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The decision support indicators (DSIs) are specifically designed to inform local and regional stakeholders on the characteristics of a predicted event to facilitate decision-making. They can be classified as conventional, impact-based and event-based DSIs. This study aims to develop methodologies for calculating event-based DSIs and to evaluate the usefulness of different classes of DSIs for climate impact assessment and climate actions by learning about users' perceptions. The DSIs are calculated based on an ensemble of hydrological projections in western Norway under two representative concentration pathway (RCP) scenarios. The definitions, methodologies and results of the indicators are summarized in questionnaires and evaluated by key stakeholders in terms of understandability, importance, plausibility and applicability. Based on the feedback, we conclude that the conventional DSIs are still preferred by stakeholders and an appropriate selection of conventional DSIs may overcome the understanding problems between the scientists and stakeholders. The DSIs based on well-known historical events are easy to understand and can be a useful tool to convey climate information to the public. However, they are not readily implemented by stakeholders in the decision-making process. The impact-based DSI is generally easy to understand and important but it can be restricted to specific impact sectors.

Perhaps more than any other “-omics” endeavor, the accuracy and level of detail obtained from mapping the major connection pathways in the living human brain with diffusion MRI depend on the capabilities of the imaging technology used. The current tools are remarkable; allowing the formation of an “image” of the water diffusion probability distribution in regions of complex crossing fibers at each of half a million voxels in the brain. Nonetheless our ability to map the connection pathways is limited by the image sensitivity and resolution, and also the contrast and resolution in encoding of the diffusion probability distribution. The goal of our Human Connectome Project (HCP) is to address these limiting factors by re-engineering the scanner from the ground up to optimize the high b-value, high angular resolution diffusion imaging needed for sensitive and accurate mapping of the brain's structural connections. Our efforts were directed based on the relative contributions of each scanner component. The gradient subsection was a major focus since gradient amplitude is central to determining the diffusion contrast, the amount of T2 signal loss, and the blurring of the water PDF over the course of the diffusion time. By implementing a novel 4-port drive geometry and optimizing size and linearity for the brain, we demonstrate a whole-body sized scanner with Gmax=300mT/m on each axis capable of the sustained duty cycle needed for diffusion imaging. The system is capable of slewing the gradient at a rate of 200T/m/s as needed for the EPI image encoding. In order to enhance the efficiency of the diffusion sequence we implemented a FOV shifting approach to Simultaneous MultiSlice (SMS) EPI capable of unaliasing 3 slices excited simultaneously with a modest g-factor penalty allowing us to diffusion encode whole brain volumes with low TR and TE. Finally we combine the multi-slice approach with a compressive sampling reconstruction to sufficiently undersample q-space to achieve a DSI scan in less than 5min. To augment this accelerated imaging approach we developed a 64-channel, tight-fitting brain array coil and show its performance benefit compared to a commercial 32-channel coil at all locations in the brain for these accelerated acquisitions. The technical challenges of developing the over-all system are discussed as well as results from SNR comparisons, ODF metrics and fiber tracking comparisons. The ultra-high gradients yielded substantial and immediate gains in the sensitivity through reduction of TE and improved signal detection and increased efficiency of the DSI or HARDI acquisition, accuracy and resolution of diffusion tractography, as defined by identification of known structure and fiber crossing. •Approach for advancing the sensitivity of the diffusion connectivity measurement.•Optimization of Gmax=300mT/m gradient, RF coil and sequence.•Improved sensitivity and diffusion contrast in high quality DSI/Q Ball.

The Medium Energy X-ray telescope (ME) is one of the three main telescopes on board the Insight hard X-ray modulation telescope ( Insight- HXMT) astronomy satellite. ME contains 1728 pixels of Si-PIN detectors sensitive in 5–30 keV with a total geometrical area of 952 cm 2 . The application specific integrated circuit (ASIC) chip, VA32TA6, is used to achieve low power consumption and low readout noise. The collimators define three kinds of field of views (FOVs) for the telescope, 1°×4°, 4°×4°, and blocked ones. Combination of such FOVs can be used to estimate the in-orbit X-ray and particle background components. The energy resolution of ME is ~3 keV at 17.8 keV (FWHM) and the time resolution is 255 μs. In this paper, we introduce the design and performance of ME.

Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.

