Hydroxamic acid pre-adsorption raises the efficiency of cosensitized solar cells.

Dye-sensitized solar cells (DSCs) convert light into electricity by using photosensitizers adsorbed on the surface of nanocrystalline mesoporous titanium dioxide (TiO2) films along with electrolytes or solid charge-transport materials1-3. They possess many features including transparency, multicolour and low-cost fabrication, and are being deployed in glass facades, skylights and greenhouses4. Recent development of sensitizers5-10, redox mediators11-13 and device structures14 has improved the performance of DSCs, particularly under ambient light conditions14-17. To further enhance their efficiency, it is pivotal to control the assembly of dye molecules on the surface of TiO2 to favour charge generation. Here we report a route of pre-adsorbing a monolayer of a hydroxamic acid derivative on the surface of TiO2 to improve the dye molecular packing and photovoltaic performance of two newly designed co-adsorbed sensitizers that harvest light quantitatively across the entire visible domain. The best performing cosensitized solar cells exhibited a power conversion efficiency of 15.2% (which has been independently confirmed) under a standard air mass of 1.5 global simulated sunlight, and showed long-term operational stability (500 h). Devices with a larger active area of 2.8 cm2 exhibited a power conversion efficiency of 28.4% to 30.2% over a wide range of ambient light intensities, along with high stability. Our findings pave the way for facile access to high-performance DSCs and offer promising prospects for applications as power supplies and battery replacements for low-power electronic devices18-20 that use ambient light as their energy source.

Low-loss contacts on textured substrates for inverted perovskite solar cells.

Inverted perovskite solar cells (PSCs) promise enhanced operating stability compared to their normal-structure counterparts1-3. To improve efficiency further, it is crucial to combine effective light management with low interfacial losses4,5. Here we develop a conformal self-assembled monolayer (SAM) as the hole-selective contact on light-managing textured substrates. Molecular dynamics simulations indicate that cluster formation during phosphonic acid adsorption leads to incomplete SAM coverage. We devise a co-adsorbent strategy that disassembles high-order clusters, thus homogenizing the distribution of phosphonic acid molecules, and thereby minimizing interfacial recombination and improving electronic structures. We report a laboratory-measured power conversion efficiency (PCE) of 25.3% and a certified quasi-steady-state PCE of 24.8% for inverted PSCs, with a photocurrent approaching 95% of the Shockley-Queisser maximum. An encapsulated device having a PCE of 24.6% at room temperature retains 95% of its peak performance when stressed at 65 °C and 50% relative humidity following more than 1,000 h of maximum power point tracking under 1 sun illumination. This represents one of the most stable PSCs subjected to accelerated ageing: achieved with a PCE surpassing 24%. The engineering of phosphonic acid adsorption on textured substrates offers a promising avenue for efficient and stable PSCs. It is also anticipated to benefit other optoelectronic devices that require light management.

Pseudo-halide anion engineering for α-FAPbI3 perovskite solar cells.

Metal halide perovskites of the general formula ABX3-where A is a monovalent cation such as caesium, methylammonium or formamidinium; B is divalent lead, tin or germanium; and X is a halide anion-have shown great potential as light harvesters for thin-film photovoltaics1-5. Among a large number of compositions investigated, the cubic α-phase of formamidinium lead triiodide (FAPbI3) has emerged as the most promising semiconductor for highly efficient and stable perovskite solar cells6-9, and maximizing the performance of this material in such devices is of vital importance for the perovskite research community. Here we introduce an anion engineering concept that uses the pseudo-halide anion formate (HCOO-) to suppress anion-vacancy defects that are present at grain boundaries and at the surface of the perovskite films and to augment the crystallinity of the films. The resulting solar cell devices attain a power conversion efficiency of 25.6 per cent (certified 25.2 per cent), have long-term operational stability (450 hours) and show intense electroluminescence with external quantum efficiencies of more than 10 per cent. Our findings provide a direct route to eliminate the most abundant and deleterious lattice defects present in metal halide perovskites, providing a facile access to solution-processable films with improved optoelectronic performance.

Replication elongates short DNA, reduces sequence bias and develops trimer structure.

