Quinazolines compounds are a significant class of multifunctional medicinal agents in the biological and pharmaceutical industries. Quinazoline-incorporating compounds have recently gained significant attention due to their various applications, especially in diverse bioactivities. Quinazolines derivatives have a range of pharmacological effects, making such heterocyclic systems an essential core in bioactive structures. Techniques for synthesizing or modifying this ring structure were always evolving and intriguing. They were of considerable interest as intermediates in the synthesis of various beneficial heterocyclic compounds. The incorporation of sugar in modified cyclic and acyclic forms with the structure of a heterocyclic quinazolines system revealed potential structures with various bioactivities. Cancer is the world's largest and most serious healthcare problem, identified by aberrant cell proliferation caused by an imbalance between cell division and cell death. There have been many chemotherapeutic drugs developed to treat cancer, but quinazolines derivatives are one of the most potent and have few side effects.

A new series of amino acid derivatives linked to coumarin has been synthesized as CK2 inhibitors. Compound 7c outperforms doxorubicin in cell lines, while compound 5b shows superior CK2 inhibition compared to roscovetine.

Dry machining of aluminum (Al) 2024 alloy has been performed with four different cutting inserts (cemented carbide, titanium nitride (TiN) coated, titanium aluminum nitride (TiAlN) coated, and polycrystalline diamond (PCD) coated), and their performance is assessed for tool wear and workpiece surface roughness. Design of experiments and response surface methodology (RSM) was performed to optimize the cutting parameters. TiAlN coated inserts presented an average ≈ 21%, ≈ 36%, and ≈ 58% less tool wear than the uncoated cemented carbide, TiN and PCD coated inserts, respectively. While TiN coated inserts exhibited an average ≈ 17%, ≈ 37%, and ≈ 42% less workpiece surface roughness than the uncoated cemented carbide, TiAlN, and PCD coated inserts, respectively. PCD coated inserts have greater mechanical properties, but due to the poor adhesion strength of the coating, it performed worst regarding tool wear and workpiece surface roughness. Energy dispersive X-ray spectroscopy (EDX) analysis of the chips validated our findings that the adhesion of coated tools is also very important for the evaluation of machining performance other than mechanical properties. It is concluded that the mechanical properties and adhesion of the coated tools are both important in assessing the tool wear and workpiece surface roughness. Also, the research community and industry need to consider adhesion strength of the coated tools for better machining performance.

Salicylates are the group of chemicals that has salicylic acid as the parent compound and have the ability to treat inflammation, pain syndromes, brain and cardiovascular disorders. The present study was designed to investigate the protective effect of loaded sodium salicylates on nanoparticles to treat brain toxicity induced by cisplatin and decrease the drug side effects. Cisplatin 20mg/kg BW was given alone or in combination with sodium salicylate loaded on nanoparticles (Si-Sc-NPs) [100 mg/kg BW] for three weeks. The obtained results indicate that Si-ScNPs decrease oxidative stress markers such as malondialdehyde (MDA), nitric oxide (NO), and paraoxonase-1 (PON-1) activity in brain. There were also decreased in Monocyte Chemo-attractant Protein-1 (MCP-1) activities and Nuclear Factor kappa beta (NF-k beta) level of brain tissue while the Butyrylcholinesterase (BChE) activities of brain tissue increased after Si-Sc-NPs treatment. Histopathological results showed that cisplatin group showed several neurodegenerative changes, on the other hand, group treated with Si-Sc-NPs +Cis showed improvement in almost brain structure and mild pyknotic nuclei and apoptotic neurons were observed.

Sustainable Design implies making decisions at various scales of the built environment (buildings, communities, land use patterns, urban support systems) in ways that support environmental quality, social equity, and economic vitality. The undergraduate minor in Sustainable Design is jointly offered by the Department of Architecture and the Department of Landscape Architecture and Environmental Planning but also includes interdisciplinary courses across campus. The minor is open to all majors at UC Berkeley.

