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General General

Rise of nations: Why do empires expand and fall?

In Chaos (Woodbury, N.Y.)

We consider centralized networks composed of multiple satellites arranged around a few dominating super-egoistic centers. These so-called empires are organized using a divide and rule framework enforcing strong center-satellite interactions while keeping the pairwise interactions between the satellites sufficiently weak. We present a stochastic stability analysis, in which we consider these dynamical systems as stable if the centers have sufficient resources while the satellites have no value. Our model is based on a Hopfield type network that proved its significance in the field of artificial intelligence. Using this model, it is shown that the divide and rule framework provides important advantages: it allows for completely controlling the dynamics in a straight-forward way by adjusting center-satellite interactions. Moreover, it is shown that such empires should only have a single ruling center to provide sufficient stability. To survive, empires should have switching mechanisms implementing adequate behavior models by choosing appropriate local attractors in order to correctly respond to internal and external challenges. By an analogy with Bose-Einstein condensation, we show that if the noise correlations are negative for each pair of nodes, then the most stable structure with respect to noise is a globally connected network. For social systems, we show that controllability by their centers is only possible if the centers evolve slowly. Except for short periods when the state approaches a certain stable state, the development of such structures is very slow and negatively correlated with the size of the system's structure. Hence, increasing size eventually ends up in the "control trap."

Vakulenko S, Lyakhov D A, Weber A G, Lukichev D, Michels D L


General General

A two-stage deep learning algorithm for talker-independent speaker separation in reverberant conditions.

In The Journal of the Acoustical Society of America

Speaker separation is a special case of speech separation, in which the mixture signal comprises two or more speakers. Many talker-independent speaker separation methods have been introduced in recent years to address this problem in anechoic conditions. To consider more realistic environments, this paper investigates talker-independent speaker separation in reverberant conditions. To effectively deal with speaker separation and speech dereverberation, extending the deep computational auditory scene analysis (CASA) approach to a two-stage system is proposed. In this method, reverberant utterances are first separated and separated utterances are then dereverberated. The proposed two-stage deep CASA system significantly outperforms a baseline one-stage deep CASA method in real reverberant conditions. The proposed system has superior separation performance at the frame level and higher accuracy in assigning separated frames to individual speakers. The proposed system successfully generalizes to an unseen speech corpus and exhibits similar performance to a talker-dependent system.

Delfarah Masood, Liu Yuzhou, Wang DeLiang


General General

Identification of key discriminating variables between spinner dolphin (Stenella longirostris) whistle types.

In The Journal of the Acoustical Society of America

Descriptions of the six different spinner dolphin (Stenella longirostris) whistle types were developed from a random sample of 600 whistles collected across a 2-yr period from a Fijian spinner dolphin population. An exploratory multivariate visualization suggested an inverse relationship between delta and minimum frequency (58.6%) as well as whistle duration (18.1%) as the most discriminating variables in this dataset. All three of these variables were deemed to be significant when considered jointly in a multivariate analysis of variance (MANOVA): delta frequency (F5594 = 27.167, p < 0.0001), minimum frequency (F5594 = 14.889, p < 0.0001), and duration (F5594 = 24.303, p < 0.0001). Significant differences between at least two of the whistle types were found for all five acoustic parameters in univariate analysis of variation (ANOVA) tests. Constant and sine whistles were found to be the most distinctive whistles, whereas upsweep and downsweep whistles were the most similar. The identification of which parameters differ most markedly between whistle types and the relatively high explanatory power of this study's results provide a logical starting point for objective classification of spinner dolphin whistle types using machine learning techniques.

Simpson Samanunu D, Miller Cara E


Cardiology Cardiology

Sphingolipid composition of circulating extracellular vesicles after myocardial ischemia.

