Miracle-dz/UniFAR-FTUs huggingface dataset:
Odorant Receptors and Odorant-Binding Proteins as Insect Pest Control Targets: A Comparative Analysis

Recently, two alternative targets in insect periphery nerve system have been explored for environmentally-friendly approaches in insect pest management, namely odorant-binding proteins (OBPs) and odorant receptors (ORs). Located in insect antennae, OBPs are thought to be involved in the transport of odorants to ORs for the specific signal transduction of behaviorally active odorants. There is rich information on OBP binding affinity and molecular docking to bioactive compounds as well as ample 3D crystal structures due to feasible production of recombinant proteins. Although these provide excellent opportunities for them to be considered as pest control targets and a tool to design pest control agents, the debates on their binding specificity represent an obstacle. On the other hand, ORs have recently been functionally characterized with increasing evidence for their specificity, sensitivity and functional roles in pest behaviors. However, a major barrier to use ORs for semiochemical discovery is the lack of 3D crystal structures. Thus, OBPs and ORs have not been analyzed comparatively together so far for their feasibility as pest control targets. Here, we summarize the state of OBPs and ORs research in terms of its application in insect pest management. We discuss the suitability of both proteins as pest control targets and their selection toward the discovery of new potent semiochemicals. We argue that both proteins represent promising targets for pest control and can be used to identify new super-ligands likely present in nature and with reduced risk of resistance development than insect pesticides currently used in agriculture. We discuss that with the massive identification of OBPs through RNA-seq and improved binding affinity measurements, these proteins could be reconsidered as suitable targets for semiochemical discovery.
Weakly-Supervised Neural Semantic Parsing with a Generative Ranker

Weakly-supervised semantic parsers are trained on utterance-denotation pairs, treating logical forms as latent. The task is challenging due to the large search space and spuriousness of logical forms. In this paper we introduce a neural parser-ranker system for weakly-supervised semantic parsing. The parser generates candidate tree-structured logical forms from utterances using clues of denotations. These candidates are then ranked based on two criterion: their likelihood of executing to the correct denotation, and their agreement with the utterance semantics. We present a scheduled training procedure to balance the contribution of the two objectives. Furthermore, we propose to use a neurally encoded lexicon to inject prior domain knowledge to the model. Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving results within the state-of-the-art range.
How Long Does It Take for Patients to Complete PROMIS Scores?: An Assessment of PROMIS CAT Questionnaires Administered at an Ambulatory Sports Medicine Clinic

Background: Challenges exist in routinely collecting patient-reported outcomes (PROs) from patients at a busy ambulatory clinic. A number of validated Patient-Reported Outcomes Measurement Information System (PROMIS) subdomains allow for efficient PRO administration. Purpose: To determine the time to completion (TTC) of 3 PROMIS computer adaptive test (CAT) scores. CAT questionnaires were administered at the ambulatory clinic with the following PROMIS subdomains: Pain Interference (PI), Depression, and Physical Function for lower extremity (PF) or for upper extremity (UE). The secondary purpose was to determine the influence of patient demographic factors on TTC. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Patients were recruited from 3 fellowship-trained upper extremity and sports medicine orthopaedic surgery clinics. PROMIS CAT questionnaires were administered to consecutive patients during the study period (July 2017–September 2017). The start and completion times of each CAT were recorded. The primary outcome of interest was TTC of the questionnaires. Patients were stratified into age quartiles to determine the impact of age on TTC. Patient demographic information, such as sex, race, and ethnicity, was determined retroactively. Results: A total of 1178 questionnaire sets consisting of 3658 individual PROMIS forms were analyzed. The mean TTC was 3.29 minutes for all 4 forms in aggregate, with PROMIS PI, PF, UE, and Depression taking on average 1.05, 0.74, 0.96, and 0.57 minutes to complete, respectively. Patients from the oldest age quartile (mean ± SD, 70.3 ± 7.5 years) had a statistically significant longer TTC as compared with the second quartile (41.2 ± 4.7 years) (3.70 vs 2.87 minutes; P < .05). Asian patients had the longest PROMIS PF TTC, while white patients completed PF with the shortest TTC (1.28 vs 0.68 minutes; P < .05). Patients of unstated ethnicity had a longer TTC for PF as compared with their Hispanic/Latino and non-Hispanic/Latino counterparts (0.91 vs 0.30 and 0.70 minutes; P < .05). Conclusion: PROMIS CAT forms are efficient tools for collecting patient-reported outcomes in the ambulatory orthopaedic surgery clinic. Older patients, Asian patients, and patients of unstated ethnicity took longer to complete the forms.
Educational Case: Thyroid Neoplasms

