Miracle-dz/UniFAR-FTUs huggingface dataset:
A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma

Objective. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dose imposed by high-resolution micro cone-beam CT. Approach. A novel deep-learning approach is developed to enable BLT-based tumor targeting and treatment planning for orthotopic rat GBM models. The proposed framework is trained and validated on a set of realistic Monte Carlo simulations. Finally, the trained deep learning model is tested on a limited set of BLI measurements of real rat GBM models. Significance. Bioluminescence imaging (BLI) is a 2D non-invasive optical imaging modality geared toward preclinical cancer research. It can be used to monitor tumor growth in small animal tumor models effectively and without radiation burden. However, the current state-of-the-art does not allow accurate radiation treatment planning using BLI, hence limiting BLI’s value in preclinical radiobiology research. Results. The proposed solution can achieve sub-millimeter targeting accuracy on the simulated dataset, with a median dice similarity coefficient (DSC) of 61%. The provided BLT-based planning volume achieves a median encapsulation of more than 97% of the tumor while keeping the median geometrical brain coverage below 4.2%. For the real BLI measurements, the proposed solution provided median geometrical tumor coverage of 95% and a median DSC of 42%. Dose planning using a dedicated small animal treatment planning system indicated good BLT-based treatment planning accuracy compared to ground-truth CT-based planning, where dose-volume metrics for the tumor fall within the limit of agreement for more than 95% of cases. Conclusion. The combination of flexibility, accuracy, and speed of the deep learning solutions make them a viable option for the BLT reconstruction problem and can provide BLT-based tumor targeting for the rat GBM models.
Molecular detection, typing, and virulence potential of Salmonella Serotypes isolated from poultry feeds.

Salmonella contamination in poultry feed is one of the main issues in poultry industry and public health. The aim of the present study was molecular detection and typing of Salmonella serotypes isolated from poultry feeds. Moreover, we determined the antibiotic resistance pattern and the ability of biofilm formation in the serotypes. To this end, eighty feed samples were collected from aviculture depots. Salmonella serotypes were identified by culture and PCR methods. For serological identification, a slide agglutination test was used. BOXAIR and rep-PCR methods were applied to evaluate the diversity of serotypes. The disc diffusion method was performed to evaluate the antibiotic susceptibility of serotypes to sixteen antibiotics. Biofilm formation was also assessed by the microtiter-plate test. From a total of 80 feed samples, 30 samples were contaminated with Salmonella spp. , which were divided into 5 different serotypes belonging to B, C, and D serogroups. BOXAIR-PCR (D value [DI] 0.985) and rep-PCR (DI 0.991) fingerprinting of isolates revealed 23 and 19 reproducible fingerprint patterns, respectively. A higher antibiotic resistance was observed to ampicillin and doxycycline (100% each), followed by chloramphenicol (83.33%) and tetracycline (73.33%). Multidrug resistance (MDR) was detected in all Salmonella serotypes. Half of the serotypes possessed the ability of biofilm formation with varied adhesion strengths. These results revealed the high and unexpected prevalence of Salmonella serotypes in poultry feed with MDR and biofilm formation ability. BOXAIR and rep-PCR revealed a high diversity of Salmonella serotypes in feeds and subsequently indicated variation in the source of Salmonella spp. The unknown sources harboring high diversity of Salmonella serotypes indicated poor control, which could cause problems for feed manufacturing.
Gender congruence and emotion effects in cross-modal associative learning: Insights from ERPs and pupillary responses.

