Stony Brook University
  • Stony Brook, New York, United States
Recent publications
Zinc (Zn) is a new class of bioresorbable metal that has potential for cardiovascular stent material, orthopedic implants, wound closure devices, etc. However, pure Zn is not ideal for these applications due to its low mechanical strength and localized degradation behavior. Alloying is the most common/effective way to overcome this limitation. Still, the choice of alloying element is crucial to ensure the resulting alloy possesses sufficient mechanical strength, suitable degradation rate, and acceptable biocompatibility. Hereby, we proposed to blend selective transition metals (i.e., vanadium-V, chromium-Cr, and zirconium-Zr) to improve Zn's properties. These selected transition metals have similar properties to Zn and thus are beneficial for the metallurgy process and mechanical property. Furthermore, the biosafety of these elements is of less concern as they all have been used as regulatory approved medical implants or a component of an implant such as Ti6Al4V, CoCr, or Zr-based dental implants. Our study showed the first evidence that blending with transition metals V, Cr, or Zr can improve Zn's properties as bioresorbable medical implants. In addition, three in vivo implantation models were explored in rats: subcutaneous, aorta, and femoral implantations, to target the potential clinical applications of bioresorbable Zn implants.
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
Background The Src tyrosine kinase phosphorylates effector proteins to induce expression of the podoplanin (PDPN) receptor in order to promote tumor progression. However, nontransformed cells can normalize the growth and morphology of neighboring transformed cells. Transformed cells must escape this process, called “contact normalization”, to become invasive and malignant. Contact normalization requires junctional communication between transformed and nontransformed cells. However, specific junctions that mediate this process have not been defined. This study aimed to identify junctional proteins required for contact normalization. Methods Src transformed cells and oral squamous cell carcinoma cells were cultured with nontransformed cells. Formation of heterocellular adherens junctions between transformed and nontransformed cells was visualized by fluorescent microscopy. CRISPR technology was used to produce cadherin deficient and cadherin competent nontransformed cells to determine the requirement for adherens junctions during contact normalization. Contact normalization of transformed cells cultured with cadherin deficient or cadherin competent nontransformed cells was analyzed by growth assays, immunofluorescence, western blotting, and RNA-seq. In addition, Src transformed cells expressing PDPN under a constitutively active exogenous promoter were used to examine the ability of PDPN to override contact normalization. Results We found that N-cadherin (N-Cdh) appeared to mediate contact normalization. Cadherin competent cells that expressed N-Cdh inhibited the growth of neighboring transformed cells in culture, while cadherin deficient cells failed to inhibit the growth of these cells. Results from RNA-seq analysis indicate that about 10% of the transcripts affected by contact normalization relied on cadherin mediated communication, and this set of genes includes PDPN. In contrast, cadherin deficient cells failed to inhibit PDPN expression or normalize the growth of adjacent transformed cells. These data indicate that nontransformed cells formed heterocellular cadherin junctions to inhibit PDPN expression in adjacent transformed cells. Moreover, we found that PDPN enabled transformed cells to override the effects of contact normalization in the face of continued N-Cdh expression. Cadherin competent cells failed to normalize the growth of transformed cells expressing PDPN under a constitutively active exogenous promoter. Conclusions Nontransformed cells form cadherin junctions with adjacent transformed cells to decrease PDPN expression in order to inhibit tumor cell proliferation. Plain English Summary Cancer begins when a single cell acquires changes that enables them to form tumors. During these beginning stages of cancer development, normal cells surround and directly contact the cancer cell to prevent tumor formation and inhibit cancer progression. This process is called contact normalization. Cancer cells must break free from contact normalization to progress into a malignant cancer. Contact normalization is a widespread and powerful process; however, not much is known about the mechanisms involved in this process. This work identifies proteins required to form contacts between normal cells and cancer cells, and explores pathways by which cancer cells override contact normalization to progress into malignant cancers. Graphical abstract
Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm ² , which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm ² ) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.
The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
The hyperinflammatory state leading to an aberrant cytokine production, culminating in acute respiratory distress syndrome, sepsis and multi-organ dysfunction contribute much to the pathophysiologies of severe COVID-19. These severe patients have similar clinical manifestations with patients suffering from certain auto-inflammatory disorders and cytokine storm syndromes. Interestingly, anakinra (blocking both IL-1α and IL-1β) has shown promises in treating these patients with hyperinflammatory disorders, sepsis with multiorgan failures. Another inflammasome, AIM2, involved in production of IL-1 has also been found to be implicated in COVID-19. IL-1β, a known procoagulant, causes induction of tissue factor with increasing vascular endothelial permeability loss ensuing in hypercoagulability-one of the cardinal features of the disease. Hence, anakinra a 17kD recombinant human IL-1 receptor antagonist, used widely in Rheumatoid Arthritis treatments might prove efficacious in attenuating the hyperinflammatory state of the disease. Indeed, some of the controlled clinical trials have shown anakinra to effectively decrease mortality and hospital stay. Targeted cytokine blocking are always preferable in comparison with non-specific blocking (steroids) as it is more restrained with the chances of dampening of systemic immune system being much less. Early cell death and neutrophil migration have been one of the pivotal events in COVID-19 pathogenesis. Hence, suPAR levels which measures IL-1α (necroptosis) and S100A8/A9 (neutrophil migration) can perhaps be a good early biomarker predicting the disease progression. Lastly and importantly, as the vaccines are raised against spike protein and the different variants of concern are known to evade the neutralizing antibodies by varying degrees, it will be deserving to assess anakinra, against the variants of concern as an immunomodulatory drug.
Advances in cardiac surgical operative techniques and myocardial protection have dramatically improved outcomes in the past two decades. An unfortunate and unintended consequence is that 80% of the preventable morbidity and mortality following cardiac surgery now originates outside of the operating room. Our hope is that a renewed emphasis on evidence-based best practice and standardized perioperative care will reduce overall morbidity and mortality and improve patient-centric care. The Perioperative Quality Initiative (POQI) and Enhanced Recovery After Surgery–Cardiac Society (ERAS® Cardiac) have identified significant evidence gaps in perioperative medicine related to cardiac surgery, defined as areas in which there is significant controversy about how best to manage patients. These five areas of focus include patient blood management, goal-directed therapy, acute kidney injury, opioid analgesic reduction, and delirium.
Ultrafast control of structural and electronic properties of various quantum materials has recently sparked great interest. In particular, photoinduced switching between distinct topological phases has been considered a promising route to realize quantum computers. Here we use first-principles and effective Hamiltonian methods to show that in ZrTe 5 , lattice distortions corresponding to all three types of zone-center infrared optical phonon modes can drive the system from a topological insulator to a Weyl semimetal. Thus achieved Weyl phases are robust, highly tunable, and one of the cleanest due to the proximity of the Weyl points to the Fermi level and a lack of other carriers. We also find that Berry curvature dipole moment, induced by the dynamical inversion symmetry breaking, gives rise to various nonlinear effects that oscillate with the amplitude of the phonon modes. These nonlinear effects present an ultrafast switch for controlling the Weyltronics-enabled quantum system.
Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities. In this study, higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities. To make good use of the derivative information, a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors. Two widely used differential operators, i.e., the gradient operator and Hessian operator, are utilized to generate the first and second order derivative images. These derivative volumetric images are used to produce two angle-based and two vector-based (including both angle and magnitude) textures. Next, a vector-based co-occurrence matrix is proposed to extract texture features which are fed to a random forest classifier to perform polyp classifications. To evaluate the performance of our method, experiments are implemented over a private colorectal polyp dataset obtained from computed tomographic colonography. We compare our method with four existing state-of-the-art methods and find that our method can outperform those competing methods over 4%-13% evaluated by the area under the receiver operating characteristics curves.
