Relational transformation in family therapy supervision: a qualitative inquiry in a postmodern collaborative learning community

This study presents an emerging qualitative inquiry about relational transformation in family therapy supervision at the Houston Galveston Institute (HGI), a postmodern collaborative learning community. Ethno-phenomenology is used as a blended methodology. The researcher acknowledged the participants performing co-researchers of their experiences of relational transformation through supervision relationships. Ten participants, seven supervisors and three supervisors, described their experiences of transformation through their supervision relationships at a postmodern collaborative learning community, the HGI. All participants defined transformation and the process of relational transformation in reference to their own experiences of transformation and their understandings of theory of social construction. As they described their experiences of supervision, supervision relationships, and the learning community at HGI, they also shared their stories, addressing the characteristics of the community that facilitate transformation in them. In addition, participants discussed how a learning community could become a relational, transforma-generative context that eventually facilitates and encourages transformation in all its members who are the learners of family therapy

Using monoclonal antibodies to label living root hairs: a novel tool for studying cell wall microarchitecture and dynamics in <i>Arabidopsis</i>

Background&lt;p&gt;&lt;/p&gt; The Arabidopsis root hair represents a valuable cell model for elucidating polar expansion mechanisms in plant cells and the overall biology of roots. The deposition and development of the cell wall is central to the root hair expansion apparatus. During this process, incorporation of specific wall polymers into the growing wall architecture constitutes a critical spatio-temporal event that controls hair size and growth rate and one that is closely coordinated with the cell’s endomembrane, cytoskeletal and signal transduction apparatuses.&lt;p&gt;&lt;/p&gt; Results&lt;p&gt;&lt;/p&gt; In this study, the protocol for live cell labeling of roots with monoclonal antibodies that bind to specific wall polymers is presented. This method allows for rapid assessment of root hair cell wall composition during development and assists in describing changes to cell wall composition in transgenic mutant lines. Enzymatic “unmasking” of specific polymers prior to labeling allows for refined interpretation of cell wall chemistry. Live cell immunofluorescence data may also be correlated with transmission electron microscopy-based immunogold labeling.&lt;p&gt;&lt;/p&gt; Conclusions&lt;p&gt;&lt;/p&gt; Live Arabidopsis root hairs may be labeled with cell wall polymer-specific antibodies. This methodology allows for direct visualization of cell wall dynamics throughout development in stable transgenic plant lines. It also provides an important new tool in the elucidation of the specific interactions occurring between membrane trafficking networks, cytoskeleton and the cell wall deposition/remodeling mechanism

Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models

Inverse problems arise in a multitude of applications, where the goal is to recover a clean signal from noisy and possibly (non)linear observations. The difficulty of a reconstruction problem depends on multiple factors, such as the structure of the ground truth signal, the severity of the degradation, the implicit bias of the reconstruction model and the complex interactions between the above factors. This results in natural sample-by-sample variation in the difficulty of a reconstruction task, which is often overlooked by contemporary techniques. Recently, diffusion-based inverse problem solvers have established new state-of-the-art in various reconstruction tasks. However, they have the drawback of being computationally prohibitive. Our key observation in this paper is that most existing solvers lack the ability to adapt their compute power to the difficulty of the reconstruction task, resulting in long inference times, subpar performance and wasteful resource allocation. We propose a novel method that we call severity encoding, to estimate the degradation severity of noisy, degraded signals in the latent space of an autoencoder. We show that the estimated severity has strong correlation with the true corruption level and can give useful hints at the difficulty of reconstruction problems on a sample-by-sample basis. Furthermore, we propose a reconstruction method based on latent diffusion models that leverages the predicted degradation severities to fine-tune the reverse diffusion sampling trajectory and thus achieve sample-adaptive inference times. We utilize latent diffusion posterior sampling to maintain data consistency with observations. We perform experiments on both linear and nonlinear inverse problems and demonstrate that our technique achieves performance comparable to state-of-the-art diffusion-based techniques, with significant improvements in computational efficiency.Comment: 14 pages, 6 figures, preliminary versio

