Spin-glass behavior in KxRu4-yNiyO8 hollandite materials

We report the synthesis and comprehensive ac and dc susceptibility measurements of KxRu4−yNiyO8 hollandite. The value of the relative frequency shift, δTf , has been determined as 0.025 which is within the range expected for spin-glass systems (0.005–0.06). Additionally, the characteristic flipping time of a single spin flip, τ0, and the dynamical critical exponent, −zv, were determined to have values of 5.82×10−8 s and 6.1(3), respectively from the power law. While the value of τ0 is comparatively very large, −zv is consistent with what is expected for spin-glass systems. Field-cooled hysteresis behavior demonstrates a small increase in the remnant magnetization (at 2 K) on increasing the strength of the cooling field, suggesting that the degree of short-range correlations increases consistent with the formation of larger spin clusters. Thermoremnant magnetization data indicate an exponential-like decay of the magnetization as a function of time with the remnant magnetization remaining nonzero. However, it is clear from these data that multiple components contribute to the decay behavior. Collectively, these data confirm spin-glass character for K0.73(3)Ni1.9(5)Ru2.1(5)O8 and clearly demonstrate that the magnetic behavior of this material is far from simplistic

Impact of Cardio-Renal-Metabolic Comorbidities on Cardiovascular Outcomes and Mortality in Type 2 Diabetes Mellitus

BACKGROUND: We evaluated the incremental contribution of chronic kidney disease (CKD) to the risk of major adverse cardiovascular (CV) events (MACE), heart failure (HF), and all-cause mortality (ACM) in type 2 diabetes mellitus (T2DM) patients and its importance relative to the presence of other cardio-renal-metabolic (CaReMe) comorbidities. METHODS: Patients (≥40 years) were identified at the time of T2DM diagnosis from US (Humedica/Optum) and UK (Clinical Practice Research Datalink) databases. Patients were monitored post-diagnosis for modified MACE (myocardial infarction, stroke, ACM), HF, and ACM. Adjusted hazard ratios were obtained using Cox proportional-hazards regression to evaluate the relative risk of modified MACE, HF, and ACM due to CKD. Patients were stratified by the presence or absence of atherosclerotic CV disease (ASCVD) and age. RESULTS: Between 2011 and 2015, of 227,224 patients identified with incident T2DM, 40,063 (17.64%) had CKD. Regardless of prior ASCVD, CKD was associated with higher risk of modified MACE, HF, and ACM; this excess hazard was more pronounced in older patients with prior ASCVD. In time-to-event analyses in the overall cohort, patients with T2DM + CKD or T2DM + CKD + hypertension + hyperlipidemia had increased risks for modified MACE, HF, and ACM versus patients with T2DM and no CaReMe comorbidities. Patients with CKD had higher risks for and shorter times to modified MACE, HF, and ACM than those without CKD. CONCLUSION: In T2DM patients, CKD presence was associated with higher risk of modified MACE, HF, and ACM. This may have risk-stratification implications for T2DM patients based on background CKD and highlights the potential importance of novel renoprotective strategies

SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain

Health Care Resource Utilization and Related Costs of Patients With CKD From the United States: A Report From the DISCOVER CKD Retrospective Cohort

Introduction: It is well established that chronic kidney disease (CKD) results in a significant burden on patients’ health and health care providers. However, detailed estimates of the health care resource utilization (HCRU) of CKD are limited, particularly those which consider severity, comorbidities, and payer type. This study aimed to bridge this evidence gap by reporting contemporary HCRU and costs in patients with CKD across the US health care providers. Methods: Cost and HCRU estimates of CKD and reduced kidney function without CKD (estimated glomerular filtration rate [eGFR]: 60−75 and urine albumin-to-creatinine ratio [UACR]: <30) were derived for US patients included in the DISCOVER CKD cohort study, using linked inpatient and outpatient data from the limited claims-EMR data set (LCED) and TriNetX database. Patients with a history of transplant or undergoing dialysis were not included. HCRU and costs were stratified by CKD severity using UACR and eGFR. Results: Overall health care costs ranged from $26,889 (A1) to$42,139 (A3), and from $28,627 (G2) to$42,902 (G5) per patient per year (PPPY), demonstrating a considerable early disease burden which continued to increase with declining kidney function. The PPPY costs of later stage CKD were particularly notable for patients with concomitant heart failure ($50,191 [A3]) and those covered by commercial payers ($55,735 [A3]). Conclusions: Health care costs and resource use associated with CKD and reduced kidney function pose a substantial burden across health care systems and payers, increasing in line with CKD progression. Early CKD screening, particularly of UACR, paired with proactive disease management may provide both an improvement to patient outcomes and a significant HCRU and cost saving to health care providers

