Fragment-based drug design offers multiple routes to advance from fragments. One approach is to build structure-activity relationships (SAR) from analogue series in direct-to-biology workflows. Analogues can be prepared by automated chemistry and tested as crude reaction mixtures (CRMs) without purification, but assay noise often leads to hit resynthesis, potentially discarding false negatives and reducing SAR dataset size. High-throughput (HT) X-ray crystallography has the potential to address these issues by resolving hits directly from 100s-1000s of CRMs. However, no systematic analytics exist for extracting SAR models from HT crystallographic evaluation of CRMs. Here, we demonstrate that crystallographic SAR (xSAR) can be extracted from CRMs evaluated via HT X-ray crystallography. We developed a simple rule-based ligand scoring scheme that identifies conserved chemical features associated with crystallographic binding and non-binding. Applied to a crystallographic dataset of 957 fragment elaborations in CRMs targeting PHIP(2), a therapeutically relevant bromodomain, our xSAR model demonstrated effectiveness in two proof-of-concept experiments. First, it recovered 26 missed binders in the initial dataset (false negatives), doubling the hit rate and denoising the dataset. Second, it enabled a prospective virtual screen that identified novel hits with informative chemistries and measurable binding affinities. This work establishes a proof-of-concept that xSAR models can be directly extracted from large-scale crystallographic readouts of CRMs, offering a valuable methodology to build SAR models and accelerate design-make-test iterations without requiring CRM hit resynthesis and confirmation. This invites future work to utilise advanced analytics and modelling techniques to further strengthen purification-agnostic workflows.
Diffusion MRI is widely used to characterize tissue microstructure, but standardization remains challenging, particularly for advanced models or regions with crossing fibers. Phantoms provide controlled environments to assess measurement repeatability independent of biological variability. This study evaluated the repeatability of higher-order diffusion tensor metrics using a novel anisotropic diffusion phantom designed to mimic white matter tract geometry. The phantom, containing linear, crossing (30°, 45°, 90°), and bifurcating synthetic fiber bundles, was scanned seven times using a GE Healthcare 3.0 T MRI system. Four acquisition protocols were evaluated: 30-direction DTI (b = 1000s/mm2), 60 and 90-direction High Angular Resolution Diffusion Imaging (HARDI; b = 1300s/mm2), and 30-direction Diffusion Kurtosis Imaging (DKI; b = 250, 500, 750, 1000, 1500, 2000, 2500, 3000 s/mm2). Repeatability was quantified using coefficient of variation (CoV) and intraclass correlation coefficient (ICC) for scalar diffusion metrics across six regions of interest. Fiber orientation distribution functions (fODFs) were analyzed to assess crossing fiber resolution accuracy. DTI-derived metrics demonstrated excellent repeatability, with fractional anisotropy (FA) CoV < 10% and mean, axial, and radial diffusivities < 3%. DKI-derived metrics exhibited greater variability, though kurtosis FA remained stable (CoV ∼7%). Generalized FA showed improved reliability with increased angular resolution (ICC = 0.8445 for 90-direction HARDI). fODFs accurately resolved crossing fibers at 90° (RMSE = 3.49°) and 45° (RMSE = 8.92°) but failed at 30° separation. The phantom provides reliable repeatability for standard DTI metrics and demonstrates utility for quality assurance of advanced diffusion models with high angular resolution protocols.
Structure determination by X-ray diffraction is limited by crystal size and can be compromised by radiation damage when using very intense X-ray radiation. X-ray structure determination from partial diffraction data sets combined from multiple crystals is a potential solution, but its exploitation in chemistry and materials science is largely unrealized. Here we report the use of synchrotron radiation for multi-crystal X-ray diffraction (MCXRD) adapted for structure determination of metal-organic framework (MOF) materials with crystal dimensions too small for conventional single-crystal diffraction studies. We further show that radiation-induced chemical changes and degradation of diffraction quality can be alleviated. Our approach encompasses both rotation- and stationary-MCXRD measurements for 10 to 1000s of crystals with software-optimized combination of the multiple data sets. We report the crystal structures of six MOFs: MOF-919(Sc/Cu), MET-2, MIL-88B(Cr)-1,4-NDC, PCN-260(Sc), UiO-66, and UiO-66-MoO4 with unit cell dimensions ranging from 18-114 Å and crystal sizes from 0.5-480 µm3. This approach can address the challenges of structure determination in a regime of particle size and sample radiation sensitivity that lies between existing single-crystal X-ray diffraction and the emerging field of electron diffraction. MCXRD can provide accurate atomic-resolution structure determination for some of the most challenging cases in chemistry and materials science.
