Miracle-dz/UniFAR-FTUs huggingface dataset:
Editorial: Biomarkers in Leishmaniasis

Leishmaniasis is one of the most deadly, yet neglected, of all tropical diseases (Alvar et al., 2012). The eco-epidemiological characteristics of leishmaniasis render it a complex problem. There are over 20 species of leishmaniasis pathogens transmitted by different species of sand fly (Phlebotomus spp.). Transmission may be anthroponotic or zoonotic, the latter entailing different animal reservoirs. Depending on the species of Leishmania and the immune response to it, the disease may present as cutaneous leishmaniasis (CL), mucocutaneous leishmaniasis (MCL), visceral leishmaniasis (VL), or post-kala azar dermal leishmaniasis (PKDL) (Burza et al., 2018). The complexity of leishmaniasis means different strategies are needed if it is to be controlled and eliminated (Matlashewski et al., 2014; Rijal et al., 2019). For example: - Diagnostic methods need to be faster and simpler, but sensitive and robust, and allow early diagnoses to be made. Follow-up methods are also needed that confirm patient responses and help predict the risk of relapse. - Efficient therapies for the different forms of the disease need to be developed, both for the immunocompetent and the immunodepressed. - Methods of controlling animal reservoirs—especially domestic animal reservoirs—need to be developed; alongside better vectorial control these should reduce the transmission of the parasite. - The capacity of asymptomatic carriers to pass on the disease needs to be examined—an important challenge for control programs. - A vaccine against the different forms of leishmaniasis needs to be developed; this might be the best means of control and protection, but work is needed on how to immunize most effectively for the lowest cost. Biomarkers have a central role to play in the above challenges by providing information on patient immune status, the response to treatment, exposure to vectors, the role of animal reservoirs, and the epidemiology of infection, etc. New biomarkers need to be found that will allow the development of tools for assessing the effectiveness of treatments, that can confirm when a cure has been achieved, to identify asymptomatic persons and rates of transmission in endemic areas, to develop rapid, non-invasive tests, and for checking the immune response to experimental vaccines (Ibarra-Meneses et al., submitted). This Research Topic, entitled “Biomarkers in Leishmaniasis”, is a collection of 19 articles, some of which examine the latest advances in biomarkers of the different types of leishmaniasis, while others report original research into biomarker identification and characterization.
Relationships Between Neurodegeneration and Vascular Damage in Diabetic Retinopathy

Diabetic retinopathy (DR) is a common complication of diabetes and constitutes a major cause of vision impairment and blindness in the world. DR has long been described exclusively as a microvascular disease of the eye. However, in recent years, a growing interest has been focused on the contribution of neuroretinal degeneration to the pathogenesis of the disease, and there are observations suggesting that neuronal death in the early phases of DR may favor the development of microvascular abnormalities, followed by the full manifestation of the disease. However, the mediators that are involved in the crosslink between neurodegeneration and vascular changes have not yet been identified. According to our hypothesis, vascular endothelial growth factor (VEGF) could probably be the most important connecting link between the death of retinal neurons and the occurrence of microvascular lesions. Indeed, VEGF is known to play important neuroprotective actions; therefore, in the early phases of DR, it may be released in response to neuronal suffering, and it would act as a double-edged weapon inducing both neuroprotective and vasoactive effects. If this hypothesis is correct, then any retinal stress causing neuronal damage should be accompanied by VEGF upregulation and by vascular changes. Similarly, any compound with neuroprotective properties should also induce VEGF downregulation and amelioration of the vascular lesions. In this review, we searched for a correlation between neurodegeneration and vasculopathy in animal models of retinal diseases, examining the effects of different neuroprotective substances, ranging from nutraceuticals to antioxidants to neuropeptides and others and showing that reducing neuronal suffering also prevents overexpression of VEGF and vascular complications. Taken together, the reviewed evidence highlights the crucial role played by mediators such as VEGF in the relationship between retinal neuronal damage and vascular alterations and suggests that the use of neuroprotective substances could be an efficient strategy to prevent the onset or to retard the development of DR.
Realization of a Tapered Slot Array as Both Decoupling and Radiating Structure for 4G/5G Wireless Devices