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice

Purpose: This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression. / Methods: Nonsystematic literature review using the search combinations "Artificial Intelligence," "Deep Learning," "Machine Learning," "Neural Networks," "Bayesian Networks," "Glaucoma Diagnosis," and "Glaucoma Progression." Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted. / Results: Numerous AI strategies provide promising levels of specificity and sensitivity for structural (e.g. optical coherence tomography [OCT] imaging, fundus photography) and functional (visual field [VF] testing) test modalities used for the detection of glaucoma. Area under receiver operating curve (AROC) values of > 0.90 were achieved with every modality. Combining structural and functional inputs has been shown to even more improve the diagnostic ability. Regarding glaucoma progression, AI strategies can detect progression earlier than conventional methods or potentially from one single VF test. / Conclusions: AI algorithms applied to fundus photographs for screening purposes may provide good results using a simple and widely accessible test. However, for patients who are likely to have glaucoma more sophisticated methods should be used including data from OCT and perimetry. Outputs may serve as an adjunct to assist clinical decision making, whereas also enhancing the efficiency, productivity, and quality of the delivery of glaucoma care. Patients with diagnosed glaucoma may benefit from future algorithms to evaluate their risk of progression. Challenges are yet to be overcome, including the external validity of AI strategies, a move from a "black box" toward "explainable AI," and likely regulatory hurdles. However, it is clear that AI can enhance the role of specialist clinicians and will inevitably shape the future of the delivery of glaucoma care to the next generation. / Translational Relevance: The promising levels of diagnostic accuracy reported by AI strategies across the modalities used in clinical practice for glaucoma detection can pave the way for the development of reliable models appropriate for their translation into clinical practice. Future incorporation of AI into healthcare models may help address the current limitations of access and timely management of patients with glaucoma across the world

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials

Organometallic neptunium(III) complexes

Studies of transuranic organometallic complexes provide a particularly valuable insight into covalent contributions to the metal–ligand bonding, in which the subtle differences between the transuranium actinide ions and their lighter lanthanide counterparts are of fundamental importance for the effective remediation of nuclear waste. Unlike the organometallic chemistry of uranium, which has focused strongly on UIII and has seen some spectacular advances, that of the transuranics is significantly technically more challenging and has remained dormant. In the case of neptunium, it is limited mainly to NpIV. Here we report the synthesis of three new NpIII organometallic compounds and the characterization of their molecular and electronic structures. These studies suggest that NpIII complexes could act as single-molecule magnets, and that the lower oxidation state of NpII is chemically accessible. In comparison with lanthanide analogues, significant d- and f-electron contributions to key NpIII orbitals are observed, which shows that fundamental neptunium organometallic chemistry can provide new insights into the behaviour of f-elements

Disfluency in dialogue:an intentional signal from the speaker?

Disfluency is a characteristic feature of spontaneous human speech, commonly seen as a consequence of problems with production. However, the question remains open as to why speakers are disfluent: Is it a mechanical by-product of planning difficulty, or do speakers use disfluency in dialogue to manage listeners' expectations? To address this question, we present two experiments investigating the production of disfluency in monologue and dialogue situations. Dialogue affected the linguistic choices made by participants, who aligned on referring expressions by choosing less frequent names for ambiguous images where those names had previously been mentioned. However, participants were no more disfluent in dialogue than in monologue situations, and the distribution of types of disfluency used remained constant. Our evidence rules out at least a straightforward interpretation of the view that disfluencies are an intentional signal in dialogue. © 2012 Psychonomic Society, Inc

Quantifying The Causes of Differences in Tropospheric OH within Global Models

The hydroxyl radical (OH) is the primary daytime oxidant in the troposphere and provides the main loss mechanism for many pollutants and greenhouse gases, including methane (CH4). Global mean tropospheric OH differs by as much as 80% among various global models, for reasons that are not well understood. We use neural networks (NNs), trained using archived output from eight chemical transport models (CTMs) that participated in the POLARCAT Model Intercomparison Project (POLMIP), to quantify the factors responsible for differences in tropospheric OH and resulting CH4 lifetime (τCH4) between these models. Annual average τCH4, for loss by OH only, ranges from 8.0–11.6 years for the eight POLMIP CTMs. The factors driving these differences were quantified by inputting 3-D chemical fields from one CTM into the trained NN of another CTM. Across all CTMs, the largest mean differences in τCH4 (ΔτCH4) result from variations in chemical mechanisms (ΔτCH4 = 0.46 years), the photolysis frequency (J) of O3→O(1D) (0.31 years), local O3 (0.30 years), and CO (0.23 years). The ΔτCH4 due to CTM differences in NOx (NO + NO2) is relatively low (0.17 years), though large regional variation in OH between the CTMs is attributed to NOx. Differences in isoprene and J(NO2) have negligible overall effect on globally averaged tropospheric OH, though the extent of OH variations due to each factor depends on the model being examined. This study demonstrates that NNs can serve as a useful tool for quantifying why tropospheric OH varies between global models, provided essential chemical fields are archived

Time Markers and Temporal Illusions

Peer reviewe