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The ability to transport energy is a fundamental property of the two-dimensional Dirac fermions in graphene. Electronic thermal transport in this system is relatively unexplored and is expected to show unique fundamental properties and to play an important role in future applications of graphene, including optoelectronics, plasmonics, and ultrasensitive bolometry. Here, we present measurements of bipolar thermal conductances due to electron diffusion and electron-phonon coupling and infer the electronic specific heat, with a minimum value of 10k_{B} (10^{-22} J/K) per square micron. We test the validity of the Wiedemann-Franz law and find that the Lorenz number equals 1.32×(π^{2}/3)(k_{B}/e)^{2}. The electron-phonon thermal conductance has a temperature power law T^{2} at high doping levels, and the coupling parameter is consistent with recent theory, indicating its enhancement by impurity scattering. We demonstrate control of the thermal conductance by electrical gating and by suppressing the diffusion channel using NbTiN superconducting electrodes, which sets the stage for future graphene-based single-microwave photon detection.

BackgroundExisting criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting ‘less-than-median-survival risk’ in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet.MethodsTo ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features.ResultsThe classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively.ConclusionMachine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.

Background: The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents. Methods: 474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8–18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2–L3 disc space and another image at the L4–L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student’s t-test against the reference standards. Results: For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS). Conclusion: These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.

The membrane-aerated biofilm reactor (MABR) is a novel method for the biological treatment of wastewaters and has been successfully applied for nitrification. To improve the design and adaptation of MABR processes for colder climates and varying temperatures, the temperature dependence of a counter-diffusional biofilm’s nitrification performance was investigated. A lab-scale MABR system with silicone hollow fibre membranes was operated at various temperatures between 8 and 30°C, and batch tests were performed to determine the ammonia oxidation kinetics. Biofilm samples were taken at 8 and 24°C and analysed with 16S rRNA sequencing to monitor changes in the microbial community composition, and a mathematical model was used to study the temperature dependence of mass transfer. A high nitrification rate (3.08 g N m–2 d–1) was achieved at 8°C, and temperature dependence was found to be low (θ = 1.024–1.026) compared to suspended growth processes. Changes in the community composition were moderate, Nitrospira defluvii remaining the most dominant species. Mass transfer limitations were shown to be largely responsible for the observed trends, consistent with other biofilm processes. The results show that the MABR is a promising technology for low temperature nitrification, and appropriate management of the mass transfer resistance can optimise the process for both low and high temperature operation.

Abstract Background Atrial fibrillation (AF) is a progressive condition, which is characterized by inflammation/fibrosis of left atrial (LA) wall, an increase in the LA size/volumes, and decrease in LA function. We sought to investigate the relationship of anatomical and functional parameters obtained by cardiovascular magnetic resonance (CMR), with AF recurrence in paroxysmal AF (pAF) patients after catheter ablation. Methods We studied 80 consecutive pAF patients referred for ablation, between January 2014 and December 2019, who underwent pre- and post-ablation CMR while in sinus rhythm. LA volumes were measured using the area–length method and included maximum, minimum, and pre-atrial-contraction volumes. CMR-derived LA reservoir strain (ℇR), conduit strain (ℇCD), and contractile strain (ℇCT) were measured by computer assisted manual planimetry. We used a multivariate logistical regression to estimate the independent predictors of AF recurrence after ablation. Results Mean age was 58.6 ± 9.4 years, 75% men, mean CHA2DS2-VASc score was 1.7, 36% had prior cardioversion and 51% were taking antiarrhythmic drugs. Patients were followed for a median of 4 years (Q1–Q3 = 2.5–6.2 years). Of the 80 patients, 21 (26.3%) patients had AF recurrence after ablation. There were no significant differences between AF recurrence vs. no recurrence groups in age, gender, CHA2DS2-VASc score, or baseline comorbidities. At baseline, patients with AF recurrence compared to without recurrence had lower LV end systolic volume index (32 ± 7 vs 37 ± 11 mL/m2; p = 0.045) and lower ℇCT (7.1 ± 4.6 vs 9.1 ± 3.7; p = 0.05). Post-ablation, patients with AF recurrence had higher LA minimum volume (68 ± 32 vs 55 ± 23; p = 0.05), right atrial volume index (62 ± 20 vs 52 ± 19 mL/m2; p = 0.04) and lower LA active ejection fraction (24 ± 8 vs 29 ± 11; p = 0.05), LA total ejection fraction (39 ± 14 vs 46 ± 12; p = 0.02), LA expansion index (73.6 ± 37.5 vs 94.7 ± 37.1; p = 0.03) and ℇCT (6.2 ± 2.9 vs 7.3 ± 1.7; p = 0.04). Adjusting for clinical variables in the multivariate logistic regression model, post-ablation minimum LA volume (OR 1.09; CI 1.02–1.16), LA expansion index (OR 0.98; CI 0.96–0.99), and baseline ℇR (OR 0.92; CI 0.85–0.99) were independently associated with AF recurrence. Conclusion Significant changes in LA volumes and strain parameters occur after AF ablation. CMR derived baseline ℇR, post-ablation minimum LAV, and expansion index are independently associated with AF recurrence.

