Miracle-dz/UniFAR-FTUs huggingface dataset:
BRAF mutation in cytologically indeterminate thyroid nodules: after reclassification of a variant thyroid carcinoma

Purpose Fine-needle aspiration biopsy (FNAB) is regarded by the Bethesda system as the gold-standard investigation for stratifying the risk of malignancy of a thyroid nodule. However, some limitations affect the adequacy of the obtained materials, resulting in 30% of the cytological results remaining in the indeterminate category. We aimed to investigate the diagnostic value of the BRAF mutation in cytologically indeterminate thyroid nodules after the reclassification of a variant thyroid carcinoma. Patients and methods In this prospective diagnostic study, 76 patients with FNAB findings of atypia of undetermined significance (AUS) and suspicious for malignancy (SUS) were included. The BRAF V600 mutation from FNAB was confirmed by a PCR-based method (Sanger sequencing combined with allele-specific real-time PCR techniques) and immunohistochemistry (IHC). Pathological specimens and features, including noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), were reviewed and compared to the FNAB results. Results Using the PCR-based method, the BRAF mutation was positive in 13/76 cases (17.1%), with the diagnostic values of 16.7% sensitivity, 100% specificity, 100% positive predictive value (PPV), and 82.8% negative predictive value (NPV) in the AUS compared to 73.3% sensitivity, 100% specificity, 100% PPV, and 20% NPV in the SUS. For the IHC technique, only 20 of the 76 cytological specimens were qualified for testing. The BRAF mutation was positive in 13/20 cases, with the diagnostic values of 100% sensitivity, 63.6% specificity, 42.9% PPV, and 100% NPV in the AUS compared to 100% sensitivity and PPV in the SUS. The BRAF mutation was not found in the pathological reports for NIFTP. Conclusion The malignancy rate is high in our data, with specific and acceptable accuracy rates for the BRAF mutation from FNAB found by using the PCR-based method. NIFTP has been introduced after the pathological reclassification. Molecular diagnosis might be useful to establish the nature of the disease.
Efficacy and safety of abaloparatide for the treatment of post-menopausal osteoporosis

ABSTRACT Introduction: Osteoporosis is a skeletal disorder characterized by loss of bone mass and strength affecting up to 30–50% of postmenopausal women worldwide. Current therapeutic options include antiresorptives such as aminobisphosphonates or denosumab and osteoanabolic compounds such as teriparatide. Areas covered: In this review, the authors summarize the clinical development, safety and efficacy profile of abaloparatide, a new osteoanabolic agent recently marketed in the US for the treatment of postmenopausal osteoporosis in women who are at high risk for fracture or who fail antiresorptive therapy. Expert opinion: Abaloparatide is a 1-34 PTH related peptide-like molecule that has been modified in order to potentiate the osteoanabolic effect. In its pivotal phase 3 trial in postmenopausal women with osteoporosis, subcutaneous abaloparatide 80 mcg/day reduced the risk of vertebral, nonvertebral, major osteoporotic, and clinical fractures compared with placebo and reduced the risk of major osteoporotic fractures compared with teriparatide. These results, together with a reduced prevalence of hypercalcemia and a lower cost of the marketed compound, point toward improved cost effectiveness with abaloparatide versus teriparatide. However, some concerns have been raised due to a somewhat higher occurrence of adverse effects (particularly with palpitations and increased heart rate) or the resultant discontinuation due to these adverse effects when compared to teriparatide.
Role of Sentinel Node Biopsy in Endometrial Cancer