Accurate needle placement is essential for prostate biopsy. Recently, transperineal prostate biopsies are receiving renewed interest due to concern over infection from conventional transrectal biopsies. However, accurate needle placement is more challenging in the transperineal approach than in the transrectal approach due to the long insertion distance leading to a large targeting error and repeated insertion attempts. Improved procedure planning tools that can predict the deviation of the needle can potentially reduce the targeting error and number of insertion attempts. Prediction of deflection magnitude requires a model of biopsy needle deflection, which in turn requires information about tissue material properties. However, material properties of tissue in patients cannot be easily obtained. Accounting for this uncertainty in patient tissue properties requires a model capable of quantifying uncertainty in needle deflection as a function of a distribution of tissue properties. A Monte Carlo uncertainty quantification requires 1000s of samples, but it is not possible to obtain this many samples in a short enough time for intraoperative procedure planning using published needle deflection prediction models. This work seeks to develop a model of needle deflection fast enough for use in intraoperative procedure planning, validate this model against experimental results, and integrate it into a Monte Carlo uncertainty quantification model. This work used a mechanics-based model of biopsy needle deflection to train a Fourier feature neural network (FFNN) model in order to make predictions with a low computational cost. Both models were validated against experimental data. The neural network model was used in a Monte Carlo uncertainty quantification model to quantify uncertainty in needle deflection arising from uncertain tissue mechanical properties. This work (1) implemented a mechanics-based model and a FFNN model. Both models were validated against previously published experiments carried out with tissue phantoms. Both models showed close agreement with the experimental data. (2) We showed that our FFNN model was more accurate than a baseline ordinary least squares model, introducing only about 0.3-mm tip deflection error compared to the mechanics-based model. We also showed that our FFNN model makes unbiased predictions with respect to the amount of deflection. (3) We demonstrated a Monte Carlo uncertainty quantification model of needle deflection with a low computational cost of about 20 CPU s. We used our uncertainty quantification model to show how the depth, stiffness, and magnitude of uncertainty in a layer of tissue affect needle deflection. In addition, we showed a simple clinical example of the use of our model. This work demonstrates a Monte Carlo uncertainty quantification model of needle deflection with a low computational cost. This method shows promise for future applications in procedure planning for prostate biopsies as well as other transperineal procedures conducted with flexible needles such as cryoablation and brachytherapy.
This study aimed to investigate the feasibility of MR diffusion imaging of fresh prostate needle biopsy cores in a 14 Tesla vertical bore NMR system. Biopsies sampled from the index tumor and a presumed tumor-free area of eight prostatectomy specimens underwent diffusion-weighted imaging using conventional pulsed gradient spin echoes (PGSE) and stimulated echo acquisition mode (STEAM) with different gradient separation times between 10 and 400 ms at nominal b values ranging from 0 up to 5000 s/mm2. The apparent diffusion coefficient (ADC) was determined over the b-value range 0-1000 s/mm2. Kurtosis and biexponential models were fitted to measured signals at effective b values within the range 0-2000 and 0-5000 s/mm2, respectively. Moreover, monoexponential models were fitted over the same b-value ranges. After imaging, all biopsies underwent histopathological evaluation. For all diffusion gradient separation times evaluated, the ADC of histopathologically confirmed tumor and normal tissue was not significantly different (p = 0.140 at 10 ms gradient separation time). Irrespective of the investigated tissue type, for an increasing gradient separation time up to 150 ms ADC decreased. For longer times up to 400 ms no further change was evident. For short diffusion gradient separation times, the Akaike information criterion (AIC) favored the biexponential model in almost all voxels, but for longer diffusion gradient separation times, in a majority of voxels the AIC favored the monoexponential model over either the kurtosis or biexponential model. Imaging of fresh prostate biopsies is feasible. At short diffusion times, in agreement with the presence of at least two distinct compartments, the signal decay is well described by a biexponential model. At long diffusion times, the ADC is lower, and a monoexponential model tends to be more appropriate, indicating the presence of exchange between compartments.
