Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor Area Highlighting Using T2WI, ADC Map, and DWI Sequence Fusion on bpMRI Images for Better Prostate Cancer Diagnosis

Prostate cancer is the second most common cancer in men worldwide. The results obtained in magnetic resonance imaging examinations are used to decide the indication, type, and location of a prostate biopsy and contribute information about the characterization or aggressiveness of detected cancers, including tumor progression over time. This study proposes a method to highlight prostate lesions with a high and very high risk of being malignant by overlaying a T2-weighted image, apparent diffusion coefficient map, and diffusion-weighted image sequences using 204 pairs of slices from 80 examined patients. It was reviewed by two radiologists who segmented suspicious lesions and labeled them according to the prostate imaging-reporting and data system (PI-RADS) score. Both radiologists found the algorithm to be useful as a “first opinion”, and they gave an average score on the quality of the highlight of 9.2 and 9.3, with an agreement of 0.96.
Improvement of process control in sheet metal forming by considering the gradual properties of the initial sheet metal

In scientific studies, sheet metal is usually considered as a two-dimensional body. Thus, it is accepted that material properties are in most cases regarded homogeneous in thickness direction. However, a gradation of certain properties becomes apparent when going beyond the standard characterization methods for sheet metals, which can for example, influence the springback behavior and the thinning of the sheet after forming. Thus, the aim of this work is to further improve the prediction accuracy of springback after forming in simulations, by implementing several inhomogeneous properties over the sheet thickness in an existing material model. For this purpose, the entire procedure from the identification of the inhomogeneous properties for describing the gradation to the implementation in a numerical model and its validation by comparing experimental and simulated bending operations is carried out on a DC04 cold-forming steel in order to prove its influence on the springback behavior. It is shown that including graded material properties in simulations does indeed have an impact on the prediction quality of springback and that the information about inhomogeneous properties can be provided by existing characterization methods with a high local resolution like electron backscatter diffraction or X-ray stress analysis. In a further step, it was possible to validate the improvement in numerical accuracy by comparing the prediction of the springback angle from both the existing and the extended model with experimental bending results. Both the initial model as well as the model supplemented with the 3D properties provide a good prediction accuracy in the solution heat treated material state. For the predeformed material, however, the initial numerical model predicts a springback angle of about 13°, which deviates remarkably from the experimentally obtained mean value of about 17°. The extended model delivers a significantly improved accuracy in springback prediction in relation to the initial prediction (deviation of 4°) with a minor deviation of only about 0.8°, which proves the importance of considering the gradation of material properties in thickness direction for an overall higher dimensional accuracy of sheet metal products.
Femtosecond solid-state lasers

The emergence of new ultrafast optical modulation techniques has opened the way towards a new femtosecond laser technology based on solid-state gain media. The authors address the requirements for stable ultrashort pulse generation in these novel femtosecond sources. The theoretical considerations are backed up by experimental results obtained with a number of different laser systems. The conclusions drawn from the presented theoretical and experimental investigations provide general guidelines for the design and optimization of a wide range of femtosecond solid-state laser oscillators. >
Light emission from wavelength-tunable microcavities

We report a novel microcavity design that allows the control in wavelength of the resonant modes of a microcavity operating in the visible spectrum. We have been able to shift the modes by up to 56 nm. The device is comprised of a thin film of a fluorescent semiconducting polymer combined with an electrically switchable liquid crystal layer, which are sandwiched between the two cavity mirrors. This structure serves as a tool for further experimental studies of the properties of microcavities and also has considerable technological potential, providing, for example, an efficient wavelength converter or tunable light source.
Positional Information Storage in Sequence Patterns

We build a model of storage of well-defined positional information in probabilistic sequence patterns. Once a pattern is defined, it is possible to judge the effect of any mutation in it. We show that the frequency of beneficial mutations can be high in general and the same mutation can be either advantageous or deleterious depending on the pattern’s context. The model allows to treat positional information as a physical quantity, formulate its conservation law and to model its continuous evolution in a whole genome, with meaningful applications of basic physical principles such as optimal efficiency and channel capacity. A plausible example of optimal solution analytically describes phase transitions-like behavior. The model shows that, in principle, it is possible to store error-free information on sequences with arbitrary low conservation. The described theoretical framework allows one to approach from novel general perspectives such long-standing paradoxes as excessive junk DNA in large genomes or the corresponding G- and C-values paradoxes. We also expect it to have an effect on a number of fundamental concepts in population genetics including the neutral theory, cost-of-selection dilemma, error catastrophe and others.
Knowledge and Practices of Disinfectants and Sanitizers Use during COVID-19 Pandemic in Jordan

