Malware detection with artificial intelligence: A systematic literature review

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is limited by the widespread use of obfuscation. Dynamic analysis is not impacted by obfuscation but is defeated by ubiquitous anti-analysis techniques and requires more computational power. Sophisticated and evasive malware is challenging to extract authentic discriminatory features from and, combined with poor quality datasets, this can lead to a situation where a model achieves high accuracy with only one specific dataset

Relation Between Oxidant/Antioxidant Status and Postpartum Anestrous Conditions

The aim of the present study was to detect the relation between oxidant/antioxidant status and postpartum anestrous (PPA) conditions in dairy cows.The postpartum period is a very critical time that influenced mainly in cattle reproduction. A little information is obtainable in literature concerning antioxidant defense mechanisms during anestrus.The purpose of the following study is detection of the relation between oxidant/antioxidant status and postpartum anestrous (PPA) condition in dairy cows. Seventy five postpartum anestrous (PPA) and twenty five normal cyclic Holstein Friesian pluriparous dairy cows were selected on the basis of their reproductive history gained from farm records. Depend on the rectal findings and ultrasonography in addition to progesterone profile the studied animals were classified into three groups (each 25 animals) as inactive ovaries group, persistent corpus luteum group and silent heat group. Blood samples from anestrous and normal cyclic animals were gathered at day 0, day 10, day 21. These samples were utilized for detection of MDA, Vitamin C, Nitric Oxide and Total antioxidant capacity. Results of the present study revealed that MDA and Nitric Oxide were be significantly (plt0.05) higher in the groups of PPA than the normal cyclic group. Vitamin C (Ascorbic acid)nbsp levels were seen to be significantly (plt0.05) lower in normal cyclic animals in comparison to inactive ovaries group and persistent C.L group ,while there is no significant difference with the silent heat group . No statistically significant difference was detected in the total antioxidant capacity between the group of silent heat and the normal cyclic group, while the groups of persistent C.L and inactive ovaries were found to have statistically significant difference (plt0.05) with the normal cyclic group. It is concluded that supplementing diets with optimal levels of micronutrients with antioxidant capabilities is a good advice to farmers to avoid post-partum anestrum. Moreover, early approaches to conflict the progression of stress and to promote the antioxidant defense mechanisms of dairy cattle during times of increased metabolic demands appears to be Pertinent

Kaleidoscopic associations between life outside home and the technological environment that shape occupational injustice as revealed through cross-sectional statistical modelling

BACKGROUND: Everyday life outside home and accessing a variety of places are central to occupation. Technology is ever more taken for granted, even outside home, and for some may culminate in occupational injustice. This study aims to explore the association between everyday technologies (ET), particularly out of home, and the number of places older adults with and without dementia go to, in rural and urban environments. METHOD: The Everyday Technology Use Questionnaire, and Participation in Activities and Places Outside Home Questionnaire, were administered with 128 people in England. Six logistic regression models explored the association between ET and the number of places people went to, with other demographic factors (i.e., rurality, diagnosis, deprivation). RESULTS: The amount of out of home technologies a person perceived relevant and relative levels of neighbourhood deprivation were most persistently associated with the number of places people went to. Associations with ability to use technology, diagnosis, and education were more tentative. In no model was rurality significant. All models explained a low proportion of variance and lacked sensitivity to predict the outcome. CONCLUSION: For a minority of people, perceptions of the technological environment are associated with other personal and environmental dimensions. Viewed kaleidoscopically, these associations assemble to generate an impermanent, fragmented view of occupational injustice that may jeopardise opportunities outside home. However, there will be other influential factors not identified in this study. Greater attention to the intersections between specific environmental dimensions may deepen understanding of how modifications can be made to deliver occupational justice

Social Participation in Relation to Technology Use and Social Deprivation: A Mixed Methods Study Among Older People with and without Dementia

