Disbursal of Text Steganography in the Space of Double-Secure Algorithm

Hackers reveal sensitive information from encrypted conversations as the globe has become one community. As a result, steganography and cryptography are used together to safeguard sensitive data more effectively than previous approaches and newly developed algorithms. A double-secure algorithm for text steganography (DSTS) is the safest approach compared to the paragraph hiding method (PHM) and capital alphabet shape encoding (CASE) method. In this context, the paragraph hiding method uses ASCII coding, which is not a standard method, and the CASE method has a security issue that if someone understands its applicability, it can be easily attacked. DSTS algorithm overcomes its limitations by using UTF-16 coding and enhances the security of CASE with a one-time pad (OTP). The one-time pad is a theoretically unbreakable cryptographic method. Still, it is not feasible for long messages, however, it is usable for short messages such as a password or intelligence agency’s more secure messages. DSTS degrades the average hiding capacity in a PHM from 2.06% to 1% due to UTF-16 coding. Using the UTF-8 method, the authors have improved MDSTS and the CMPHM by 6.49% and 7.76% hiding capacity, which outperforms in existing procedures; in the CMPHM, there is one reason for the improvement in hiding capacity is using the LZW compression method. CMHPM algorithm optimization standard deviation is less than that of the HPM, DSTS, and MDSTS, ultimately increasing speed.

Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative

Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA. Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively. Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN-based approaches. To foster such developments we make all segmentations publicly available together with this publication.

A typological overview of Eegimaa (Jóola Banjal)

This paper presents some of the most prominent properties of Eegimaa, a Jóola/Diola2 language spoken in the Basse-Casamance (Southern Senegal). The phonological features examined include [ATR] vowel harmony, backness harmony, lenition, and Eegimaa’s typologically unusual geminate consonants. Most of the paper, however, focuses on Eegimaa morphology. My analysis of the noun class system separates morphological classes from agreement classes (genders), and presents the most important principles of semantic categorization, including shape encoding. I also show that Eegimaa classifies nouns and verbs by the same overt linguistic means, namely, noun class prefixes. I argue that this overt classification of nouns and verbs reflects parallel semantic categorization of entities and events. Other prominent typological features include associative plural marking and nominal TAM marking with the inactualis suffix, which also expresses alienability contrasts.

Cholinergic Midbrain Afferents Modulate Striatal Circuits and Shape Encoding of Action Control

SummaryAssimilation of novel strategies into a consolidated action repertoire is a crucial function for behavioral adaptation and cognitive flexibility. Acetylcholine in the striatum plays a pivotal role in such adaptation and its release has been causally associated with the activity of cholinergic interneurons. Here we show that the midbrain, a previously unknown source of acetylcholine in the striatum, is a major contributor to cholinergic transmission in the striatal complex. Neurons of the pedunculopontine and laterodorsal tegmental nuclei synapse with striatal cholinergic interneurons and give rise to excitatory responses that, in turn, mediate inhibition of spiny projection neurons. Inhibition of acetylcholine release from midbrain terminals in the striatum impairs action shifting and mimics the effects observed following inhibition of acetylcholine release from striatal cholinergic interneurons. These results suggest the existence of two hierarchically-organized modes of cholinergic transmission in the striatum where cholinergic interneurons are modulated by cholinergic neurons of the midbrain.