Covid-19 Patients' Hospital Occupancy Prediction During the Recent Omicron Wave via some Recurrent Deep Learning Architectures

This paper described a suggested model to predict bed occupancy for Covid-19 patients by country during the rapid spread of the Omicron variant. This model can be used to make decisions on the introduction or alleviation of restrictive measures and on the prediction of oxygen and health human resource requirements. To predict Covid-19 hospital occupancy, we tested some recurrent deep learning architectures. To train the model, we referred to Covid-19 hospital occupancy data from 15 countries whose curves started their regressions during January 2022. The studied period covers the month of December 2021 and the beginning of January 2022, which represents the period of strong contagion of the omicron variant around the world. The evolution sequences of hospital occupancy, vaccination percentages and median ages of populations were used to train our model. The results are very promising which could help to better manage the current pandemic peak

Hepatocellular carcinoma: a clinicopathological study of 64 cases

Hepatocellular carcinoma (HCC) is the most common of all liver cancers and is a major worldwide public health problem. The aim of this study was to provide an updated overview on clinicopathological features, treatment and outcome of HCC. In our retrospective study, we reviewed 64 cases of HCC that were diagnosed at the pathology department of Mongi Slim hospital over a fifteen-year period (2000- 2014). Relevant clinical information and microscopic slides were retrospectively reviewed. Our study group included 38 men and 26 women (sex ratio M/F = 1,26) aged between 8 and 83 years (mean = 56,64 years). The presenting clinical symptoms were dominated by abdominal pain (n=34), followed by altered general health (n=25) and jaundice (n=4). Fifty-five patients underwent surgical treatment. Liver transplantation was performed in two cases and transarterial chemoembolization was achieved in seven cases. Histopathological examination of the surgical or biopsy specimen established the diagnosis of conventional HCC in 55 cases, fibrolamellar carcinoma in 6 cases and clear cell HCC in 3 cases. Seven patients with HCC died postoperatively. Local recurrence of the tumour occurred in three cases and two patients had distant metastases postoperatively. The other patients are still being followed-up. Hepatocellular carcinoma is associated with a high rate of mortality because of early invasion, widespread metastasis and lack of effective therapeutic modalities. Accurate diagnosis and staging of these tumours is critical for optimal treatment planning and for determining prognosis.Keywords: Liver, hepatocellular carcinoma, cancer, patholog

Lightweight IDS for UAV Networks: A Periodic Deep Reinforcement Learning-based Approach

The use of intrusion detection systems (IDS) has become crucial for modern networks. To ensure the targeted performance of such networks, diverse techniques were introduced to enhance system reliability. Many network designs have adapted the use of Unmanned Aerial Vehicles (UAVs) to provide wider coverage and meet performance targets. However, the cybersecurity aspect of UAVs has not been fully considered. In this paper, we propose a lightweight intrusion detection and prevention system (IDPS) module for UAVs. The IDPS module is trained using Deep Reinforcement Learning (DRL), specifically Deep Q-learning (DQN), to enable UAVs to autonomously detect suspicious activities and to take necessary action to ensure the security of the network. A customized reward function is used to take into consideration the dataset unbalanced nature, which encourages the IDPS module to detect minor classes. Also, considering the limited availability of resources for UAVs, a periodic offline-learning approach is introduced to ensure that UAVs are capable to learn and adapt to the evolution of intrusion attacks autonomously. Numerical simulations show the efficiency of the proposed IDPS in detecting suspicious activities and corroborating the advantages brought by the periodic offline learning in comparison with similar online learning approaches, in terms of accuracy and energy consumption

Deep Learning and Uniform LBP Histograms for Position Recognition of Elderly People with Privacy Preservation

For the elderly population, falls are a vital health problem especially in the current context of home care for COVID-19 patients. Given the saturation of health structures, patients are quarantined, in order to prevent the spread of the disease. Therefore, it is highly desirable to have a dedicated monitoring system to adequately improve their independent living and significantly reduce assistance costs. A fall event is considered as a specific and brutal change of pose. Thus, human poses should be first identified in order to detect abnormal events. Prompted by the great results achieved by the deep neural networks, we proposed a new architecture for image classification based on local binary pattern (LBP) histograms for feature extraction. These features were then saved, instead of saving the whole image in the series of identified poses. We aimed to preserve privacy, which is highly recommended in health informatics. The novelty of this study lies in the recognition of individuals’ positions in video images avoiding the convolution neural networks (CNNs) exorbitant computational cost and Minimizing the number of necessary inputs when learning a recognition model. The obtained numerical results of our approach application are very promising compared to the results of using other complex architectures like the deep CNNs

The impact of macroeconomic variables on Stock ‎market in United Kingdom

The key objective of this study is to shed light on the relationship between the stock market ‎and macroeconomic factors (Interest rate, Consumer Price Index, Exchange rate) in United ‎Kingdom for the period Pre Global Financial Crisis 2008 (GFC); from January 1999 to ‎December 2007. The finding of Johansen Cointegration, and Granger and Toda Yamamoto ‎‎(TY) Causality tests show respectively that there is no co-integration between variables, no ‎causal relation is detected from macro factors to stock return, and a unidirectional causal ‎relation is depicted from exchange rate to stock price. While from VAR Granger non ‎Causality/Block Exogeneity Wald Tests results, both inflation (INF) and exchange rate ‎growth (EXCG) Granger cause the UK stock market Return. Moreover, the ARDL ‎specification show a stable long run effect of all considered macroeconomic factors on the ‎UK stock price. Precisely, the results of the ECM show that all considered macroeconomic ‎factors drives UK stock price toward long-run equilibrium at a fast speed.

