Miracle-dz/UniFAR-FTUs huggingface dataset:
Dosimetric verification of IMPT using a commercial heterogeneous phantom

Abstract The purpose of this study was to propose a verification method and results of intensity‐modulated proton therapy (IMPT), using a commercially available heterogeneous phantom. We used a simple simulated head and neck and prostate phantom. An ionization chamber and radiochromic film were used for measurements of absolute dose and relative dose distribution. The measured doses were compared with calculated doses using a treatment planning system. We defined the uncertainty of the measurement point of the ionization chamber due to the effective point of the chamber and mechanical setup error as 2 mm and estimated the dose variation base on a 2 mm error. We prepared a HU‐relative stopping power conversion table and fluence correction factor that were specific to the heterogeneous phantom. The fluence correction factor was determined as a function of depth and was obtained from the ratio of the doses in water and in the phantom at the same effective depths. In the simulated prostate plan, composite doses of measurements and calculations agreed within ±1.3% and the maximum local dose differences of each field were 10.0%. Composite doses in the simulated head and neck plan agreed within 4.0% and the maximum local dose difference for each field was 12.0%. The dose difference for each field came within 2% when taking the measurement uncertainty into consideration. In the composite plan, the maximum dose uncertainty was estimated as 4.0% in the simulated prostate plan and 5.8% in the simulated head and neck plan. Film measurements showed good agreement, with more than 92.5% of points passing a gamma value (3%/3 mm). From these results, the heterogeneous phantom should be useful for verification of IMPT by using a phantom‐specific HU‐relative stopping power conversion, fluence correction factor, and dose error estimation due to the effective point of the chamber.
Structure–function relationship in the ‘termination upstream ribosomal binding site’ of the calicivirus rabbit hemorrhagic disease virus

Abstract Caliciviruses use a termination/reinitiation mechanism for translation of their minor capsid protein VP2. A sequence element of about 80 nucleotides denoted ‘termination upstream ribosomal binding site’ (TURBS) is crucial for reinitiation. RNA secondary structure probing and computer aided secondary structure prediction revealed a rather low degree of secondary structure determinants for the TURBS of the rabbit hermorrhagic disease virus. Mutation analysis showed that prevention of duplex formation had major impact on the VP2 expression levels. Restoration of complementarity of the respective sequences by reciprocal mutation at least partially restored reinitiating rates. Synthetic TURBS structures preserving only the secondary structure forming sequences and the known short motifs important for TURBS function were found to drive reinitiation when the altered sequence could be predicted to allow establishment of the crucial secondary structures of the TURBS.
The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG

Electroencephalographic (EEG) recordings are contaminated by instrumental, environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact detection is a critical task for real-time applications where the signal is used to give a continuous feedback to the user. In these applications, it is therefore necessary to estimate online a signal quality index (SQI) in order to stop the feedback when the signal quality is unacceptable. In this paper, we introduce the Riemannian potato field (RPF) algorithm as such SQI. It is a generalization and extensionof theRiemannian potato, a previouslypublished real-time artifact detection algorithm, whose performance is degraded as the number of channels increases. The RPF overcomes this limitation by combining the outputs of several smaller potatoes into a unique SQI resulting in a higher sensitivity and specificity, regardless of the number of electrodes. We demonstrate these results on a clinical dataset totalizing more than 2200 h of EEG recorded at home, that is, in a non-controlled environment.
Cognitive Deficits in Euthymic Patients With Bipolar Disorder: State or Trait Marker?

Abstract Cognitive deficits have been demonstrated in people in the euthymic phase of bipolar disorder. This cross-sectional study compared euthymic bipolar disorder patients (n = 30) with never psychiatrically ill controls (n = 30) on a neuropsychological test battery containing tasks of executive function, the Wisconsin Card Sorting Test (WCST), attention and working memory, digits forward and backward, and speed of information processing digit symbol. Scores on the Mini-Mental State Examination (MMSE) and vocabulary test did not differ between the groups. The bipolar group was significantly impaired compared with controls on various indices of executive function on the WCST and on the digit tests. The impaired performance on the digit tests, but not the WCST, was significantly associated with medication status, notably prescribed benzodiazepines. There was no significant effect of severity or course of illness on performance. The findings support the hypothesis that impairments in executive function are present between illness episodes in bipolar disorder, and so they are not simply state markers.
CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