The emissions from coal power plants have serious implication on the environment protection, and there is an increasing effort around the globe to control these emissions by the flue gas cleaning technologies. This research was carried out on the limestone forced oxidation (LSFO) flue gas desulfurization (FGD) system installed at the 2*660 MW supercritical coal-fired power plant. Nine input variables of the FGD system: pH, inlet sulfur dioxide (SO2), inlet temperature, inlet nitrogen oxide (NOx), inlet O-2, oxidation air, absorber slurry density, inlet humidity, and inlet dust were used for the development of effective neural network process models for a comprehensive emission analysis constituting outlet SO2, outlet Hg, outlet NOx, and outlet dust emissions from the LSFO FGD system. Monte Carlo experiments were conducted on the artificial neural network process models to investigate the relationships between the input control variables and output variables. Accordingly, optimum operating ranges of all input control variables were recommended. Operating the LSFO FGD system under optimum conditions, nearly 35% and 24% reduction in SO(2)emissions are possible at inlet SO(2)values of 1500 mg/m(3)and 1800 mg/m(3), respectively, as compared to general operating conditions. Similarly, nearly 42% and 28% reduction in Hg emissions are possible at inlet SO(2)values of 1500 mg/m(3)and 1800 mg/m(3), respectively, as compared to general operating conditions. The findings are useful for minimizing the emissions from coal power plants and the development of optimum operating strategies for the LSFO FGD system.

Jessica Chin is an Artist/Designer/Researcher focusing on blending creativity with mechanical design. She has been collaborating with leading research and development laboratories including the Modeling, Analysis, and Predcition (MAP) Laboratory at Northeastern University in Boston, Mass. and the Center for STEM Education at Northeastern. For the past four years, Chin was a researcher working on the development of a predictive model for chronic wound tracking. In addition, she also supports a National Science Foundation initative to increase STEM (Science, Technology, Engineering, and Mathematics) in K-12 Education.

Aim: The aim of this study was to investigate the role of leptin receptor (LEPR) gene polymorphism and adipokines in the pathogenesis of knee osteoarthritis (OA) in Egyptian female patients. Materials and Methods: Ninety-five Egyptian females were classified into three groups: group I (control): 32 healthy females, Group II: included 30 non obese knee osteoarthritic patients and Group III: included 33 obese knee osteoarthritic patients. Genotyping of rs1137101 at LEPR gene was analyzed using allelic discrimination assay by real-time polymerase chain reaction technique, and then, adipokines and nitric oxide (NO) levels were measured. Results: The frequency of GG genotype was found to be significantly higher in Group III when compared to Group II and controls (26% to 4%, 1%) (P < 0.001), while AA genotype was the most frequent in the control group (75%) (P < 0.001). rs1137101 was correlated with knee OA in the dominant genetic model (GG + GA vs. AA) in Group II (odds ratio = 8, 95% CI [2.6–24.2], P <.001). Moreover, there was a significant increase in serum levels of leptin, resistin, and NO with a concomitant decrease in adiponectin level in Group III as compared to the other two groups confirming the role of these adipokines in OA. Conclusion: Our findings suggested that the genetic variation in rs1137101 is involved in the pathogenesis of both obesity and knee OA in Egyptian female patients. In addition, it seems that adipokines and oxidative stress are important factors linking obesity, adiposity, and inflammation in OA.

AbstractIn the current work, the effects of design (groove depth and groove width) and operational (temperature and velocity) parameters on aerodynamic performance parameters (coefficient of drag and coefficient of lift) of an isolated passenger car tire have been investigated. The study is conducted by using neural network-based Monte-Carlo analysis on computational fluid dynamics (CFD). The computer experiments are designed to obtain the causal relationship between tire design, operational, and aerodynamic performance parameters. The Reynolds-averaged Navier–Stokes equations-based RealizableK-εmodel has been employed to analyze the variations in flow patterns around an isolated tire. The design parameters are varied over wide range and full factorial design, while considering temperature and velocity is completely explored to draw conclusive results. The multi-layer perceptron type neural network with the back-propagation algorithm is trained to map any non-linearity in causal relationships. The sensitivity analysis is performed to find the relationship between control variables and performance indicators. The importance of control variable is determined by both sensitivity and significance analyses and the paired interaction analysis is performed between selected control variables to find the interactive behavior of corresponding variables. The design parameter of groove width with 6.8% and 41% reduction in drag and lift coefficient, respectively, and conventionally overlooked operational parameter of velocity with 4% and 35% impact on drag and lift coefficient, respectively, are found to be the most significant variables. The air trapped between the longitudinal grooves and the road is found to follow the beam theory. The interaction of the groove depth and width is found to be significant with respect to coefficient of lift based on the air beam concept. The interaction of groove width and velocity is found to be significant with respect to both coefficients of lifts and drag.

