Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of manual and dose reduction modes of a MORITA R100 CBCT.

OBJECTIVE: To compare effective dose differences when acquiring (1) dose reduction mode and (2) manual mode in a MORITA R100 CBCT. METHODS: 24 exposure protocols with different technique factors were performed in both the dose reduction mode and the manual mode in a Veraviewepocs 3D R100 cone beam CT device. 48 TLD were placed in a RANDO head phantom at 24 different sites. Effective doses were calculated according to the formalism published in the 103rd ICRP report. RESULTS: Effective doses for the dose reduction mode protocols ranged from 14 to 156 µSv [mean = 57 µSv, standard deviation (SD) = 37 µSv], whereas effective doses for the manual mode protocols ranged from 22 to 267 µSv (mean = 94 µSv, SD = 65 µSv). Thus, across all protocols, the dose reduction mode leads to a drop of the effective dose by 38 % (SD = 6 %). CONCLUSIONS: The estimated effective doses are significantly lower if dose reduction protocols are acquired.
Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2015

Purpose This study presents the 2015 nationwide cancer statistics in Korea, including the incidence, survival, prevalence, and mortality. Materials and Methods Cancer incidence data from 1999 to 2015 was obtained from the Korea National Cancer Incidence Database and followed until December 31, 2016. Mortality data from 1983 to 2015 were obtained from Statistics Korea. The prevalence was defined as the number of cancer patients alive on January 1, 2016, among all cancer patients diagnosed since 1999. Crude and age-standardized rates (ASRs) for incidence, mortality and prevalence and 5-year relative survivals were also calculated. Results Herein, 214,701 and 76,855 Koreans were newly diagnosed and died from cancer in 2015, respectively. The ASRs for cancer incidence and mortality in 2015 were 258.9 and 82.0 per 100,000, respectively. The overall cancer incidence rate has increased significantly by 3.4% annually from 1999 to 2012, and started to decrease after 2012 (2012-2015, annual percent change, –6.1%). However, the overall cancer mortality has decreased 2.7% annually since 2002. The 5-year relative survival rate for patients diagnosed with cancer between 2011 and 2015 was 70.7%, an improvement from the 41.2% for patients diagnosed between 1993 and 1995. Conclusion Age-standardized cancer incidence rates have decreased since 2012 and mortality rates have declined since 2002; however, the 5-year survival rates have improved remarkably from 1993-1995 to 2011-2015 in Korea.
Viperin Deficiency Promotes Polarization of Macrophages and Secretion of M1 and M2 Cytokines

Viperin is a multifunctional protein that was first identified in human primary macrophages treated with interferon-γ and in human fibroblasts infected with human cytomegalovirus. This protein plays a role as an anti-viral protein and a regulator of cell signaling pathways or cellular metabolism when induced in a variety of cells such as fibroblasts, hepatocytes and immune cells including T cells and dendritic cells. However, the role of viperin in macrophages is unknown. Here, we show that viperin is basally expressed in murine bone marrow cells including monocytes. Its expression is maintained in bone marrow monocyte-derived macrophages (BMDMs) depending on macrophage colony-stimulating factor (M-CSF) treatment but not on granulocyte-macrophage colony-stimulating factor (GM-CSF) treatment. In wild type (WT) and viperin knockout (KO) BMDMs differentiated with M-CSF or G-MCSF, there are little differences at the gene expression levels of M1 and M2 macrophage markers such as inducible nitric oxide synthase (iNOS) and arginase-1, and cytokines such as IL-6 and IL-10, indicating that viperin expression in BMDMs does not affect the basal gene expression of macrophage markers and cytokines. However, when BMDMs are completely polarized, the levels of expression of macrophage markers and secretion of cytokines in viperin KO M1 and M2 macrophages are significantly higher than those in WT M1 and M2 macrophages. The data suggest that viperin plays a role as a regulator in polarization of macrophages and secretion of M1 and M2 cytokines.
Antimicrobial Prophylaxis for Adult Patients With Cancer-Related Immunosuppression: ASCO and IDSA Clinical Practice Guideline Update.