Background Policy documents like Vision and Change and the Next Generation Science Standards emphasize the importance of using constructed-response assessments to measure student learning, but little work has examined the extent to which administration conditions (e.g., participation incentives, end-of-course timing) bias inferences about learning using such instruments. This study investigates potential biases in the measurement of evolution understanding (one time point) and learning (pre-post) using a constructed-response instrument. Methods The constructed-response ACORNS instrument (Assessment of COntextual Reasoning about Natural Selection) was administered at the beginning of the semester, during the final exam, and at end of the semester to large samples of North American undergraduates (N = 488–1379, 68–96% participation rate). Three ACORNS scores were studied: number of evolutionary core concepts (CC), presence of evolutionary misconceptions (MIS), and presence of normative scientific reasoning across contexts (MODC). Hierarchical logistic and linear models (HLMs) were used to study the impact of participation incentives (regular credit vs. extra credit) and end-of-course timing (final exam vs. post-test) on inferences about evolution understanding (single time point) and learning (pre-post) derived from the three ACORNS scores. The analyses also explored whether results were generalizable across race/ethnicity and gender. Results Variation in participation incentives and end-of-course ACORNS administration timing did not meaningfully impact inferences about evolution understanding (i.e., interpretations of CC, MIS, and MODC magnitudes at a single time point); all comparisons were either insignificant or, if significant, considered to be small effect sizes. Furthermore, participation incentives and end-of-course timing did not meaningfully impact inferences about evolution learning (i.e., interpretations of CC, MIS, and MODC changes through time). These findings were consistent across race/ethnicity and gender groups. Conclusion Inferences about evolution understanding and learning derived from ACORNS scores were in most cases robust to variations in participation incentives and end-of-course timing, suggesting that educators may have some flexibility in terms of when and how they deploy the ACORNS instrument.
As one of the significant features in the cache-enabled networks, in-network caching improves the efficiency of content dissemination by offloading content from the remote content provider onto the network that is closer to the users, and creates an opportunity for multicast. Since the multicast paradigm is a promising method for sending data to multiple users while saving bandwidth, this paper exploits the multi-rate multicast paradigm to address the caching problem in the cache-enabled environment which jointly considers the content caching and transmission rate allocation. The pure caching design, which is developed in most existing works, can only achieve limited performance; therefore, we argue that caching should be jointly designed. Our joint model can accommodate the diverse requirements of users by serving them with content in the nearby cache at different transmission rates, which is different from the single-rate multicast paradigm where users can only share the same rate in the same multicast group. We prove that the proposed maximization problem is a biconvex optimization problem. To solve this problem, we exploit the decomposable structure of the joint maximization to develop a heuristic solution that consists of caching decision and rate allocation algorithms. We reduce the search space of the heuristic algorithm to further reduce computation and communication complexity. We carry out an extensive packet-level simulation to evaluate the performance of our proposal compared with some benchmark schemes. Simulation results show that the proposed heuristic algorithm performs well compared with the benchmarks.
Cooperative fault management (CFM) is designed herein to control different types of renewable energy resources cooperatively during electrical faults. This paper studies systems with a high penetration of photovoltaic (PV) energy and wind energy. First, CFM leverages power converters of PV farms to boost the ride-through capability of nearby doubly-fed induction generators (DFIGs). By controlling PV farms’ output voltages to change smoothly during both fault initiation and fault clearance, the widely used crowbar in DFIGs is less likely to be activated. Crowbar activation adversely makes DFIGs lose controllability and absorb reactive power. The second contribution is the development of a software-defined CFM controller and a controller-in-the-loop demonstration of the real-time performance of this optimization-based CFM. CFM capitalizes on distributed optimization formulation to enable flexibility, plug-and-play, and privacy-preserving. Computation time, however, is a major concern for optimization-based dynamics control. Real-time controller-in-the-loop simulation results show optimization-based CFM can output reference values around 60 ms and is quick enough for dynamic control.