A series of novel 2-(pyridin-4-yl)quinazolin-4(3H)-ones bearing different heterocycle cores as potential PI3K inhibitors have been synthesized and evaluated via the MTT assay for their antiproliferative properties against selected HePG-2, MCF-7, and HCT116 cancer cell lines. Among them, compound 9 displayed significant activity against HePG-2 (IC50 = 60.29 ± 1.06 μM) comparable to doxorubicin as a reference anticancer drug (IC50 = 69.60 ± 1.50 μM). Kinase inhibitory assessment of target products against PI3K and docking studies revealed the promising binding affinities which match with the binding mode of the ligand, SW13 towards the active site of PI3K. Therefore, this work represents a promising matrix for developing novel potential anticancer candidates.

This paper describes the original intent and curriculum design of two manufacturing certificate programs funded by a National Science Foundation (NSF) Advanced Technological Education (ATE) award at a community college in Massachusetts. It also describes salient features of this project including a focus on recruiting Liberal Arts majors for emerging jobs in the manufacturing sector, as well as the requirement of an experiential learning component. The paper further discusses what the team learned about student recruitment and employer engagement over the next three years. It also discusses how the team responded to the emerging needs of the student and manufacturing community through collaboration and teamwork. Finally, the paper presents a set of tools and recommendations for institutions interested in developing new academic programs in manufacturing to engage with all the stakeholders including prospective students, departments and other partners.

Piston ring and cylinder liner (PRCL) interface is a major contributor to the overall frictional and wear losses in an IC engine. Physical vapor deposition (PVD) based ceramic coatings on liners and rings are being investigated to address these issues. High temperature requirements for applications of conventional coating systems compromise the mechanical properties of the substrate materials. In the current study, experimental investigation of tribo-mechanical properties is conducted for various titanium nitride (TiN) coated PRCL interfaces in comparison with a commercial PRCL system. Low-temperature PVD based TiN coating is successfully achieved on the grey cast iron cylinder liner samples. Surface roughness of the grey cast iron cylinder liner substrates and the thickness of TiN coating are varied. A comprehensive comparative analysis of various PRCL interfaces is presented and all the trade-offs between various mechanical and tribological performance parameters are summarized. Coating thickness between 5 and 6 micrometres reports best tribo-mechanical behaviour. Adhesion and hardness are found to be superior for the TiN coatings deposited on cylinder liner samples with higher roughness, i.e., ~ 5-micron Ra. Maximum 62 % savings on the COF is reported for a particular PRCL system. Maximum 97% saving in cylinder liner wear rate is reported for another PRCL system.

A series of newly synthesized compounds of quinazolinone by various substituents was screened for its pharmacological activities. These included their action as antibacterial agents against pathogenic bacteria ( Staphylococcus aureus , Streptococcus pneumoniae , Escherichia coli , Klebsiella pneumoniae , and Pseudomonas aeruginosa ) and as antifungal agents against Aspergillus niger and pathogenic yeast ( Candida albicans ). The presently investigated compounds were synthesized in higher yields, and the structure features were elucidated on the basis of IR, 1 H‐NMR, and mass and elemental analysis data. These compounds were also evaluated as antioxidant agent. The results revealed that six compounds ( 2a , 11b , 11a , 2b , 13a , and 3c ) exhibited higher antimicrobial activity against the tested pathogenic strains. In addition, it was found that compound 6a exhibited a radical scavenging activity higher than other studied compounds.

