Adaptive feature thresholding for off-line signature verification

This paper introduces Adaptive Feature Thresholding (AFT) which is a novel method of person-dependent off-line signature verification. AFT enhances how a simple image feature of a signature is converted to a binary feature vector by significantly improving its representation in relation to the training signatures. The similarity between signatures is then easily computed from their corresponding binary feature vectors. AFT was tested on the CEDAR and GPDS benchmark datasets, with classification using either a manual or an automatic variant. On the CEDAR dataset we achieved a classification accuracy of 92% for manual and 90% for automatic, while on the GPDS dataset we achieved over 87% and 85% respectively. For both datasets AFT is less complex and requires fewer images features than the existing state of the art methods, while achieving competitive results

Crosstalk and the Dynamical Modularity of Feed-Forward Loops in Transcriptional Regulatory Networks

Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (approximate to 20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically different state. Despite the potential for modularity between sparsely connected network motifs, Escherichia coli (E. coli) appears to favor crosstalk wherein at least one of the cross-linked FFLs remains modular. A gene ontology analysis reveals that stress response processes are significantly overrepresented in the cross-linked motifs found within E. coli. Although the daunting complexity of biological networks affects the dynamical properties of individual network motifs, some resist and remain modular, seemingly insulated from extrinsic perturbations-an intriguing possibility for nature to consistently and reliably provide certain network functionalities wherever the need arise

First-passage time analysis of a one-dimensional diffusion-reaction model: application to protein transport along DNA

<p>Abstract</p> <p>Background</p> <p>Proteins search along the DNA for targets, such as transcription initiation sequences, according to one-dimensional diffusion, which is interrupted by micro- and macro-hopping events and intersegmental transfers that occur under close packing conditions.</p> <p>Results</p> <p>A one-dimensional diffusion-reaction model in the form of difference-differential equations is proposed to analyze the nonequilibrium protein sliding kinetics along a segment of bacterial DNA. A renormalization approach is used to derive an expression for the mean first-passage time to arrive at sites downstream of the origin from the occupation probabilities given by the individual transport equations. Monte Carlo simulations are employed to assess the validity of the proposed approach, and all results are interpreted within the context of bacterial transcription.</p> <p>Conclusions</p> <p>Mean first-passage times decrease with increasing reaction rates, indicating that, on average, surviving proteins more rapidly locate downstream targets than their reaction-free counterparts, but at the price of increasing rarity. Two qualitatively different screening regimes are identified according to whether the search process operates under “small” or “large” values for the dissociation rate of the protein-DNA complex. Lower bounds are placed on the overall search time for varying reactive conditions. Good agreement with experimental estimates requires the reaction rate reside near the transition between both screening regimes, suggesting that biology balances a need for rapid searches against maximum exploration during each round of the sliding phase.</p

Distance dependence of photoinduced long-range electron transfer in zinc/ruthenium-modified myoglobins

An experimental investigation of the distance dependence of long-range electron transfer in zinc/ruthenium-modified myoglobins has been performed. The modified proteins were prepared by substitution of zinc mesoporphyrin IX diacid (ZnP) for the heme in each of four previously characterized pentaammineruthenium(III) (a_5Ru;a = NH_3) derivatives of sperm whale myoglobin (Mb): a_5Ru(His-48)Mb, a_5Ru(His-12)Mb, a_5Ru(His-116)Mb, a_5Ru(His-81)Mb. Electron transfer from the ZnP triplet excited state (^3ZnP*) to Ru^3+, ^3ZnP*-Ru^3+ → ZnP^+-Ru^2+ (ΔE° ~ 0.8V) was measured by time-resolved transient absorption spectroscopy: rate constants (k_f) are 7.0 × 10^4 (His-48), 1.0 × 10^2 (His-12), 8.9 × 10^1 (His-116), and 8.5 × 10^1 (His-81) s^-1 at 25 °C. Activation enthalpies calculated from the temperature dependences of the electron-transfer rates over the range 5-40 °C are 1.7 ± 1.6 (His-48), 4.7 ± 0.9 (His-12), 5.4 ± 0.4 (His-116), and 5.6 ± 2.5 (His-81) kcal mol^-1. Electron-transfer distances (d = closest ZnP edge to a_5Ru(His) edge; angstroms) were calculated to fall in the following ranges: His-48, 11.8-16.6; His-12, 21.5-22.3; His-116, 19.8-20.4; His-81, 18.8-19.3. The rate-distance equation is k_f = 7.8 × 10^8 exp[-0.9l(d - 3)] s^-1 . The data indicate that the ^3ZnP*-Ru(His-12)^3+ electronic coupling may be enhanced by an intervening tryptophan (Trp-14)

Transcriptional network growing models using motif-based preferential attachment

Understanding relationships between architectural properties of gene-regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics, as it can provide insights into, e.g., disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs – i.e., small-node subgraphs that occur more abundantly in GRNs than expected from chance alone. Because these transcriptional modules represent “building blocks” of complex networks and exhibit a wide range of functional and dynamical properties, they may contribute to the remarkable robustness and dynamical stability associated with the whole of GRNs. Here, we developed network-construction models to better understand this relationship, which produce randomized GRNs by using transcriptional motifs as the fundamental growth unit in contrast to other methods that construct similar networks on a node-by-node basis. Because this model produces networks with a prescribed lower bound on the number of choice transcriptional motifs (e.g., downlinks, feed-forward loops), its fidelity to the motif distributions observed in model organisms represents an improvement over existing methods, which we validated by contrasting their resultant motif and degree distributions against existing network-growth models and data from the model organism of the bacteriumEscherichia coli. These models may therefore serve as novel testbeds for further elucidating relationships between the topology of transcriptional motifs and network-wide dynamical properties

Taking a PEEK into YOLOv5 for Satellite Component Recognition via Entropy-based Visual Explanations

The escalating risk of collisions and the accumulation of space debris in Low Earth Orbit (LEO) has reached critical concern due to the ever increasing number of spacecraft. Addressing this crisis, especially in dealing with non-cooperative and unidentified space debris, is of paramount importance. This paper contributes to efforts in enabling autonomous swarms of small chaser satellites for target geometry determination and safe flight trajectory planning for proximity operations in LEO. Our research explores on-orbit use of the You Only Look Once v5 (YOLOv5) object detection model trained to detect satellite components. While this model has shown promise, its inherent lack of interpretability hinders human understanding, a critical aspect of validating algorithms for use in safety-critical missions. To analyze the decision processes, we introduce Probabilistic Explanations for Entropic Knowledge extraction (PEEK), a method that utilizes information theoretic analysis of the latent representations within the hidden layers of the model. Through both synthetic in hardware-in-the-loop experiments, PEEK illuminates the decision-making processes of the model, helping identify its strengths, limitations and biases