Artificial Neural Network for Predicting Car Performance Using JNN

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas

Neural Network-Based Water Quality Prediction

Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water quality monitoring and decision-making for stakeholders and policymakers

Dental pulp pluripotent-like stem cells (DPPSC), a new stem cell population with chromosomal stability and osteogenic capacity for biomaterials evaluation

Background: Biomaterials are widely used to regenerate or substitute bone tissue. In order to evaluate their potential use for clinical applications, these need to be tested and evaluated in vitro with cell culture models. Frequently, immortalized osteoblastic cell lines are used in these studies. However, their uncontrolled proliferation rate, phenotypic changes or aberrations in mitotic processes limits their use in long-term investigations. Recently, we described a new pluripotent-like subpopulation of dental pulp stem cells derived from the third molars (DPPSC) that shows genetic stability and shares some pluripotent characteristics with embryonic stem cells. In this study we aim to describe the use of DPPSC to test biomaterials, since we believe that the biomaterial cues will be more critical in order to enhance the differentiation of pluripotent stem cells. Methods: The capacity of DPPSC to differentiate into osteogenic lineage was compared with human sarcoma osteogenic cell line (SAOS-2). Collagen and titanium were used to assess the cell behavior in commonly used biomaterials. The analyses were performed by flow cytometry, alkaline phosphatase and mineralization stains, RT-PCR, immunohistochemistry, scanning electron microscopy, Western blot and enzymatic activity. Moreover, the genetic stability was evaluated and compared before and after differentiation by short-comparative genomic hybridization (sCGH). Results: DPPSC showed excellent differentiation into osteogenic lineages expressing bone-related markers similar to SAOS-2. When cells were cultured on biomaterials, DPPSC showed higher initial adhesion levels. Nevertheless, their osteogenic differentiation showed similar trend among both cell types. Interestingly, only DPPSC maintained a normal chromosomal dosage before and after differentiation on 2D monolayer and on biomaterials. Conclusions: Taken together, these results promote the use of DPPSC as a new pluripotent-like cell model to evaluate the biocompatibility and the differentiation capacity of biomaterials used in bone regeneration

Low Birth Weight Prediction Using JNN

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy

OGX-427 inhibits tumor progression and enhances gemcitabine chemotherapy in pancreatic cancer

Despite many advances in oncology, almost all patients with pancreatic cancer (PC) die of the disease. Molecularly targeted agents are offering hope for their potential role in helping translate the improved activity of combination chemotherapy into improved survival. Heat shock protein 27 (Hsp27) is a chaperone implicated in several pathological processes such as cancer. Further, Hsp27 expression becomes highly upregulated in cancer cells after chemotherapy. Recently, a modified antisense oligonucleotide that is complementary to Hsp27 (OGX-427) has been developed, which inhibits Hsp27 expression and enhances drug efficacy in cancer xenograft models. Phase II clinical trials using OGX-427 in different cancers like breast, ovarian, bladder, prostate and lung are in progress in the United States and Canada. In this study, we demonstrate using TMA of 181 patients that Hsp27 expression and phosphorylation levels increase in moderately differentiated tumors to become uniformly highly expressed in metastatic samples. Using MiaPaCa-2 cells grown both in vitro and xenografted in mice, we demonstrate that OGX-427 inhibits proliferation, induces apoptosis and also enhances gemcitabine chemosensitivity via a mechanism involving the eukaryotic translation initiation factor 4E. Collectively, these findings suggest that the combination of Hsp27 knockdown with OGX-427 and chemotherapeutic agents such as gemcitabine can be a novel strategy to inhibit the progression of pancreas cancer