Poor sedimentation stability remains a critical bottleneck hindering the widespread application of magnetorheological fluids (MRFs). To address this challenge, we propose a synergistic strategy that combines plasma surface engineering with micro-nano dual-dispersed particles to simultaneously tailor interfacial properties and suspension microstructure. Graphite-coated Fe/FeN core-shell magnetic nanoparticles (Fe/FeN@G) were synthesized through the pyrolysis of ferrocene in high-temperature plasma and subsequently fluorinated in a low-temperature CF4 plasma to obtain Fe/FeN@PFG. Unlike previous studies that individually employed plasma-treated carbonyl iron particles or bidisperse micro-nano systems, the present work integrates fluorinated Fe/FeN@PFG nanoparticles as a nanosized secondary phase with micron-sized carbonyl iron (CI) at a fixed solid loading of 50 wt % without additional stabilizing additives, thereby constructing an additive-free, plasma-engineered bidisperse MRF. Plasma fluorination introduces a fluorine-containing organic layer (C-F/C-F2 bonds), reduces the contact angle with silicone oil from 65.7° to 40.3°, and promotes the formation of a shear-sensitive three-dimensional thixotropic network. The resulting Fe/FeN@PFG-CI MRF exhibits a high zero-field viscosity of 73.2 Pa·s at 0.01 s-1, providing structural antisedimentation support, while maintaining a low viscosity of 0.111 Pa·s at 1000 s-1 to ensure good flowability. Despite the enhanced off-state viscosity and elasticity, the system retains reversible magnetorheological responses and fast thixotropic recovery. Sedimentation stability is markedly improved, as reflected by a turbiscan stability index of only 0.51 after 5.3 h and a sedimentation ratio of 85% after 72 h. These findings demonstrate that coupling core-shell nanoparticle design with CF4 plasma fluorination offers a cost-effective and scalable route to high-stability, high-performance MRFs that complement and extend existing stabilization strategies.
Global efforts to mitigate anthropogenic pressures on biodiversity and ecosystems will often be realised through management at landscape-scales (i.e., in the range of 100s-1000 s km2). In consequence, we need to measure biodiversity responses at landscape-scales to ensure mitigations are effectively protecting and restoring ecosystems. Yet many countries currently lack monitoring programmes that can generate indicators of biodiversity at these scales. Localised monitoring (e.g., 1 km2) is often amalgamated into national-scale indicators, however, this leaves a substantial gap in the middle of this spatial gradient, limiting availability of information at decision-relevant scales. Here, using the United Kingdom as a case study, we explored the suitability of seven sources of biodiversity data which could be used to construct landscape-scale indicators. We surveyed 70, mostly UK-based, monitoring experts for their opinions on structured and unstructured in-person surveys, camera traps, eDNA, drones, passive acoustic recorders, and satellite remote sensing. We assessed data source utility to construct indicators reflecting Essential Biodiversity Variables, i.e., as holistic measures of taxa or ecosystems rather than assessments of individual management interventions. All seven data sources were deemed suitable, and experts expected developments in technology and infrastructure to greatly increase this potential over the next decade. However, there are technical, analytical, logistical and financial barriers to establishing monitoring networks that could yield the requisite data for landscape-scale indicators. Resolving these issues requires substantial research, policy commitment and investment, but landscape-scale indicators will be essential for the UK to undertake adaptive management and monitor nature recovery.
Background/Objectives: Whole-blood viscosity (WBV) is increasingly used in cardiovascular risk assessment; however, inter-device comparability may depend on shear-rate definition. We performed a paired comparison of two scanning capillary viscometers to evaluate shear-dependent analytical agreement and its impact on clinical classification. Methods: In 300 identical blood samples, WBV was measured using Rheovis 2000A and Hemovister. Systolic WBV was defined at 300 s-1 for both devices (shear-matched), whereas clinically defined diastolic WBV corresponded to 1 s-1 for Rheovis 2000A and 5 s-1 for Hemovister. Agreement was assessed using linear regression and Bland-Altman analysis. Hematocrit tertiles were examined as effect modifiers. Clinical agreement was evaluated using quadratic weighted Cohen's κ. Results: Across matched shear rates (1000 to 1 s-1), Hemovister yielded consistently higher WBV values than Rheovis 2000A, with statistically significant inter-device differences at all shear levels except 1000 s-1. The magnitude of bias increased progressively as shear rate decreased, reaching -8.34 mPa·s at 1 s-1. Under shear-matched systolic conditions (300 s-1), the mean difference was -0.25 mPa·s (limits of agreement -1.72 to 1.22). In contrast, under clinically defined diastolic conditions (1 vs. 5 s-1), the mean difference was 14.54 mPa·s (3.93 to 25.15), increasing across hematocrit tertiles. Clinical agreement was fair for systolic (κ = 0.31; 95% CI 0.24 to 0.39) and moderate for diastolic WBV (κ = 0.44; 95% CI 0.37 to 0.51). Notably, among samples classified as high by Hemovister, 72.8% (systolic) and 54.0% (diastolic) were reclassified as normal by Rheovis 2000A. Conclusions: Inter-device agreement in WBV measurement is strongly shear-dependent. Although numerical divergence increases at low shear, categorical concordance may remain moderate when device-specific reference thresholds are applied. Harmonization of shear definitions and reference frameworks may therefore be essential for consistent cross-platform interpretation.
