WILLINGNESS OF FOOD INDUSTRY COMPANIES IN CO-FINANCING COLLECTIVE AGRICULTURAL MARKETING (CAM) ACTIONS

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On the number of tetrahedra with minimum, unit, and distinct volumes in three-space

We formulate and give partial answers to several combinatorial problems on volumes of simplices determined by $n$ points in 3-space, and in general in $d$ dimensions. (i) The number of tetrahedra of minimum (nonzero) volume spanned by $n$ points in \RR^3 is at most ${2/3}n^3-O(n^2)$, and there are point sets for which this number is ${3/16}n^3-O(n^2)$. We also present an $O(n^3)$ time algorithm for reporting all tetrahedra of minimum nonzero volume, and thereby extend an algorithm of Edelsbrunner, O'Rourke, and Seidel. In general, for every k,d\in \NN, $1\leq k \leq d$, the maximum number of $k$-dimensional simplices of minimum (nonzero) volume spanned by $n$ points in \RR^d is $\Theta(n^k)$. (ii) The number of unit-volume tetrahedra determined by $n$ points in \RR^3 is $O(n^{7/2})$, and there are point sets for which this number is $\Omega(n^3 \log \log{n})$. (iii) For every d\in \NN, the minimum number of distinct volumes of all full-dimensional simplices determined by $n$ points in \RR^d, not all on a hyperplane, is $\Theta(n)$.Comment: 19 pages, 3 figures, a preliminary version has appeard in proceedings of the ACM-SIAM Symposium on Discrete Algorithms, 200

The role of apoptosis repressor with a CARD domain (ARC) in the therapeutic resistance of renal cell carcinoma (RCC): the crucial role of ARC in the inhibition of extrinsic and intrinsic apoptotic signalling

Background: Renal cell carcinomas (RCCs) display broad resistance against conventional radio- and chemotherapies, which is due at least in part to impairments in both extrinsic and intrinsic apoptotic pathways. One important anti-apoptotic factor that is strongly overexpressed in RCCs and known to inhibit both apoptotic pathways is ARC (apoptosis repressor with a CARD domain). Methods: Expression and subcellular distribution of ARC in RCC tissue samples and RCC cell lines were determined by immunohistochemistry and fluorescent immunohistochemistry, respectively. Extrinsic and intrinsic apoptosis signalling were induced by TRAIL (TNF-related apoptosis-inducing ligand), ABT-263 or topotecan. ARC knock-down was performed in clearCa-12 cells using lentiviral transduction of pGIPZ. shRNAmir constructs. Extrinsic respectively intrinsic apoptosis were induced by TRAIL (TNF-related apoptosis-inducing ligand), ABT263 or topotecan. Potential synergistic effects were tested by pre-treatment with topotecan and subsequent treatment with ABT263. Activation of different caspases and mitochondrial depolarisation (JC-1 staining) were analysed by flow cytometry. Protein expression of Bcl-2 family members and ARC in RCC cell lines was measured by Western blotting. Statistical analysis was performed by Student’s t-test. Results: Regarding the extrinsic pathway, ARC knockdown strongly enhanced TRAIL-induced apoptosis by increasing the activation level of caspase-8. Regarding the intrinsic pathway, ARC, which was only weakly expressed in the nuclei of RCCs in vivo, exerted its anti-apoptotic effect by impairing mitochondrial activation rather than inhibiting p53. Topotecan- and ABT-263-induced apoptosis was strongly enhanced following ARC knockdown in RCC cell lines. In addition, topotecan pre-treatment enhanced ABT-263-induced apoptosis and this effect was amplified in ARC-knockdown cells. Conclusion: Taken together, our results are the first to demonstrate the importance of ARC protein in the inhibition of both the extrinsic and intrinsic pathways of apoptosis in RCCs. In this context, ARC cooperates with anti-apoptotic Bcl-2 family members to exert its strong anti-apoptotic effects and is therefore an important factor not only in the therapeutic resistance but also in future therapy strategies (i.e., Bcl-2 inhibitors) in RCC. In sum, targeting of ARC may enhance the therapeutic response in combination therapy protocols

The common truncation variant in pancreatic lipase related protein 2 (PNLIPRP2) is expressed poorly and does not alter risk for chronic pancreatitis

A nonsense variant (p.W358X) of human pancreatic lipase related protein 2 (PNLIPRP2) is present in different ethnic populations with a high allele frequency. In cell culture experiments, the truncated protein mainly accumulates inside the cells and causes endoplasmic reticulum stress. Here, we tested the hypothesis that variant p.W358X might increase risk for chronic pancreatitis through acinar cell stress. We sequenced exon 11 of PNLIPRP2 in a cohort of 256 subjects with chronic pancreatitis (152 alcoholic and 104 non-alcoholic) and 200 controls of Hungarian origin. We observed no significant difference in the distribution of the truncation variant between patients and controls. We analyzed mRNA expression in human pancreatic cDNA samples and found the variant allele markedly reduced. We conclude that the p.W358X truncation variant of PNLIPRP2 is expressed poorly and has no significant effect on the risk of chronic pancreatitis