As a combination of microwave Multiple-Input Multiple-Output (MIMO) antenna system and mm-wave antenna array, an integrated design is proposed to provide multi-band, high port isolation, and high gain antenna solution for 4G/5G wireless devices. In this work, an array of tapered slots is used as a decoupling structure at microwave frequency and as an antenna array at mm-wave frequency. The proposed geometry consists of a dual-band monopole MIMO antenna system operating at 4G (2.6 GHz) and 5G (3.5 GHz) sub-6 GHz bands. The wideband port isolation of more than 25 dB is achieved by introducing a tapered slot array structure. One of the key design features is the utilization of these slots as an antenna array with a peak realized gain of 15 dBi at 28 GHz, making this array dual-functional, thus more feasible for compact 4G/5G handheld devices. The proposed design is printed on an RO-5880 substrate with an overall size of 70 mm <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 50 mm <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 0.51 mm. The measured −10-dB impedance matching bandwidth covers two sub-6 GHz bands (from 2.45 GHz to 2.85 GHz and from 3.18 GHz to 3.68 GHz), and the wide mm-wave band from 25 GHz to 30 GHz.
17β-Estradiol Attenuates Neuropathic Pain Caused by Spared Nerve Injury by Upregulating CIC-3 in the Dorsal Root Ganglion of Ovariectomized Rats

17β-estradiol plays a role in pain sensitivity, analgesic drug efficacy, and neuropathic pain prevalence, but the underlying mechanisms remain unclear. Here, we investigated whether voltage-gated chloride channel-3 (ClC-3) impacts the effects of 17β-estradiol (E2) on spared nerve injury (SNI)-induced neuropathic pain in ovariectomized (OVX) female Sprague Dawley rats that were divided into OVX, OVX + SNI, OVX + SNI + E2, OVX + SNI + E2 + DMSO (vehicle, dimethyl sulfoxide), or OVX + SNI + E2+Cltx (ClC-3-blocker chlorotoxin) groups. Changes in ClC-3 protein expression were monitored by western blot analysis. Behavioral testing used the paw withdrawal threshold to acetone irritation and paw withdrawal thermal latency (PWTL) to thermal stimulation. Immunofluorescence indicated the localization and protein expression levels of ClC-3. OVX + SNI + E2 rats were subcutaneously injected with 17β-estradiol once daily for 7 days; a sheathed tube was implanted, and chlorotoxin was injected for 4 days. Intrathecal Cltx to OVX and OVX + SNI rats was administered for 4 consecutive days (days 7–10 after SNI) to further determine the contribution of ClC-3 to neuropathic pain. Patch clamp technology in current clamp mode was used to measure the current threshold (rheobase) dorsal root ganglion (DRG) neurons and the minimal current that evoked action potentials (APs) as excitability parameters. The mean number of APs at double-strength rheobase verified neuronal excitability. There was no difference in behaviors and ClC-3 expression after OVX. Compared with OVX + SNI rats, OVX + SNI + E2 rats showed a lower paw withdrawal threshold to the acetone stimulus, but the PWTL was not significantly different, indicating increased sensitivity to cold but not to thermal pain. Co-immunofluorescent data revealed that ClC-3 was mainly distributed in A- and C-type nociceptive neurons, especially in medium/small-sized neurons. 17β-estradiol administration was associated with increased expression of ClC-3. 17β-estradiol-induced increase in ClC-3 expression was blocked by co-administration of Cltx. Cltx causes hyperalgesia and decreased expression of ClC-3 in OVX rats. Patch clamp results suggested that 17β-estradiol attenuated the excitability of neurons induced by SNI by up-regulating the expression of ClC-3 in the DRG of OVX rats. 17β-estradiol administration significantly improved cold allodynia thresholds in OVX rats with SNI. The mechanism for this decreased sensitivity may be related to the upregulation of ClC-3 expression in the DRG.
Secure DoF for the MIMO MAC: The Case of Knowing Eavesdropper's Channel Statistics Only