Lymphadenectomy, for early stages of endometrial cancer (EC), provides a low detection rate of lymphatic metastasis, without having demonstrated a therapeutic effect; so that the collection and histological analysis of the sentinel lymph node (SLN) might be an alternative to lymphadenectomy. The contribution of SLN to surgical staging represents a change in the paradigm of lymphadenectomy in EC, being an intermediate approach between not assessing the condition of the lymph nodes and complete pelvic and paraaortic dissection. Accurate identification of the main uterine drainage pathway increases the likelihood of detecting metastases during lymphatic mapping. In addition, pathological assessment by the ultrastaging of the SLN is the most important advance in the SLN biopsy (SLNB) technique. The application of the SLNB presumes a decrease in surgical and long-term morbidity, with an increase in the detection of lymphatic metastasis, mainly at the expense of detecting low tumour volume, selecting the group of patients that would benefit from a modification in adjuvant therapy. The SLNB can be established as an oncologically safe and effective method in the surgical staging of early-stage EC. Prospective studies are required to determine optimal behaviour and prognosis in the detection of low-volume metastases.
An Efficient Augmented Lagrangian Based Method for Constrained Lasso

Variable selection is one of the most important tasks in statistics and machine learning. To incorporate more prior information about the regression coefficients, the constrained Lasso model has been proposed in the literature. In this paper, we present an inexact augmented Lagrangian method to solve the Lasso problem with linear equality constraints. By fully exploiting second-order sparsity of the problem, we are able to greatly reduce the computational cost and obtain highly efficient implementations. Furthermore, numerical results on both synthetic data and real data show that our algorithm is superior to existing first-order methods in terms of both running time and solution accuracy.
ANALYZING WIND-DRIVEN RAIN ON A BUILDING FACADE USING THE LASER PRECIPITATION MONITOR (LPM)

This paper is concerned with a building situated in the west of Scotland which faces severe weather conditions with high wind speeds and driving rain occurring frequently. This results in extensive damage to the building fabric, and affects the internal climate which leads to a serious issue in building construction. In this study, a three-dimensional numerical model of airflow around the building is investigated. This investigation is part of ongoing research on wind-driven rain which has established the importance of moisture stresses, wind flow and rain impacts on a commercial building. The full scale experimental facility which includes anemometers, rain gauge, driving rain gauges and sensors is described. To supplement this, a sophisticated laser precipitation monitor (disdrometer) was installed, which collects rain data every 1-minute. This provides accurate measurement of drop size and speed of the falling raindrops which plays an important role in wind-driven rain studies. Comparisons with numerical simulations and experimental data were predicted. Results shows that the average predominantly wind direction for the sample test period is from south-west (223 from north) and the driving rain during this period is 86.8 mm for the west facing façade and 193.4 mm for the south facade. From the computational fluid dynamics (CFD) simulations, it is predicted that the change in velocity profile along the laser strip is due to the test building and the disdrometer head itself, which ultimately affects the driving rain calculations.
A High-Multi Target Resolution Focal Plane Array-Based Laser Detection and Ranging Sensor

This paper introduces a digital-assisted multiple echo detection scheme, which utilizes the waste time of the full serial data readout period in a focal plane array (FPA)-based laser detection and ranging (LADAR) receiver. With the support of an external digital signal processor (DSP) and additional analog memory inserted into the receiver, the proposed readout scheme can effectively enhance multi-target resolution (MTR) three times higher than the conventional FPA-based LADAR, while maintaining low power consumption and a small area. A prototype chip was fabricated in a 0.18-μm CMOS process with an 8 × 8 FPA configuration, where each single receiver pixel occupied an area of 100 μm × 100 μm. The single receiver achieved an MTR of 20 ns with 7.47 mW power dissipation, an input referred noise current of 4.48 pA/√Hz with a bandwidth 530 MHz, a minimum detectable signal (MDS) of 340 nA, a maximum walk error of 2.2 ns, and a maximum non-linearity of 0.05% among the captured multiple echo images.
First Report of Plasmodiophora brassicae on Cabbage in Eastern North Carolina.

