Miracle-dz/UniFAR-FTUs huggingface dataset:
Pigmentation and vision: Is GPR143 in control?

Albinism, typically characterized by decreased melanin synthesis, is associated with significant visual deficits owing to developmental changes during neurosensory retina development. All albinism is caused by genetic mutations in a group of diverse genes including enzymes, transporters, G‐protein coupled receptor. Interestingly, these genes are not expressed in the neurosensory retina. Further, regardless of cause of albinism, all forms of albinism have the same retinal pathology, the extent of which is variable. In this review, we explore the possibility that this similarity in retinal phenotype is because all forms of albinism funnel through the same final common pathway. There are currently seven known genes linked to the seven forms of ocular cutaneous albinism. These types of albinism are the most common, and result in changes to all pigmented tissues (hair, skin, eyes). We will discuss the incidence and mechanism, where known, to develop a picture as to how the mutations cause albinism. Next, we will examine the one form of albinism which causes tissue‐specific pathology, ocular albinism, where the eye exhibits the retinal albinism phenotype despite near normal melanin synthesis. We will discuss a potential way to treat the disease and restore normal retinal development. Finally, we will briefly discuss the possibility that this same pathway may intersect with the most common cause of permanent vision loss in the elderly.
User Modeling Using LSTM Networks

The LSTM model presented is capable of describing a user of a particular website without human expert supervision. In other words, the model is able to automatically craft features which depict attitude, intention and the overall state of a user. This effect is achieved by projecting the complex history of the user (sequence data corresponding to his actions on the website) into fixed-size vectors of real numbers. The representation obtained may be used to enrich typical models used in e-commerce: click-through rate, conversion rate, recommender systems etc. The goal of this paper is to demonstrate a way of creating the mentioned projection, which we called user2vec, and present possible benefits of incorporating this solution to enhance conversion rate model. Thus enriched model’s superiority is due not only to its increased internal complexity but also to its capability of learning from wider data – it indirectly analyzes actions of all website users, rather than being limited to the users who clicked on an ad.
Forecasting with Fourier Residual Modified ARIMAModel-An Empirical Case of Inbound Tourism Demand in New Zealand

Tourism, one of the gigantic industries in a country, has been considered as a complexly integrated and self-contained economic activity. Key determinants of the tourism demand have not been being fully identified in literature, so varied levels of the forecast accuracy existing in distinct formations of forecasting models, such as the autoregressive integrated moving average (ARIMA) and its joint Fourier modified model, are investigated in this paper. With a certain degree of Fourier-modification factors joined, the model performance is found to be significantly boosted. In an empirical study for the inbound-tourism demand forecasting in New Zealand, the Fourier-modified seasonal ARIMA model, named FSARIMA(1 , 0 , 1 )(1 , 1 , 1 )12 , is highly recommended due to its satisfactory forecasting power for the historical data. We further employ this model to provide the New Zealand’s tourism projecting demand in 2013 so as to assist policy makers as well as related organizations in early establishing their appropriate strategies for sustaining growth in this extremely-intensified competitive industry. Key–Words: ARIMA model, Tourism demand, Fourier modification, New Zealand tourism, Tourism forecasting
Carbapenemase-Producing Carbapenem-Resistant Enterobacteriaceae from Bangkok, Thailand, and Their Detection by the Carba NP and Modified Carbapenem Inactivation Method Tests.

AIM The purpose of the study was to determine the epidemiology of carbapenemase genes among carbapenem-resistant Enterobacteriaceae and evaluate the Carba NP and modified carbapenem inactivation method (mCIM) tests in their detection. MATERIALS AND METHODS A total of 287 nonduplicated Enterobacteriaceae isolates, which were at least resistant to one of the carbapenems, were identified and detected for carbapenemase genes by multiplex PCR covering blaKPC, blaNDM, blaVIM, blaIMP, and blaOXA-48-like. All positive genes were then sequenced. These isolates were phenotypically tested for the production of carbapenemases by mCIM and Carba NP tests to evaluate the efficacy of these methods. RESULTS Seven species of carbapenem-resistant isolates mainly Klebsiella pneumoniae, Escherichia coli, and Enterobacter cloacae were detected. Of these isolates, three families of carbapenemase genes, including blaNDM (blaNDM-1, -4, -5, -9), blaOXA (blaOXA-48, -181, -232), and blaIMP-14, were found. Of these, 223 (77.70%) carried at least one of the carbapenemase genes. The blaNDM was detected in 160/223 (71.75%) isolates, of which 153/160 (95.63%) were the blaNDM-1. Three types of the blaOXA-48-like group, blaOXA-48, blaOXA-181, and blaOXA-232, were found, 91/104 (87.5%) harbored the blaOXA-232. In addition, 25.11% (56/223) of the carbapenemase-producing isolates harbored a combination of blaNDM and blaOXA-48-like. Phenotypic detection methods, mCIM and Carba NP, showed 100% sensitivity and specificity to blaNDM, blaIMP-14, and blaOXA-48, while the mCIM was positive in all blaOXA-181 and blaOXA-232 isolates, only 12.5% (1/8) and 28.95% (11/38), respectively, were detected by the Carba NP test. CONCLUSIONS This study revealed a unique prevalence of carbapenemase genes in Bangkok, Thailand, as well as demonstrated the efficacy and limitation of phenotypic detection methods of carbapenemase in the area where blaNDM-1 and blaOXA-232 were predominant.
CMOS-Memristive Analog Multiplier Design

