BoostNet: Bootstrapping detection of socialbots, and a case study from Guatemala

We present a method to reconstruct networks of socialbots given minimal input. Then we use Kernel Density Estimates of Botometer scores from 47,000 social networking accounts to find clusters of automated accounts, discovering over 5,000 socialbots. This statistical and data driven approach allows for inference of thresholds for socialbot detection, as illustrated in a case study we present from Guatemala.Comment: 7 pages, 4 figure

PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of Multiple Structures on Temporal Bone CT Scans

The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among critical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based pipeline as the first fully automated algorithm for segmenting multiple temporal bone structures from CT volumes, including the sigmoid sinus, facial nerve, inner ear, malleus, incus, stapes, internal carotid artery and internal auditory canal. The proposed fully convolutional network, PWD-3DNet, is a patch-wise densely connected (PWD) three-dimensional (3D) network. The accuracy and speed of the proposed algorithm was shown to surpass current manual and semi-automated segmentation techniques. The experimental results yielded significantly high Dice similarity scores and low Hausdorff distances for all temporal bone structures with an average of 86% and 0.755 millimeter (mm), respectively. We illustrated that overlapping in the inference sub-volumes improves the segmentation performance. Moreover, we proposed augmentation layers by using samples with various transformations and image artefacts to increase the robustness of PWD-3DNet against image acquisition protocols, such as smoothing caused by soft tissue scanner settings and larger voxel sizes used for radiation reduction. The proposed algorithm was tested on low-resolution CTs acquired by another center with different scanner parameters than the ones used to create the algorithm and shows potential for application beyond the particular training data used in the study

Hydrophobicity drives the systemic distribution of lipid-conjugated siRNAs via lipid transport pathways

Efficient delivery of therapeutic RNA beyond the liver is the fundamental obstacle preventing its clinical utility. Lipid conjugation increases plasma half-life and enhances tissue accumulation and cellular uptake of small interfering RNAs (siRNAs). However, the mechanism relating lipid hydrophobicity, structure, and siRNA pharmacokinetics is unclear. Here, using a diverse panel of biologically occurring lipids, we show that lipid conjugation directly modulates siRNA hydrophobicity. When administered in vivo, highly hydrophobic lipid-siRNAs preferentially and spontaneously associate with circulating low-density lipoprotein (LDL), while less lipophilic lipid-siRNAs bind to high-density lipoprotein (HDL). Lipid-siRNAs are targeted to lipoprotein receptor-enriched tissues, eliciting significant mRNA silencing in liver (65%), adrenal gland (37%), ovary (35%), and kidney (78%). Interestingly, siRNA internalization may not be completely driven by lipoprotein endocytosis, but the extent of siRNA phosphorothioate modifications may also be a factor. Although biomimetic lipoprotein nanoparticles have been explored for the enhancement of siRNA delivery, our findings suggest that hydrophobic modifications can be leveraged to incorporate therapeutic siRNA into endogenous lipid transport pathways without the requirement for synthetic formulation

Higher Adherence to the Mediterranean Dietary Pattern Is Inversely Associated With Severity of COVID-19 and Related Symptoms: A Cross-Sectional Study

Background and AimsAdherence to the Mediterranean diet (MD) has been associated with a decreased risk of developing a variety of chronic diseases that are comorbidities in COVID-19 patients. However, its association to the severity and symptoms of COVID-19 are still unknown. This study aimed to examine the association between adherence to the MD pattern and COVID-19 severity and symptoms in Iranian hospitalized patients.MethodsIn this cross-sectional study, 250 COVID-19 patients aged 18 to 65 were examined. We employed a food frequency questionnaire (FFQ) to obtain data on dietary intake of participants in the year prior to their COVID-19 diagnosis. COVID-19 severity was determined using the National Institutes of Health's Coronavirus Disease 2019 report. Additionally, symptoms associated with COVID-19, inflammatory markers, and other variables were evaluated. The scoring method proposed by Trichopoulou et al. was used to assess adherence to the MD.ResultsThe participants' mean age was 44.1 ± 12.1 years, and 46% of them had severe COVID-19. Patients who adhered more closely to the MD had lower serum C-reactive protein levels (7.80 vs. 37.36 mg/l) and erythrocyte sedimentation rate (14.08 vs. 42.65 mm/h). Those with the highest MD score were 77% less likely to have severe COVID-19 after controlling for confounding variables. The MD score was also found to be inversely associated with COVID-19 symptoms, including dyspnea, cough, fever, chills, weakness, myalgia, nausea and vomiting, and sore throat.ConclusionHigher adherence to the MD was associated with a decreased likelihood of COVID-19 severity and symptoms, as well as a shorter duration of hospitalization and convalescence, and inflammatory biomarkers

Small-molecule-induced DNA damage identifies alternative DNA structures in human genes.

