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ABSTRACTWe investigated whether individuals with Down (DS) or Williams (WS) syndrome are capable of conditional learning, a hippocampus-dependent process enabling flexible reasoning, using three touch-screen tasks: a visual learning task to assess unconditional learning (A > B and C > D), a 3-item conditional learning task (A > B, B > C), and a 5-item conditional learning task (A > B, B > C, C > D, D > E) to further assess transitive inference abilities. We compared the performance of individuals with DS or WS to typically developing 3–12-year-old children and 20–30-year-old adults. Most participants exhibited unconditional learning, but only 12% of individuals with DS, 15% of individuals with WS, and 42% of children exhibited conditional learning, as compared to 95% of young adults; only adults demonstrated transitive inference. Our findings indicate that conditional learning and transitive inference, which depend on brain structures impacted in these disorders, including the hippocampus and the prefrontal cortex, are beyond reach for most individuals with DS and WS.

AbstractThe study explores media discourse through the lexical and semantic realization of text. For that, online hard news on the disruption of tension due to targeting Iranian Major General Qassem Soleimani as the Commander of Islamic Revolutionary Guard Corps (IRGC) on 3rd January 2020 in Iraq was selected from “Tehran Times” and “USA Today.” The “Appraisal Analysis” of Martin and White under “Attitude” and “Graduation” resources were employed to analyze the lexical strategies. The dataset comprises 322 words from the “Tehran Times” and 1727 words from “USA Today.” The study reveals that both newspapers have presented their views viasocial actors (Irani President Rouhani & U.S. President Donald Trump) as an external voice. President Rouhani presented in negative invoked instances with determination to respond in “Tehran Times.” At the same time, “USA Today” portrayed Donald Trump on a positive with correct measures as the need of an hour. The findings suggest “USA Today” only had inscribed judgment and invoked stances. Both newspapers had represented “subjectivity” along with “objectivity” by presenting social actors’ positive image in their local newspapers through direct speech that showed the ideological orientation of newspapers.

Mutations in BRCA1and BRCA2significantly elevate the risk of developing breast and ovarian cancer. Limited data exists regarding the prevalence of BRCAmutations, and optimal, cost-effective testing strategies in developing countries like India. This study aimed to evaluate the utility of a Next Generation Sequencing (NGS) panel for BRCA1/2mutation testing among women diagnosed with, or at risk of developing hereditary breast and ovarian cancers. We also aimed to identify population specific BRCA1/2mutation hotspots, to enable the development of more affordable testing strategies. We identified 921 patients with breast and ovarian cancer who underwent mutation testing. The target enrichment was followed by targeted NGS in 772 patients and an allele-specific PCR (ASPCR) based genotyping for BRCA1:c.68_69delAG (or 185delAG), was carried out in 149 patients. We identified 188 (20.4%) patients with BRCA1/2variants: 118 (62.8%) with pathogenic/likely pathogenic and 70 (37.2%) with VUS. The 185delAG was identified as a recurrent mutation in the Southern Indian population, accounting for 24.6% of the pathogenic variants. In addition, a family history of breast, ovary, pancreas, or prostate (BOPP) cancer was found to be associated with an increased risk of identifying a deleterious BRCA1/2variant [OR = 2.11 (95% CI 1.45–3.07) p≤ 0.001]. These results suggest that Targeted NGS is a sensitive and specific strategy for BRCAtesting. For Southern Indian patients, a two-tiered approach can be considered: Initial screening with ASPCR for BRCA1185delAG followed by NGS for those testing negative. Expanding the gene panel and identifying other population-specific mutation hot spots is a promising area with potential for improvements in testing and treatment strategies.

Graphical abstract:

Wireless sensor networks, which play a significant role in monitoring complex environments that change rapidly over time, were used in the Artificial Intelligence method. External factors or the device designers themselves are both responsible for this complex behavior. Sensor networks often use machine learning techniques to adapt to such conditions, eliminating the need for excessive redesign. Cyber-physical systems (CPS) appeared as the promising option for improving physical-virtual interactions. The quality of the system containing processing information is primarily determined by the system function. There are many benefits obtained while combining Artificial Intelligence (AI) and Cyber-Physical Systems (CPSs) in buildings. In CPS-based indoor environment has various design schemes containing measurement and intelligent buildings in the control system consisting of detection, tracking, execution, and communication modules. The Multi-Agent System (MAS) is the smallest control unit that simulates among neurons and it flexibly provides the information. To mimic the interactions between human neurons, multi-agents are used. In this paper, the CPS’s information world is built on the fundamental principle of granular formal concepts and the theory of granular computing is investigated. The calculation module is used by Back-Propagation Neural Network (BPNN) for pattern recognition and classification by environmental information. Various parameters namely the normalized root mean square error, peak signal-to-noise ratio, mean square error, and the mean absolute error are chosen as the objective assessment criteria to assess the benefits of the proposed method and the effectiveness of the proposed system is proven.

Nowadays, research has focused more on the behavior of the thermodecomposition of new lignocellulosic materials to improve scientific understanding of biomass as an energy resource. The present work focused on determining the thermogravimetric characteristics and kinetic modeling of Piptocoma discolorpyrolysis and combustion processes as a contribution towards understanding its energetic use in the Ecuadorian Amazon region. Thermogravimetric analysis was applied to study the thermal decomposition of P. discolorbiomass under inert atmosphere conditions and in the presence of oxygen at a heating rate of 5 °C/min, from room temperature up to 900 °C. A mechanism of three independent parallel reactions was employed to detail the pyrolysis, as well as an additional step to model the combustion process in order to include the char oxidation reaction. The model succeeded in reproducing the degradation curves for pyrolysis and combustion with a very good fit between the experimental and calculated data. We obtained variation coefficient values of less than 5% (pyrolysis 3.19% and combustion 2.97%). Based on these results, it is confirmed that biomass obtained from P. discolorcan be used as a source for energy generation using thermoconversion processes. The thermogravimetric and kinetic parameters provide useful information for designing a technically, economically, and environmentally feasible system for the energetic use of the biomass from P. discoloras a renewable resource in the Pastaza province of the Ecuadorian Amazon region.