Background: The use of antimicrobials has been expanded during the COVID-19 pandemic. This study aims to assess the knowledge and practices of disinfectants and sanitizers use among Jordanian people during the (COVID-19) pandemic. Methods: A web-based cross-sectional descriptive questionnaire was distributed across Jordan between August and September 2020. The questionnaire consisted of three sections inquiring about demographics and general characteristics of the surveyed sample, evaluating the respondents’ knowledge about disinfectants, as well as respondents’ practices. The questionnaire was completed by 403 literate adult respondents. Results: Our results indicate that Jordanian adults have used disinfectants increasingly during the COVID-19 outbreak. Knowledge of our study sample was considerably affected by gender (p=0.044), income (p=0.001), and profession (p<0.001). 80.8% of those participants reported skin-related side effects due to disinfectant use during the pandemic. The most used disinfectants were ethanol, followed by soap and water. Generally, study respondents showed positive practices toward the use of disinfectants during the time of the pandemic with few high-risk practices reported. Interestingly, the positive practices applied by Jordanian adults were minimally and not significantly affected by the knowledge about antimicrobials’ safe and effective use. Conclusions: There is an urgent need for a structured effort to increase public awareness regarding the safe and effective use of disinfectants against SARS-CoV-2 transmission.
A 13.5-nA Quiescent Current LDO With Adaptive Ultra-Low-Power Mode for Low-Power IoT Applications

This brief presents a low-dropout regulator (LDO) for power-efficient Internet of Things (IoT) devices. When IoT devices are in low-power standby mode, the proposed LDO senses the load current and enters an ultra-low-power mode to enable an extremely low-power system. The proposed LDO alternates between two modes of operation. One is the ultra-low-power mode in which the core circuit of the LDO is turned off to save power, and the other is an active mode in which all circuit components are on. The successful operation of the proposed scheme is demonstrated by implementing and measuring silicon LDO chips, resulting in an extremely low-quiescent current of 13.5 nA and a superior figure-of-merit (FOM).
Effect of a narrow channel inserted in the conventional ice block mold with brine injection on the productivity and ice formation behavior of the low temperature brine bathing ice production process

ABSTRACT Block ice is cuboid in shape. It is produced by filling water into a mold and bathing it in a brine pool. For 270 × 560 × 1500 mm3 the production batch time is 24 hours. The aim of this work is to improve the conventional mold so that it has a batch time shorter than the low electrical cost duration of 11 hours. A narrow channel having nearly the same width as the mold was installed inside the mold. The brine is injected from the middle of the channel's open end at the mold base by a ½ inch nozzle. A CFD model was developed and validated with the experimental data. It was found that the optimum pool temperature and injection flow rate may be approximated to be −10 and 80 lpm, respectively. Heat transfer can be assumed to be in one dimension in the mold depth direction. As long as the channel height is less than that of the mold, the batch time will be comparable to that of the conventional mold. Regardless of the pool temperature, for a certain channel height, the channel can contribute to the productivity at approximately the same rate. The batch time was 9.73 hours.
An Algorithm of Hierarchical Clustering Based on Correcting Class Center

In order to solve the problem of class center offset caused by merger of class of traditional hierarchical clustering algorithm,an algorithm of hierarchical clustering is proposed based on correcting class center.The accuracy of proposed algorithm is improved.And it inherits the non-dependnce of the initial clustering center of the traditional hierarchical algorithm.After analysis,the algorithm obtains a good clustering result for the known and unknown number of clusters.It is proved that the proposed algorithm is not only more precise than traditional hierarchical clustering algorithm,but also the clustering results show that the algorithm is effective in image segmentation.
On the investigation of fast electron beam filamentation in laser-irradiated solid targets using multi-MeV proton emission

The transverse filamentation of beams of fast electrons transported in solid targets irradiated by ultraintense (5 × 1020 W cm−2), picosecond laser pulses is investigated experimentally. Filamentation is diagnosed by measuring the uniformity of a beam of multi-MeV protons accelerated by the sheath field formed by the arrival of the fast electrons at the rear of the target, and is investigated for metallic and insulator targets ranging in thickness from 50 to 1200 µm. By developing an analytical model, the effects of lateral expansion of electron beam filaments in the sheath during the proton acceleration process is shown to account for measured increases in proton beam nonuniformity with target thickness for the insulating targets.
Multimodal Emotion Recognition From EEG Signals and Facial Expressions

