Search

Cytotoxic T Lymphocyte Antigen 4 (CTLA4) is an important immune checkpoint protein and has been utilized as an immunotherapeutic target against melanoma. Our previous studies have shown that CTLA4 is expressed at low levels in primary human melanocytes but is upregulated in mutant-BRAF/NRAS melanoma cells and tissues. Despite predominantly intracellular localization of CTLA4, its intracellular function remains highly contradictory and unsubstantiated. We found that ectopic expression of Ctla4 in mouse melanoma cell lines substantially promoted lung colonization in allograft model systems in syngeneic mice. Moreover, similar results were observed in immunocompromised recipient mice, suggesting an intracellular cell-autonomous tumorigenic role of Ctla4 in melanoma. These finding led us to investigated intracellular functions of CTLA4 and its potential roles in melanomagenesis. Through the Ingenuity Pathway Analysis of our proteomics data, we found apoptosis as a major affected pathway in Ctla4-expressing mouse melanoma cells. We treated the cells (B2905-Ctla4-ee and EV control) with doxorubicin to induce apoptosis, followed by assessment by Annexin V assay. We found that Ctla4 expressing mouse melanoma cells were significantly resistant to doxorubicin-induced apoptosis. In reciprocal experiment, we generated (by CRISPR-Cas9) CTLA4-knockout A2058 human melanoma cells that exhibit high endogenous CTLA4 expression and we found that knockout cells showed significantly increased apoptosis than the parental cells. Moreover, we also observed significant upregulation of anti-apoptotic proteins (Bnip3, Birc6, Pak1, Mcl-1, Survivin, Rac1/Cdc42, Bcl-2, and Bnip3l) and downregulation of pro-apoptotic proteins (p53 and Bad) in CTLA4-expressing human melanoma cell lines. CTLA4-expressing cells also showed significantly higher invasion in Matrigel-coated transwell assay. These findings lead us to conclude that CTLA4 plays a significant role in regulating apoptosis and promoting melanoma progression in BRAF/NRAS mutant cells. Citation Format: Hasan Raza Kazmi, Xuan Mo, Bo Zhou, Sara Preston-Alp, Scott Gross, Hassaan Wajeeh, Carmen Merali, Jonathan Soboloff, Salim Merali, M. Raza Zaidi. A novel anti apoptotic function of CTLA4 in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5652.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

CTLA4 is a cell surface receptor on T cells that functions as an immune checkpoint molecule to enforce tolerance to cognate antigens. Anti–CTLA4 immunotherapy is highly effective at reactivating T-cell responses against melanoma, which is postulated to be due to targeting CTLA4 on T cells. Here, we report that CTLA4 is also highly expressed by most human melanoma cell lines, as well as in normal human melanocytes. Interferon-γ (IFNG) signaling activated the expression of the human CTLA4 gene in a melanocyte and melanoma cell–specific manner. Mechanistically, IFNG activated CTLA4 expression through JAK1/2-dependent phosphorylation of STAT1, which bound a specific gamma-activated sequence site on the CTLA4 promoter, thereby licensing CBP/p300-mediated histone acetylation and local chromatin opening. In melanoma cell lines, elevated baseline expression relied upon constitutive activation of the MAPK pathway. Notably, RNA-seq analyses of melanoma specimens obtained from patients who had received anti–CTLA4 immunotherapy (ipilimumab) showed upregulation of an IFNG-response gene expression signature, including CTLA4 itself, which correlated significantly with durable response. Taken together, our results raise the possibility that CTLA4 targeting on melanoma cells may contribute to the clinical immunobiology of anti–CTLA4 responses. Significance: These findings show that human melanoma cells express high levels of the immune checkpoint molecule CTLA4, with important possible implications for understanding how anti-CTLA4 immunotherapy mediates its therapeutic effects. Cancer Res; 78(2); 436–50. ©2017 AACR.

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.

Cutaneous malignant melanoma is one of the few major cancers that continue to exhibit a positive rate of increase in the developed world. A wealth of epidemiological data has undisputedly implicated ultraviolet radiation (UVR) from sunlight and artificial sources as the major risk factor for melanomagenesis. However, the molecular mechanisms of this cause-and-effect relationship remain murky and understudied. Recent efforts on multiple fronts have brought unprecedented expansion of our knowledge base on this subject and it is now clear that melanoma is caused by a complex interaction between genetic predisposition and environmental exposure, primarily to UVR. Here we provide an overview of the effects of the macroenvironment (UVR) on the skin microenvironment and melanocyte-specific intrinsic (mostly genetic) landscape, which conspire to produce one of the deadliest malignancies.