Social and emotional cues from faces and voices are highly relevant and have been reliably demonstrated to attract attention involuntarily. However, there are mixed findings as to which degree associating emotional valence to faces occurs automatically. In the present study, we tested whether inherently neutral faces gain additional relevance by being conditioned with either positive, negative, or neutral vocal affect bursts. During learning, participants performed a gender-matching task on face-voice pairs without explicit emotion judgments of the voices. In the test session on a subsequent day, only the previously associated faces were presented and had to be categorized regarding gender. We analyzed event-related potentials (ERPs), pupil diameter, and response times (RTs) of N = 32 subjects. Emotion effects were found in auditory ERPs and RTs during the learning session, suggesting that task-irrelevant emotion was automatically processed. However, ERPs time-locked to the conditioned faces were mainly modulated by the task-relevant information, that is, the gender congruence of the face and voice, but not by emotion. Importantly, these ERP and RT effects of learned congruence were not limited to learning but extended to the test session, that is, after removing the auditory stimuli. These findings indicate successful associative learning in our paradigm, but it did not extend to the task-irrelevant dimension of emotional relevance. Therefore, cross-modal associations of emotional relevance may not be completely automatic, even though the emotion was processed in the voice.
Diabetes Prevalence and Duration Data Extracted from Outpatient Records Representative for the Bulgarian Population

This paper presents new data about diabetes prevalence and illness duration from a population of patients with diabetes Type 1(43,818) and Type 2 (457,247). Unlike the usual approach that employs adjusted estimates in similar prevalence reports, this study extracts data from a large number of original clinical documents such as all the outpatient records (6,887,876) issued in Bulgaria to all the 501,065 patients with diabetics during 2018 (9.77% of all the 5,128,172 patients recorded in 2018, 4.43% male and 5.35% female). Diabetes prevalence data are described in terms of distributions of Type 1 and Type 2 diabetes over age and gender. It is mapped to an Observational Medical Outcomes Partnership Common Data Model that is publicly available. The distribution of Type 2 diabetics agrees with peak values of BMI established in related research. A major novelty in this research are the data about the diabetes illness duration. It is an essential metric for evaluating the quality of processes evolving over time. Accurate estimates are obtained for the duration measured in years of Type 1 (95% CI 10.92, 11.08) and Type 2 (95% CI 7.97, 8.02) diabetics from the Bulgarian population. The patients with diabetes Type 1 have longer diabetes duration relatively to those with Type 2. It is recommended to include this metric in official diabetes prevalence reports.
SVJedi-graph: improving the genotyping of close and overlapping structural variants with long reads using a variation graph

Abstract Motivation Structural variation (SV) is a class of genetic diversity whose importance is increasingly revealed by genome resequencing, especially with long-read technologies. One crucial problem when analyzing and comparing SVs in several individuals is their accurate genotyping, that is determining whether a described SV is present or absent in one sequenced individual, and if present, in how many copies. There are only a few methods dedicated to SV genotyping with long-read data, and all either suffer of a bias toward the reference allele by not representing equally all alleles, or have difficulties genotyping close or overlapping SVs due to a linear representation of the alleles. Results We present SVJedi-graph, a novel method for SV genotyping that relies on a variation graph to represent in a single data structure all alleles of a set of SVs. The long reads are mapped on the variation graph and the resulting alignments that cover allele-specific edges in the graph are used to estimate the most likely genotype for each SV. Running SVJedi-graph on simulated sets of close and overlapping deletions showed that this graph model prevents the bias toward the reference alleles and allows maintaining high genotyping accuracy whatever the SV proximity, contrary to other state of the art genotypers. On the human gold standard HG002 dataset, SVJedi-graph obtained the best performances, genotyping 99.5% of the high confidence SV callset with an accuracy of 95% in less than 30 min. Availability and implementation SVJedi-graph is distributed under an AGPL license and available on GitHub at https://github.com/SandraLouise/SVJedi-graph and as a BioConda package.
Surface-Wave Dispersion Approach for Evaluating Statistical Models That Account for Shear-Wave Velocity Uncertainty

