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Cold storage is deemed one of the main elements in food safety management to maintain food quality. The temperature, relative humidity (RH), and air quality in cold storage rooms (CSRs) should be carefully controlled to ensure food quality and safety during cold storage. In addition, the components of CSR are exposed to risks caused by the electric current, high temperature surrounding the compressor of the condensing unit, snow and ice accumulation on the evaporator coils, and refrigerant gas leakage. These parameters affect the stored product quality, and the real-time sending of warnings is very important for early preemptive actionability against the risks that may cause damage to the components of the cold storage rooms. The IoT-based control (IoT-BC) with multipurpose sensors in food technologies presents solutions for postharvest quality management of fruits during cold storage. Therefore, this study aimed to design and evaluate a IoT-BC system to remotely control, risk alert, and monitor the microclimate parameters, i.e., RH, temperature, CO2, C2H4, and light and some operating parameters, i.e., the temperature of the refrigeration compressor, the electrical current, and the energy consumption for a modified CSR (MCSR). In addition, the impacts of the designed IoT-BC system on date fruit quality during cold storage were investigated compared with a traditional CSR (TCSR) as a case study. The results showed that the designed IoT-BC system precisely controlled the MCSR, provided reliable data about the interior microclimate atmosphere, applied electrical current and energy consumption of the MCSR, and sent the necessary alerts in case of an emergency based on real-time data analytics. There was no significant effect of the storage time on the most important quality attributes for stored date fruit in the MCSR compared with the TCSR. As a result, the MCSR maintained high-quality attributes of date fruits during cold storage. Based on the positive impact of the designed IoT-BC system on the MCSR and stored fruit quality, this modification seems quite suitable for remotely managing cold storage facilities.

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Drought is the most severe problem for agricultural production, and the intensity of this problem is increasing in most cultivated areas around the world. Hence improving water productivity is the primary purpose of sustainable agriculture. This study aimed to use cloud IoT solutions to control a modern subsurface irrigation system for improving irrigation management of date palms in arid regions. To achieve this goal, we designed, constructed, and validated the performance of a fully automated controlled subsurface irrigation system (CSIS) to monitor and control the irrigation water amount remotely. The CSIS is based on an autonomous sensors network to instantly collect the climatic parameters and volumetric soil water content in the study area. Therefore, we employed the ThingSpeak cloud platform to host sensor readings, perform algorithmic analysis, instant visualize the live data, create event-based alerts to the user, and send instructions to the IoT devices. The validation of the CSIS proved that automatically irrigating date palm trees controlled by the sensor-based irrigation scheduling (S-BIS) is more efficient than the time-based irrigation scheduling (T-BIS). The S-BIS provided the date palm with the optimum irrigation water amount at the opportune time directly in the functional root zone. Generally, the S-BIS and T-BIS of CSIS reduced the applied irrigation water amount by 64.1% and 61.2%, respectively, compared with traditional surface irrigation (TSI). The total annual amount of applied irrigation water for CSIS with S-BIS method, CSIS with T-BIS method, and TSI was 21.04, 22.76, and 58.71 m3 palm-1, respectively. The water productivity at the CSIS with S-BIS (1.783 kg m-3) and T-BIS (1.44 kg m-3) methods was significantly higher compared to the TSI (0.531 kg m-3). The CSIS with the S-BIS method kept the volumetric water content in the functional root zone next to the field capacity compared to the T-BIS method. The deigned CSIS with the S-BIS method characterized by the positive impact on the irrigation water management and enhancement on fruit yield of the date palm is quite proper for date palm irrigation in the arid regions.

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Food security has become an increasingly important challenge for all countries globally, particularly as the world population continues to grow and arable lands are diminishing due to urbanization. Water scarcity and lack of labor add extra negative influence on traditional agriculture and food production. The problem is getting worse in countries with arid lands and harsh climate, which exacerbates the food gap in these countries. Therefore, smart and practical solutions to promote cultivation and combat food production challenges are highly needed. As a controllable environment, greenhouses are the perfect environment to improve crops' production and quality in harsh climate regions. Monitoring and controlling greenhouse microclimate is a real problem where growers have to deal with various parameters to ensure the optimal growth of crops. This paper shows our research in which we established a multi-tier cloud-based Internet of things (IoT) platform to enhance the greenhouse microclimate. As a case study, we applied the IoT platform on cucumber cultivation in a soilless medium inside a commercial-sized greenhouse. The established platform connected all sensors, controllers, and actuators placed in the greenhouse to provide long-distance communication to monitor, control, and manage the greenhouse. The implemented platform increased the cucumber yield and enhanced its quality. Moreover, the platform improved water use efficiency and decreased consumption of electrical energy. Based on the positive impact on water use efficiency and enhancement on cucumber fruit yield and quality, the established platform seems quite suitable for the soilless greenhouse cultivation in arid regions.