Objective The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram. Materials and Methods This retrospective study was approved by the Institutional Review Board. Ten chest CT scans were performed and reconstructed with the B10f, B30f, B50f, and B70f kernels. The dataset was divided into six, two, and two examinations for training, validation, and testing, respectively. We constructed a CNN architecture consisting of six convolutional layers, each with a 3 × 3 kernel with 64 filter banks. Quantitative performance was evaluated using root mean square error (RMSE) values. To validate clinical use, image conversion was conducted on 30 additional chest CT scans reconstructed with the B30f and B50f kernels. The influence of image conversion on emphysema quantification was assessed with Bland–Altman plots. Results Our scheme rapidly generated conversion results at the rate of 0.065 s/slice. Substantial reduction in RMSE was observed in the converted images in comparison with the original images with different kernels (mean reduction, 65.7%; range, 29.5–82.2%). The mean emphysema indices for B30f, B50f, converted B30f, and converted B50f were 5.4 ± 7.2%, 15.3 ± 7.2%, 5.9 ± 7.3%, and 16.8 ± 7.5%, respectively. The 95% limits of agreement between B30f and other kernels (B50f and converted B30f) ranged from −14.1% to −2.6% (mean, −8.3%) and −2.3% to 0.7% (mean, −0.8%), respectively. Conclusion CNN-based CT kernel conversion shows adequate performance with high accuracy and speed, indicating its potential clinical use.
Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images

An efficient and precise liver extraction from computed tomography (CT) images is a crucial step for computer-aided hepatic diseases diagnosis and treatment. Considering the possible risk to patient's health due to X-ray radiation of repetitive CT examination, low-dose CT (LDCT) is an effective solution for medical imaging. However, inhomogeneous appearances and indistinct boundaries due to additional noise and streaks artifacts in LDCT images often make it a challenging task. This study aims to extract a liver model from LDCT images for facilitating medical expert in surgical planning and post-operative assessment along with low radiation risk to the patient. Our method carried out liver extraction by employing residual convolutional neural networks (LER-CN), which is further refined by noise removal and structure preservation components. After patch-based training, our LER-CN shows a competitive performance relative to state-of-the-art methods for both clinical and publicly available MICCAI Sliver07 datasets. We have proposed training and learning algorithms for LER-CN based on back propagation gradient descent. We have evaluated our method on 150 abdominal CT scans for liver extraction. LER-CN achieves dice similarity coefficient up to 96.5<inline-formula><tex-math notation="LaTeX">$\pm \text{1.8}\%$</tex-math></inline-formula>, decreased volumetric overlap error up to 4.30<inline-formula><tex-math notation="LaTeX">$\pm \text{0.58}\%$</tex-math></inline-formula>, and average symmetric surface distance less than 1.4 <inline-formula><tex-math notation="LaTeX">$\pm \text{0.5mm}$</tex-math></inline-formula>. These findings have shown that LER-CN is a favorable method for medical applications with high efficiency allowing low radiation risk to patients.
Weighted Extreme Sparse Classifier and Local Derivative Pattern for 3D Face Recognition

A novel weighted hybrid classifier and a high-order, local normal derivative pattern descriptor are proposed for 3D face recognition. The local derivative pattern (LDP) captures the detailed information based on the local derivative variation in different directions. The LDP is computed on three normal maps in <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-, <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>-, and <inline-formula> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula>-directions and on different scales. The surface normal captures the orientation of a surface at each point of 3D data. More informative local shape information is extracted using the surface normal, as compared to depth. The <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>th-order LDP on the surface normal is proposed to encode the more detailed features from the <inline-formula> <tex-math notation="LaTeX">$(n-1)$ </tex-math></inline-formula>th-order’s local derivative direction variations. An extreme learning machine (ELM)-based autoencoder, using a multilayer network structure, is employed to select more discriminant features and to provide a faster training speed. A weighted hybrid framework is proposed to handle facial challenges using a combination of the ELM and the sparse representation classifier (SRC). The advantage of speed for the ELM and the accuracy for the SRC in a weighted scheme is used to enhance the performance of the recognition system. Experimental results regarding four famous 3D face databases illustrate the generalization and effectiveness of the proposed method in terms of both computational cost and recognition accuracy.
CRISPR/Cas9-mediated knockout of CD47 causes hemolytic anemia with splenomegaly in C57BL/6 mice

CD47 (integrin-associated protein), a multi-spanning transmembrane protein expressed in all cells including red blood cells (RBCs) and leukocytes, interacts with signal regulatory protein α (SIRPα) on macrophages and thereby inhibits phagocytosis of RBCs. Recently, we generated a novel C57BL/6J CD47 knockout (CD47−/− hereafter) mouse line by employing a CRISPR/Cas9 system at Center for Mouse Models of Human Disease, and here report their hematological phenotypes. On monitoring their birth and development, CD47−/− mice were born viable with a natural male-to-female sex ratio and normally developed from birth through puberty to adulthood without noticeable changes in growth, food/water intake compared to their age and sex-matched wild-type littermates up to 26 weeks. Hematological analysis revealed a mild but significant reduction of RBC counts and hemoglobin in 16 week-old male CD47−/− mice which were aggravated at the age of 26 weeks with increased reticulocyte counts and mean corpuscular volume (MCV), suggesting hemolytic anemia. Interestingly, anemia in female CD47−/− mice became evident at 26 weeks, but splenomegaly was identified in both genders of CD47−/− mice from the age of 16 weeks, consistent with development of hemolytic anemia. Additionally, helper and cytotoxic T cell populations were considerably reduced in the spleen, but not in thymus, of CD47−/− mice, suggesting a crucial role of CD47 in proliferation of T cells. Collectively, these findings indicate that our CD47−/− mice have progressive hemolytic anemia and splenic depletion of mature T cell populations and therefore may be useful as an in vivo model to study the function of CD47.
Conceptually Rich, Perceptually Sparse: Object Representations in 6-Month-Old Infants’ Working Memory