Perfectness of clustered graphs

Given a clustered graph (G,V), that is, a graph G=(V,E) together with a partition V of its vertex set, the selective coloring problem consists in choosing one vertex per cluster such that the chromatic number of the subgraph induced by the chosen vertices is minimum. This problem can be formulated as a covering problem with a 0–1 matrix M(G,V). Nevertheless, we observe that, given (G,V), it is NP-hard to check if M(G,V) is conformal (resp. perfect). We will give a sufficient condition, checkable in polynomial time, for M(G,V) to be conformal that becomes also necessary if conformality is required to be hereditary. Finally, we show that M(G,V) is perfect for every partition V if and only if G belongs to a superclass of threshold graphs defined with a complex function instead of a real one.Fil: Bonomo, Flavia. Consejo Nacional de Invest.cientif.y Tecnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Instituto de Investigaciones Matematicas; ArgentinaFil: Cornaz, Deni. Université Paris-Dauphine; FranciaFil: Ekim, Tinaz. Boğaziçi University; TurquíaFil: Ries, Bernard. Université Paris-Dauphine; Franci

Generalized vertex coloring problems using split graphs

Graph theory experienced a remarkable increase of interest among the scientific community during the last decades. The vertex coloring problem (Min Coloring) deserves a particular attention rince it has been able to capture a wide variety of applications. For mathematicians, it is interesting for an additional reason: it is extremely hard to solve it in an efficient way. In this thesis, we introduce several problems generalizing the usual vertex coloring problem, and hence, extending its application domain. We say that a graph is (p, k)-colorable if its vertex set can be partitioned into p cliques and k stable sets. Then, for a given p (respectively k), one may ask the following questions: how to choose p cliques (respectively k stable sets) to be removed from the graph such that the number of stable sets (respectively cliques) partitioning the remaining vertices is minimized? These are called (p, k)-coloring problems. We also introduce Min Split-coloring which is, given a graph G, the problem of minimizing k such that G is (k, k)-colorable. Along the saine line, given a graph G, the objective of the problem Min Cocoloring is to minimize p + k such that G is (p, k)-colorable. All these problems, called together generalized coloring problems, are obviously at least as difficult as Min Coloring. The purpose of this dissertation is to study generalized coloring problems in nome restricted classes of graphs in order to bring a new insight on the relative difficulties of these problems. To this end, we detect in a more precise way the limits between NP-hard and polynomially solvable problems. Chapter 1 introduces generalized coloring problems by emphasizing nome preliminary results which will guide the questions to handle in the following chapters. Chapter 2 exposes the first clans of graphs, namely cacti, where Min Split-coloring is shown to be polynomially solvable. We also observe that generalized coloring problems can be polynomially solved in triangulated graphs. The main result of Chapter 3 is a new characterization of cographs: it is equivalent to say that G is a cograph, and to state that, for every subgraph G' ⊆ G, G' is (p, k)-colorable if and only if G' [V \ K] is (p – 1, k)-colorable, where K induces a maximum clique of G'. This result implies simple combinatorial algorithme to solve all generalized coloring problems; the one for Min Cocoloring improves the best time complexity known so far. In Chapter 4, we handle the recognition of polar graphs which can be seen as a particular (p, k)-coloring, where p cliques are independent (i.e., not linked at all) and k stable sets form a complete k-partite graph. It is known that the recognition of polar graphs is NP-complete. Here, we determine the first clans of graphs, namely cographs, where the polar graphs can be recognized in polynomial time, more precisely in time O(n log n). We also give a characterization by forbidden subgraphs. In the came manner, we characterize monopolar cographs, i.e., cographs admitting a polar partition with at most one clique or at most one stable set. Chapter 5 is devoted to generalized coloring problems in line graphs. Here, we detect the first classes of graphs, namely line graphs of trees, line graphs of bipartite graphs and line graphs of line-perfect graphs, where generalized coloring problems diverge in terms of NP-hardness. In Chapter 6 we study the approximability of generalized coloring problems in line graphs, in comparability graphs and in general graphs. We derive approximation algorithms with a performance guarantee using both the standard approximation ratio and the differential approximation ratio. We show that both Min Split-coloring and Min Cocoloring are at least as hard as Min Coloring to approximate from the standard approximation ratio point of view, whereas, they admit a polynomial time differential approximation scheme and Min Coloring only a constant differential approximation ratio. We also show that Min Cocoloring reduces to Min Split-coloring in all classes of graphs closed under addition of disjoint cliques and under join of a complete k-partite graph. In Chapter 7, we handle two different applications of Min Split-coloring in permutation graphs. They give birth to a new problem, called Min Threshold-coloring, that we study in the came spirit as the other generalized coloring problems. In the last chapter, we present several open questions arising from this thesis