This paper examines the role of oil market uncertainty on currency carry trade payoffs. We find that oil market uncertainty can impact currency carry trade excess returns. When oil market uncertainty rises, expected currency excess returns will increase. Our findings are robust to alternative measures of oil market uncertainty and, after controlling for traditional uncertainties, different types of oil shocks. The results also hold well in both developed and emerging markets, as well as for oil-related currencies, non-oil currencies, commodity currencies, and non-commodity currencies. Additionally, oil market uncertainty can be priced in the cross section of currency carry trade excess returns. This effect can be explained by investors becoming more risk averse under high oil market uncertainty and requiring greater compensation for bearing such risk. Moreover, our measure of oil market uncertainty, the downside risk from the oil market, is quite different from that of traditional aggregate measures.

We examine the ability of idiosyncratic skewness to explain the cross section of commodity futures returns at both the characteristic and factor levels. We find that idiosyncratic skewness negatively and significantly predicts cross-sectional commodity futures returns, and largely accounts for the skewness effect documented in previous studies. Furthermore, we find that a long/short trading strategy based on the idiosyncratic skewness generates significant abnormal returns, which cannot be explained by the traditional risk factors in commodity futures markets and persists up to 12 months. Moreover, idiosyncratic skewness appears to be a priced risk factor in commodity futures markets. Finally, we find that a novel asymmetry measure, IE, is better at capturing the effect of idiosyncratic skewness than the traditional measure.

Cutaneous malignant melanoma is one of the few major cancers that continue to exhibit a positive rate of increase in the developed world. A wealth of epidemiological data has undisputedly implicated ultraviolet radiation (UVR) from sunlight and artificial sources as the major risk factor for melanomagenesis. However, the molecular mechanisms of this cause-and-effect relationship remain murky and understudied. Recent efforts on multiple fronts have brought unprecedented expansion of our knowledge base on this subject and it is now clear that melanoma is caused by a complex interaction between genetic predisposition and environmental exposure, primarily to UVR. Here we provide an overview of the effects of the macroenvironment (UVR) on the skin microenvironment and melanocyte-specific intrinsic (mostly genetic) landscape, which conspire to produce one of the deadliest malignancies.

To enhance the toughness of metal matrix nanocomposites, we demonstrate a strategy that involves the introduction of spatial arrays of nanoparticles. Specifically, we describe an approach to synthesize a microstructure characterized by arrays of fiber-like nanoparticle-rich (NPR) zones that contain spherical nanoparticles of boron carbide (sn-B4C) embedded in an ultrafine grained (UFG) aluminum alloy matrix. A combination of cryomilling and hot-extrusion was used to obtain this particular microstructure, and the mechanical behavior and operative strengthening and deformation mechanisms were investigated in detail. When compared to an equivalent unreinforced material, the presence of the array of NPR zones contributed to a 26% increase in tensile strength. Moreover, when compared to a nanocomposite containing a homogeneous distribution of nanoparticles, a 30% increase in toughness was observed. High nanohardness values obtained for the NPR zones and the observation of “pull-out” phenomena on fracture surfaces, suggest that the NPR zones behave as “hard” fiber-like units that can effectively sustain tensile loading and thereby enhance the strengthening efficiency of sn-B4C. Also, the presence of the array of NPR zones surrounded by nanoparticle-free (NPF) zones led to an enhancement in strength with limited loss in ductility. This behavior was rationalized on the basis of a low value of the Schmid factor in regions adjacent to NPR zones, coupled with the ease of dislocation movement in NPF zones. Finally, the ratio of the plastic zone size to the size of the “hard” NPR zones is proposed as an important factor that governs the overall toughness of the nanocomposite.

Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. A common class of approaches relies on object tracking and trajectory analysis. Very recently, sparse reconstruction techniques have been employed in video anomaly detection. The fundamental underlying assumption of these methods is that any new feature representation of a normal/anomalous event can be approximately modeled as a (sparse) linear combination prelabeled feature representations (of previously observed events) in a training dictionary. Sparsity can be a powerful prior on model coefficients but challenges remain in the detection of anomalies involving multiple objects and the ability of the linear sparsity model to effectively allow for class separation. The proposed research addresses both these issues. First, we develop a new joint sparsity model for anomaly detection that enables the detection of joint anomalies involving multiple objects. This extension is highly nontrivial since it leads to a new simultaneous sparsity problem that we solve using a greedy pursuit technique. Second, we introduce nonlinearity into, that is, kernelize. The linear sparsity model to enable superior class separability and hence anomaly detection. We extensively test on several real world video datasets involving both single and multiple object anomalies. Results show marked improvements in detection of anomalies in both supervised and unsupervised scenarios when using the proposed sparsity models.