Social participation is a modifiable determinant for health and wellbeing among older people; however, social participation is increasingly dependent on technology use. This study investigated social participation in relation to Everyday Technology use and social deprivation of the living environment, among older people with and without dementia in the United Kingdom. Sixty-four people with dementia and sixty-four people without dementia were interviewed using standardized questionnaires: The Participation in ACTivities and Places OUTside Home Questionnaire and Everyday Technology Use Questionnaire. A mixed methods approach integrated statistical analyses and content analysis of free-text responses, through data visualizations. Small, statistically significant associations were found between social participation and Everyday Technology use outside home, for participants with dementia (Rs = 0.247; p = 0.049) and without dementia (Rs = 0.343; p = 0.006). A small, statistically significant association was identified between social participation and social deprivation in the living environment, among only participants with dementia (Rs = 0.267, p = 0.033). The content analysis and graphical joint display revealed motivators, considerations that require extra attention, and strategies for managing social participation. The results underline how Everyday Technology use can be assistive to social participation but also the need to consider social deprivation of the living environment, especially among people with dementia

Molecular diagnosis in recessive pediatric neurogenetic disease can help reduce disease recurrence in families.

BackgroundThe causes for thousands of individually rare recessive diseases have been discovered since the adoption of next generation sequencing (NGS). Following the molecular diagnosis in older children in a family, parents could use this information to opt for fetal genotyping in subsequent pregnancies, which could inform decisions about elective termination of pregnancy. The use of NGS diagnostic sequencing in families has not been demonstrated to yield benefit in subsequent pregnancies to reduce recurrence. Here we evaluated whether genetic diagnosis in older children in families supports reduction in recurrence of recessive neurogenetic disease.MethodsRetrospective study involving families with a child with a recessive pediatric brain disease (rPBD) that underwent NGS-based molecular diagnosis. Prenatal molecular testing was offered to couples in which a molecular diagnosis was made, to help couples seeking to prevent recurrence. With this information, families made decisions about elective termination. Pregnancies that were carried to term were assessed for the health of child and mother, and compared with historic recurrence risk of recessive disease.ResultsBetween 2010 and 2016, 1172 families presented with a child a likely rPBD, 526 families received a molecular diagnosis, 91 families returned to the clinic with 101 subsequent pregnancies, and 84 opted for fetal genotyping. Sixty tested negative for recurrence for the biallelic mutation in the fetus, and all, except for one spontaneous abortion, carried to term, and were unaffected at follow-up. Of 24 that genotyped positive for the biallelic mutation, 16 were electively terminated, and 8 were carried to term and showed features of disease similar to that of the older affected sibling(s). Among the 101 pregnancies, disease recurrence in living offspring deviated from the expected 25% to the observed 12% ([95% CI 0·04 to 0·20], p = 0·011).ConclusionsMolecular diagnosis in an older child, coupled with prenatal fetal genotyping in subsequent pregnancies and genetic counselling, allows families to make informed decisions to reduce recessive neurogenetic disease recurrence

Field efficiency and selectivity effects of selected insecticides on cotton aphid, Aphis gossypii Glover (Homoptera: Aphididea) and its predators

Cotton aphid, Aphis gossypii Glover (Homoptera: Aphididae) is a key pest of cotton plants in Egypt. A two-year field study was conducted at Faculty of Agriculture, Assiut University, Egypt, during 2013 and 2014 growing seasons to determined the efficiency of acetamiprid, imidacloprid, thiamethoxam, dinotefuran, pirimicarb and  malathion on cotton aphid and selectivity effects of these insecticides on Coccinella undecimpunctata L. and Chrysoperla carnea (Stephens). The results indicated that thiamethoxam, dinotefuran, acetamiprid and imidacloprid proved to be the most effective insecticides in reducing cotton aphid population up to 21 days after treatment throughout both seasons and caused an average reduction percentage ranged from 73.58 to 96.42%%, whereas pirimicarb and malathion showed the lowest reduction with an average ranged 38.08 to 66.68 % at different exposure dates during 2013 and 2014 seasons. In addition, the selectivity effects of acetamiprid, imidacloprid, pirimicarb and malathion reduced the population of C. undecimpunctata with an average ranged from 78.05 to 96.43% and were classified as harmful. Thiamethoxam reduced the population with an average ranged from 68.72 to 69.20% and was classified as moderately harmful. Dinotefuran showed a slightly harmful effect to C. undecimpunctata with an average reduction 44.3 and 41.81% during 2013 and 2014 seasons. On the other hand, acetamiprid and dinotefuran caused a significant reduction in the population of C. carnea with an average ranged from 28.28 to 56.52% and were classified as harmless. Thiamethoxam and imidacloprid reduced the population with an average ranged from 55.53 and 64.39% and were classified as moderately harmful. By contrast, malathion and pirimicarb showed the highest reduction in the population with an average ranged from 67.15 to 96.57% and were classified as harmful during both seasons. These results suggested that, the selection of a suitable insecticide in an IPM program to control the cotton aphid not only depends on its efficiency against the aphid but also its toxicity to natural enemies (predators and parasitoids) and its persistence