The origin of molecular evolution required the replication of short oligonucleotides to form longer polymers. Prebiotically plausible oligonucleotide pools tend to contain more of some nucleobases than others. It has been unclear whether this initial bias persists and how it affects replication. To investigate this, we examined the evolution of 12-mer biased short DNA pools using an enzymatic model system. This allowed us to study the long timescales involved in evolution, since it is not yet possible with currently investigated prebiotic replication chemistries. Our analysis using next-generation sequencing from different time points revealed that the initial nucleotide bias of the pool disappeared in the elongated pool after isothermal replication. In contrast, the nucleotide composition at each position in the elongated sequences remained biased and varied with both position and initial bias. Furthermore, we observed the emergence of highly periodic dimer and trimer motifs in the rapidly elongated sequences. This shift in nucleotide composition and the emergence of structure through templated replication could help explain how biased prebiotic pools could undergo molecular evolution and lead to complex functional nucleic acids.

Glutamatergic synaptic input to glioma cells drives brain tumour progression.

A network of communicating tumour cells that is connected by tumour microtubes mediates the progression of incurable gliomas. Moreover, neuronal activity can foster malignant behaviour of glioma cells by non-synaptic paracrine and autocrine mechanisms. Here we report a direct communication channel between neurons and glioma cells in different disease models and human tumours: functional bona fide chemical synapses between presynaptic neurons and postsynaptic glioma cells. These neurogliomal synapses show a typical synaptic ultrastructure, are located on tumour microtubes, and produce postsynaptic currents that are mediated by glutamate receptors of the AMPA subtype. Neuronal activity including epileptic conditions generates synchronised calcium transients in tumour-microtube-connected glioma networks. Glioma-cell-specific genetic perturbation of AMPA receptors reduces calcium-related invasiveness of tumour-microtube-positive tumour cells and glioma growth. Invasion and growth are also reduced by anaesthesia and the AMPA receptor antagonist perampanel, respectively. These findings reveal a biologically relevant direct synaptic communication between neurons and glioma cells with potential clinical implications.

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study.

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.

A diminished sciatic nerve structural integrity is associated with distinct peripheral sensory phenotypes in individuals with type 2 diabetes.

AIMS/HYPOTHESIS: Quantitative sensory testing (QST) allows the identification of individuals with rapid progression of diabetic sensorimotor polyneuropathy (DSPN) based on certain sensory phenotypes. Hence, the aim of this study was to investigate the relationship of these phenotypes with the structural integrity of the sciatic nerve among individuals with type 2 diabetes. METHODS: Seventy-six individuals with type 2 diabetes took part in this cross-sectional study and underwent QST of the right foot and high-resolution magnetic resonance neurography including diffusion tensor imaging of the right distal sciatic nerve to determine the sciatic nerve fractional anisotropy (FA) and cross-sectional area (CSA), both of which serve as markers of structural integrity of peripheral nerves. Participants were then assigned to four sensory phenotypes (participants with type 2 diabetes and healthy sensory profile [HSP], thermal hyperalgesia [TH], mechanical hyperalgesia [MH], sensory loss [SL]) by a standardised sorting algorithm based on QST. RESULTS: Objective neurological deficits showed a gradual increase across HSP, TH, MH and SL groups, being higher in MH compared with HSP and in SL compared with HSP and TH. The number of participants categorised as HSP, TH, MH and SL was 16, 24, 17 and 19, respectively. There was a gradual decrease of the sciatic nerve's FA (HSP 0.444, TH 0.437, MH 0.395, SL 0.382; p=0.005) and increase of CSA (HSP 21.7, TH 21.5, MH 25.9, SL 25.8 mm2; p=0.011) across the four phenotypes. Further, MH and SL were associated with a lower sciatic FA (MH unstandardised regression coefficient [B]=-0.048 [95% CI -0.091, -0.006], p=0.027; SL B=-0.062 [95% CI -0.103, -0.020], p=0.004) and CSA (MH ß=4.3 [95% CI 0.5, 8.0], p=0.028; SL B=4.0 [95% CI 0.4, 7.7], p=0.032) in a multivariable regression analysis. The sciatic FA correlated negatively with the sciatic CSA (r=-0.35, p=0.002) and markers of microvascular damage (high-sensitivity troponin T, urine albumin/creatinine ratio). CONCLUSIONS/INTERPRETATION: The most severe sensory phenotypes of DSPN (MH and SL) showed diminishing sciatic nerve structural integrity indexed by lower FA, likely representing progressive axonal loss, as well as increasing CSA of the sciatic nerve, which cannot be detected in individuals with TH. Individuals with type 2 diabetes may experience a predefined cascade of nerve fibre damage in the course of the disease, from healthy to TH, to MH and finally SL, while structural changes in the proximal nerve seem to precede the sensory loss of peripheral nerves and indicate potential targets for the prevention of end-stage DSPN. TRIAL REGISTRATION: ClinicalTrials.gov NCT03022721.

