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In this paper, I engage with the works of Richard Bodéüs about Aristotle’s understanding of the relationship between law, virtue, and education. I argue that there is an important difference between the demands of the law and those of reason, especially in the defective, but more common, regimes. This difference is also found in the best regime possible for most cities, the mixed regime Aristotle calls ‘polity’ (or, in Greek,politeia), insofar as it represents a balance between oligarchy and democracy. To educate citizens in this regime requires what Aristotle calls “political philosophy.”

From bacteria following simple chemical gradients to the brain distinguishing complex odour information, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks, but has been limited to the recognition of a set of no more than four patterns, each composed of four distinct DNA molecules. Winner-take-all computation has been suggested as a potential strategy for enhancing the capability of DNA-based neural networks. Compared to the linear-threshold circuits and Hopfield networks used previously, winner-take-all circuits are computationally more powerful, allow simpler molecular implementation and are not constrained by the number of patterns and their complexity, so both a large number of simple patterns and a small number of complex patterns can be recognized. Here we report a systematic implementation of winner-take-all neural networks based on DNA-strand-displacement reactions. We use a previously developed seesaw DNA gate motif, extended to include a simple and robust component that facilitates the cooperative hybridization that is involved in the process of selecting a ‘winner’. We show that with this extended seesaw motif DNA-based neural networks can classify patterns into up to nine categories. Each of these patterns consists of 20 distinct DNA molecules chosen from the set of 100 that represents the 100 bits in 10 × 10 patterns, with the 20 DNA molecules selected tracing one of the handwritten digits ‘1’ to ‘9’. The network successfully classified test patterns with up to 30 of the 100 bits flipped relative to the digit patterns ‘remembered’ during training, suggesting that molecular circuits can robustly accomplish the sophisticated task of classifying highly complex and noisy information on the basis of similarity to a memory.

In her magisterial Plato's Philosophers, Catherine Zuckert presents a radically new interpretation of Plato's dialogues. In doing so, she insists we must overcome reading them through the lens of Aristotle, whose influence has obscured the true nature of Plato's philosophy. However, in her works dealing with Aristotle's political science, Zuckert indicates several advantages of his approach to understanding politics. In this article, I explore the reasons why Zuckert finds Aristotle a problematic guide to Plato's philosophy as well as what she sees as the character and benefits of Aristotle's political theory. I conclude by suggesting a possible reconciliation between Zuckert's Aristotle and her Plato, insofar as both the Socrates whom Plato made his hero and Aristotle agree that political communities will rarely direct citizens toward virtue by means of law and that we must instead look to informal means of doing so.

Biochemical circuits made of rationally designed DNA molecules are proofs of concept for embedding control within complex molecular environments. They hold promise for transforming the current technologies in chemistry, biology, medicine and material science by introducing programmable and responsive behaviour to diverse molecular systems. As the transformative power of a technology depends on its accessibility, two main challenges are an automated design process and simple experimental procedures. Here we demonstrate the use of circuit design software, combined with the use of unpurified strands and simplified experimental procedures, for creating a complex DNA strand displacement circuit that consists of 78 distinct species. We develop a systematic procedure for overcoming the challenges involved in using unpurified DNA strands. We also develop a model that takes synthesis errors into consideration and semi-quantitatively reproduces the experimental data. Our methods now enable even novice researchers to successfully design and construct complex DNA strand displacement circuits., DNA circuits hold promise for advancing information-based molecular technologies, yet it is challenging to design and construct them in practice. Thubagere et al. build DNA strand displacement circuits using unpurified strands whose sequences are automatically generated from a user-friendly compiler.

Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, p < 0.001). This method also showed statistically significantly improved recall rate compared to a 2-cue baseline method using fewer vessel cues (30.7% versus 67.7%, p < 0.001). These results demonstrate that marginal artery localization on CTC is feasible by combining a discriminative classifier (i.e., random forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse. Published by Elsevier B.V.