We devise and experimentally validate a theoretical model to account for lost photon counts during the exposure time of a time-correlated single photon counting (TCSPC)-based QuantICAM single photon avalanche diode array camera. The work is motivated by the quest for TCSPC-based wide-field time-resolved fluorescence anisotropy imaging (TR-FAIM), implemented by the acquisition of images at orthogonal polarization. For accurate, quantitatively correct TR-FAIM, the two images must be acquired under equivalent conditions and any photons lost during the camera exposures must be precisely quantified. Our model is based on a binomial distribution with a single adjustable parameter. We plot the recorded versus the true photon counts for exposure times of 250 µs and 1000 µs, using photons with random arrival times and from fluorescence decays. Our model describes the experimental data well and the correct number of excitation cycles during the exposure time is extracted from least-squares fits of the binomial model to the experimental data. On the basis of this model, we account for lost photons in TCSPC-based TR-FAIM and show that a compensation for lost photons is essential to obtain quantitatively correct steady-state anisotropy andG-factor histograms in TR-FAIM. We also show that, under the conditions used, the rotational correlation time, initial anisotropyr0and hindered rotation parameterr∞histograms are only marginally affected by lost photons. Our work thus paves the way for robust and reliable TCSPC-based TR-FAIM.
Orientation-dependent transverse relaxation in human brain white matter (WM) has been widely reported, yet its biophysical origins remain debated. This study investigates the relative contributions of the magic angle effect (MAE) and susceptibility-based mechanisms at 3 T and 7 T. Publicly available diffusion tensor imaging (DTI) datasets from 25 young adults in the Human Connectome Project, acquired with b-values of [0,1000] and [0,2000] s/mm2, were analyzed. Using a cone-based framework that incorporates a generalized MAE model, the magnitudes of orientation-dependent transverse relaxation rates (R2a) were derived from T2-weighted images (b = 0) and compared across the two field strengths. Additionally, R2a values obtained from gradient-echo (GRE) signals reported in previous literature were evaluated at both 3 T and 7 T. Classical relaxation theory predicts a ratio η = R2a(7 T) / R2a(3 T) ≃ 1 if MAE dominates, or approximately η = 5.4 if susceptibility effects prevail. Model parameters were consistent across DTI datasets with different non-zero b-values. For b = 1000 s/mm2, R2a increased from 4.0 ± 1.1 s-1 at 3 T to 5.6 ± 1.6 s-1 at 7 T, yielding a ratio η < 1.5. This increase suggests a partial contribution of susceptibility effects to the measured R2a, estimated at 8.3 ± 10.2% at 3 T and 34.5 ± 42.2% at 7 T. In contrast, GRE-based η values were close to unity. These findings suggest that MAE is the predominant mechanism underlying orientation-dependent transverse relaxation in WM at 3 T, offering a revised interpretation that contrasts with prior susceptibility-based explanations.