AbstractA number of strategies exist to account for the epistemic uncertainty and aleatory variability in shear-wave velocity (Vs) profiles used in site response analyses. Epistemic uncertainty may be accounted for by using median and bounding-type profiles (e.g., ±20%), while aleatory variability may be accounted for by using Vs randomization procedures. A robust, quantitative method to help judge how well these statistically derived Vs profiles represent actual subsurface stiffness or layering conditions is currently not available. This paper presents a surface-wave dispersion approach for evaluating statistical models meant to account for Vs uncertainty in site response. Specifically, surface-wave dispersion data from two geologically disparate sites were used to generate 1,000 Vs profiles and layered earth models whose theoretical dispersion curves fit within the uncertainty bounds of the experimental data collected at each site. Additionally, theoretical dispersion curves generated from statistical .
Soluble CD163 predicts outcome in both chemoimmunotherapy and targeted therapy–treated mantle cell lymphoma

Key Points • High level of the soluble macrophage marker CD163 is associated with shorter PFS and OS in MCL. • Soluble CD163 is prognostic in both rituximab-chemotherapy–treated and rituximab, ibrutinib and lenalidomide–treated MCL.
Supplementation with Sodium Butyrate Modulates the Composition of the Gut Microbiota and Ameliorates High-Fat Diet-Induced Obesity in Mice.

BACKGROUND Short-chain fatty acids (SCFAs) have been reported to ameliorate obesity. However, the underlying mechanisms require further investigation. OBJECTIVE The aim of this study was to determine the role of butyrate, an SCFA, in the regulation of obesity, low-grade chronic inflammation, and alterations of microbiota composition in mice. METHODS Male C57BL/6J mice, 4-5 wk of age, were divided into 3 groups (n = 8 mice/group): low-fat diet (LFD; 10% energy from fat), high-fat diet (HFD; 45% energy from fat), or high-fat diet plus sodium butyrate (HSB). HSB mice received sodium butyrate at a concentration of 0.1 M in drinking water for 12 wk. Measures of inflammation, obesity, and intestinal integrity were assessed. Serum lipopolysaccharide (LPS) concentrations were measured in the 3 groups. Fecal samples were collected for gut microbiota analysis. RESULTS In HFD mice, body weight gain and hepatic triglyceride (TG), serum interleukin-6 (IL-6), and serum tumor necrosis factor (TNF)-α levels were 1-4 times higher than those in LFD mice (P < 0.05); they were 34-42% lower in HSB mice compared with HFD mice (P < 0.05). The HFD group had 28%-48% lower mRNA expression of both Tjp1 and Ocln in the ileum and colon compared with levels in LFD or HSB mice (P < 0.05), whereas there was no difference in expression levels between LFD and HSB mice. Furthermore, in HSB mice, serum LPS concentration was 53% lower compared with that in HFD mice but still 23% higher than that in LFD mice (P < 0.05). Results from principal component analysis showed that HSB and LFD mice had a similar gut microbiota structure, which was significantly different from that in HFD mice (P < 0.05). CONCLUSIONS Sodium butyrate administration beneficially changed HFD-induced gut microbiota composition and improved intestinal barrier, leading to lower serum LPS concentrations. These changes may correspond with improvements in obesity-related lipid accumulation and low-grade chronic inflammation.
Modeling linear and nonlinear relationship between temperature and development rate of Amblyseuis swirskii (Acari: Phytoseiidae)

Abstract In recent years, the utilization of biocontrol agents to administer agriculture pests has received more attention, which has conduce to an growth in companies generating biocontrol agents, including predators and parasitoids. Amblyseius swirskii Athias-Henriot as a part of predatory communities in greenhouses is currently used worldwide as a biocontrol agent against small insects and various mites, especially to suppress the population of the two-spotted spider mite (TSSM), Tetranychus urticae Koch. To improve mass rearing of A. swirskii and optimize its application in integrated pest management programs, its development rate was determined at seven constant temperatures ranging from 15 to 34 (±1)°C, 50±10% RH and a photoperiod of 16:8 (L:D) h under laboratory conditions. None of the eggs hatched at 34°C and no development was observed. To determine the lower temperature threshold (T0) and thermal constant (K) of different stages of the predator, two linear models (ordinary and Ikemoto) were used. In addition, 26 nonlinear models were fitted to evaluate the development rate at different temperatures. The lower temperature threshold (T0) and thermal constant (K) of total immature stages were estimated by the ordinary (3.72°C and 133.22 DD) and Ikemoto (10.64°C and 86.51DD) linear models. Based on the Akaike information criterion (AIC), the best model for the description of the temperature-dependent development rate of the egg, larval, protonymphal, and dutonymphal stages was the Ratkowsky model and for the whole pre-adult stage, it was the Logan-6 model. Our results provided a detailed evaluation of the thermal requirements of A. swirskii, which can be important in improving the role of this mite in biological control programs.
Side effects of insecticides alone or in a mixture with acaricide on life history traits and demographic parameters of Brevipalpus yothersi