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub. com/doi/10.1177/2374289517715040.
Landsat-based vegetation abundance and surface temperature for surface urban heat island studies: the tale of Greater Amman Municipality

ABSTRACT This study aimed at characterizing the spatial and temporal characteristics of vegetation abundance (represented by At-Sensor Normalized Difference Vegetation Index (ASNDVI) and/or Land Surface Normalized Difference Vegetation Index (LSNDVI)) and surface temperature (represented by At-Sensor Brightness Temperature (ASBT) and/or Land Surface Temperature (LST)) and investigating the types of the relationships between these variables in different seasons in different years using Landsat data in Greater Amman Municipality in Jordan. Correlation analysis using Pearson’s correlation coefficient showed that using either ASNDVI or LSNDVI and either ASBT or LST should lead to the same results. Change analysis from 1987–2016 using map algebra showed that the majority of the municipality was subjected to both no change and small increase in vegetation abundance in summer and winter and both no change and small increase in surface temperature in summer and both no change and small decrease in surface temperature in winter. Vegetation abundance variability reflected partly the land cover characteristics of the municipality while surface temperature variability indicated the potential of the presence of an inverted surface urban heat island pattern. Correlation analysis using contingency tables showed the presence of negative relationship in summer and positive relationship in winter between vegetation abundance and surface temperature.
End-to-End Neural Entity Linking

Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention - entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy.
Group Recommendations Based on Hesitant Fuzzy Sets

Group recommender systems (GRSs) recommend items that are used by groups of people because certain activities, such as listening to music, watching a movie, dining in a restaurant, etc., are social events performed by groups of people sharing their tastes, and their choices affect all of them. GRSs help groups of people making choices in overloaded search spaces according to all group members preferences. A common GRS scheme aggregates users preferences to generate a group preference profile. However, the aggregation process may imply a loss of information, negatively affecting different properties of the GRS such as diversity of group recommendations, which is an important quality factor because of such recommendations are targeted to groups formed by users with individual and possibly conflicting preferences. To avoid and manage the loss of information caused by aggregation, this paper proposes to keep all group members preferences by using hesitant fuzzy sets (HFSs) and interpreting such information like the group hesitation about their preferences that will be used in the group recommendation process. To evaluate the performance and rank quality of the HFS GRS proposal, a case study is carried out.
Optical in vivo imaging detection of preclinical models of gut tumors through the expression of integrin αVβ3

Optical imaging and Fluorescent Molecular Tomography (FMT) are becoming increasingly important for the study of different preclinical models of cancer, providing a non-invasive method for the evaluation of tumor progression in a relatively simple and fast way. Intestinal tumors, in particular colorectal cancer (CRC), represent a major cause of cancer-related death in Western countries: despite the presence of a number of preclinical models of intestinal carcinogenesis, there is a paucity of information about the possibility to detect intestinal tumors using fluorescent probes and optical in vivo imaging. Herein, we identify the detection of integrin αvβ3 by FMT and optical imaging as an effective approach to assess the occurrence and progression of intestinal carcinogenesis in genetic and chemically-induced mouse models. For this purpose, a commercially available probe (IntegriSense), recognizing integrin αvβ3, was injected in APC+/min mice bearing small intestinal adenomas or CRC: FMT analysis allowed a specific tumor detection, further confirmed by subsequent ex vivo imaging or conventional histology. In addition, IntegriSense detection by FMT allowed the longitudinal monitoring of tumor growth. Taken together, our data indicate the possibility to use integrin αvβ3 for the visualization of intestinal tumors in preclinical models.
Security Analysis of the Khudra Lightweight Cryptosystem in the Vehicular Ad-hoc Networks

With the enlargement of wireless technology, vehicular ad-hoc networks (VANETs) are rising as a hopeful way to realize smart cities and address a lot of vital transportation problems such as road security, convenience, and efficiency. To achieve data confidentiality, integrity and authentication applying lightweight cryptosystems is widely recognized as a rather efficient approach for the VANETs. The Khudra cipher is such a lightweight cryptosystem with a typical Generalized Feistel Network, and supports 80-bit secret key. Up to now, little research of fault analysis has been devoted to attacking Khudra. On the basis of the single nibble-oriented fault model, we propose a differential fault analysis on Khudra. The attack can recover its 80-bit secret key by introducing only 2 faults. The results in this study will provides vital references for the security evaluations of other lightweight ciphers in the VANETs.
Prediction system for activity recognition with compressed video