A common task in brain image analysis includes diagnosis of a certain medical condition wherein groups of healthy controls and diseased subjects are analyzed and compared. On the other hand, for two groups of healthy participants with different proficiency in a certain skill, a distinctive analysis of the brain function remains a challenging problem. In this study, we develop new computational tools to explore the functional and anatomical differences that could exist between the brain of healthy individuals identified on the basis of different levels of task experience/proficiency. Toward this end, we look at a dataset of amateur and professional chess players, where we utilize resting-state functional magnetic resonance images to generate functional connectivity (FC) information. In addition, we utilize T1-weighted magnetic resonance imaging to estimate morphometric connectivity (MC) information. We combine functional and anatomical features into a new connectivity matrix, which we term as the functional morphometric similarity connectome (FMSC). Since, both the FC and MC information is susceptible to redundancy, the size of this information is reduced using statistical feature selection. We employ off-the-shelf machine learning classifier, support vector machine, for both single- and multi-modality classifications. From our experiments, we establish that the saliency and ventral attention network of the brain is functionally and anatomically different between two groups of healthy subjects (chess players). We argue that, since chess involves many aspects of higher order cognition such as systematic thinking and spatial reasoning and the identified network is task-positive to cognition tasks requiring a response, our results are valid and supporting the feasibility of the proposed computational pipeline. Moreover, we quantitatively validate an existing neuroscience hypothesis that learning a certain skill could cause a change in the brain (functional connectivity and anatomy) and this can be tested via our novel FMSC algorithm.

Anaerobic digestion of food waste (FW) is typically limited to large reactors due to high hydraulic retention times (HRTs). Technologies such as anaerobic membrane reactors (AnMBRs) can perform anaerobic digestion at lower HRTs while maintaining high chemical oxygen demand (COD) removal efficiencies. This study evaluated the effect of HRT and organic loading rate (OLR) on the stability and performance of a side-stream AnMBR in treating diluted fresh food waste (FW). The reactor was fed with synthetic FW at an influent concentration of 8.24 (± 0.12) g COD/L. The OLR was increased by reducing the HRT from 20 to 1 d. The AnMBR obtained an overall removal efficiency of >97 and >98% of the influent COD and total suspended solids (TSS), respectively, throughout the course of operation. The biological process was able to convert 76% of the influent COD into biogas with 70% methane content, while the cake layer formed on the membrane gave an additional COD removal of 7%. Total ammoniacal nitrogen (TAN) and total nitrogen (TN) concentrations were found to be higher in the bioreactor than in the influent, and average overall removal efficiencies of 17.3 (± 5) and 61.5 (± 3)% of TAN and TN, respectively, were observed with respect to the bioreactor concentrations after 2 weeks. Total phosphorus (TP) had an average removal efficiency of 40.39 (± 5)% with respect to the influent. Membrane fouling was observed when the HRT was decreased from 7 to 5 d and was alleviated through backwashing. This study suggests that the side-stream AnMBR can be used to successfully reduce the typical HRT of wet anaerobic food waste (solids content 7%) digesters from 20 days to 1 day, while maintaining a high COD removal efficiency and biogas production.

