菲律賓

Backpropagation neural networks were used to forecast daily rainfall with minimal error for Metro Manila in order to have an inexpensive way of accurately predicting weather. Calamities brought on by heavy rainfall have caused great economic, infrastructure and human loses. Neural networks have the ability to discern complex patterns in noisy data; this makes it a viable method for weather forecasting. Daily precipitation, humidity, rain indication, sea level pressure, temperature and maximum sustained wind speed for January 2000 to December 2010 were acquired from the Philippine Atmospheric Geophysical and Astrological Services Administration. The neural network made use of Python 2.7.2 and the backpropagation program by Neil Schemenauer (python.org). It considered different neural network architectures with a total of 2844 data sets for training and 708 data sets for testing. Each neural network’s accuracy was measured with a graph of the actual and predicted values, correlation coefficient, and root mean square error. It was observed that the neural network with architecture 5-8-1 yielded the most accurate results as it had the highest correlation coefficient of 0.48599 and smallest root mean square error of 14.84. It was also observed that the trends of the predicted values followed that of the target values. This suggests that it is possible to create a neural network with a moderate correlation given daily weather data. It is recommended that further researches make use of hourly data instead of daily data for more accurate results. Other variables, which might affect rainfall, not in this study should also be considered. This research could aid in the anticipation of calamities and the decision making involved in shipping, fishing and aviation industries.

Parameters of traffic, road availability, and fare were integrated into a web-based application for determining the best public transport routes within Metro Manila in order to assist commuters in their travel planning, whether for business or for pleasure. A user-friendly interface was developed to obtain a user’s place of origin and destination, as well as preferences in travel time, mode of transportation, and cost of journey. By accessing the traffic roadway network of the metropolis, a real-time situation of road availability was obtained, and used in a modified Dijkstra’s shortest-path algorithm to produce a model of a real-time adaptive transport network of Metro Manila. From the model, an optimal route that considers the user’s preferences can be determined. This project will be immensely useful in helping both businessmen and tourists in planning their routes that will save on time and money.

The researchers aim to construct an emergency mobile phone charging station that runs on renewable energy and will serve as a cost-efficient alternative to more traditional power banks. Circuit components include a 20V / 6W solar panel supplemented by a hand-crank gear mechanism integrated with a 6V / 1A lead-acid battery, a usb output and an adjustable switch-mode power supply (SMPS) to convert excess voltage into current. Initial voltage and current outputs were measured under varying resistances. It was determined that the set-up satisfied the minimum voltage and current requirement for charging a mobile phone (5V / 1A). A subsequent phone charging test was executed using a Samsung Galaxy J2 (3.85V Li-ion battery 7.70W, Charge Voltage: 4.4V / 2000mAh) wherein it charged on an average of 0.277% per minute for the solar panel and an average of 0.263% per minute for the hand crank gear mechanism. A Mann-Whitney U statistical test was conducted to determine if the charging rate of the charging station had a significant difference from a commercially available power bank’s. The calculated UA: (4) from the test was below the lower limit and the UB: (217) was above the upper limit which indicated that there was a significant difference between the charging rates. While the efficiency was lower than the commercial power bank’s, it can still be used as an alternative charging method especially during emergencies and disasters.

Heavy metals contaminate many bodies of water, posing a health risk to not only organisms that live and use the water in these areas, but also to the humans that live nearby. Chlorella vulgaris, a microalga, is one organism whose chlorophyll a fluorescence can indicate the presence of these substances, detecting any changes in concentrations using fluorescence microscopy and other fluorescence devices. The study explores the sensitivity of C. vulgaris to the heavy metal zinc where the algae was exposed to five concentrations of zinc: 0 ppm, 5 ppm, 10 ppm, 50 ppm, and 100 ppm. The fluorescence of the samples was observed with a fluorescence microscope on days 0, 4, 7, and 12, where the algal samples were adapted to the dark for 5 minutes, then exposed to light for 90 seconds. The values of the minimal and maximal fluorescence of the samples in the dark were noted. There is a significant difference in the values of the minimal fluorescence, maximal fluorescence, and maximum quantum yield, a value derived from the minimal and maximal fluorescence, at the highest concentration, 100 ppm, from the other treatments for the entirety of the experiment. The significantly low values at 100 ppm and the calculated EC50 of 75.70 ppm indicate that C. vulgaris is indeed a viable indicator for zinc detection at this and higher concentrations of zinc.

Plants are potential low-cost alternatives for cancer treatment. Rubus fraxinifolius or “sapinit” has been found to possess phytochemicals with anti-cancer potential. This project aimed to evaluate the antiangiogenic properties of methanolic R. fraxinifolius fruit crude extracts through the chick chorioallantoic membrane (CAM) assay. Through phytochemical screening, leucoanthocyanins, phenols, and tannins were detected. For the CAM assay, 10, 20, and 30 μg/μL extracts, distilled water, methanol, and retinoic acid were applied on 60 ten-day-old chicken eggs. The CAM photographs were analyzed using ImageJ Software. The mean percent inhibitions (MPI) of total length and vascular density from both analyses were subjected to One-Way Analysis of Variance (ANOVA). The ANOVA for the MPI of total length, followed by a Tukey post hoc test, show that only retinoic acid treatment has significantly higher MPI (p = 0.0010). Meanwhile, the results for the MPI of vascular density show no significant differences between all groups (p = 0.1630). It is possible that the concentrations used in the study may not be the concentrations needed to achieve optimal antiangiogenesis. The results may also be due to the absence of phytochemicals that exhibit significant antiangiogenic properties such as alkaloids. Lower concentrations and isolated phytochemicals may also be tested.

