The hazardous air pollutants like benzene, toluene, ethylbenzene and xylene (BTEX) are considered as toxic because of their role in ozone formation and adverse effects on human health. Owing to this, ...the present study was carried out at six spatially distributed sites in Delhi from November 2017- June 2018. Activated charcoal tubes were used to collect samples of BTEX and were further analyzed using GC-FID. The minimum BTEX concentration was found at institutional site (9.94 μg/m3) and maximum at roadside site (103.12 μg/m3) with the average of 46.66 μg/m3. Also, the levels of BTEX were 1.18–1.74 times higher during rush hours as compared to non-rush hours. The high T/B ratio (2.26–3.41) observed is the indication of the traffic-originated sources of emission. The cancer risks calculated for benzene at probability 0.50 ranged as 1.29E-06 – 1.80E-05, whereas 4.09E-06 – 3.40E-05 at probability 0.95, which were higher than the acceptable value of 1.0E-06. The non-cancer health risks in terms of hazard index were observed less than unity i.e. within acceptable limit. The total ozone formation potential (OFP) was obtained as 207.51 ± 123.40 μg/m3 with maximum potential by toluene. Such high levels of BTEX, cancer risks and OFP obtained in the study especially at roadside and connectivity hub are harmful for people residing near these areas, and also to large commuters, who are exposed to such emissions during travelling.
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•Average BTEX level of all sampling sites was 46.66 μg/m3.•BTEX levels were found maximum at roadside site followed by connectivity hub site during morning rush-hours.•Cancer risks obtained as more than 1E-06, showing significant long-term health effects.•Hazard quotient of non-cancer risks obtained <1, indicates no adverse effects of BTEX on human health.•The total ozone formation potential of BTEX was obtained as 207.51±123.40 μg/m3.
Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning ...problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.
Lipases, triacylglycerol hydrolases, are an important group of biotechnologically relevant enzymes and they find immense applications in food, dairy, detergent and pharmaceutical industries. Lipases ...are by and large produced from microbes and specifically bacterial lipases play a vital role in commercial ventures. Some important lipase-producing bacterial genera include Bacillus, Pseudomonas and Burkholderia. Lipases are generally produced on lipidic carbon, such as oils, fatty acids, glycerol or tweens in the presence of an organic nitrogen source. Bacterial lipases are mostly extracellular and are produced by submerged fermentation. The enzyme is most commonly purified by hydrophobic interaction chromatography, in addition to some modern approaches such as reverse micellar and aqueous two-phase systems. Most lipases can act in a wide range of pH and temperature, though alkaline bacterial lipases are more common. Lipases are serine hydrolases and have high stability in organic solvents. Besides these, some lipases exhibit chemo-, regio- and enantioselectivity. The latest trend in lipase research is the development of novel and improved lipases through molecular approaches such as directed evolution and exploring natural communities by the metagenomic approach.
We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. Effectively, this is a novel approach to determining the scaling ...relation between a cluster's Sunyaev-Zel'dovich (SZ) effect signal and mass. The deep-learning algorithm used is mResUNet, which is a modified feed-forward deep-learning algorithm that broadly combines residual learning, convolution layers with different dilation rates, image regression activation, and a U-Net framework. We train and test the deep-learning model using simulated images of the microwave sky that include signals from the cosmic microwave background, dusty and radio galaxies, and instrumental noise as well as the cluster's own SZ signal. The simulated cluster sample covers the mass range 1 × 1014 M < M200c < 8 × 1014 M at z = 0.7. The trained model estimates the cluster masses with a 1 uncertainty ΔM/M ≤ 0.2, consistent with the input scatter on the SZ signal of 20%. We verify that the model works for realistic SZ profiles even when trained on azimuthally symmetric SZ profiles by using the Magneticum hydrodynamical simulations.
