Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of carcinoid syndrome symptoms and long-term use of somatostatin analogs on quality of life in patients with carcinoid syndrome

Abstract To evaluate association of carcinoid syndrome (CS) symptom burden and somatostatin analog (SSA) duration with quality of life (QoL) using Functional Assessment of Cancer Therapy-General (FACT-G) and Patient-Reported Outcomes Measurement Information System (PROMIS-29) instruments. Adults who received treatment for CS symptoms in the US were recruited to participate in a cross-sectional online survey (July–October, 2016). Demographic, clinical, and QoL questions (FACT-G, 29 CS-related supplemental questions, PROMIS-29) were included. Descriptive and multivariable regression analyses adjusting for demographic and clinical characteristics followed. Most (98%) of the 117 patients received SSAs in the prior month. Multivariable regression analysis showed ≥4 bowel movements/day (vs <4) and each additional CS symptom was associated with 7.1 (P = .043) and 3.4 (P = .034) point FACT-G total score decreases, respectively. Requiring bed rest (vs normal activity) was associated with significant decreases in FACT-G total score (P < .001). There were similar associations for FACT-G subscales, supplemental questions, and PROMIS-29. After adjustment, FACT-G total score was significantly higher (11.3 points; P = .033) for patients treated with SSA >8 years versus <2.7 years. CS symptom burden was observed to be associated with lower QoL scores, measured by FACT-G. Patients with >8 years SSA treatment duration versus <2.7 years had higher QoL.
John A. Galloway, MD, FACP: A Pharmaceutical Physician and Leader in Diabetes Therapy

John A. Galloway, MD, FACP. There have been extraordinary individuals active in patient care, clinical research, professional and patient organizations, and pharmaceutical science who devoted their careers to constantly focusing on and campaigning for “what is best for the patient. ” Dr. John A. Galloway has been that leader, whose clinical curiosity, insights, dedication, and commitment to others brought insulin therapy from the impurified, low-potency animal insulins to the modern insulins of today. Born in Omaha, Nebraska, in 1928, Galloway moved to Washington, DC, and graduated from St. Albans School in 1946 and then from the University of Pennsylvania in Philadelphia in 1950 with a B.A. in English and Pre-Med. After 2 years in the U.S. Army and serving in combat in Korea, 1st Lt. Galloway was released to start medical school at the University of Nebraska College of Medicine in Omaha, where he gained his MD in 1956. Upon completion of his internship, he began his residency in internal medicine at Temple University Hospital in Philadelphia, serving as Chief Medical Resident from 1959–1960. This was followed by a Fellowship in Diabetes, Endocrinology and Nuclear Medicine with Charles R. Shuman, MD, at Temple, where Galloway and Shuman published the first report in the medical literature on the use of computers to analyze large volumes of clinical data in a study of surgery in patients with diabetes (1). After a report in the Journal of Clinical Investigation by Solomon A. Berson, MD, and Rosalyn S. Yalow, PhD, a physicist, on a method they had developed for measuring insulin in the blood using immunologic techniques (the radioimmunoassay or RIA) (2), Shuman arranged for Galloway to attend the first workshop that Berson and Yalow gave on how to perform their assay. Also selected to attend were Lisa G. Heding from Novo Laboratory (now …
Integrating Aerial and Street View Images for Urban Land Use Classification

Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.
Open Targets Platform: new developments and updates two years on

Abstract The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The associations are displayed in an intuitive user interface (https://www.targetvalidation.org), and are available through a REST-API (https://api.opentargets.io/v3/platform/docs/swagger-ui) and a bulk download (https://www.targetvalidation.org/downloads/data). In addition to target-disease associations, we also aggregate and display data at the target and disease levels to aid target prioritisation. Since our first publication two years ago, we have made eight releases, added new data sources for target-disease associations, started including causal genetic variants from non genome-wide targeted arrays, added new target and disease annotations, launched new visualisations and improved existing ones and released a new web tool for batch search of up to 200 targets. We have a new URL for the Open Targets Platform REST-API, new REST endpoints and also removed the need for authorisation for API fair use. Here, we present the latest developments of the Open Targets Platform, expanding the evidence and target-disease associations with new and improved data sources, refining data quality, enhancing website usability, and increasing our user base with our training workshops, user support, social media and bioinformatics forum engagement.
Quantification of somatic mutation flow across individual cell division events by lineage sequencing