PURPOSE To provide an updated joint ASCO/Infectious Diseases Society of America (IDSA) guideline on antimicrobial prophylaxis for adult patients with immunosuppression associated with cancer and its treatment. METHODS ASCO and IDSA convened an update Expert Panel and conducted a systematic review of relevant studies from May 2011 to November 2016. The guideline recommendations were based on the review of evidence by the Expert Panel. RESULTS Six new or updated meta-analyses and six new primary studies were added to the updated systematic review. RECOMMENDATIONS Antibacterial and antifungal prophylaxis is recommended for patients who are at high risk of infection, including patients who are expected to have profound, protracted neutropenia, which is defined as < 100 neutrophils/µL for > 7 days or other risk factors. Herpes simplex virus-seropositive patients undergoing allogeneic hematopoietic stem-cell transplantation or leukemia induction therapy should receive nucleoside analog-based antiviral prophylaxis, such as acyclovir. Pneumocystis jirovecii prophylaxis is recommended for patients receiving chemotherapy regimens that are associated with a > 3.5% risk for pneumonia as a result of this organism (eg, those with ≥ 20 mg prednisone equivalents daily for ≥ 1 month or on the basis of purine analog usage). Treatment with a nucleoside reverse transcription inhibitor (eg, entecavir or tenofovir) is recommended for patients at high risk of hepatitis B virus reactivation. Recommendations for vaccination and avoidance of prolonged contact with environments that have high concentrations of airborne fungal spores are also provided within the updated guideline. Additional information is available at www.asco. org/supportive-care-guidelines .
Philadelphia Telemedicine Glaucoma Detection and Follow-up Study: Analysis of Unreadable Fundus Images

Purpose: The purpose of this study was to ascertain determinants of unreadable fundus images for participants enrolled in the Philadelphia Telemedicine Glaucoma Detection and Follow-up Study. Methods: Individuals were screened for glaucoma at 7 primary care practices and 4 Federally Qualified Health Centers using telemedicine. Screening (visit 1) included fundus photography, assessing family history of glaucoma, and intraocular pressure (IOP) measurements. Participants with an unreadable image in at least one eye were deemed unreadable and invited to return for a confirmatory eye examination (visit 2). Results: A total of 906 participants completed the visit 1 eye screening and 17.1% (n=155/906) were “unreadable. ” In the multivariable logistic regression analysis, older age, male sex, smoking, and worse visual acuity were significantly associated with an unreadable fundus image finding at the eye screening (P<0.05). Of the 89 participants who were invited for the confirmatory eye examination solely for unreadable images and attended visit 2, 58 (65.2%) were diagnosed with at least one ocular pathology. The most frequent diagnoses were cataracts (n=71; 15 visually significant, 56 nonvisually significant), glaucoma suspects (n=27), and anatomical narrow angle (n=10). Conclusions: Understanding the causes of unreadable fundus images will foster improvements in telemedicine techniques to optimize the predictive accuracy, efficiency, and cost in ophthalmology. A high proportion of participants with unreadable images (65.2%) in our study were diagnosed with some ocular pathology, indicating that the finding of an unreadable fundus image warrants a referral for a comprehensive follow-up eye examination.
Persistent sex disparities in clinical outcomes with percutaneous coronary intervention: Insights from 6.6 million PCI procedures in the United States