Executing video analytics tasks using a large camera network is a challenging problem in the field of video processing. Video compression is a necessary step to reduce video data size before transmission. However, the performance of video analytics tasks generally degrade as video quality drops. This paper considers how to find the optimal point between video compression and performance for the video analytics task of activity recognition. We propose a system that predicts the success or failure of a video analytics task under different compression parameters without executing the task. The system is designed to automatically select the best compression rate for each video to maintain an acceptable detection accuracy. Our experiments indicate that such a system has the potential to improve overall performance across a variety of different activity sets selected from the UCF101 dataset [1].
Skin Disorders and Primary Tumor Location as Prognostic Factors in Patients with Metastatic Colorectal Cancer Treated with Cetuximab and Chemotherapy

Background: Cetuximab-induced skin disorder is common in colorectal cancer (CRC), and is known to affect prolonged overall survival (OS). Patients with left-sided CRC survive longer than those with right-sided CRC, among those treated with combination cetuximab and chemotherapy. However, no study has evaluated patient prognosis in terms of OS and progression-free survival (PFS) in relation to both tumor location and skin disorder. This study aimed to determine the incidence of skin disorder according to tumor location and analyze the relationship of tumor location and skin disorder with OS. Methods: Patients with metastatic colorectal cancer (mCRC) treated with standard chemotherapy and cetuximab as first-line therapy were included. Differences in the incidence of skin disorders due to the location of the primary tumors were compared in the same patient. The OS and PFS in relation to the location of the primary tumors and presence or absence of skin disorder were also compared. Results: Total frequency of each skin disorder as rash acneiform, paronychia, and dry skin in patients with left- and right-sided mCRC was 70%, 70%, and 43% and 27%, 36%, and 27%, respectively. The median OS was 8.9 months for mCRC on the left-sided without skin disorder and 56.3 months for mCRC on the left-sided with skin disorder. In comparison, the median OS was 10.4 months for mCRC on the right-sided without skin disorder and 11.3 months for mCRC on the right-sided with skin disease (left-sided with skin disorder versus other three group; P<0.001). Conclusions: Primary tumor location and the presence of skin disorder are important factors in patients with mCRC who receive cetuximab. In particular, our results show the new fact that the left-sided and right-sided mCRC survival time were comparable if there is no skin disorder caused by cetuximab.
From words to action: Implicit attention to antisocial semantic cues predicts aggression and amygdala reactivity to angry faces in healthy young women

Implicit measures of aggressiveness are able to circumvent response biases that plague self-reports, but it is unclear how they link to neural activation during aggressive interactions and to aggression-related endocrine function. Here, we tested whether an implicit attentional bias toward antisocial semantic information was associated with endogenous testosterone (T) and cortisol (C) levels, as well as with aggressive behavior and amygdala reactivity to angry faces in a separate competitive paradigm. On Day one, participants (39 healthy young women) completed an emotional word Stroop task in which they had to indicate the font color of antisocial, prosocial, or neutral words. On Day two, we measured subjects' brain activity during a competitive reaction time task in which the female opponent displayed angry or neutral facial expressions at the start of each trial and provoked participants with increasingly strong sound blasts. T and C were measured in saliva during a regular weekday as well as before and after scanning. We previously showed that aggression was associated with enhanced amygdala reactivity to angry faces in this sample. The present analyses revealed that subjects were slower to identify the font color of antisocial relative to neutral words, and that this attentional bias predicted higher aggression. T and C were uncorrelated with Stroop scores. Crucially, the relationship between implicit attention to antisocial words and aggression was mediated by amygdala reactivity to angry faces. Our data indicate that a tendency to dwell on implicit hostile cues reflects enhanced responsivity to overt anger displays.
SSD‐enhanced uncoded space‐time labeling diversity

Space‐time labeling diversity (STLD) has been shown to be an efficient technique for improving the bit error rate (BER) performance of an uncoded space‐time coded modulation system. In this paper, signal space diversity (SSD) is incorporated into the uncoded STLD system to further enhance the system BER performance. A tight closed‐form union bound on the BER of the proposed system is derived and is used to optimize the rotation angle of the SSD scheme. Simulation results are used to confirm the theoretical bound derived for the system. The results also show performance gains of approximately 2.0 dB at a BER of 10−6 and 1.6 dB at a BER 10−4 from incorporating SSD into the uncoded STLD system using 16QAM and 64QAM, respectively. Furthermore, a low complexity detection scheme based on orthogonal projection is formulated for the proposed scheme and, in comparison with the optimal maximum‐likelihood detector, is shown to result in a 56% and 95% reduction in computation complexity for the 16QAM and 64QAM versions of the proposed system, respectively.
An information privacy culture instrument to measure consumer privacy expectations and confidence

