AI-based analysis of super-resolution microscopy: Biological discovery in the absence of ground truth

The nanoscale resolution of super-resolution microscopy has now enabled the use of fluorescent based molecular localization tools to study whole cell structural biology. Machine learning based analysis of super-resolution data offers tremendous potential for discovery of new biology, that by definition is not known and lacks ground truth. Herein, we describe the application of weakly supervised learning paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the molecular architecture of subcellular macromolecules and organelles.Comment: 14 pages, 3 figure

A Systematic Design of a Compact Wideband Hybrid Directional Coupler Based on Printed RGW Technology

Printed ridge gap waveguide (PRGW) is considered among the state of art guiding technologies due to its low signal distortion and low loss at Millimeter Wave (mmWave) spectrum, which motivates the research community to use this guiding structure as a host technology for various passive microwave and mmWave components. One of the most important passive components used in antenna beam-switching networks is the quadrature hybrid directional coupler providing signal power division with 90° phase shift. A featured design of a broadband and compact PRGW hybrid coupler is propose in this paper. A novel design methodology, based on mode analysis, is introduced to design the objective coupler. The proposed design is suitable for mmWave applications with small electrical dimensions ( 1.2λo×1.2λo ), low loss, and wide bandwidth. The proposed hybrid coupler is fabricated on Roger/RT 6002 substrate material of thickness 0.762 mm. The measured results highlight that the coupler can provide a good return loss with a bandwidth of 26.5% at 30 GHz and isolation beyond 15 dB. The measured phase difference between the coupler output ports is equal 90∘± 5∘ through the interested operating bandwidth. A clear agreement between the simulated and the measured results over the assigned operating bandwidth has been illustrated

Temporal Dynamics and Impact of Climate Factors on the Incidence of Zoonotic Cutaneous Leishmaniasis in Central Tunisia

Old world cutaneous leishmaniasis is a vector-borne disease occurring in rural areas of developing countries. The main reservoirs are the rodents Psammomys obesus and Meriones shawi. Zoonotic Leishmania transmission cycle is maintained in the burrows of rodents where the sand fly Phlebotomus papatasi finds the ideal environment and source of blood meals. In the present study we showed seasonality of the incidence of disease during the same cycle with an inter-epidemic period ranging from 4 to 7 years. We evaluated the impact of climate variables (rainfall, humidity and temperature) on the incidence of zoonotic cutaneous leishmaniais in central Tunisia. We confirmed that the risk of disease is mainly influenced by the humidity related to the months of July to September during the same season and mean rainfall lagged by 12 to 14 months

Mobile station movement direction prediction (MMDP) based handover scanning for mobile WiMAX system

Mobile WiMAX is a broadband technology that is capable of delivering triple play services (voice, data, and video). However, mobility in mobile WiMAX system is still an issue when the mobile station (MS) moves and its connection is handed over between base stations (BSs). In the handover process, scanning is one of the required phases to find the target BS. During the handover scanning process, the MS must synchronize with all the advertised neighbour BSs (nBSs) to select the best BS candidate for the incoming handover action. Without terminating the connection between the SBS and MS, the SBS will schedule the scanning intervals and sleep-intervals (also called interleaving interval) to MS for the handover scanning. However, during the scanning interval period, all the coming transmissions will be paused. Therefore, the redundant or unnecessary scanning of neighbouring BS cause delay and MAC overhead which may affect real-time applications. In this paper, the MS movement direction prediction (MMDP) based handover scanning scheme is introduced to overcome the mobile WiMAX handover scanning issue. It based on dividing the BS coverage area is into zones and sectors. According to the signal quality; there are three zones, no handover (No-HO), low handover (Low-HO) and high handover (High-HO) zones respectively and six sectors. In this scheme, only two BSs can become candidates; the two that the MS moves toward them will be chosen as the candidate for the handover scanning purpose. Hence, the handover scanning process repetition will be reduced with these two shortlisted BS candidates instead of scanning all nBSs. Thus, MMDP will reduce scanning delay and the number of exchange messages during the handover scanning comparing to the conventional scanning scheme. Although, the MMDP may need an extra computational time, the prediction and scanning process will be finished before the MS reach the High-HO zone, which mean the end-user’s running application will be affected. Simulation results show that the proposed MMDP scheme reduces the total handover scanning delay and scanning interval duration by 25 and 50 % respectively. Also, the size of scanning message is reduced, which leads to reduced signalling overhead

Moving Beyond the Stigma: Understanding and Overcoming the Resistance to the Acceptance and Adoption of Artificial Intelligence Chatbots

