Fresh Produce as a Potential Vector and Reservoir for Human Bacterial Pathogens: Revealing the Ambiguity of Interaction and Transmission

The consumer demand for fresh produce (vegetables and fruits) has considerably increased since the 1980s for more nutritious foods and healthier life practices, particularly in developed countries. Currently, several foodborne outbreaks have been linked to fresh produce. The global rise in fresh produce associated with human infections may be due to the use of wastewater or any contaminated water for the cultivation of fruits and vegetables, the firm attachment of the foodborne pathogens on the plant surface, and the internalization of these agents deep inside the tissue of the plant, poor disinfection practices and human consumption of raw fresh produce. Several investigations have been established related to the human microbial pathogens (HMPs) interaction, their internalization, and survival on/within plant tissue. Previous studies have displayed that HMPs are comprised of several cellular constituents to attach and adapt to the plant’s intracellular niches. In addition, there are several plant-associated factors, such as surface morphology, nutrient content, and plant–HMP interactions, that determine the internalization and subsequent transmission to humans. Based on documented findings, the internalized HMPs are not susceptible to sanitation or decontaminants applied on the surface of the fresh produce. Therefore, the contamination of fresh produce by HMPs could pose significant food safety hazards. This review provides a comprehensive overview of the interaction between fresh produce and HMPs and reveals the ambiguity of interaction and transmission of the agents to humans

The Battle between Bacteria and Bacteriophages: A Conundrum to Their Immune System

Bacteria and their predators, bacteriophages, or phages are continuously engaged in an arms race for their survival using various defense strategies. Several studies indicated that the bacterial immune arsenal towards phage is quite diverse and uses different components of the host machinery. Most studied antiphage systems are associated with phages, whose genomic matter is double-stranded-DNA. These defense mechanisms are mainly related to either the host or phage-derived proteins and other associated structures and biomolecules. Some of these strategies include DNA restriction-modification (R-M), spontaneous mutations, blocking of phage receptors, production of competitive inhibitors and extracellular matrix which prevent the entry of phage DNA into the host cytoplasm, assembly interference, abortive infection, toxin–antitoxin systems, bacterial retrons, and secondary metabolite-based replication interference. On the contrary, phages develop anti-phage resistance defense mechanisms in consortium with each of these bacterial phage resistance strategies with small fitness cost. These mechanisms allow phages to undergo their replication safely inside their bacterial host’s cytoplasm and be able to produce viable, competent, and immunologically endured progeny virions for the next generation. In this review, we highlight the major bacterial defense systems developed against their predators and some of the phage counterstrategies and suggest potential research directions

Optimization of Germination Conditions Of Melia volkensii By Response Surface Methodology

Germination of Melia volkensii was modelled using response surface methodology (rsm) whereby second order models were developed and the associated response surfaces analyzed. The factors under investigation were soil pH, temperature, chemical concentration and length of time of seed pre-treatment. Four chemicals were used for seed pre-treatment. These were potassium nitrate (KNO3), hydroge

Analysing Four Factor Second Order Models Using Response Surface Methodology with Application in Germination of Melia volkensii

Second order models are useful in situations where there are curvilinear effects present in the true response function. Such models have real life applications in a wide variety of fields such as agriculture, biology, and business among others. In such cases the problem is twofold. First is to fit a model for the relationship between the dependent variable and the explanatory variables. Second is to find the values of the predictor variables that optimize the response. The objectives here were to fit second order models involving four independent variables as well as to obtain values for the explanatory variables that optimize the dependent variable. Response surface methodology (RSM) is used both to fit the models as well as to analyze the fitted surfaces. The data obtained by simulation were from a four factor rotatable central composite design (CCD). Results included the fitted models and the tests of adequacy of fit for the models. Optimal values for the independent variables were also given. Contour and surface plots are presented to give a pictorial view of the nature of the response surface. As an application a model for the germination of Melia volkensii experiment was fitted and optimal values of temperature, soil pH and chemical concentration obtained. The work in this paper can be directly applied in many instances where an investigator studies the relationship between four predictor variables and a response. With some relevant adjustments this can be extended to any number of explanatory variables

Markov Chain Model for Time Series and its Application to Forecasting Stock Market Prices

A Markov chain is a discrete-valued Markov process; discrete-valued means that the state space of possible values of the Markov chain is finite or countable. This paper seeks to forecast stock market prices using Markov Chain Model (MCM). A discrete state space is defined for an MCM which is used to calculate fitting probability matrices. Time series data of day closing prices of KenGen Company as listed in the Nairobi Stock Exchange for the period 4th January, 2016 to 31st August 2018, will be used. One of the advantages of this forecasting technique is its flexibility whereby it just requires the ability to calculate the probability at any given point. Numerical analysis is done using R. This forecasting technique is useful, not only to KenGen Company, but also to other companies listed in the NSE, the share brokers as well as the shareholders, and any other individual or company interested in trading in the share market

Time series analysis of Nandi county government revenue using seasonal autoregressive integrated moving average (SARIMA) model