Abstract Brevipalpus yothersi is an important pest to citrus crop due to its ability to transmit the virus that causes citrus leprosis. The exposure of target and non-target organisms to different pesticides is a reality in field conditions. For successful management, it is essential to determine the effects of citrus commonly used pesticides on leprosis mites to determine how pesticides can interfere with the leprosis mite population. Therefore, this study aimed to evaluate the impact of insecticides, alone or associated with spirodiclofen, on B. yothersi life history traits and demographic parameters under laboratory and greenhouse conditions. Results suggest that cypermethrin and imidacloprid, both alone and in a mixture with spirodiclofen, phosmet in a mix with spirodiclofen and spirodiclofen alone did not interfere significantly in the fecundity and fertility of B. yothersi in laboratory conditions. In the same condition, only phosmet at 15.6, 125.0, and 250.0 mg.L-1 reduced the fecundity of the mites. Brevipalpus yothersi population varied with the insecticide and mixture type in the greenhouse condition. Most insecticides and acaricide, alone or in a mixture, decreased pre-adult development, whereas female adult longevity was similar to control. Spirodiclofen combined with phosmet and imidacloprid decreased female adult longevity. Only spirodiclofen in a mixture with cypermethrin and phosmet alone did not interfere significantly with the intrinsic rate of population increase. The other insecticides and mixtures reduced that parameter. The mean time of one mite generation was decreased when the mites were exposed to all insecticides and acaricide combinations, spirodiclofen and phosmet alone. Therefore, cypermethrin, phosmet, imidacloprid, and spirodiclofen, alone or in a mixture, negatively impacted the development time and demographic parameters of B. yothersi.
Using automated vehicle positioning to improve efficiency in vehicle platooning

Vehicle platooning allows a follower vehicle to reduce energy consumption by following closely behind a cooperative lead vehicle. In a previous work, we described a lidar-based vehicle detection and tracking approach that measures the extents of the back surface of the lead vehicle and allows for automated lane level positioning. In this work, we introduce a follower vehicle energy model and compare the performance of a vehicle centering strategy based on rear backplane geometry as opposed to center of mass and bounding box approaches. Energy efficiency improvements are discussed with respect to the computational complexity of each approach.
Global context-aware feature modulation networks for unified multi-scale super-resolution

Abstract. Compared with training multiple single-scale models to achieve different scale image super-resolution (SR), using the internal correlation of multiple scales to aggregate multi-scale learning into a single model can effectively reduce the network parameters and improve efficiency. Correspondingly, it is important to study how to take the advantage of inter-scale correlation at a fixed model size for the performance improvement of multi-scale SR. We combine the capture of richer context dependencies and convolutional response modulation to improve the discriminative ability of the model, and then propose a global context-aware feature modulation network, which has a stronger representational capability for multi-scale SR. Specifically, we first model the global context based on the self-attention mechanism, which can effectively extract rich global information from convolutional features. Then, we propose to modulate the feature responses using global contextual dependencies in spatial and channel dimensions respectively. Further, we construct the global context-aware feature modulation block, which can be stacked to form a deep feature modulation network and applied to the unified multi-scale SR architecture to learn inter-scale correlation. Extensive experiments demonstrate that the proposed global context-aware modulation is effective, which can improve the performance of multi-scale SR task both quantitatively and qualitatively.
Insulator self-explosion detection method based on deep learning for transmission line components in ultrahigh voltage aerial images

