Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of acute changes of inflammation on appetite and food intake among older hospitalised patients

Abstract The present study aimed to investigate the effect of acute changes in serum C-reactive protein (CRP) on appetite and food intake among older hospitalised patients. A total of 200 patients (age range 65–94 years, 62·5 % women) participated in this prospective longitudinal observational study. Risk of malnutrition was measured according to the Mini Nutritional Assessment Short Form. The Simplified Nutritional Appetite Questionnaire (SNAQ) and Edmonton Symptom Assessment System (ESAS) were used to evaluate patients’ appetite at the time of hospital admission (baseline) and after 7 d (follow-up). Food intake was measured according to the plate diagram and serum CRP was analysed at baseline and follow-up. At baseline, 30·5 % of the patients had moderate to severe inflammation, 31·0 % were malnourished and 48·0 % had food intake <75 % of the meals offered. Also, 32·5 and 23·5 % reported poor and very poor appetite or severe loss of appetite according to the SNAQ and ESAS, respectively. Of the patients, 40 % displayed a pronounced reduction in median CRP levels by −1·2 mg/dl and 19 % demonstrated an increase in median CRP levels by +1·2 mg/dl. Appetite significantly improved (P = 0·006) in patients with a decrease in CRP level and deteriorated in those with an increase in CRP level (P = 0·032). Changes in CRP levels did not show any significant impact on food intake. In a regression analysis, changes of inflammation were the major independent predictor for changes of patients’ appetite. We conclude that inflammation has a significant impact on appetite and should therefore be considered in the diagnosis and treatment of malnutrition.
Morphological reassessment and molecular assessment of Sargassum (Fucales: Phaeophyceae) species from the Gulf of California, Mexico

Systematic molecular studies have not been performed on ecologically important brown algae ( Sargassum ) in the Gulf of California, Mexico, where 42 specific and infraspecific names have been recorded within this genus. We conducted phylogenetic analyses of nuclear ribosomal ITS and mitochondrial cox 1 and cox 3 sequences, along with detailed morphological observations of Sargassum collected in the Gulf of California. We confirmed the presence of six species, five previously described— S . herporhizum , S . horridum , S . johnstonii , S . lapazeanum , and S . sinicola , as well as the newly described Sargassum ulixei sp. nov. which is characterized by terete primary axes arising from the stipe, lanceolate to elliptical sessile ecostate blades with smooth margins, cryptostomata, ellipsoidal vesicles with a short mucron, and cylindrical to ellipsoidal receptacles. The individual analyses and subsequent phylogenetic analysis recognized two groups previously described by Dawson, one as Sargassum and other as Horridum . Our studies suggest that the Gulf of California is a region with endemic Sargassum species, and is clearly distinguished from the Pacific side of Baja California, where recently introduced species are common. Based on phylogenetic relationships, we propose two geographical origins for the Sargassum from the Gulf of California: a first group originating from the proto-Gulf, related to species from the northern hemisphere (including five species); and a second group, containing S . sinicola , introduced when the southern end opened at a later stage.
Occurrence of lancefield group C streptococcal species in strangles cases of foals in Punjab, Pakistan.

Three equine rearing districts of Punjab, Pakistan including Sargodha, Okara and Faisalabad were surveyed. The occurrence of strangles was highest in foals during February to the start of May compared to winter and summer seasons. Strangles-inflicted foals showed inability to eat, strangled breathing with roaring sounds, swollen submandibular and submaxillary lymph nodes, abscessation in the lymph nodes, hyperthermia and anorexia with general debilitating conditions. Morbidity and case fatality rates were 78 and 2%, respectively. Pus samples were collected from 70 foals showing burst lymph node abscesses and processed on blood agar. Typical growth showing pinpoint mucoid colonies, with beta hemolysis and catalase negative characteristics were subjected to API Strep 20 system. The isolates of streptococcus recovered from pus samples include S. equi subspecies equi (54%), S. equi subspecies zooepidemicus (11%), S. dysgalactia subspecies equisimilis (11%) and mixed isolates of S. equisimilis and S. equi (23%). Streptococcus species were found sensitive to Penicillin and Cefotoxime, whereas Gentamycine and Tetracycline were ineffective.
Call Attention to Stances: Detect Rumor with a Stance Attention Network