Artificial intelligence chatbots may fundamentally transform academic research, automate mundane tasks, and enhance productivity. However, the integration of artificial intelligence chatbots (AIc) is impeded by a complex stigma deeply rooted in individuals’ misconceptions and apprehension, including concerns about academic integrity, job displacement, data privacy, and algorithmic bias. The aim of this study was to scrutinize the origins and impacts of the stigma associated with artificial intelligence chatbots within the realm of academic research and to propose strategies to mitigate such stigmas. This study draws parallels between the reception of artificial intelligence chatbots and previous transformative technologies, presenting case studies illustrating the spectrum of responses to the integration of artificial intelligence chatbots into academic research. This study identifies the need for a shift in mindset from perceiving artificial intelligence chatbots as threats to recognizing them as facilitators of efficiency and innovation. It also underscores the importance of understanding these models as tools that aid researchers but do not replace the need for human expertise and judgment. We further highlighted the role of education, transparency, regulation, and ethical guidelines in overcoming the stigma associated with artificial intelligence chatbots. Given how adaptable people are, the surrounding stigma will likely fade with time. We support a cooperative strategy with continuing education and discussion to maximize the benefits of artificial intelligence chatbots while minimizing their drawbacks, hopefully paving the way for their ethical and successful application in scholarly research

Fuzzy Logic Based Self-Adaptive Handover Algorithm for MobileWiMAX.

It is well known that WiMAX is a broadband technology that is capable of delivering triple play (voice, data, and video) services. However, mobility in WiMAX system is still a main issue when the mobile station (MS) moves across the base station (BS) coverage and be handed over between BSs. Among the challenging issues in mobile WiMAX handover are unnecessary handover, handover failure and handover delay, which may affect real-time applications. The conventional handover decision algorithm in mobile WiMAX is based on a single criterion, which usually uses the received signal strength indicator (RSSI) as an indicator, with the other fixed handover parameters such as handover threshold and handover margin. In this paper, a fuzzy logic based self-adaptive handover (FuzSAHO) algorithm is introduced. The proposed algorithm is derived from the self-adaptive handover parameters to overcome the mobile WiMAX ping-pong handover and handover delay issues. Hence, the proposed FuzSAHO is initiated to check whether a handover is necessary or not which depends on its fuzzy logic stage. The proposed FuzSAHO algorithm will first self-adapt the handover parameters based on a set of multiple criteria, which includes the RSSI and MS velocity. Then the handover decision will be executed according to the handover parameter values. Simulation results show that the proposed FuzSAHO algorithm reduces the number of ping-pong handover and its delay. When compared with RSSI based handover algorithm and mobility improved handover (MIHO) algorithm, respectively, FuzSAHO reduces the number of handovers by 12.5 and 7.5 %, respectively, when the MS velocity is <17 m/s. In term of handover delay, the proposed FuzSAHO algorithm shows an improvement of 27.8 and 8 % as compared to both conventional and MIHO algorithms, respectively. Thus, the proposed multi-criteria with fuzzy logic based self-adaptive handover algorithm called FuzSAHO, outperforms both conventional and MIHO handover algorithms

Contextualising the pervasive impact of macroeconomic austerity on prison health in England: A qualitative study among international policymakers

Background: Prisons offer the state the opportunity to gain access to a population that is at particularly high risk of ill-health. Despite the supportive legal and policy structures surrounding prison rehabilitation, the oppressive nature of the austerity policy in England threatens its advanced improvement.Methods: Using grounded theory methodology, this is the first interdisciplinary qualitative study to explore the impact of macroeconomic austerity on prison health in England from the perspective of 29 international prison policymakers.Results: The far-reaching impact of austerity in England has established a regressive political system that shapes the societal attitude towards social issues, which has exacerbated the existing poor health of the prisoners. Austerity has undermined the notion of social collectivism, imposed a culture of acceptance among prison bureaucrats and the wider community, and normalised the devastating impacts of prison instability. These developments are evidenced by the increasing levels of suicide, violence, radicalisation and prison gangs among prisoners, as well as the imposition of long working hours and the high levels of absenteeism among prison staff.Conclusions: This study underscores an important and yet unarticulated phenomenon that despite being the fifth largest economy in the world, England’s poorest, marginalised and excluded population continues to bear the brunt of austerity. Reducing the prison population, using international obligations as minimum standards to protect prisoners’ right to health and providing greater resources would create a more positive and inclusive system, in line with England’s international and domestic commitments to the humane treatment of all people

Increased Neural Activity of a Mushroom Body Neuron Subtype in the Brains of Forager Honeybees

Honeybees organize a sophisticated society, and the workers transmit information about the location of food sources using a symbolic dance, known as ‘dance communication’. Recent studies indicate that workers integrate sensory information during foraging flight for dance communication. The neural mechanisms that account for this remarkable ability are, however, unknown. In the present study, we established a novel method to visualize neural activity in the honeybee brain using a novel immediate early gene, kakusei, as a marker of neural activity. The kakusei transcript was localized in the nuclei of brain neurons and did not encode an open reading frame, suggesting that it functions as a non-coding nuclear RNA. Using this method, we show that neural activity of a mushroom body neuron subtype, the small-type Kenyon cells, is prominently increased in the brains of dancer and forager honeybees. In contrast, the neural activity of the two mushroom body neuron subtypes, the small-and large-type Kenyon cells, is increased in the brains of re-orienting workers, which memorize their hive location during re-orienting flights. These findings demonstrate that the small-type Kenyon cell-preferential activity is associated with foraging behavior, suggesting its involvement in information integration during foraging flight, which is an essential basis for dance communication