Comparison of locoregional recurrence risk among nipple-sparing mastectomy, skin-sparing mastectomy, and simple mastectomy in patients with ductal carcinoma in situ: a single-center study.

BACKGROUND: In invasive breast cancer, there are no differences among the mid- and long-term oncological safety results of nipple-sparing mastectomy (NSM), skin-sparing mastectomy (SSM), and simple mastectomy (SM). There are several reports comparing NSM and SSM with SM in the context of ductal carcinoma in situ (DCIS); however, the eligibility criteria vary among institutions, and there are no reports that compare all three surgical methods simultaneously within the same institution. This study aimed to compare the local recurrence and survival rates of the three techniques (NSM, SSM, and SM) in Japanese patients undergoing mastectomy for DCIS. METHODS: Patients undergoing NSM, SSM, or SM at our institution between 2006 and 2015 were identified, and their outcomes were analyzed. RESULTS: The mean follow-up period was 80.4 months (standard deviation [SD]: 37.1 months). NSM was performed in 152 cases, SSM in 49, and SM in 44. Five of 245 patients developed local recurrences. Four of these patients had invasive cancer. The primary endpoints of 5-year cumulative local recurrence were 2.4% (95% confidence interval [CI]: 0.0-5.0) for NSM, 2.2% (95% CI: 0.0-6.3) for SSM, and 0% (95% CI: 0.0-0.0) for SM. There were no significant differences among the 5-year local recurrence rates. CONCLUSIONS: In this single-center, retrospective study, the oncological safety of SSM and NSM for DCIS was comparable to that of conventional SM.

Statistical shape modeling of the geometric morphology of the canine femur, tibia, and patella.

Bone morphometry varies among dogs of different sizes and breeds. Studying these differences may help understand the predisposition of certain breeds for specific orthopedic pathologies. This study aimed to develop a statistical shape model (SSM) of the femur, patella, and tibia of dogs without any clinical orthopeadic abnormalities to analyze and compare morphological variations based on body weight and breed. A total of 97 CT scans were collected from different facilities and divided based on breed and body weight. The 3D models of the bones were obtained and aligned to a coordinate system. The SSM was created using principal component analysis (PCA) to analyze shape variations. The study found that the first few modes of variation accounted for a significant percentage of the total variation, with size/scale being the most prominent factor. The results provide valuable insights into normal anatomical variations and can be used for future research in understanding pathological bone morphologies and developing 3D imaging algorithms in veterinary medicine.

DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries.

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms of clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation of both dental structures and proximate tissues. This underpins the pillars of early pathological detection and meticulous disease progression monitoring. Nonetheless, conventional segmentation frameworks often encounter significant setbacks attributable to the intrinsic limitations of X-ray imaging, including compromised image fidelity, obscured delineation of structural boundaries, and the intricate anatomical structures of dental constituents such as pulp, enamel, and dentin. To surmount these impediments, we propose the Deformable Convolution and Mamba Integration Network, an innovative 2D dental X-ray image segmentation architecture, which amalgamates a Coalescent Structural Deformable Encoder, a Cognitively-Optimized Semantic Enhance Module, and a Hierarchical Convergence Decoder. Collectively, these components bolster the management of multi-scale global features, fortify the stability of feature representation, and refine the amalgamation of feature vectors. A comparative assessment against 14 baselines underscores its efficacy, registering a 0.95% enhancement in the Dice Coefficient and a diminution of the 95th percentile Hausdorff Distance to 7.494.

Whole-brain glucose metabolic pattern differentiates minimally conscious state from unresponsive wakefulness syndrome.