In clinical practice and medical imaging research , automated computer-aided detection (CADe) is an important tool. While many methods can achieve high sensitivities, they typically suffer from high false positives (FP) per patient. In this study, we describe a two-stage coarse-to-fine approach using CADe candidate generation systems that operate at high sensitivity rates (close to \(100\%\) recall). In a second stage, we reduce false positive numbers using state-of-the-art machine learning methods, namely deep convolutional neural networks (ConvNet). The ConvNets are trained to differentiate hard false positives from true-positives utilizing a set of 2D (two-dimensional) or 2.5D re-sampled views comprising random translations, rotations, and multi-scale observations around a candidate’s center coordinate. During the test phase, we apply the ConvNets on unseen patient data and aggregate all probability scores for lesions (or pathology). We found that this second stage is a highly selective classifier that is able to reject difficult false positives while retaining good sensitivity rates. The method was evaluated on three data sets (sclerotic metastases, lymph nodes, colonic polyps) with varying numbers patients (59, 176, and 1,186, respectively). Experiments show that the method is able to generalize to different applications and increasing data set sizes. Marked improvements are observed in all cases: sensitivities increased from 57 to 70%, from 43 to 77% and from 58 to 75% for sclerotic metastases, lymph nodes and colonic polyps, respectively, at low FP rates per patient (3 FPs/patient).

Political theorists have recently emphasized the popular dimension of Aristotle’s political thought, and many have called attention to Aristotle’s assertion that certain multitudes should share in the city’s deliberations. In this article, I explore the “part of virtue and prudence” Aristotle believes necessary for a multitude to participate in political life. I argue, first, that military service helps citizens develop the “part of virtue” necessary for political participation and, second, that the “part of prudence” Aristotle has in mind is sunesis. I argue, moreover, that military virtue helps citizens acquire sunesis and guides its exercise. Aristotle recognizes the limitations and potential dangers of military virtue and attempts to avoid these, in part by offering a new understanding of military virtue to offset the Spartan. Understanding the “part of virtue and prudence” citizens have helps understand their role in a polity but also points to how those citizens, and their regime, can be improved.

Resilin has emerged as a promising new biomaterial possessing attractive properties for tissue engineering applications. To date, proteins with repeating resilin motifs have been expressed with molecular weights less than 30 kDa. This work describes the development of resilin-based proteins (repeating motif derived from Anopheles gambiae) 50 kDa in size. A modular cloning scheme was utilized and features a recursive cloning technique that can seamlessly and precisely tune the number of resilin repeats. Previously-established resilin expression protocols (based on the Studier auto-induction method) were employed to express the proteins in Escherichia coli BL21(DE3)pLysS. Western blot and densitometry results demonstrated that only ~50% of expressed proteins were the desired molecular weight. This finding suggested that either protein truncation or degradation occurred during protein expression. Preventing leaky expression, lowering the culture temperature, and harvesting during exponential phase resulted in up to 94% of the expressed proteins having the desired molecular weight. These expression conditions differ from previously-published resilin expression methods and are recommended when expressing proteins with a larger number of repetitive resilin sequences.

Aristotle uses the same word—politeia—to describe both the genus of “regimes” and a particular species within that genus. I argue that this usage is a common practice in Aristotle's practical works and identifies the most developed species within its genus. Aristotle thus sees the regime of polity as more appropriate for developed communities than the regimes often taken to represent his ideals, i.e., kingship and aristocracy. Aristotle's understanding of the capacities, and limitations, of the multitude leads him to propose the mixed regime of polity as the best regime generally possible. While polity differs from the best regime simply discussed in Book VII, it still offers rich possibilities for both political and theoretical activity.

This article argues that Book I of the Politics represents Aristotle's critique of Plato's Eleatic Stranger on the specific character of political rule and the knowledge required for political rule, and that this critique produces a different understanding of the proper division of regimes and the relationship between political theory and practice. These differences can be traced to a more fundamental disagreement about nature: Aristotle sees nature as generally hospitable to human life and argues that the natural end or goal of political association is not mere life but the good life, while the Eleatic perceives nature as hostile and proposes a minimalist politics, aimed primarily at preserving life. Although the Eleatic's view of nature might appear to be closer to and more compatible with modern political thought, the conception of nature Aristotle presents in his Politics offers richer possibilities for political theory and political life.

