248 research outputs found
Modeling Three-dimensional Invasive Solid Tumor Growth in Heterogeneous Microenvironment under Chemotherapy
A systematic understanding of the evolution and growth dynamics of invasive
solid tumors in response to different chemotherapy strategies is crucial for
the development of individually optimized oncotherapy. Here, we develop a
hybrid three-dimensional (3D) computational model that integrates
pharmacokinetic model, continuum diffusion-reaction model and discrete cell
automaton model to investigate 3D invasive solid tumor growth in heterogeneous
microenvironment under chemotherapy. Specifically, we consider the effects of
heterogeneous environment on drug diffusion, tumor growth, invasion and the
drug-tumor interaction on individual cell level. We employ the hybrid model to
investigate the evolution and growth dynamics of avascular invasive solid
tumors under different chemotherapy strategies. Our simulations reproduce the
well-established observation that constant dosing is generally more effective
in suppressing primary tumor growth than periodic dosing, due to the resulting
continuous high drug concentration. In highly heterogeneous microenvironment,
the malignancy of the tumor is significantly enhanced, leading to inefficiency
of chemotherapies. The effects of geometrically-confined microenvironment and
non-uniform drug dosing are also investigated. Our computational model, when
supplemented with sufficient clinical data, could eventually lead to the
development of efficient in silico tools for prognosis and treatment strategy
optimization.Comment: 41 pages, 8 figure
Modeling the impact of extreme summer drought on conventional and renewable generation capacity: methods and a case study on the Eastern U.S. power system
The United States has witnessed a growing prevalence of droughts in recent
years, posing significant challenges to water supplies and power generation.
The resulting impacts on power systems, including reduced capacity and the
potential for power outages, underscore the need for accurate assessment
methods to ensure the reliable operation of the nation's energy infrastructure.
A critical step is to evaluate the usable capacity of a regional power system's
generation fleet, which is a complex undertaking and requires precise modeling
of the effects of hydrological and meteorological conditions on diverse
generating technologies. This paper proposes a systematic, analytical approach
for assessing the impacts of extreme summer drought events on the available
capacity of hydro, thermal, and renewable energy generators. More specifically,
the systematic framework provides plant-level capacity derating models for
hydroelectric, once-through cooling thermoelectric, recirculating cooling
thermoelectric, combustion turbine, solar PV, and wind turbine systems.
Application of the proposed impact assessment framework to the 2025 generation
fleet of the real-world power system in the PJM and SERC regions yields
insightful results. By examining the daily usable capacity of 6,055 at-risk
generators throughout the study region, we find that in the event of the
recurrence of the 2007 southeastern summer drought in the near future, the
usable capacity of all at-risk power plants may experience a substantial
decrease compared to a typical summer, falling within the range of 71% to 81%.
The sensitivity analysis reveals that the usable capacity would experience a
more pronounced decline under more severe drought conditions. The findings of
this study offer valuable insights, enabling stakeholders to enhance the
resilience of power systems against the potential effects of extreme drought in
the future.Comment: 15 pages, 16 figure
Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms
In this paper, we investigate the design and implementation of machine
learning (ML) based demodulation methods in the physical layer of visible light
communication (VLC) systems. We build a flexible hardware prototype of an
end-to-end VLC system, from which the received signals are collected as the
real data. The dataset is available online, which contains eight types of
modulated signals. Then, we propose three ML demodulators based on
convolutional neural network (CNN), deep belief network (DBN), and adaptive
boosting (AdaBoost), respectively. Specifically, the CNN based demodulator
converts the modulated signals to images and recognizes the signals by the
image classification. The proposed DBN based demodulator contains three
restricted Boltzmann machines (RBMs) to extract the modulation features. The
AdaBoost method includes a strong classifier that is constructed by the weak
classifiers with the k-nearest neighbor (KNN) algorithm. These three
demodulators are trained and tested by our online open dataset. Experimental
results show that the demodulation accuracy of the three data-driven
demodulators drops as the transmission distance increases. A higher modulation
order negatively influences the accuracy for a given transmission distance.