AIMS: The patient being minimally conscious state (MCS) may benefit from wake-up interventions aimed at improving quality of life and have a higher probability of recovering higher level of consciousness compared to patients with the unresponsive wakefulness syndrome (UWS). However, differentiation of the MCS and UWS poses challenge in clinical practice. This study aimed to explore glucose metabolic pattern (GMP) obtained from 18F-labeled-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) in distinguishing between UWS and MCS. METHODS: Fifty-seven patients with disorders of consciousness (21 cases of UWS and 36 cases of MCS) who had undergone repeated standardized Coma Recovery Scale-Revised (CRS-R) evaluations were enrolled in this prospective study. 18F-FDG-PET was carried out in all patients and healthy controls (HCs). Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate GMPs. The expression score of whole-brain GMP was obtained, and its diagnostic accuracy was compared with the standardized uptake value ratio (SUVR). The diagnostic efficiency was validated by one-year later clinical outcomes. RESULTS: UWS-MCS GMP exhibited hypometabolism in the frontal-parietal cortex, along with hypermetabolism in the unilateral lentiform nucleus, putamen, and anterior cingulate gyrus. The UWS-MCS-GMP expression score was significantly higher in UWS compared to MCS patients (0.90 ± 0.85 vs. 0 ± 0.93, p < 0.001). UWS-MCS-GMP expression score achieved an area under the curve (AUC) of 0.77 to distinguish MCS from UWS, surpassing that of SUVR based on the frontoparietal cortex (AUC = 0.623). UWS-MCS-GMP expression score was significantly correlated with the CRS-R score (r = -0.45, p = 0.004) and accurately predicted the one-year outcome in 73.7% of patients. CONCLUSION: UWS and MCS exhibit specific glucose metabolism patterns, the UWS-MCS-GMP expression score significantly distinguishes MCS from UWS, making SSM/PCA a potential diagnostic methods in clinical practice for individual patients.

A hybrid statistical morphometry free-form deformation approach to 3D personalized foot-ankle models.

Foot and ankle joint models are widely used in the biomechanics community for musculoskeletal and finite element analysis. However, personalizing a foot and ankle joint model is highly time-consuming in terms of medical image collection and data processing. This study aims to develop and evaluate a framework for constructing a comprehensive 3D foot model that integrates statistical shape modeling (SSM) with free-form deformation (FFD) of internal bones. The SSM component is derived from external foot surface scans (skin measurements) of 50 participants, utilizing principal component analysis (PCA) to capture the variance in foot shapes. The derived surface shapes from SSM then guide the FFD process to accurately reconstruct the internal bone structures. The workflow accuracy was established by comparing three model-generated foot models against corresponding skin and bone geometries manually segmented and not part of the original training set. We used the top ten principal components representing 85 % of the population variation to create the model. For prediction validation, the average Dice similarity coefficient, Hausdorff distance error, and root mean square error were 0.92 ± 0.01, 2.2 ± 0.19 mm, and 2.95 ± 0.23 mm for soft tissues, and 0.84 ± 0.03, 1.83 ± 0.1 mm, and 2.36 ± 0.12 mm for bones, respectively. This study presents an efficient approach for 3D personalized foot model reconstruction via SSM generation of the foot surface that informs bone reconstruction based on FFD. The proposed workflow is part of the open-source Musculoskeletal Atlas Project linked to OpenSim and makes it feasible to accurately generate foot models informed by population anatomy, and suitable for rigid body analysis and finite element simulation.

Monitoring multistage healthcare processes using state space models and a machine learning based framework.

Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are often characterized by a multistage structure in which several components, states or stages form the final products or outcomes. In such complex scenarios, Multistage Process Monitoring (MPM) techniques become invaluable for monitoring distinct states of the process over time. However, the healthcare sector has seen limited studies employing MPM. This study aims to fill this gap by developing an MPM control chart tailored for healthcare data to promote early detection, confirmation, and patient safety. As it is important to detect unnatural conditions in healthcare processes at an early stage, the statistical control charts are combined with machine learning techniques (i.e., we deal with Intelligent Control Charting, ICC) to enhance detection ability. Through Monte Carlo simulations, our method demonstrates better performance compared to its statistical counterparts. To underline the practical application of the proposed ICC framework, real data from a two-stage thyroid cancer surgery is utilized. This real-world case serves as a compelling illustration of the effectiveness of the developed MPM control chart in a healthcare setting.

