3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

Acute-on-chronic Liver Failure: MELD Score 30-day Mortality Predictability and Etiology in a Pakistani Population

Background: Cirrhosis is a pathological condition that ultimately leads to liver failure. Acute on chronic liver failure (ACLF) has a high short term mortality rate. Viral hepatitis is the most common cause of liver failure in our local population. We carried out this study to identity the 30-day mortality and etiology of patients presenting with ACLF using Model for End-Stage Liver Disease (MELD) score predictability. Methodology: This was a descriptive case series, conducted at Sheikh Zayed Hospital, Lahore, Pakistan from January 31, 2018 to July 30, 2018. One hundred and eighty five patients who met the inclusion criteria were enrolled using 95% confidence level and 4% margin of error. Data was entered and analyzed with SPSS version 23.0. Numerical variables including age was presented by Mean ± S.D. Categorical variables i.e. gender, etiology of acute-on-chronic liver failure and 30-day mortality were presented by frequency and percentage. Data was stratified for age, gender, duration of chronic liver disease and MELD grade to address the effect modifiers. Post-stratification chi-square test was calculated using 95% significance (p≤0.05). Results: Majority of the enrolled patients were male (74.6%) while only 25.4% of the patients were female. One hundred and thirty patients (70.3%) had underlying viral hepatitis while twelve patients (6.5%) and forty three patients (23.2%) presented with alcoholic liver disease and drug-induced ACLF, respectively. Eighty patients (43.2%) died within 30 days of admission.The 30-day mortality with respect to MELD grade was statistically significant (p<0.001) with the highest mortality noted in grade-IV and thirty five patients (43.8%) dying within 30 days of admission (p<0.001). Grade-II and III MELD scores also contributed to the 30-day mortality with twenty three patients (28.8%) and nineteen patients (23.8%) dying within 30 days of admission (p<0.001). Conclusion: MELD scores are able to accurately predict the short-term mortality in patients with ACLF and viral hepatitis was the most common etiology in our population. Early detection and use of appropriate prognostic models may alleviate mortality and morbidity in paitents with ACLF

Nonstationary spatiotemporal Bayesian data fusion for pollutants in the near‐road environment

Concentrations of near‐road air pollutants (NRAPs) have increased to very high levels in many urban centers around the world, particularly in developing countries. The adverse health effects of exposure to NRAPs are greater when the exposure occurs in the near‐road environment as compared to background levels of pollutant concentration. Therefore, there is increasing interest in monitoring pollutant concentrations in the near‐road environment. However, due to various practical limitations, monitoring pollutant concentrations near roadways and traffic sources is generally rather difficult and expensive. As an alternative, various deterministic computer models that provide predictions of pollutant concentrations in the near‐road environment, such as the research line‐source dispersion model (RLINE), have been developed. A common feature of these models is that their outputs typically display systematic biases and need to be calibrated in space and time using observed pollutant data. In this paper, we present a nonstationary Bayesian data fusion model that uses a novel data set on monitored pollutant concentrations (nitrogen oxides or NOx and fine particulate matter or PM2.5) in the near‐road environment and, combining it with the RLINE model output, provides predictions at unsampled locations. The model can also be used to evaluate whether including the RLINE model output leads to improved pollutant concentration predictions and whether the RLINE model output captures the spatial dependence structure of NRAP concentrations in the near‐road environment. A defining characteristic of the proposed model is that we model the nonstationarity in the pollutant concentrations by using a recently developed approach that includes covariates, postulated to be the driving force behind the nonstationary behavior, in the covariance function.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151876/1/env2581.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151876/2/env2581_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151876/3/ENV_2581-Supp-0001-BDF_supp_material.pd

Synthesis, structural and antibacterial study of new silver complex with 3-acetyl-2H chromene-2-one

A new silver complex [Ag(C11H8O3)2]NO3 was synthesized by the reaction of silver nitrateand coumarin based ligand (3-acetyl-2H-chromene-2-one) through solution method. The product was characterized using different analytical techniques like melting point, Infrared spectroscopy, Raman spectroscopy, powder X-ray diffraction, thermogravimetric analysis, scanning electron microscopy, atomic absorption spectroscopy and mass spectrometry. An antibacterial study of the complex was also studied for its possible use in medical treatment. KEY WORDS: Silver complex, Acetyl coumarin, Vibrational analysis, Antibacterial study Bull. Chem. Soc. Ethiop. 2016, 30(3), 403-411DOI: http://dx.doi.org/10.4314/bcse.v30i3.