Emotion recognition has attracted attention in recent years. It is widely used in healthcare, teaching, human-computer interaction, and other fields. Human emotional features are often used to recognize different emotions. Currently, there is more and more research on multimodal emotion recognition based on the fusion of multiple features. This paper proposes a deep learning model for multimodal emotion recognition based on the fusion of electroencephalogram (EEG) signals and facial expressions to achieve an excellent classification effect. First, a pre-trained convolution neural network (CNN) is used to extract the facial features from the facial expressions. Next, the attention mechanism is introduced to extract more critical facial frame features. Then, we apply CNNs to extract spatial features from original EEG signals, which use a local convolution kernel and a global convolution kernel to learn the features of left and right hemispheres channels and all EEG channels. After feature-level fusion, the fusion features of the facial expression features and EEG features are fed into the classifier for emotion recognition. This paper conducted experiments on the DEAP and MAHNOB-HCI datasets to evaluate the performance of the proposed model. The accuracy of valence dimension classification is 96.63%, and arousal dimension classification is 97.15% on the DEAP dataset, while 96.69% and 96.26% on the MAHNOB-HCI dataset. The experimental results show that the proposed model can effectively recognize emotions.
The Penrose, Ammann and DA tiling spaces are Cantor set fiber bundles

Anderson and Putnam have recently shown that the space of all tilings of a substitution tiling scheme is a special case of the present author's ‘expanding attractor’. This resurrects an earlier conjecture of the present author in this special case: The tiling space of a substitution tiling space is a fiber bundle over the torus, with Cantor set fiber. If true, this could be important in the classification of these spaces, just as in the one-dimensional case. These spaces were known (in these two disciplines) to be locally the product of a disk and a Cantor set. Farrell and Jones gave an example showing our more inclusive conjecture is false. Here we show that the Penrose, Ammann and ‘stepped plane’ tiling spaces are in fact such bundles. Among dynamicists, the stepped plane tiling space (in special cases) is known as the ‘DA’ attractor of Smale. The formalism of substitution systems provides a Markov partition, and this is certainly part of their intuitive appeal. However, in dynamics such partitions are rare in dimensions greater than 2 and do not occur for the DA attractor. We consider a weaker concept, that omits this requirement. Finally, we construct an example like the Farrell–Jones example, in sufficient detail that it can be visualized as a smooth tiling space, as opposed to the more rigid geometric tiling space.
Evaluation of Bortezomib-BeEAM (2BeEAM) as Chemotherapy Regimen Prior to ASCT in Patients with Mantle Cell Lymphoma

Simple Summary In patients with mantle cell lymphoma (MCL), an effective and safe high-dose chemotherapy regimen as consolidation of first-line treatment prior to ASCT is a matter of ongoing exploration due to unsatisfying outcomes with a relevant rate of relapse in the following course of disease. In our study, we evaluated the use of bortezomib together with standard HDCT of BeEAM (2BeEAM) in 11 patients with an MCL in a single institution study, assessing feasibility, toxicities, and survival rates. More than half of the patients presented with peripheral sensory and/or motor neuropathy. Progression-free survival and overall survival were 64% after a median follow-up time of 22 months. Moreover, 55% of the patients were in complete remission. These findings suggest the addition of bortezomib is associated with substantial toxicities without having a superior benefit in MCL therapy. Thus, we could not identify an obvious clinical benefit compared to standard treatment. Abstract (1) Background: First-line therapy in fit MCL patients may comprise high-dose chemotherapy (HDCT) with autologous transplantation to consolidate remission before maintenance treatment. However, optimization of HDCT is an unmet clinical need given the substantial relapse rate of first-line treatment, while the use of bortezomib is a promising candidate to be added to standard HDCT. (2) Methods: We analyzed 11 consecutive patients with MCL who received bortezomib added to standard BeEAM (2BeEAM) HDCT at a single academic institution. We assessed safety, feasibility, toxicities, and survival rates. (3) Results: All patients had stage III or IV disease. We found that six patients (55%) developed new or worsening of preexisting peripheral neuropathy following administration of 2BeEAM HDCT. One patient relapsed within the first six months after HDCT, whereas three patients never reached complete remission. After a median follow-up of 22 months, the PFS was 64% and the OS 64% at the last follow-up assessment. At this time, 55% of patients were in CR. (4) Conclusions: The use of bortezomib added to standard BeEAM HDCT is associated with relevant toxicities, particularly with regards to additional neuropathy. Moreover, the anti-lymphoma efficacy of 2BeEAM HDCT appears to be modest; therefore, other therapeutic options should be evaluated for consolidation in this patient group.
D-optimal design for a quadratic log contrast model for experiments with mixtures

