Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and validation of nomograms for the prediction of low muscle mass and radiodensity in gastric cancer patients.

BACKGROUND The skeletal muscle mass index (SMI) and skeletal muscle radiodensity (SMD) are important components of sarcopenia and malnutrition. However, their assessment requires additional resources in cancer patients, which is inconvenient for the early detection of sarcopenia and malnutrition. OBJECTIVES This study aimed to develop and validate nomograms for the prediction of low muscle mass and muscle radiodensity and to examine the application value of the nomograms in the diagnoses of sarcopenia and malnutrition. METHODS A total of 1315 patients diagnosed with gastric cancer between July 2014 and May 2019 were included. Random resampling with an 80/20 split ratio was performed to obtain a training cohort (n = 1056) and a validation cohort (n = 259). Nomograms were separately constructed for low SMI (LSMI) and low SMD (LSMD) in the training cohort based on prospectively collected preoperative data. The performance of the nomograms was assessed using the AUC, calibration curve, and Hosmer-Lemeshow test. The application values of the nomograms in the diagnoses of sarcopenia and malnutrition were also evaluated. RESULTS Age, BMI, hemoglobin concentration, and gait speed were included in the nomogram for LSMI predictions. These variables, in addition to sex, were included in the nomogram for LSMD predictions. The diagnostic nomograms exhibited good discrimination, with AUCs of 0.818 (95% CI, 0.791-0.845) for the LSMI nomogram and 0.788 (95% CI, 0.761-0.815) for the LSMD diagnostic nomogram in the training cohort. Calibration was also excellent. The agreement ratios between the nomograms and actual observations in the total population were 92.3% and 95.6% for sarcopenia and malnutrition, respectively. Prognostic nomograms exhibited similar performance in the validation cohort. CONCLUSIONS Diagnostic nomograms consisting of preoperative factors can successfully predict LSMI and LSMD. These models facilitate early identification and timely interventions for at-risk populations.
Efficient Channel Coding for Dimmable Visible Light Communications System

Visible light communication (VLC) offers wireless communication within short-range based on wavelength converters and light-emitting diode (LED). In the VLC system, conventional forward error correction (FEC) codes are not guaranteed to provide flicker mitigation and dimming support. Consequently, modified coding schemes are introduced for reliable VLC. These methods require complicated coding structures, use of lookup tables, and the addition of large redundancy, resulting to increased computational complexity and low transmission efficiency. In this article, we propose a coding scheme that is flicker-free and enhances the transmission efficiency for VLC systems. The proposed scheme is based on polar codes (PC) and Knuth balancing code with enhanced prefix coding technique. The results show that the proposed algorithm exhibits improved transmission efficiency compared to the PC without and with run-length limited code, for dimming values 75% (or 25%) and 87.5% (or 12.5%). Also, the proposed scheme presents a significant bit error rate (BER) performance gain compared to the schemes in literature. The proposed scheme is flicker-free, provides a simple encoding structure, does not utilize lookup tables, generates minimal number of redundancies for energy efficiency. Thus, the approach is flexible, and it is more suitable for real-time VLC systems.
FCN-SFW: Steel Structure Crack Segmentation Using a Fully Convolutional Network and Structured Forests

Tiny cracks that exist in steel beams have poor continuity and low contrast in images, posing a huge challenge to crack detection using image-based approaches. When complex backgrounds exist, the existing deep learning methods are usually unable to perform effective feature transfer and fusion for crack feature mapping, and they cannot accurately distinguish crack features from similar backgrounds. In this article, we propose a fusion segmentation algorithm, using the fully convolutional network (FCN) and structured forests with wavelet transform (SFW) to detect tiny cracks in steel beams. First, five neural networks based on the FCN framework are constructed to extend the global characteristics of tiny cracks. Second, a fine edge detection approach using multi-scale structured forests and wavelet maximum modulus edge detection to refine the characteristics of tiny cracks are proposed. Here, a competitive training strategy is used to address the SFW parameter optimization problem. Finally, we fuse the multiple probability maps, acquired from both the optimal FCN model and the SFW classifier, into a merged map, which can segment tiny cracks with robustness better than the comparison approaches. The experimental results show that compared with state-of-the-art algorithms and other segmentation approaches, the proposed algorithm realizes better segmentation in terms of quantitative metrics.
Alumination of the purely siliceous mesoporous molecular sieve MCM-41 and its hydrothermal conversion into zeolite Na-A

