Review of recent literature relating to the motivation of early adolescents toward a permanent interest in reading

The problem of this investigation was twofold. First, it was undertaken to determine major factors in motivating early adolescents toward permanent interest in reading. Second, it was undertaken to determine means related to these factors by which early adolescents can be encouraged toward such an interest

Mechanismen der Kontaktaktivierung während einer Infektion mit Streptococcus pyogenes

Das humane Kontaktsystem aktiviert sowohl Blutgerinnung als auch Entzündung. Wie ich im Rahmen meiner Studien erstmalig gezeigt habe, kann die Aktivierung des Systems durch die Bakterien selbst als auch durch Wirtsstrukturen, die als Antwort auf die Bakterien freigesetzt werden, erfolgen. So unterstützt das Kontaktsystems während einer Infektion die Aktivierung der lokalen angeborenen Immunantwort. Andererseits haben pathogene Bakterien Mechanismen entwickelt Kontaktfaktoren zu aktivieren, was mit einer erhöhten Virulenz einhergeht.The human contact system activates both coagulation and inflammation. As I showed for the first time in my studies, the system can be activated by the bacteria themselves as well as by host structures that are released in response to the bacteria. The role of the contact system during an infection appears ambiguous, because on the one hand the activation on the surface of bacteria supports the local innate immune response. On the other hand, pathogenic bacteria in particular have developed mechanisms to activate contact factors, which is associated with increased virulence

Attentional Feature Fusion

Feature fusion, the combination of features from different layers or branches, is an omnipresent part of modern network architectures. It is often implemented via simple operations, such as summation or concatenation, but this might not be the best choice. In this work, we propose a uniform and general scheme, namely attentional feature fusion, which is applicable for most common scenarios, including feature fusion induced by short and long skip connections as well as within Inception layers. To better fuse features of inconsistent semantics and scales, we propose a multi-scale channel attention module, which addresses issues that arise when fusing features given at different scales. We also demonstrate that the initial integration of feature maps can become a bottleneck and that this issue can be alleviated by adding another level of attention, which we refer to as iterative attentional feature fusion. With fewer layers or parameters, our models outperform state-of-the-art networks on both CIFAR-100 and ImageNet datasets, which suggests that more sophisticated attention mechanisms for feature fusion hold great potential to consistently yield better results compared to their direct counterparts. Our codes and trained models are available online.Comment: Accepted by WACV 202

Predicting urban tree cover from incomplete point labels and limited background information

Trees inside cities are important for the urban microclimate, contributing positively to the physical and mental health of the urban dwellers. Despite their importance, often only limited information about city trees is available. Therefore in this paper, we propose a method for mapping urban trees in high-resolution aerial imagery using limited datasets and deep learning. Deep learning has become best-practice for this task, however, existing approaches rely on large and accurately labelled training datasets, which can be difficult and expensive to obtain. However, often noisy and incomplete data may be available that can be combined and utilized to solve more difficult tasks than those datasets were intended for. This paper studies how to combine accurate point labels of urban trees along streets with crowd-sourced annotations from an open geographic database to delineate city trees in remote sensing images, a task which is challenging even for humans. To that end, we perform semantic segmentation of very high resolution aerial imagery using a fully convolutional neural network. The main challenge is that our segmentation maps are sparsely annotated and incomplete. Small areas around the point labels of the street trees coming from official and crowd-sourced data are marked as foreground class. Crowd-sourced annotations of streets, buildings, etc. define the background class. Since the tree data is incomplete, we introduce a masking to avoid class confusion. Our experiments in Hamburg, Germany, showed that the system is able to produce tree cover maps, not limited to trees along streets, without providing tree delineations. We evaluated the method on manually labelled trees and show that performance drastically deteriorates if the open geographic database is not used

Attention as Activation

Activation functions and attention mechanisms are typically treated as having different purposes and have evolved differently. However, both concepts can be formulated as a non-linear gating function. Inspired by their similarity, we propose a novel type of activation units called attentional activation (ATAC) units as a unification of activation functions and attention mechanisms. In particular, we propose a local channel attention module for the simultaneous non-linear activation and element-wise feature refinement, which locally aggregates point-wise cross-channel feature contexts. By replacing the well-known rectified linear units by such ATAC units in convolutional networks, we can construct fully attentional networks that perform significantly better with a modest number of additional parameters. We conducted detailed ablation studies on the ATAC units using several host networks with varying network depths to empirically verify the effectiveness and efficiency of the units. Furthermore, we compared the performance of the ATAC units against existing activation functions as well as other attention mechanisms on the CIFAR-10, CIFAR-100, and ImageNet datasets. Our experimental results show that networks constructed with the proposed ATAC units generally yield performance gains over their competitors given a comparable number of parameters

Extracellular aspartic protease SAP2 of Candida albicans yeast cleaves human kininogens and releases proinflammatory peptides, Met-Lys-bradykinin and des-Arg(9)-Met-Lys-bradykinin

Bradykinin-related peptides, universal mediators of inflammation collectively referred to as the kinins, are often produced in excessive amounts during microbial infections. We have recently shown that the yeast Candida albicans, the major fungal pathogen to humans, can exploit two mechanisms to enhance kinin levels at the sites of candidial infection, one depending on adsorption and activation of the endogenous kinin-generating system of the host on the fungal cell wall and the other relying on cleavage of kinin precursors, the kininogens, by pathogen-secreted proteases. This work aimed at assigning this kininogenase activity to the major secreted aspartic protease of C. albicans (SAP2). The purified SAP2 was shown to cleave human kininogens, preferably the low molecular mass form (LK) and optimally in an acidic environment (pH 3.5-4.0), and to produce two kinins, Met-Lys-bradykinin and its derivative, {[}Hydroxyproline(3)]-Met-Lys-bradykinin, both of which are capable of interacting with cellular bradykinin receptors of the B2 subtype. Additionally, albeit with a lower yield, des-Arg(9)-Met-Lys-bradykinin, an effective agonist of B1-subtype receptors, was released. The pathophysiological potential of these kinins and des-Arg-kinin was also proven by presenting their ability to stimulate human promonocytic cells U937 to release proinflammatory interleukin 1 beta (IL-1 beta) and IL-6

Scattered tree death contributes to substantial forest loss in California

In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups ( ≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires