Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Cerebral Blood Flow and Metabolism Combination of Dynamic and Integral Methods for Generating Reproducible Functional Cbf Images

A new method to measure regional CBF is presented, applying both dynamic and integral analyses to a dynamic sequence of positron emission tomographic scans collected during and following the administration of H2150 (inhalation of CI502). The dynamic analysis is used to correct continuously monitored arterial whole-blood activity for delay and dispersion relative to tissue scans. An integral analysis including corrections for this delay and dispersion is then used to calculate CBF on a pixel-The measurement of regional CBF (rCBF) using positron emission tomography (PET) and 150_ labelled water is a well-established clinical research tool. Both intravenous injection of H2150 and inha­ lation of CI502 have been used. In the latter case the 150 label is rapidly transferred to the water pool in the lung capillary bed (West and Dollery, 1962). A number of different implementations have been described, such as the steady-state technique (Frackowiak et aI., 1980), the integrated projection technique (Huang et aI., 1983), the autoradiographic technique (Raichle et aI., 1983; Kanno et aI. , 1984), and various dynamic techniques. Each of these methods has its relative advantages and limitations. For example, the steady-state technique has low temporal resolution, is statistically vulnerable to changes at high flow values (Lammert sma et aI., 1981b), and makes inefficient use of the total radio­ ac tivity administered to the subject. The autoradio­ graphic and integrated projection techniques pro­ duce results that are reported to be scan time de-675 by-pixel basis. Normal values and reproducibility over a 2-h period are presented, together with the results of val­ idation and simulation studies. The results indicate that the single-tissue compartment model adequately de­ scribes the distribution of H2150 in the brain, without recourse to postulating a nonexchanging water pool. Key Words: Delay and dispersion-Functional images-150-labelled water-Regional cerebral blood flow-Sim­ ulation. This is probably because they do not take into ac­ count delay and dispersion of the measured arterial blood curve relative to the recorded tissue data (lida et aI., 1986; Lammertsma et aI., 1989). The scan time dependency of the autoradiographic tech­ nique may also result from using an incorrect value for the volume of distribution of water, upon which the later phase of the data is dependent. Recently a dynamic method, the build-up tech­ nique (Lammert sma et aI., 1989), has been de­ scribed, which performs an intrinsic correction for the delay and dispersion of the measured arterial blood curve. This build-up technique provides …
Neural network structure optimization for Hefei Light Source II β function correction

In this study, we address the design challenges related to hyperparameters, such as the number of layers and nodes in deep neural networks, by introducing an Improved Genetic Algorithm-based method for optimizing neural network structures (IGA-DNN). We apply this method to the practical problem of β function correction in particle accelerators and develop a storage ring β function correction scheme based on IGA-DNN. We compare our approach with traditional genetic algorithm-optimized neural networks to evaluate its performance. Our results reveal that the neural network optimized by the improved genetic algorithm reduces the number of layers by three and decreases training time by a factor of three, leading to a more efficient model. Moreover, the accuracy of β function simulation correction is enhanced using the IGA-DNN method. This approach can also be extended to optimize other optical parameters and tackle multi-parameter optimization problems, showcasing its versatility and potential for broader applications across various fields.
Effect of Tillage Systems and Foliar Boron Application on Growth and Yield of Soft Wheat (Triticum aestivum L.)

