Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 ...pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.
Starting at birth, large numbers of sex-selective abortions in some parts of India lead to skewed gender ratios in the general population.2 The biases against girls in India are often firmly ...entrenched in the sociocultural milieu, and extend to multiple aspects of health care.3 Consequently, there is a gender differential in infant and under-5 mortality. 4,5 In The Lancet Oncology, Kanu Priya Bhatia and colleagues systematically document a definite gender bias in favour of boys with cancer in India. IndianFaces/Shutterstock.com Using data from three hospital-based cancer registries, Bhatia and colleagues also show that the male bias was more pronounced at registration or diagnosis, but was not apparent for treatment (after taking into account sex ratio at diagnosis).6 This is a positive finding and would need validation from other cohort studies. Treatment abandonment, which is a major cause of treatment failure in resource-poor settings, has been reported to be higher in girls with cancer in India than in boys, suggesting a challenge in accessing treatment even after diagnosis, with low literacy and poor socioeconomic status being key determinants.7,8 Notably, according to Bhatia and colleagues' findings, fewer girls than boys underwent haematopoietic stem cell transplant, even after adjusting for the sex ratio at diagnosis.
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for ...simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieves an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identifies patients with high CVD mortality risks (AUC of 0.768). We validate our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects. Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.
Machining processes involve thermo-mechanical loading, which influences the tool wear and surface quality. Nano lubricants are effective in reducing the friction between tool-work contact surfaces. ...However, nanofluids prepared with plant-based oils are more desirable in view of ecological concerns. The present work aims to investigate the machining and tribological performance of novel vegetable oil-based nanofluid. Al
2
O
3
and ZrO nanoparticles have been dispersed in Jatropha oil to develop biodegradable nanofluids and are investigated for their dispersion stability and anti-corrosion characteristics. The prepared nanofluids have been found to be stable for 48 h via UV–vis and zeta potential studies. The anti-corrosion capability of the nanofluids has been confirmed for their suitability in a corrosive environment. Tribological characteristics of the nanofluids have been investigated using a pin-on-disc wear test with Hastelloy C-276 and tungsten carbide mating pair. The experimental findings have shown a noticeable reduction in the coefficient of friction and wear loss. The coefficient of friction has been reduced by 83.3%, 85%, 80%, and 81.6% using JO, JO + 0.5% Al
2
O
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, JO + 0.5% ZrO, and JO + 0.5% Al
2
O
3
+ 0.5% ZrO respectively as compared to dry condition. Furthermore, wear loss has been decreased by 51.7%, 72.3%, 61.6% and 73.1% using JO, JO + 0.5% Al
2
O
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, JO + 0.5% ZrO and JO + 0.5% Al
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+ 0.5% ZrO in comparison with dry condition. Also, the machining performance of nanofluids has shown a significant decrease in cutting forces and surface roughness. The ultrasonically produced atomized mist of nanofluids has resulted in a decrease in tool wear and produces chip segmentation. The prepared unitary and hybrid nanofluids have shown an immense potential to address the environmental concerns of machining difficult-to-cut materials.
Graphical abstract
The COVID‐19 pandemic has unfolded to be the most challenging global health crisis in a century. In 11 months since its first emergence, according to WHO, the causative infectious agent SARS‐CoV‐2 ...has infected more than 100 million people and claimed more than 2.15 million lives worldwide. Moreover, the world has raced to understand the virus and natural immunity and to develop vaccines. Thus, within a short 11 months a number of highly promising COVID‐19 vaccines were developed at an unprecedented speed and are now being deployed via emergency use authorization for immunization. Although a considerable number of review contributions are being published, all of them attempt to capture only a specific aspect of COVID‐19 or its therapeutic approaches based on ever‐expanding information. Here, we provide a comprehensive overview to conceptually thread together the latest information on global epidemiology and mitigation strategies, clinical features, viral pathogenesis and immune responses, and the current state of vaccine development.
Toxin-antitoxin (TA) systems are highly conserved in members of the Mycobacterium tuberculosis (Mtb) complex and have been proposed to play an important role in physiology and virulence. Nine of ...these TA systems belong to the mazEF family, encoding the intracellular MazF toxin and its antitoxin, MazE. By overexpressing each of the nine putative MazF homologues in Mycobacterium bovis BCG, here we show that Rv1102c (MazF3), Rv1991c (MazF6) and Rv2801c (MazF9) induce bacteriostasis. The construction of various single-, double- and triple-mutant Mtb strains reveals that these MazF ribonucleases contribute synergistically to the ability of Mtb to adapt to conditions such as oxidative stress, nutrient depletion and drug exposure. Moreover, guinea pigs infected with the triple-mutant strain exhibits significantly reduced bacterial loads and pathological damage in infected tissues in comparison with parental strain-infected guinea pigs. The present study highlights the importance of MazF ribonucleases in Mtb stress adaptation, drug tolerance and virulence.