Magic-sized semiconductor clusters (MSCs) synthesized in organic solvents typically exhibit poor compatibility with aqueous media, which hampers electron transfer and leads to weak electrochemiluminescence (ECL), thereby limiting their biological and environmental applications. Here we report that cation-induced assembly confers aqueous stability on Cd15Se12 MSCs and simultaneously boosts their ECL efficiency. Using cysteine (Cys) as a stabilizing ligand, we prepared water-dispersible Cd15Se12-Cys MSCs and introduced multivalent metal cations (Mn+=Zn2+, Cd2+, K+, Na+, Al3+) to electrostatically bind the surface carboxylate groups, thereby driving spontaneous intercluster assembly. The assembled Cd15Se12-Cys-Mn+ MSCs exhibited a 10-fold enhancement in ECL intensity compared to unassembled counterparts, along with excellent signal stability over 1000 s of continuous operation. Mechanistic studies revealed that the cation valence critically dictates the assembly mode and ECL efficiency. Notably, Zn2+-modified assemblies functioned as highly efficient ECL emitters in a biosensing platform, enabling sensitive lactate detection in sweat with a broad linear range (0.01-50 mM). This work establishes cation-induced assembly as a general strategy to achieve aqueous-stable MSCs with enhanced ECL performance, opening new opportunities for their application in biosensing and environmental monitoring.
Routine clinical magnetic resonance diffusion-weighted imaging (DWI) is generally performed with 2D echo planar sequences. A single thick-slab 3D approach could offer higher signal-to-noise ratio and better slice resolution. This has not been adopted due to the difficulty to avoid motion-induced phase error that interfere with multi-shot spatial encoding. To introduce a new approach for 3D brain DWI: rather than relying on navigator echoes for phase correction, moment-nulled diffusion encoding gradients are used to minimize phase variations at the source. A standard 2D echo planar imaging sequence was modified to incorporate moment-nulled diffusion encoding gradients and a second phase encoding gradient for spatial multi-shot encoding along the slice select direction. The single thick-slab 3D diffusion-weighted imaging sequence was evaluated with brain scans in healthy volunteers on a 3 Tesla scanner. Incorporation of both first and second order moment nulling achieved substantial, albeit not comprehensive, reduction of motion-related ghosting artifacts. Without such motion compensation or with first order moment nulling only, motion-related artifacts were consistently more severe. Even though the approach comes with a penalty in echo time-at a diffusion weighting of 1000 s/mm2, 119 ms for the moment-nulled 3D acquisition versus 82 ms for the conventional 2D acquisition-the measured ratio between SNR3D and SNR2D for a 92-slice scan was 0.99. This proof-of-concept shows that first and second order moment nulling may be a viable avenue for enabling 3D diffusion imaging. At higher slice numbers the SNR3D is expected to clearly surpass corresponding SNR2D. However, further investigation into echo time reduction and correction of residual phase variations is needed before the 3D approach is viable for translation into a clinical setting. Specifically, with higher gradient strength shorter echo times can be realized. Moreover, this reduces third and higher order gradient moments and associated residual phase shifts.
Capacitorless two-transistor-zero-capacitor (2T0C) dynamic random-access memories (DRAMs) offer scalability, simplified processing, and design flexibility by eliminating storage capacitors. We propose 2T0C DRAMs using aluminum-doped indium tin zinc oxide (Al:ITZO) thin-film transistors (TFTs) that enhance both retention time and memory window through device-level engineering. To suppress off-state leakage, N2O plasma treatment was applied, which enabled fine-tuning the threshold voltage (Vth) control via oxygen vacancy reduction, as confirmed by XPS analysis. Additionally, by adjusting the channel width-to-length (W/L) ratio of the read transistor (RTR), three key objectives were achieved. First, the write transistor (WTR) Vth was also engineered to enable hold-state operation at 0 V write word line (WWL), enabling ultra-low-power operation. Second, optimization of the RTR W/L ratio effectively suppressed charge loss, resulting in significantly improved retention characteristics. Third, the memory window was maximized by balancing the intrinsic trade-off between the RTR Vth and on-current (I on). As a result, we achieved retention times exceeding 1000 s and a ∼13-fold increase in memory window. These results demonstrate the feasibility of Al:ITZO-based 2T0C DRAMs for next-generation memory systems with improved scalability and energy efficiency.