Purely siliceous mesoporous [Si]-MCM-41 has been aluminated using an aqueous solution of sodium aluminate, NaAlO2, to form [Si,Al]-MCM-41 with the framework Si/Al ratio as low as 1.9. 27Al and 29Si magic-angel-spinning (MAS) NMR show that all aluminium is incorporated in the framework. The well resolved X-ray diffraction (XRD) pattern of calcined [Si,Al]-MCM-41 indicates that the mesopore system is stable and that the channel diameter decreases with the increased degree of incorporation of Al. The intensity of lines corresponding to Si(nAl) building blocks in the 29Si MAS NMR spectra reflect changes in the local atomic arrangement upon alumination. Increased reaction temperature at a constant concentration of NaAlO2 enhances the efficiency of the alumination, but increased concentration at constant temperature disrupts the framework structure. Upon treatment with a concentrated (>1 mol 1–1) solution of NaAlO2 at moderate temperatures (100 ± 20 °C), [Si]-MCM-41 is transformed into crystalline zeolite Na-A.
Community Understanding of the Importance of Social Distancing Using Sentiment Analysis in Twitter

The government may use social media, such as Twitter, to socialize a policy or a program to society. We may predict whether a program is successful or not by analyzing the sentiment of societies towards such program or communities through their tweets. The latest program of Indonesia’s government during the COVID-19 pandemic is to make people do social distancing. It is socialized using the hashtag of stay at home appeal (#dirumahaja). The objective of this study is to analyze the understanding of societies regarding this program through people’s tweet. We compared two classification algorithms (Naive Bayes and Random Forest), using tokenization and unigram features to build classification model of tweet sentiment. The tweets that included some hashtags regarding social distancing program, were collected with 5101 tweets in total. The highest accuracy is obtained using the Random Forest algorithm and term weighting feature, which yielded 95.98%. From the model we found that the number of positive sentiments is greater than the negative sentiment. Which can be concluded that the societies are understand and agree to the social distancing program.
Operational Design Domain Requirements for Improved Performance of Lane Assistance Systems: A Field Test Study in The Netherlands

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the driving environment characteristics that affect the performance of automated vehicles. In this context, a field test with Lane Departure Warning (LDW) and Lane Keeping Systems (LKS)-enabled vehicles was conducted in the Netherlands. Empirical data from the experiment was used to estimate the impact of driving environment components such as weather condition and lane width on the performance of the automated vehicles. Driving at night in the presence of streetlights with rain resulted in least detection performance for both the vehicles as compared to other visibility conditions. As for lane-keeping performance, the LKS positioned the vehicle significantly more to the left of the lane on left-curves than on straight sections. The LKS also positioned the vehicle more left on lanes with a width less than 250 cm than on wider lanes. These findings were translated into levels of service of the Operational Design Domain (ODD). Each level of service corresponded to a performance level of the lane assistance systems, classified as “High”, “Medium”, and “Low”, and defined using indicators.
Effect of Pt Additions in the Interconnections on the Resistivity and Power Output of Solar Modules

