University Students’ Readiness for Job Opportunities in Big Data Analytics

This study aims to explore the students’ level of readiness in taking up job opportunities in big data analytics and determine the contributing factors to students’ readiness. In addition, the crucial factors that need to be resolved are identified. This job field requires some significant criteria such as, willing to work as a team, self-effort, and specialised skills such as data visualisations and data storytelling, big data analysis, and basic knowledge on tools for big data analytics. Intellipaat.com, a platform that offers various professional online training courses, has ranked position in big data analytics and data science as the highest paying jobs in 2019. However, from 2019 onwards, Malaysia has been predicted to suffer a shortfall of data analysis professionals of up to 7,000-15,000. Our educational institutions are being encouraged to create more graduates to meet this need. The question arises on whether students are prepared and willing to work in this sector once they graduate. An online survey was constructed and distributed to all UiTM students enrolled in various bachelor’s degrees and master’s programmes. One hundred and thirty-nine students participated in this survey. A graphical tool for data tabulation was presented using a box-and-whisker plot. Additionally, correlation analysis and multiple regression were used to determine the relationship and factors that can contribute the students’ readiness for job opportunities in big data analytics. The results from the box-and-whisker plot have discovered an excellent sign of students’ readiness towards job opportunities in big data analytics. Correlation analyses has shown a weak to moderate relationship among factors and multiple linear regression analyses revealed the data visualisation including storytelling skill (DVSS) and teamwork (TW) have significantly given some impacts on the students’ opportunity in big data analytics career. The results of this study are expected to provide insights into students’ readiness for job opportunities in big data analytics.

Statistical Analysis of X-ray Images and Laboratory Studies of COVID-19 Patients

The large increase in COVID-19 cases that continue occurring in Mexico has generated the need to learn more about the damage caused by this virus, for which it has been proposed to carry out a statistical study of the 164 ray images database X and 29 laboratory studies provided by the Salamanca State Center for Critical Care (CECCS) of patients diagnosed with COVID-19 and not COVID-19, the purpose is to analyze if there is any correlation between X-rays and laboratory studies. First, the entire database was organized by patient, both for X-ray and laboratory studies. Afterwards, the statistical process of the laboratory studies was carried out. Subsequently, we worked on the selection of patients with a greater number of X-rays, segment of these the area of the lungs, since it was our area of interest. In these radiographs, the statistical process of entropy and box-and-whisker plot is performed. Finally, the results of some laboratory studies are correlated with the entropy and box-and-whisker plot results.

Form and content of Jamaican cannabis edibles

Background In 2017, the Jamaican government banned the sale of cannabis-infused edibles after reports of over-intoxication in adults and children. There is a general lack of public awareness regarding the risk involved with edible dosage. Vandrey et al. in 2015 reported that random cannabis edibles sampled from dispensaries in California and Washington in the USA failed to meet the basic labeling standards for pharmaceuticals (Vandrey et al.; JAMA 2015). This study aims to measure the levels of THC and CBD in a variety of edibles available locally in order to establish current cannabinoid content and to report on safety and packaging. This study is deemed necessary as no such study has been done to measure the potency levels of edibles and to raise awareness of the potential risk to children. Methods Forty-five cannabis-infused edible items were collected as convenience samples over a 4-year period (2014–2018) and analyzed. The QuEChERS technique (modified) was used to extract cannabinoids from each item. The extracts were then derivatized with MSTFA prior to analysis by gas chromatography-mass spectrometry (GC-MS). The descriptive statistics were calculated using the Statistical Package for Social Sciences—SPSS Software. Descriptive statistics presented include the mean, median, standard deviation, and range for each product category. The distribution of data with a box and whisker plot and frequency of THC to CBD ratios with a histogram was also presented. Results Edibles on the Jamaican market comprise baked goods, candies, frozen foods, and beverages. Eighty-six percent of samples were poorly labeled and failed to meet basic labeling requirements. None of the packages were observed to be child-proof. THC levels ranged from 0.01 to 99.9 mg per product while CBD levels ranged from 0.001 to 69.2 mg per product. The highest THC and CBD levels were detected in cookies and brownies. Thirty percent of the samples had THC levels greater than the recommended 10 mg THC per serving. Conclusion The lack of proper labeling and the wide range of THC levels in cannabis edibles raises public health concerns for all consumers including inexperienced persons who may be at a greater risk of overdosing. Concern must also be raised over the possibility that the attractive and tasty ways in which the drug is being presented might tempt young children and teens to take cannabis. Impact statement It is hoped that this information will raise public awareness of the current potential danger these edibles pose to children and inexperienced users and that policymakers will see the need for the imposition of suitable regulations.