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PURPOSE: High-dose chemotherapy with autologous stem-cell rescue (SCR) is a key component of high-risk neuroblastoma (HRNB) therapy. Carboplatin, etoposide, and melphalan (CEM) or busulfan and melphalan (Bu/Mel) are the most evaluated, effective high-dose chemotherapy for HRNB on the basis of results from major cooperative group studies. Toxicity profiles vary between these regimens, and practice variation exists regarding the preferred high-dose therapy (HDT). We sought to evaluate the safety of HDT and autologous SCR for HRNB in a resource-limited country (Egypt) compared with the resource-rich United States. PATIENTS AND METHODS: We performed a retrospective comparative review of single CEM-based HDT/SCR outcomes through day 100 for HRNB at the Fred Hutchinson Cancer Research Center (FH) in the United States (2005 to 2015) versus Bu/Mel-based HDT at El-Sheikh Zayed Specialized Hospital (SZ) in Egypt (2009 to 2015). RESULTS: Forty-four patients at FH and 77 patients at SZ were reviewed. Pretransplant hepatic comorbidities were significantly higher at SZ (29 of 77 v nine of 44; P = .05), with 19 of 77 patients at SZ having hepatitis infection. Engraftment was delayed after SZ-Bu/Mel therapy compared with FH-CEM therapy for neutrophils (median 12 days v 10 days, respectively; P < .001) and platelets (median 20 days v 18 days, respectively; P < .001). Sinusoidal obstruction syndrome occurred later, after SZ-Bu/Mel therapy (median 19 days v 7 days; P = .033), and four of eight cases were fatal (six of eight patients had underlying hepatitis infection), whereas three of three cases after FH-CEM therapy were moderately severe. Resource utilization associated with the number of days with fever, antibiotic use, and the number of transfusions administered was significantly higher after FH-CEM therapy than after SZ-Bu/Mel therapy. CONCLUSION: Use of autologous stem-cell transplantation is feasible in the context of a resource-limited country.

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Background: P-glycoprotein (P-gp), a membrane transporter encoded by the multidrug resistance-1 (MDR1) gene, influences pharmacokinetics and metabolism of anticancer drugs and contributes to multidrug resistance phenotype in acute lymphoblastic leukemia (ALL). Genetic variation ofMDR1 in ALL patients is increasingly recognized as a factor influencing response to treatment. Aim: To investigate the possible role of MDR-1 gene polymorphisms (C3435T, C1236T and C4125A) as risk factors for the development and clinical outcome of ALL in Egyptian children. Materials and Methods: Genotyping of MDR-1 C3435T, C1236T and C4125A single nucleotide polymorphisms (SNPs) was accomplished using a polymerase chain reaction­restriction fragment length polymorphism (RFLP-PCR) assay with 120 childhood ALL patients and 100 healthy controls. Results: Homozygous T with the C3435T SNP showed a protective effect as compared to homozygous C (OR=0.748) while heterozygous CT correlated with a poor outcome (high risk, drug unresponsiveness, relapse and high percentage of death). Additionally, the T allele of the C1236T SNP showed a significant relation with ALL risk (OR=1.6). However, there were no significant differences in the genotype and allele frequencies of MDR-1 SNPs between patients and controls. Only one genotype (CC) and one allele of MDR-1 (C4125A) were seen. Neither CA/AA genotypes nor A alleles were present in ALL patients and normal controls. TC was the predominant haplotype in both groups, while CT proved to be minor. The cumulative incidence of relapse was higher with the CC genotype of C1236T as compared with TT. Conclusion: From our preliminary data, the CT genotype of C3435T is associated with a poor ALL outcome while the CC genotype of C1236T is related with an increased incidence of relapse. Although our results provide assistance for oncologist choice of individual therapeutic strategy taking the patient genetic repertoire into consideration, further investigations with larger sample size should be conducted to validate our results.

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BACKGROUND: Breast cancer is one of the major death causing diseases of the women in the world. Every year more than million women are diagnosed with breast cancer more than half of them will die because of inaccuracies and delays in diagnosis of the disease. High accuracy in cancer prediction is important to improve the treatment quality and the survivability rate of patients. OBJECTIVES: In this paper, we are going to propose a new and robust breast cancer prediction and diagnosis system based on the Rough Set (RS). Also, introducing the robust classification process based on some new and most effective attributes. Comparing and evaluating the performance of our proposed approach with the clinical, Radial Basis Function, and Artificial Neural Networks classification schemes. METHODS: The dataset used in our experiments consists of 60 samples obtained from the National Cancer Institute (NCI) of Egypt. We have used the RS theory to robustly find dependence relationships among data, and evaluate the importance of attributes through: Results: Conclusion: We have introduced the robustness of the RS theory in early predicting and diagnosing the breast cancer. This lay more importance to the contribution and efficiency of RS theory in the field of computational biology.

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