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"Nguyen, Tu"
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The thinsulin program : the breakthrough solution to help you lose weight and stay thin
A two-stage weight loss program draws on decades of medical and psychiatric expertise to explain how to regulate insulin levels, rather than calories, to promote fat burning and prevent the body from storing unnecessary fat.
Book
Deep learning-based facial emotion recognition for human–computer interaction applications
One of the most significant fields in the man–machine interface is emotion recognition using facial expressions. Some of the challenges in the emotion recognition area are facial accessories, non-uniform illuminations, pose variations, etc. Emotion detection using conventional approaches having the drawback of mutual optimization of feature extraction and classification. To overcome this problem, researchers are showing more attention toward deep learning techniques. Nowadays, deep-learning approaches are playing a major role in classification tasks. This paper deals with emotion recognition by using transfer learning approaches. In this work pre-trained networks of Resnet50, vgg19, Inception V3, and Mobile Net are used. The fully connected layers of the pre-trained ConvNets are eliminated, and we add our fully connected layers that are suitable for the number of instructions in our task. Finally, the newly added layers are only trainable to update the weights. The experiment was conducted by using the CK + database and achieved an average accuracy of 96% for emotion detection problems.
Journal Article
Global and national high blood pressure burden and control
High blood pressure is a leading modifiable cause of premature death and one of WHO's global targets for the prevention of non-communicable diseases. [...]these are still estimates based on the best available data.6 The disappointing message of this study, however, is that despite much research, health systems, and global policy efforts, progress has been slow in the global control of hypertension.7 There is an urgent need for a transformation and innovative approaches to reduce the burden of hypertension globally. From a medical model point of view, digital transformation such as telemonitoring, home blood pressure monitoring, text message reminders to improve adherence, and other digital health interventions to encourage healthy behaviours, or simpler medical regimens such as initial treatment with a combination therapy—such as a single pill containing ultra-low-dose quadruple combination therapy—should be considered to address barriers to blood pressure control.9–12 Finally the standstill in global prevalence and the global control rates of approximately 20% should serve as an important global wakeup call that cardiovascular disease is going to be a main burden of disease for many years to come, especially if we carry on like this.
Journal Article
Fine-tuned support vector regression model for stock predictions
In this paper, a new machine learning (ML) technique is proposed that uses the fine-tuned version of support vector regression for stock forecasting of time series data. Grid search technique is applied over training dataset to select the best kernel function and to optimize its parameters. The optimized parameters are validated through validation dataset. Thus, the tuning of this parameters to their optimized value not only increases model’s overall accuracy but also requires less time and memory. Further, this also minimizes the model from being data overfitted. The proposed method is used to analysis different performance parameters of stock market like up-to-daily and up-to-monthly return, cumulative monthly return, its volatility nature and the risk associated with it. Eight different large-sized datasets are chosen from different domain, and stock is predicted for each case by using the proposed method. A comparison is carried out among the proposed method and some similar methods of same interest in terms of computed root mean square error and the mean absolute percentage error. The comparison reveals the proposed method to be more accurate in predicting the stocks for the chosen datasets. Further, the proposed method requires much less time than its counterpart methods.
Journal Article
Surface modifications of superparamagnetic iron oxide nanoparticles with citric acid, selenium, and silver combining with polymer blend as antibacterial agent
Antibiotic resistance is a growing global health crisis. This study introduces a novel nanocomposite material incorporating superparamagnetic iron oxide nanoparticles (SPION), citric acid (CA), selenium (Se), silver (Ag), and a polymer matrix (M8) consisting of polyethylene glycol, polyvinylpyrrolidone, chitosan, and polyvinyl alcohol. The novel materials were used to inhibit Pseudomonas aeruginosa (PA), Staphylococcus aureus (SA), and Salmonella enterica (SE). The optimal molar ratio of SPION:CA: CSPION:Se: CSPION/Se/Ag was 1:2:0.5, yielding nanoparticles with an average size of 15.09 ± 2.95 nm (FE-SEM) and a saturation magnetization of 21.51 emu/g (VSM). XRD confirmed the coexistence of Fe₃O₄, Se, and Ag crystalline phases, while FTIR revealed ionic and hydrogen bonding interactions between the polymers, citric acid, and metal nanoparticles. EDS analysis validated the successful incorporation of Se (7.06 wt%) and Ag (22.00 wt%). At this ratio, the inhibition percentage against PA, SA, and SE (using the minimum inhibitory concentration method) at 50% dilution is 99.99 ± 0.52%, 99.31 ± 2.74%, and 47.41 ± 3.69%, respectively. The superior performance is attributed to synergistic effects between SPION, Se, Ag, and the polymer blend, offering a promising approach for combating antibiotic-resistant bacteria.
