Efficacy of Visual Surveys for White-Nose Syndrome at Bat Hibernacula

White-Nose Syndrome (WNS) is an epizootic disease in hibernating bats caused by the fungus Pseudogymnoascus destructans. Surveillance for P. destructans at bat hibernacula consists primarily of visual surveys of bats, collection of potentially infected bats, and submission of these bats for laboratory testing. Cryptic infections (bats that are infected but display no visual signs of fungus) could lead to the mischaracterization of the infection status of a site and the inadvertent spread of P. destructans. We determined the efficacy of visual detection of P. destructans by examining visual signs and molecular detection of P. destructans on 928 bats of six species at 27 sites during surveys conducted from January through March in 2012–2014 in the southeastern USA on the leading edge of the disease invasion. Cryptic infections were widespread with 77% of bats that tested positive by qPCR showing no visible signs of infection. The probability of exhibiting visual signs of infection increased with sampling date and pathogen load, the latter of which was substantially higher in three species (Myotis lucifugus, M. septentrionalis, and Perimyotis subflavus). In addition, M. lucifugus was more likely to show visual signs of infection than other species given the same pathogen load. Nearly all infections were cryptic in three species (Eptesicus fuscus, M. grisescens, and M. sodalis), which had much lower fungal loads. The presence of M. lucifugus or M. septentrionalis at a site increased the probability that P. destructans was visually detected on bats. Our results suggest that cryptic infections of P. destructans are common in all bat species, and visible infections rarely occur in some species. However, due to very high infection prevalence and loads in some species, we estimate that visual surveys examining at least 17 individuals of M. lucifugus and M. septentrionalis, or 29 individuals of P. subflavus are still effective to determine whether a site has bats infected with P. destructans. In addition, because the probability of visually detecting the fungus was higher later in winter, surveys should be done as close to the end of the hibernation period as possible

A Detailed Review on Plant Leaf Disease Detection and Classification Methodologies using Deep Learning Techniques

The rapid emergence and evolution of deep learning methodologies in the field of plant disease classification and detection has resulted in significant progress. Their application has revolutionized the way agriculture is done. This paper provides an overview of the advancements in utilizing deep learning models to address the crucial task of identifying and categorizing plant diseases. By harnessing the power of deep convolutional neural networks (CNNs) and transfer learning, researchers have achieved remarkable accuracy in disease classification, often surpassing traditional methods. This study also delves into the challenges that persist in this field, such as the scarcity of labeled data and potential biases in models. To address these concerns, the integration of visualization techniques is explored, allowing for better model interpretation and transparency. The collaborative efforts of agricultural experts and machine learning researchers are deemed crucial for overcoming these challenges and driving the future direction of research. Looking ahead, the interdisciplinary approach is anticipated to play a pivotal role in refining deep learning models for plant disease detection. A seamless collaboration between domain-specific professionals, machine learning experts, and agricultural practitioners is essential to foster innovation, enhance the reliability of models, and create a sustainable agricultural ecosystem. With the integration of cutting-edge architectures, emerging technologies like edge computing, and broader datasets, the field is poised to bring about transformative changes in agricultural practices, bolstering crop health and productivity

Fast detector/first responder : interactions between the superior colliculus-pulvinar pathway and stimuli relevant to primates

Primates are distinguished from other mammals by their heavy reliance on the visual sense, which occurred as a result of natural selection continually favoring those individuals whose visual systems were more responsive to challenges in the natural world. Here we describe two independent but also interrelated visual systems, one cortical and the other subcortical, both of which have been modified and expanded in primates for different functions. Available evidence suggests that while the cortical visual system mainly functions to give primates the ability to assess and adjust to fluid social and ecological environments, the subcortical visual system appears to function as a rapid detector and first responder when time is of the essence, i.e., when survival requires very quick action. We focus here on the subcortical visual system with a review of behavioral and neurophysiological evidence that demonstrates its sensitivity to particular, often emotionally charged, ecological and social stimuli, i.e., snakes and fearful and aggressive facial expressions in conspecifics. We also review the literature on subcortical involvement during another, less emotional, situation that requires rapid detection and response—visually guided reaching and grasping during locomotion—to further emphasize our argument that the subcortical visual system evolved as a rapid detector/first responder, a function that remains in place today. Finally, we argue that investigating deficits in this subcortical system may provide greater understanding of Parkinson's disease and Autism Spectrum disorders (ASD)