Crude oil is an influential commodity in the real economy and stock markets of the world. However, the literature focuses exclusively on using the first or second moment of oil prices and ignores the information content in higher order moments. Our paper addresses this gap by investigating the relationship between the skewness of oil returns and expected stock returns in China’s A-share market. We find that the relationship between the skewness of oil returns and expected stock returns is significantly negative. This finding is robust even after considering firm characteristics, lagged oil returns, different energy dependencies, and changes in the supply and demand of oil, and is sustained across industries. This negative relationship can be interpreted as investors’ preference for skewness. Investors tend to overweight the tails of positively skewed oil returns to capture lottery-like payoffs, leading to net long positions and overpricing, which in turn lead to negative average returns. We also find that there are structural patterns for the relationship between the skewness of oil returns and expected stock returns. The negative relationship only exists in falling stock markets and changes under extremely low volatility.

Ginkgo biloba leaf extract has been widely used in dietary supplements and more recently in some foods and beverages. In addition to the well-known flavonol glycosides and terpene lactones, G. biloba leaves are also rich in amino acids. To determine the content of free amino acids, a reliable method has been established by using hydrophilic interaction ultra-HPLC coupled with ESI-MS. 20 free amino acids were simultaneously determined without derivatization in 12 min. The proposed method was fully validated in terms of linearity, sensitivity, repeatability, as well as recovery. Furthermore, the principal component analysis was applied to different G. biloba leaves collected in November (after fruit harvest season), which revealed that the samples from different production areas exhibited regional disparity in different clusters in accordance with their various hydrophilic interaction chromatograms coupled with mass profiles. The established approach could be helpful for evaluation of the potential values as dietary supplements and the quality control of G. biloba leaves, which might also be utilized for the investigation of other medicinal herbs containing amino acids.

On the basis of liquid chromatography coupled with triple quadrupole mass spectrometry working in multiple reaction monitoring mode, an analytical method has been established to simultaneously determine flavonol glycosides, terpene lactones, biflavones, proanthocyanidins, and ginkgolic acids inGinkgo bilobaleaves. Chromatographic separation was carried out on an Acquity BEH C18column (100 mm × 2.1 mm, 1.7 μm) with gradient elution of acetonitrile and 0.10% formic acid (v/v) at a flow rate of 0.4 mL/min, and column temperature 30°C. The developed method was validated in terms of linearity, accuracy, precision, stability, and sensitivity. The optimized method was successfully applied to analyze twenty-twoG. bilobaleaf samples of fruit cultivars collected from different places in China. Furthermore, hierarchical clustering analysis (HCA) was performed to evaluate and classify the samples according to the contents of the twenty-four chemical constituents. All of the results demonstrated that the developed method was useful for the overall evaluation of the quality ofG. bilobaleaves, and this study was also helpful for the comprehensive utilization and development ofG. bilobaresources.

Two new fatty alcohols named as (7S,8R,11S)-nonacosanetriol (1) and (10R,12R,15S)-nonacosanetriol (2), along with eight known compounds including ginkgolic acid (3), hydroginkgolic acid (4), sciadopitysin (5), ginkgetin (6), isoginkgetin (7), ginkgolide A (8), ginkgolide B (9) and ginkgolide C (10) have been isolated from the petroleum ether extract of Ginkgo biloba sarcotesta. Their structures were elucidated by means of chemical and extensive spectroscopic analysis. The absolute stereochemistry of compounds 1 and 2 was elucidated on the spectroscopic analysis of the R- and S-MTPA esters. Compounds 1 and 2 exhibited slight activity of antithrombin and moderate activity of antiplatelet aggregation in vitro. This was the first report regarding the anticoagulative activities of biflavonoids in G. biloba, and isoginkgetin (7) showed significant antithrombin and antiplatelet aggregation activity.