A statistical shape analysis for the assessment of the main geometrical features of the distal femoral medullary canal.

Statistical Shape Models (SSMs) are widely used in orthopedics to extract the main shape features from bone regions (e.g., femur). This study aims to develop an SSM of the femoral medullary canal, investigate its anatomical variability, and assess variations depending on canal length. The canals were isolated from 72 CT femur scans, through a threshold-based segmentation. A region of interest (ROI) was selected; sixteen segments were extracted from the ROI, ranging from 25% of the full length down to the most distal segment. An SSM was developed to identify the main modes of variation for each segment. The number of Principal Components (PCs) needed to explain at least 90% of the shape variance were three/four based on the length of the canal segment. The study examined the relationship between the identified PCs and geometric parameters like length, radius of curvature, ellipticity, mean diameter, and conicity, reporting range and percentage variation of these parameters for each segment. The SSMs provide insights into the anatomical variability of the femoral canal, emphasizing the importance of considering different segments to capture shape variations at various canal length. These findings can contribute for the design of personalized orthopedic implants involving the distal femur.

Evaluation of the Simple Suspension Method for Oral Anticancer Drugs.

The simple suspension method (SSM) involves administering tablets or capsules using a tube after disintegration and suspension in hot water without crushing or opening the capsule. Particularly, for anticancer drugs, it is an excellent method of administration that reduces the risk of exposure during dispensing. In contrast, information on SSM for individual drugs is insufficient. Anticancer drugs present a therapeutic challenge because their information is limited. We investigated whether SSM is possible with 36 anticancer drugs. Furthermore, we examined the pH of the suspension of these drugs, for which no information on SSM is available. We found that suspension was possible for 24 of the 36 drugs. Furthermore, the pH of the suspension was measured, which provided important information when considering dissolution solutions other than hot water. Little changes in the pH were observed before or after passing through the tube. The results of this study may improve medication adherence in patients with cancer experiencing dysphagia.

How to Measure "Spillover Spread".

Since the development of the first instrument in the late 1960s, flow cytometry (FC) has become a powerful tool in both the clinical and research space. As one of the earliest single-cell analytical techniques, flow cytometry can measure thousands of cells in minutes, allowing researchers an unprecedented understanding of the biology of their system of interest. There are commercial systems available that can measure over 40 different parameters at the same time. The most common assay, immunophenotyping, involves labeling cells with fluorescently conjugated antibodies. The process of fluorescence occurs when a fluorescent molecule first absorbs a photon of light, which promotes an electron to a higher energy state. This energy is released by the emission of a photon of lower energy (thus a higher wavelength). The emitted photon will be within a range of visible wavelengths. When measured on a flow cytometer, this results in the fluorescent signal being measured not just in the primary detector but also in one or more secondary detectors. Termed "spillover," this is when the fluorescent signal measured in a detector other than the intended one creates a problem in identifying the real signal. The process of compensation is used to address this spectral spillover. However, in correcting for the spillover by compensation, the spread of the data is revealed. This spread can be quantified, and, here, we discuss two methods that can be used to identify and measure this spectral spread for any combination of fluorochromes. The output of these methods is useful in experimental design and monitoring instrument quality control. Armed with this information, the researcher can better design polychromatic panels to minimize the impact of spread on their data.

Sanshimao formula inhibits the hypoxia-induced pro-angiogenesis of hepatocellular carcinoma cells partly through regulating MKK6/p38 signaling pathway.