Schizotypy and facial emotion processing

The ability to accurately interpret facial emotion is crucial to social being and our capacity to correctly interpret threat-related expressions has obvious adaptive value. Healthy individuals appear to process facial emotions rapidly, accurately and effortlessly, while individuals with schizophrenia often present with marked impairment in emotion processing. The hypothesis of continuity between schizophrenia and normal behaviour suggests that the signs and symptoms of the disorder also occur to varying, lesser degrees in the general population. This thesis presents a series of studies that explore the limits of facial emotion processing in healthy individuals, and its relationship with schizotypal personality traits. The first paper describes a set of three studies that use eye tracking techniques to explore the limits of rapid emotion processing. It is shown that we can quickly orient attention towards emotional faces even when the faces are task-irrelevant, presented for very brief intervals, and located well into peripheral vision. The remaining studies explore whether high schizotypes have similarities to individuals with schizophrenia in the way that they process facial emotion. High schizotypes were significantly less accurate at discriminating facial emotions and significantly more likely to misperceive neutral faces as angry, offering support for continuum models of visual hallucinatory experiences. A further study revealed that high relative to low schizoptypes feel as though they are exposed to angry faces for longer. It is argued that this experience itself may serve to maintain hypervigilance to social threat. Finally, laterality biases during face perception were explored. Contrary to the predictions of continuum models of schizophrenia, high schizotypes had an increased left side / right hemisphere bias for face processing. In summary, the thesis offers partial support for the hypothesis of continuity between the impairments in emotion discrimination observed in individuals with schizophrenia, and normal, healthy variation in facial emotion processing

Informatics for EEG biomarker discovery in clinical neuroscience

Neurological and developmental disorders (NDDs) impose an enormous burden of disease on children throughout the world. Two of the most common are autism spectrum disorder (ASD) and epilepsy. ASD has recently been estimated to affect 1 in 68 children, making it the most common neurodevelopmental disorder in children. Epilepsy is also a spectrum disorder that follows a developmental trajectory, with an estimated prevalence of 1%, nearly as common as autism. ASD and epilepsy co-occur in approximately 30% of individuals with a primary diagnosis of either disorder. Although considered to be different disorders, the relatively high comorbidity suggests the possibility of common neuropathological mechanisms. Early interventions for NDDs lead to better long-term outcomes. But early intervention is predicated on early detection. Behavioral measures have thus far proven ineffective in detecting autism before about 18 months of age, in part because the behavioral repertoire of infants is so limited. Similarly, no methods for detecting emerging epilepsy before seizures begin are currently known. Because atypical brain development is likely to precede overt behavioral manifestations by months or even years, a critical developmental window for early intervention may be opened by the discovery of brain based biomarkers. Analysis of brain activity with EEG may be under-utilized for clinical applications, especially for neurodevelopment. The hypothesis investigated in this dissertation is that new methods of nonlinear signal analysis, together with methods from biomedical informatics, can extract information from EEG data that enables detection of atypical neurodevelopment. This is tested using data collected at Boston Children’s Hospital. Several results are presented. First, infants with a family history of ASD were found to have EEG features that may enable autism to be detected as early as 9 months. Second, significant EEG-based differences were found between children with absence epilepsy, ASD and control groups using short 30-second EEG segments. Comparison of control groups using different EEG equipment supported the claim that EEG features could be computed that were independent of equipment and lab conditions. Finally, the potential for this technology to help meet the clinical need for neurodevelopmental screening and monitoring in low-income regions of the world is discussed

