Miracle-dz/UniFAR-FTUs huggingface dataset:
Fetal Cells in Maternal Blood

Prenatal genetic diagnosis has advanced immensely over the last several decades. In the 1950s. genetic counseling was the only modality available. Couples could be provided recurrence risk figures for the few recognized Mendelian conditions, but otherwise no diagnosis could be made, and no intervention was possible. This changed abruptly in 1968 when diagnosis in utero became possible through amniocentesis. In 1982 to 1984, first-trimester prenatal diagnosis was shown to be feasible through chorionic villus sampling, a first-trimester procedure later shown to be as safe and effective as amniocentesis. ' Despite these notable advances, shortcomings remain in prenatal genetic diagnosis. Specifically, both chorionic villus sampling and amniocentesis are invasive procedures that carry a low but finite risk of pregnancy loss. Therefore, these tests cannot be offered to the entire population, but rather only to women at increased risk for genetic disorders. Examples include those with previously affected offspring (e.g., with a Mendelian trait) or advanced maternal age (235 years at delivery). Noninvasive methods would be highly desirable. Methods currently available to clinical geneticists include ultrasonographic assessment and maternal serum analyte screening. Unfortunately, noninvasive tests lack the sensitivity of invasive tests. Although certain studies have suggested that a relatively high proportion of structural anomalies are detectable by ultrasound, this did not prove to be the case in a large, recent, randomized trial of low-risk women in the United States. In this trial,* 15,151 low-risk patients were randomized between those receiving routine ultrasound and those receiving ultrasound only for specific indications. In the indicated ultrasound arm, 35% of anomalies were detected, whereas only 11% were detected in the control (routine ultrasound) arm. Unfortunately, only slightly less than half the anomalies detected were recognized earlier than 24 weeks of gestation (i.e., 31 out of 187 in the routine ultrasound arm). Pregnancy outcome was thus not often altered. Maternal serum analyte screening is also less than optimal. Screening in women less than 35 years of age will detect perhaps 60% of Down syndrome cases, although the rate rises to approximately 90% in women over the age of 35 years.3 Because neither ultrasound nor serum screening approaches the sensitivity of invasive screening, attention has continued to focus on the idea of recovering fetal
Traversing the Continuous Spectrum of Image Retrieval with Deep Dynamic Models

We introduce the first work to tackle the image retrieval problem as a continuous operation. While the proposed approaches in the literature can be roughly categorized into two main groups: category- and instance-based retrieval, in this work we show that the retrieval task is much richer and more complex. Image similarity goes beyond this discrete vantage point and spans a continuous spectrum among the classical operating points of category and instance similarity. However, current retrieval models are static and incapable of exploring this rich structure of the retrieval space since they are trained and evaluated with a single operating point as a target objective. Hence, we introduce a novel retrieval model that for a given query is capable of producing a dynamic embedding that can target an arbitrary point along the continuous retrieval spectrum. Our model disentangles the visual signal of a query image into its basic components of categorical and attribute information. Furthermore, using a continuous control parameter our model learns to reconstruct a dynamic embedding of the query by mixing these components with different proportions to target a specific point along the retrieval simplex. We demonstrate our idea in a comprehensive evaluation of the proposed model and highlight the advantages of our approach against a set of well-established discrete retrieval models.
Segment-Specific Kinetics of mRNA, cRNA, and vRNA Accumulation during Influenza Virus Infection

Influenza A virus (IAV) is a respiratory pathogen that has caused significant mortality throughout history and remains a global threat to human health. Although much is known about IAV replication, the regulation of IAV replication dynamics is not completely understood. ABSTRACT Influenza A virus (IAV) is a segmented negative-sense RNA virus and is the cause of major epidemics and pandemics. The replication of IAV is complex, involving the production of three distinct RNA species, namely mRNA, cRNA, and viral RNA (vRNA), for all eight genome segments. While understanding IAV replication kinetics is important for drug development and improving vaccine production, current methods for studying IAV kinetics have been limited by the ability to detect all three different RNA species in a scalable manner. Here, we report the development of a novel pipeline using total stranded RNA sequencing (RNA-Seq), which we named influenza virus enumerator of RNA transcripts (InVERT), that allows for the simultaneous quantification of all three RNA species produced by IAV. Using InVERT, we provide a full landscape of the IAV replication kinetics and found that different groups of viral genes follow different kinetics. The segments coding for RNA-dependent RNA polymerase (RdRP) produced more vRNA than mRNA, while some other segments (NP, NS, and hemagglutinin [HA]) consistently made more mRNA than vRNA. vRNA expression levels did not correlate with cRNA expression, suggesting complex regulation of vRNA synthesis. Furthermore, by studying the kinetics of a virus lacking the capacity to generate new polymerase complexes, we found evidence that further supports a model in which cRNA synthesis requires newly synthesized RdRP and that incoming RdRP can only generate mRNA. Overall, InVERT is a powerful tool for quantifying IAV RNA species to elucidate key features of IAV replication. IMPORTANCE Influenza A virus (IAV) is a respiratory pathogen that has caused significant mortality throughout history and remains a global threat to human health. Although much is known about IAV replication, the regulation of IAV replication dynamics is not completely understood. This is due in part to both technical limitations and the complicated replication of the virus, which has a segmented genome and produces three distinct RNA species for each gene segment. We developed a new approach that allows the methodical study of IAV replication kinetics, shedding light on many interesting features of IAV replication biology. This study advances our understanding of the kinetics of IAV replication and will help to facilitate future research in the field.
Lattice homology, formality, and plumbed L-space links