Serum soluble CD26/DPP4 titer variation is a potential prognostic biomarker in cancer therapy with a humanized anti-CD26 antibody

Background The phase I trial of the humanized anti-CD26 monoclonal antibody YS110 for CD26-expressing tumors was conducted recently. The present study identifies a potential prognostic biomarker for CD26-targeted therapy based on the phase I data. Methods Box and Whisker plot analysis, Scatter plot analysis, Peason product moment correlation/Spearman’s rank-difference correlation, Bar graph analysis, and Receiver Operating Characteristics (ROC) were used to examine the correlation between sCD26 titer variation with YS110 administration and tumor volume change, RECIST criteria evaluation and progression free survival (PFS). Mechanism for serum sCD26 titer variation was confirmed by in vitro experimentation. Results Serum sCD26/DPP4 titer was reduced following YS110 administration and gradually recovered until the next infusion. Serum sCD26/DPP4 titer before the next infusion was sustained at lower levels in Stable Disease (SD) cases compared to Progressive Disease cases. ROC analysis defined the cut-off level of serum sCD26/DPP4 titer variation at day 29 pre/post for the clinical outcome of SD as tumor response or PFS. In vitro experimentation confirmed that YS110 addition reduced sCD26 production from CD26-expressing tumor and non-tumor cells. Conclusions Our study indicates that serum sCD26/DPP4 titer variation in the early phase of YS110 treatment is a predictive biomarker for evaluating therapeutic efficacy.

Serum soluble CD26/DPP4 titer variation is a potential prognostic biomarker in cancer therapy with a humanized anti-CD26 antibody

Background The phase I trial of the humanized anti-CD26 monoclonal antibody YS110 for CD26-expressing tumors was conducted recently. The present study identifies a potential prognostic biomarker for CD26-targeted therapy based on the phase I data. Methods Box and Whisker plot analysis, Scatter plot analysis, Peason product moment correlation/Spearman’s rank-difference correlation, Bar graph analysis, and Receiver Operating Characteristics (ROC) were used to examine the correlation between sCD26 titer variation with YS110 administration and tumor volume change, RECIST criteria evaluation and progression free survival (PFS). Mechanism for serum sCD26 titer variation was confirmed by in vitro experimentation. Results Serum sCD26/DPP4 titer was reduced following YS110 administration and gradually recovered until the next infusion. Serum sCD26/DPP4 titer before the next infusion was sustained at lower levels in Stable Disease (SD) cases compared to Progressive Disease cases. ROC analysis defined the cut-off level of serum sCD26/DPP4 titer variation at day 29 pre/post for the clinical outcome of SD as tumor response or PFS. In vitro experimentation confirmed that YS110 addition reduced the sCD26 production from CD26-expressing tumor and non-tumor cells. Conclusions Our study indicates that serum sCD26/DPP4 titer variation in the early phase of YS110 treatment is a predictive biomarker for evaluating therapeutic efficacy.

Magnetic fabric and archaeomagnetic analyses of anthropogenic ash horizons in a cave sediment succession (Crvena Stijena site, Montenegro)

SUMMARY An archaeomagnetic, rock magnetic and magnetic fabric study has been carried out on seven anthropogenic ash horizons in the Middle Palaeolithic sedimentary level XXIV at the rock shelter of Crvena Stijena (‘Red Rock’), Montenegro. The study has multiple goals, including the identification of iron bearing minerals formed during combustion, assessment of the suitability of these combustion features for recording the Earth´s magnetic field direction, revelation of the magnetic fabric and its significance in the characterization of cave (rock shelter) burnt facies, and identification of post-burning alteration processes. Magnetite has been identified as the main ferromagnetic component of the ash. The ash layers exhibit a high thermomagnetic reversibility in contrast to the irreversible behaviour of their subjacent burnt black layers which is related to the different temperatures attained. Seven mean archaeomagnetic directions were obtained with acceptable statistical values indicating that these features recorded the field direction at the time of burning. However, some of them are out of the expected range of secular variation for mid-latitude regions suggesting post-burning alterations. The magnetic fabric of the ash was characterized by anisotropy of low field magnetic susceptibility measurements. Statistical analysis (box and whisker plot) of the basic anisotropy parameters, such as foliation, lineation, degree of anisotropy and the shape parameter, along with the alignment of the principal susceptibilities on stereoplots, revealed variation among the ash units. The diverse, oblate to prolate, lineated or strongly foliated, quasi-horizontally and vertically oriented fabrics of the units may indicate different slope processes, such as orientation by gravity, solifluction, run-off water, quasi-vertical migration of groundwater and post-burning/post-depositional alteration of the fabric by rockfall impact. In sum, the magnetic characterization of the ash layers has shown the occurrence of different post-burning alteration processes previously not identified at the site. Alteration processes in prehistoric combustion features are often identified from macroscopic observations but our study demonstrates that multiple processes can affect them and are usually unnoted because they take place on a microscopic scale. Their identification is critical for a correct chronological and cultural interpretation of a site (e.g. collection of samples for dating, stratigraphic displacement of remains), especially if significant alterations are involved. Magnetic methods are therefore a powerful but underutilized tool in palaeolithic research for the identification and evaluation of taphonomic processes affecting prehistoric fires.

Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier

The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.