Double-Lepton Polarization Asymmetries in B->K_1 l^+ l^- Decay in Universal Extra Dimension Model

Double-lepton polarization asymmetries for the exclusive decay B->K_1 l^+ l^- in the Universal Extra Dimension (UED) Model is studied. It is obtained that double-lepton polarization asymmetries are very sensitive to the UED model parameters. Experimental measurements of double lepton polarizations can give valuable information on the physics beyond the Standard Model (SM).Comment: 15 pages, 10 figure

The analgesic and anticonvulsant effects of piperine in mice

Piperine, is the major active principal of black pepper. In traditional medicine, black pepper has been used as an analgesic, anti-inflammatory agent and in the treatment of epilepsy. This study was conducted to evaluate the in vivo analgesic and anticonvulsant effects of piperine in mice. The analgesic and anticonvulsant effects of piperine were studied in mice using acetic acid-induced writhing, tail flick assay, pentylenetetrazole (PTZ)- and picrotoxin (PIC)-induced seizures models. The intraperitoneal (i.p.) administration of piperine (30, 50 and 70 mg/kg) significantly inhibited (P\u3c0.01) the acetic acid-induced writhing in mice, similar to the effect of indomethacin (20 mg/kg i.p.). In the tail flick assay, piperine (30 and 50 mg/kg, i.p.) and morphine (5 mg/kg, i.p.) caused a significant increase (P\u3c0.01) in the reaction time of mice. Pre-treatment of animals with naloxone (5 mg/kg i.p.), reversed the analgesic effects of both piperine and morphine in the tail flick assay. Piperine (30, 50 and 70 mg/kg, i.p.) and standard drugs, valproic acid (200 mg/kg, i.p.), carbamazepine (30 mg/kg, i.p.) and diazepam (1 mg/kg, i.p.) significantly (P\u3c0.01) delayed the onset of PTZ-and PIC-induced seizures in mice. These findings indicate that piperine exhibits analgesic and anticonvulsant effects possibly mediated via opioid and GABA-ergic pathways respectively. Moreover, piperine being the main constituent of black pepper, may be contributing factor in the medicinal uses of black pepper in pain and epileps

Effects of lipoproteins on cyclo-oxygenase and lipoxygenase pathways in human platelets

The products of arachidonic acid (AA) metabolism in platelets play an important role in platelet shape change, adhesion and aggregation which may participate in the pathogenesis of ischemic heart disease and thrombosis. Since lipoproteins are also involved in the pathogenesis of thrombo-embolic disorders, the effect of human lipoproteins (HDL, LDL, VLDL) on AA metabolism in human platelets was investigated. Lipoproteins were separated by density gradient zonal ultracentrifugation. The effects of lipoproteins on production of AA metabolites in human platelets i.e., thromboxane A2 (TXA2) and hydroxy-eicosatetraenoic acids (HETEs) were examined using radiometric thin layer chromatography coupled with automated data integrator system. In human platelets, HDL inhibited 12-HETE and TXA2 formation in a concentration-dependent manner. LDL had a strong inhibitory effect on TXA2 production and a weak inhibitory effect on 12-HETE production. VLDL had no effect on platelet AA metabolism. These findings point to a new facet of lipoproteins action and suggest that lipoproteins may have a physiological role in the regulation of AA metabolism in platelets

Altered platelet activating factor metabolism in insulin dependent diabetes mellitus

Diabetes mellitus is associated with several abnormalities of platelet function. Recent studies have shown that the blood level of platelet activating factor (PAF), a potent inducer of platelet aggregation, is elevated in insulin dependent diabetes mellitus (IDDM) and remains unchanged in non-insulin dependent diabetes mellitus (NIDDM) patients. However, the mechanism of this increase in PAF levels has not been determined. In this study we have measured the activity of plasma PAF acetylhydrolase (an enzyme that regulates PAF levels) and lipoprotein levels in control subjects and diabetic patients. The data presented show that plasma PAF acetylhydrolase activity is significantly decreased in IDDM and is not altered in NIDDM patients. The lipoprotein levels were similar in control and diabetic subjects and there was no correlation between lipoprotein levels and PAF acetylhydrolase activity. These results suggest that the elevated levels of PAF in IDDM patients could be due to a decrease in plasma PAF acetylhydrolase activity

A Robust Internet of Drones Security Surveillance Communication Network Based on IOTA

cations. The rise in drone usage underscores privacy and security challenges concerning flight boundaries, data collection in public and private domains, and data storage and dissemination. Such issues highlight the drones’ capability to communicate and securely store data over potentially insecure channels. Recognizing these challenges and gaps in the research, this paper introduces an efficient and secure security surveillance model for the Internet of Drones (IoD). Our model ensures secure communication between Ground Stations (GS) and Drones, effectively addressing various attack types. Particularly, surveillance drones are vulnerable to physical capture attacks. We delve into a scenario where a network drone is physically apprehended. Leveraging the information stored within the drone, the attacker could potentially access the session. This paper proposes a solution to counter such threats. Through experiments using MATLAB and VScode, we evaluate our network’s efficiency and scalability in relation to the surge in transactions. The findings reveal our model’s prowess in handling large-scale networks. Specifically, when transactions surpass 1000 per minute, our model achieves approximately a 20% reduction in processing time compared to existing studies. Moreover, our approach facilitates about 80% enhanced communication efficiency relative to the contemporary state- of-the-art frameworks. A security analysis via AVISPA further corroborates the robustness and security of our proposed communication strategy against diverse attack types