Clubroot, caused by Plasmodiophora brassicae Woronin, has occurred for at least 50 years in three counties in northwestern North Carolina, but has not been reported previously from eastern North Carolina, where most commercial cabbage is produced. In the fall of 1995, clubroot was observed in a direct seeded, commercial cabbage field in Plymouth, NC. Diseased cabbage plants were stunted and roots exhibited clublike swellings. Clubs were randomly harvested from roots of five plants to obtain a composite isolate to determine which race(s) of P. brassicae are infecting cabbage in eastern North Carolina. Three experiments were conducted, using the procedure of Williams (2). Four replicates of 10, 1-week-old seedlings of eight different crucifer cultivars were inoculated by dipping in a spore suspension (1 × 108 cysts/ml) of P. brassicae and planted in pasteurized potting mix. Seedlings dipped in sterile water served as controls. Inoculated seedlings were incubated in a greenhouse at 18 to 28°C for 6 to 8 weeks and assessed for clubroot incidence and severity. The isolate of P. brassicae from eastern North Carolina was most virulent on cabbage (Brassica oleracea var. capitata cv. Jersey Queen), collard (B. oleracea var. acephala cv. Vates), and wild mustard (B. nigra); moderately virulent on canola (B. napus cv. Brutor) and rutabaga (B. napus cvs. Laurentian and Wilhelmsburger); and least virulent on cabbage (cv. Badger Shipper). Canola (B. napus cv. Nevin) and control seedlings were not infected and exhibited no symptoms. Similar results were obtained for all experiments. Based on these results, the isolate of P. brassicae from eastern North Carolina was designated as race 6 and pathotype 5 according to Williams (2) and Some (1), respectively. However, further experiments with single-cyst-derived isolates from individual clubs obtained from different geographic locations are needed to accurately characterize field populations of P. brassicae on cabbage in eastern North Carolina. References: (1) A. Some et al. Plant Pathol. 45:432, 1996. (2) P. H. Williams. Phytopathology 56:624, 1966.
Early Abnormal Heartbeat Multistage Classification by using Decision Tree and K-Nearest Neighbor

Heart diseases contribute to the highest cause of death around the world particularly for middle aged and elderly people. There are various types of heart disease symptoms. One of the most common types is Arrhythmia which is considered as a dangerous heart condition since the symptom itself may initiate more chronic heart diseases and result in death if it is not treated earlier. However, the detection of Arrhythmia by humans is regarded as a challenging task because the natures of the symptom appear at random times. Therefore, an automatic detection method of abnormal heartbeat in ECG (electrocardiogram) data is needed to overcome the issue. In this paper, a novel multistage classification approach using K-Nearest Neighbor and decision tree of the 3 segments in the ECG cycle is proposed to detect Arrhythmia heartbeat from the early minute of ECG data. Specific attributes based on feature extraction in each heartbeat are used to classify the Normal Sinus Rhythm and Arrhythmia. The experimental result shows that the proposed multistage classification approach is able to detect the Arrhythmia heartbeat with 90.6% accuracy for the P and the Q peak segments, 91.1% accuracy for the Q, R and S peak segments and lastly, 97.7% accuracy for the S and the T peak segments, outperforming the other data mining techniques.
Cysteamine Protects Neurons from Mutant Huntingtin Toxicity.

BACKGROUND The potential benefit of cysteamine for Huntington's disease has been demonstrated in HD animal models. Cysteamine and its derivate cystamine were shown to reduce neuropathology and prolong lifespan. Human studies have demonstrated safety, and suggestive results regarding efficacy. Despite all the studies available in vivo, there are only few in vitro studies, and the mechanism of action of cysteamine remains unclear. OBJECTIVE The objective of this study is to assess the capacity of cysteamine for neuroprotection against mutant Huntingtin in vitro using cellular models of HD, and to provide initial data regarding mechanism of action. METHODS We tested the neuroprotective properties of cysteamine in vitro in our primary neuron and iPSC models of HD. RESULTS Cysteamine showed a strong neuroprotective effect (EC50 = 7.1 nM) against mutant Htt-(aa-1-586 82Q) toxicity, in a nuclear condensation cell toxicity assay. Cysteamine also rescued mitochondrial changes induced by mutant Htt. Modulation of the levels of cysteine or glutathione failed to protect neurons, suggesting that cysteamine neuroprotection is not mediated through cysteine metabolism. Taurine and Hypotaurine, which are metabolites of cysteamine can protect neurons against Htt toxicity, but the inhibition of the enzyme converting cysteamine to hypotaurine does not block either protective activity, suggesting independent protective pathways. Cysteamine has been suggested to activate BDNF secretion; however, cysteamine protection was not blocked by BDNF pathway antagonists. CONCLUSIONS Cysteamine was strongly neuroprotective with relatively high potency. We demonstrated that the main neuroprotective pathways that have been proposed to be the mechanism of protection by cysteamine can all be blocked and still not prevent the neuroprotective effect. The results suggest the involvement of other yet-to-be-determined neuroprotective pathways.
Excessive Reactive Iron Impairs Hematopoiesis by Affecting Both Immature Hematopoietic Cells and Stromal Cells