Automated computer-aided detection (CADe) in medical imaging has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities but at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities of $\sim$100% but at high FP levels. By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study. In this second stage, we generate $N$ 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations with respect to each ROI's centroid coordinates. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the trained ConvNets are employed to assign class (e.g., lesion, pathology) probabilities for a new set of $N$ random views that are then averaged at each ROI to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three different data sets with different numbers of patients: 59 patients for sclerotic metastases detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve CADe performance markedly in all cases. CADe sensitivities improved from 57% to 70%, from 43% to 77% and from 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively., Comment: 2D vs 2.5D vs 3D inputs and comparison to other standard classifiers such as SVM have been addressed by more experimentation and two completely new sections and figures. Results and Discussions have been updated accordingly

We propose a new method for detecting abdominal lymphadenopathy by utilizing a random forest statistical classifier to create voxel-level lymph node predictions, i.e. initial detection of enlarged lymph nodes. The framework permits the combination of multiple statistical lymph node descriptors and appropriate feature selection in order to improve lesion detection beyond traditional enhancement filters. We show that Hessian blobness measurements alone are inadequate for detecting lymph nodes in the abdominal cavity. Of the features tested here, intensity proved to be the most important predictor for lymph node classification. For initial detection, candidate lesions were extracted from the 3D prediction map generated by random forest. Statistical features describing intensity distribution, shape, and texture were calculated from each enlarged lymph node candidate. In the last step, a support vector machine (SVM) was trained and tested based on the calculated features from candidates and labels determined by two experienced radiologists. The computer-aided detection (CAD) system was tested on a dataset containing 30 patients with 119 enlarged lymph nodes. Our method achieved an AUC of 0.762±0.022 and a sensitivity of 79.8% with 15 false positives suggesting it can aid radiologists in finding enlarged lymph nodes.

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.

Modular proteins have emerged as powerful tools in tissue engineering because both the mechanical and biochemical properties can be precisely controlled through amino acid sequence. Resilin is an attractive candidate for use in modular proteins because it is well-known for having low stiffness, high fatigue lifetime, and high resilience. However, no studies have been conducted to assess resilin's compressive properties, cytocompatibility with clinically relevant cells, or effect on cell spreading. We designed a modular protein containing repeating sequences of a motif derived from Anopheles gambiae and cell-binding domains derived from fibronectin. Rapid cross-linking with tris(hydroxymethyl)phosphine was observed. The hydrogels had a complex modulus of 22 ± 1 kPa and yield strain of 63%. The elastic modulus in compression, or unconfined compressive modulus, was 2.4 ± 0.2 MPa, which is on the same order as human cartilage. A LIVE/DEAD assay demonstrated that human mesenchymal stem cells cultured on the resilin-based protein had a viability of 95% after three days. A cell-spreading assay revealed that the cells interacted with the fibronectin-derived domain in a sequence-specific manner and resulted in a mean cell area ~1.4-fold larger than when cells were seeded on a sequence-scrambled negative control protein. These results demonstrate that our resilin-based biomaterial is a promising biomaterial for cartilage tissue engineering.

In this book, Kevin M. Cherry compares the views of Plato and Aristotle about the practice, study and, above all, the purpose of politics. The first scholar to place Aristotle's Politics in sustained dialogue with Plato's Statesman, Cherry argues that Aristotle rejects the view of politics advanced by Plato's Eleatic Stranger, contrasting them on topics such as the proper categorization of regimes, the usefulness and limitations of the rule of law, and the proper understanding of phronēsis. The various differences between their respective political philosophies, however, reflect a more fundamental difference in how they view the relationship of human beings to the natural world around them. Reading the Politics in light of the Statesman sheds new light on Aristotle's political theory and provides a better understanding of Aristotle's criticism of Socrates. Most importantly, it highlights an enduring and important question: should politics have as its primary purpose the preservation of life, or should it pursue the higher good of living well?