Mutation data reveal the dynamic equilibrium between DNA damage and repair processes in cells and are indispensable to the understanding of age-related diseases, tumor evolution, and the acquisition of drug resistance. However, available genome-wide methods have a limited ability to resolve rare somatic variants and the relationships between these variants. Here, we present lineage sequencing, a new genome sequencing approach that enables somatic event reconstruction by providing quality somatic mutation call sets with resolution as high as the single-cell level in subject lineages. Lineage sequencing entails sampling single cells from a population and sequencing subclonal sample sets derived from these cells such that knowledge of relationships among the cells can be used to jointly call variants across the sample set. This approach integrates data from multiple sequence libraries to support each variant and precisely assigns mutations to lineage segments. We applied lineage sequencing to a human colon cancer cell line with a DNA polymerase epsilon (POLE) proofreading deficiency (HT115) and a human retinal epithelial cell line immortalized by constitutive telomerase expression (RPE1). Cells were cultured under continuous observation to link observed single-cell phenotypes with single-cell mutation data. The high sensitivity, specificity, and resolution of the data provide a unique opportunity for quantitative analysis of variation in mutation rate, spectrum, and correlations among variants. Our data show that mutations arrive with nonuniform probability across sublineages and that DNA lesion dynamics may cause strong correlations between certain mutations.
Overfishing and Climate Drives Changes in Biology and Recruitment of the Indian Oil Sardine Sardinella longiceps in Southeastern Arabian Sea

The recent fluctuations in abundance of the Indian oil sardine Sardinella longiceps, a tropical small pelagic clupeid fish, was investigated in the light of overfishing and variations in its habitat ecology in southeastern Arabian Sea. In 2012, its landings peaked to an all-time record making it the fifth largest sardine fishery in the world, and within 3 years the catches were reduced to nearly a tenth of that level. This study examined the fishery dependant factors such as effort, catch rates and expansion of fishing area; the biological variations in fish size, maturity and recruitment; and tried to relate this to the environmental variations in the sardine habitat and food availability. The 2012 mega harvest was a result of a 2-time increase in gear size and engine capacity of fishing crafts and a 3.7-time increase in fishing effort. The female maturation process was strongly influenced primarily by rainfall and then by upwelling and the resulting influx of cold nutrient-rich water in the habitat from April much before the start of the monsoon in June. After 2013, the weak monsoons and the 2015 El Nino Southern Oscillation resulted in a warmer (by an average of 1.1°C) period which negatively impacted the maturation process. The abundance of jellyfishes which are larval and young fish predators in the habitat negatively affected recruitment after 2013. The mismatch in timing of phytoplankton productivity and sardine larvae in the habitat also affected the recruitment success. These environmental divergences coupled with the excessive capture (beyond maximum sustainable yields) of spawning stock and juveniles from 2010 has resulted in this biological catastrophe which has affected the livelihood of thousands of small-scale fishers. A more responsive fisheries administration with timely restriction on fishing effort and protection of spawning stocks by way of fishery closure would have helped minimize the impacts.
Quantitative fluorescence imaging determines the absolute number of locked nucleic acid oligonucleotides needed for suppression of target gene expression

Abstract Locked nucleic acid based antisense oligonucleotides (LNA-ASOs) can reach their intracellular RNA targets without delivery modules. Functional cellular uptake involves vesicular accumulation followed by translocation to the cytosol and nucleus. However, it is yet unknown how many LNA-ASO molecules need to be delivered to achieve target knock down. Here we show by quantitative fluorescence imaging combined with LNA-ASO microinjection into the cytosol or unassisted uptake that ∼105 molecules produce >50% knock down of their targets, indicating that a substantial amount of LNA-ASO escapes from endosomes. Microinjected LNA-ASOs redistributed within minutes from the cytosol to the nucleus and remained bound to nuclear components. Together with the fact that RNA levels for a given target are several orders of magnitude lower than the amounts of LNA-ASO, our data indicate that only a minor fraction is available for RNase H1 mediated reduction of target RNA. When non-specific binding sites were blocked by co-administration of non-related LNA-ASOs, the amount of target LNA-ASO required was reduced by an order of magnitude. Therefore, dynamic processes within the nucleus appear to influence the distribution and activity of LNA-ASOs and may represent important parameters for improving their efficacy and potency.
Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR

High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy.
Heterocyclic electrophiles as new MurA inhibitors

An electrophilic fragment library of small heterocycles was developed and characterized in the surrogate GSH‐reactivity assay and aqueous stability test that revealed their potential as covalent warheads. Screening the library against MurA from Staphylococcus aureus (MurASA) and Escherichia coli (MurAEC) identified heterocyclic fragments with significant inhibitory potency. The validated heterocyclic warhead library might be useful for developing targeted covalent inhibitors for other targets of interest with a new design strategy incorporating heterocyclic electrophiles as warheads.
Reducing postoperative morbidity of mini-invasive percutaneous nephrolithotomy

Background: The aim of this study was to provide a randomized controlled trial comparing single B-mode ultrasound guidance and color doppler ultrasound guidance in minimally invasive percutaneous nephrolithotomy. Methods: Three hundred patients with renal calculus were prospectively randomly assigned into 2 groups. In group 1 (150 patients), minimally invasive percutaneous nephrolithotomy (m-PCNL) were managed with single B-mode ultrasound guidance; In group 2 (150 patients), m-PCNL were managed with color Doppler ultrasound guidance and a needle bracket in order to guide placement at a target location beneath the skin. The characteristics of patients, operation, complications and prognosis, including body temperature, urine culture, and hematologic tests after the operation were recorded and compared. Results: Our vessel-sparing technique showed a statistically significant decrease in hemoglobin drop, postoperative procalcitonin values, the frequency of postoperative fever, systemic inflammatory response syndrome, and urosepsis (P < .05). Conclusion: Using color Doppler ultrasound in real time and a needle bracket to detect and avoid main renal blood vessels decreased incidences of hemorrhagic complications and postoperative infection.
Two New Polarimetric Feature Parameters for the Recognition of the Different Kinds of Buildings in Earthquake-Stricken Areas Based on Entropy and Eigenvalues of PolSAR Decomposition

Rapidly and accurately obtaining collapsed building information for earthquake-stricken areas can help to effectively guide the implementation of the emergency response and can reduce disaster losses and casualties. This work is focused on rapid building earthquake damage detection in urban areas using a single post-earthquake polarimetric synthetic aperture radar (PolSAR) image. In an earthquake-stricken area, the buildings include both damaged buildings and undamaged buildings. The undamaged buildings are made up of both parallel buildings, whose array direction is parallel to the flight direction, and oriented buildings, whose array direction is not parallel to the flight direction. The damaged buildings after an earthquake are made up of completely collapsed buildings and residual damaged parallel walls and oriented walls of the damaged buildings. Therefore, we divide the buildings in earthquake-stricken areas into five kinds: intact parallel buildings, damaged parallel walls, collapsed buildings, intact oriented buildings, and damaged oriented walls. The two new polarimetric feature parameters of λ_H and H_λ are proposed to recognize the five kinds of buildings, and the Wishart supervised classification method is employed to further improve the extraction accuracy of the damaged buildings and undamaged buildings.
Plant growth‐promoting archaea trigger induced systemic resistance in Arabidopsis thaliana against Pectobacterium carotovorum and Pseudomonas syringae

Archaea have inhabited the earth for a long period of time and are ubiquitously distributed in diverse environments. However, few studies have focused on the interactions of archaea with other organisms, including eukaryotes such as plants, since it is difficult to cultivate sufficient numbers of archaeal cells for analysis. In this study, we investigated the interaction between soil archaea and Arabidopsis thaliana. We demonstrate for the first time that soil archaea promote plant growth and trigger induced systemic resistance (ISR) against the necrotrophic bacterium Pectobacterium carotovorum subsp. carotovorum SCC1 and biotrophic bacterium Pseudomonas syringae pv. tomato DC3000. Ammonia-oxidizing archaeon Nitrosocosmicus oleophilus MY3 cells clearly colonized the root surface of Arabidopsis plants, and increased resistance against both pathogenic species via the salicylic acid-independent signalling pathway. This mechanism of bacterial resistance resembles that underlying soil bacteria- and fungi-mediated ISR signalling. Additionally, volatile emissions from N. oleophilus MY3 were identified as major archaeal determinants that elicit ISR. Our results lay a foundation for archaea-plant interactions as a new field of research.
Combination of Surgery and Nd:YAG Laser Therapy for Recalcitrant Viral Warts: A Successful Therapeutic Approach for Immunosuppressed Patients.