A field experiment was carried out in the Um Al-Akf region,(5 km from the center of Al-Muthanna Governorate), in the season (2021-2022), with the aim of studying the effect of tillage systems and foliar spraying of boron on the growth and yield characteristics of soft wheat. The experiment was carried out according to the Split Block Design method, using sector design. Completely randomized (RCBD) and with three replications, the experiment included two factors, the first factor was the tillage systems (no-till and Reduce tillage at a depth of (10 cm) and conventional tillage at a depth of (20 cm) and operated in the vertical position, and the second factor was spraying boron element (0, 50, 100, 150 mg B L-1), and operated in the horizontal position. The results of the experiment showed the superiority of the no-till system significantly, as the highest averages were recorded in the characteristic of plant height 79.66 cm, leaf area 38.84 cm2, the number of spikes 276.8 spikes m-2, the grain yield 3.540 ton ha-1, and the biological yield 8.285 ton ha-1, As for the spraying of boron, the concentration of 150 mg B L-1 was significantly superior, as it recorded the highest averages for the characteristic of the weight of a thousand grains 36.30 g, the grain yield 3.498 ton ha-1, and the biological yield 8.713 ton ha-1, while the interaction between tillage and boron spraying systems showed superiority Significant in the weight of 1000 grains, as the treatment of interference (Reduce Tillage × 100 mg B L-1) recorded the highest average weight per thousand tablets was 38.03 g.
3D based generative PROTAC linker design with reinforcement learning

Proteolysis targeting chimera (PROTAC), has emerged as an effective modality to selectively degrade disease-related proteins by harnessing the ubiquitin-proteasome system. Due to PROTACs' hetero-bifunctional characteristics, in which a linker joins a warhead binding to a protein of interest (POI), conferring specificity and a E3-ligand binding to an E3 ubiquitin ligase, this could trigger the ubiquitination and transportation of POI to the proteasome, followed by degradation. The rational PROTAC linker design is challenging due to its relatively large molecular weight and the complexity of maintaining the binding mode of warhead and E3-ligand in the binding pockets of counterpart. Conventional linker generation method can only generate linkers in either 1D SMILES or 2D graph, without taking into account the information of ternary structures. Here we propose a novel 3D linker generative model PROTAC-INVENT which can not only generate SMILES of PROTAC but also its 3D putative binding conformation coupled with the target protein and the E3 ligase. The model is trained jointly with the RL approach to bias the generation of PROTAC structures toward pre-defined 2D and 3D based properties. Examples were provided to demonstrate the utility of the model for generating reasonable 3D conformation of PROTACs. On the other hand, our results show that the associated workflow for 3D PROTAC conformation generation can also be used as an efficient docking protocol for PROTACs.
A Multi-Conjugate Adaptive Optics view of Bulge Globular Clusters

The Galactic bulge is one of the most massive and inaccessible regions of the Galaxy, where thick clouds of dust along the line of sight almost totally absorb the optical light. Bulge globular clusters (GCs) are useful tools to constrain the properties of this system, as they share the same kinematics and chemical composition with bulge field stars. However, these systems have been widely excluded from large surveys due to the huge extinction, and they remain in many cases only poorly known so far. The aim of this Thesis is to contribute to fill this gap by studying the stellar populations and structural parameters of bulge GCs by means of state-of- the-art high resolution near-infrared (NIR) instruments. We exploited the capabilities offered by the Multi-Conjugate Adaptive Optics system GeMS combined with the GSAOI imager at the Gemini South Telescope, in Chile. We mainly focused on two extreme cases. The first is Liller 1, one of the most obscured and therefore least studied systems in the Galaxy. It probe the benefit of using an Adaptive Optics system on an 8 meter telescope. The second is NGC 6624, which is well-studied and therefore allows a detailed comparison and assessment of the GeMS+GSAOI performance. Based on these data we performed an in-depth characterization of the instruments in terms of Point Spread Function modeling, Strehl Ratio and Encircled Energy distribution variations within the field of view. We derived also the first analytic solution to correct the geometric distortions of this system. Taking advantage of the exquisite quality and depth of the NGC 6624 images, in combination with deep HST NIR observations of 47 Tucanae, we performed a detailed analysis aimed at probing reliability and limits of the so-called "MS-knee" as a tool to derive accurate absolute ages in NIR CMDs of GC.
A new species of Simulium (Asiosimulium) (Diptera: Simuliidae) from northeastern Thailand, with its phylogenetic relationships with related species in the subgenus Asiosimulium