Intermetallic compound (IMC) layers growth in the interconnections of solar modules is inevitable once the solar modules are in use. Higher temperatures (warmer climates) may accelerate the growth of these compounds, leading to faster degradation of the solar modules. In this paper, the effect of Platinum (Pt) additions on the interfacial reaction, contact resistivity and power output of solar module have been investigated. The changes in IMC morphology was studied using the scanning electron microscope (SEM) with an energy dispersive X-ray (EDX). The contact resistivities of solder joints were measured using the transfer length method. Solar modules with varying amounts of Pt additions were constructed and their power outputs under natural sunlight were compared to the reference solar module constructed with a commercial Sn-3.0Ag-0.5Cu (SAC305) solder. Experimental results showed that minute Pt additions effectively suppressed the IMC growth during isothermal aging at 150°C for up to 500 hours. This was reflected in the reduced contact resistivities ranging between 20->60% when 0.10wt. %-0.50wt. % of Pt were added to the SAC solder. In trend with the reduced resistivities, the solar modules containing Pt recorded higher power outputs and lower degradation rate under natural sunlight exposure. Pt is believed to dissolve and form an ternary compound, (Cu,Pt)6Sn5 which is less susceptible to decompose into the more detrimental Cu3Sn IMC layer, thus slowing down the overall degradation of the solar panels.
Targeting HPV in gynaecological cancers – Current status, ongoing challenges and future directions

Despite the success of preventive vaccination, the Human Papilloma Virus still accounts for 266,000 deaths annually, as the main causative factor of cervical, vaginal, anal, penile and oropharyngeal cancers. Human Papilloma Virus infects epithelial cells, driving tumourigenesis primarily from incorporation of DNA into the host cellular genome. Translation of two particular Human Papilloma Virus–specific oncoproteins, E6 and E7, are the key drivers of malignancy. If diagnosed early cervical, vaginal and vulval cancers have good prognosis and are treated with curative intent. However, metastatic disease carries a poor prognosis, with first-line systemic treatment providing only modest increase in outcome. Having shown promise in other solid malignancies, immune checkpoint inhibition and therapeutic cancer vaccines have been directed towards Human Papilloma Virus–associated gynaecological cancers, mindful that persistent Human Papilloma Virus infection drives malignancy and is associated with immunosuppression and lack of T-cell immunity. In this review, we discuss novel therapeutic approaches for targeting Human Papilloma Virus–driven gynaecological malignancies including vaccination strategies, use of immunomodulation, immune checkpoint inhibitors and agents targeting Human Papilloma Virus–specific oncoproteins. We also highlight the evolving focus on exciting new treatments including adoptive T-cell therapies.
Product integration of compact roll-to-roll processed polymer solar cell modules: methods and manufacture using flexographic printing, slot-die coating and rotary screen printing

The improvement of the performance of roll-to-roll processed polymer solar cell modules through miniaturization of the device outline is described. The devices were prepared using full roll-to-roll processing comprising flexographic printing, slot-die coating and rotary screen printing to create 5 mm wide lines of ZnO, P3HT:[60/70]PCBM, PEDOT:PSS and silver on an ITO-PET substrate. The lines were spaced by 1 mm and the devices were completed by encapsulation using roll-to-roll lamination on both sides using a pressure sensitive adhesive and a multilayered barrier material having a UV-filter with a cut-off at 390 nm, oxygen and water vapor transmission rates of respectively 0.01 cm3 m−2 bar−1 day−1 and 0.04 g m−2 day−1. The final modules comprised 16 serially connected cells. The technical yield was 89% based on the criterion that the Voc had to be larger than 7.2 V. This set of modules gave respectively a voltage, current, fill factor and power conversion efficiency of 8.47 ± 0.41 V, −23.20 ± 4.10 mA, 35.4 ± 2.8% and 1.96 ± 0.34% in the case of modules based on P3HT:[60]PCBM. A total of 1960 modules were prepared for each run and the best power conversion reached was 2.75% for devices based on P3HT:[70]PCBM. The solar cell modules were used to demonstrate the complete manufacture of a small lamp entirely using techniques of flexible electronics. The solar cell module was used to charge a polymer lithium ion battery through a blocking diode. The entire process was fully automated and demonstrates the capacity of polymer solar cells in the context of flexible and printed electronics. Finally a comparison was made between the learning curve for OPV and crystalline silicon solar cells in terms of the cost per watt peak and the cumulative watt peak. OPV as a technology was found to have a significantly steeper learning curve.
Identifying Fatigue Indicators Using Gait Variability Measures: A Longitudinal Study on Elderly Brisk Walking