Generation of random chordal graphs using subtrees of a tree

Chordal graphs form one of the most studied graph classes. Several graph problems that are NP-hard in general become solvable in polynomial time on chordal graphs, whereas many others remain NP-hard. For a large group of problems among the latter, approximation algorithms, parameterized algorithms, and algorithms with moderately exponential or sub-exponential running time have been designed. Chordal graphs have also gained increasing interest during the recent years in the area of enumeration algorithms. Being able to test these algorithms on instances of chordal graphs is crucial for understanding the concepts of tractability of hard problems on graph classes. Unfortunately, only few published papers give algorithms for generating chordal graphs. Even in these papers, only very few methods aim for generating a large variety of chordal graphs. Surprisingly, none of these methods is directly based on the “intersection of subtrees of a tree” characterization of chordal graphs. In this paper, we give an algorithm for generating chordal graphs, based on the characterization that a graph is chordal if and only if it is the intersection graph of subtrees of a tree. Upon generating a random host tree, we give and test various methods that generate subtrees of the host tree. We compare our methods to one another and to existing ones for generating chordal graphs. Our experiments show that one of our methods is able to generate the largest variety of chordal graphs in terms of maximal clique sizes. Moreover, two of our subtree generation methods result in an overall complexity of our generation algorithm that is the best possible time complexity for a method generating the entire node set of subtrees in an “intersection of subtrees of a tree” representation. The instances corresponding to the results presented in this paper, and also a set of relatively small-sized instances are made available online.publishedVersio

Arabinogalactan Proteins and the Extracellular Matrix of Charophytes: A Sticky Business

Charophytes represent the group of green algae whose ancestors invaded land and ultimately gave rise to land plants 450 million years ago. While Zygnematophyceae are believed to be the direct sister lineage to embryophytes, different members of this group (Penium, Spirogyra, Zygnema) and the advanced thallus forming Coleochaete as well as the sarcinoid basal streptophyte Chlorokybus were investigated concerning their vegetative extracellular matrix (ECM) properties. Many taxa exhibit adhesion phenomena that are critical for affixing to a substrate or keeping cells together in a thallus, however, there is a great variety in possible reactions to e.g., wounding. In this study an analysis of adhesion mechanisms revealed that arabinogalactan proteins (AGPs) are most likely key adhesion molecules. Through use of monoclonal antibodies (JIM13) or the Yariv reagent, AGPs were located in cell surface sheaths and cell walls that were parts of the adhesion focal zones on substrates including wound induced rhizoid formation. JIM5, detecting highly methyl-esterfied homoglacturonan and JIM8, an antibody detecting AGP glycan and LM6 detecting arabinans were also tested and a colocalization was found in several examples (e.g., Zygnema) suggesting an interplay between these components. AGPs have been described in this study to perform both, cell to cell adhesion in algae forming thalli and cell to surface adhesion in the filamentous forms. These findings enable a broader evolutionary understanding of the function of AGPs in charophyte green algae

Premonitory urges are associated with decreased grey matter thickness within the insula and sensorimotor cortex in young people with Tourette syndrome

Tourette syndrome (TS) is a neurological disorder characterized by vocal and motor tics and is associated with cortical–striatal–thalamic–cortical circuit (CSTC) dysfunction and hyperexcitability of cortical limbic and motor regions, which are thought to lead to the occurrence of tics. Importantly, individuals with TS often report that their tics are preceded by ‘premonitory sensory phenomena’ (PSP) that are described as uncomfortable cognitive or bodily sensations that precede the execution of a tic, and are experienced as a strong urge for motor discharge. While the precise role played by PSP in the occurrence of tics is controversial, PSP are nonetheless of considerable theoretical and clinical importance in TS, not least because they form the core component in many of the behavioural therapies that are currently used in the treatment of tic disorders. In this study, we investigated the brain structure correlates of PSP. Specifically, we conducted a whole-brain analysis of cortical (grey matter) thickness in 29 children and young adults with TS and investigated the association between grey matter thickness and PSP. We demonstrate for the first time that PSP are inversely associated with grey matter thickness measurements within the insula and sensori-motor cortex. We also demonstrate that grey matter thickness is significantly reduced in these areas in individuals with TS relative to a closely age- and gender-matched group of typically developing individuals and that PSP ratings are significantly correlated with tic severity