OBJECTIVES: Sanshimao (SSM) is a traditional Chinese medicine formula for advanced hepatocellular carcinoma (HCC). This study was designed to investigate the effect of SSM on HCC-induced angiogenesis and to explore the potential mechanism. METHODS: The endothelial cells were cultured with HCC cells conditioned medium in the 1% oxygen atmosphere to imitate tumor hypoxia microenvironment. EA.hy926 cells migration and tubulogenesis were detected by tube formation and scratch-wound assay. The protein microarray was employed to explore SSM-targeted proteins in Huh7 cells. We also established an animal model to observe the effects of SSM on angiogenesis in vivo. RESULTS: The data indicated that SSM reduced HCC-induced migration and tube formation of EA.hy926 cells at low dose under hypoxic conditions. These effects might be partly owing to suppression of HIF-1α-induced vascular endothelial growth factorα expression in Huh7 cells. Moreover, this inhibition was in an MKK6/P38-dependent way. Besides, Huh7 subcutaneous tumor models in nude mice further demonstrated the inhibition of SSM on tumor weight might be exerted partly by reduction of angiogenesis via blocking MKK6/P38 signaling pathways. CONCLUSION: SSM inhibits HCC-induced pro-angiogenesis under hypoxic conditions via suppression of MKK6/P38 signaling pathways, which is favorable for HCC tumor growth.

Graphitic Carbon Nitride/MOFs Hybrid Composite as Highly Selective and Sensitive Electrodes for Calcium Ion Detection.

The metal-organic framework (MOF) is a class of materials that exhibits a notable capacity for electron transfer. This unique framework design offers potential applications in various fields, including catalysis, gas storage, and sensing. Herein, we focused on a specific type of MOF called Ti-MOF. To enhance its properties and functionality, the composite material was prepared by incorporating graphitic carbon nitride (g-C3N4) into the Ti-MOF structure. This composite, known as g-C3N4@Ti-MOF, was selected as the active material for ion detection, specifically targeting calcium ions (Ca2+). To gain a comprehensive understanding of the structural and chemical properties of the g-C3N4@Ti-MOF composite, several analytical techniques were employed to characterize the prepared g-C3N4@Ti-MOF composite, including X-ray diffraction (XRD), SEM-EDX, and FT-IR. For comparison, different pastes were prepared by mixing Ti-MOF or g-C3N4@Ti-MOF, graphite, and o-NPOE as a plasticizer. The divalent Nernstian responses of the two best electrodes, I and II, were 28.15 ± 0.47 and 29.80 ± 0.66 mV decade-1, respectively, with concentration ranges of 1 µM-1 mM and 0.1 µM-1 mM with a content 1.0 mg Ti-MOF: 250 mg graphite: 0.1 mL o-NPOE and 0.5 mg g-C3N4@Ti-MOF: 250 mg graphite: 0.1 mL o-NPOE, respectively. The electrodes showed high sensitivity and selectivity for Ca2+ ions over different species. The suggested electrodes have been successfully employed for Ca2+ ion measurement in various real samples with excellent precision (RSD = 0.74-1.30%) and accuracy (recovery = 98.5-100.2%), and they exhibited good agreement with the HPLC.

Prognostic assessment of liver cirrhosis and its complications: current concepts and future perspectives.

Liver cirrhosis is an irreversible stage of chronic liver disease with varying clinical course. Acute decompensation of liver cirrhosis represents a watershed in prognosis and is characterized by the occurrence of clinical complications such as ascites, jaundice, hepatic encephalopathy, infections, or portal-hypertensive hemorrhages. Emergent data indicate that an acute decompensation can be subdivided into stable decompensated cirrhosis (SDC), unstable decompensated cirrhosis (UDC), pre-acute-on chronic liver failure (pre-ACLF) and acute-on chronic liver failure (ACLF), while the mortality risk varies greatly between the respective subgroups. ACLF is the most severe form of acutely decompensated cirrhosis and characterized by the development of organ failure(s) and a high short-term mortality. Due to the dynamic disease course of acute decompensation, it is paramount to detect patients at particular risk for severe complications those at high risk for developing ACLF as early as possible in order to initiate optimal management. This review describes new concepts and perspectives in the definition and classification of decompensated cirrhosis and provides on overview on emerging predictive scoring systems, non-invasive measurement methods and new biomarkers, which allow an early identification of patients with acute decompensation at risk.

