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University of Massachusetts Amherst
Mathematics and Statistics Department Faculty Publication Series
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Articles 1 - 5 of 5
Full-Text Articles in Life Sciences
Investigating Optimal Chemotherapy Options For Osteosarcoma Patients Through A Mathematical Model, Trang M. Le, Sumeyye Su, Leili Shahriyari
Investigating Optimal Chemotherapy Options For Osteosarcoma Patients Through A Mathematical Model, Trang M. Le, Sumeyye Su, Leili Shahriyari
Mathematics and Statistics Department Faculty Publication Series
Since all tumors are unique, they may respond differently to the same treatments. Therefore, it is necessary to study their characteristics individually to find their best treatment options. We built a mathematical model for the interactions between the most common chemotherapy drugs and the osteosarcoma microenvironments of three clusters of tumors with unique immune profiles. We then investigated the effects of chemotherapy with different treatment regimens and various treatment start times on the behaviors of immune and cancer cells in each cluster. Saliently, we suggest the optimal drug dosages for the tumors in each cluster. The results show that abundances …
Easing Covid-19 Lockdown Measures While Protecting The Older Restricts The Deaths To The Level Of The Full Lockdown, A. S. Fokas, J. Cuevas-Maraver, P. G. Kevrekidis
Easing Covid-19 Lockdown Measures While Protecting The Older Restricts The Deaths To The Level Of The Full Lockdown, A. S. Fokas, J. Cuevas-Maraver, P. G. Kevrekidis
Mathematics and Statistics Department Faculty Publication Series
Guided by a rigorous mathematical result, we have earlier introduced a numerical algorithm, which using as input the cumulative number of deaths caused by COVID-19, can estimate the effect of easing of the lockdown conditions. Applying this algorithm to data from Greece, we extend it to the case of two subpopulations, namely, those consisting of individuals below and above 40 years of age. After supplementing the Greek data for deaths with the data for the number of individuals reported to be infected by SARS-CoV-2, we estimated the effect on deaths and infections in the case that the easing of the …
Unraveling The Mechanisms Of Surround Suppression In Early Visual Processing, Yao Li, Lai-Sang Young
Unraveling The Mechanisms Of Surround Suppression In Early Visual Processing, Yao Li, Lai-Sang Young
Mathematics and Statistics Department Faculty Publication Series
This paper uses mathematical modeling to study the mechanisms of surround suppression in the primate visual cortex. We present a large-scale neural circuit alistic modeling work are used. The remaining parameters are chosen to produce model outputs that emulate experimentally observed size-tuning curves. Our two main results are: (i) we discovered the character of the long-range connections in Layer 6 responsible for surround effects in the input layers; and (ii) we showed that a net-inhibitory feedback, i.e., feedback that excites I-cells more than E-cells, from Layer 6 to Layer 4 is conducive to producing surround properties consistent with experimental data. …
Gemini: A Computationally-Efficient Search Engine For Large Gene Expression Datasets, Timothy Defreitas, Hachem Saddiki, Patrick Flaherty
Gemini: A Computationally-Efficient Search Engine For Large Gene Expression Datasets, Timothy Defreitas, Hachem Saddiki, Patrick Flaherty
Mathematics and Statistics Department Faculty Publication Series
Background
Low-cost DNA sequencing allows organizations to accumulate massive amounts of genomic data and use that data to answer a diverse range of research questions. Presently, users must search for relevant genomic data using a keyword, accession number of meta-data tag. However, in this search paradigm the form of the query – a text-based string – is mismatched with the form of the target – a genomic profile.
Results
To improve access to massive genomic data resources, we have developed a fast search engine, GEMINI, that uses a genomic profile as a query to search for similar genomic profiles. GEMINI …
Robust Optimization Of Biological Protocols, Patrick Flaherty, Ronald W. Davis
Robust Optimization Of Biological Protocols, Patrick Flaherty, Ronald W. Davis
Mathematics and Statistics Department Faculty Publication Series
When conducting high-throughput biological experiments, it is often necessary to develop a protocol that is both inexpensive and robust. Standard approaches are either not cost-effective or arrive at an optimized protocol that is sensitive to experimental variations. Here, we describe a novel approach that directly minimizes the cost of the protocol while ensuring the protocol is robust to experimental variation. Our approach uses a risk-averse conditional value-at-risk criterion in a robust parameter design framework. We demonstrate this approach on a polymerase chain reaction protocol and show that our improved protocol is less expensive than the standard protocol and more robust …