In recent decades, the rate of introduction of non-indigenous macroalgae has increased. While invasive seaweeds often outcompete native species for substrata, their direct effects on marine life are rarely described. Here, we describe ‘red water’ events caused by the decay of blooms of the invasive red seaweed, Dasysiphonia japonica, in Great South Bay, NY, USA, and the ability of water from such events to induce rapid and significant mortality in larval and juvenile fish (Menidia beryllina, Menidia menidia, and Cyprinodon variegatus) and larval bivalves (Mercenaria mercenaria and Crassostrea virginica). All species studied experienced significant (p<0.05) reductions in survival when exposed to macroalgae in a state of decay, seawater in which the alga was previously decayed, or both. Both bivalve species experienced 50–60% increases in mortality when exposed to decaying D. japonica for ∼ one week, despite normoxic conditions. Among fish, significant increases (40–80%) in mortality were observed after 24 h exposure to decayed D. japonica and one-week exposures caused, on average, 90% mortality in larval M. beryllina, 50% mortality in juvenile (∼3 cm) M. menidia, and 50% mortality in larval C. variegatus. All fish and bivalve mortality occurred under normoxic conditions (dissolved oxygen (DO) >7 mg L–1) and low ammonium levels (< 20 µM), with the exception of C. variegatus, which expired under conditions of decayed D. japonica coupled with reduced DO caused by the alga. Screening of water with decayed D. japonica using liquid chromatography-mass spectrometry revealed compounds with mass-to-charge ratios matching caulerpin, a known algal toxin that causes fish and shellfish mortality, and several other putative toxicants at elevated levels. Collectively, the high levels of mortality (50–90%) of larval and juvenile fish and bivalves exposed to decaying D. japonica under normoxic conditions coupled with the observation of ‘red water’ events in estuaries collectively indicate the red seaweed, D. japonica, can create harmful algal blooms (HABs).
Coastal regions in tropical countries encompass a diverse set of highly productive ecosystems with underlying acid sulfate (AS) soil materials. Muthurajawela Marsh is a tropical, saline, peat bog on the western coast of Sri Lanka and is known to contain AS soil materials. It is a critically important coastal wetland ecosystem and provides a multitude of benefits and services to the surrounding environment and the people in the area. At present, the AS soil materials in the marsh are at risk of exposure due to development activities in the surrounding areas. In this study, net acidity (NA) was quantified using an acid base accounting approach which includes retained acidity (RA) in addition to actual and potential sulfidic acidity (AA and PSA). The NA and the other soil characteristics were investigated in three soil profiles down to 1.5 m, along a north–south transect. All sites contained hypersulfidic soil materials as confirmed by field pH (pHF) > 4, field oxidation pH (pHFOX) < 4 and sulfide content > 0.01%. Net acidity values ranged from 23 to 4000 mol H+ t-1which was above the recommended management requirement value for peat and medium clay soils (i.e. 18 and 36 mol H+ t−1). At the northern site (S1), PSA was the main contributor to the NA, indicating future risk if the site were to become exposed to air. Both AA and RA were major contributing fractions at the middle (S2) and southern (S3) sites, with a possible imminent acidity discharge. All sites lack inherent buffering capacity, consequently, acidity can be released from the oxidation of the AS soil materials leading to the potential impact on the marsh ecosystem. The findings of this study indicate that human activities should be carefully managed to minimize the hazards that can occur due to exposing AS soils in the marsh.
The classification of imbalanced data has presented a significant challenge for most well-known classification algorithms that were often designed for data with relatively balanced class distributions. Nevertheless skewed class distribution is a common feature in real world problems. It is especially prevalent in certain application domains with great need for machine learning and better predictive analysis such as disease diagnosis, fraud detection, bankruptcy prediction, and suspect identification. In this paper, we propose a novel tree-based algorithm based on the area under the precision–recall curve for variable selection in the classification context. Our algorithm, named as the “Precision–Recall curve classification tree”, or simply the “PRC classification tree” modifies two crucial stages in tree building. The first stage is to maximize the area under the precision–recall curve in node variable selection. The second stage is to maximize the harmonic mean of recall and precision (F-measure) for threshold selection. We found the proposed PRC classification tree, and its subsequent extension, the PRC random forest, work well especially for class-imbalanced data sets. We have demonstrated that our methods outperform their classic counterparts, the usual CART and random forest for both synthetic and real data. Furthermore, the ROC classification tree proposed by our group previously, based on the area under the ROC curve, has shown good performance in imbalanced data. Their combination, the PRC–ROC tree, has also shows great promise in identifying the minority class.