Diabetes is a serious chronic disease marked by high levels of blood glucose. It results from issues related to how insulin is produced and/or how insulin functions in the body. In the long run, uncontrolled blood sugar can damage the vessels that supply blood to important organs such as heart, kidneys, eyes, and nerves. Currently there are no effective algorithms to automatically recommend insulin dosage level considering the characteristics of a diabetic patient. The objective of this work is to develop and validate a general reinforcement learning framework and a related learning model for personalized treatment and management of Type 1 diabetes and its complications. This research presents a model-free reinforcement learning (RL) algorithm to recommend insulin level to regulate the blood glucose level of a diabetic patient considering his/her state defined by A1C level, alcohol usage, activity level, and BMI value. In this approach, an RL agent learns from its exploration and response of diabetic patients when they are subject to different actions in terms of insulin dosage level. As a result of a treatment action at time step t, the RL agent receives a numeric reward depending on the response of the patient’s blood glucose level. At each stage the reward for the learning agent is calculated as a function of the difference between the glucose level in the patient body and its target level. The RL algorithm is trained on ten years of the clinical data of 87 patients obtained from the Mass General Hospital. Demographically, 59% of patients are male and 41% of patients are female; the median of age is 54 years and mean is 52.92 years; 86% of patients are white and 47% of 87 patients are married. The performance of the algorithm is evaluated on 60 test cases. Further the performance of Support Vector Machine (SVM) has been applied for Lantus class prediction and results has been compared with Q-learning algorithm recommendation. The results show that the RL recommendations of insulin levels for test patients match with the actual prescriptions of the test patients. The RL gave prediction with an accuracy of 88% and SVM shows 80% accuracy. Since the RL algorithm can select actions that improve patient condition by taking into account delayed effects, it has a good potential to control blood glucose level in diabetic patients.

Natural bentonite clay (mainly sodium-rich montmorillonite) both purified and acid activated using HCl, H2SO4, and H3PO4 acids were applied to the catalytic fast pyrolysis (CFP) of lignin. The modifications in crystalline structure and acidity of the activated clay were investigated via XRD, N2-adsorption-desorption, FTIR, TGA and NH3-TPD analysis. The activity and selectivity of the clays were evaluated for lignin conversion in down- flow micro reactor in the temperature range 500–650°0C in flowing Nitrogen (oxygen-free). The yields of high value added monocyclic aromatics such as BTX (benzene- toluene- xylenes) and naphthalene were increased by catalytic upgrading using HCl-activated bentonite clay. The selectivity for o&p-xylenes, naphthalene, and methyl naphthalene were greatly enhanced using HCl-activated bentonite in the temperature range 550–650°C. The catalyst: lignin ratio 3:1 and the nitrogen flow rate 75ml/min, at 600°C were the optimum conditions.

Most of the current problems can be solved by referring to the solutions of the previous problems. Case Based reasoning (CBR) is one of the methods that solves a problem by retrieving the similar problems from the past and adapting the solutions of the past problems to solve the new problem. Recent studies that apply CBR include time as a parameter to retrieve most effective solutions that vary with time. This approach is more helpful in healthcare area in which one needs to look at historical evidence to find an accurate diagnostic or treatment regime. Hence, in this study, a time-based CBR is applied to track the outcomes of the drug therapy on hypertensive patients and find the most effective drug as a prescription. Initially, episodes in each patient’s medical records are chronologically ordered such that the oldest episode is placed first in the episode sequence and the latest episode is placed the last. It is assumed that the first episode of each patient is the first instance of diagnose; so when a new patient comes for checkup, his/her state (health condition) is compared with the initial state of the past patients. Therefore, the retrieval process calculates the similarity between the new patient’s current state and the most similar patients at their first episodes in the patient records. Due to the diversity of therapies for matching patients, the best treatment couldn’t be determined without knowing the efficacy of the different treatments. Therefore, the subsequent episodes of matching patients are examined to find the best treatment for the new patient. This might even require using a combination of treatments from all matching patients to find a good treatment for the new patient. After the treatment is defined for the first visit, the record of the new patient is stored in the library for future case retrieval. This method is a novel approach to personalized treatment of patients having chronic disease by tracking the medical records past patients over a long period of time. The current approach for treating the hypertensive patients uses evidence-based guidelines for managing the disease. However, this approach is more general and doesn’t take into account all the patient characteristics such as lab results and physical examination parameters. In the current approach the similarity between patients can’t be leveraged; the change of the treatment regime is based only on the risk parameter. However, in this method several parameters are being checked for efficiency of the medication. In contrast, the proposed CBR-based method personalizes the treatment based on what worked well for similar patients. In this paper, the clinical records of hypertensive patients are provided by a Boston based hospital. The preliminary results confirm that the proposed approach will give good recommendation for hypertension treatment.