Journal Article
Amino acids in cancer
Over 90 years ago, Otto Warburg’s seminal discovery of aerobic glycolysis established metabolic reprogramming as one of the first distinguishing characteristics of cancer1. The field of cancer metabolism subsequently revealed additional metabolic alterations in cancer by focusing on central carbon metabolism, including the citric acid cycle and pentose phosphate pathway. Recent reports have, however, uncovered substantial non-carbon metabolism contributions to cancer cell viability and growth. Amino acids, nutrients vital to the survival of all cell types, experience reprogrammed metabolism in cancer. This review outlines the diverse roles of amino acids within the tumor and in the tumor microenvironment. Beyond their role in biosynthesis, they serve as energy sources and help maintain redox balance. In addition, amino acid derivatives contribute to epigenetic regulation and immune responses linked to tumorigenesis and metastasis. Furthermore, in discussing the transporters and transaminases that mediate amino acid uptake and synthesis, we identify potential metabolic liabilities as targets for therapeutic intervention.Cancer: How tumors hijack basic building blocksCancer changes how the body uses amino acids, the building blocks that all proteins are made of. A better understanding of these changes could lead to new cancer therapies. Cancer was already known to alter the body’s sugar metabolism to feed extra energy to fast-growing tumors. Recent reports have revealed that cancer also rewires amino acid metabolism. Jiyeon Kim at the University of Illinois at Chicago, USA, and co-workers have reviewed how cancer co-opts amino acids. They report that tumors use amino acids as an energy source and antioxidant precursor to balance their production of toxic reactive oxygen species. Amino acids are also instrumental in annotating the epigenetic code to enhance or suppress expression of tumor-related genes. The review illuminates a promising new approach to cancer therapeutics.
Journal Article
Electrochemical Sensor Based on Reduced Graphene Oxide/Double-Walled Carbon Nanotubes/Octahedral Fe3O4/Chitosan Composite for Glyphosate Detection
In this work, reduced graphene oxide/double-walled carbon nanotubes/octahedral-Fe3O4/chitosan composite material modified screen-printed gold electrodes (rGO/DWCNTs/Oct-Fe3O4/Cs/SPAuE) under inhibition of urease enzyme was developed for the determination of glyphosate (GLY). The electrochemical behaviors of GLY on these electrodes were evaluated by square wave voltammetry (SWV). With the electroactive surface area is 1.7 times higher than that of bare SPAuE, the rGO/DWCNTs/Oct-Fe3O4/Cs/SPAuE for detection of GLY shows a low detection limit (LOD) of ~ 0.08 ppb in a large concentration range of 0.1–1000 ppb. Moreover, it is also successfully applied to the determination of GLY in river water samples with recoveries and relative standard deviations (RSDs) from 98.7% to 106.9% and from 0.79% to 0.87%, respectively. The developed composite will probably provide an universal electrochemical sensing platform that is very promising for environmental monitoring.
Journal Article
Surface modifications of graphitic carbon nitride with metal particles and polymer blend as antibacterial and dye removal agents
In this study, novel dual-function composite was developed to remove dye and inhibit the growth of bacteria. In short, a metal oxide zinc oxide particles (ZnOP), copper oxide particles (CuOP), nickel oxide particles (NiOP), and zinc/nickel/copper oxide particles (ZNC) which has the average size (obtained from FE-SEM) of 165.8 ± 31.1, 117.3 ± 36.0, 97.0 ± 29.3, 108.3 ± 73.4 nm, respectively was modified with a polymer blend (M8F), consisting of polyvinyl alcohol, chitosan, polyvinylpyrrolidone, polyethylene glycol and graphitic carbon nitride (g-C
3
N
4
). The composite that can inhibit the growth of
Pseudomonas aeruginosa (PA)
while can remove the most methylene blue (MB), malachite green (MG), and Congo red (CR) is ZNC/M8F/g-C
3
N
4
. Additionally, ZnO/M8F/g-C
3
N
4
can be used as dual-function composite to remove methyl orange (MO) and inhibit the growth of
Staphylococcus aureus (SA)
. In the case of
Salmonella enterica
(
SE
), none of the materials can inhibit at least 50% of the bacteria. Additionally, none of the materials can kill
SA
,
PA
, and
SE
. Hence, ZNC/M8F/g-C
3
N
4
and ZnO/M8F/g-C
3
N
4
must be further modified to remove all of the metioned dye and inhibit the growth of mentioned bacteria better. The dye removing and bacteria growth inhibition experiments were done using adsorption and minimum inhibition concentration method.
Journal Article
Assessing the carbonisation temperatures recorded by ancient charcoals for δ13C-based palaeoclimate reconstruction
Ancient charcoal fragments, produced by the use of wood as fuel in archaeological contexts or during natural or anthropic forest fires, persist in soil and sediments over centuries to millennia. They thus offer a unique window to reconstruct past climate, especially palaeo-precipitation regimes thanks to their stable carbon isotope composition. However, the initial δ
13
C of wood is slightly modified as a function of the carbonisation temperature. Carbonisation-induced
13
C fractionation is classically investigated through a transfer function between experimental carbonisation temperatures and the carbon content. This approach assumes that the carbon content is conservative through time in ancient charcoals and neglects the potential impact of post-depositional oxidation occurring in soils and sediments. In the present study, we first show that post-depositional oxidation can lead to a large underestimation of past carbonisation temperatures, thereby minimising the estimation of carbonisation-induced
13
C fractionations and possibly biasing δ
13
C-based climate reconstructions. Secondly, by combining carbon content, Fourier-transform infrared and Raman spectroscopy, we propose a new framework to assess the carbonisation temperatures registered in ancient charcoals. This new framework paves the way to reassessing δ
13
C-based climate reconstruction.
Journal Article