The implementation of analog multiplication process in analog domain is a challenging task, which involves complex circuits with large on-chip area and high power consumption to achieve highly linear multiplication performance. Therefore, such multipliers cannot be used for large scale problems. This paper addresses these issues and proposes four quadrant analog CMOS-memristive analog multiplier design aiming to reduce on-chip area and power consumption of the circuit. The multiplier is designed using TSMC 180 nm CMOS technology and simulated in SPICE. The proposed multiplier allows to reduce on-chip area and power consumption by 25% and 5%, respectively.
Biophysical characterization of antibodies with isothermal titration calorimetry

Antibodies play a key role in the immune response. Since antibodies bind antigens with high specificity and tight affinity, antibodies are an important reagent in experimental biology, assay development, biomedical research and diagnostics. Monoclonal antibodies are therapeutic drugs and used for vaccine development. Antibody engineering, biophysical characterization, and structural data have provided a deeper understanding of how antibodies function, and how to make better drugs. Isothermal titration calorimetry (ITC) is a label-free binding assay, which measures affinity, stoichiometry, and binding thermodynamics for biomolecular interactions. When thermodynamic data are used together with structural and kinetic data from other assays, a complete structure-activity-thermodynamics profile can be constructed. This review article describes ITC, and discusses several applications on how data from ITC provides insights into how antibodies function, guide antibody engineering, and aid design of new therapeutic drugs.
Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem

The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD) for a set of jobs when their weights equal 1 (unweighted problem). This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithm is proposed to find approximate solutions. Through computational experiments, the heuristic algorithms’ performance is evaluated with problems up to 500 jobs.
A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: highlighting the predictive power of lactose and electrical conductivity

Sub-clinical mastitis (SCM) affects milk composition. In this study, we hypothesise that large-scale mining of milk composition features by pattern recognition models can identify the best predictors of SCM within the milk composition features. To this end, using data mining algorithms, we conducted a large-scale and longitudinal study to evaluate the ability of various milk production parameters as indicators of SCM. SCM is the most prevalent disease of dairy cattle, causing substantial economic loss for the dairy industry. Developing new techniques to diagnose SCM in its early stages improves herd health and is of great importance. Test-day Somatic Cell Count (SCC) is the most common indicator of SCM and the primary mastitis surveillance approach worldwide. However, test-day SCC fluctuates widely between days, causing major concerns for its reliability. Consequently, there would be great benefit to identifying additional efficient indicators from large-scale and longitudinal studies. With this intent, data was collected at every milking (twice per day) for a period of 2 months from a single farm using in-line electronic equipment (346 248 records in total). The following data were analysed: milk volume, protein concentration, lactose concentration, electrical conductivity (EC), milking time and peak flow. Three SCC cut-offs were used to estimate the prevalence of SCM: Australian ≥ 250 000 cells/ml, European ≥200 000 cells/ml and New Zealand ≥ 150 000 cells/ml. At first, 10 different Attribute Weighting Algorithms (AWM) were applied to the data. In the absence of SCC, lactose concentration featured as the most important variable, followed by EC. For the first time, using attribute weighted modelling, we showed that the concentration of lactose in milk can be used as a strong indicator of SCM. The development of machine-learning expert systems using two or more milk variables (such as lactose concentration and EC) may produce a predictive pattern for early SCM detection.
Dental pain and oral health-related quality of life in individuals with severe dental anxiety

Abstract Objectives: To analyze the prevalence and level of dental pain among adult individuals with severe dental anxiety (DA), and the association between dental pain and oral health-related quality of life (OHRQoL). Methods: The study was based on 170 adult individuals with DA referred to a specialized DA clinic. All patients answered a questionnaire including questions on DA (DAS, DFS), OHRQoL (OIDP) and dental pain. An adapted clinical examination and a panoramic radiograph revealed the present oral status. Results: The prevalence of dental pain was high (77.6%) and among those reporting pain the intensity was high (49.0–61.0 on a VAS). One or more problems during the last 6 months with the mouth or teeth affecting the individual’s daily activities were reported in 85.3% of the participants. Individuals who reported dental pain had lower OHRQoL compared with those who did not report dental pain (p < .001). Dental pain and number of decayed teeth were associated with the probability of poor OHRQoL (OR = 8.2, p < .001, OR = 3.8, p = .008). Conclusion: This study revealed a high prevalence and a high level of dental pain among adult individuals with severe DA. Having dental pain was associated with poor OHRQoL.
Morphological analysis using a sequence decoder