Electrocatalytic Nitrate and Nitrite Reduction toward Ammonia Using Cu2O Nanocubes: Active Species and Reaction Mechanisms.

The electrochemical reduction of nitrate (NO3-) and nitrite (NO2-) enables sustainable, carbon-neutral, and decentralized routes to produce ammonia (NH3). Copper-based materials are promising electrocatalysts for NOx- conversion to NH3. However, the underlying reaction mechanisms and the role of different Cu species during the catalytic process are still poorly understood. Herein, by combining quasi in situ X-ray photoelectron spectroscopy (XPS) and operando X-ray absorption spectroscopy (XAS), we unveiled that Cu is mostly in metallic form during the highly selective reduction of NO3-/NO2- to NH3. On the contrary, Cu(I) species are predominant in a potential region where the two-electron reduction of NO3- to NO2- is the major reaction. Electrokinetic analysis and in situ Raman spectroscopy was also used to propose possible steps and intermediates leading to NO2- and NH3, respectively. This work establishes a correlation between the catalytic performance and the dynamic changes of the chemical state of Cu, and provides crucial mechanistic insights into the pathways for NO3-/NO2- electrocatalytic reduction.

Expansion of Neisseria meningitidis Serogroup C Clonal Complex 10217 during Meningitis Outbreak, Burkina Faso, 2019.

During January 28-May 5, 2019, a meningitis outbreak caused by Neisseria meningitidis serogroup C (NmC) occurred in Burkina Faso. Demographic and laboratory data for meningitis cases were collected through national case-based surveillance. Cerebrospinal fluid was collected and tested by culture and real-time PCR. Among 301 suspected cases reported in 6 districts, N. meningitidis was the primary pathogen detected; 103 cases were serogroup C and 13 were serogroup X. Whole-genome sequencing revealed that 18 cerebrospinal fluid specimens tested positive for NmC sequence type (ST) 10217 within clonal complex 10217, an ST responsible for large epidemics in Niger and Nigeria. Expansion of NmC ST10217 into Burkina Faso, continued NmC outbreaks in the meningitis belt of Africa since 2019, and ongoing circulation of N. meningitidis serogroup X in the region underscore the urgent need to use multivalent conjugate vaccines in regional mass vaccination campaigns to reduce further spread of those serogroups.

Navigator-based motion compensation for liver BOLD measurement with five-echo SAGE EPI and breath-hold task.

