Miracle-dz/UniFAR-FTUs huggingface dataset:
A Nanoceutical Agent for Chemoprevention of Bilirubin Encephalopathy

Background Targeted degradation of bilirubin in vivo may enable safer and more effective approach to manage incipient bilirubin encephalopathy consequent to severe neonatal hyperbilirubinemia (SNH). This report builds on the use of a spinel structured mixed-valence transition metal oxide (trimanganese tetroxide) nanoparticle duly functionalized with biocompatible ligand citrate (C-Mn3O4 NP) having the ability to degrade bilirubin without photo-activation. Method The efficiency of C-Mn3O4 NP in in vivo degradation of serum bilirubin and amelioration of severe bilirubin encephalopathy and associated neurobehavioral changes was evaluated in C57BL/6j animal model of SNH. Results Oral single dose (0.25 mg kg-1 body weight) of the NPs efficiently reduced serum bilirubin levels (both conjugated and unconjugated) in study mice. It prevents bilirubin-induced neurotoxicity with reduction of SNH as observed by neurobehavioral and movement studies of SNH-mice. Pharmacokinetic data suggests intestinal reabsorption of the NPs and explain sustainable action. Biodistribution, pharmacokinetics, and biocompatibility of the NPs were tested during sub-chronic exposure. Conclusion Thus, we report preliminary studies that explore an affordable chemoprevention mechanism to acutely prevent or minimize bilirubin neurotoxicity in newborn infants. IMPACT STATEMENT Despite several attempts, no pharmaco-therapeutics are available for the treatment of severe neonatal hyperbilirubinemia (SNH) and associated neurotoxicity. Our newly developed nanodrug, citrate functionalized Mn3O4 nanoparticles (C-Mn3O4 NPs), can efficiently ameliorate SNH and associated neurotoxicity as investigated in preclinical rodent model. Chemoprevention effect of the nanodrug is found to be safe and sustainable. If successfully translated into clinical trials, C-Mn3O4 NPs could become the first drug to treat SNH.
Corporate Social Responsibility And Marketing Performance

ABSTRACT The link between corporate social responsibility (CSR) initiatives and company performance has been researched extensively, but the findings have been inconclusive or even conflicting. The link between CSR and marketing performance—one of the dimensions of company performance—has not been investigated so far, however. This study examines the relationship between CSR activities (in particular, corporate community and environmental activities) and marketing performance (measured by market share) and uncovers a positive relationship. The findings of this study demonstrate that the level of advertising intensity positively moderates the relationship between CSR and market share.
Layer-dependent loss and enhancement of geniculostriate and retinotectal pathways in adult human amblyopia

Abnormal visual experience during critical period leads to reorganization of neuroarchitectures in primate visual cortex. However, developmental plasticity of human subcortical visual pathways remains elusive. Using high-resolution fMRI and pathway-selective visual stimuli, we investigated layer-dependent response properties and connectivity of subcortical visual pathways of adult human amblyopia. Stimuli presented to the amblyopic eye showed selective response loss in the parvocellular layers of the lateral geniculate nucleus, and also reduced the connectivity to V1. Amblyopic eye’s response to isoluminant chromatic stimulus was significantly reduced in the superficial layers of the superior colliculus, while the fellow eye’s response robustly increased in the deeper layers associated with increased cortical feedbacks. Therefore, amblyopia led to selective reduction of parvocellular feedforward signals in the geniculostriate pathway, whereas loss and enhancement of parvocellular feedback signals in the retinotectal pathway. These findings shed light for future development of new tools for treating amblyopia and tracking the prognosis. Highlights Amblyopia impairs feedforward processing in the P layers of the LGN Layer-dependent loss and enhancement of cortical feedback signals in the SC Pathway-specific abnormalities explain amblyopic deficits in visual acuity and attention Significance statement How abnormal visual experiences during critical period shape the function and wire the neural circuits of human subcortex remains largely unknown. With high-resolution fMRI and visual stimuli to preferentially activate layer-dependent response in human subcortical pathways, the current study clearly demonstrates that amblyopia shifts the homeostatic interocular balance of human subcortex in a pathway-specific manner. Amblyopia led to selective loss of parvocellular feedforward signals in the geniculostriate pathway, whereas deficit and enhancement of parvocellular feedback signals in the retinotectal pathway. These pathwayspecific functional abnormalities provide the neural basis for amblyopic deficits in visual acuity, control of eye movement and attention. It sheds light for future development of new tools for treating amblyopia and tracking the prognosis.
Adaptive Potential Field-Based Path Planning for Complex Autonomous Driving Scenarios