We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder generates the sequence of characters for the lemma followed by a sequence of individual morphological features. We show that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and outperform whole-tag models. In addition, generating morphological features as a sequence rather than e.g.\ an unordered set allows our model to produce an arbitrary number of features that represent multiple inflectional groups in morphologically complex languages. We obtain state-of-the art results in nine languages of different morphological complexity under low-resource, high-resource and transfer learning settings. We also introduce TrMor2018, a new high accuracy Turkish morphology dataset. Our Morse implementation and the TrMor2018 dataset are available online to support future research\footnote{See \url{https://github.com/ai-ku/Morse.jl} for a Morse implementation in Julia/Knet \cite{knet2016mlsys} and \url{https://github.com/ai-ku/TrMor2018} for the new Turkish dataset. }.
Assessing the Competency and Acceptability of Community Health Worker Provision of Standard Days Method® in Family Planning Services in Gisagara District, Rwanda

Abstract This study assesses the competency and acceptability of community‐based provision of Standard Days Method® (SDM) to first‐time users in Rwanda. The national strategy equips community health workers (CHWs) to resupply pills, injectables and condoms to existing clients. With the aim of expanding access, SDM provision to first‐time users was added to the method mix in Gisagara district and assessed with a 12 month prospective, mixed methods study. Thirty percent of SDM clients had never used a method of family planning and 58 percent had not been using a method for at least three months. Eighty‐seven percent of CHWs correctly screened clients to use SDM and 92 percent accurately explained how to use CycleBeads to prevent pregnancy. After being counseled by the CHWs, 89 percent of clients reported knowledge of all key steps required in using SDM to prevent pregnancy. Nearly all SDM clients (99 percent) believed that CHWs were able to counsel them adequately. These results suggest that CHWs were able to offer SDM as part of their family planning responsibilities, and the study adds to the evidence on the role of CHWs in expanding contraceptive access and choice.
Binary Space Partitions for Axis-Aligned Fat Rectangles

It is shown that for any $n$ disjoint axis-aligned fat rectangles in three-space there is a binary space partition (BSP) of $O(n\log^8 n)$ size and $O(\log^5 n)$ height and it can be constructed in $O(n \,\mathrm{polylog}\, n)$ time. This improves earlier bounds of Agarwal et al. [SIAM J. Comput. , 29 (2000), pp. 1422-1448]. On the other hand, for every $n\in \mathbb{N}$, there are $n$ disjoint axis-aligned fat rectangles in $\mathbb{R}^3$ such that their smallest axis-aligned BSP has $\Omega(n\log n)$ size.
A Variable Neighborhood Search for Flying Sidekick Traveling Salesman Problem

An innovative model of parcel distribution is emerging from the accelerated evolution of drones and the effort of logistic companies to proceed faster deliveries at a reduced cost. This new modality originated the Flying Sidekick Traveling Salesman Problem (FSTSP) in which customers are served either by a truck or a drone. Additionally, this variant of the Traveling Salesman Problem (TSP) presents several new restrictions concerning the drone such as endurance and payload capacity. This work proposes a hybrid heuristic that the initial solution is created from the optimal TSP solution reached by a Mixed-Integer Programming (MIP) solver. Next, an implementation of the General Variable Neighborhood Search is used to obtain the delivery routes of truck and drone. Computational experiments show the potential of the algorithm to improve the total delivery time up to 67.79%. New best-known solutions (BKS) are established for all FSTSP instances that results are reported in the literature. Furthermore, a new set of instances based on well-known TSPLIB instances is provided.
Data Driven Models of Short-Term Synaptic Plasticity

Simple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of representation of the postsynaptic response. While this approach has proven useful in many contexts, for the purpose of determining the type of process underlying a stochastic output train, it ignores the ordering of the responses, leaving an important characterizing feature on the table. In this paper we use a broader class of information measures on output only, and specifically construct hidden Markov models (HMMs) (known as epsilon machines or causal state models) to differentiate between synapse type, and classify the complexity of the process. We find that the machines allow us to differentiate between processes in a way not possible by considering distributions alone. We are also able to understand these differences in terms of the dynamics of the model used to create the output response, bringing the analysis full circle. Hence this technique provides a complimentary description of the synaptic filtering process, and potentially expands the interpretation of future experimental results.
Chemogenetic Enhancement of Axon Regeneration Following Peripheral Nerve Injury in the SLICK-A Mouse