Mangrove ecosystems constitute a large portion of the coastline in the tropical and subtropical regions of Earth and are characterized by their salinity and tidal variation which results in frequent anaerobic conditions and a wide range of redox potential. Such conditions make mangroves hotspots for microbial diversity, and the microbial community plays essential roles in the functioning and maintenance of the ecosystem. The complex microbial communities that inhabit the sediment of mangroves plays a crucial role in the coupling of biogeochemical cycles between the land and ocean. Hence, the objectives of this work were to characterise Actinophytocolasp. K4-08, to assess the antibacterial ability of the crude extracts obtained from Actinophytocolasp. K4-08 growth culture and to determine potential compounds present in the extract through gas chromatography-mass spectrometry (GC-MS) profiling. Actinophytocolasp. K4-08, a rare actinomycete was previously isolated from mangrove forest sediment in Kuantan, Pahang, Malaysia. Actinophytocolasp. K4-08 colonies appeared in a round-irregular shape with formation of powdery white aerial mycelia spores around the colony and dense, white-creamed substrate hyphae in the middle. Scanning electron microscopy showed a regular round spore chain with short branching. This bacterium could tolerate up to 10% sodium chloride (NaCl) and able to uti-lise gentiobiose, D-raffinose, α-D-glucose, D-galactose, 3-methyl glucose, D-fucose, L-fucose, inosine, D-galacturonic acid, citric acid, acetic acid and formic acid as carbon sources and resistance to minocycline and aztreonam antibiotics. PKS-I and NRPS genes, usually related to secondary metabolite ability were detected in this bacterium. Three crude extracts were prepared – methanol, ethyl acetate and acetone. Methanolic and ethyl acetate extracts exhibited strong antibacterial activity against Bacillus subtiliswhile acetone showed weak antibacterial activity. Further analysis was conducted on methanolic extract through Fourier-transform infrared spectroscopy and GC-MS. Fourier-transform infrared spectrum produced peaks at 3,327; 2,927; 1,636; 1,411 and 1,018 cm–1which corresponded to O–H stretching bond in alcohol, stretching C–H in alkane group, stretching of C=C in alkene group, C–H bending in alkane group and stretching carbonyl group in primary alcohol respectively. GC-MS profiling identified 9 compounds and they were 3,4-dihydroxy-3,4-dimethylhexane-2,5-dione, 2,4-di-tert-butylphenol (2,4-DTBP), 1-docosene, hexadecanoic acid methyl ester, methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate, 1-nonadecene, linoleic acid methyl ester, oleic acid methyl ester, and stearic acid methyl ester. Linoleic acid methyl ester, oleic acid methyl ester and hexadecanoic acid methyl ester are usually linked to antioxidant activity whereas 2,4-DTBP is secondary metabolite that is associated with many biological potentials. However, the findings of this study indicate the promising potential of rare actinomycete Actinophytocolasp. K4-08 to be developed as antibacterial agent towards the applications in the medicinal and pharmaceutical industries.

In this paper, we propose a novel transformer-based classification algorithm for the brain computer interface (BCI) using a motor imagery (MI) electroencephalogram (EEG) signal. To design the MI classification algorithm, we apply an up-to-date deep learning model, the transformer, that has revolutionized the natural language processing (NLP) and successfully widened its application to many other domains such as the computer vision. Within a long MI trial spanning a few seconds, the classification algorithm should give more attention to the time periods during which the intended motor task is imagined by the subject without any artifact. To achieve this goal, we propose a hierarchical transformer architecture that consists of a high-level transformer (HLT) and a low-level transformer (LLT). We break down a long MI trial into a number of short-term intervals. The LLT extracts a feature from each short-term interval, and the HLT pays more attention to the features from more relevant short-term intervals by using the self-attention mechanism of the transformer. We have done extensive tests of the proposed scheme on four open MI datasets, and shown that the proposed hierarchical transformer excels in both the subject-dependent and subject-independent tests.

This research aims to design a human body temperature detection system based on the Internet of Things (IoT) and featuring the output with human voice. The implementation of the IoT concept using the MQTT Protocol. The stages in developing this system are needs analysis, design, develop, testing and evaluation. Principally, this system works as follows, once the temperature sensor detects a human temperature, the microcontroller will process this temperature data (1) convert the data into human voice and (2) sending (publish) the data to a web server in the internet. In this research uses Adafruit.com as Mqtt broker. Anyone subscribes to the feeds of the Adafruit will receive such temperature data automatically. To determine the output of the human temperature we must adjust with +3 from the original result of MLX90614 in the normal room temperature. By this adjustment we have the best of error average 0.96% and an average difference of result 0.35°C. It means, by adjustment +3 from the original result of the sensor will conform with the standards of IEC No. 13B-23 and the 13th revision of Melexis in 2019, where the maximum of error tolerance is 5% and the temperature difference is 0.5°C. For distance testing, the result is that the farther the object from the MLX90614 sensor will result the greater errors value and the object closer to the sensor, will result the smaller error. The average delay time to send data to the Adafruit Webserver and android application is 1.53 seconds.