Guanine-rich DNA sequences that can adopt non-Watson-Crick structures in vitro are prevalent in the human genome. Whether such structures normally exist in mammalian cells has, however, been the subject of active research for decades. Here we show that the G-quadruplex-interacting drug pyridostatin promotes growth arrest in human cancer cells by inducing replication- and transcription-dependent DNA damage. A chromatin immunoprecipitation sequencing analysis of the DNA damage marker γH2AX provided the genome-wide distribution of pyridostatin-induced sites of damage and revealed that pyridostatin targets gene bodies containing clusters of sequences with a propensity for G-quadruplex formation. As a result, pyridostatin modulated the expression of these genes, including the proto-oncogene SRC. We observed that pyridostatin reduced SRC protein abundance and SRC-dependent cellular motility in human breast cancer cells, validating SRC as a target of this drug. Our unbiased approach to define genomic sites of action for a drug establishes a framework for discovering functional DNA-drug interactions

Comparison of partially and fully chemically-modified siRNA in conjugate-mediated delivery in vivo

Abstract Small interfering RNA (siRNA)-based drugs require chemical modifications or formulation to promote stability, minimize innate immunity, and enable delivery to target tissues. Partially modified siRNAs (up to 70% of the nucleotides) provide significant stabilization in vitro and are commercially available; thus are commonly used to evaluate efficacy of bio-conjugates for in vivo delivery. In contrast, most clinically-advanced non-formulated compounds, using conjugation as a delivery strategy, are fully chemically modified (100% of nucleotides). Here, we compare partially and fully chemically modified siRNAs in conjugate mediated delivery. We show that fully modified siRNAs are retained at 100x greater levels in various tissues, independently of the nature of the conjugate or siRNA sequence, and support productive mRNA silencing. Thus, fully chemically stabilized siRNAs may provide a better platform to identify novel moieties (peptides, aptamers, small molecules) for targeted RNAi delivery

Investigating the effects of nurse post-discharge follow-up phone calls on the self-efficacy of patients caregivers suffering from stroke

Introduction: Caring fora person suffering from a chronic illness creates a lot of stress for the caregiver and family of the patient. Self-efficacy is a tool to improve health and training to the caregiver; promoting self-efficacy is a significant procedure in patient care and behavioral modification. The purpose of this study was to determine the effectiveness of follow-up calls by nurses on the self-efficacy of stroke patients'caregivers. Methodology: In this research, a clinical trial study was carried out using pre-test and post-test with the participation of 70 stroke patients' caregivers. They have selected through simple sampling and were randomly assigned to control and intervention groups. Demographic information questionnaire and Sherer general self-efficacy scale were selected as tools for collecting data. Data were analyzed through independent t-test, paired t-test and Chi-Square using SPSS software version 21. Results: In the present study, the mean age of caregivers was 36.48 +/- 10.44, and the majority of caregivers were patients' children. There was no significant difference in mean score of self-efficacy between the two groups before the intervention (P >= 0.05), but after the intervention, a significant difference (P <=.05) was observed following an increase in the mean score of self-efficacy in the intervention group. The comparison of self-efficacy score in the intervention group before and after the study demonstrated that the mean score of self-efficacy increased to 63.87 +/- 8.79 before the intervention and 68.43 +/- 7.17 after the intervention. This increase was statistically significant (P = .001). Conclusion: According to the results of the current study, it can be concluded that the nurse's follow-up phone call program resulted in the self-efficacy of caregivers of patients suffering from stroke

Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks

Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study, two separate sets of strawberry images, along with their associated diseases, were collected and subjected to resizing and augmentation. Subsequently, a combined dataset consisting of nine classes was utilized to fine-tune three distinct pretrained models: vision transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first five and last five layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the vision transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population

Refining 3-D object models constructed from multiple FS sonar images by space carving

A sequential space carving method has been developed for the 3-D reconstruction of objects from multitude of forward-look sonar images captured at known sonar poses [1]. The 3-D space within common viewing volume of several camera poses is divided into small volumetric pixels (voxels). Projecting each onto various images, all voxels satisfying certain consistency measure are maintained. Conversely, voxels violating the consistency measure are carved out as inadmissible. The 3-D space comprising of all admissible voxels is taken as the potential object interior. Finally, the boundary of this volume defines the object surfaces. The method applying a binary admissibility criteria does not make use of image intensity values from any one of multitude of available sonar views. Here, the refinement of generated 3-D surface model is explored based on optimization formulations that minimize the discrepancy between the sonar intensity measurements and predicted values by the sonar image formation model. Comparison among various formulations leads to a selected one with desirable properties for improving the surface model