PURPOSE: The purpose of this work is to apply multi-echo spin- and gradient-echo (SAGE) echo-planar imaging (EPI) combined with a navigator-based (NAV) prospective motion compensation method for a quantitative liver blood oxygen level dependent (BOLD) measurement with a breath-hold (BH) task. METHODS: A five-echo SAGE sequence was developed to quantitatively measure T2 and T2* to depict function with sufficient signal-to-noise ratio, spatial resolution and sensitivity to BOLD changes induced by the BH task. To account for respiratory motion, a navigator was employed in the form of a single gradient-echo projection readout, located at the diaphragm along the inferior-superior direction. Prior to each transverse imaging slice of the spin-echo EPI-based readouts, navigator acquisition and fat suppression were incorporated. Motion data was obtained from the navigator and transmitted back to the sequence, allowing real-time adjustments to slice positioning. Six healthy volunteers and three patients with liver carcinoma were included in this study. Quantitative T2 and T2* were calculated at each time point of the BH task. Parameters of t value from first-level analysis using a general linear model and hepatovascular reactivity (HVR) of Echo1, T2 and T2* were calculated. RESULTS: The motion caused by respiratory activity was successfully compensated using the navigator signal. The average changes of T2 and T2* during breath-hold were about 1% and 0.7%, respectively. With the help of NAV prospective motion compensation whole liver t values could be obtained without motion artifacts. The quantified liver T2 (34.7 ± 0.7 ms) and T2* (29 ± 1.2 ms) values agreed with values from literature. In healthy volunteers, the distribution of statistical t value and HVR was homogeneous throughout the whole liver. In patients with liver carcinoma, the distribution of t value and HVR was inhomogeneous due to metastases or therapy. CONCLUSIONS: This study demonstrates the feasibility of using a NAV prospective motion compensation technique in conjunction with five-echo SAGE EPI for the quantitative measurement of liver BOLD with a BH task.

Fetal CCL2 signaling mediates offspring social behavior and recapitulates effects of prenatal stress.

Maternal stress during pregnancy is prevalent and associated with increased risk of neurodevelopmental disorders in the offspring. Maternal and offspring immune dysfunction has been implicated as a potential mechanism by which prenatal stress shapes offspring neurodevelopment; however, the impact of prenatal stress on the developing immune system has yet to be elucidated. Furthermore, there is evidence that the chemokine C-C motif chemokine ligand 2 (CCL2) plays a key role in mediating the behavioral sequelae of prenatal stress. Here, we use an established model of prenatal restraint stress in mice to investigate alterations in the fetal immune system, with a focus on CCL2. In the placenta, stress led to a reduction in CCL2 and Ccr2 expression with a concomitant decrease in leukocyte number. However, the fetal liver exhibited an inflammatory phenotype, with upregulation of Ccl2, Il6, and Lbp expression, along with an increase in pro-inflammatory Ly6CHi monocytes. Prenatal stress also disrupted chemokine signaling and increased the number of monocytes and microglia in the fetal brain. Furthermore, stress increased Il1b expression by fetal brain CD11b+ microglia and monocytes. Finally, intra-amniotic injections of recombinant mouse CCL2 partially recapitulated the social behavioral deficits in the adult offspring previously observed in the prenatal restraint stress model. Altogether, these data suggest that prenatal stress led to fetal inflammation, and that fetal CCL2 plays a role in shaping offspring social behavior.

Magnetic resonance neurography in spinal cord injury: Imaging findings and clinical significance.

BACKGROUND AND PURPOSE: It is unknown whether changes to the peripheral nervous system following spinal cord injury (SCI) are relevant for functional recovery or the development of neuropathic pain below the level of injury. Magnetic resonance neurography (MRN) at 3 T allows detection and localization of structural and functional nerve damage. This study aimed to combine MRN and clinical assessments in individuals with chronic SCI and nondisabled controls. METHODS: Twenty participants with chronic SCI and 20 controls matched for gender, age, and body mass index underwent MRN of the L5 dorsal root ganglia (DRG) and the sciatic nerve. DRG volume, sciatic nerve mean cross-sectional area (CSA), fascicular lesion load, and fractional anisotropy (FA), a marker for functional nerve integrity, were calculated. Results were correlated with clinical assessments and nerve conduction studies. RESULTS: Sciatic nerve CSA and lesion load were higher (21.29 ± 5.82 mm2 vs. 14.08 ± 4.62 mm2 , p < 0.001; and 8.70 ± 7.47% vs. 3.60 ± 2.45%, p < 0.001) in individuals with SCI compared to controls, whereas FA was lower (0.55 ± 0.11 vs. 0.63 ± 0.08, p = 0.022). DRG volumes were larger in individuals with SCI who suffered from neuropathic pain compared to those without neuropathic pain (223.7 ± 53.08 mm3 vs. 159.7 ± 55.66 mm3 , p = 0.043). Sciatic MRN parameters correlated with electrophysiological results but did not correlate with the extent of myelopathy or clinical severity of SCI. CONCLUSIONS: Individuals with chronic SCI are subject to a decline of structural peripheral nerve integrity that may occur independently from the clinical severity of SCI. Larger volumes of DRG in SCI with neuropathic pain support existing evidence from animal studies on SCI-related neuropathic pain.