Triage is the process by which nurses manage hospital emergency departments by assigning patients varying degrees of urgency. While triage algorithms such as the Emergency Severity Index (ESI) have been standardized worldwide, many of them are highly inconsistent, which could endanger the lives of thousands of patients. One way to improve on nurses’ accuracy is to use machine learning models (ML), which can learn from past data to make predictions. We tested six ML models: random forest, XGBoost, logistic regression, support vector machines, k-nearest neighbors, and multilayer perceptron. These models were tasked with predicting whether a patient would be admitted to the intensive care unit (ICU), another unit in the hospital, or be discharged. After training on data from more than 30,000 patients and testing using 10-fold cross-validation, we found that all six models outperformed ESI. Of the six, the random forest model achieved the highest average accuracy in predicting both ICU admission (81% vs. 69% using ESI; p<0.001) and hospitalization (75% vs. 57%; p<0.001). These models were then added to an Android application, which would accept patient data, predict their triage, and then add them to a priority-ordered waiting list. This approach may offer significant advantages over conventional triage: mainly, it has a higher accuracy than nurses and returns predictions instantaneously. It could also stand-in for triage nurses entirely in disasters, where medical personnel must deal with a large influx of patients in a short amount of time.

Currently, no method can completely eliminate the human immunodeficiency virus (HIV) in an infected person. HIV employs an accessory protein called Nef that forms a complex with cellular AP-1, preventing detection of HIV-infected cells. Lovastatin has been recently identified to inhibit the formation of said Nef-AP-1 complex, but its effective concentration is remarked to be far higher than other Nef inhibitors. This study aims to develop a modified lovastatin molecule exhibiting higher binding affinity to the HIV-1 Nef protein than lovastatin in silico. Modified lovastatin molecules based on the interaction map of lovastatin with Nef were modeled, and flexible ligand-flexible receptor docking to the Nef binding site was performed using AutoDock Vina. Residues within the Nef binding site identified by Liu et al. (2019) to be crucial (Glu-63, Val-66, Phe-68, Asp-108, Leu-112, Tyr-115) were set as flexible. Fragment-based drug design was utilized to append molecular fragments to lovastatin in order to maximize its interactions with said crucial residues. From the fragment-based approach, molecule F4 ((1S,3S)‐8‐{2‐[(2R,4R)‐4‐chloro‐6‐oxooxan‐2‐yl]ethyl}‐3‐(hydroxymethyl)‐7‐methyl‐1,2,3,4‐tetrahydronaphthalen‐1‐yl 4‐aminobenzoate) exhibited a binding affinity of -9.0 kcal/mole, and its estimated IC50 ranges between 0.25-0.51 μM which is at least 7.5 times lower than the reported IC50 of lovastatin from literature. This study presents insights on the key modifications to improve lovastatin as an HIV-1 Nef inhibitor and pertinent information about the Nef binding site for future drug development studies.

Currently, no method can completely eliminate the human immunodeficiency virus (HIV) in an infected person. HIV employs an accessory protein called Nef that forms a complex with cellular AP-1, preventing detection of HIV-infected cells. Lovastatin has been recently identified to inhibit the formation of said Nef-AP-1 complex, but its effective concentration is remarked to be far higher than other Nef inhibitors. This study aims to develop a modified lovastatin molecule exhibiting higher binding affinity to the HIV-1 Nef protein than lovastatin in silico. Modified lovastatin molecules based on the interaction map of lovastatin with Nef were modeled, and flexible ligand-flexible receptor docking to the Nef binding site was performed using AutoDock Vina. Residues within the Nef binding site identified by Liu et al. (2019) to be crucial (Glu-63, Val-66, Phe-68, Asp-108, Leu-112, Tyr-115) were set as flexible. Fragment-based drug design was utilized to append molecular fragments to lovastatin in order to maximize its interactions with said crucial residues. From the fragment-based approach, molecule F4 ((1S,3S)‐8‐{2‐[(2R,4R)‐4‐chloro‐6‐oxooxan‐2‐yl]ethyl}‐3‐(hydroxymethyl)‐7‐methyl‐1,2,3,4‐tetrahydronaphthalen‐1‐yl 4‐aminobenzoate) exhibited a binding affinity of -9.0 kcal/mole, and its estimated IC50 ranges between 0.25-0.51 μM which is at least 7.5 times lower than the reported IC50 of lovastatin from literature. This study presents insights on the key modifications to improve lovastatin as an HIV-1 Nef inhibitor and pertinent information about the Nef binding site for future drug development studies.

Triage is the process by which nurses manage hospital emergency departments by assigning patients varying degrees of urgency. While triage algorithms such as the Emergency Severity Index (ESI) have been standardized worldwide, many of them are highly inconsistent, which could endanger the lives of thousands of patients. One way to improve on nurses’ accuracy is to use machine learning models (ML), which can learn from past data to make predictions. We tested six ML models: random forest, XGBoost, logistic regression, support vector machines, k-nearest neighbors, and multilayer perceptron. These models were tasked with predicting whether a patient would be admitted to the intensive care unit (ICU), another unit in the hospital, or be discharged. After training on data from more than 30,000 patients and testing using 10-fold cross-validation, we found that all six models outperformed ESI. Of the six, the random forest model achieved the highest average accuracy in predicting both ICU admission (81% vs. 69% using ESI; p<0.001) and hospitalization (75% vs. 57%; p<0.001). These models were then added to an Android application, which would accept patient data, predict their triage, and then add them to a priority-ordered waiting list. This approach may offer significant advantages over conventional triage: mainly, it has a higher accuracy than nurses and returns predictions instantaneously. It could also stand-in for triage nurses entirely in disasters, where medical personnel must deal with a large influx of patients in a short amount of time.