Junfeng Zeng,1 Lihong Shi,1 Yongbo Liu,2 Jian Yang,3 Luqing Zhang,4 Huifang Song,1 Yunhe Zhao,1 Jing Yang,1 Xiaolong Cheng,5 Li Lu1,6 1School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 2Department of Radiology, Peking University Care Lu’an Hospital, Changzhi, Shanxi, People’s Republic of China; 3School of Biomedical Sciences, The University of Hong Kong, People’s Republic of China; 4School of Basic Medical Sciences, Nanjing University, Nanjing, Jiangsu, People’s Republic of China; 5Science and Technology Department, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 6Key Laboratory of Cellular Physiology of Chinese Ministry of Education, Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of ChinaCorrespondence: Li Lu, School of Basic Medical Sciences, Shanxi Medical University, Key Laboratory of Cellular Physiology of Chinese Ministry of Education, Taiyuan, Shanxi, 030001, People’s Republic of China, Email luli@sxmu.edu.cn Xiaolong Cheng, Science and Technology Department, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, People’s Republic of China, Email chengxl@sxmu.edu.cnBackground: DSI Studio is an advanced imaging software specifically designed for the analysis of diffusion magnetic resonance imaging (dMRI). Its key features, which include fiber reconstruction, fiber tracking, and 3D visualization, have established its significant role in neuroscience research.Objective: A solid understanding of spatial relationships is crucial for students studying anatomy. However, there has been limited research in Chinese medical education regarding the integration of 3D imaging technology into anatomical instruction. To address this gap, we conducted an innovative study utilizing DSI Studio to enhance neuroanatomy education.Methods: An innovative study was conducted utilizing DSI Studio for fiber reconstruction and 3D visualization in neuroanatomy workshops. A total of 38 students participated in hands-on sessions, with 13 completing pre-training surveys and 19 completing post-training surveys. The students’ understanding of neuroanatomy prior to training, as well as their performance and experiences during the neuroanatomy learning process, were systematically recorded. Additionally, the effectiveness of DSI Studio software in enhancing neuroanatomy learning was assessed in conjunction with the reconstruction capabilities of the software.Results: The application of DSI Studio significantly improved students’ visualization of neural structures, surpassing traditional teaching limitations. It enhanced their understanding of three-dimensional brain anatomy, boosted enthusiasm, and improved learning efficiency. The workshops supported the students’ progression through the knowledge acquisition phases—understanding, mastery, and application.Conclusion: DSI Studio demonstrates potential as an educational tool in neuroanatomy, offering a supportive and flexible learning environment conducive to achieving learning objectives. Our findings preliminarily support the adoption of DSI Studio’s fiber reconstruction technology in undergraduate medical education.Keywords: neuroanatomy, DSI studio, spatial understanding ability, fiber reconstruction

Purpose Advances in neuroimaging have provided an understanding of the precuneus’(PCu) involvement in functions such as visuospatial processing and cognition. While the PCu has been previously determined to be apart of a higher-order default mode network (DMN), recent studies suggest the presence of possible dissociations from this model in order to explain the diverse functions the PCu facilitates, such as in episodic memory. An improved structural model of the white-matter anatomy of the PCu can demonstrate its unique cerebral connections with adjacent regions which can provide additional clarity on its role in integrating information across higher-order cerebral networks like the DMN. Furthermore, this information can provide clinically actionable anatomic information that can support clinical decision making to improve neurologic outcomes such as during cerebral surgery. Here, we sought to derive the relationship between the precuneus and underlying major white-mater bundles by characterizing its macroscopic connectivity. Methods Structural tractography was performed on twenty healthy adult controls from the Human Connectome Project (HCP) utilizing previously demonstrated methodology. All precuneus connections were mapped in both cerebral hemispheres and inter-hemispheric differences in resultant tract volumes were compared with an unpaired, corrected Mann–Whitney U test and a laterality index (LI) was completed. Ten postmortem dissections were then performed to serve as ground truth by using a modified Klingler technique with careful preservation of relevant white matter bundles. Results The precuneus is a heterogenous cortical region with five major types of connections that were present bilaterally. (1) Short association fibers connect the gyri of the precuneus and connect the precuneus to the superior parietal lobule and the occipital cortex. (2) Four distinct parts of the cingulum bundle connect the precuneus to the frontal lobe and the temporal lobe. (3) The middle longitudinal fasciculus from the precuneus connects to the superior temporal gyrus and the dorsolateral temporal pole. (4) Parietopontine fibers travel as part of the corticopontine fibers to connect the precuneus to pontine regions. (5) An extensive commissural bundle connects the precuneus bilaterally. Conclusion We present a summary of the anatomic connections of the precuneus as part of an effort to understand the function of the precuneus and highlight key white-matter pathways to inform surgical decision-making. Our findings support recent models suggesting unique fiber connections integrating at the precuneus which may suggest finer subsystems of the DMN or unique networks, but further study is necessary to refine our model in greater quantitative detail.