Background Prior studies have reported inconsistencies in the baseline risk profile, comorbidity burden and their association with clinical outcomes in women compared to men. More importantly, there is limited data around the sex differences and how these have changed over time in contemporary percutaneous coronary intervention (PCI) practice. Methods and results We used the Nationwide Inpatient Sample to identify all PCI procedures based on ICD-9 procedure codes in the United States between 2004–2014 in adult patients. Descriptive statistics were used to describe sex-based differences in baseline characteristics and comorbidity burden of patients. Multivariable logistic regressions were used to investigate the association between these differences and in-hospital mortality, complications, length of stay and total hospital charges. Among 6,601,526 patients, 66% were men and 33% were women. Women were more likely to be admitted with diagnosis of NSTEMI (non-ST elevation acute myocardial infarction), were on average 5 years older (median age 68 compared to 63) and had higher burden of comorbidity defined by Charlson score ≥3. Women also had higher in-hospital crude mortality (2.0% vs 1.4%) and any complications compared to men (11.1% vs 7.0%). These trends persisted in our adjusted analyses where women had a significant increase in the odds of in-hospital mortality men (OR 1.20 (95% CI 1.16,1.23) and major bleeding (OR 1.81 (95% CI 1.77,1.86). Conclusion In this national unselected contemporary PCI cohort, there are significant sex-based differences in presentation, baseline characteristics and comorbidity burden. These differences do not fully account for the higher in-hospital mortality and procedural complications observed in women.
A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China

A segmented processing approach of eigenvector spatial filtering (ESF) regression is proposed to detect the relationship between NDVI and its environmental factors like DEM, precipitation, relative humidity, precipitation days, soil organic carbon, and soil base saturation in central China. An optimum size of 32 × 32 is selected through experiments as the basic unit for image segmentation to resolve the large datasets to smaller ones that can be performed in parallel and processed more efficiently. The eigenvectors from the spatial weights matrix (SWM) of each segmented image block are selected as synthetic proxy variables accounting for the spatial effects and aggregated to construct a global ESF regression model. Results show precipitation and humidity are more influential than other factors and spatial autocorrelation plays a vital role in vegetation cover in central China. Despite the increase in model complexity; the parallel ESF regression model performs best across all performance criteria compared to the ordinary least squared linear regression (OLS) and spatial autoregressive (SAR) models. The proposed parallel ESF approach overcomes the computational barrier for large data sets and is very promising in applying spatial regression modeling to a wide range of real world problem solving and forecasting.
Dietary Probiotic Effect of Lactococcus lactis WFLU12 on Low-Molecular-Weight Metabolites and Growth of Olive Flounder (Paralichythys olivaceus)

The use of probiotics is considered an attractive biocontrol method. It is effective in growth promotion in aquaculture. However, the mode of action of probiotics in fish in terms of growth promotion remains unclear. The objective of the present study was to investigate growth promotion effect of dietary administration of host-derived probiotics, Lactococcus lactis WFLU12, on olive flounder compared to control group fed with basal diet by analyzing their intestinal and serum metabolome using capillary electrophoresis mass spectrometry with time-of flight (CE-TOFMS). Results of CE-TOFMS revealed that 53 out of 200 metabolites from intestinal luminal metabolome and 5 out of 171 metabolites from serum metabolome, respectively, were present in significantly higher concentrations in the probiotic-fed group than those in the control group. Concentrations of metabolites such as citrulline, tricarboxylic acid cycle (TCA) intermediates, short chain fatty acids, vitamins, and taurine were significantly higher in the probiotic-fed group than those in the control group. The probiotic strain WFLU12 also possesses genes encoding enzymes to help produce these metabolites. Therefore, it is highly likely that these increased metabolites linked to growth promotion in olive flounder are due to supplementation of the probiotic strain. To the best of our knowledge, this is the first study to show that dietary probiotics can greatly influence metabolome in fish. Findings of the present study may reveal important implications for maximizing the efficiency of using dietary additives to optimize fish health and growth.
Uterine Artery Doppler in Pregnancy: Women with PCOS Compared to Healthy Controls