A practical method is suggested for solving complicated D-optimal design problems analytically. Using this method the author has solved the problem for a quadratic log contrast model for experiments with mixtures introduced by J. Aitchison and J. Bacon-Shone. It is found that for a symmetric subspace of the finite dimensional simplex, the vertices and the centroid of this subspace are the only possible support points for a D-optimal design. The weights that must be assigned to these support points contain irrational numbers and are constrained by a system of three simultaneous linear equations, except for the special cases of 1- and 2-dimensional simplexes where the situation is much simpler. Numerical values for the solution are given up to the 19-dimensional simplex
Assessing arthropod diversity metrics derived from stream environmental DNA: spatiotemporal variation and paired comparisons with manual sampling

Background Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods. Methods We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment. Results Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.
DEVELOPING PROSPECTIVE MATHEMATICS TEACHERS' TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE (TPACK): A CASE OF NORMAL DISTRIBUTION

In a teacher development experiment, thirteen (2 males, 11 females) prospective mathematics teachers took a 10-lecture mathematics education course, which integrated technology (Fathom dynamic data software), pedagogy, and content (statistical distribution) knowledge. They also experienced three rounds of simulation teaching and one round of authentic teaching, which is a “Same Content Different Lesson Constructs” (SCDLC or TongKeYiGou in Chinese)activity held among three prospective teachers and two practicing teachers. In-depth interview, classroom observation and videotaping, and document collection were used to collect data. Computer assisted qualitative data analysis software, NVivo, was used to analyze the data. Three case reports showed the significant changes of Technological Pedagogical Content Knowledge(TPACK) of prospective teachers. TPACK, TongKeYiGou, Fathom dynamic data software, normal distribution, data literacy
Integration of Metabolomics and Transcriptomics to Reveal the Metabolic Characteristics of Exercise-Improved Bone Mass

(1) Background: Exercise is effective in promoting and maintaining bone mass. The aim of this study was to detect the exercise-induced metabolic changes in bone tissue of zebrafish. (2) Methods: Thirty-eight zebrafish (Danio rerio, six months old) were analyzed. The exercise group (n = 19) received 8 weeks of counter-current swimming training. The control group (n = 19) was not subjected to exercise. Mineralization was quantified, and alkaline phosphatase (Alp) and anti-tartrate acid phosphatase (Trap) activities were estimated (n = 12). The metabolomics (n = 12) and transcriptomics (n = 14) data of bone tissue were used for the integration analyses. (3) Results: The results showed that the exercise training improved the bone mineralization of zebrafish, e.g., the exercise group (5.74 × 104 ± 7.63 × 103) had a higher mean optical density than the control group (5.26 × 104 ± 8.56 × 103, p = 0.046) for the caudal vertebrae. The amount of mineralized matrix in scales of the exercised zebrafish was also higher (0.156 ± 0.012 vs. 0.102 ± 0.003, p = 0.005). Both histological staining and biochemical analysis revealed increased Alp activity (0.81 ± 0.26 vs. 0.76 ± 0.01, p = 0.002) and decreased Trap activity (1.34 ± 0.01 vs. 1.36 ± 0.01, p = 0.005) in the exercise group. A total of 103 different metabolites (DMs, VIP ≥ 1, fold change (FC) ≥ 1.20 or ≤0.83, p < 0.050) were identified. Alanine, aspartate and glutamate metabolism, β-alanine metabolism, pyrimidine metabolism, and pantothenate and CoA biosynthesis were the significantly enriched metabolic pathways (p < 0.050). A total of 35 genes (q ≤ 0.050 (BH), |Log2FC| ≥ 0.5) were coenriched with the 103 DMs in the four identified pathways. Protein–protein interaction network analysis of the 35 genes showed that entpd3, entpd1, and cmpk2 were the core genes. (4) Conclusions: The results of this study suggest that alanine, aspartate and glutamate metabolism, β-alanine metabolism, pyrimidine metabolism, and pantothenate and CoA biosynthesis contributed to exercise-induced improvements in bone mass.
H3 Lysine 4 Methylation Is Required for Full Activation of Genes Involved in α-Ketoglutarate Availability in the Nucleus of Yeast Cells after Diauxic Shift

We show that in S. cerevisiae the metabolic diauxic shift is associated with a H3 lysine 4 tri-methylation (H3K4me3) increase which involves a significant fraction of transcriptionally induced genes which are required for the metabolic changes, suggesting a role for histone methylation in their transcriptional regulation. We show that histone H3K4me3 around the start site correlates with transcriptional induction in some of these genes. Among the methylation-induced genes are IDP2 and ODC1, which regulate the nuclear availability of α-ketoglutarate, which, as a cofactor for Jhd2 demethylase, regulates H3K4 tri-methylation. We propose that this feedback circuit could be used to regulate the nuclear α-ketoglutarate pool concentration. We also show that yeast cells adapt to the absence of Jhd2 by decreasing Set1 methylation activity.