Submarine groundwater discharge (SGD) is an important source of nutrients and metals to the coastal ocean, affects coastal ecosystems, and is gaining recognition as a relevant water resource. SGD is usually quantified using geochemical tracers such as radon or radium. However, a few studies have also used dissolved silicon (DSi) as a tracer for SGD, as DSi is usually enriched in groundwater when compared to surface waters. In this study, we discuss the potential of DSi as a tracer in SGD studies based on a literature review and two case studies from contrasting environments. In the first case study, DSi is used to calculate SGD fluxes in a tropical volcanic-carbonate karstic region (southern Java, Indonesia), where SGD is dominated by terrestrial groundwater discharge. The second case study discusses DSi as a tracer for marine SGD (i.e. recirculated seawater) in the tidal flat area of Spiekeroog (southern North Sea), where SGD is dominantly driven by tidal pumping through beach sands. Our results indicate that DSi is a useful tracer for SGD in various lithologies (e.g. karstic, volcanic, complex) to quantify terrestrial and marine SGD fluxes. DSi can also be used to trace groundwater transport processes in the sediment and the coastal aquifer. Care has to be taken that all sources and sinks of DSi are known and can be quantified or neglected. One major limitation is that DSi is used by siliceous phytoplankton and therefore limits its applicability to times of the year when primary production of siliceous phytoplankton is low. In general, DSi is a powerful tracer for SGD in many environments. We recommend that DSi should be used to complement other conventionally used tracers, such as radon or radium, to help account for their own shortcomings.

PurposeThe purpose of this paper is to describe and categorise how current literature contributes to sales and operations planning (S&OP) research on how contextual variables affect S&OP design and to frame future areas for context-based S&OP research.Design/methodology/approachThe method used was a systematic literature review. Studies for review were obtained through a keyword search of five relevant databases, manual searches of relevant journals and snowballing of citations in relevant papers. In total, 571 papers published between 2000 and 2017 were assessed, and 68 papers were included in the review.FindingsThe review found that S&OP design depends on industry, dynamic complexity, detail complexity and organisational characteristics. The findings of the literature review suggest that future research should study the roles of industry, complexity, system and process and organisational characteristics in S&OP design.Research limitations/implicationsThe findings revealed several gaps in the literature on context-dependent S&OP design. To address these gaps, an agenda for future S&OP contingency research is developed.Practical implicationsThe findings revealed which contextual areas and specific S&OP design issues must be considered when designing and implementing S&OP.Originality/valueThis study focussed on identifying relevant research on S&OP design by analysing the contribution of literature to a research framework inspired by contingency-based research of operations and supply chain management.

The effects of variations in the heat treatment process of milk on its quality and flavor are inevitable. This study investigated the effect of direct steam injection and instantaneous ultra-high-temperature (DSI-IUHT, 143 °C, 1–2 s) sterilization on the physicochemical properties, whey protein denaturation (WPD) rate, and volatile compounds (VCs) of milk. The experiment compared raw milk as a control with high-temperature short-time (HTST, 75 °C 15 s and 85 °C 15 s) pasteurization and indirect ultra-high-temperature (IND-UHT, 143 °C, 3–4 s) sterilization. The results showed no significant differences (p > 0.05) in physical stability between milk samples with different heat treatments. The DSI-IUHT and IND-UHT milks presented smaller particle sizes (p < 0.05) and more concentrated distributions than the HTST milk. The apparent viscosity of the DSI-IUHT milk was significantly higher than the other samples (p < 0.05) and is consistent with the microrheological results. The WPD of DSI-IUHT milk was 27.52% lower than that of IND-UHT milk. Solid-phase microextraction (SPME) and solvent-assisted flavor evaporation (SAFE) were combined with the WPD rates to analyze the VCs, which were positively correlated with ketones, acids, and esters and negatively associated with alcohols, heterocycles, sulfur, and aldehydes. The DSI-IUHT samples exhibited a higher similarity to raw and HTST milk than the IND-UHT samples. In summary, DSI-IUHT was more successful in preserving the milk’s quality due to its milder sterilization conditions compared to IND-UHT. This study provides excellent reference data for the application of DSI-IUHT treatment in milk processing.

The Fucheng-1 (FC-1) satellite has successfully transitioned from its initial operational phase and is now undergoing a detailed performance assessment for time-series deformation monitoring. This study evaluates the surface deformation monitoring capabilities of the newly launched FC-1 satellite using the interferometric synthetic aperture radar (InSAR) technique, particularly in urban applications. By analyzing the observation data from 20 FC-1 scenes and 20 Sentinel-1 scenes, deformation velocity maps of a university in Mianyang city were obtained using persistent scatterer interferometry (PSI) and distributed scatterer interferometry (DSI) techniques. The results show that thanks to the high resolution of 3 × 3 m of the FC-1 satellite, significantly more PS points and DS points were detected than those detected by Sentinel-1, by 13.4 times and 17.9 times, respectively. The distribution of the major deformation areas detected by both satellites in the velocity maps is generally consistent. FC-1 performs better than Sentinel-1 in monitoring densely structured and vegetation-covered areas. Its deformation monitoring capability at the millimeter level was further validated through comparison with leveling measurements, with average errors and root mean square errors of 1.761 mm and 2.172 mm, respectively. Its high-resolution and high-precision interferometry capabilities make it particularly promising in the commercial remote sensing market.