Breast cancer (BC) is one of the most prevalent cancers in women. Triple-negative breast cancer (TNBC) in which the three major receptors i.e. estrogen receptor (ER), progesterone receptor (PR) and ...human epidermal growth factor receptor 2 (HER2), are absent is known to express the most aggressive phenotype and increased metastasis which results in the development of resistance to chemotherapy. It offers various therapeutic advantages in treating BC and TNBC. Nanotechnology offers various unique characteristics such as small size (nanometric), active and passive targeting, and the ability to attach multiple targeting moieties, controlled release, and site-specific targeting. This review focuses on conventional drug therapies, recent treatment strategies, and unique therapeutic approaches available for BC and TNBC. The role of breast cancer stem cells in the recurrence of BC and TNBC has also been highlighted. Several chemotherapeutic agents delivered using nanocarriers such as polymeric nanoparticles/micelles, metallic/inorganic NPs, and lipid-based NPs (Liposome, solid-lipid nanoparticles (SLNs), and nanostructured lipid carriers (NLCs)), etc. with excellent responses in the treatment of BC/TNBC along with breast cancer stem cells have been discussed in details. Moreover, the application of nanomedicine including CRISPR nanoparticle, exosomes for the treatment of BC/TNBC and other molecular targets available such as poly (ADP-ribose) polymerase (PARP), epidermal growth factor receptor (EGFR), Vascular endothelial growth factor (VEGF), etc. for further exploration have also been discussed.
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•BC and TNBC prevalence & recurrence attributed to tumor heterogeneity & individual variations.•The identification of the role of stem cells in tumor progression and metastasis is the need of the era.•Nanotechnology-based approaches offer significant potential to address the unmet needs of cancer treatments.•Phytoconstituents can offer broad spectrum of solutions to target multiple pathways involved in breast cancer progression.•The future of BC therapy lies in close collaboration among phytochemists, molecular biologists, & formulation scientists.
Abstract
We present a new application of deep learning to reconstruct the cosmic microwave background (CMB) temperature maps from images of the microwave sky and to use these reconstructed maps to ...estimate the masses of galaxy clusters. We use a feed-forward deep-learning network, mResUNet, for both steps of the analysis. The first deep-learning model, mResUNet-I, is trained to reconstruct foreground and noise-suppressed CMB maps from a set of simulated images of the microwave sky that include signals from the CMB, astrophysical foregrounds like dusty and radio galaxies, instrumental noise as well as the cluster’s own thermal Sunyaev–Zel’dovich signal. The second deep-learning model, mResUNet-II, is trained to estimate cluster masses from the gravitational-lensing signature in the reconstructed foreground and noise-suppressed CMB maps. For SPTpol-like noise levels, the trained mResUNet-II model recovers the mass for 10
4
galaxy cluster samples with a 1
σ
uncertainty
Δ
M
200
c
est
/
M
200
c
est
=
0.108 and 0.016 for input cluster mass
M
200
c
true
=
10
14
M
⊙
and 8 × 10
14
M
⊙
, respectively. We also test for potential bias on recovered masses, finding that for a set of 10
5
clusters the estimator recovers
M
200
c
est
=
2.02
×
10
14
M
⊙
, consistent with the input at 1% level. The 2
σ
upper limit on potential bias is at 3.5% level.
Seed priming technique has a marvelous potential in enhancing seed germination and crop establishment under limited soil moisture conditions, which ultimately increases yield. Therefore, we ...investigated the effects of seed priming on physiology, growth, yield and antioxidant defense system of pearl millet (Pennisetum glaucum L.) under rain-fed condition. The experiments were conducted under laboratory as well as field conditions comprising three treatments i.e., non-primed seeds (control, T0), priming with tap water (hydropriming) (T1) and priming with 2% KNO3 2% for 6 hours at 25°C followed by shade drying (T2). The results showed that chlorophyll content (10.37-14.15%) and relative water content (RWC) (12.70-13.01%) increased whereas proline (-19.44 to -25%) and soluble sugar (-15.51 to -29.13%) contents decreased on account of seed priming in pearl millet under field conditions. The seed priming significantly improved the plant height, final plant stand and grain weight which resulted in increased yield. Enhanced activities of superoxide dismutase (SOD) (5.89 to 8.10 unit/g/seed/min), catalase (CAT) (22.54 to 39.67 µmol/min/g/seed) and ascorbate peroxidase (APX) (8.92 to 22.10 µmol/cm/min/g) and concomitant decrease in H2O2 and malondialdehyde (MDA) content suggests their role in imparting oxidative tolerance at initial stages of growth in primed seed. The lab studies suggest that the improved yield might be attributes to increased seed germination and seedling vigor. It is recommended that the hydropriming (tap water) or KNO3 (2%) priming of seeds for 6 hours under ambient conditions is effective to enhance growth and yield of pearl millet under rainfed conditions.
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