Iron overload is the accumulation of excess iron in the body that may occur as a result of various genetic disorders or as a consequence of repeated blood transfusions. The surplus iron is then stored in the liver, pancreas, heart and other organs, which may lead to chronic liver disease or cirrhosis, diabetes and heart disease, respectively. In addition, excessive iron may impair hematopoiesis, although the mechanisms of this deleterious effect is not entirely known. In this study, we found that ferrous ammonium sulfate (FeAS), induced growth arrest and apoptosis in immature hematopoietic cells, which was mediated via reactive oxygen species (ROS) activation of p38MAPK and JNK pathways. In in vitro hematopoiesis derived from embryonic stem cells (ES cells), FeAS enhanced the development of dysplastic erythroblasts but inhibited their terminal differentiation; in contrast, it had little effect on the development of granulocytes, megakaryocytes, and B lymphocytes. In addition to its directs effects on hematopoietic cells, iron overload altered the expression of several adhesion molecules on stromal cells and impaired the cytokine production profile of these cells. Therefore, excessive iron would affect whole hematopoiesis by inflicting vicious effects on both immature hematopoietic cells and stromal cells.
Emerging DFT Methods and Their Importance for Challenging Molecular Systems with Orbital Degeneracy

We briefly present some of the most modern and outstanding non-conventional density-functional theory (DFT) methods, which have largely broadened the field of applications with respect to more traditional calculations. The results of these ongoing efforts reveal that a DFT-inspired solution always exists even for pathological cases. Among the set of emerging methods, we specifically mention FT-DFT, OO-DFT, RSX-DFT, MC-PDFT, and FLOSIC-DFT, complementing the last generation of existing density functionals, such as local hybrid and double-hybrid expressions.
A test for the correct specification of marginal structural models

Marginal structural models (MSMs) allow estimating the causal effect of a time‐varying exposure on an outcome in the presence of time‐dependent confounding. The parameters of MSMs can be estimated utilizing an inverse probability of treatment weight estimator under certain assumptions. One of these assumptions is that the proposed causal model relating the outcome to exposure history is correctly specified. However, in practice, the true model is unknown. We propose a test that employs the observed data to attempt validating the assumption that the model is correctly specified. The performance of the proposed test is investigated with a simulation study. We illustrate our approach by estimating the effect of repeated exposure to psychosocial stressors at work on ambulatory blood pressure in a large cohort of white‐collar workers in Québec City, Canada. Code examples in SAS and R are provided to facilitate the implementation of the test.
Application of Classification Tree Method to Determine Factors Affecting Somatic Cell Count in Holstein Cows