With the advent of web2.0 era, social media such as microblogging platforms have become important information distribution channels. Considering the amount of users on microblogging platforms, the rumor spreading on microblogging platform could have a negative effect on individuals, groups and the whole society. Hence, automatic rumor detection method is required. Stance features are a crucial for rumor detection, because users discussing rumors tend to express more querying and denying stances. However, different user stances have different importance. Motivated by this inspiration, in this paper, we propose a Rumor Detection Model with a Stance Attention Network (RDM-SAN). The RDM-SAN consists of three modules: stance module, rumor module and integrate module. The first module is a stance module, in which recurrent neural network and convolutional neural network are used to capture grammatical and event-related information released by individual users respectively. Then attention mechanism is leveraged to make sure that the most valuable information is contained in the event stance latent representation. The second module is a rumor module in which the content features and temporal features of a microblogging event are captured. The third module is an integrate module in which stance representations and rumor representations are concatenated together to detect rumors. Experiments on a real-world dataset from Weibo platform demonstrate that our proposed model RDM-SAN improves the performance of rumor detection in terms of both efficiency and accuracy compared to other methods, and the accuracy of our model achieves 94.6%.
Voiceye: A Multimodal Inclusive Development Environment

People with physical impairments who are unable to use traditional input devices (i.e. mouse and keyboard) are often excluded from technical professions (e.g. web development). Alternative input methods such as eye gaze tracking and speech recognition have become more readily available in recent years with both being explored independently to support people with physical impairments in coding activities. This paper describes a novel multimodal application ("Voiceye") that combines voice input, gaze interaction, and mechanical switches as an alternative approach for writing code. The system was evaluated with non-disabled participants who have coding experience (N=29) to assess the feasibility of the application in writing HTML and CSS code. Results found that Voiceye was perceived positively and enabled successful completion of coding tasks. A follow-up study with disabled participants (N=5) demonstrated that this method of multimodal interaction can support people with physical impairments in writing and editing code.
Designing Mind(set) and Setting for Profound Emotional Experiences in Virtual Reality

Virtual reality (VR) has the potential to support profound emotional experiences, such as experiencing awe when virtually viewing the Earth from space. In doing so, VR can potentially both give people positive emotional experiences contributing to their overall well-being and give researchers a way to study these profound emotional experiences in a more controlled environment. Through a design refinement process, we explored the potential influence of the "set and setting''--one's mindset and the physical and social environment--when transitioning people into and out of VR designed to support profound emotional experiences. We present our findings from a design refinement session and a case study exploring how set and setting may support the profound emotional experience of awe. We discuss common themes in user experience and trends of awe-related behavioural and introspective measures. Our results contribute to the discourse around the role of the design of set and setting in overall user experience.
Fc Gamma Receptors and Their Role in Antigen Uptake, Presentation, and T Cell Activation

The cellular uptake, intracellular processing, and presentation of foreign antigen are crucial processes for eliciting an effective adaptive host response to the majority of pathogens. The effective recognition of antigen by T cells requires that it is first processed and then presented on MHC molecules that are expressed on other cells. A critical step leading to the presentation of antigen is delivering the foreign cargo to an intracellular compartment where the antigen can be processed and loaded onto MHC molecules. Fc-gamma receptors (FcγRs) recognize IgG-coated targets, such as opsonized pathogens or immune complexes (ICs). Cross-linking leads to internalization of the cargo with associated activation of down-stream signaling cascades. FcγRs vary in their affinity for IgG and intracellular trafficking, and therefore have an opportunity to regulate antigen presentation by controlling the shuttling and processing of their cargos. In this way, they critically influence physiological and pathophysiological adaptive immune cell functions. In this review, we will cover the contribution of FcγRs to antigen-presentation with a focus on the intracellular trafficking of IgG-ICs and the pathways that support this function. We will also discuss genetic evidence linking FcγR biology to immune cell activation and autoimmune processes as exemplified by systemic lupus erythematosus (SLE).
The Effect of Acid Sanitizers on the Microbiome of Re-use Water