To demonstrate improved image quality and lesion conspicuity in prostate diffusion-weighted imaging (DWI) using an inside-out nonlinear gradient coil that provides locally strong gradients (200-500 500 mT/m) at typical prostate positions. Before applying the nonlinear gradient coil to DWI with Echo Planar Imaging (EPI) readout, we investigated geometric distortion and eddy currents, and proposed necessary corrections. We then developed two DWI protocols (bmax = 1000 and 3000 s/mm2) with minimized echo time (TE) and tested them on volunteers and patients. We validated apparent diffusion coefficient (ADC) maps from the nonlinear gradient acquisition against the reference (linear gradients only). We quantified improvements in signal-to-noise ratio (SNR), lesion contrast-to-noise ratio (CNR), and lesion-to-normal-tissue contrast ratio in the compartmental map of restricted diffusion. Corrections effectively reduced nonlinear-gradient DWI artifacts. ADC maps from linear- and nonlinear-gradient-encoded studies agreed well, with a normalized root-mean-square-error of ∼10%, a common level of ADC variation. TE was significantly reduced from 57 to 42-47 ms for moderate b-values (≤ 1000 s/mm2) and from 72 to 42-54 ms for high b-values (≤ 3000 s/mm2). Consequently, SNR increased by 3%-38% (median 16%, p < 0.01) and 7%-38% (median 26%, p < 0.01), respectively. Lesion CNR improved by a median of 133% at b = 2000 s/mm2 and 217% at b = 3000 s/mm2. The restricted diffusion component in lesions was more conspicuous at short TE, with a median 23% increase in lesion-to-normal-tissue contrast ratio (p = 0.02). The inside-out nonlinear gradient coil enhances prostate DWI.
RNA synthesis by eukaryotic polymerases requires existing polynucleotides to serve as templates or primers. Here, we describe primer- and template-free RNA generation by human terminal nucleotidyltransferase 4B (TENT4B) via de novo polymerization of free nucleotides. We observed that recombinant TENT4B (rTENT4B) consumes ATP to yield inorganic pyrophosphate in the absence of a primer or template, concurrent with the appearance of oligomeric poly-adenosine RNA products. Remarkably, 5' labels on γ-phosphate-modified ATP or GTP are retained during polymerization in the presence of unlabeled nucleotide triphosphates (NTPs). These polymers are created at a similar efficiency irrespective of the inclusion of a primer, indicating robust RNA synthesis by rTENT4B from free NTPs. While canonical purine NTPs are favored, nucleotide diphosphates can also serve as substrates for rTENT4B-mediated de novo RNA polymerization. rTENT4B-mediated RNA synthesis using free adenosine nucleotides shows high processivity to generate 1000s-mers, whereas guanosine nucleotide polymerization is strongly and uniformly self-limited and yields a 3'-exonuclease-resistant oligonucleotide. Interrogation of other RNA polymerases reveals potential capacity for de novo polymerization using free ATP, albeit at significantly higher substrate concentrations and lower efficiency compared to rTENT4B. Our data provide definitive evidence of efficient template-free de novo RNA synthesis by a eukaryotic polymerase.
To evaluate diffusion tensor imaging (DTI) for assessing facial nerve root exit zone (REZ) microstructure in hemifacial spasm (HFS) and predicting microvascular decompression (MVD) outcomes. Data from 60 patients with primary unilateral HFS and 30 healthy volunteers were retrospectively examined. High-resolution 3.0T DTI was performed on all participants. DTI data (b=1000 s/mm2, 32 directions) were processed using FMRIB Software Library. The fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were derived from the facial nerve REZ. Group comparisons and receiver operating characteristic curve analysis assessed diagnostic performance. Preoperative DTI parameters were also evaluated for predicting MVD outcomes. In HFS, FA on the symptomatic side (0.35±0.06) was significantly lower than that on the contralateral side (0.48±0.05) and controls (0.49±0.04, P<0.001). MD and RD were significantly elevated (both P<0.001), while AD remained stable. The area under the curve (AUC) for FA in diagnosing HFS was 0.968 (sensitivity 86.00%, specificity 98.00%). Multivariate logistic regression analysis showed that the preoperative FA value was an independent predictor of postoperative outcomes (odds ratio=0.752, P=0.005), and the optimal cutoff value for FA was ≤ 0.378 (AUC=0.780). DTI noninvasively quantifies facial nerve REZ microstructural damage in HFS, characterized by reduced FA and elevated RD. As diagnostic and prognostic biomarkers, FA differentiates pathological compression from asymptomatic contact, supporting clinical decisions.