Real-time detection of fatigue in the elderly during physical exercises can help identify the stability and thus falling risks which are commonly achieved by the investigation of kinematic parameters. In this study, we aimed to identify the change in gait variability parameters from inertial measurement units (IMU) during a course of 60 min brisk walking which could lay the foundation for the development of fatigue-detecting wearable sensors. Eighteen elderly people were invited to participate in the brisk walking trials for 60 min with a single IMU attached to the posterior heel region of the dominant side. Nine sets of signals, including the accelerations, angular velocities, and rotation angles of the heel in three anatomical axes, were measured and extracted at the three walking times (baseline, 30th min, and 60th min) of the trial for analysis. Sixteen of eighteen participants reported fatigue after walking, and there were significant differences in the median acceleration (p = 0.001), variability of angular velocity (p = 0.025), and range of angle rotation (p = 0.0011), in the medial–lateral direction. In addition, there were also significant differences in the heel pronation angle (p = 0.005) and variability and energy consumption of the angles in the anterior–posterior axis (p = 0.028, p = 0.028), medial–lateral axis (p = 0.014, p = 0.014), and vertical axis (p = 0.002, p < 0.001). Our study demonstrated that a single IMU on the posterior heel of the dominant side can address the variability of kinematics parameters for elderly performing prolonged brisk walking and could serve as an indicator for walking instability, and thus fatigue.
9. Pharmacologic Approaches to Glycemic Treatment: Standards of Medical Care in Diabetes-2021.

The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee (https://doi.org/10.2337/dc21-SPPC), are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction (https://doi.org/10.2337/dc21-SINT). Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes. org/SOC.
Shallow-depth sequencing of cell-free DNA for Hodgkin and diffuse large B-cell lymphoma (differential) diagnosis: a standardized approach with underappreciated potential

Shallow-depth sequencing of cell-free DNA, an inexpensive and standardized approach to obtain molecular information on tumors non-invasively, has been insufficiently explored for the diagnosis of lymphoma and disease follow-up. This study collected 318 samples, including longitudinal liquid and paired solid biopsies, from a prospectively- recruited cohort of 38 Hodgkin lymphoma (HL) and 85 aggressive B-cell non-HL patients, represented by 81 diffuse large B-cell lymphoma (DLBCL) cases. Following sequencing, copy number alterations and viral read fractions were derived and analyzed. At diagnosis, liquid biopsies showed detectable copy number alterations in 84.2% of HL patients (88.6% for classical HL) and 74.1% of DLBCL patients. Of the DLBCL patients, copy number profiles between liquid-solid pairs were highly concordant (r=0.815±0.043); and, compared to tissue, HL liquid biopsies had abnormalities with higher amplitudes (P=0.010). This implies that tumor DNA is more abundant in plasma. Additionally, 39.5% of HL and 13.6% of DLBCL cases had a significantly elevated number of plasma Epstein-Barr virus DNA fragments, achieving a sensitivity of 100% compared to the current standard. A longitudinal analysis determined that, when detectable, copy number patterns were similar across (re)staging moments in refractory or relapsed patients. Further, the overall profile anomaly correlated highly with the total metabolic tumor volume (P<0.001). To conclude, as a proof of principle, we demonstrate that liquid biopsy-derived copy numbers can aid diagnosis: e.g., by differentiating HL from DLBCL, random forest modeling is represented by an area under the receiver operating characteristic curve of 0.967. This application is potentially useful when tissue is difficult to obtain or when biopsies are small and inconclusive.
Uncouple Generative Adversarial Networks for Transferring Stylized Portraits to Realistic Faces