Due to global climate change, water is becoming increasingly scarce. The poultry industry is a major consumer of fresh water; therefore, in order to reduce their environmental footprint, companies are in the beginning stages of evaluating water for re-use. Re-use water is classified as water from the poultry plant that has been collected, sanitized, and used again during poultry processing. Necessarily, this water must be potable as it otherwise produces a significant risk to the poultry industry. One of the most commonly used sanitizers in processing water is peracetic acid (PAA). At high concentrations, PAA is corrosive to the metal equipment in the plant and introduces a significant occupational hazard to plant workers. Inorganic sanitizers, such as sodium bisulfate (SBS), have documented antimicrobial and sanitation effects on a variety of surfaces. In this study, SBS and PAA were compared for their ability to sanitize re-use water collected from a local poultry plant. Fresh, commercial poultry processing plant re-use water was collected at the end of a processing shift and used within 1 h of the collection. Microcosms were created to simulate the sanitation environment and a time course collection of live microorganisms were collected and evaluated for aerobic plate counts, total Enterobacteriaceae, and Salmonella load. The microcosms contained either SBS (1, 2, or 3%) or 200 ppm PAA throughout the course of the study. Water samples were collected at 0, 15, 30, and 60 min post-sanitation. The water was evaluated for total Enterobacteriaceae and microbial load using traditional plating methods and the total DNA was extracted and sequenced using the 16S rDNA Illumina MiSeq v3 Platform targeting the V4 region of the prokaryote rDNA molecule. All sanitation methods resulted in nearly undetectable XLD and APC counts by 60 m (P < 0.05), with 1 and 2% SBS having a slight rebounding of APC counts by 60 m. The sequences were analyzed using the QIIME2.2018. 08 pipeline. Changes in alpha diversity and beta diversity were indicated across time and by treatment group, which may reflect the cumulative effects of the sanitation treatment (killing, delayed killing, and the emergence of resistant populations). Overall, based on the ANCOM results, SBS groups had less Pseudmonas and more Bacteroides while PAA groups had greater Actiobacter and Gallinobacterium (Q < 0.05). The results of this study indicate that SBS is as efficacious as PAA for decreasing bacterial loads in poultry re-use water, but it may favor different populations of bacteria in that water. Therefore, while more studies are needed to evaluate the changes in the microbiome when re-use water is applied to carcasses, evidence presented herein suggest that the choice of sanitizer in water re-use sanitation may impact downstream microbial loads.
Motivational and Affective Factors Underlying Consumer Dropout and Transactional Success in eCommerce: An Overview

It is estimated that more than half of all online transactions are abandoned before completion. This paper investigates the psychological factors that influence online shopping behavior, with a view to improving transactional success rates. Through a review of the literature, we identify a range of factors which predict abandonment of online shopping, highlighting affective and motivational dimensions in addition to processing style and characteristics of the consumer, device, and product. We conclude that online purchasing and payment systems that boost consumers’ motivation to buy and prevent or attenuate negative affective states will demonstrate the greatest rates of transactional success. However, with rapid advancement in technology, continued research is needed to fully understand the potential impact on future online purchasing behavior.
Question Chunk Extraction for Improving Question Similarity

Intention classification is one of the challenging issues of question understanding. The same intention could be described in different styles e.g., "when", "what time. " It is more challenging for a language that has no word separator, flexible grammar and lack of annotated corpus. In Thai, a word "when" could be described as "เมื่อไหร่", "ตอนไหน", "กี่โมง", "เวลาไหน. " The underlining text are question identifier i.e., "when" "which" "how many" and "which", respectively. With above limitations, traditional word segmentation could not tokenize question words properly especially on question tokens. As a result, word similarity or word suggestion services using word embedding, which heavily relies on word segmentation could not provide a decent result when it is a question. We proposed a question chunk extraction method since question should be tokenized into a chunk instead of a word. This can improve quality of word segmentation and can improve question similarity results. A question chunk (QC) is a combination of question word (QW) and focus word (FW.) We compared our question chunk based word embedding (QCWE) using QC and traditional word embedding (TWE) using traditional word segmentation, the results showed that our QCWE outperformed the TWE in all aspects.
Overview of Poultry Eimeria Life Cycle and Host-Parasite Interactions

Apicomplexan parasites of the genus Eimeria are organisms which invade the intestinal tract, causing coccidiosis, an enteric disease of major economic importance worldwide. The disease causes high morbidity ranging from an acute, bloody enteritis with high mortality, to subclinical disease. However, the presence of intestinal lesions depends on the Eimeria species. The most important poultry Eimeria species are: E. tenella, E. necatrix, E. acervulina, E. maxima, E. brunetti, E. mitis, and E. praecox. Key points to better understanding the behavior of this species are the host-parasite interactions and its life cycle. The present paper reviews the literature available regarding the life cycle and the initial host-parasite interaction. More studies are needed to better understand these interactions in poultry Eimerias, taking into account that almost all the information available was generated from other apicomplexan parasites that generate human disease.
Local landscape composition and configuration around North American Breeding Bird Survey routes

Large-scale monitoring of avian species is often conducted by observers driving rural roads and highways. Surveys along roads allow for easy and reliable access over time and the ability to survey large geographic areas in a relatively short period of time. However, if the landscape directly adjacent to these roadside transects is not representative of the greater landscape inhabited by the bird species, then estimates of abundance along the route may not be accurate for the greater landscape. Moreover, inferences regarding abundance, distribution, population trend, and habitat use in the greater landscape may not be valid. Using land cover data from the National Land Cover Database (NLCD), we recently conducted an assessment that revealed a satisfactorily high level of similarity in land cover composition between landscapes immediately adjacent to North American Breeding Bird Survey (BBS) routes (Buffer distance of 0.4 km on either side of route) and the larger landscapes (buffer distance of 10 km) in wh.
Validity, Reliability, and Diagnostic Cut-off of the Kinyarwandan Version of the Hamilton Depression Rating Scale in Rwanda

Introduction: In Rwanda, major depressive disorder affects 11.9% of the population and up to 35% of genocide survivors. Mental health services remain underutilized due to stigma and lack of awareness. Increasing the ability and capacity to diagnose and treat mental disorders is considered important to close this gap. We describe the translation, validity, and reliability assessment of the Hamilton Depression Rating Scale (HDRS) as a diagnostic tool for moderate to severe depression in Rwanda. Methods: The HDRS-21 was translated by a multi-group taskforce. We validated the translation against expert assessment in a comparative study on a sample of patients living with depression and of healthy volunteers. Psychometric properties, namely internal structure, reliability, and external validity were assessed using confirmatory factor analysis, three reliability calculations, and correlation analysis, respectively. Maximized Youden’s index was used for determining diagnostic cut-off. Results: The translated version demonstrated a kappa of 0.93. We enrolled 105 healthy volunteers and 105 patients with confirmed mild to severe depression. In the confirmatory factor analysis, HDRS had good factor loadings of 0.32–0.80. Reliability coefficients above 0.92 indicated strong internal consistency. External validity was shown by good sensitivity (0.95) and specificity (0.94) to differentiate depression from absence of depression. At a cut-off point of 17 for the diagnosis of depression, sensitivity and specificity were both 0.95 relative to gold standard. Conclusion: The validated HDRS in Kinyarwanda with diagnostic cut-off provides mental healthcare staff with an accurate tool to diagnose moderate to severe depression, enabling closure of the diagnosis and treatment gap.
A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges

Interactive reinforcement learning (RL) has been successfully used in various applications in different fields, which has also motivated HCI researchers to contribute in this area. In this paper, we survey interactive RL to empower human-computer interaction (HCI) researchers with the technical background in RL needed to design new interaction techniques and propose new applications. We elucidate the roles played by HCI researchers in interactive RL, identifying ideas and promising research directions. Furthermore, we propose generic design principles that will provide researchers with a guide to effectively implement interactive RL applications.
Enhancement of Fault Tolerance in Kafka Pipeline Architecture

Fault tolerance act as the most important factor to improve the reliability of the real-time messaging system. Checkpointing and replication are the main factors to promote the performance of fault tolerance. The paper emphasizes the message recovery scheme based on checkpointing. Apache Kafka is a highly scalable publishing-subscribing messaging system. However, the message recovery process becomes one of the challenges when server failure occurred. Apache Kafka needs to catch up with the lost messages in the failure case. To overcome the problem, Kafka requires to check the failure case by identifying the optimal checkpoint interval in Kafka configuration. The paper focuses on editing the default checkpoint interval (DCI) to recover the lost messages. Kafka needs to estimate the overall total overhead cost after defining the optimal checkpoint interval. The proposed system intends to recover lost messages and reduce the total overhead cost, according to the evaluation of Kafka. The paper measures the total overhead cost by using a fixed checkpoint interval (FCI) method. Conforming to the experimental results, the system achieves the problems of message loss and improve the main factor of fault tolerance in Kafka. The system also measures the total overhead cost by using a fixed checkpoint interval (FCI) method. Experimental results outperform the message recovery and save time approximately 50% than the original system.
Sustainable Cities and Society

The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three di ﬀ erent measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the ﬁ rst measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 ﬂ are-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The ﬁ ndings signify the promises of dynamic clustering for lock-down coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for signi ﬁ cantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.
Modelling the effects of climate variability on habitat suitability of jumbo flying squid, Dosidicus gigas, in the Southeast Pacific Ocean off Peru

Wei Yu1,4, Qian Yi1,2,3,4, Xinjun Chen1,2,3,4*, and Yong Chen5,4 College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China Collaborative Innovation Center for Distant-Water Fisheries, Shanghai 201306, China School of Marine Sciences, University of Maine, Orono, ME 04469, USA