We define a link lattice complex for plumbed links, generalizing constructions of Ozsv\'ath, Stipsicz and Szab\'o, and of Gorsky and N\'emethi. We prove that for all plumbed links in rational homology 3-spheres, the link lattice complex is homotopy equivalent to the link Floer complex as an $A_\infty$-module. Additionally, we prove that the link Floer complex of a plumbed L-space link is a free resolution of its homology. As a consequence, we give an algorithm to compute the link Floer complexes of plumbed L-space links, in particular of algebraic links, from their multivariable Alexander polynomial.
Learning Multi-Attention Context Graph for Group-Based Re-Identification

Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. However, most existing methods focus on (single) person re-identification (re-id), ignoring the fact that people often walk in groups in real scenarios. In this work, we take a step further and consider employing context information for identifying groups of people, i.e., group re-id. On the one hand, group re-id is more challenging than single person re-id, since it requires both a robust modeling of local individual person appearance (with different illumination conditions, pose/viewpoint variations, and occlusions), as well as full awareness of global group structures (with group layout and group member variations). On the other hand, we believe that person re-id can be greatly enhanced by incorporating additional visual context from neighboring group members, a task which we formulate as group-aware (single) person re-id. In this paper, we propose a novel unified framework based on graph neural networks to simultaneously address the above two group-based re-id tasks, i.e., group re-id and group-aware person re-id. Specifically, we construct a context graph with group members as its nodes to exploit dependencies among different people. A multi-level attention mechanism is developed to formulate both intra-group and inter-group context, with an additional self-attention module for robust graph-level representations by attentively aggregating node-level features. The proposed model can be directly generalized to tackle group-aware person re-id using node-level representations. Meanwhile, to facilitate the deployment of deep learning models on these tasks, we build a new group re-id dataset which contains more than <inline-formula><tex-math notation="LaTeX">$3.8K$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>. </mml:mo><mml:mn>8</mml:mn><mml:mi>K</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="qin-ieq1-3032542.gif"/></alternatives></inline-formula> images with <inline-formula><tex-math notation="LaTeX">$1.5K$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>5</mml:mn><mml:mi>K</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="qin-ieq2-3032542.gif"/></alternatives></inline-formula> annotated groups, an order of magnitude larger than existing group re-id datasets. Extensive experiments on the novel dataset as well as three existing datasets clearly demonstrate the effectiveness of the proposed framework for both group-based re-id tasks.
Data mining and automatic OLAP schema generation

Data mining aims at extraction of previously unidentified information from large databases. It can be viewed as an automated application of algorithms to discover hidden patterns and to extract knowledge from data. Online Analytical Processing (OLAP) systems, on the other hand, allow exploring and querying huge datasets in interactive way. These OLAP systems are the predominant front-end tools used in data warehousing environments and the OLAP system's market has developed rapidly during the last few years. Several works in the past emphasized the integration of OLAP and data mining. More recently, data mining techniques along with OLAP have been applied in decision support applications to analyze large data sets in an efficient manner. However, in order to integrate data mining results with OLAP the data has to be modeled in a particular type of OLAP schema. An OLAP schema is a collection of database objects, including tables, views, indexes and synonyms. Schema generation process was considered a manual task but in the recent years research communities reported their work in automatic schema generation. In this paper, we reviewed literature on the schema generation techniques and highlighted the limitations of the existing works. The review reveals that automatic schema generation has never been integrated with data mining. Hence, we propose a model for data mining and automatic schema generation of three types namely star, snowflake, and galaxy. Hierarchical clustering technique of data mining was used and schema from the clustered data was generated. We have also developed a prototype of the proposed model and validated it via experiments of real-life data set. The proposed model is significant as it supports both integration and automation process.
Some Joules Are More Precious Than Others: Managing Renewable Energy in the Datacenter∗