Determining the genome-wide kinship coefficient seems unhelpful in distinguishing consanguineous couples with a high versus low risk for adverse reproductive outcome

Background: Offspring of consanguineous couples are at increased risk of congenital disorders. The risk increases as parents are more closely related. Individuals that have the same degree of relatedness according to their pedigree, show variable genomic kinship coefficients. To investigate whether we can differentiate between couples with high- and low risk for offspring with congenital disorders, we have compared the genomic kinship coefficient of consanguineous parents with a child affected with an autosomal recessive disorder with that of consanguineous parents with only healthy children, corrected for the degree of pedigree relatedness. Methods: 151 consanguineous couples (73 cases and 78 controls) from 10 different ethnic backgrounds were genotyped on the Affymetrix platform and passed quality control checks. After pruning SNPs in linkage disequilibrium, 57,358 SNPs remained. Kinship coefficients were calculated using three different toolsets: PLINK, King and IBDelphi, yielding five different estimates (IBDelphi, PLINK (all), PLINK (by population), King robust (all) and King homo (by population)). We performed a one-sided Mann Whitney test to investigate whether the median relative difference regarding observed and expected kinship coefficients is bigger for cases than for controls. Furthermore, we fitted a mixed effects linear model to correct for a possible population effect. Results: Although the estimated degrees of genomic relatedness with the different toolsets show substantial variability, correlation measures between the different estimators demonstrated moderate to strong correlations. Controls have higher point estimates for genomic kinship coefficients. The one-sided Mann Whitney test did not show any evidence for a higher median relative difference for cases compared to controls. Neither did the regression analysis exhibit a positive association between case–control status and genomic kinship coefficient. Conclusions: In this case–control setting, in which we compared consanguineous couples corrected for degree of pedigree relatedness, a higher degree of genomic relatedness was not significantly associated with a higher likelihood of having an affected child. Further translational research should focus on which parts of the genome and which pathogenic mutations couples are sharing. Looking at relatedness coefficients by determining genome-wide SNPs does not seem to be an effective measure for prospective risk assessment in consanguineous parents

Absence of mutations in four genes encoding for congenital cataract and expressed in the human brain in Tunisian families with cataract and mental retardation

<p>Abstract</p> <p>Background</p> <p>To identify the genetic defect associated with autosomal recessive congenital cataract (ARCC), mental retardation (MR) and ARCC, MR and microcephaly present in most patients in four Tunisian consanguineous families.</p> <p>Methods</p> <p>We screened four genes implicated in congenital cataract by direct sequencing in two groups of patients; those affected by ARCC associated to MR and those who presented also microcephaly. Among its three genes <it>PAX6</it>, <it>PITX3 </it>and <it>HSF4 </it>are expressed in human brain and one gene <it>LIM2 </it>encodes for the protein MP20 that interact with the protein galectin-3 expressed in human brain and plays a crucial role in its development. All genes were screened by direct sequencing in two groups of patients; those affected by ARCC associated to MR and those who presented also microcephaly.</p> <p>Results</p> <p>We report no mutation in the four genes of congenital cataract and its flanking regions. Only variations that did not segregate with the studied phenotypes (ARCC associated to MR, ARCC associated with MR and microcephaly) are reported. We detected three intronic variations in <it>PAX6 </it>gene: IVS4 -274insG (intron 4), IVS12 -174G>A (intron12) in the four studied families and IVS4 -195G>A (intron 4) in two families. Two substitutions polymorphisms in <it>PITX3 </it>gene: c.439 C>T (exon 3) and c.930 C>A (exon4) in one family. One intronic variation in <it>HSF4 </it>gene: IVS7 +93C>T (intron 7) identified in one family. And three intronic substitutions in <it>LIM2 </it>gene identified in all four studied families: IVS2 -24A>G (intron 2), IVS4 +32C>T (intron 4) and c.*15A>C (3'-downstream sequence).</p> <p>Conclusion</p> <p>Although the role of the four studied genes: <it>PAX6</it>, <it>PITX3</it>, <it>HSF4 </it>and <it>LIM2 </it>in both ocular and central nervous system development, we report the absence of mutations in all studied genes in four families with phenotypes associating cataract, MR and microcephaly.</p