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.</p

COVID-19 mortality may be reduced among fully vaccinated solid organ transplant recipients.

BACKGROUND: Solid organ transplant (SOT) recipients are at increased risk for morbidity and mortality from COVID-19 due to their immunosuppressed state and reduced immunogenicity from COVID-19 mRNA vaccines. This investigation examined the association between COVID-19 mRNA vaccination status and mortality among SOT recipients diagnosed with COVID-19. METHODS & FINDINGS: A retrospective, registry-based chart review was conducted investigating COVID-19 mortality among immunosuppressed solid organ transplant (SOT) recipients in a large metropolitan healthcare system in Houston, Texas, USA. Electronic health record data was collected from consecutive SOT recipients who received a diagnostic SARS-CoV-2 test between March 1, 2020, and October 1, 2021. The primary exposure was COVID-19 vaccination status at time of COVID-19 diagnosis. Patients were considered \u27fully vaccinated\u27 at fourteen days after completing their vaccine course. COVID-19 mortality within 60 days and intensive care unit admission within 30 days were primary and secondary endpoints, respectively. Among 646 SOT recipients who were diagnosed with COVID-19 at Houston Methodist Hospital between March 2020, and October 2021, 70 (10.8%) expired from COVID-19 within 60 days. Transplanted organs included 63 (9.8%) heart, 355 (55.0%) kidney, 108 (16.7%) liver, 70 (10.8%) lung, and 50 (7.7%) multi-organ. Increasing age was a risk factor for COVID-19 mortality, while vaccination within 180 days of COVID-19 diagnosis was protective in Cox proportional hazard models with hazard ratio 1.04 (95% CI: 1.01-1.06) and 0.31 (0.11-0.90), respectively). These findings were confirmed in the propensity score matched cohort between vaccinated and unvaccinated patients. CONCLUSIONS: This investigation found COVID-19 mortality may be significantly reduced among immunosuppressed SOT recipients within 6 months following vaccination. These findings can inform vaccination policies targeting immunosuppressed populations worldwide

Twitter: a useful tool for studying elections?

The 2015 General Election in the UK was the first to take place in the UK with Twitter as an important part of the social media landscape. This pilot project looked at 16 constituencies along England’s South Coast in order to investigate what impact, if any, Twitter had had on the campaign and the result and to investigate the efficacy, or otherwise, of using Twitter as a tool for studying election campaigns in terms of candidate and local party activism. On the basis of an analysis of almost half a million tweets the analysis concluded that there appeared to be a correlation between the rate at which parties and/or candidates responded to incoming tweets and their relative electoral performance but this was not demonstrable for all parties (it applied in particular to Labour and UKIP candidates). In addition, high rates of reply also appeared to have a positive impact on constituency turnout figures. The findings are not yet conclusive but suggest that Twitter could be a good indicator of general levels of local party activism. The research also sought to understand how candidates used Twitter differently and established a number of candidate ‘classifiers’. It also investigated the issues agenda that was dominating Twitter conversations during the campaign and found that Twitter’s agenda was closer to the public’s than was that of the national media. The research also monitored the regional and local media in the 16 constituencies and discovered that their issues agenda was closer still to the public’s. Overall it is difficult to conclude that Twitter had a major impact on the election campaign