Objective
Assess if deep learning–based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs).
Methods
This reader study included 173 ...images from cancer-positive patients (n = 98) and 346 images from cancer-negative patients (n = 196) selected from National Lung Screening Trial (NLST). Eight readers, including three radiology residents, and five board-certified radiologists, participated in the observer performance test. AI algorithm provided image-level probability of pulmonary nodule or mass on CXRs and a heatmap of detected lesions. Reader performance was compared with AUC, sensitivity, specificity, false-positives per image (FPPI), and rates of chest CT recommendations.
Results
With AI, the average sensitivity of readers for the detection of visible lung cancer increased for residents, but was similar for radiologists compared to that without AI (0.61 95% CI, 0.55–0.67 vs. 0.72 95% CI, 0.66–0.77,
p
= 0.016 for residents, and 0.76 95% CI, 0.72–0.81 vs. 0.76 95% CI, 0.72–0.81,
p
= 1.00 for radiologists), while false-positive findings per image (FPPI) was similar for residents, but decreased for radiologists (0.15 95% CI, 0.11–0.18 vs. 0.12 95% CI, 0.09–0.16,
p
= 0.13 for residents, and 0.24 95% CI, 0.20–0.29 vs. 0.17 95% CI, 0.13–0.20,
p
< 0.001 for radiologists). With AI, the average rate of chest CT recommendation in patients positive for visible cancer increased for residents, but was similar for radiologists (54.7% 95% CI, 48.2–61.2% vs. 70.2% 95% CI, 64.2–76.2%,
p
< 0.001 for residents and 72.5% 95% CI, 68.0–77.1% vs. 73.9% 95% CI, 69.4–78.3%,
p
= 0.68 for radiologists), while that in cancer-negative patients was similar for residents, but decreased for radiologists (11.2% 95% CI, 9.6–13.1% vs. 9.8% 95% CI, 8.0–11.6%,
p
= 0.32 for residents and 16.4% 95% CI, 14.7–18.2% vs. 11.7% 95% CI, 10.2–13.3%,
p
< 0.001 for radiologists).
Conclusions
AI algorithm can enhance the performance of readers for the detection of lung cancers on chest radiographs when used as second reader.
Key Points
• Reader study in the NLST dataset shows that AI algorithm had sensitivity benefit for residents and specificity benefit for radiologists for the detection of visible lung cancer.
• With AI, radiology residents were able to recommend more chest CT examinations (54.7% vs 70.2%, p < 0.001) for patients with visible lung cancer.
• With AI, radiologists recommended significantly less proportion of unnecessary chest CT examinations (16.4% vs. 11.7%, p < 0.001) in cancer-negative patients.
Deep learning (DL) based solutions have been proposed for interpretation of several imaging modalities including radiography, CT, and MR. For chest radiographs, DL algorithms have found success in ...the evaluation of abnormalities such as lung nodules, pulmonary tuberculosis, cystic fibrosis, pneumoconiosis, and location of peripherally inserted central catheters. Chest radiography represents the most commonly performed radiological test for a multitude of non-emergent and emergent clinical indications. This study aims to assess accuracy of deep learning (DL) algorithm for detection of abnormalities on routine frontal chest radiographs (CXR), and assessment of stability or change in findings over serial radiographs.
We processed 874 de-identified frontal CXR from 724 adult patients (> 18 years) with DL (Qure AI). Scores and prediction statistics from DL were generated and recorded for the presence of pulmonary opacities, pleural effusions, hilar prominence, and enlarged cardiac silhouette. To establish a standard of reference (SOR), two thoracic radiologists assessed all CXR for these abnormalities. Four other radiologists (test radiologists), unaware of SOR and DL findings, independently assessed the presence of radiographic abnormalities. A total 724 radiographs were assessed for detection of findings. A subset of 150 radiographs with follow up examinations was used to asses change over time. Data were analyzed with receiver operating characteristics analyses and post-hoc power analysis.
About 42% (305/ 724) CXR had no findings according to SOR; single and multiple abnormalities were seen in 23% (168/724) and 35% (251/724) of CXR. There was no statistical difference between DL and SOR for all abnormalities (p = 0.2-0.8). The area under the curve (AUC) for DL and test radiologists ranged between 0.837-0.929 and 0.693-0.923, respectively. DL had lowest AUC (0.758) for assessing changes in pulmonary opacities over follow up CXR. Presence of chest wall implanted devices negatively affected the accuracy of DL algorithm for evaluation of pulmonary and hilar abnormalities.
DL algorithm can aid in interpretation of CXR findings and their stability over follow up CXR. However, in its present version, it is unlikely to replace radiologists due to its limited specificity for categorizing specific findings.
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