Simulium (Asiosimulium) khongchiamense sp. nov. is described based on females, males, pupae, and mature larvae collected from Khong Chiam District, Ubon Ratchathani Province, northeastern Thailand. It is characterized in the female by the medium-long sensory vesicle, scutum with 3 dark longitudinal vittae and elongate cercus; in the male by the number of upper-eye (large) facets in 17 or 18 vertical rows and 18 or 19 horizontal rows, hind basitarsus moderately enlarged and ventral plate with the posterior margin moderately concave medially; in the pupa by the head and thoracic integument sparsely covered with tubercles and gill of arborescent type with 32 or 33 filaments; and in the larva by the postgenal cleft deep, reaching the posterior margin of the hypostoma and sheath of the subesophageal ganglion dark pigmented. DNA analysis based on COI gene of all known species of the subgenus Asiosimulium, except for S. shanense and S. suchitrae, indicated that this new species can be clearly differentiated from all other related species (S. phurueaense, S. oblongum, S. saeungae, S. furvum, and S. wanchaii) with interspecific genetic distances ranging between 4.79% and 19.18%. This is the eighth species of the subgenus Asiosimulium. Taxonomic notes are given to distinguish this new species from the 7 known species members in its same subgenus. Additionally, keys to species of all members in the subgenus Asiosimulium are provided.
Comparative study of conventional and synchrotron X-ray electron densities on molecular crystals

Four electron density quality single crystal X-ray diffraction datasets on molecular crystals of melamine were obtained from both state-of-the-art laboratory instruments (Rigaku Synergy with Ag Kα and Mo Kα, Stoe Stadivari with Mo Kα) and an older X-ray diffractometer (Oxford Diffraction Supernova with Mo Kα) and the data quality and electron density models were compared with results from a high-quality synchrotron dataset (SPring-8).
Reservoir Computing With Dynamic Reservoir using Cascaded DNA Memristors

This article proposes molecular and DNA memristors where the state is defined by a single output variable. In past molecular and DNA memristors, the state of the memristor was defined based on two output variables. These memristors cannot be cascaded because their input and output sizes are different. We introduce a different definition of state for the molecular and DNA memristors. This change allows cascading of memristors. The proposed memristors are used to build reservoir computing (RC) models that can process temporal inputs. An RC system consists of two parts: reservoir and readout layer. The first part projects the information from the input space into a high-dimensional feature space. We also study the input-state characteristics of the cascaded memristors and show that the cascaded memristors retain the memristive behavior. The cascade connections in a reservoir can change dynamically; this allows the synthesis of a dynamic reservoir as opposed to a static one in the prior work. This reduces the number of memristors significantly compared to a static reservoir. The inputs to the readout layer correspond to one molecule per state instead of two; this significantly reduces the number of molecular and DSD reactions for the readout layer. A DNA RC system consisting of DNA memristors and a DNA readout layer is used to detect seizures from intra-cranial electroencephalogram (iEEG). We also demonstrate that a DNA RC system consisting of three cascaded DNA memristors and a DNA readout layer can be used to solve the time-series prediction task. The proposed approach can reduce the number of DNA strand displacement (DSD) reactions by three to five times compared to prior approaches.
Organizational Context and Quality Indicators in Nursing Homes: A Microsystem Look

The association of organizational context with quality of care in nursing homes is not well understood at the clinical microsystem (care unit) level. This cross-sectional study examined the associations of unit-level context with 10 unit-level quality indicators derived from the Minimum Data Set 2.0. Study settings comprised 262 care units within 91 Canadian nursing homes. We assessed context using unit-aggregated care-aide-reported scores on the 10 scales of the Alberta Context Tool. Mixed-effects regression analysis showed that structural resources were negatively associated with antipsychotics use (B = −.06; p = .001) and worsened late-loss activities of daily living (B = −.03, p = .04). Organizational slack in time was negatively associated with worsened pain (B = −.04, p = .01). Social capital was positively associated with delirium symptoms (B = .12, p = .02) and worsened depressive symptoms (B = .10, p = .01). The findings suggested that targeting interventions to modifiable contextual elements and unit-level quality improvement will be promising.
Membrane Tension Inhibits Lipid Mixing by Increasing the Hemifusion Stalk Energy