To help rebuild wild fisheries while increasing seafood production, China has made a substantial effort over the past 40 years to support the development of sea ranching. However, the efficacy of this strategy remains largely uncertain, and there are still intense debates on its future growth and optimization. In this paper, we review the definition and concept of sea ranching in China and trace the history of Chinese policies, measures, and investments concerning sea ranching development from 1979 to 2020. We present the current status of sea ranching in China according to its major enhancement activities, geographic distribution, and management mode, each of which shows striking "south-north differences". In addition, combined with a systematic review of 150 articles, we first performed a nationwide evaluation of sea ranching performance in China. Results showed higher abundance, biomass, and/or species richness of marine organisms in artificial reef areas than adjacent sites, but this was not true for taxa such as non-economic benthos and phytoplankton. Additionally, mean recapture rates of the four species groups varied from 1.55 to 10.15%, with the highest recapture rate for crabs (mean ± SD = 10.15% ± 5.35%). We also found statistically significant correlations between the effect size of artificial reefs on the abundance of economically important fish and non-economic benthos and management mode. The effect size of artificial reefs on non-economic benthos biomass was significantly influenced by the reef area and density. Based on these findings, we concluded that there are three major challenges to the development of sea ranching in China, namely, habitat bottlenecks, recruitment bottlenecks, and management bottlenecks. In response to these challenges, we proposed specific recommendations focusing on funding allocation, utilization of habitat-based enhancement tools, hatchery releases, data sharing, and regional cooperative management. Graphical abstract
Ambulatory assessment methods (e.g., ecological momentary assessment [EMA], daily diary) are used to study the impact of daily stress on physical and mental health and behavior. However, there is relatively little information about how question format might influence responding. In two ambulatory studies, the effect of question format (i.e., binary yes/no vs. event checklist) on reporting of everyday stressors was evaluated. Study 1 included 58 urban caregivers of children with asthma in a diary design, and study 2 included 27 overweight/obese adults in an EMA design. Participants in both studies completed surveys that included questions about stressor occurrence and severity with one question format each week (binary vs. checklist); participants were randomized to question order, counterbalanced across weeks. Results suggested that participants in study 1, but not study 2, reported more stressors when provided a checklist vs. yes/no question. Stressor severity ratings did not systematically differ across question format. Additional research on question format across samples and designs could help to inform future ambulatory study design.
Grid forming inverters provide voltage and frequency regulations for microgrids; in the meantime, new challenges are introduced for microgrid protections. For instance, inverters’ control strategies can affect protection behaviors, and low short-circuit ratios and bi-directional power flows also make protection operations complex. Protection schemes based on conventional principles such as overcurrent and distance relays do not always provide reliable, sensitive, or selective operations. This paper devises a traveling wave protection approach for microgrids using a wavelet-driven deep neural network named WaveletKernelNet (WKN). Compared with conventional methods, the presented approach provides enhanced sensitivity, higher selectivity, and better identification of various faults in microgrids. Extensive case studies validate the efficacy and excellent performance of the devised approach.
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10,733 members
Gang He
  • Department of Technology and Society
Stalin Vilcarromero
  • Department of Medicine/ Infectious Disease
Rafael Arcesio Delgado Ruiz
  • Department of Prosthodontics and Digital Technology
Patricia Wright
  • Department of Anthropology
Gaurav Lalwani
  • Department of Biomedical Engineering
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Stony Brook University, 11794-5000, Stony Brook, New York, United States
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Michael Alan Bernstein
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