Six-month-old infants can store representations of multiple objects in working memory but do not always remember the objects’ features (e.g., shape). Here, we asked whether infants’ object representations (a) may contain conceptual content and (b) may contain this content even if perceptual features are forgotten. We hid two conceptually distinct objects (a humanlike doll and a nonhuman ball) one at a time in two separate locations and then tested infants’ memory for the first-hidden object by revealing either the original hidden object or an unexpected other object. Using looking time, we found that infants remembered the categorical identity of the hidden object but failed to remember its perceptual identity. Our results suggest that young infants may encode conceptual category in a representation of an occluded object, even when perceptual features are lost.
Body Fat Percentage, Body Mass Index, Fat Mass Index and the Ageing Bone: Their Singular and Combined Roles Linked to Physical Activity and Diet

This study took a multi-analytical approach including group differences, correlations and unit-weighed directional z-score comparisons to identify the key mediators of bone health. A total of 190 participants (18–80 years) were categorized by body fat%, body mass index (BMI) and fat mass index (FMI) to examine the effect of differing obesity criteria on bone characteristics. A subset of 50 healthy-eating middle-to-older aged adults (44–80 years) was randomly selected to examine any added impact of lifestyle and inflammatory profiles. Diet was assessed using a 3-day food diary, bone mineral density (BMD) and content (BMC) by dual energy x-ray absorptiometry in the lumbar, thoracic, (upper and lower) appendicular and pelvic areas. Physical activity was assessed using the Baecke questionnaire, and endocrine profiling was assessed using multiplex luminometry. Obesity, classed via BMI, positively affected 20 out of 22 BMC- and BMD-related outcome measures, whereas FMI was associated with 14 outcome measures and adiposity only modulated nine out of 22 BMC- and BMD-related outcome measures. Whilst bivariate correlations only linked vitamin A and relative protein intake with BMD, the Z-score composite summary presented a significantly different overall dietary quality between healthy and osteopenic individuals. In addition, bivariate correlations from the subset revealed daily energy intake, sport-based physical activity and BMI positive mediators of seven out of 10 BMD sites with age and body fat% shown to be negative mediators of bone characteristics. In conclusion, whilst BMI is a good indicator of bone characteristics, high body fat% should also be the focus of osteoporosis risk with ageing. Interestingly, high BMI in conjunction with moderate to vigorous activity supplemented with an optimal diet (quality and quantity) are identified as positive modulators of bone heath.
Measuring Ultra-Weak Protein Self-Association by Non-ideal Sedimentation Velocity

Ultra-weak self-association can govern the macroscopic solution behavior of concentrated macromolecular solutions ranging from food products to pharmaceutical formulations and the cytosol. For example, it can promote dynamic assembly of multi-protein signaling complexes, lead to intracellular liquid–liquid phase transitions, and seed crystallization or pathological aggregates. Unfortunately, weak self-association is technically extremely difficult to study, as it requires very high protein concentrations where short intermolecular distances cause strongly correlated particle motion. Additionally, protein samples near their solubility limit in vitro frequently show some degree of polydispersity. Here we exploit the strong mass-dependent separation of assemblies in the centrifugal field to study ultra-weak binding, using a sedimentation velocity technique that allows us to determine particle size distributions while accounting for colloidal hydrodynamic interactions and thermodynamic non-ideality (Chaturvedi, S. K.; et al. Nat. Commun.2018, 9, 4415; DOI: 10.1038/s41467-018-06902-x). We show that this approach, applied to self-associating proteins, can reveal a time-average association state for rapidly reversible self-associations from which the free energy of binding can be derived. The method is label-free and allows studying mid-sized proteins at millimolar protein concentrations in a wide range of solution conditions. We examine the performance of this method with hen egg lysozyme as a model system, reproducing its well-known ionic-strength-dependent weak self-association. The application to chicken γS-crystallin reveals weak monomer–dimer self-association with KD = 24 mM, corresponding to a standard free energy change of approximately −9 kJ/mol, which is a large contribution to the delicate balance of forces ensuring eye lens transparency.
Blood–brain barrier pericytes as a target for HIV-1 infection