Purpose This paper aims to propose an information privacy culture index framework (IPCIF) with a validated information privacy culture index instrument (IPCII) to measure information privacy culture across nations. The framework is based on consumers’ privacy expectations, their actual experiences when organisations process their personal information and their general privacy concerns. Design/methodology/approach A survey method was deployed to collect data in South Africa – the first participating country in the study – to start building a global information privacy culture index (IPCI) and to validate the questionnaire. Findings The IPCI revealed that there seems to be a disconnect between what consumers expect in terms of privacy and the way in which organisations are honouring (or failing to honour) those expectations, which results in a breach of trust and the social contract being violated. Practical implications Governments, information regulators and organisations can leverage the results of the privacy culture index to implement corrective actions and controls aimed at addressing the gaps identified from a consumer and compliance perspective. The validated IPCII can be used by both academia and industry to measure the information privacy culture of an institution, organisation or country to identify what to improve to address consumer privacy expectations and concerns. Originality/value The IPCIF and validated IPCII are the first tools that combine the concepts of consumer expectations and their confidence levels in whether organisations are meeting their privacy expectations, which are in line with the fair information practice principles and the privacy guidelines of the Organisation for Economic Cooperation and Development, to determine gaps and define improvement plans.
Üniversite Öğrencilerinin Algıladıkları Anne Baba Tutumları ve Flört Kaygısı Arasındaki İlişkinin İncelenmesi

Bu arastirma, universite ogrencilerinin algiladiklari anne tutumlari ve baba tutumlarinin flort kaygisi duzeylerine etkisinin incelenmesi amaciyla yapilmistir. Arastirmanin calisma grubunu; 2016 – 2017 egitim-ogretim yilinda Erzincan Universitesi Egitim Fakultesi bunyesinde bulunan 11 anabilim dalinda ogrenim gormekte olan 582 ogrenciden tabakali orneklem yontemiyle belirlenen 413 (252’si Erkek, 161’i kadin) ogrenci olusturmaktadir. Arastirmada veri toplama araclari olarak Algilanan Anne Tutum Olcegi, Algilanan Baba Tutum Olcegi, Flort Kaygisi Olcegi ve Kisisel Bilgi Formu uygulanmistir. Bu arastirma tarama modelinde betimsel bir calismadir. Verilerin analizinde t-testi, Tek Yonlu Varyans Analizi Regrasyon analizi kullanilmistir. Arastirma sonucunda cinsiyet degiskeni ile algilanan anne tutumlari, algilanan baba tutumlari ve flort kaygisi puanlari arasinda istatistiksel acidan anlamli farklilik oldugu gorulmustur. Ogrencilerin yasadiklari bolge degiskeni ile algiladiklari anne ve baba tutumlari arasinda anlamli bir fark bulunamazken, flort kaygisiyla anlamli bir farklilik bulunmustur. Anne calisma degiskenine gore algiladiklari anne tutum puanlari arasinda istatistiksel acidan anlamli bir fark vardir. Anne calisma degiskeni ile flort kaygisi puanlari arasinda anlamli bir farklilik yoktur. Baba calisma degiskeni ile algilanan baba tutum puanlari ve flort kaygisi puanlari arasinda istatistiksel acidan anlamli bir farklilik yoktur. Ogrencilerin anne babalarinin evlilik sekli degiskeni ile algiladiklari anne tutum puanlari ve flort kaygisi puanlari arasinda anlamli fark bulunamamistir. Ogrencilerin anne babalarinin evlilik sekli degiskeni ile algiladiklari baba tutum puanlari arasinda anlamli farklilik gorulmustur.
Predictive pollen-based biome modeling using machine learning

This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published African pollen dataset and a global ecosystem classification scheme. To test the applicability of traditional and machine-learning based classification models for the task of biome prediction from high dimensional modern pollen data, we train a total of eight classification models, including Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Classification Decision Tree, Random Forest, Neural Network, and Support Vector Machine. The ability of each model to predict biomes from pollen data is statistically tested on an independent test set. The Random Forest classifier outperforms other models in its ability correctly classify biomes given pollen data. Out of the eight models, the Random Forest classifier scores highest on all of the metrics used for model evaluations and is able to predict four out of five biome classes to high degree of accuracy, including arid, montane, tropical and subtropical closed and open systems, e.g. forests and savanna/grassland. The model has the potential for accurate reconstructions of past biomes and awaits application to fossil pollen sequences. The Random Forest model may be used to investigate vegetation changes on both long and short time scales, e.g. during glacial and interglacial cycles, or more recent and abrupt climatic anomalies like the African Humid Period. Such applications may contribute to a better understanding of past shifts in vegetation cover and ultimately provide valuable information on drivers of climate change.