Physical layer security has attracted research attention as a means to achieve secure communication without the need for complicated upper layer encryption techniques. The secure degrees of freedom (SDoF) of various networks in the absence of instantaneous eavesdropper channel state information is still unknown. In this work, we study the SDoF of a multiple access network composed of two transmitters and a single receiver in the presence of an eavesdropper. All parties are equipped with multiple antennas and are subject to Gaussian noise in addition to fading channel conditions. A realistic, worst case scenario, where the channel state information (CSI) for the channels between the trusted parties is known to everyone, while the trusted parties can only estimate the channel statistics (environment based) of the eavesdropper is considered. The asymptotic secure network sum capacity (aka sum SDoF) is provided utilizing a novel proposed comprehensive upperbound along with a novel achievable scheme based on exploiting jamming.
Co-ML: a case for Collaborative ML acceleration using near-data processing

The growing importance of Machine Learning (ML) has led to a proliferation of accelerator designs that target ML workloads. The majority of these designs focus on accelerating compute-intensive regions of ML workloads such as general matrix multiplications (GEMMs) and convolutions. While this is a legitimate approach, we observe in this work that ML workloads also comprise data-intensive computations that manifest low compute-to-byte ratios and can often contribute considerably to the total execution time. Further, we also observe that, the presence of such computations opens up an exciting opportunity for near-data processing (NDP) architectures as they often provision for higher memory bandwidth that can benefit such computations. Based on the above observations, in this work we make a case for a more collaborative approach to ML acceleration, termed Co-ML, in which memory plays an active role and is responsible for NDP-amenable computations while the compute-intensive computations are executed on the host accelerator as before. We demonstrate how even a relatively simple NDP design can increase performance of data-intensive computations in ML by up to 20×. Further, for a suite of ML workloads we demonstrate that Co-ML can deliver speedups as high as 20% with average speedups of 14%. Finally, we show that with increasing efforts to build better accelerators for compute-intensive computations, these benefits will likely increase.
Finding Language Classes

For migrants and refugees, settling in a host country often proves hugely problematic. The ability to communicate marks a significant step in the process of resettlement. The current high number of migrants coming to Europe has therefore meant that the need to improve and increase the availability of language provision has become more pressing. Nevertheless, due to the complexity of financial and legal regulations and of policy restrictions, access to and information about available courses are in practice hard to find, and non-funded volunteer initiatives are scarcely visible at all. To address this problem, a newly developed language-course wizard provides orientation for refugees and migrants. Findings indicate that its visual representation is apt to span a broad range of cultural as well as educational backgrounds. Our work demonstrates the need for a detailed approach to understanding the challenges that refugees and other migrants face in practice. Failing this, services frequently do not reach those they are intended for.
Refining Concepts by Machine Learning

In this paper we deal with machine learning methods and algorithms applied in learning simple concepts by their refining or explication. The method of refining a simple concept of an object O consists in discovering a molecular concept that defines the same or a very similar object to the object O. Typically, such a molecular concept is a professional definition of the object, for instance a biological definition according to taxonomy, or legal definition of roles, acts, etc. Our background theory is Transparent Intensional Logic (TIL). In TIL concepts are explicated as abstract procedures encoded by natural language terms. These procedures are defined as six kinds of TIL constructions. First, we briefly introduce the method of learning with a supervisor that is applied in our case. Then we describe the algorithm ‘Framework’ together with heuristic methods applied by it. The heuristics is based on a plausible supply of positive and negative (near-miss) examples by which learner’s hypotheses are refined and adjusted. Given a positive example, the learner refines the hypothesis learnt so far, while a near-miss example triggers specialization. Our heuristic methods deal with the way refinement is applied, which includes also its special cases generalization and specialization.
Characterization of the acoustic community of vocal fishes in the Azores

