Intelligent OS X malware threat detection with code inspection

With the increasing market share of Mac OS X operating system, there is a corresponding increase in the number of malicious programs (malware) designed to exploit vulnerabilities on Mac OS X platforms. However, existing manual and heuristic OS X malware detection techniques are not capable of coping with such a high rate of malware. While machine learning techniques offer promising results in automated detection of Windows and Android malware, there have been limited efforts in extending them to OS X malware detection. In this paper, we propose a supervised machine learning model. The model applies kernel base Support Vector Machine (SVM) and a novel weighting measure based on application library calls to detect OS X malware. For training and evaluating the model, a dataset with a combination of 152 malware and 450 benign were is created. Using common supervised Machine Learning algorithm on the dataset, we obtain over 91% detection accuracy with 3.9% false alarm rate. We also utilize Synthetic Minority Over-sampling Technique (SMOTE) to create three synthetic datasets with different distributions based on the refined version of collected dataset to investigate impact of different sample sizes on accuracy of malware detection. Using SMOTE datasets we could achieve over 96% detection accuracy and false alarm of less than 4%. All malware classification experiments are tested using cross validation technique. Our results reflect that increasing sample size in synthetic datasets has direct positive effect on detection accuracy while increases false alarm rate in compare to the original dataset

COVAD survey 2 long-term outcomes: unmet need and protocol

Vaccine hesitancy is considered a major barrier to achieving herd immunity against COVID-19. While multiple alternative and synergistic approaches including heterologous vaccination, booster doses, and antiviral drugs have been developed, equitable vaccine uptake remains the foremost strategy to manage pandemic. Although none of the currently approved vaccines are live-attenuated, several reports of disease flares, waning protection, and acute-onset syndromes have emerged as short-term adverse events after vaccination. Hence, scientific literature falls short when discussing potential long-term effects in vulnerable cohorts. The COVAD-2 survey follows on from the baseline COVAD-1 survey with the aim to collect patient-reported data on the long-term safety and tolerability of COVID-19 vaccines in immune modulation. The e-survey has been extensively pilot-tested and validated with translations into multiple languages. Anticipated results will help improve vaccination efforts and reduce the imminent risks of COVID-19 infection, especially in understudied vulnerable groups

Distribution of total nitrogen in soils of the tropical highlands of Cameroon

Understanding the factors controlling soil total N (TN) is helpful in simulating N cycling at local and regional scales. This study was conducted with the aim to; (i) understand the distribution of TN in specific soil horizons (A and B horizons) of three reference soil groups: Acrisols, Cambisols and Ferralsols, common in humid tropical environments, and (ii) to identify factors controlling TN variations among the various soil groups. Twenty-eight Acrisols, 21 Cambisols and 8 Ferralsols profiles spanning a wide range of precipitation gradients, vegetation type/land use and parent materials, from the Northwestern Highlands (NWH) of Cameroon were considered. Soil properties were analyzed following standard procedures. TN had very high coefficients of variation (>35%) in all the reference soil groups (RSGs), with highest mean TN (0.31 +/- 0.03%) observed in the A horizons of Acrisols and the lowest (0.05 +/- 0.01%) in B horizons of Ferralsols. Variations in TN content were significantly (p < 0.05) influenced by parent material, land use type, precipitation and slope gradient. In surface (A) horizons of all the RSGs, TN correlated positively and significantly with clay (p < 0.05), silt (p < 0.01) and soil organic carbon (SOC) (p < 0.01), and negatively with sand (p < 0.01). This observation was slightly different in subsurface (B) horizons. This study provides data which contributes to a better understanding of soil fertility in tropical highlands

COVAD survey 2 long-term outcomes: unmet need and protocol

Vaccine hesitancy is considered a major barrier to achieving herd immunity against COVID-19. While multiple alternative and synergistic approaches including heterologous vaccination, booster doses, and&nbsp;antiviral drugs have been developed, equitable vaccine uptake remains the foremost strategy to manage pandemic. Although none of the currently approved vaccines are live-attenuated, several reports of disease flares, waning protection, and acute-onset syndromes have emerged as short-term adverse events after vaccination. Hence, scientific literature falls short when discussing potential long-term effects in vulnerable cohorts. The COVAD-2 survey follows on from the baseline COVAD-1 survey with the aim&nbsp;to collect patient-reported data on the long-term safety and tolerability of COVID-19 vaccines in immune modulation. The e-survey has been extensively pilot-tested and validated with translations into multiple languages. Anticipated results will help improve vaccination efforts and reduce the imminent risks of COVID-19&nbsp;infection, especially in understudied&nbsp;vulnerable groups