The success of surgical resection in epilepsy patients depends on preserving functionally critical brain regions, while removing pathological tissues. Being the gold standard, electro-cortical stimulation mapping (ESM) helps surgeons in localizing the function of eloquent cortex through electrical stimulation of electrodes placed directly on the cortical brain surface. Due to the potential hazards of ESM, including increased risk of provoked seizures, electrocorticography based functional mapping (ECoG-FM) was introduced as a safer alternative approach. However, ECoG-FM has a low success rate when compared to the ESM. In this study, we address this critical limitation by developing a new algorithm based on deep learning for ECoG-FM and thereby we achieve an accuracy comparable to ESM in identifying eloquent language cortex. In our experiments, with 11 epilepsy patients who underwent presurgical evaluation (through deep learning-based signal analysis on 637 electrodes), our proposed algorithm obtained an accuracy of 83.05% in identifying language regions, an exceptional 23% improvement with respect to the conventional ECoG-FM analysis (∼60%). Our findings have demonstrated, for the first time, that deep learning powered ECoG-FM can serve as a stand-alone modality and avoid likely hazards of the ESM in epilepsy surgery. Hence, reducing the potential for developing post-surgical morbidity in the language function.

Graphene oxide (GO) has been widely reported and used for treatment of heavy metals from different waste streams. Although their use as additives for membranes has greatly enhanced membrane properties, there is still a bottleneck in obtaining membranes with high heavy-metal rejection efficiencies while maintaining high flux, mechanical strength, and porosity. In the present study, different compositions of GO (0–1 wt %)-blended membranes were prepared using 1-methyl-2-pyrrolidone (NMP) as solvent and water with 5% ethanol as non-solvent, and studied for the rejection of the chosen model heavy-metal lead. The prepared membranes were characterized for hydrophilicity, membrane porosity, flux, permeability, pore-size, mechanical strength, and membrane morphology. From the results, it was inferred that membranes having maximum GO in their blend (1 wt %) showed better hydrophilicity (water contact angle 34.2°), porosity (82.2%), permeability (52.1 L/m2 h bar), and pure water flux (163.71 L/m2 h) at 3-bar pressure as opposed to other compositions. The pore sizes of the membranes ranged between 18 to 24 nm. Tensile strength tests showed the role of GO as a positive reinforcement on the mechanical properties of membranes through Young’s modulus (188.13 ± 15.36 MPa) for the membrane having 0.25 wt % GO composition. Environmental Scanning Electron Microscopy (ESEM) images displayed the dense top layer supported by a porous, finger-like structure, obtained from instantaneous de-mixing favored by NMP and GO. The observed reduction in flux of lead solution for GO-blended membranes was due to osmotic pressure build-up caused by the retained nitrate salt by GO on the retentate side of the membrane. A maximum rejection of 98% was achieved with 1 wt % GO membrane at 1-bar pressure with flux of 43.62 L/m2 h, which decreased to 94% at 3-bar pressure with flux of 142.95 L/m2 h. These results showed how the application of NMP as solvent and GO as an additive could facilitate in obtaining high-flux and high-rejection membranes.

We demonstrate that the Hanle effect can be tuned between magnetically induced absorption (MIA) and magnetically induced transmission (MIT) simply by changing the polarization of the input laser beam. The experiments are done using closed hyperfine transitions of the D2 line of 133Cs —Fg = 3 → Fe = 2 and Fg = 4 → Fe = 5. The former shows a transformation from MIT to MIA, while the latter shows the opposite behavior. A qualitative explanation based on optical pumping and coherences among the magnetic sublevels of the ground state is borne out by a detailed density-matrix calculation. To increase the coherence time, the experiments are done in a Cs vapor cell with paraffin coating on the walls. The observed linewidth is extremely narrow (∼ 0.1 mG) compared to previous work in this area, making this a promising technique for all kinds of precision measurements. [ABSTRACT FROM AUTHOR]