Mechanisms of Resistance to Decitabine in the Myelodysplastic Syndrome

Purpose: The DNA methylation inhibitor 5-aza-29-deoxycytidine (DAC) is approved for the treatment of myelodysplastic syndromes (MDS), but resistance to DAC develops during treatment and mechanisms of resistance remain unknown. Therefore, we investigated mechanisms of primary and secondary resistance to DAC in MDS. Patients and Methods: We performed Quantitative Real-Time PCR to examine expression of genes related to DAC metabolism prior to therapy in 32 responders and non-responders with MDS as well as 14 patients who achieved a complete remission and subsequently relapsed while on therapy (secondary resistance). We then performed quantitative methylation analyses by bisulfite pyrosequencing of 10 genes as well as Methylated CpG Island Amplification Microarray (MCAM) analysis of global methylation in secondary resistance. Results: Most genes showed no differences by response, but the CDA/DCK ratio was 3 fold higher in non-responders than responders (P,.05), suggesting that this could be a mechanism of primary resistance. There were no significant differences at relapse in DAC metabolism genes, and no DCK mutations were detected. Global methylation measured by the LINE1 assay was lower at relapse than at diagnosis (P,.05). On average, the methylation of 10 genes was lower at relapse (16.1%) compared to diagnosis (18.1%) (P,.05).MCAM analysis showed decreased methylation of an average of 4.5 % (range 0.6%– 9.7%) of the genes at relapse. By contrast, new cytogenetic changes were found in 20 % of patients

Hydrogen Sulfide Donor NaHS Improves Metabolism and Reduces Muscle Atrophy in Type 2 Diabetes: Implication for Understanding Sarcopenic Pathophysiology

Sarcopenia, a loss of muscle mass and functionality, constitutes a major contributor to disability in diabetes. Hydrogen sulfide (H2S) dynamics and muscle mass regulatory signaling were studied in GK rats, a model for type 2 diabetes (T2D). GK rats exhibited a number of features that are consistent with sarcopenia and T2D including loss of muscle mass and strength, in addition to glucose intolerance, insulin resistance, and impaired β-cell responsiveness to glucose. Mechanistically, activation levels of Akt, a key modulator of protein balance, were decreased in T2D. Consequently, we confirmed reduced activity of mTOR signaling components and higher expression of atrophy-related markers typified by FoxO1/atrogin-1/MuRF1 and myostatin-Smad2/3 signaling during the course of diabetes. We observed in GK rat reduced antioxidant capacity (↓GSH/GSSG) and increased expression and activity of NADPH oxidase in connection with augmented rate of oxidation of lipids, proteins, and DNA. H2S bioavailability and the expression of key enzymes involved in its synthesis were suppressed as a function of diabetes. Interestingly, GK rats receiving NaHS displayed increased muscle Akt/mTOR signaling and decreased expression of myostatin and the FoxO1/MuRF1/atrogin-dependent pathway. Moreover, diabetes-induced heightened state of oxidative stress was also ameliorated in response to NaHS therapy. Overall, the current data support the notion that a relationship exists between sarcopenia, heightened state of oxidative stress, and H2S deficiency at least in the context of diabetes. Moreover, treatment with a potent H2S donor at an early stage of diabetes is likely to mitigate the development of sarcopenia/frailty and predictably reduces its devastating sequelae of amputation

Temporal distribution of non-methane hydrocarbon (NMHC) in a developing equatorial island

Non-methane hydrocarbons (NMHC) play a vital role in the formation of ozone in urban environment, where vehicle emissions are dominant. Ozone is known for its negative impacts on human health and environment. This study examines the prevalence of NMHC using central fitting distribution for equatorial area of Penang Island, Malaysia. The results of the diurnal variations of the NMHC concentrations measured during the years 2005 and 2006 showed that the concentrations were varying over the 24-h period while peaking from 8:00 to 10:00 am. In 2006, the maximum concentration of NMHC at the island station was 0.30 ppm, and the minimum level was 0.15 ppm. In term of probability distribution, the NMHC concentrations surrounding the station were well represented by Weibull distribution in 2005 and lognormal distribution in 2006 with an accuracy of 99.6 and 99.4 %, respectively. Moreover, the predicted number of days exceeds the US standard for NMHC (0.24 ppm) were 107 days. The Pearson’s correlation analysis showed a significant correlation (average r > 0.85) between NMHC concentrations and the measured CO emissions which indicate that they have a common source which is vehicular emissions. The factor analysis results showed that the temperature and humidity were the main contributors to the variance of NMHC concentrations. This study suggests that a special attention should be taken into consideration because of the potential health threat posed to the human

Functionally-focused algorithmic analysis of high resolution microarray-CGH genomic landscapes demonstrates comparable genomic copy number aberrations in MSI and MSS sporadic colorectal cancer - Fig 3

<p>Aberrational frequencies in MSS (a) and MSI (b) CRC. The aberrational length X-axis in a and b are log transformed, with median shown as solid line and mean as dashed line. c) Shows the average number of genomic aberrations per patient in MSI and MSS CRC.</p