Effect of repeated applications of fipronil on arthropod populations in experimental plot studies

The effect of two applications of fipronil on arthropod populations were studied under experimental plot conditions using 3-month old Cuphea ignea. Eighty-one families belonging to 12 orders of Arthropoda were trapped before spraying. The four dominant orders were Hymenoptera (28.6%), Homoptera (19.1 %), Collembola (17.8 %) and Diptera (16.2 %). Other orders were present in small numbers i. e. Hemiptera, Coleoptera, Orthoptera, Thysanoptera, Araneida, Acarina, Lepidoptera and Isopoda. The abundance of arthropods was reduced to 44 and 47 families after the first and second sprayings, respectively. The percentage population of Collembola increased significantly after the first and second sprayings as compared to the number before treatment. The percentage population ofHomoptera (Aleyrodidae) increased after the first spray but declined after the second spray. The family Isotomidae (Collembola) increased significantly after the first and second sprays. Some orders such as Isopoda and Lepidoptera disappeared after the plot was treated with fipronil

Illicit Activity Detection in Large-Scale Dark and Opaque Web Social Networks

Many online chat applications live in a grey area between the legitimate web and the dark net. The Telegram network in particular can aid criminal activities. Telegram hosts “chats” which consist of varied conversations and advertisements. These chats take place among automated “bots” and human users. Classifying legitimate activity from illegitimate activity can aid law enforcement in finding criminals. Social network analysis of Telegram chats presents a difficult problem. Users can change their username or create new accounts. Users involved in criminal activity often do this to obscure their identity. This makes establishing the unique identity behind a given username challenging. Thus we explored classifying users from their language usage in their chat messages.The volume and velocity of Telegram chat data place it well within the domain of big data. Machine learning and natural language processing (NLP) tools are necessary to classify this chat data. We developed NLP tools for classifying users and the chat group to which their messages belong. We found that legitimate and illegitimate chat groups could be classified with high accuracy. We also were able to classify bots, humans, and advertisements within conversations

Evaluation of Parkinson’s disease early diagnosis using single-channel EEG features and auditory cognitive assessment

BackgroundParkinson’s disease (PD) often presents with subtle early signs, making diagnosis difficult. F-DOPA PET imaging provides a reliable measure of dopaminergic function and is a primary tool for early PD diagnosis. This study aims to evaluate the ability of machine-learning (ML) extracted EEG features to predict F-DOPA results and distinguish between PD and non-PD patients. These features, extracted using a single-channel EEG during an auditory cognitive assessment, include EEG feature A0 associated with cognitive load in healthy subjects, and EEG feature L1 associated with cognitive task differentiation.MethodsParticipants in this study are comprised of cognitively healthy patients who had undergone an F-DOPA PET scan as a part of their standard care (n = 32), and cognitively healthy controls (n = 20). EEG data collected using the Neurosteer system during an auditory cognitive task, was decomposed using wavelet-packet analysis and machine learning methods for feature extraction. These features were used in a connectivity analysis that was applied in a similar manner to fMRI connectivity. A preliminary model that relies on the features and their connectivity was used to predict initially unrevealed F-DOPA test results. Then, generalized linear mixed models (LMM) were used to discern between PD and non-PD subjects based on EEG variables.ResultsThe prediction model correctly classified patients with unrevealed scores as positive F-DOPA. EEG feature A0 and the Delta band revealed distinct activity patterns separating between study groups, with controls displaying higher activity than PD patients. In controls, EEG feature L1 showed variations between resting state and high-cognitive load, an effect lacking in PD patients.ConclusionOur findings exhibit the potential of single-channel EEG technology in combination with an auditory cognitive assessment to distinguish positive from negative F-DOPA PET scores. This approach shows promise for early PD diagnosis. Additional studies are needed to further verify the utility of this tool as a potential biomarker for PD

Accumulation of Heavy Metals in Selected Vegetables, Their Availability and Correlation in Lithogenic and Nonlithogenic Fractions of Soils from Some Agricultural Areas in Malaysia

ABSTRACT Heavy metal content was determined in selected vegetables cultivated in some highland and lowland areas in Peninsular Malaysia. Leafy vegetables were represented by convolvulus (Ipomoea aquatica) and green mustard or sawi (Brassica rapa var. parachinensis), tubers and bulbs by sweet potato (Ipomoea batatas) and onion (Allium cepa), and fruity vegetables by chilly (Capsicum annuum), brinjal (Solanum melongena) and long bean (Vigna sinensis), respectively. Heavy metals from lithogenic and nonlithogenic soil fractions were studied at Cameron Highlands situated in the Pahang state and at lowland areas in Klang, Bangi, Gombak and Sepang districts in the Selangor state. The aim of the study was to investigate the availability of heavy metals and their potential uptake by vegetables in selected agricultural areas. The metals analysed were ferrum (Fe), zinc (Zn) cadmium (Cd), manganese (Mn), plumbum (Pb), copper (Cu) and chromium (Cr). Three soil samples were collected from each area and sampling was done at 1-30 cm depth. Extraction of heavy metals was carried out using sequential extraction and four fractions were produced comprising the easily leachable and ion exchange fraction, the acid reducible fraction, the oxidation organic fraction and the resistant fraction respectively. Heavy metal content in plant and soil samples were determined by atomic absorption spectrophotometry following standard methods (AOAC). Most metals were found at concentrations normally observed in vegetables grown in uncontaminated agricultural areas, with zinc (Zn) and manganese (Mn) content being highest, followed by copper (Cu), plumbum (Pb) and cadmium (Cd). However, the levels of potentially toxic metals such as Pb, Cd and Cr in the vegetables studied were found to be below the stipulated levels. Analysis of soil samples showed that the highest concentrations of heavy metals were obtained from the resistant fraction as compared to the other soil fractions. Concentration of Fe and Pb was found to be high in Sepang, whereas that of Cu was highest in Gombak and Cd levels were generally high in Sepang and Gombak. In contrast, the concentration of metals in the easily leachable and ion exchange fractions were low. Since differential uptake and accumulation of metals in the various plant parts are influenced by the availability of metals from the latter two fractions of the soils, the results indicate that availability of heavy metals to the cultivated plants (and thus, its consequent health risk to consumers) is also low. Based on the results obtained, the availability of heavy metals can be arranged as follows: Zn > Mn > Cd > Cu > Pb > Fe. The agricultural soils were found to contain high levels of Fe, Mn and Zn, whilst Cd and Cr were found in very low levels, well below the critical soil levels listed for arable land