A framework for proactive safety evaluation of intersection using surrogate safety measures and non-compliance behavior.

In recent years, identifying road users' behavior and conflicts at intersections have become an essential data source for evaluating traffic safety. According to the Federal Highway Administration (FHWA), in 2020, more than 50% of fatal and injury crashes occurred at or near intersections, necessitating further investigation. This study developed an innovative artificial intelligence (AI)-based video analytic tool to assess intersection safety using surrogate safety measures and non-compliance behavior. To extract the trajectory data, a real-time AI detection model - YOLO-v5 with a tracking framework based on the DeepSORT algorithm was deployed. 54 h of high-resolution video data were collected at six signalized intersections (including three 3-leg and three 4-leg intersections) in Glassboro, New Jersey. Non-compliance behaviors, such as redlight running and pedestrian crossing outside the crosswalk, are captured to better understand the risky behaviors at these locations. The proposed approach achieved an accuracy of 92% to 98% for detecting and tracking road users' trajectories. Additionally, the developed tool also provided directional traffic volumes, pedestrian volumes, vehicles running a red light, pedestrian crossing outside the crosswalk events, and PET and TTC for crossing conflicts between vehicles. Furthermore, an extreme value theory (EVT) was used to estimate the number of crashes at each intersection utilizing the frequency of PETs and TTCs. Finally, the intersections were ranked based on the calculated score considering the severity of crashes. Overall, the developed tool and the crash estimation, as well as the model and ranking method, can provide valuable information for engineers and policymakers to assess the safety of intersections and implement effective countermeasures to mitigate intersection-involved crashes.

In-situ growth of porous rod-like tungsten oxide for electrochemical determination of cupric ion.

Preconcentration can effectively enhance the detection performance of electrodes in the electrochemical detection of heavy metal ions, but it also presents challenges for real-time monitoring. Several attempts have been made to optimize preconcentration by improving the adsorption capacity or detection mechanism of the electrode. The valence transfer of tungsten oxide between W5+/W6+ can participate in the reduction between the electrode material and heavy metal ions, playing a role in preconcentration to some extent. Therefore, we developed a WO3/SSM electrochemical sensor for the detection of Cu(II) that utilizes the valence variation property of WO3. The crystallinity and microstructure of the WO3/SSM electrode can be regulated by controlling the deposition parameters, and we prepared three types of WO3/SSM with different morphologies to identify the influence of the electrochemical effective surface area. The proposed electrode shows high performance as a Cu(II) sensor under short preconcentration time (60 s), with an excellent sensitivity of 14.113 µA µM-1 cm-2 for 0.1-10.0 µM and 4.7356 µA µM-1 cm-2 for 10.0-20.0 µM. Overall, the combined effect of morphology and valence transfers shortens the preconcentration time and optimizes preconcentration while ensuring excellent electrode performance. This WO3/SSM electrode is expected to drive great advances in the application of tungsten oxide in the electrochemical detection of heavy metal ions.

Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies.

BACKGROUND: Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies. METHODS: We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson's Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses ("gold standard") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA). RESULTS: The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941). CONCLUSIONS: SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.

Outstanding feasibility of spleen stiffness measurement by 100-Hz vibration-controlled transient elastography.

This novel spleen-dedicated FibroScan has high success rate and is easy to operate. The spleen stiffness is correlated with liver stiffness, which reflects the liver fibrosis stage. However, whether SSM is able to reflect the severity of liver disease warrants further observation.

Solid-Supported Membrane (SSM)-Based Electrophysiology Assays Using Surface Electrogenic Event Reader Technology (SURFE²R) in Early Drug Discovery.