A monolithic MEMS-CMOS rangefinder using piezoelectric micromachined ultrasonic transducers (PMUTs) is presented. A dual-electrode PMUT design is used where the center electrode is driven externally to transmit an ultrasound pulse and the outer ring electrode is used to sense the returning echoes. We designed, fabricated, and characterized devices at two nominal Si thicknesses, 2 μm and 5 μm, designed to operate nominally at 200 kHz and 400 kHz respectively. The devices were fabricated by wafer-scale eutectic bonding of a 0.18 μm CMOS signal processing ASIC to the PMUT array. The ASIC provides each PMUT with a dedicated receiver, resulting in low parasitics and noise. Using the sensor for pulse-echo range finding, the maximum range of a single PMUT was found to be 98 cm round trip at 411 kHz.

Fas-associated protein with death domain (FADD) has been implicated in T lymphocytes, but the nature of FADD-dependent mechanism in early T cell development has not been completely elucidated. In this study, using T-cell specific deletion mice, we observed that FADD deficiency in thymocytes led to increased apoptosis and reduced cell numbers, which may be attributed to the reduction of Glut1 expression and correspondingly decreased glucose uptake. Furthermore, an abnormal transduction of Akt signaling was discovered in FADD−/− thymocytes, which may be responsible for the declined Glut1 expression. Collectively, our results demonstrate the new function of FADD in glucose metabolism and survival of early T cells.

Recently, sparsity based classification has been applied to video anomaly detection. A linear model is assumed over video features (e.g. trajectories) such that the feature representation of a new event is written as a sparse linear combination of existing feature representations in the dictionary. Sparsity based video anomaly detection shows promise but open challenges remain in that existing methods assume object specific and class specific event dictionaries making them applicable mostly in highly structured scenarios. Second, using conventional sparsity models on matrices/vectors, the computational burden is often high. In this work, we advocate a more general and practical sparsity model using a low-rank structure on the matrix of sparse coefficients. We find that enforcing a low-rank structure can ease the rigidity of traditional row-sparse constraints on sparse coefficient vectors/matrices. Because low-rank matrices are of course not always sparse, an additional l 1 regularization term is added. Further, if rank is substituted by its convex nuclear norm alternative, then significant computational benefits can be obtained over existing methods in sparsity based video anomaly detection. Experimental evaluation on benchmark video datasets reveal, our method is competitive with state-of-the art while providing robustness benefits under occlusion.

Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. Recently, sparse reconstruction techniques have been used for image classification, and shown to provide excellent robustness to occlusion. This progress has also been leveraged for sparsity based video anomaly detection where test trajectories are expressed as sparse linear combinations of example trajectories from a given (normal or anomalous) class. While sparsity based anomaly detection techniques are promising, they pose practical challenges due to their increased computational burden and the need for generous manually labeled training (even if only for normal event trajectories). Our work focuses on overcoming these limitations. Our central contribution is a dictionary design and optimization technique that can effectively reduce the size of training dictionaries that enable sparsity based classification/anomaly detection without adversely influencing detection performance. We also suggest the use of state of the art automatic trajectory clustering techniques for initializing dictionaries which can alleviate the burden of manual labeling. Experimental results show that significant computational advantages can be obtained with the proposed techniques with little performance loss over using large and manually labeled dictionaries of example trajectories.

A novel and generally applicable approach was established for the herb-herb interaction analysis when decocting together by using ultra-high-performance liquid chromatography coupled with a triple quadrupole electrospray tandem mass spectrometer and fuzzy chemical identification with poly-proportion design. A simple programme was originally developed for the rapid identification and classification of herbal constituents on the basis of the establishment of herbal constituent databases, recognition of the reference compound peaks, selection of the diagnostic ions or fragmentation pathways, classification of chemical groups and formation of group networks. In this study, the exact structures of the chemical constituents did not need to be determined, and only the constituents attributed to different groups were further considered for quantitative analysis. Such a novel approach was successfully applied to kansui-licorice interaction analysis when decocting together. A total of 26 constituents from kansui and 45 constituents from licorice were classified into different chemical groups, and they were further quantitatively analyzed on the basis of semi-symmetric proportion design. The results showed that kansui could significantly promote the concentration of most triterpenoid saponins, phenylpropanoids and their glycosides (the constituents from licorice) in solution when co-decocting, and licorice could clearly promote the concentration of most diterpenes and triterpenes (the constituents from kansui) in solution, potentially explaining the incompatibility of kansui and licorice. Overall, the presently developed strategy should be useful for the interaction analysis for complex mixtures containing various complicated constituents, such as herbal, environmental, agricultural and biological samples.