Liberal Arts (BA) graduates are, more often than not, either underemployed or unemployed in the field(s) for which they received their degree. This is more so true in hard economic and recessionary times. It is also well known that BA graduates are well rounded by virtue of their education and are more adept at changing careers. Advanced manufacturing is one such career where BA graduates may excel, especially in entry-level positions such as CAD operators, CNC programmers, production supervisors, and in support staff roles. The challenge is how to prepare these non-technical majors (BA graduates) for technical careers (advanced manufacturing). This paper presents an internship model that is part of a 12-month fast track certificate in advanced manufacturing to enable BA graduates to gain both the technical skills and experiential knowledge they need to secure jobs in advanced manufacturing. This paper describes the certificate academic program, corresponding courses, and the recruitment process of BA graduates to provide context. It then focuses on the details of the internship model: recruiting industry partners to provide internships, preparing students for the internships, the management and support system of these internships, and lessons learned so far. These research findings are part of an NSF, 3-year grant that investigates a transformation model of BA graduates for careers in advanced manufacturing.

Evidence exists that a thriving manufacturing sector increases the number of stable, well-paying jobs leading to the growth of the US economy. The manufacturing industry is the fifth largest employer in Massachusetts and benefits from the state's diversified economy, according to a recent Jobs For the Future (JFF) report. At the same time, several states in the US, including Massachusetts, are consistently reporting a skills gap that results in a shortage of a skilled workforce leading to thousands of unfilled positions in this sector. This paper describes an innovative approach that prepares college graduates to launch a second career in the growing advanced manufacturing sector while bringing together various stakeholders including higher education institutions, workforce investment boards and industry. The paper describes the range of services provided to students in the new manufacturing certificate programs at a community college in Massachusetts and opportunities that exist for additional collaboration.

The post synthesis of Al3+ or Zr4+ substituted MCM-48 framework with controlled acidity is challenging because the functional groups exhibiting acidity often jeopardize the framework integrity. Herein, we report the post-synthesis of two hierarchically porous MCM-48 composed of either aluminum (Al3+) or zirconium (Zr4+) clusters with high throughput. All prepared catalysts have been characterized by HR-TEM, XRD, IR, N2-adsorption, NH3-TPD, TGA and MAS NMR. They exhibit BET surface areas of 597 and 1112m2g⿿1 for 8.4% Al/MCM-48 and 2.9% Zr/MCM-48, respectively. XRD analysis reveals that the hierarchical porosity of parental MCM-48 is reserved even after incorporation of Al3+or Zr4+. Zr/MCM-48 catalysts are demonstrate a superior performance versus that of Al/MCM-48 and MCM-48 because of the mild (ZrO2) or nil (SiO2) Lewis acidity contributed from Zr-μ2-O group as well as smaller pore sizes suitable for the restriction of unwanted side reactions. The reaction conditions which were affecting the catalytic pyrolysis and final products were gas flow rate, pyrolysis temperature, and catalyst to lignin ratio. A total of 49% of BTX product were obtained over 2.9% Zr/MCM-48 at 600°C. The Lewis acid character was the governing factor which helps in pyrolysis and directly affects the BTX formation.

It is well recognized that manufacturing is making a comeback to the US, from the outsourcing that took place between 1980–2010. The need for advanced manufacturing careers is also well documented by many manufacturing organizations, substantiated by the report entitled “A National Strategic Plan for Advanced Manufacturing” which was released by the Executive Office of the President National Science and Technology Council’s in February 2012. The Association for Manufacturing Excellence (AME) points out that at the height of the recession, 32% of manufacturers reported that they had jobs unfilled because they could not find people with requisite skills. It is also well documented that liberal arts (BA) graduates suffer from mal-employment problems; they are either underemployed or unemployed. To solve this problem, this paper describes an innovative solution of transforming BA graduates to take on advanced manufacturing positions to meet the skilled workforce needs and fill these positions. This paper briefly describes the program, but focuses mainly on one aspect of it: industry partnerships. We describe the importance of industry partners to the proposed solution. We also discuss industry needs.