The objective of this study was to investigate possible differences in uterine artery pulsatility index (UtAPI) between pregnant women with PCOS and healthy controls and to explore possible effects of metformin on UtAPI. Material and Methods. The study was conducted in a tertiary center. Forty-eight pregnant women diagnosed with PCOS before pregnancy and 124 healthy pregnant women were included. Women with PCOS were randomly assigned to metformin 2000 mg daily or a placebo. UtAPI was measured five times during 1st and 2nd trimesters of pregnancy in women with PCOS and four times in healthy controls. Results. There was no difference in UtAPI between PCOS women and healthy controls at any point in time (p = 0.34–0.77). In women with PCOS, randomly assigned to metformin 2000 mg or placebo, UtAPI was unaffected by metformin two hours after intake of the first dose of study medication (p = 0.34). All PCOS women, regardless of randomization, had higher UtAPI two hours after intake of study medication and a meal compared to before a meal (p = 0.02). Conclusions. In the first and second trimesters of pregnancy, there was no difference in UtAPI between women with PCOS and healthy controls. Metformin had no immediate effect on the UtAPI. Interestingly, blood flow decreased after a meal, suggesting that time since last meal should be taken into consideration when interpreting the results of UtAPI measurements in pregnancy. This trial is registered with ClinicalTrials.gov (NCT00466622) Metformin in Pregnant PCOS women (PregMet) (NCT00159536).
Prior Knowledge, Episodic Control and Theory of Mind in Autism: Toward an Integrative Account of Social Cognition

Over the last 30 years, research has explored theory of mind (ToM), the ability to attribute mental states to oneself and to others. Work on ToM in typical and atypical populations has shed light on the neurocognitive mechanisms underlying social understanding and interaction. The ToM hypothesis has long been regarded as one comprehensive explanation of the severe cognitive and behavioral impairments encountered by individuals with autism. However, high-functioning individuals can often pass both first-order and second-order false belief tasks using cognitive compensation strategies. To provide more sensitive measures of mental state attribution abilities, researchers have introduced more difficult, “advanced” theory of mind tasks. In this article, we argue that in attempting to bypass compensation strategies, these new advanced ToM tasks, such as the Faux Pas and the Strange Stories tasks, impose cognitive demands beyond those specific to the domain of ToM. We then provide an integrative account of social deficits in autism that takes into account several distinct components of mental state understanding, including both general cognitive capacities and processes specific to ToM. We argue that a number of related cognitive abilities, including episodic cognitive control and inferencing from prior knowledge, are necessary to understand how both people with autism and typical development navigate challenging, real-life social situations.
Mobile Mechatronic/Robotic Orthotic Devices to Assist–Rehabilitate Neuromotor Impairments in the Upper Limb: A Systematic and Synthetic Review

This paper overviews the state-of-the-art in upper limb robot-supported approaches, focusing on advancements in the related mechatronic devices for the patients' rehabilitation and/or assistance. Dedicated to the technical, comprehensively methodological and global effectiveness and improvement in this inter-disciplinary field of research, it includes information beyond the therapy administrated in clinical settings-but with no diminished safety requirements. Our systematic review, based on PRISMA guidelines, searched articles published between January 2001 and November 2017 from the following databases: Cochrane, Medline/PubMed, PMC, Elsevier, PEDro, and ISI Web of Knowledge/Science. Then we have applied a new innovative PEDro-inspired technique to classify the relevant articles. The article focuses on the main indications, current technologies, categories of intervention and outcome assessment modalities. It includes also, in tabular form, the main characteristics of the most relevant mobile (wearable and/or portable) mechatronic/robotic orthoses/exoskeletons prototype devices used to assist-rehabilitate neuromotor impairments in the upper limb.
IoU is not submodular

This short article aims at demonstrate that the Intersection over Union (or Jaccard index) is not a submodular function. This mistake has been made in an article which is cited and used as a foundation in another article. The Intersection of Union is widely used in machine learning as a cost function especially for imbalance data and semantic segmentation.
Effect of Ultrafiltration of Milk Prior to Fermentation on Mass Balance and Process Efficiency in Greek-Style Yogurt Manufacture