Ginkgo biloba leaf extract has been widely used in dietary supplements and more recently in some foods and beverages. In addition to the well-known flavonol glycosides and terpene lactones, G. biloba leaves are also rich in amino acids. To determine the content of free amino acids, a reliable method has been established by using hydrophilic interaction ultra-HPLC coupled with ESI-MS. 20 free amino acids were simultaneously determined without derivatization in 12 min. The proposed method was fully validated in terms of linearity, sensitivity, repeatability, as well as recovery. Furthermore, the principal component analysis was applied to different G. biloba leaves collected in November (after fruit harvest season), which revealed that the samples from different production areas exhibited regional disparity in different clusters in accordance with their various hydrophilic interaction chromatograms coupled with mass profiles. The established approach could be helpful for evaluation of the potential values as dietary supplements and the quality control of G. biloba leaves, which might also be utilized for the investigation of other medicinal herbs containing amino acids.

Video anomaly detection can be used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and other suspicious activities. A common class of approaches relies upon object tracking and trajectory analysis. A key challenge is the ability to effectively handle occlusions among objects and their trajectories. Another challenge is the detection of joint anomalies between multiple moving objects. Recently sparse reconstruction techniques have been used for image classification, and shown to provide excellent robustness to occlusion. This paper proposes a new joint sparsity model for anomaly detection that effectively addresses both the robustness to occlusion and the detection of joint anomalies involving multiple objects. Experimental results on real and synthetic data demonstrate the effectiveness of our approach for both single-object and multi-object anomalies.

This work presented a new analytical methodology based on hydrophilic interaction ultra-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry in multiple-reaction monitoring mode for analysis of 24 underivatized free amino acids (FAAs) in functional foods. The proposed method was first reported and validated by assessing the matrix effects, linearity, limit of detections and limit of quantifications, precision, repeatability, stability and recovery of all target compounds, and it was used to determine the nutritional substances of FAAs in ginkgo seeds and further elucidate the nutritional value of this functional food. The result showed that ginkgo seed turned out to be a good source of FAAs with high levels of several essential FAAs and to have a good nutritional value. Furthermore, the principal component analysis was performed to classify the ginkgo seed samples on the basis of 24 FAAs. As a result, the samples could be mainly clustered into three groups, which were similar to areas classification. Overall, the presented method would be useful for the investigation of amino acids in edible plants and agricultural products.

An important computational problem in color imaging is the design of color transforms that map color between devices or from a device-dependent space (e.g., RGB/CMYK) to a device-independent space (e.g., CIELAB) and vice versa. Real-time processing constraints entail that such nonlinear color transforms be implemented using multidimensional lookup tables (LUTs). Furthermore, relatively sparse LUTs (with efficient interpolation) are employed in practice because of storage and memory constraints. This paper presents a principled design methodology rooted in constrained convex optimization to design color LUTs on a simplex topology. The use of n simplexes, i.e., simplexes in n dimensions, as opposed to traditional lattices, recently has been of great interest in color LUT design for simplex topologies that allow both more analytically tractable formulations and greater efficiency in the LUT. In this framework of n-simplex interpolation, our central contribution is to develop an elegant iterative algorithm that jointly optimizes the placement of nodes of the color LUT and the output values at those nodes to minimize interpolation error in an expected sense. This is in contrast to existing work, which exclusively designs either node locations or the output values. We also develop new analytical results for the problem of node location optimization, which reduces to constrained optimization of a large but sparse interpolation matrix in our framework. We evaluate our n -simplex color LUTs against the state-of-the-art lattice (e.g., International Color Consortium profiles) and simplex-based techniques for approximating two representative multidimensional color transforms that characterize a CMYK xerographic printer and an RGB scanner, respectively. The results show that color LUTs designed on simplexes offer very significant benefits over traditional lattice-based alternatives in improving color transform accuracy even with a much smaller number of nodes.

The different sisal fibers concrete secimens were maded with the different percentage content sisal fibers. They were tested on the compressive strength, tensile strength, flexural strength and dry shrinkage, etc, the study finds that reinforcing the concrete by sisal fibers of certain content can significantly improve the performance of tensile strength, flexural strength, shrinkage resistance and impact resistance, but not the compressive strength. This technology can be widely used in the pavement construction and maintenance.

This chapter evaluates the stability of the Huanglashi landslide in the Three Gorges of the Yangtze River. By means of the uncertainty evaluation model for the Huanglashi landslide, this chapter calculates and evaluates the stability of the landslide at present and after water storage, and then analyzes the stability after the completion of the Three Gorges project. The stability analysis can be used for predicting the stability tendency and the prevention of future disasters.