Large cloud-computing datacenters now host a wide range of business applications, ranging from e-commerce websites to search engines to data mining. Increasingly, these datacenters use renewable energy from wind turbines or solar panels to reduce their dependence on costly and less clean energy from the grid [17, 4, 1]. This paper argues that datacenter management should be revised to maximize the use of such off-grid renewables.1 Our argument is similar in spirit to the case for energyefficient management [18, 7, 11]. But efficiency alone may not take full advantage of renewables. For instance, energy-efficient management may batch writes so disks can sleep for long durations. But sometimes renewables are intermittently unavailable, long sleeps may miss opportunities to use them. Operating systems and cluster management software already manage energy, CPU cycles, and other low-level resources. To be sure, many past techniques will probably carry over to renewables. However, unlike other managed resources, renewables are available only when the wind blows or the sun shines. Such intermittency makes it hard for datacenters to use renewables effectively. For example, windy nights that follow calm afternoons would produce renewables only during traditionally light workload times for datacenters [15, 2]. In the first part of this paper, we model a datacenter that gets power from intermittent wind renewables. Our re-
Carotid Artery Calcification: A Digital Panoramic-Based Study

Objective: The aim of this study was to estimate the incidence of carotid artery calcification (CAC) in a sample of Lebanese population using digital panoramic radiographs. Materials and Methods: Panoramic radiographs of 500 patients (281 females and 219 males), aged between 18 and 88 years (mean: 47.9 years), were assessed for CAC. Data collected were analyzed statistically using IBM® SPSS® for Windows version 20.0 (SPSS, Chicago, IL, USA). Results: CAC were found in 34 cases (6.8%), among them, 23 females (8.18%) and 11 males (5.02%). Six of all the calcifications were on the right side, against six on the left side, and 22 on both sides. The mean age of patients affected with CAC was 60.9 years (ranging from 18 to 88 years). Chi-square test showed no statistical significance between gender and CAC, while Spearman correlation analysis showed positive low correlation with age (r = 0.179). Conclusion: CAC can be found on routine panoramic radiographs taken in dental clinics; dentists should automatically refer the patients in question for specialized medical evaluation.
Microfluidics meets MEMS

The use of planar fluidic devices for performing small-volume chemistry was first proposed by analytical chemists, who coined the term "miniaturized total chemical analysis systems" (/spl mu/TAS) for this concept. More recently, the /spl mu/TAS field has begun to encompass other areas of chemistry and biology. To reflect this expanded scope, the broader terms "microfluidics" and "lab-on-a-chip" are now often used in addition to /spl mu/TAS. Most microfluidics researchers rely on micromachining technologies at least to some extent to produce microflow systems based on interconnected micrometer-dimensioned channels. As members of the microelectromechanical systems (MEMS) community know, however, one can do more with these techniques. It is possible to impart higher levels of functionality by making features in different materials and at different levels within a microfluidic device. Increasingly, researchers have considered how to integrate electrical or electrochemical function into chips for purposes as diverse as heating, temperature sensing, electrochemical detection, and pumping. MEMS processes applied to new materials have also resulted in new approaches for fabrication of microchannels. This review paper explores these and other developments that have emerged from the increasing interaction between the MEMS and microfluidics worlds.
A Rule Restatement of Yurok Allomorphy