An adaptive potential field is designed to adapt the acceleration/deceleration and mass of the obstacle. The potential fields are established in a transformed road coordinate system to improve the feasibility and robustness. A path planning method is proposed based on the designed adaptive potential field to improve the driving safety and the ride comfort of autonomous vehicles in complex driving scenarios, which including the cut-in, emergency braking, obstacle sudden accelerating during overtaking and the curve road driving scenarios. The effectiveness of the proposed method is validated by simulations with constructed and real data, respectively. The $TTC\text{s}$ (Time-to-Collision) and the maximum lateral accelerations are used to evaluate the improvements on safety and ride comfort. The results show that both the driving safety and ride comfort are efficiently improved by using the proposed approach in emergency braking and accelerating scenarios. Meanwhile, the proposed method can be well applied in a curve road driving environment.
Distinct cryo-EM Structure of α-synuclein Filaments derived by Tau

Recent structural studies of ex vivo amyloid filaments extracted from human patients demonstrated that the ex vivo filaments associated with different disease phenotypes adopt diverse molecular conformations distinct from those in vitro amyloid filaments. A very recent cryo-EM structural study also revealed that ex vivo α-synuclein filaments extracted from multiple system atrophy (MSA) patients adopt quite distinct molecular structures from those of in vitro α-synuclein filaments, suggesting the presence of co-factors for α-synuclein aggregation in vivo. Here, we report structural characterizations of α-synuclein filaments derived by a potential co-factor, tau, using cryo-EM and solid-state NMR. Our cryo-EM structure of the tau-promoted α-synuclein filament at 4.0 Å resolution is somewhat similar to one of the polymorphs of in vitro α-synuclein filaments. However, the N- and C-terminal regions of the tau-promoted α-synuclein filament have different molecular conformations. Our structural studies highlight the conformational plasticity of α-synuclein filaments, requiring additional structural investigation of not only more ex vivo α-synuclein filaments, but also in vitro α-synuclein filaments formed in the presence of diverse co-factors to better understand molecular basis of diverse molecular conformations of α-synuclein filaments.
Tremor Suppression With Mechanical Vibration Stimulation

Tremor, which is one of the most common movement disorders, is a repetitive movement that is caused by periodic muscle contraction and relaxation. To suppress tremors of upper-limb tremor patients, such as essential tremor (ET) patients, many kinds of devices have been developed. On the other hand, when mechanical vibration stimulation is applied to a human muscle, sustained muscle contraction, which is referred to as the tonic vibration reflex (TVR), is induced in the stimulated muscle. In this study, a novel tremor suppression method that utilizes the periodic TVR to induce muscle contraction/relaxation to generate the counterphase motion of the ET is proposed and applied to the forearm pronation-supination ET. In the proposed method, periodic vibration stimulation is applied to generate the periodic TVR in the pronator teres muscle and/or supinator muscle. First, the results confirmed that the TVR can be induced by applying mechanical vibration stimulation to the pronator teres muscle and supinator muscle since the forearm pronation-supination tremor is one of the key features of the ET. Furthermore, the findings also confirmed that the TVR intensity that is induced in these muscles can be adjusted by changing the vibration stimulation frequency. Second, the results show that the counterphase motion of the ET (i.e., periodic pronation-supination motion) can be generated by applying the proposed method. The effectiveness of the proposed method for tremor suppression is evaluated by comparing the generated motion with the ET motion.
Makeup Presentation Attacks: Review and Detection Performance Benchmark

The application of facial cosmetics may cause substantial alterations in the facial appearance, which can degrade the performance of facial biometrics systems. Additionally, it was recently demonstrated that makeup can be abused to launch so-called makeup presentation attacks. More precisely, an attacker might apply heavy makeup to obtain the facial appearance of a target subject with the aim of impersonation or to conceal their own identity. We provide a comprehensive survey of works related to the topic of makeup presentation attack detection, along with a critical discussion. Subsequently, we assess the vulnerability of a commercial off-the-shelf and an open-source face recognition system against makeup presentation attacks. Speciﬁcally, we focus on makeup presentation attacks with the aim of impersonation employing the publicly available Makeup Induced Face Spooﬁng (MIFS) and Disguised Faces in the Wild (DFW) databases. It is shown that makeup presentation attacks might seriously impact the security of face recognition systems. Further, we propose different image pair-based, i.e. differential, attack detection schemes which analyse differences in feature representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection systems employ various types of feature extractors including texture descriptors, facial landmarks, and deep (face) representations. To distinguish makeup presentation attacks from genuine, i.e. bona ﬁde presentations, machine learning-based classiﬁers are used. The classiﬁers are trained with a large number of synthetically generated makeup presentation attacks utilising a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database and a subset of the DFW database reveal that deep face representations achieve competitive detection equal error rates of 0.7% and 1.8%, respectively.
Avaliação da cinética de biorremediação de benzeno em água através da Pseudomona aeruginosa.