Verrucae vulgares are benign epidermal tumours induced by cutaneous infection with different subtypes of human papilloma virus (HPV). Spontaneous regression can occur, but treatment is usually challenging and protracted. Immunocompromised patients have a higher risk of malignant HPV-induced skin tumours and recalcitrant viral warts, which are more difficult to treat than in immunocompetent individuals. In immunocompromised patients, complications appear more frequently and severely, therefore the therapeutic regime must be chosen carefully considering possible impaired wound healing or toxicity of therapeutic agents. Therapeutic approaches can be surgical, chemical or immunomodulatory or by laser, but only limited data exist on effective treatment in immunocompromised patients.
Application of Data Science Technology on Research of Circulatory System Disease Prediction Based on a Prospective Cohort

Chronic diseases represented by circulatory diseases have gradually become the main types of diseases affecting the health of our population. Establishing a circulatory system disease prediction model to predict the occurrence of diseases and controlling them is of great significance to the health of our population. This article is based on the prospective population cohort data of chronic diseases in China, based on the existing medical cohort studies, the Kaplan–Meier method was used for feature selection, and the traditional medical analysis model represented by the Cox proportional hazards model was used and introduced. Support vector machine research methods in machine learning establish circulatory system disease prediction models. This paper also attempts to introduce the proportion of the explanation variation (PEV) and the shrinkage factor to improve the Cox proportional hazards model; and the use of Particle Swarm Optimization (PSO) algorithm to optimize the parameters of SVM model. Finally, the experimental verification of the above prediction models is carried out. This paper uses the model training time, Accuracy rate(ACC), the area under curve (AUC)of the Receiver Operator Characteristic curve (ROC) and other forecasting indicators. The experimental results show that the PSO-SVM-CSDPC disease prediction model and the S-Cox-CSDPC circulation system disease prediction model have the advantages of fast model solving speed, accurate prediction results and strong generalization ability, which are helpful for the intervention and control of chronic diseases.
Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy

Introduction: Golden retriever muscular dystrophy (GRMD), an X‐linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD. Methods: To assess severity in the GRMD, we analyzed texture features extracted from multi‐parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines. Results: A single feature from qualitative images can provide 89% maximal accuracy. Furthermore, 2 features from T1w, T2m, or Dixon images provided highest accuracy. When considering a tradeoff between scan‐time and computational complexity, T2m images provided good accuracy at a lower acquisition and processing time and effort. Conclusions: The combination of MRI texture features improved the classification accuracy for assessment of disease progression in GRMD with evaluation of the heterogenous nature of skeletal muscles as reflection of the histopathological changes. Muscle Nerve 59:380–386, 2019
High-Precision Camera Localization in Scenes with Repetitive Patterns

This article presents a high-precision multi-modal approach for localizing moving cameras with monocular videos, which has wide potentials in many intelligent applications, including robotics, autonomous vehicles, and so on. Existing visual odometry methods often suffer from symmetric or repetitive scene patterns, e.g., windows on buildings or parking stalls. To address this issue, we introduce a robust camera localization method that contributes in two aspects. First, we formulate feature tracking, the critical step of visual odometry, as a hierarchical min-cost network flow optimization task, and we regularize the formula with flow constraints, cross-scale consistencies, and motion heuristics. The proposed regularized formula is capable of adaptively selecting distinctive features or feature combinations, which is more effective than traditional methods that detect and group repetitive patterns in a separate step. Second, we develop a joint formula for integrating dense visual odometry and sparse GPS readings in a common reference coordinate. The fusion process is guided with high-order statistics knowledge to suppress the impacts of noises, clusters, and model drifting. We evaluate the proposed camera localization method on both public video datasets and a newly created dataset that includes scenes full of repetitive patterns. Results with comparisons show that our method can achieve comparable performance to state-of-the-art methods and is particularly effective for addressing repetitive pattern issues.