Ultrafiltration (UF) can be used to concentrate yogurt to produce Greek-style yogurt (GSY) (UF-YOG), but this generates acid whey permeate, which is an environmental issue. However, when UF is applied before fermentation (UF-MILK), a nonacidified whey permeate is generated. For this study, two model GSYs (UF-YOG and UF-MILK) were produced to compare the composition, UF performance, and energy consumption of the two processes. For UF-MILK, skim milk was ultrafiltered with a 30 kDa spiral-wound UF membrane to achieve a 3× volume reduction factor (VRF). The retentate was fermented to a pH of 4.5. The UF-YOG process was the same except that regular yogurt was ultrafiltered. Both GSYs had similar protein (~10%) and solid content (~17%). As expected, lactic acid/lactate was not detected in UF-MILK permeate, while 7.3 g/kg was recovered from the UF-YOG permeate. Permeation flux values (11.6 to 13.3 L m−2 h−1) and total flux decline (47% to 50%) were constant during UF-MILK, whereas drastic decreases in these two membrane performance indicators (average flux: 38.5 to 10.9 L m−2 h−1; total flux decline: 2% to 38%) were calculated for UF-YOG. Moreover, for UF-YOG, UF membrane performance never recovered, even when drastic and repeated cleaning steps were applied. Energy consumption was 1.6 kwh/kg GSY and remained constant for UF-MILK, whereas it increased from 0.6 to 1.5 kwh/kg GSY for UF-YOG. Our results show that, although the composition of GSYs was similar for both processes, the UF step of yogurt concentration affected process efficiency due to drastic and permanent membrane fouling.
Using Predictive Analytics to Identify Children at High Risk of Defaulting From a Routine Immunization Program: Feasibility Study

Background Despite the availability of free routine immunizations in low- and middle-income countries, many children are not completely vaccinated, vaccinated late for age, or drop out from the course of the immunization schedule. Without the technology to model and visualize risk of large datasets, vaccinators and policy makers are unable to identify target groups and individuals at high risk of dropping out; thus default rates remain high, preventing universal immunization coverage. Predictive analytics algorithm leverages artificial intelligence and uses statistical modeling, machine learning, and multidimensional data mining to accurately identify children who are most likely to delay or miss their follow-up immunization visits. Objective This study aimed to conduct feasibility testing and validation of a predictive analytics algorithm to identify the children who are likely to default on subsequent immunization visits for any vaccine included in the routine immunization schedule. Methods The algorithm was developed using 47,554 longitudinal immunization records, which were classified into the training and validation cohorts. Four machine learning models (random forest; recursive partitioning; support vector machines, SVMs; and C-forest) were used to generate the algorithm that predicts the likelihood of each child defaulting from the follow-up immunization visit. The following variables were used in the models as predictors of defaulting: gender of the child, language spoken at the child’s house, place of residence of the child (town or city), enrollment vaccine, timeliness of vaccination, enrolling staff (vaccinator or others), date of birth (accurate or estimated), and age group of the child. The models were encapsulated in the predictive engine, which identified the most appropriate method to use in a given case. Each of the models was assessed in terms of accuracy, precision (positive predictive value), sensitivity, specificity and negative predictive value, and area under the curve (AUC). Results Out of 11,889 cases in the validation dataset, the random forest model correctly predicted 8994 cases, yielding 94.9% sensitivity and 54.9% specificity. The C-forest model, SVMs, and recursive partitioning models improved prediction by achieving 352, 376, and 389 correctly predicted cases, respectively, above the predictions made by the random forest model. All models had a C-statistic of 0.750 or above, whereas the highest statistic (AUC 0.791, 95% CI 0.784-0.798) was observed in the recursive partitioning algorithm. Conclusions This feasibility study demonstrates that predictive analytics can accurately identify children who are at a higher risk for defaulting on follow-up immunization visits. Correct identification of potential defaulters opens a window for evidence-based targeted interventions in resource limited settings to achieve optimal immunization coverage and timeliness.