This may be an appropriate occasion to consider a more concise, rule-based statement of the occurrence of the prefixes (? )weand ? uas third-person markers in nouns and verbs in Yurok, a language of north California. Their syntactic functions and their meanings have been treated already in Robins (1958; 1962). The choice between these two allomorphs is wholly predictable phonologically. Hitherto their distribution has been stated by the simple listing of their environments: (? )weoccurs before t, t' c, c' n, 1, f, s, s, r, y, h, 2 except in the sequence 2.1 vuoccurs before p, k, k' k", m, w, w (p' and k'", which would clearly belong to this set, are not recorded in root-initial position). The question arises of how to state this distribution in terms of rules and features. Two sets of features are currently available, the Jakobsonian acoustically defined features set out in Jakobson et al. (1952; 1956) and the articulatorily based features proposed by Chomsky and Halle (1968) and further treated by Schane(1973). Since (2)weoccurs in the majority of environments and is in paradigmatic association with the firstand second-person prefixes (2)neand k'e-, where no such allomorphy is found, the rule is best set out as 2we-u ? -, irrespective of a possible diachronic reconstruction (Robins 1962:16-18). The deletion of ? in (2)weafter pause or a consonant, of e before root-initial h (and the subsequent deletion of h), except in roots beginning with hi and hi:, and certain other more general morpheme and word structure rules need not be discussed here (Robins 1958:9-10, 26). Two questions have to be answered: the identification of the controlling features and the different effects of ? and ? w. Jakobson's [+grave] covers all environments in which the rule operates except for ? w, since in all other consonant sequences only the first consonant is relevant
Epidemiology of Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is rapidly becoming the most common liver disease worldwide. The prevalence of NAFLD in the general population of Western countries is 20–30%. About 2–3% of the general population is estimated to have non-alcoholic steatohepatitis (NASH), which may progress to liver cirrhosis and hepatocarcinoma. As a rule, the prevalence of NAFLD is higher in males and increases with increasing age, and it is influenced by the diagnostic method and the characteristics of the population, especially lifestyle habits. Population-based studies provide better estimates of the prevalence of NAFLD as compared to autoptic and clinical studies, but few such studies have been performed to date. The diagnosis of NAFLD in population studies is usually obtained by ultrasonography, which is known to underestimate the prevalence of fatty liver. The Dallas Heart Study and the Dionysos Study reported that 30% of the adults in the USA and 25% in Italy have NAFLD. In these studies, 79% and 55% of patients with NAFLD had normal aminotransferase levels, showing that liver enzymes are not surrogate markers of NAFLD in the general population. Noninvasive markers such as the fatty liver index obtained from the Dionysos Study may be useful to screen for NAFLD in the general population. The most important risk factors for NAFLD are male gender, age, obesity, insulin resistance and the cardiometabolic alterations that define the metabolic syndrome. The prevalence of NAFLD is 80–90% in obese adults, 30–50% in patients with diabetes and up to 90% in patients with hyperlipidemia. The prevalence of NAFLD among children is 3–10%, rising up to 40–70% among obese children. Moreover, pediatric NAFLD increased from about 3% a decade ago to 5% today, with a male-to-female ratio of 2:1. The incidence and natural history of NAFLD are still not well defined, but it is recognized that the majority of individuals with NAFLD do not develop NASH. The incidence of NAFLD is probably increasing in Western countries, strictly linked to lifestyle habits.
The generation of theta rhythm in hippocampal formation maintained in vitro

The most spectacular example of oscillations and synchrony which appear in the brain is the rhythmic slow activity (theta) of the limbic cortex. Theta rhythm is the best synchronized electroencephalographic activity that can be recorded from the mammalian brain. Hippocampal formation is considered to be the main structure involved in the generation of this activity. Although detailed studies of the physiology and pharmacology of theta‐band oscillations have been carried out since the early 1950s, the first demonstration of atropine‐sensitive theta rhythm, recorded in completely deafferented hippocampal slices of a rat, was performed in the second half of the 1980s. Since the discovery of cholinergically induced in vitro theta rhythm recorded from hippocampal formation slices, the central mechanisms underlying theta generation have been successfully studied in in vitro conditions. Most of these experiments were focused on the basic question regarding the similarities between the cholinergically induced theta activity and theta rhythm examined in vivo. The results of numerous in vitro experiments strongly suggest that cholinergically induced theta rhythm recorded in hippocampal slices is a useful analogue of theta observed in intact animals, and could be helpful in searching for the mechanisms of oscillations and synchrony in the central nervous system neuronal networks. The objective of the present review is to discuss the main results of experiments concerning theta oscillations recorded in in vitro conditions. It is our intent to provide, on the basis of these results, the characteristics of essential mechanisms underlying the generation of atropine‐sensitive in vitro theta.
Detection of Cyberattacks and Anomalies in Cyber-Physical Systems: Approaches, Data Sources, Evaluation

Cyberattacks on cyber-physical systems (CPS) can lead to severe consequences, and therefore it is extremely important to detect them at early stages. However, there are several challenges to be solved in this area; they include an ability of the security system to detect previously unknown attacks. This problem could be solved with the system behaviour analysis methods and unsupervised or semi-supervised machine learning techniques. The efficiency of the attack detection system strongly depends on the datasets used to train the machine learning models. As real-world data from CPS systems are mostly not available due to the security requirements of cyber-physical objects, there are several attempts to create such datasets; however, their completeness and validity are questionable. This paper reviews existing approaches to attack and anomaly detection in CPS, with a particular focus on datasets and evaluation metrics used to assess the efficiency of the proposed solutions. The analysis revealed that only two of the three selected datasets are suitable for solving intrusion detection tasks as soon as they are generated using real test beds; in addition, only one of the selected datasets contains both network and sensor data, making it preferable for intrusion detection. Moreover, there are different approaches to evaluate the efficiency of the machine learning techniques, that require more analysis and research. Thus, in future research, the authors aim to develop an approach to anomaly detection for CPS using the selected datasets and to conduct experiments to select the performance metrics.