RESUMO – Os compostos monoaromáticos, quando liberados no meio ambiente, podem inviabilizar a exploração de recursos hídricos. Entre estas substâncias, o benzeno se destaca, pois provoca sérios danos mesmo em baixas concentrações. A biorremediação consiste na utilização de grupos microbianos capazes de degradar hidrocarbonetos, promovendo a destruição permanente dos resíduos. Este trabalho investigou a cinética de biodegradação do benzeno em água, a partir da avaliação do seu consumo pela bactéria Pseudomona aeruginosa para as concentrações de 200, 160, 120, 80 e 50 mg/L. Para isto, os modelos cinéticos de Monod e Andrews desenvolvidos para uma operação em batelada foram discretizados numericamente pelo método de Runge-Kutta. Os resultados obtidos mostraram que não foi necessário um tempo maior do que 2,5 horas para microrganismo degradar o anel aromático. Por fim, o modelo de Monod conseguiu predizer satisfatoriamente os dados experimentais.
Improving Query Quality for Transductive Learning in Learning to Rank

In traditional transductive learning, all queries are used in learning to rank in order to generate pseudo-labels when sufficient training data are not available. However, low quality queries may affect retrieval performance in transductive learning. We thus think that it is important to improve the quality of queries in transductive learning to train an effective ranking model. By using a small number of reliable samples and data close to the boundaries of classification, we propose building a query quality estimator by establishing a relationship between the benefits of good retrieval performance and features of the normalized query commitment that influence query quality. In our proposed transduction model, all queries available are filtered by the proposed query quality estimator and only high quality queries that enhance the effectiveness of retrieval such that they yield performance-related benefits, are used to generate pseudo-labels for learning to rank. Queries that can degrade performance benefits are discarded while creating the pseudo-labels. Pseudo-labels aggregated by high quality queries in transductive learning are then leveraged in learning to rank scenarios without sufficient training data. The results of extensive experiments on the standard LETOR 4.0 dataset showed that our proposed method can outperform strong baselines and the average normalized discounted cumulative gain is enhanced up to 7.77% in some case. INDEX TERMS Transductive learning, query quality, retrieval performance, learning to rank
Departure Time and Route Choices With Accurate Information Under Binary Stochastic Bottleneck Capacity in the Morning Commute

Uncertainty, a critical factor of causing congestion and extra travel costs in the commute, can be mitigated by providing information. This paper studies the welfare effects of accurate pretrip information on departure time and route choices in the morning commute under binary stochastic bottleneck capacity. We consider a classical two-route network. Each route has a single bottleneck where congestion occurs during the rush hours. The two routes’ bottleneck capacities vary from day-to-day due to events such as bad weather, accidents, and temporary road closures. We derive all equilibrium solutions in consideration of the differences between routes in free-flow travel time, the shadow value of travel time, the severity of bottleneck capacity reductions, and the degree of correlation between two routes in travel conditions. Furthermore, we investigate the benefit changes from zero-information to full-information and prove that accurate pre-trip information about the bottleneck conditions is strictly welfare-improving. Finally, these theoretical results are supplemented by case studies that show examples of benefit gains from pre-trip information. INDEX TERMS Departure time choice, route choice, bottleneck, congestion, pre-trip information, uncertainty.
Personalised Services in Social Situations

We present a qualitative study of how personal accounts on online services, such as Tinder, Netflix and Spotify, may be shared in particular social situations. We draw from agency theory's focus on principal-agent relationships and Goffman's work on frames in analysing situations where others are allowed to use personal accounts, either for a shared purpose or on behalf of the account owner. We deploy Goffman's concepts of regrounding to understand how interests behind activities are transformed and brackets to draw attention to the boundaries of different frames, and how these are incurred or broken in situations that exceed personal account use. Based on a set of 43 written descriptions of account sharing, we depict how employing others to act as agents to use one's personal accounts may lead to playful or serious use. Additionally, we discuss consequentiality of sharing personalised services, considering both what services might reveal about the account owner and how sharing takes place in the context of relationships. We contribute by illustrating how users' relationships with personalised services are complicated by the different interests that are served when accounts are shared.
Using Data to Approach the Unknown

Medical practices have always been data oriented: healthcare providers decisions are based on data, both clinically generated and patient reported. With the increased use of patient-generated health data (PGHD), other types of data are entering providers? practices and influencing patient-provider collaboration. We studied the use of PGHD and its related data practices in the context of fertility, a health concern that is uncertain, complex, and data intensive. We interviewed 14 patients who are facing or have faced challenges to conceive and 5 healthcare providers specialized in infertility. Our findings show that patients and providers use PGHD in different ways but with the common goal of exploring 'the unknown' generated by the uncertainties of fertility. Providers use patients? data in a rational protocol, aiming to identify possible causes of infertility and define a treatment course. Patients use data in a much more emotional way, learning about their bodies while struggling with data interpretation challenges. By analyzing these data practices, we discuss the principles behind their differences and describe how they have individual benefits for each specific group. We then suggest that fertility technologies need to consider such principles, highlight the existing boundary between patients' and providers' data practices, and focus on bridging instead of merging them in order to facilitate collaboration and maintain their independent benefits.
Lessons from the Old Masters on Assessing Equity and Efficiency: A Primer for Fiscal Policymakers