Scalable machine learning algorithms using path signatures

In this thesis, we consider the integration of path signatures–a mathematical object rooted in rough path theory–with scalable machine learning algorithms to address challenges in sequential and structured data modelling. The key topics considered include: • Path Signatures: Introduced as a hierarchical and theoretically robust feature representation for sequential data, path signatures faithfully capture dynamics while offering properties like invariance to reparameterization and tree-like equivalence. Challenges like computational overheads are addressed through novel algorithms throughout the thesis. • Gaussian Processes: We demonstrate embedding signature kernels into Gaussian process models, offering an expressive probabilistic modelling approach for sequential data while tackling computational barriers via sparse variational inference techniques. This approach enhances performance on probabilistic time series classification tasks. • Seq2Tens Framework: Combines signature features with deep learning, using iterations of low-rank layers to mitigate computational costs while retaining expressiveness. Applications include time series classification, mortality prediction, generative modelling. • Graph Representation: Extends path signatures to graph data and connects them to hypo-elliptic diffusions, combined with low-rank techniques offering scalable architectures for capturing global and local graph structures, and outperforming conventional graph neural networks on tasks requiring long-range reasoning. • Random Fourier Signature Features: Introduces scalable random feature-based approximations for signature kernels with supporting theoretical results, overcoming computational limitations for large datasets while retaining state-of-the-art performance. • Recurrent Sparse Spectrum Signature Gaussian Processes: Combines Random Fourier Signature Features with Gaussian Processes and a forgetting mechanism for adaptive con text focus in time series forecasting, bridging short- and long-term dependencies.The topics are divided into separate, self-contained chapters that can be read independently

Investigation and simulation of the Planetary Combination Hybrid Electric Vehicle

The purpose of this study was the detailed examination of a Planetary Combination Hybrid Electric Vehicle design (PC-Hybrid). The PC-Hybrid unites all the advantages of the existing hybrid electric vehicle powertrain concepts, such as series, parallel and combination, while eliminating the disadvantages of each.;The PC-Hybrid powertrain is built up of an internal combustion engine, and two electric motor/alternators connected together via a planetary gear set. Several different powertrain configuration layouts were investigated as possible setups of the PC-Hybrid and the two most promising ones were chosen for further investigation and simulation. A control strategy has been developed for the optimal operation PC-Hybrid configurations. A computer program was written to simulate the fuel economy of the PC-Hybrid.;A Hybrid Vehicle Simulator, HVSim (developed at WVU), was used as the basis of the computer simulation and was used to compare the fuel consumption of the PC-Hybrid design to a baseline conventional vehicle setup as well as to the currently existing hybrid electric vehicle configurations. The program uses a backward-looking simulation model that calculates the speed and torque required of the engine, the motor and the alternator for a given driving cycle. Once the engine, motor and alternator speed and torque are calculated, HVSim uses efficiency maps of the engine and motor to define their efficiency. Using the instantaneous efficiency HVSim defines the power loss in each component and calculates the fuel consumption of the simulated vehicle.;The simulation results show that the fuel economy of the PC-Hybrid is better than that of a comparable Series HEV on the FTP City cycle and better than that of a comparable Parallel HEV on the Highway FET cycle while maintaining similar performance to the stock conventional vehicle. In addition the exhaust gas emissions may be reduced, compared to conventional vehicle or a parallel HEV, due to the reduced requirement for transient engine operation

Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

We develop a Bayesian approach to learning from sequential data by using Gaussian processes (GPs) with so-called signature kernels as covariance functions. This allows to make sequences of different length comparable and to rely on strong theoretical results from stochastic analysis. Signatures capture sequential structure with tensors that can scale unfavourably in sequence length and state space dimension. To deal with this, we introduce a sparse variational approach with inducing tensors. We then combine the resulting GP with LSTMs and GRUs to build larger models that leverage the strengths of each of these approaches and benchmark the resulting GPs on multivariate time series (TS) classification datasets. Code available at https://github.com/tgcsaba/GPSig.Comment: Near camera ready version for ICML 2020. Previous title: "Variational Gaussian Processes with Signature Covariances

Coregulated Genes Link Sulfide:Quinone Oxidoreductase and Arsenic Metabolism in Synechocystis sp. Strain PCC6803

Although the biogeochemistry of the two environmentally hazardous compounds arsenic and sulfide has been extensively investigated, the biological interference of these two toxic but potentially energy-rich compounds has only been hypothesized and indirectly proven. Here we provide direct evidence for the first time that in the photosynthetic model organism Synechocystis sp. strain PCC6803 the two metabolic pathways are linked by coregulated genes that are involved in arsenic transport, sulfide oxidation, and probably in sulfide-based alternative photosynthesis. Although Synechocystis sp. strain PCC6803 is an obligate photoautotrophic cyanobacterium that grows via oxygenic photosynthesis, we discovered that specific genes are activated in the presence of sulfide or arsenite to exploit the energy potentials of these chemicals. These genes form an operon that we termed suoRSCT, located on a transposable element of type IS4 on the plasmid pSYSM of the cyanobacterium. suoS (sll5036) encodes a light-dependent, type I sulfide:quinone oxidoreductase. The suoR (sll5035) gene downstream of suoS encodes a regulatory protein that belongs to the ArsR-type repressors that are normally involved in arsenic resistance. We found that this repressor has dual specificity, resulting in 200-fold induction of the operon upon either arsenite or sulfide exposure. The suoT gene encodes a transmembrane protein similar to chromate transporters but in fact functioning as an arsenite importer at permissive concentrations. We propose that the proteins encoded by the suoRSCT operon might have played an important role under anaerobic, reducing conditions on primordial Earth and that the operon was acquired by the cyanobacterium via horizontal gene transfer

Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections

Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutativity, in the sense that reordering the elements of a sequence can completely change its meaning. We use a classical mathematical object -- the tensor algebra -- to capture such dependencies. To address the innate computational complexity of high degree tensors, we use compositions of low-rank tensor projections. This yields modular and scalable building blocks for neural networks that give state-of-the-art performance on standard benchmarks such as multivariate time series classification and generative models for video.Comment: 33 pages, 5 figures, 6 table