To determine whether a new deep learning (DL) based phase corrected (DLPC) reconstruction model can enhance image quality of diffusion weighted images of the prostate acquired at 1.5 T compared to a commercially available DL based product. A retrospective study of 30 consecutive patients undergoing conventional multiparametric MRI (mpMRI) of the prostate on a single 1.5 T scanner was performed. Diffusion image datasets reconstructed with a commercially available DL product and a new DLPC model were assessed. Qualitative image assessment was performed by three board certified radiologists using a 5-point Likert scale across four features and inter-rater agreement was estimated using Gwet's AC2 statistic. Quantitative image comparison was performed by assessing SNR of acquired intermediate b-value (b = 1000 s/mm2) diffusion images. The Wilcoxon matched-pairs signed rank test was used to assess differences between techniques. Image noise was assessed using the edge function. Median patient age was 70 years (interquartile range: 66.0-75.3). All radiologists perceived less noise and better image quality for all DLPC image sets compared to commercial DL images (p < 0.05). Significantly higher SNR was observed for the acquired intermediate b-value diffusion images reconstructed with DLPC (median SNR: 49.4 vs 27.5; p < 0.001), and mean ADC values did not significantly differ between DLPC and DL images (p = 0.63). Edge analyses demonstrated significantly reduced noise for DLPC images (p < 0.001). DLPC image reconstruction of diffusion weighted prostate image datasets reduces image noise and improves SNR over a commercial DL product at 1.5 T.
In order to implement a three-dimensional (3D) 2-transistor-0-capacitor (2T0C) DRAM cell using a thermally sensitive InGaZnO (IGZO) channel, the impact of electrical interactions between the write transistor (WTr) and the read transistor (RTr) in a novel structure was investigated, and a control methodology was established. First, the adoption of a discrete active island pattern led to the suppression of parasitic channels. As a result, the subthreshold hump was eliminated, and an excellent subthreshold swing characteristic of 154.4 mV dec-1 was achieved. Second, memory characterization revealed that electrical coupling effects resulting from parasitic capacitance and electrostatic effects in the 3D stacked structure compromise the storage node voltage (VSN) charging efficiency. These can be attributed to the extended BE, which serves to reduce the device footprint, and the voltage applied to activate the RTr to read the VSN value, respectively. Finally, the strategic placement of the asymmetric S/D electrodes in the vertical-channel thin-film transistor ensured excellent operational stability with an SN variation below 0.03 V after 1000 s. Consequently, long-term linear multilevel operation of 3 bits was achieved under various write operation conditions.
Laser power stability is critical for precision laser systems. Here, we present a fully fiber-coupled laser power stabilization system with a compact, lower power consumption Variable Optical Attenuator (VOA). This system can provide stabilized laser output both in-loop and out-of-loop to meet various requirements. A 12-h stability test demonstrated that the power was stabilized at 2.4 mW, The relative stability of laser power in terms of root mean square and peak to peak was 0.0273% and 0.1298%, respectively, while the Allan deviation was 6.64 × 10-6 at 1 s and 1.11 × 10-4 at 1000 s. The low-power operation and compact dimensions of VOAs contribute to the development of laser stabilization systems with enhanced miniaturization, power efficiency, and reliability.
The glymphatic system clears brain waste, including amyloid-β (Aβ), and it is shown that its dysfunction may contribute to Alzheimer's disease (AD) pathology. This dysfunction can be evaluated using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. This study summarizes the AD literature on the glymphatic system evaluated through neuroimaging methods. We searched PubMed, Scopus, Embase, and Web of Science databases to find relevant neuroimaging studies. 24 studies were included in this systematic review and meta-analysis. We observed a significant reduction in DTI-ALPS index among patients with AD, compared to healthy controls (standardized mean difference (SMD) of -1.044 (95% CI: -1.304, -0.784) in DTI studies with 1000 s/mm2 b-values and an SMD of -1.063 (95% CI: -1.278, -0.847) in studies with b-value of 2000 s/mm2). Moreover, our study reflected a significant correlation between the DTI-ALPS index and cognitive function assessed by Mini-Mental State Examination (95% CI: 0.37 to 0.51, z-score: 0.44), Montreal Cognitive Assessment (95% CI: 0.45 to 0.61, z-score: 0.54), and Clinical Dementia Rating (95% CI: -0.63 to -0.28, z-score: -0.47). In conclusion, our systematic review and meta-analysis revealed a significant dysfunction of the glymphatic system in patients with AD, compared to healthy participants. These findings suggest the DTI-ALPS index as a linked index to cognitive performance among patients with AD and as a potential parameter in assessing the progression of AD.