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The uncertainty of High Impact Low Frequency (HILF) events is a test of the resilience of mankind. The recent COVID-19 pandemic has agonized the systems worldwide shattering the health and economy. The automobile industry was among the major affected worldwide due to dependency on labour, network structure, and nature of operations. Yet, the industry could operate to cater to healthcare equipment demands using repurposing, an adaptation strategy. Given this, the present study quantifies the losses in production and Value Addition (VA) in the industry and investigates the effects of adaptation at a firm level. To compute the losses, a non-pandemic scenario was enacted using Seasonal Autoregressive Integrated Moving Average (SARIMA) to present the IIP and VA values in the absence of the pandemic. The VA Losses were computed as the difference between actual and forecasted measures. This was followed by a firm-level analysis of the effects of repurposing using a scalable Discrete Event Simulation (DES) model. Using two scenarios, an automobile component manufacturing press shop that handles 114 jobs using 28 machines was chosen to understand the benefits of repurposing raw materials to produce ventilators and oxygen support equipment. Results indicated a VA Loss of INR 198.46 billion during the initial phases of the pandemic from March to July 2020. Scenario 2, which contributed 5% of raw materials every month and repurposed after fulfilling demand, was effective. An additional 321 and 864 additional ventilators or oxygen concentrators were produced in Scenario 2 than in Scenario 1 over the 14 months. Firm-level implementation evidences the benefits of operationalizing the adaptation strategies. More insights for managers and policymaking have been discussed. [ABSTRACT FROM AUTHOR]

Relaxation enhanced angiography without contrast and triggering (REACT) is a non-contrast-enhanced angiography technique that highlights blood and suppresses the low T2 and T1 background tissues by using a combination of T2prep and inversion modules, respectively. In this work, the T2prep module is modified to effectively perform both T2prep and inversion. This eliminates the need for a separate inversion pulse. The modified sequence (iREACT) is compared with REACT through a subjective evaluation based on blood vessel delineation, muscle and fat suppression efficacy. Further an objective analysis was performed for these parameters by measuring the blood-muscle and water-fat contrast ratios. iREACT shows comparable suppression of muscle and delineation of blood vessels whereas fat suppression efficacy is reduced at tissue boundaries and the edge of larger field of views. Efficient background suppression is achieved with a reduced RF load. This is a step towards safe and easy non-contrast MR angiography. [ABSTRACT FROM AUTHOR]

Background and Purpose: This work aims at optimizing and studying the feasibility of imaging the brachial plexus at 1.5T using 3D nerve-SHeath signal increased with INKed rest-tissue RARE imaging (3D SHINKEI) neurography sequence by comparing with routine sequences. Materials and Methods: The study was performed on a 1.5T Achieva scanner. It was designed in two parts: (a) Optimization of SHINKEI sequence at 1.5T; and (b) Feasibility study of the optimized SHINKEI sequence for generating clinical quality magnetic resonance neurography images at 1.5T. Simulations and volunteer experiments were conducted to optimize the T2 preparation duration for optimum nerve-muscle contrast at 1.5T. Images from the sequence under study and other routine sequences from 24 patients clinically referred for brachial plexus imaging were scored by a panel of radiologists for diagnostic quality. Injury detection efficacy of these sequences were evaluated against the surgical information available from seven patients. Results: T2 preparation duration of 50 ms gives the best contrast to noise between nerve and muscle. The images of 3D SHINKEI and short-term inversion recovery turbo spin-echo sequences are of similar diagnostic quality but significantly better than diffusion weighted imaging with background signal suppression. In comparison with the surgical findings, 3D SHINKEI has the lowest specificity; however, it had the highest sensitivity and predictive efficacy compared to other routine sequences. Conclusion: 3D SHINKEI sequence provides a good nerve-muscle contrast and has high predictive efficacy of nerve injury, indicating that it is a potential screening sequence candidate for brachial plexus scans at 1.5T also. [ABSTRACT FROM AUTHOR]

Background: A randomized trial in glioblastoma patients with methylated-MGMT (m-MGMT) found an improvement in median survival of 16.7 months for combination therapy with temozolomide (TMZ) and lomustine, however the approach remains controversial and relatively under-utilized. Therefore, we sought to determine whether comprehensive genomic analysis can predict which patients would derive large, intermediate, or negligible benefits from the combination compared to single agent chemotherapy. Methods: Comprehensive genomic information from 274 newly diagnosed patients with methylated-MGMT glioblastoma (GBM) was downloaded from TCGA. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotypes representing hallmark behavior of cancers. In silico responses to TMZ, lomustine, and combination treatment were biosimulated. Efficacy scores representing the effect of treatment for each treatment strategy were generated and compared to each other to ascertain the differential benefit in drug response. Results: Differential benefits for each drug were identified, including strong, modest-intermediate, negligible, and deleterious (harmful) effects for subgroups of patients. Similarly, the benefits of combination therapy ranged from synergy, little or negligible benefit, and deleterious effects compared to single agent approaches. Conclusions: The benefit of combination chemotherapy is predicted to vary widely in the population. Biosimulation appears to be a useful tool to address the disease heterogeneity, drug response, and the relevance of particular clinical trials observations to individual patients. Biosimulation has potential to spare some patients the experience of over-treatment while identifying patients uniquely situated to benefit from combination treatment. Validation of this new artificial intelligence tool is needed. [ABSTRACT FROM AUTHOR]