Fusion of biological membranes is fundamental in various physiological events. The fusion process involves several intermediate stages with energy barriers that are tightly dependent on the mechanical and physical properties of the system, one of which is membrane tension. As previously established, the late stages of fusion, including hemifusion diaphragm and pore expansions, are favored by membrane tension. However, a current understanding of how the energy barrier of earlier fusion stages is affected by membrane tension is lacking. Here, we apply a newly developed experimental approach combining micropipette-aspirated giant unilamellar vesicles and optically trapped membrane-coated beads, revealing that membrane tension inhibits lipid mixing. We show that lipid mixing is 6 times slower under a tension of 0.12 mN/m compared with tension-free membranes. Furthermore, using continuum elastic theory, we calculate the dependence of the hemifusion stalk formation energy on membrane tension and intermembrane distance and find the increase in the corresponding energy barrier to be 1.6 kBT in our setting, which can explain the increase in lipid mixing time delay. Finally, we show that tension can be a significant factor in the stalk energy if the pre-fusion intermembrane distance is on the order of several nanometers, while for membranes that are tightly docked, tension has a negligible effect.
TET3-mediated DNA oxidation is essential for intestinal epithelial cell response to stressors

Significance Recent single-cell transcriptomic studies predicted TET enzymes, which are DNA dioxygenases capable of changing the epigenome, among risk genes in ulcerative colitis patients. However, the role of TET-mediated epigenetic reprogramming affecting intestinal epithelial physiology under conditions of homeostasis and in response to stressors remains largely unclear. Here, we described an essential role for TET3-mediated DNA production of 5hmC to protect intestinal epithelial integrity from pathogenic infection and chemical stressors by promoting the expression of genes involved in Notch and Wnt signaling pathways.
Mucosal antibody responses following Vaxzevria vaccination

Mucosal antibodies play a key role in protection against breakthrough COVID‐19 infections and emerging viral variants. Intramuscular adenovirus‐based vaccination (Vaxzevria) only weakly induces nasal IgG and IgA responses, unless vaccinees have been previously infected. However, little is known about how Vaxzevria vaccination impacts the ability of mucosal antibodies to induce Fc responses, particularly against SARS‐CoV‐2 variants of concern (VoCs). Here, we profiled paired mucosal (saliva, tears) and plasma antibodies from COVID‐19 vaccinated only vaccinees (uninfected, vaccinated) and COVID‐19 recovered vaccinees (COVID‐19 recovered, vaccinated) who both received Vaxzevria vaccines. SARS‐CoV‐2 ancestral‐specific IgG antibodies capable of engaging FcγR3a were significantly higher in the mucosal samples of COVID‐19 recovered Vaxzevria vaccinees in comparison with vaccinated only vaccinees. However, when IgG and FcγR3a engaging antibodies were tested against a panel of SARS‐CoV‐2 VoCs, the responses were ancestral‐centric with weaker recognition of Omicron strains observed. In contrast, salivary IgA, but not plasma IgA, from Vaxzevria vaccinees displayed broad cross‐reactivity across all SARS‐CoV‐2 VoCs tested. Our data highlight that while intramuscular Vaxzevria vaccination can enhance mucosal antibodies responses in COVID‐19 recovered vaccinees, restrictions by ancestral‐centric bias may have implications for COVID‐19 protection. However, highly cross‐reactive mucosal IgA could be key in addressing these gaps in mucosal immunity and may be an important focus of future SARS‐CoV‐2 vaccine development.
Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression

Depression is a common mental health condition that often occurs in association with other chronic illnesses, and varies considerably in severity. Electronic Health Records (EHRs) contain rich information about a patient's medical history and can be used to train, test and maintain predictive models to support and improve patient care. This work evaluated the feasibility of implementing an environment for predicting mental health crisis among people living with depression based on both structured and unstructured EHRs. A large EHR from a mental health provider, Mersey Care, was pseudonymised and ingested into the Natural Language Processing (NLP) platform CogStack, allowing text content in binary clinical notes to be extracted. All unstructured clinical notes and summaries were semantically annotated by MedCAT and BioYODIE NLP services. Cases of crisis in patients with depression were then identified. Random forest models, gradient boosting trees, and Long Short-Term Memory (LSTM) networks, with varying feature arrangement, were trained to predict the occurrence of crisis. The results showed that all the prediction models can use a combination of structured and unstructured EHR information to predict crisis in patients with depression with good and useful accuracy. The LSTM network that was trained on a modified dataset with only 1000 most-important features from the random forest model with temporality showed the best performance with a mean AUC of 0.901 and a standard deviation of 0.006 using a training dataset and a mean AUC of 0.810 and 0.01 using a hold-out test dataset. Comparing the results from the technical evaluation with the views of psychiatrists shows that there are now opportunities to refine and integrate such prediction models into pragmatic point-of-care clinical decision support tools for supporting mental healthcare delivery.
Changes in Health Care Costs, Survival, and Time Toxicity in the Era of Immunotherapy and Targeted Systemic Therapy for Melanoma

Key Points Question How have health care costs, survival, and time toxicity changed after the adoption of adjuvant and palliative immunotherapies and targeted therapies for melanoma? Findings This cohort study, which evaluated matched cohorts of 731 patients with melanoma, found a substantial increase in systemic therapy costs in 2018 to 2019 compared with 2007 to 2012. Survival improved for all stages in 2018 to 2019 compared with 2007 to 2012, and time toxicity was similar between eras. Meaning These data highlight the trade-off with new effective therapies, for which there are greater health care costs and time toxicity but an associated improvement in patient survival.
Improved functional portmanteau tests

ABSTRACT Functional time series is a popular method of forecasting in functional data analysis. The Box-Jenkins methodology for model building, with the aim of forecasting, includes three iterative steps of model identification, parameter estimation and diagnostic checking. Portmanteau tests are one of the most popular diagnostic checking tools. In particular, they are applied to find if the residuals of the fitted model are white noise. Gabrys and Kokoszka [Portmanteau test of independence for functional observations. J Am Stat Assoc. 2007;102(480):1338–1348. ] proposed a portmanteau test of independence for functional observation based on Box and Pierce's statistic. Their statistic is too sensitive to the lag value, specially when the sample size is small. Here, two modifications of Gabrys and Kokoszka statistic are presented, which have superior properties in small samples. The efficiency of the modified statistics is demonstrated through a simulation study.
Expert-Guided Knowledge Distillation for Semi-Supervised Vessel Segmentation

In medical image analysis, blood vessel segmentation is of considerable clinical value for diagnosis and surgery. The predicaments of complex vascular structures obstruct the development of the field. Despite many algorithms have emerged to get off the tight corners, they rely excessively on careful annotations for tubular vessel extraction. A practical solution is to excavate the feature information distribution from unlabeled data. This work proposes a novel semi-supervised vessel segmentation framework, named EXP-Net, to navigate through finite annotations. Based on the training mechanism of the Mean Teacher model, we innovatively engage an expert network in EXP-Net to enhance knowledge distillation. The expert network comprises knowledge and connectivity enhancement modules, which are respectively in charge of modeling feature relationships from global and detailed perspectives. In particular, the knowledge enhancement module leverages the vision transformer to highlight the long-range dependencies among multi-level token components; the connectivity enhancement module maximizes the properties of topology and geometry by skeletonizing the vessel in a non-parametric manner. The key components are dedicated to the conditions of weak vessel connectivity and poor pixel contrast. Extensive evaluations show that our EXP-Net achieves state-of-the-art performance on subcutaneous vessel, retinal vessel, and coronary artery segmentations.