Stylized portraits widely exist in artwork or paintings. It is interesting to restore the original identity of portrait artworks, but consider that the rarity and the style diversity of these artworks, it is difficult to pair and obtain sufficient training data to restore their original identities by existing methods. Therefore, it is challenging to explore a method to restore a single stylized portrait to its original identity. Although CycleGAN can convert paintings into realistic photographs in unpaired datasets, it was not developed specifically for portraits, and photo-realistic faces require more accurate structures, thus the visual results obtained with CycleGAN are not satisfactory. In this paper, we propose Uncouple-Generative Adversarial Networks (UncGANs) for transferring stylized portraits to realistic faces. Our UncGANs framework is inspired by CariGANs to tackle the visual problem in CycleGAN for obtaining realistic faces from stylized portraits. In addition, we introduce three losses, namely, the semantic style consistency loss and the cycle consistency loss to effectively guide the training of generators and discriminators on unpaired datasets, the global and local adversarial loss ensure the consistency of appearance characteristics before and after translation, and the location consistency loss to establish the precise correspondence between the source domain and the target domain as well as assist the discriminators. Extensive experimental results and comparisons with state-of-the-art methods including Style, Deep-Image-Analogy, UNIT, MUNIT, CycleGAN, CP-GAN, and PS2-MAN demonstrate that our framework is better at generating realistic faces from stylized portraits with accurate structures and features.
Analyses of Risk, Racial Disparity, and Outcomes Among US Patients With Cancer and COVID-19 Infection.

Importance Patients with specific cancers may be at higher risk than those without cancer for coronavirus disease 2019 (COVID-19) and its severe outcomes. At present, limited data are available on the risk, racial disparity, and outcomes for COVID-19 illness in patients with cancer. Objectives To investigate how patients with specific types of cancer are at risk for COVID-19 infection and its adverse outcomes and whether there are cancer-specific race disparities for COVID-19 infection. Design, Setting, and Participants This retrospective case-control analysis of patient electronic health records included 73.4 million patients from 360 hospitals and 317 000 clinicians across 50 US states to August 14, 2020. The odds of COVID-19 infections for 13 common cancer types and adverse outcomes were assessed. Exposures The exposure groups were patients diagnosed with a specific cancer, whereas the unexposed groups were patients without the specific cancer. Main Outcomes and Measures The adjusted odds ratio (aOR) and 95% CI were estimated using the Cochran-Mantel-Haenszel test for the risk of COVID-19 infection. Results Among the 73.4 million patients included in the analysis (53.6% female), 2 523 920 had at least 1 of the 13 common cancers diagnosed (all cancer diagnosed within or before the last year), and 273 140 had recent cancer (cancer diagnosed within the last year). Among 16 570 patients diagnosed with COVID-19, 1200 had a cancer diagnosis and 690 had a recent cancer diagnosis of at least 1 of the 13 common cancers. Those with recent cancer diagnosis were at significantly increased risk for COVID-19 infection (aOR, 7.14 [95% CI, 6.91-7.39]; P < .001), with the strongest association for recently diagnosed leukemia (aOR, 12.16 [95% CI, 11.03-13.40]; P < .001), non-Hodgkin lymphoma (aOR, 8.54 [95% CI, 7.80-9.36]; P < .001), and lung cancer (aOR, 7.66 [95% CI, 7.07-8.29]; P < .001) and weakest for thyroid cancer (aOR, 3.10 [95% CI, 2.47-3.87]; P < .001). Among patients with recent cancer diagnosis, African Americans had a significantly higher risk for COVID-19 infection than White patients; this racial disparity was largest for breast cancer (aOR, 5.44 [95% CI, 4.69-6.31]; P < .001), followed by prostate cancer (aOR, 5.10 [95% CI, 4.34-5.98]; P < .001), colorectal cancer (aOR, 3.30 [95% CI, 2.55-4.26]; P < .001), and lung cancer (aOR, 2.53 [95% CI, 2.10-3.06]; P < .001). Patients with cancer and COVID-19 had significantly worse outcomes (hospitalization, 47.46%; death, 14.93%) than patients with COVID-19 without cancer (hospitalization, 24.26%; death, 5.26%) (P < .001) and patients with cancer without COVID-19 (hospitalization, 12.39%; death, 4.03%) (P < .001). Conclusions and Relevance In this case-control study, patients with cancer were at significantly increased risk for COVID-19 infection and worse outcomes, which was further exacerbated among African Americans. These findings highlight the need to protect and monitor patients with cancer as part of the strategy to control the pandemic.