In the current study, the effect of phenotypic factors on Somatic Cell Count (SCC) was evaluated by using classification tree technique. The current study used a total of 1,972,031 test day records of SCC in parity 1 to 4 collected from 1,281 Iranian HolsteinFriesian cows’ herds through 2004–2013. The SCC records were converted to binary trait, defined as 1: If SCC≤ 200,000, and 2: Otherwise. The CART (Classification And Regression Tree) algorithm for classification trees, with GINI index and Entropy function as the division criteria, was used to develop the tree. Statistical analysis was performed using ‘rpart’ package in R software. The constructed tree had 12 leaves and it was 6 levels deep. The results of classification tree procedure for ranking of importance of the variables responsible for the variation in SCC were, respectively, parity, test-day milk production, year of calving, season of calving, and days in milk (stage of lactation). Based on the obtained classification tree, different combination of variables associated with SCC could be identified. According to the classification tree, the lowest amount of SCC was expected in the group of cows that were in the 1st or 2nd parity; their test-day milk production was > 30 kg; they were calved from year 2004 to 2013; and their calving season was autumn or winter.
Strontium Promotes the Proliferation and Osteogenic Differentiation of Human Placental Decidual Basalis- and Bone Marrow-Derived MSCs in a Dose-Dependent Manner

The osteogenic potential of mesenchymal stromal cells (MSCs) varies among different tissue sources. Strontium enhances the osteogenic differentiation of bone marrow-derived MSCs (BM-MSCs), but whether it exerts similar effects on placental decidual basalis-derived MSCs (PDB-MSCs) remains unknown. Here, we compared the influence of strontium on the proliferation and osteogenic differentiation of human PDB- and BM-MSCs in vitro. We found that 1 mM and 10 mM strontium, but not 0.1 mM strontium, evidently promoted the proliferation of human PDB- and BM-MSCs. These doses of strontium showed a comparable alkaline phosphatase activity in both cell types, but their osteogenic gene expressions were promoted in a dose-dependent manner. Strontium at doses of 0.1 mM and 1 mM elevated several osteogenic gene expressions of PDB-MSCs, but not those of BM-MSCs at an early stage. Nevertheless, they failed to enhance the mineralization of either cell type. By contrast, 10 mM strontium facilitated the osteogenic gene expression as well as the mineralization of human PDB- and BM-MSCs. Collectively, this study demonstrated that human PDB- and BM-MSCs shared a great similarity in response to strontium, which promoted their proliferation and osteogenic differentiation in a dose-dependent manner.
A Novel Substrate-Binding Site in the X-ray Structure of an Oxidized E. coli Glyceraldehyde 3-Phosphate Dehydrogenase Elucidated by Single-Wavelength Anomalous Dispersion

Escherichia coli (E. coli), one of the most frequently used host for the expression of recombinant proteins, is often affected by the toxic effect of the exogenous proteins that is required to express. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is a multi-functional protein that has been used as a control marker for basal function and it is known to undergo cysteine oxidation under different types of cellular stress. Here, we report the 3D structure of the endogenous GAPDH purified from stressed E. coli cells expressing a eukaryotic protein. The structure was solved at 1.64 Å using single-wavelength anomalous dispersion (SAD) phasing with a selenium-modified enzyme. Interestingly, each GAPDH monomer contains a molecule of glyceraldehyde-3 phosphate in a non-previously identified site. Furthermore, the catalytic Cys149 is covalently attached to a ~300 Da molecule, possibly glutathione. This modification alters the conformation of an adjacent alpha helix in the catalytic domain, right opposite to the NAD+ binding site. The conformation of the alpha helix is stabilized after soaking the crystals with NAD+. These results exemplify the effects that the overexpression of an exogenous protein has over the host proteins and sheds light on the structural changes that large oxidant molecules on the catalytic cysteine produce for the GAPDH enzyme.
Research on the Transformation and Upgrading Path and Selection of Traditional Industries from the Perspective of Big Data

With the emergence of a new generation of information technology, big data has become an important driving force for current social development. Digitalization has become the main direction of the transformation and upgrading of traditional industries. As the product of current informatization, big data includes data quantity, data quality and data analysis ability. It is used as two different ways to interpret the value creation of big data, making it clear that it can promote the transformation and upgrading of traditional industries through value creation. Then, it puts forward the traditional industrial transformation and upgrading path from the perspective of big data, namely the linear path of "traditional industry + digital" and the transitional non-linear "digital + traditional industry". Its path selection will be analyzed by combining external and internal factors.