Sounds produced by teleost fishes are an important component of marine soundscapes, making passive acoustic monitoring (PAM) an effective way to map the presence of vocal fishes with a minimal impact on ecosystems. Based on a literature review, we list the known soniferous fish species occurring in Azorean waters and compile their sounds. We also describe new fish sounds recorded in Azores seamounts. From the literature, we identified 20 vocal fish species present in Azores. We analysed long-term acoustic recordings carried out since 2008 in Condor and Princesa Alice seamounts and describe 20 new putative fish sound sequences. Although we propose candidates as the source of some vocalizations, this study puts into evidence the myriad of fish sounds lacking species identification. In addition to identifying new sound sequences, we provide the first marine fish sound library for Azores. Our acoustic library will allow to monitor soniferous fish species for conservation and management purposes.
Provider Perspectives on Integrating Sensor-Captured Patient-Generated Data in Mental Health Care

The increasing ubiquity of health sensing technology holds promise to enable patients and health care providers to make more informed decisions based on continuously-captured data. The use of sensor-captured patient-generated data (sPGD) has been gaining greater prominence in the assessment of physical health, but we have little understanding of the role that sPGD can play in mental health. To better understand the use of sPGD in mental health, we interviewed care providers in an intensive treatment program (ITP) for veterans with post-traumatic stress disorder. In this program, patients were given Fitbits for their own voluntary use. Providers identified a number of potential benefits from patients' Fitbit use, such as patient empowerment and opportunities to reinforce therapeutic progress through collaborative data review and interpretation. However, despite the promise of sensor data as offering an "objective" view into patients' health behavior and symptoms, the relationships between sPGD and therapeutic progress are often ambiguous. Given substantial subjectivity involved in interpreting data from commercial wearables in the context of mental health treatment, providers emphasized potential risks to their patients and were uncertain how to adjust their practice to effectively guide collaborative use of the FitBit and its sPGD. We discuss the implications of these findings for designing systems to leverage sPGD in mental health care.
Vicariously Experiencing it all Without Going Outside

The livestreaming industry in China is gaining greater traction than its European and North American counterparts and has a profound impact on the stakeholders' online and offline lives. An emerging genre of livestreaming that has become increasingly popular in China is outdoor livestreaming. With outdoor livestreams, streamers broadcast outdoor activities, travel, or socialize with passersby in outdoor settings, often for 6 or more hours, and viewers watch such streams for hours each day. However, given that professionally produced content about travel and outdoor activities are not very popular, it is currently unknown what makes this category of livestreams so engaging and how these techniques can be applied to other content or genres. Thus, we conducted a mixed methods study consisting of a survey (N=287) and interviews (N = 20) to understand how viewers watch and engage with outdoor livestreams in China. The data revealed that outdoor livestreams encompass many categories of content, environments and passersby behaviors create challenges and uncertainty for viewers and streamers, and viewers watch livestreams for surprising lengths of time (e.g., sometimes more than 5 continuous hours). We also gained insights into how live commenting and virtual gifting encourage engagement. Lastly, we detail how the behaviors of dedicated fans and casual viewers differ and provide implications for the design of livestreaming services that support outdoor activities.
3-Stage Complexity- and Accuracy-Efficient Synchronization Scheme for Wireless IoT Systems

This paper proposes a 3-stage complexity- and accuracy-efficient detection scheme with simplified detector analysis using the NPSS signal for time and frequency synchronization in NB-IoT systems. Through analysis and simulation, we elucidate that the proposed scheme can lead to substantial battery longevity by both of a lower detector complexity and a more memory saving over existing scheme.
Poster: Let History not Repeat Itself (this Time) -- Tackling WebAuthn Developer Issues Early On