The impact of beef sire breed on dystocia, stillbirth, gestation length, health, and lactation performance of cows that carry beef × dairy calves.

In the United States, it is becoming common for dairy herds to mate a portion of cows to beef semen to create a value-added calf. The objectives of this study were to determine if dystocia risk, stillbirth (SB) risk, gestation length (GL), probability of early-lactation clinical disease events, early-lactation culling risk, or subsequent milk production differ between cows that carried calves sired by different beef breeds and those that carried Holstein-sired calves. Records from 10 herds contained 75,256 lactations from 39,249 cows that had calves with known Holstein or beef breed sires from the years 2010 to 2023. Calf sire breeds with ≥150 records included in analyses were Holstein, Angus, Simmental, Limousin, crossbred beef, and Charolais. Additional beef sire breeds that existed in lower frequency (n < 150 records) were condensed together and classified as "other." Because GL is a continuous variable, sire breed inclusion criteria were reduced to n ≥ 100 records; thus, Wagyu sires were included as their own breed group. Some records did not contain all variables of interest, thus models included fewer lactations depending on variable. Binomial generalized mixed models evaluated dystocia risk (defined as calving ease score ≥4 or calving ease score ≥3), SB risk, clinical health event risk (defined as lameness, mastitis, metabolic, reproductive, other, or any health events occurring within 60 d in milk [DIM]), and early culling risk (defined as death or culling within 60 DIM). Gestation length and test-date milk, fat, and protein yields were evaluated with mixed models. Calves sired by crossbred beef bulls had a greater probability of being stillborn (5%; 95% confidence interval lower = 2.9% upper = 9.0%) than those sired by Holstein bulls (2%; 95% confidence interval lower = 1.5%, upper = 2.7%). All beef-sired calves increased GL from that of Holstein-sired calves (277 ± 0.15 d) with Limousin (282 ± 0.81 d) and Wagyu-sired calves (285 d ± 0.79) resulting in the longest GL. The risk of dystocia, clinical health events, and early-lactation culling did not differ by calf sire breed nor did subsequent milk and component yield. Generally, carrying a calf sired by the beef breeds included in this study did not negatively affect the dairy cow.

Managing open loop water resource value chain through IoT focused decision and information integration (DII) modelling using fuzzy MCDM approach.

Water is a limited and invaluable resource that is essential for human survival. Negligence and unregulated water use have brought about a global water crisis. Proper management with a relevant decision and information integration approach can aid water to continue as a renewable resource. The water and wastewater industry must shift from outmoded, inefficient techniques to more sustainable, data-driven solutions to address water concerns and improve public health. The Internet of Things (IoT) has emerged as an innovative strategy for decision and information integration to drive an open-loop Water Value Chain (WVC) efficiently. The IoT-driven network allows objects to connect and communicate, gather data in real-time, analyze data and develop reasonable decision - making insights instantaneously. This study aims to find the enablers of IoT for an open-loop WVC. It examines 25 factors for IoT implementation in the open-loop WVC. The 25 factors are clustered into seven enablers using Principal Component Analysis (PCA). These principal components are analyzed by employing a Multi-Criteria Decision Making (MCDM) approach, i.e., the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), which helps to find the cause-effect relationship to prioritize the enablers. The fuzzy set theory is used to address the uncertainty and vagueness in experts' opinions and data deficiency problems. The study reveals that the Ecosystem of an IoT network, IoT network configuration and adaptation and data mobility in an IoT network are the most prominent enablers to consider for the implementation of IoT in an open loop WVC. The study may be helpful for regulatory agencies and enterprises in water distribution and processing for identifying and prioritizing the potential enablers of IoT in an open-loop WVC.

Fractal dynamics of individual mitochondrial oscillators measure local inter-mitochondrial coupling.