Among the three ML methods, the AdaBoost modulator achieves the best
performance
Robust Power Allocation for Integrated Visible Light Positioning and Communication Networks
Integrated visible light positioning and communication (VLPC), capable of
combining advantages of visible light communications (VLC) and visible light
positioning (VLP), is a promising key technology for the future Internet of
Things. In VLPC networks, positioning and communications are inherently
coupled, which has not been sufficiently explored in the literature. We propose
a robust power allocation scheme for integrated VLPC Networks by exploiting the
intrinsic relationship between positioning and communications. Specifically, we
derive explicit relationships between random positioning errors, following both
a Gaussian distribution and an arbitrary distribution, and channel state
information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of
positioning errors, subject to the rate outage constraint and the power
constraints, which is a chance-constrained optimization problem and generally
computationally intractable. To circumvent the nonconvex challenge, we
conservatively transform the chance constraints to deterministic forms by using
the Bernstein-type inequality and the conditional value-at-risk for the
Gaussian and arbitrary distributed positioning errors, respectively, and then
approximate them as convex semidefinite programs. Finally, simulation results
verify the robustness and effectiveness of our proposed integrated VLPC design
schemes.Comment: 13 pages, 15 figures, accepted by IEEE Transactions on Communication
Reducing climate change impacts and inequality of the global food system through diet shifts
How much and what we eat and where it is produced can create huge differences in greenhouse gas emissions. Bridging food consumption with detailed household-expenditure data, this study estimates dietary emissions from 13 food categories consumed by 201 expenditure groups in 139 countries, and further models the emission mitigation potential of worldwide adoption of the EAT–Lancet planetary health diet. We find that the consumption of groups with higher expenditures generally creates larger dietary emissions due to excessive red meat and dairy intake. As countries develop, the disparities in both emission volumes and patterns among expenditure groups tend to decrease. Global dietary emissions would fall by 17% if all countries adopted the planetary health diet, primarily attributed to decreased red meat and grains, despite a substantial increase in emissions related to increased consumption of legumes and nuts. The wealthiest populations in developed and rapidly developing countries have greater potential to reduce emissions through diet shifts, while the bottom and lower-middle populations from developing countries would cause a considerable emission increase to reach the planetary health diet. Our findings highlight the opportunities and challenges to combat climate change and reduce food inequality through shifting to healthier diets
Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks
In this paper, we investigate the performance of a practical aggregated
LiFi-WiFi system with the discrete constellation inputs from a practical view.
We derive the achievable rate expressions of the aggregated LiFi-WiFi system
for the first time. Then, we study the rate maximization problem via optimizing
the constellation distribution and power allocation jointly. Specifically, a
multilevel mercy-filling power allocation scheme is proposed by exploiting the
relationship between the mutual information and minimum mean-squared error
(MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is
proposed for obtaining the optimal probability distributions. To strike a
balance between the computational complexity and the transmission performance,
we further develop a framework that maximizes the lower bound of the achievable
rate where the optimal power allocation can be obtained in closed forms and the
constellation distributions problem can be solved efficiently by Frank-Wolfe
method. Extensive numerical results show that the optimized strategies are able
to provide significant gains over the state-of-the-art schemes in terms of the
achievable rate.Comment: 14 pages, 13 figures, accepted by IEEE Transactions on Wireless
Communication
Targeting of Embryonic Stem Cells by Peptide-Conjugated Quantum Dots
Targeting stem cells holds great potential for studying the embryonic stem cell and development of stem cell-based regenerative medicine. Previous studies demonstrated that nanoparticles can serve as a robust platform for gene delivery, non-invasive cell imaging, and manipulation of stem cell differentiation. However specific targeting of embryonic stem cells by peptide-linked nanoparticles has not been reported.Here, we developed a method for screening peptides that specifically recognize rhesus macaque embryonic stem cells by phage display and used the peptides to facilitate quantum dot targeting of embryonic stem cells. Through a phage display screen, we found phages that displayed an APWHLSSQYSRT peptide showed high affinity and specificity to undifferentiated primate embryonic stem cells in an enzyme-linked immunoabsorbent assay. These results were subsequently confirmed by immunofluorescence microscopy. Additionally, this binding could be completed by the chemically synthesized APWHLSSQYSRT peptide, indicating that the binding capability was specific and conferred by the peptide sequence. Through the ligation of the peptide to CdSe-ZnS core-shell nanocrystals, we were able to, for the first time, target embryonic stem cells through peptide-conjugated quantum dots.These data demonstrate that our established method of screening for embryonic stem cell specific binding peptides by phage display is feasible. Moreover, the peptide-conjugated quantum dots may be applicable for embryonic stem cell study and utilization
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