Nägemistaju automaatsete protsesside eksperimentaalne uurimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneVäitekiri keskendub nägemistaju protsesside eksperimentaalsele uurimisele, mis on suuremal või vähemal määral automaatsed. Uurimistöös on kasutatud erinevaid eksperimentaalseid katseparadigmasid ja katsestiimuleid ning nii käitumuslikke- kui ka ajukuvamismeetodeid. Esimesed kolm empiirilist uurimust käsitlevad liikumisinformatsiooni töötlust, mis on evolutsiooni käigus kujunenud üheks olulisemaks baasprotsessiks nägemistajus. Esmalt huvitas meid, kuidas avastatakse liikuva objekti suunamuutusi, kui samal ajal toimub ka taustal liikumine (Uurimus I). Nägemistaju uurijad on pikka aega arvanud, et liikumist arvutatakse alati mõne välise objekti või tausta suhtes. Meie uurimistulemused ei kinnitanud taolise suhtelise liikumise printsiibi paikapidavust ning toetavad pigem seisukohta, et eesmärkobjekti liikumisinformatsiooni töötlus on automaatne protsess, mis tuvastab silma põhjas toimuvaid nihkeid, ja taustal toimuv seda eriti ei mõjuta. Teise uurimuse tulemused (Uurimus II) näitasid, et nägemissüsteem töötleb väga edukalt ka seda liikumisinformatsiooni, millele vaatleja teadlikult tähelepanu ei pööra. See tähendab, et samal ajal, kui inimene on mõne tähelepanu hõlmava tegevusega ametis, suudab tema aju taustal toimuvaid sündmusi automaatselt registreerida. Igapäevaselt on inimese nägemisväljas alati palju erinevaid objekte, millel on erinevad omadused, mistõttu järgmiseks huvitas meid (Uurimus III), kuidas ühe tunnuse (antud juhul värvimuutuse) töötlemist mõjutab mõne teise tunnusega toimuv (antud juhul liikumiskiiruse) muutus. Näitasime, et objekti liikumine parandas sama objekti värvimuutuse avastamist, mis viitab, et nende kahe omaduse töötlemine ajus ei ole päris eraldiseisev protsess. Samuti tähendab taoline tulemus, et hoolimata ühele tunnusele keskendumisest ei suuda inimene ignoreerida teist tähelepanu tõmbavat tunnust (liikumine), mis viitab taas kord automaatsetele töötlusprotsessidele. Neljas uurimus keskendus emotsionaalsete näoväljenduste töötlusele, kuna need kannavad keskkonnas hakkamasaamiseks vajalikke sotsiaalseid signaale, mistõttu on alust arvata, et nende töötlus on kujunenud suuresti automaatseks protsessiks. Näitasime, et emotsiooni väljendavaid nägusid avastati kiiremini ja kergemini kui neutraalse ilmega nägusid ning et vihane nägu tõmbas rohkem tähelepanu kui rõõmus (Uurimus IV). Väitekirja viimane osa puudutab visuaalset lahknevusnegatiivsust (ingl Visual Mismatch Negativity ehk vMMN), mis näitab aju võimet avastada automaatselt erinevusi enda loodud mudelist ümbritseva keskkonna kohta. Selle automaatse erinevuse avastamise mehhanismi uurimisse andsid oma panuse nii Uurimus II kui Uurimus IV, mis mõlemad pakuvad välja tõendusi vMMN tekkimise kohta eri tingimustel ja katseparadigmades ning ka vajalikke metodoloogilisi täiendusi. Uurimus V on esimene kogu siiani ilmunud temaatilist teadustööd hõlmav ülevaateartikkel ja metaanalüüs visuaalsest lahknevusnegatiivsusest psühhiaatriliste ja neuroloogiliste haiguste korral, mis panustab oluliselt visuaalse lahknevusnegatiivsuse valdkonna arengusse.The research presented and discussed in the thesis is an experimental exploration of processes in visual perception, which all display a considerable amount of automaticity. These processes are targeted from different angles using different experimental paradigms and stimuli, and by measuring both behavioural and brain responses. In the first three empirical studies, the focus is on motion detection that is regarded one of the most basic processes shaped by evolution. Study I investigated how motion information of an object is processed in the presence of background motion. Although it is widely believed that no motion can be perceived without establishing a frame of reference with other objects or motion on the background, our results found no support for relative motion principle. This finding speaks in favour of a simple and automatic process of detecting motion, which is largely insensitive to the surrounding context. Study II shows that the visual system is built to automatically process motion information that is outside of our attentional focus. This means that even if we are concentrating on some task, our brain constantly monitors the surrounding environment. Study III addressed the question of what happens when multiple stimulus qualities (motion and colour) are present and varied, which is the everyday reality of our visual input. We showed that velocity facilitated the detection of colour changes, which suggests that processing motion and colour is not entirely isolated. These results also indicate that it is hard to ignore motion information, and processing it is rather automatically initiated. The fourth empirical study focusses on another example of visual input that is processed in a rather automatic way and carries high survival value – emotional expressions. In Study IV, participants detected emotional facial expressions faster and more easily compared with neutral facial expressions, with a tendency towards more automatic attention to angry faces. In addition, we investigated the emergence of visual mismatch negativity (vMMN) that is one of the most objective and efficient methods for analysing automatic processes in the brain. Study II and Study IV proposed several methodological gains for registering this automatic change-detection mechanism. Study V is an important contribution to the vMMN research field as it is the first comprehensive review and meta-analysis of the vMMN studies in psychiatric and neurological disorders

Requirements for Robotic Interpretation of Social Signals “in the Wild”: Insights from Diagnostic Criteria of Autism Spectrum Disorder

The last few decades have seen widespread advances in technological means to characterise observable aspects of human behaviour such as gaze or posture. Among others, these developments have also led to significant advances in social robotics. At the same time, however, social robots are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether the technological progress is sufficient to let such robots move “into the wild”. In this paper, we characterise the problems that a social robot in the real world may face, and review the technological state of the art in terms of addressing these. We do this by considering what it would entail to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD diagnosis fundamentally requires the ability to characterise human behaviour from observable aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall, we demonstrate that even with relatively clear therapist-provided criteria and current technological progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis, we provide a classification of criteria based on whether or not they depend on covert information and highlight present-day possibilities for supporting therapists in diagnosis through technological means. For social robotics, we highlight the fundamental role of covert behaviour, show that the current state-of-the-art is unable to characterise this, and emphasise that future research should tackle this explicitly in realistic settings

Visual Perception in Traumatic Brain Injury: Effects of Severity and Effort

Previous studies have found that poor effort can significantly impact psychometric performance by Traumatic Brain Injury (TBI) patients. So far, this impact has been relatively well studied in attention and memory. However, this is not the case for visual perception functions. Thus, the goal of this study was to determine to what extent TBI severity affect visual perception after controlling for effort. Results showed that mild TBI good effort group did not differ from a demographically matched control group. In contrast, a mild TBI poor effort group, a moderate-severe TBI group and a right hemisphere cerebro-vascular (CVA) group performed worse than the mild TBI good effort group and the control group. The results suggest a dose response relationship between injury severity and visual perception performance. After controlling for effort, results indicated that moderate-severe TBI, but not mild TBI, has long lasting effects on visual perception. Clinical implications are discussed