The paper discusses a unique technique developed initially at Nation Institute of Technology, Surat that is remodeled in real-world applications. The concept consists primarily of a user-friendly software facilitating direct communication with any intelligent or learning system/robot operating under known parameters of motor specifications. Any software base permitting high level PC interface without ASCII interrupt can be used for easy programming. This allows for a learning operation mode where a prevention of time lag is enabled by stored machine data, captured through movements such movements can be physically made or taught via programs to the device and such learning aspects make the machine more efficient where the robot can either perform individual actions as needed or learn new methods for the same results and can perform a series of actions continuously. Using the stored data, the machine is also capable of autonomous movements based on the path of least resistance as calculated by the time it takes to perform an act. Interfacing Technique Tool Machining Robot (ITTMR) was developed as robotic tool holder that can determine the shape and size of different OCTG pipes utilized in the downhole industry and enable it to machine appropriate threads on the pipe with no manual intervention. The process thereby completely negates any possibility of human error which can otherwise cause heavy loss on finished equipment that are rendered unusable because of threading errors on almost nearly finished complex milled parts or assemblies that are pending threads as the final operation. The purpose of the software codes is to provide a user-friendly GUI that can communicate with any machine by pulling in appropriate ACNC programs and performing the required tasks associated with the operating system and specifications of the motors/mobilization equipment's used. For the purpose of this paper, the software code is not provided. Any firmware base that permits the usage of an ASCII interrupt can be used and for the purpose of this operation, an RS323 equivalent board will also suffice for basic operations, however a complex ITTMR system has been utilized. This paper solely addresses the technique of how the threading operation is performed and does not address the process of how the pipe is bought to the machine or other associated aspects of the software to retain any possible patent applications on the same. [ABSTRACT FROM AUTHOR]

Mathematics is the cradle of all creations, without which the world cannot move an inch. Mathematical functions are 'extensively' used in physics, 'structurally' used in graphics, 'practically' used in civil engineering, 'potentially' used in mechanical engineering and in many other fields as well. One fairly common difficulty faced by the engineers and scientists is to find the right function that solves their problem. This involves a lot of time consuming tasks. The idea proposed here is an android application that captures the mathematical expression using a built-in camera which produces a java code that can be utilized for solving the inputs of their needs. Along with the code, it also displays a basic plot of the function, which will be more helpful for selecting the apt solution for any problem. There are many applications in the market which can compute the result of a mathematical equation. But, this application will give an executable java code which can be used for further problem solving. So, the application is unique in its way and this is a new dimension of using image processing for code generation. This application supports polynomial, logarithmic, trigonometric and exponential functions up to four variables. [ABSTRACT FROM AUTHOR]

An interview with Tony Hunter, biochemist at Salk Institute for Biological Studies, and James Allison, immunologist at University of Texas MD Anderson Cancer Center in Texas, is presented. Topics discussed include Hunter's research on protein kinases as the center of his studies on cancer, the use of tyrosine phosphorylation as a cancer drug target, and the prognostic biomarker for cancer immunotherapy.

The article presents the profile of professors Lily Y. Jan and Yuh Nung Jan at the University of California, San Francisco (UCSF) and winner of the 2017 Vilcek Prize in Biomedical Science. It mentions that both Lily and Yuh-Nung Jan have shared in developing groundbreaking research careers that have solved some of the most basic and crucial questions in neuroscience. The article mention that they were recognized for identifying the genes that give neurons, as wells as dendrites.

The article discusses short profile of Angelika Amon, winner of the 2019 Vilcek Prize in Biomedical Science Jan Vilceka. Topics discussed include her passion for cell biology and opportunity to launch her own laboratory. She has brought the balletic process by which cells bequeath genetic material to offspring; and her work has revealed how missteps in the process result in outcomes such as cancer.

The article presents a profile of Dan Littman, a professor of molecular immunology at New York University's Skirball Institute, who received the 2016 Vilcek Prize in Biomedical Science. Information about his life, career, and achievements are provided. Also presented are details about the Vilcek Foundation Prizes.

An interview with protein biochemist David Baker is presented. When asked how the field of protein engineering has evolved he talked about how early protein engineering approaches was by modifying naturally occurring proteins through induced random mutation and how this changed with greater understanding of protein folding. He talks about the challenges of transmembrane proteins and designing proteins held together by hydrogen-bonded networks.