In Scientific reports ; h5-index 158.0

Sphingolipids are structural components of cell membrane, displaying several functions in cell signalling. Extracellular vesicles (EV) are lipid bilayer membrane nanoparticle and their lipid composition may be different from parental cells, with a significant enrichment in sphingolipid species, especially in pathological conditions. We aimed at optimizing EV isolation from plasma and describing the differential lipid content of EV, as compared to whole plasma. As pilot study, we evaluated the diagnostic potential of lipidomic signature of circulating EV in patients with a diagnosis of ST-segment-elevation myocardial infarction (STEMI). STEMI patients were evaluated before reperfusion and 24-h after primary percutaneous coronary intervention. Twenty sphingolipid species were quantified by liquid-chromatography tandem-mass-spectrometry. EV-ceramides, -dihydroceramides, and -sphingomyelins increased in STEMI vs. matched controls and decreased after reperfusion. Their levels correlated to hs-troponin, leucocyte count, and ejection fraction. Plasma sphingolipids levels were 500-to-700-fold higher as compared to EV content; nevertheless, only sphingomyelins differed in STEMI vs. control patients. Different sphingolipid species were enriched in EV and their linear combination by machine learning algorithms accurately classified STEMI patients at pre-PCI evaluation. In conclusion, EV lipid signature discriminates STEMI patients. These findings may contribute to the identification of novel biomarkers and signaling mechanisms related to cardiac ischemia.

Burrello J, Biemmi V, Dei Cas M, Amongero M, Bolis S, Lazzarini E, Bollini S, Vassalli G, Paroni R, Barile L


General General

Erratum: Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning.

In International heart journal

An error appeared in the article entitled "Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning" by Takuya Matsumoto, Satoshi Kodera, Hiroki Shinohara, Hirotaka Ieki, Toshihiro Yamaguchi, Yasutomi Higashikuni, Arihiro Kiyosue, Kaoru Ito, Jiro Ando, Eiki Takimoto, Hiroshi Akazawa, Hiroyuki Morita, Issei Komuro (Vol. 61, No. 4, 781-786, 2020). The Figure 5on page 784 should be replaced by the following figure.

Matsumoto Takuya, Kodera Satoshi, Shinohara Hiroki, Ieki Hirotaka, Yamaguchi Toshihiro, Higashikuni Yasutomi, Kiyosue Arihiro, Ito Kaoru, Ando Jiro, Takimoto Eiki, Akazawa Hiroshi, Morita Hiroyuki, Komuro Issei


oncology Oncology


In Cancer immunology research ; h5-index 78.0

Optimum risk stratification in early-stage endometrial cancer (EC) combines clinicopathological factors and the molecular EC classification defined by The Cancer Genome Atlas (TCGA). It is unclear whether analysis of intratumoral immune infiltrate improves this. We developed a machine-learning image-based algorithm to quantify density of CD8+ and CD103+ immune cells in tumor epithelium and stroma in 695 stage I endometrioid ECs from the PORTEC-1&-2 trials. The relationship between immune cell density and clinicopathological/molecular factors was analyzed by hierarchical clustering and multiple regression. The prognostic value of immune infiltrate by cell type and location was analyzed by univariable and multivariable Cox regression, incorporating the molecular EC classification. Tumor-infiltrating immune cell density varied substantially between cases, and more modestly by immune cell type and location. Clustering revealed three groups with high, intermediate and low densities, with highly significant variation in the proportion of molecular EC subgroups between them. Univariable analysis revealed intraepithelial CD8+ cell density as the strongest predictor of EC recurrence; multivariable analysis confirmed this was independent of pathological factors and molecular subgroup. Exploratory analysis suggested this association was not uniform across molecular subgroups, but greatest in tumors with mutant p53 and absent in DNA mismatch repair deficient cancers. Thus, this work identified that quantification of intraepithelial CD8+ cells improved upon the prognostic utility of the molecular EC classification in early-stage EC.

Horeweg Nanda, de Bruyn Marco, Nout Remi A, Stelloo Ellen, Kedzierska Katarzyna Z, León-Castillo Alicia, Plat Annechien, Mertz Kirsten D, Osse Michelle, Jürgenliemk-Schulz Ina M, Lutgens Ludy C, Jobsen Jan J, van der Steen-Banasik Elzbieta M, Smit Vincent T H B M, Creutzberg Carien L, Bosse Tjalling, Nijman Hans W, Koelzer Viktor H, Church David N