The effects of chemogenetics on axon regeneration following peripheral nerve transection and repair were studied in mice expressing a Cre-dependent excitatory designer receptor exclusively activated by designer drugs (DREADD) and Cre-recombinase/yellow fluorescent protein (YFP) in a subset of motor and sensory neurons and cortical motoneurons (SLICK-A). Sciatic nerves were cut and repaired and mice were treated either once, at the time of injury, or five days per week for two weeks with clozapine N-oxide (CNO) (1 mg/kg, i.p.), or were untreated controls. Two weeks after injury, the lengths of YFP+ axon profiles were measured in nerves harvested from euthanized animals. Compared to untreated controls, regenerating axon lengths were not significantly longer in mice treated only once with CNO, but they were more than three times longer in mice receiving CNO repeatedly. Based on results of retrograde labeling experiments, axons of more sensory and motor neurons had regenerated successfully in mice receiving multiple CNO treatments than animals receiving only one treatment or no treatments. The increase in numbers of labeled sensory, but not motor neurons could be accounted for by increases in the proportion of retrogradely labeled neurons also expressing the DREADD. Chemogenetic increases in neuronal excitability represent a potent and innovative treatment to promote peripheral nerve regeneration.
Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications

The recent announcement of the production of new low-cost continuous glucose monitoring (CGM) sensors, the approval of marketed CGM sensors for making treatment decisions, and new reimbursement criteria have the potential to revolutionize CGM use. After briefly summarizing current CGM applications, we discuss how, in our opinion, these changes are expected to extend CGM utilization beyond diabetes patients, for example, to subjects with prediabetes or even healthy individuals. We also elaborate on how the integration of CGM data with other relevant information, for example, health records and other medical device/wearable sensor data, will contribute to creating a digital data ecosystem that will improve our understanding of the etiology and complications of diabetes and will facilitate the development of data analytics for personalized diabetes management and prevention.
A Study of Data Layout in Multi-channel Processing-In-Memory Architecture

In modern computing hardware, the performance gap between processor and memory is one of the most significant factors that limits overall performance improvement of computing system. Also, with the advent of multicore and manycore system, memory bandwidth per core is decreasing constantly. To solve this problem, recently, many researchers are interested in Processing-In-Memory (PIM). PIM is that processing elements are attached to memory-side, so near-memory-processing which is suitable for memory intensive application can be possible. Various researches studied PIM, but it was just single-channel memory system. In addition, PIM is a new architecture that is different with conventional computing system. Thus, common data layout cannot become optimal case for PIM. Optimal data layout is also needed to be studied. In this paper, we propose the multi-channel PIM architecture with PIM-to-PIM communication, because data that is needed to operate can be distributed over several channels. To utilize multi-channel PIM architecture properly, we also introduce data layout that can minimize the number of PIM-to-PIM communications which are overheads of the system and maximize parallelism to reduce execution time. We evaluate it about vector arithmetic operation. The result is that execution time is improved about 393% and compared to the worst case, in the optimal data layout.
Co-Occurrence of Posttraumatic Stress Disorder and Cardiovascular Disease Among Ethnic/Racial Groups in the United States

Objective Trauma and/or symptoms of posttraumatic stress disorder (PTSD) have been linked to the onset of cardiovascular disease (CVD), but the exact mechanism has not been determined. We examine whether the risk of CVD is different among those who have a history of trauma without PTSD symptoms, those who have experienced trauma and developed any symptoms of PTSD, and those with a PTSD diagnosis. Furthermore, we examine whether this association varies across ethnic/racial groups. Methods We used two data sets that form part of the Collaborative Psychiatric Epidemiology Surveys - the National Latino and Asian American Study and the National Comorbidity Survey Replication. Results We found an increased likelihood of cardiovascular events for those with a diagnosis of PTSD (odds ratio [OR] = 2.10, 95% CI = 1.32–3.33) when compared with those who had not experienced trauma. We did not find an increased risk for those who had experienced trauma without symptoms or with subclinical symptoms of PTSD. The higher likelihood of having a cardiovascular event in those with PTSD was significant for non-Latino whites (OR = 1.86, 95% CI = 1.08–3.11), Latinos (OR = 1.94, 95% CI = 1.04–3.62), and non-Latino blacks (OR = 3.73, 95% CI = 1.76–7.91), but not for Asian respondents. Conclusions The constellation of symptoms defining PTSD diagnosis reflect adverse reactions to traumatic events and indicate that complex responses to traumatic events may be a risk factor for CVD.