An interview with plant molecular biologist Caroline Dean is presented. When asked about how she became interested in vernalization, she refers to her curiosity to know about plant development. She also mentions several examples of commercially important plants that undergo vernalization. She also discusses the molecular biology of vernalization.

The article announces that Peter Walter, Franziska Michor and Rob Knight received the 2015 Vilcek Foundation Prizes, which recognize contributions of immigrant biomedical scientists in the U.S.

Background: The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug. Methods: We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells. Results: Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells. Conclusions: These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches. [ABSTRACT FROM AUTHOR]

An interview with Luciana Borio, acting chief scientist in U.S. Food and Drug Administration's (FDA). is presented. She discusses logistical challenges to conducting clinical trials of investigational products, action taken during 2014 Ebola epidemic and utility of platform trials. She provides information on future concerns for randomized controlled trial.

The article presents an interview with neuroscientist Karel Svoboda. Topics discussed include advantages of two-photon microscopy over standard fluorescence microscopy for brain tissues imaging; his role in the development of two-photon microscopy; and initial findings on brain structure and function.

An interview with professor of law and bioethics Alta Charo and geneticist George Church, about their report on human genome editing, is presented. Topics discussed include the conditions that might meet the draconian criteria for humane germline editing, the negative view of some people on human germline editing, and safety in the eventual use of human germline editing.

The article reports on the bacterial proteostasis and chaperone and their use for maintaining the homeostatic balance of the folding and disaggregation of the cell's proteins. Topics discussed include resemblance in decisions made by the proteostasis machine to those made when a patient enters a hospital; role of proteostasis machine in repairing its client proteins; and use of Escherichia coli in recognition, sorting, folding, and disaggregating of the cell's.

Background The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug. Methods We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells. Results Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells. Conclusions These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches. [ABSTRACT FROM AUTHOR]

DRAM scaling has been the prime driver of increasing capacity of main memory systems. Unfortunately, lower technology nodes worsen the cell reliability as it increases the coupling between adjacent DRAM cells, thereby exacerbating different failure modes. This paper investigates the reliability problem due to Row Hammering , whereby frequent activations of a given row can cause data loss for its neighboring rows. As DRAM scales to lower technology nodes, the threshold for the number of row activations that causes data loss for the neighboring rows reduces, making Row Hammering a challenging problem for future DRAM chips. To overcome Row Hammering, we propose two architectural solutions: First, Counter-Based Row Activation (CRA), which uses a counter with each row to count the number of row activations. If the count exceeds the row hammering threshold, a dummy activation is sent to neighboring rows proactively to refresh the data. Second, Probabilistic Row Activation (PRA), which obviates storage overhead of tracking and simply allows the memory controller to proactively issue dummy activations to neighboring rows with a small probability for all memory access. Our evaluations show that these solutions are effective at mitigating Row hammering while causing negligible performance loss ( $< $ 1 percent). [ABSTRACT FROM PUBLISHER]

The article announces the recipients of the 2014 Vilcek Foundation Prizes, including Biomedical Science awardee Thomas Jessell, and Creative Promise in Biomedical Science awardees Antonio Giraldez, Stavros Lomvardas and Pardis Sabeti.

Histatins are human salivary gland peptides with anti-microbial and anti-inflammatory activities. In this study, we hypothesized that histatin 5 binds to Porphyromonas gingivalis hemagglutinin B (HagB) and attenuates HagB-induced chemokine responses in human myeloid dendritic cells. Histatin 5 bound to immobilized HagB in a surface plasmon resonance (SPR) spectroscopy-based biosensor system. SPR spectroscopy kinetic and equilibrium analyses, protein microarray studies, and I-TASSER structural modeling studies all demonstrated two histatin 5 binding sites on HagB. One site had a stronger affinity with a KD1 of 1.9 mM and one site had a weaker affinity with a KD1 of 60.0 μM. Binding has biological implications and predictive modeling studies and exposure of dendritic cells both demonstrated that 20.0 mMhistatin 5 attenuated (p<0.05) 0.02 μMHagB-induced CCL3/MIP-1α, CCL4/MIP-1β, and TNFα responses. Thus histatin 5 is capable of attenuating chemokine responses, which may help control oral inflammation. [ABSTRACT FROM AUTHOR]

The article discusses the research of biochemists Michael Brown and Joseph Goldstein on the familial hypercholesterolemia (FH). It states that the cells of John Despota who was diagnosed with homozygous FH at the age of 12 were examined. It says that the biochemists have published papers stating that failure of cells to bind low-density lipoprotein (LDL) leads to higher cholesterol levels and FH. It adds that the research has led to development of several drugs to treat FH and related diseases.

The article announces that immunologists Richard Flavell and Ruslan Medzhitov have received the Vilcek Prize in Biomedical Science for 2013.