension. Design, Setting, and Participants This longitudinal, community-based epidemiological cohort study of adults enrolled in Framingham, Massachusetts, included 1252 participants (mean [SD] age, 35.3 [2.7] years) from the Framingham Original Cohort, of whom 790 (63.1%) were women. Each participant contributed up to 28 serial examinations of standardized resting BP measurements between 1948 and 2005. Exposures Age and systolic BP. Main Outcomes and Measures Via a segmented mixed model, we identified significant change points in the association between advancing age and increasing systolic BP among individuals categorized by their age at hypertension onset. Results Individuals maintained a relatively stable resting systolic BP level prior to hypertension onset. Systolic BP level began to rise at a more rapid rate after reaching a level of 123.2 mm Hg (95% CI, 122.7-130.1 mm Hg) in people with onset at 40 to 49 years; 122.0 mm Hg (95% CI, 120.3-123.9 mm Hg) in those with onset between 50 and 59 years, 124.9 mm Hg (95% CI, 120.2-127.9 mm Hg) in those with onset between 60 and 69 years, and 120.5 mm Hg (95% CI, 118.0-123.2 mm Hg) in those with onset between 70 and 79 years (P = .29 for between-group heterogeneity). Conclusions and Relevance We observed that individuals in the community generally maintained a systolic BP of less than 120 to 125 mm Hg, above which systolic BP increased at a relatively rapid rate toward overt hypertension. This trend was consistent whether the hypertension manifested earlier or later in life. Thus, a resting systolic BP that chronically exceeds the range of approximately 120 to 125 mm Hg may represent an important threshold of underlying vascular remodeling and signal incipient hypertension irrespective of age. Further investigations are needed to unravel the sequence of hemodynamic and vascular changes occurring prior to hypertension onset.

As noted by Warburg, many cancer cells depend on the consumption of glucose. We performed a genetic screen to identify factors responsible for glucose addiction and recovered the two subunits of the xCT antiporter (system xc−), which plays an antioxidant role by exporting glutamate for cystine. Disruption of the xCT antiporter greatly improves cell viability after glucose withdrawal, because conservation of glutamate enables cells to maintain mitochondrial respiration. In some breast cancer cells, xCT antiporter expression is upregulated through the antioxidant transcription factor Nrf2 and contributes to their requirement for glucose as a carbon source. In cells carrying patient-derived mitochondrial DNA mutations, the xCT antiporter is upregulated and its inhibition improves mitochondrial function and cell viability. Therefore, although upregulation of the xCT antiporter promotes antioxidant defence, it antagonizes glutamine metabolism and restricts nutrient flexibility. In cells with mitochondrial dysfunction, the potential utility of xCT antiporter inhibition should be further tested., The factors that limit the nutrient flexibility of cells remain largely unknown. Here, the authors identify the glutamate/cysteine antiporter xCT in a genetic screen for glucose dependency and show it determines the ability of cells to survive under conditions of low glucose by limiting the utilization of glutamine.