How can a society’s well-being be measured to include not only average incomes but also their distribution? How can the effects of policies be assessed by considering both equity and efficiency? This primer outlines the seminal contributions of influential economists of the past, including Arthur Okun, who developed a simple method to elicit people’s preferences regarding redistribution, and Anthony Atkinson, who showed how equity and efficiency can be measured simultaneously and summarized in a single, intuitive index expressed in monetary units (such as dollars). These methods are applied to recent data to gauge how countries fare when both mean incomes and their distribution are considered together, and to a hypothetical tax-and-transfer scheme assessed through a general equilibrium model for household-level data.
La Casa de Inez: Investigating History, Voice, and Memory through Embodied, Site-Specific Storytelling

Abstract As a partnership between ¡Habla! AZ and the Tempe History Museum, La Casa de Inez was a site-specific, participatory storytelling event about the ways in which the voices and identities of Latinas and Chicanas have been shaped by preceding generations of women in their families. Set against the backdrop of the historic Elias-Rodriguez House, one of the earliest remaining testaments to the first Mexican barrios in Tempe, Arizona, a group of Chicana and Latina storytellers set out to take the audience on a personal and historical journey. Through embodied storytelling and audience participation, the performers explored how their own personal and ancestral histories, as well as the histories of the Elias-Rodriguez women, whose legacies remain alive, especially in the grounds of this remarkable property, define and reflect the enormous weight and relevance of the Latina/Chicana voice.
An Approach to Mitigate Multiple Malicious Node Black Hole Attacks on VANETs

Inter-vehicular communication is a growing trend among the motor vehicle industry, as part of the shift to the Intelligent Transportation System (ITS) paradigm and the emergence of autonomous vehicles. The benefits of using vehicular communication systems are many: traffic congestion can be abated leading to increased route efficiency and fuel economy; road traffic accidents and fatalities can be prevented; and new services are made available to both driver and passengers. Due to the important functions carried out by these networks, it is essential that communication is secure and free from interference by malicious parties. But the very nature of these networks (i.e. public and dynamically forming, over a shared medium) make them susceptible to certain attack vectors. Black hole attacks are a discernible threat to vehicular communication and the availability of Vehicular Ad Hoc Networks (VANETs). Previously, we proposed a solution to mitigate these attacks in the case of a single malicious node. This countermeasure comprised a detection, accusation, and blacklisting scheme. We now extend that work to also account for cases where multiple malicious nodes are present in the same network. For cases where there is only one malicious node on multiple routes, we add a recursive element to our algorithm to handle additional detections during the accusation phase. Routes containing a node with a pending accusation are also precluded from selection. Where multiple malicious nodes exist on the same route, we adapt our former strategy to allow for multiple concurrent accusations and to find alternate routes to non-malicious nodes that need to be queried when verifying an accusation. We provide validation for our proposal using a simulation model built in NS-3. Results from simulation show the efficacy of our solution in detecting and preventing multiple malicious node black hole attacks in VANETs.
SISTEM DETEKSI SERANGAN DDOS MENGGUNAKAN ALGORITMA NAÏVE BAYES PADA JARINGAN INTERNET OF THINGS

Fokus penelitian ini ialah pada deteksi serangan distribute denial of service (DDoS), dengan mengenali pola dari serangan distribute denial of service (DDoS) yang terjadi pada jaringan internet of things, jaringan internet of things yang dibangun dalam penelitian ini memiliki empat node dengan sensor pada setiap nodenya, dimana setiap sensor akan melakukan pembacaan data yang akan dikirim ke mesin Raspberry Pi yang berfungsi sebagai node agregator yang selanjutnya akan diforward ke mesin server sekaligus sebagai dashboard visualisasi dengan menggunakan protokol komunikasi zigbee. Serangan distribute denial of service merupakan serangan yang bersifat connectionless yang melakukan flooding request sehingga dapat membuat jaringan menjadi sibuk dengan traffic yang tidak normal. Pola serangan distribute denial of service pada protokol zigbee dapat dikenali dari beberapa parameter berikut, seperti no_packet, ip_dst, frame_number, protocol, time, packet_length, ip_src, dan info_packet. Pada penelitian ini, algoritma yang digunakan untuk deteksi serangan DDoS ialah algoritma Naive Bayes, dengan hasil akhir penelitian memperoleh nilai rata-rata akurasi 99,94%, presisi 99,9%, dan nilai recall sebesar 99,9%.