Blood-brain barrier (BBB) pericytes regulate several vital functions of the neurovascular unit. Bertrand et al. argue that BBB pericytes are a previously unrecognized HIV target and reservoir, with HIV infection of BBB pericytes confirmed in cell cultures, a mouse model of HIV infection, and in human HIV-infected brains.
Antipsychotic drugs scavenge radiation-induced hydroxyl radicals and intracellular ROS formation, and protect apoptosis in human lymphoma U937 cells

Abstract Antioxidant activity has been reported for some atypical antipsychotic drugs; however, the detailed mechanism is not well known. Here, we investigated the effects of atypical antipsychotic drugs on •OH radical formation, intracellular reactive oxygen species (ROS), and apoptosis induced by ionising radiation. The reaction rate constants with •OH radicals were determined for five antipsychotic drugs as follows, in descending order: olanzapine, aripiprazole, clozapine, haloperidol, and risperidone. Experiments with aminophenyl fluorescein, a fluorescent dye, showed that olanzapine and clozapine could scavenge intracellular ROS. However, experiments with hydroxyphenyl fluorescein showed that only olanzapine inhibited ROS generation. X-irradiation-induced apoptosis in human lymphoma U937 cells was inhibited by clozapine at relatively low concentrations and by olanzapine at higher concentrations. Clozapine inhibited caspase-8 and caspase-3 activation and prevented loss of mitochondrial membrane potential. In contrast, olanzapine inhibited X-irradiation-induced p-JNK activation. Although the atypical antipsychotic drugs used here have relatively high reaction rate constants with •OH radicals in aqueous solutions, inhibition of intracellular ROS was not due to •OH radical scavenging. In addition, suppression of X-irradiation-induced apoptosis was not directly linked with intracellular ROS scavenging. When apoptosis signalling pathways were studied, clozapine-mediated inhibition of apoptosis was dependent on caspase-3 and caspase-8. In contrast, olanzapine inhibited apoptosis via down regulation of X-irradiation-induced p-JNK. These results suggested that both olanzapine and clozapine have antioxidative and antiapoptotic activities via distinct pathways, and provide useful information for better understanding of drug characteristics.
Inhibition of Yeast-to-Hypha Transition and Virulence of Candida albicans by 2-Alkylaminoquinoline Derivatives

A rapid increase in Candida albicans infection and drug resistance has caused an emergent need for new clinical strategies against this fungal pathogen. In this study, we evaluated the inhibitory activity of a series of 2-alkylaminoquinoline derivatives against C. albicans isolates. ABSTRACT A rapid increase in Candida albicans infection and drug resistance has caused an emergent need for new clinical strategies against this fungal pathogen. In this study, we evaluated the inhibitory activity of a series of 2-alkylaminoquinoline derivatives against C. albicans isolates. A total of 28 compounds were assessed for their efficacy in inhibiting the yeast-to-hypha transition, which is considered one of the key virulence factors in C. albicans. Several compounds showed strong activity to decrease the morphological transition and virulence of C. albicans cells. The two leading compounds, compound 1 (2-[piperidin-1-yl]quinolone) and compound 12 (6-methyl-2-[piperidin-1-yl]quinoline), remarkably attenuated C. albicans hyphal formation and cytotoxicity in a dose-dependent manner, but they showed no toxicity to either C. albicans cells or human cells. Intriguingly, compound 12 showed an excellent ability to inhibit C. albicans infection in the mouse oral mucosal infection model. This leading compound also interfered with the expression levels of hypha-specific genes in the cyclic AMP-protein kinase A and mitogen-activated protein kinase signaling pathways. Our findings suggest that 2-alkylaminoquinoline derivatives could potentially be developed as novel therapeutic agents against C. albicans infection due to their interference with the yeast-to-hypha transition.
Shared Multi-View Data Representation for Multi-Domain Event Detection

Internet platforms provide new ways for people to share experiences, generating massive amounts of data related to various real-world concepts. In this paper, we present an event detection framework to discover real-world events from multiple data domains, including online news media and social media. As multi-domain data possess multiple data views that are heterogeneous, initial dictionaries consisting of labeled data samples are exploited to align the multi-view data. Furthermore, a shared multi-view data representation (SMDR) model is devised, which learns underlying and intrinsic structures shared among the data views by considering the structures underlying the data, data variations, and informativeness of dictionaries. SMDR incorpvarious constraints in the objective function, including shared representation, low-rank, local invariance, reconstruction error, and dictionary independence constraints. Given the data representations achieved by SMDR, class-wise residual models are designed to discover the events underlying the data based on the reconstruction residuals. Extensive experiments conducted on two real-world event detection datasets, i.e., Multi-domain and Multi-modality Event Detection dataset, and MediaEval Social Event Detection 2014 dataset, indicating the effectiveness of the proposed approaches.