Abstract. Herein, a lightweight “You Only Look Once” (YOLOv5) ultrahigh voltage (UHV) transmission line insulator self-detonation detection algorithm based on the attention mechanism and Ghostnet is proposed to address the multi-scale features of UHV insulator aerial images. Small-scale defective pieces cannot be detected accurately by conventional algorithms in a complex power system inspection environment. To suppress the complex background interference, a YOLOv5-based spatial and channel convolution attention model is used to enhance the saliency of the target to be detected by weighting the feature layers and feature maps. The convolutional structure of the original cross stage partial (CSP_X) structure is replaced by the Ghostnet network to generate more similar feature maps by performing linear operations on the redundant feature maps so that more feature maps can be generated using fewer parameters to achieve a lightweight network and reduce the network parameters. To address the problem of missed and false detection caused by inadequate expression of the target features to be detected, the original neck feature pyramid network (FPN) + path aggregation network (PAN) structure is modified to a bidirectional FPN structure such that the target multi-scale features are effectively fused. Finally, SCYLLA-IoU is used as the loss function to accelerate the model convergence and improve the detection accuracy, and migration learning parameter sharing combined with the model training strategy of freeze and thaw training is performed for non-generalization of the network owing to small sample datasets. The effectiveness of the proposed algorithm is verified by training it with data obtained from UHV patrols. Experimental results show that the proposed algorithm accurately monitors insulator self-detonation targets in complex environments with an average detection accuracy of 97.4% and a significant reduction in the size of models and parameters, which has good practical value.
Understanding the Relationship Between Foreign Direct Investment and Economic Growth in BRICS: Panel ARDL Approach

Foreign direct investment (FDI) has gained prominence in international economics over the past three decades. Primarily, the belief that FDI influences economic growth of the host country, set the stage for the empirical research focused on the FDI–growth nexus. The growth literature, however, reveals mixed evidence regarding the role of FDI in promoting growth. Despite the conflicting evidence, developed and developing economies have attached immense economic and political importance to FDI. It is noteworthy that BRICS (Brazil, Russia, India, China and South Africa) economies are representative developing economies and have emerged as significant FDI destinations, having witnessed an immense surge in inward FDI over the past few decades. It is against this backdrop that the present study attempts to assess the impact of FDI inflows and select macroeconomic variables, namely macroeconomic stability, human capital, financial development and trade openness (TO), on the economic growth of developing BRICS economies. The study examines both short-run and long-run relationships between FDI inflows, select macroeconomic variables and economic growth by employing the dynamic panel autoregressive distributed lag (ARDL) model, unlike most of the previous studies. For this study, secondary data covering a reference period of 32 years (1987–2018) were used. The data on GDP growth (GDPG), FDI inflows, inflation (INF), human capital, private sector bank credit (proxy for financial development) and TO have been collected from the World Investment Reports published annually by United Nations Conference on Trade and Development (UNCTAD) and World Bank (World Development Indicators). The findings revealed a long-run cointegration among FDI, host country characteristics (TO, human capital, financial development and macroeconomic stability) and economic growth in BRICS.
Artificial Intelligence in Everyday Life: Educating the Public Through an Open, Distance-learning Course

With the rise of data-driven AI and its "democratization", we are all interacting with AI-enabled technologies in everyday life. However, not everyone understands how these technologies work. There are many misconceptions surrounding what they can and cannot do, and what are the risks and benefits. Thus, there is a need to educate the general public about everyday AI, upscaling their algorithmic literacy and helping them become more informed, responsible users. We describe the development and evaluation of an eight-week course open to the public, AI in Everyday Life. Pre- and post-course questionnaires were used to evaluate the impact of the course and participants' attitudes towards AI applications. The results suggest that more targeted educational approaches are needed for the general public to fully understand the strengths and weaknesses of the use of AI-enabled technologies.