The FIDO2 open authentication standard, developed jointly by the FIDO Alliance and the W3C, provides end-users with the means to use public-key cryptography in addition to or even instead of text-based passwords for authentication on the web. Its WebAuthn protocol has been adopted by all major browser vendors and recently also by major service providers (e.g., Google, GitHub, Dropbox, Microsoft, and others). Thus, FIDO2 is a very strong contender for finally tackling the problem of insecure user authentication on the web. However, there remain a number of open questions to be answered for FIDO2 to succeed as expected. In this poster, we focus specifically on the critical question of how well web-service developers can securely roll out WebAuthn in their own services and which issues have to be tackled to help developers in this task. The past has unfortunately shown that software developers struggle with correctly implementing or using security-critical APIs, such as TLS/SSL, password storage, or cryptographic APIs. We report here on ongoing work that investigates potential problem areas and concrete pitfalls for adopters of WebAuthn and tries to lay out a plan of how our community can help developers. We believe that raising awareness for foreseeable developer problems and calling for action to support developers early on is critical on the path for establishing FIDO2 as a de-facto authentication solution.
Memory system characterization of deep learning workloads

Deep neural networks (DNNs) have emerged as the prevalent approach for implementing learning tasks in many application domains. As DNN models become increasingly complex, the large amount of data generated during network computations exerts substantial pressure on the capacity and bandwidth of the memory subsystem. Consequently, memory hierarchy is quickly becoming a major bottleneck for DNN performance scaling. Optimizing the memory subsystem for DNN workloads requires a detailed understanding of the memory access behavior and the complex interactions between the processor and the memory hierarchy. Unfortunately, most of the existing memory profiling studies for DNNs rely either on high level software understanding of individual DNN layers or on analytical models. These approaches do not capture the complex feedback loop between the processor and the memory subsystem. To address this challenge, we have developed a simulation infrastructure, DLsim, which carries out detailed memory profiling of DNN workloads in a full-system simulation environment. In this paper, we report the key findings from our memory characterization analysis of five popular DNNs implemented in the widely used TensorFlow framework. Based on these findings, we identify multiple memory system optimization opportunities tailored for DNNs.
Visual Place Recognition in Changing Environments

Localization is an essential capability of mobile robots and place recognition is an important component of localization. Only having precise localization, robots can reliably plan, navigate and understand the environment around them. The main task of visual place recognition algorithms is to recognize based on the visual input if the robot has seen previously a given place in the environment. Cameras are one of the popular sensors robots get information from. They are lightweight, affordable, and provide detailed descriptions of the environment in the form of images. Cameras are shown to be useful for the vast variety of emerging applications, from virtual and augmented reality applications to autonomous cars or even fleets of autonomous cars. All these applications need precise localization. Nowadays, the state-of-the-art methods are able to reliably estimate the position of the robots using image streams. One of the big challenges still is the ability to localize a camera given an image stream in the presence of drastic visual appearance changes in the environment. Visual appearance changes may be caused by a variety of different reasons, starting from camera-related factors, such as changes in exposure time, camera position-related factors, e.g. the scene is observed from a different position or viewing angle, occlusions, as well as factors that stem from natural sources, for example seasonal changes, different weather conditions, illumination changes, etc. These effects change the way the same place in the environments appears in the image and can lead to situations where it becomes hard even for humans to recognize the places. Also, the performance of the traditional visual localization approaches, such as FABMAP or DBow, decreases dramatically in the presence of strong visual appearance changes. The techniques presented in this thesis aim at improving visual place recognition capabilities for robotic systems in the presence of dramatic visual appearance changes. To reduce the effect of visual changes on image matching performance, we exploit sequences of images rather than individual images. This becomes possible as robotic systems collect data sequentially and not in random order. We formulate the visual place recognition problem under strong appearance changes as a problem of matching image sequences collected by a robotic system at different points in time. A key insight here is the fact that matching sequences reduces the ambiguities in the data associations. This allows us to establish im-