This article presents detailed descriptions of procedures and troubleshooting tips for solid-supported membrane (SSM)-based electrophysiology assays (SURFE²R) to measure electrogenic solute carrier transporter proteins (SLCs) and assess the effects of compounds that modulate their activity. SURFE²R allows the use of the standard 96-well format, making it an ideal platform for tertiary assays in a drug-discovery campaign. The assays are performed with cell-line-derived membrane fractions or proteoliposomes containing the transporter of interest. Three main protocols are described for the isolation of membrane fractions from cell culture and the generation of proteoliposomes containing the transporter of interest. Additionally, detailed protocols for SURFE²R single concentration and dose-response experiments are included to measure the potencies of test compounds in stimulating or inhibiting transporter function (EC50 or IC50 values, respectively) and kinetic functional assays to calculate apparent affinity (kM ) and maximal velocity (Vmax ) of substrate uptake. © 2023 Sanofi. Current Protocols published by Wiley Periodicals LLC. PROTOCOL GROUP 1: Sample preparation for SSM-based electrophysiology assays Support Protocol 1: Production of cell batches Support Protocol 2: Simple isolation of cell membranes Alternate Protocol 1: Isolation of cell membranes with sucrose gradient pre-purification Support Protocol 3: Production and isolation of liposomes Support Protocol 4: Preparation of sensor with isolated cell membranes Alternate Protocol 2: Preparation of sensor with isolated proteoliposomes PROTOCOL GROUP 2: Determination of assay parameters for SSM-based electrophysiology assay Support Protocol 5: Assay with stable buffer Alternate Protocol 3: Assay with ion gradient Support Protocol 6: Determination of membrane/liposome concentration Support Protocol 7: Determination of substrate dependency kM PROTOCOL GROUP 3: Determination of advanced assay parameters for SSM-based electrophysiology assays Support Protocol 8: Assessment of ion concentration dependency Support Protocol 9: Assessment of pH dependency Support Protocol 10: Assessment of DMSO dependency Support Protocol 11: Assessment of signal stability with multiple activations PROTOCOL GROUP 4: Compound testing through SSM-based electrophysiology assays using SURFE²R apparatus Support Protocol 12: Assessment of signal specificity of a published inhibitor or unknown compound(s) Support Protocol 13: Compound wash-out Support Protocol 14: Statistical analysis.

Thermostability enhancement and insight of L-asparaginase from Mycobacterium sp. via consensus-guided engineering.

Acrylamide alleviation in food has represented as a critical issue due to its neurotoxic effect on human health. L-Asparaginase (ASNase, EC 3.5.1.1) is considered a potential additive for acrylamide alleviation in food. However, low thermal stability hinders the application of ASNase in thermal food processing. To obtain highly thermal stable ASNase for its industrial application, a consensus-guided approach combined with site-directed saturation mutation (SSM) was firstly reported to engineer the thermostability of Mycobacterium gordonae L-asparaginase (GmASNase). The key residues Gly97, Asn159, and Glu249 were identified for improving thermostability. The combinatorial triple mutant G97T/N159Y/E249Q (TYQ) displayed significantly superior thermostability with half-life values of 61.65 ± 8.69 min at 50 °C and 5.12 ± 1.66 min at 55 °C, whereas the wild-type was completely inactive at these conditions. Moreover, its Tm value increased by 8.59 °C from parent wild-type. Interestingly, TYQ still maintained excellent catalytic efficiency and specific activity. Further molecular dynamics and structure analysis revealed that the additional hydrogen bonds, increased hydrophobic interactions, and favorable electrostatic potential were essential for TYQ being in a more rigid state for thermostability enhancement. These results suggested that our strategy was an efficient engineering approach for improving fundamental properties of GmASNase and offering GmASNase as a potential agent for efficient acrylamide mitigation in food industry. KEY POINTS: • The thermostability of GmASNase was firstly improved by consensus-guided engineering. • The half-life and Tm value of triple mutant TYQ were significantly increased. • Insight on improved thermostability of TYQ was revealed by MD and structure analysis.

Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety.

The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle's (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.

Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia.

Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.