Mitochondrial inner membrane potentials in cardiomyocytes may oscillate in cycles of depolarization/repolarization when the mitochondrial network is exposed to metabolic or oxidative stress. The frequencies of such oscillations are dynamically changing while clusters of weakly coupled mitochondrial oscillators adjust to a common phase and frequency. Across the cardiac myocyte, the averaged signal of the mitochondrial population follows self-similar or fractal dynamics; however, fractal properties of individual mitochondrial oscillators have not yet been examined. We show that the largest synchronously oscillating cluster exhibits a fractal dimension, D, that is indicative of self-similar behavior with D=1.27±0.11, in contrast to the remaining network mitochondria whose fractal dimension is close to that of Brownian noise, D=1.58±0.10. We further demonstrate that fractal behavior is correlated with local coupling mechanisms, whereas it is only weakly linked to measures of functional connections between mitochondria. Our findings suggest that individual mitochondrial fractal dimensions may serve as a simple measure of local mitochondrial coupling.

Identification of Virulence Factors Involved in a Murine Model of Severe Achromobacter xylosoxidans Infection.

Achromobacter xylosoxidans (Ax) is an opportunistic pathogen and causative agent of numerous infections particularly in immunocompromised individuals with increasing prevalence in cystic fibrosis (CF). To date, investigations have focused on the clinical epidemiology and genomic comparisons of Ax isolates, yet little is known about disease pathology or the role that specific virulence factors play in tissue invasion or damage. Here, we model an acute Ax lung infection in immunocompetent C57BL/6 mice and immunocompromised CF mice, revealing a link between in vitro cytotoxicity and disease in an intact host. Mice were intratracheally challenged with sublethal doses of a cytotoxic (GN050) or invasive (GN008) strain of Ax. Bacterial burden, immune cell populations, and inflammatory markers in bronchoalveolar lavage fluid and lung homogenates were measured at different time points to assess disease severity. CF mice had a similar but delayed immune response toward both Ax strains compared to C57BL/6J mice. GN050 caused more severe disease and higher mortality which correlated with greater bacterial burden and increased proinflammatory responses in both mouse models. In agreement with the cytotoxicity of GN050 toward macrophages in vitro, mice challenged with GN050 had fewer macrophages. Mutants with transposon insertions in predicted virulence factors of GN050 showed that disease severity depended on the type III secretion system, Vi capsule, antisigma-E factor, and partially on the ArtA adhesin. The development of an acute infection model provides an essential tool to better understand the infectivity of diverse Ax isolates and enable improved identification of virulence factors important to bacterial persistence and disease.

Deciphering the Structural and Chemical Transformations of Oxide Catalysts during Oxygen Evolution Reaction Using Quick X-ray Absorption Spectroscopy and Machine Learning.

Bimetallic transition-metal oxides, such as spinel-like CoxFe3-xO4 materials, are known as attractive catalysts for the oxygen evolution reaction (OER) in alkaline electrolytes. Nonetheless, unveiling the real active species and active states in these catalysts remains a challenge. The coexistence of metal ions in different chemical states and in different chemical environments, including disordered X-ray amorphous phases that all evolve under reaction conditions, hinders the application of common operando techniques. Here, we address this issue by relying on operando quick X-ray absorption fine structure spectroscopy, coupled with unsupervised and supervised machine learning methods. We use principal component analysis to understand the subtle changes in the X-ray absorption near-edge structure spectra and develop an artificial neural network to decipher the extended X-ray absorption fine structure spectra. This allows us to separately track the evolution of tetrahedrally and octahedrally coordinated species and to disentangle the chemical changes and several phase transitions taking place in CoxFe3-xO4 catalysts and on their active surface, related to the conversion of disordered oxides into spinel-like structures, transformation of spinels into active oxyhydroxides, and changes in the degree of spinel inversion in the course of the activation treatment and under OER conditions. By correlating the revealed structural changes with the distinct catalytic activity for a series of CoxFe3-xO4 samples, we elucidate the active species and OER mechanism.