In Kenya, there are two levels of government namely the National Government (NG) and County Governments (CGs). NG’s revenue is mainly from taxation, borrowing, and grants among other sources while CGs rely on allocation from NG as well as Own Source Revenue (OSR). CG of Nandi has been using basic descriptive statistics to analyze OSR data such as line and bar graphs with minimal forecasting capabilities. This study focuses on the Nandi county revenue analysis from 2013/14 financial year (FY) to the 2018/19 FY. The analysis showed that the Nandi county revenue department collected an average monthly revenue amounting to Ksh. 19.29 million (Ksh. 231.5 million annually). This study performed a time series analysis on the revenues using Seasonal Auto-Regressive Integrated Moving Average (SARIMA). The best SARIMA model SARIMA(0,0,0)(1,0,0)[12] chosen proved to fit well in the data. The study also projected the revenue of the CG of Nandi going into the future. The average amount of monthly revenues forecasted is Ksh. 34.97 million. This means that the county government of Nandi has a potential of raising an average of Ksh. 34.97 million monthly, Ksh. 104.91 million quarterly and Ksh. 419.64 million annually

Modelling Time Default in University Fee Payment Using Cox Proportional Hazard Model

Default is the failure to pay interest or other money that is owed on time. Time default is amount of time taken to clear the given debt past the set time line. Fee payment is guided by the fee payment policy produced by all universities to guide their students on the time they are expected to clear their fee balances. The analysis of the time it takes students to pay their fee balances using Cox Proportional Hazard Model (CPHD) will use the university data collected from the students from the previous semester on how long it took them to pay their fee. The technique is non parametric, independent and robust thus allows a wide range of data. This study is inspired by (Mariusz, 2016).The main aim of this study is to find out the time it takes for students to pay their fee and their reasons using the Cox Proportional hazard Model (CPHD).The general objective of this study is to model Time Default in University Fee Payment using the Cox Proportional Hazard Model. Survival curves are drawn for comparison among different characteristics that affect the fee payment default. Analysis was performed using R and other mathematical modules will be discussed. From the analysed data several students are aware of the Fee Payment Policy and that Age_group,level of Education, Gender, and Employment do not affect fee payment timelines. This report is usefulto the Universities and other institutions of higher learning, to mitigate the problem of fee payment default hence leading to enhanced normalcy in running of the institutio

Extended Cox Model on Duration Taken to Release Cargo at Kenyan Border Entry Point: A Case Study of Malaba Osbp

Duration taken to release cargo is one of World Customs Organisation’s most significant instruments of measuring trade facilitation. It is a way of reviewing clearance procedure by measuring the time taken to release cargo at border entry points. This research therefore aimed to model clearance time using the Extended Cox model, which is a modification of the proportional hazard Cox model in which proportional hazard assumptions are not met, and tested the practicability of the model and compared the performance of Extended Cox model with the other conventional methods using secondary data on time clearance that was obtained from the KRA, Malaba OSBP, for the period 1st January 2015 to 31st July 2019. The variables in the study were both time depend (time taken from registration to release) and time independent (customs regimes, origin, destination, customs value and description of goods). The variables used in the research project were tested for proportional hazard assumptions. Extended Cox model was the most suitable model for analysis duration taken to release cargo data has it was found to have a lower AIC and BIC as compared to conventional cox model. Therefore, customs administration should embrace the use of Extended Cox model to analysis duration taken to release cargo data as it is statistically significant and give efficient results that are based on proper statistical methods. It is recommended that further studies to be done on a large data set that encompasses both entry and exit cargo in all the Kenyan border points

Monitoring Claim Processing Duration Using Statistical Quality Control

Statistical quality control is an important tool used widely at service provision fields to monitor the overall operation. The significant application of the SPC analysis elements to the operation will make the process more reliable and stable. An important SPC tool is the control charts, which can be used to detect changes in production processes and service delivery with a statistical level of confidence. The study introduces the philosophy and types of control charts, design and performance issues, and provides a review of control chart application in monitoring. Primarily, Shewhart control charts have been described in monitoring clinical services performance, with examples found in duration in which cancer patients were served and infections rate of spreading. It has also been used in monitoring the process of data collection in epidemiologic studies. Most applications describe charting outcome variables, but more examples of control charts applied to input variables are needed. Production systems are the identification of the best statistical model for the common cause of variability, grouping of data, selection of type of control chart, the cost of false alarms and lack of signals and difficulty identifying the special causes when a change is signaled. Nevertheless, carefully constructed control charts are powerful methods in monitoring

Time series analysis of air pollution trends in Kenya using environmental Kuznets curve

Air pollution occurs when harmful or excessive quantities of substances including gases are introduced into Earth's atmosphere. It may cause diseases, allergies and even death to humans; it may also cause harm to other living organisms such as animals and food crops, and may damage the natural or built environment. Both human activity and natural processes can generate air pollution. The study analyzed the relationship between economic growth and environmental degradation with particular reference to carbon emissions and deforestation. Further, the study seemed to determine the effect of economic growth on the Environmental Kuznets Curve in Kenya for the period 1990 to 2018. The analysis was based upon the environmental Kuznets curve (EKC) model that posits an Inverted-U relationship between income per capita and environmental quality. In particular, the study took into account the current process of globalization with the aim of defining the impact of the progressive global economic integration on the relationship between economic growth and environmental degradation. Past studies confirm that there is an Inverted-U relationship between income growth and carbon emissions, while the relationship result is less clear in the case of forest change. This goes against the hypothesis of the EKC, thus a need to investigate its applicability in the country. A regression analysis was done to establish the relationship between economic activities and per capita growth on environmental degradation in Kenya through forest change. It was realized that there exists a long-run relationship among the variables. The positive sign of GDP and negative sign of GDP2 confirms that EKC hypothesis is supported in the country. There is significant decrease in forest cover with increase in CO2 emission quantity and with one unit increase in forest cover there is decrease of 2.0074 units of CO2. The study is important especially in the core policy implications that can be drawn from the results of the study in relation to climate change