Spatially and Chemically Resolved Visualization of Fe Incorporation into NiO Octahedra during the Oxygen Evolution Reaction.

The activity of Ni (hydr)oxides for the electrochemical evolution of oxygen (OER), a key component of the overall water splitting reaction, is known to be greatly enhanced by the incorporation of Fe. However, a complete understanding of the role of cationic Fe species and the nature of the catalyst surface under reaction conditions remains unclear. Here, using a combination of electrochemical cell and conventional transmission electron microscopy, we show how the surface of NiO electrocatalysts, with initially well-defined surface facets, restructures under applied potential and forms an active NiFe layered double (oxy)hydroxide (NiFe-LDH) when Fe3+ ions are present in the electrolyte. Continued OER under these conditions, however, leads to the creation of additional FeOx aggregates. Electrochemically, the NiFe-LDH formation correlates with a lower onset potential toward the OER, whereas the formation of the FeOx aggregates is accompanied by a gradual decrease in the OER activity. Complementary insight into the catalyst near-surface composition, structure, and chemical state is further extracted using X-ray photoelectron spectroscopy, operando Raman spectroscopy, and operando X-ray absorption spectroscopy together with measurements of Fe uptake by the electrocatalysts using time-resolved inductively coupled plasma mass spectrometry. Notably, we identified that the catalytic deactivation under stationary conditions is linked to the degradation of in situ-created NiFe-LDH. These insights exemplify the complexity of the active state formation and show how its structural and morphological evolution under different applied potentials can be directly linked to the catalyst activation and degradation.

Navigator-based slice tracking for kidney pCASL using spin-echo EPI acquisition.

PURPOSE: To apply a navigator-based slice-tracking method to prospectively compensate respiratory motion for kidney pseudo-continuous arterial spin labeling (pCASL), using spin-echo (SE) EPI acquisition. METHODS: A single gradient-echo slice selection and projection readout at the location of the diaphragm along the inferior-superior direction was applied as a navigator. Navigator acquisition and fat suppression were inserted before each transverse imaging slice of the readouts of a 2D-SE-EPI-based pCASL sequence. Motion information was calculated after exclusion of the signal saturation in the navigator signal caused by EPI excitations. The motion information was then used to directly adjust the slice positioning in real time. RESULTS: The respiratory motion from the navigator signal was calculated, and slice positioning was changed in real time based on the motion information. We could show that motion compensation reduces kidney movement, and that the coefficients of variation across renal perfusion values were significantly reduced when motion correction was applied. The average reduction of coefficients of variation was approximately 20%, resulting in a more accurate and detailed structure of the respective perfusion maps. CONCLUSIONS: This study demonstrates the feasibility of a navigator-based slice-tracking technique in kidney imaging with a SE-EPI readout pCASL sequence to reduce kidney motion.

Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines.

Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively quantifies microvascular changes in clinical routine. Although blood capillaries cannot be resolved in typical magnetic resonance imaging (MRI) measurements, their geometry and distribution influence the integral nuclear magnetic resonance (NMR) signal from each macroscopic MRI voxel. We have numerically simulated the expected transverse relaxation in NMR voxels with different dimensions based on the realistic microvasculature in healthy and tumor-bearing mouse brains (U87 and GL261 glioblastoma). The 3D capillary structure in entire, undissected brains was acquired using light sheet fluorescence microscopy to produce large datasets of the highly resolved cerebrovasculature. Using this data, we trained support vector machines to classify virtual NMR voxels with different dimensions based on the simulated spin dephasing accountable to field inhomogeneities caused by the underlying vasculature. In prediction tests with previously blinded virtual voxels from healthy brain tissue and GL261 tumors, stable classification accuracies above 95% were reached. Our results indicate that high classification accuracies can be stably attained with achievable training set sizes and that larger MRI voxels facilitated increasingly successful classifications, even with small training datasets. We were able to prove that, theoretically, the transverse relaxation process can be harnessed to learn endogenous contrasts for single voxel tissue type classifications on tailored MRI acquisitions. If translatable to experimental MRI, this may augment diagnostic imaging in oncology with automated voxel-by-voxel signal interpretation to detect vascular pathologies.