Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical simulation and optimization of Si/BaSi2 heterojunction and BaSi2 homojunction solar cells

High light absorption material BaSi2 based heterojunction and homojunctin solar cells were simulated with the program AMPS (analysis of microelectronic and photonic structures)-1D in order to thoroughly understand the mechanism for further improvement in conversion efficiency. Simulation results demonstrated that p+-Si/n-BaSi2 heterojunction solar cells exhibited superior photoelectric performances as compared with n+-Si/p-BaSi2 solar cells. A high conversion efficiency up to of 22.7% were achieved by p+-Si (100 nm, NA = 5 × 1019 cm−3)/n-BaSi2 (2000 nm, ND = 1 × 1018 cm−3) heterojunction solar cell. For BaSi2/BaSi2 homojunction solar cells, the window layer should be designed as thin with large scale uniformity and high quality in achieving high efficiency. Both n+-BaSi2 (5 nm, ND = 5 × 1019 cm−3)/p-BaSi2 (2000 nm, NA = 1 × 1017 cm−3) homojunction and p+-BaSi2 (5 nm, NA = 5 × 1019 cm−3)/n-BaSi2 (2000 nm, ND = 1 × 1017 cm−3) homojunction solar cells gave out a high conversion efficiency of 22.5%. Both donor-like defects in p-BaSi2 and acceptor like defects in n-BaSi2 light absorption layers were identified to significantly influence the solar cell performance that all parameters deteriorated severely under high bulk defect density. Moreover, p+-Si/n-BaSi2 solar cell was more sensitive to high level interface trap defects on account of the sharp dropping down of Eff under high interface trap density over 5 × 1012 cm−2. This work provided insight essential guidance for device design and optimization in achieving high efficiency silicide solar cell with low cost.
Stepping stones to extirpation: Puma patch occupancy thresholds in an urban‐wildland matrix

Abstract Habitat loss and fragmentation are the leading causes of species range contraction and extirpation, worldwide. Factors that predict sensitivity to fragmentation include high trophic level, large body size, and extensive spatial requirements. Pumas (Puma concolor) exemplify these qualities, making them particularly susceptible to fragmentation and subsequent reductions in demographic connectivity. The chaparral‐dominated ecosystems surrounding the greater San Francisco Bay Area encompass over 10,000 km2 of suitable puma habitat, but inland waterways, croplands, urban land uses, and extensive transportation infrastructure have resulted in widespread habitat fragmentation. Pumas in this region now exist as a metapopulation marked by loss of genetic diversity, collisions with vehicles, and extensive human–puma conflict. Given these trends, we conducted a photo survey from 2017 to 2021 across 19 patches of predicted habitat and compiled a dataset of >6584 puma images. We used a logistic regression analytical framework to evaluate the hypothesis that puma patch occupancy would exhibit a threshold response explained by patch size, isolation, and habitat quality. Contrary to predictions, only variables related to patch size demonstrated any power to explain occupancy. On average, occupied patches were 18× larger than those where they were not detected (825 ± 1238 vs. 46 ± 101 km2). Although we observed pumas in patches as small as 1 km2, logistic regression models indicated a threshold occupancy probability between 300 and 400 km2, which is remarkably close to the mean male puma home range size in coastal California (~381 km2). Puma populations dependent on habitats below this value may be susceptible to inbreeding depression and human–wildlife conflict, and therefore vulnerable to extirpation. For species conservation, we suggest conflicts might be ameliorated by identifying the largest, isolated patches for public education campaigns with respect to management of domestic animals, and remaining connective parcels be identified, mapped, and prioritized for targeted mitigation.
In vivo Polycystin-1 interactome using a novel Pkd1 knock-in mouse model

PKD1 is the most commonly mutated gene causing autosomal dominant polycystic kidney disease (ADPKD). It encodes Polycystin-1 (PC1), a putative membrane protein that undergoes a set of incompletely characterized post-transcriptional cleavage steps and has been reported to localize in multiple subcellular locations, including the primary cilium and mitochondria. However, direct visualization of PC1 and detailed characterization of its binding partners remain challenging. We now report a new mouse model with HA epitopes and eGFP knocked-in frame into the endogenous mouse Pkd1 gene by CRISPR/Cas9. Using this model, we sought to visualize endogenous PC1-eGFP and performed affinity-purification mass spectrometry (AP-MS) and network analyses. We show that the modified Pkd1 allele is fully functional but the eGFP-tagged protein cannot be detected without signal amplification by secondary antibodies. Using nanobody-coupled beads and large quantities of tissue, AP-MS identified an in vivo PC1 interactome, which is enriched for mitochondrial proteins and components of metabolic pathways. These studies suggest this mouse model and interactome data will be useful to understand PC1 function, but that new methods and brighter tags will be required to track endogenous PC1.
Diabetes diminishes muscle precursor cell-mediated microvascular angiogenesis

The skeletal muscles of Type II diabetic (T2D) patients can be characterized by a reduced vessel density, corresponding to deficiencies in microvascular angiogenesis. Interestingly, T2D also inhibits the function of many myogenic cells resident within skeletal muscle, including satellite cells, which are well-known for the role they play in maintaining homeostasis. The current study was undertaken to gain a better understanding of the mechanisms whereby satellite cell progeny, muscle precursor cells (MPCs), influence microvascular angiogenesis. Network growth and the expression of genes associated with angiogenesis were reduced when microvessels were treated with conditioned media generated by proliferating MPCs isolated from diabetic, as compared to control rat skeletal muscle, a phenomenon that was also observed when myoblasts from control or diabetic human skeletal muscle were used. When only exosomes derived from diabetic or control MPCs were used to treat microvessels, no differences in microvascular growth were observed. An evaluation of the angiogenesis factors in control and diabetic MPCs revealed differences in Leptin, vascular endothelial growth factor (VEGF), IL1-β, interleukin 10, and IP-10, and an evaluation of the MPC secretome revealed differences in interleukin 6, MCP-1, VEGF, and interleukin 4 exist. Angiogenesis was also reduced in tissue-engineered skeletal muscles (TE-SkM) containing microvessels when they were generated from MPCs isolated from diabetic as compared to control skeletal muscle. Lastly, the secretome of injured control, but not diabetic, TE-SkM was able to increase VEGF and increase microvascular angiogenesis. This comprehensive analysis of the interaction between MPCs and microvessels in the context of diabetes points to an area for alleviating the deleterious effects of diabetes on skeletal muscle.
Molecular techniques: An overview of methods for the detection of bacteria

Several DNA molecular markers are now available for use in surveillance and investigation of food-borne outbreaks that were previously difficult to detect. The results from several sources of literature indicate substantially different degrees of sensitivities between conventional detection methods and molecular-based methods. The new technology is noted for increased sensitivity over the traditional culture methods which they complement. Key words : molecular techniques, fingerprinting, microorganism. African Journal of Biotechnology Vol. 2 (12), pp. 710-713, December 2003
Fatalistic beliefs, self-care, and HbA1c in Mexican men with type 2 diabetes mellitus: A cross-sectional study

The aim of this study is to examine the relationship between fatalistic beliefs, self-care, and glycemic control among Mexican men with type 2 diabetes mellitus. This is a cross-sectional study in men diagnosed with type 2 diabetes mellitus from 18 to 59 years of age from the Northeast of Mexico. Fatalistic beliefs, self-care, medication adherence, and HbA1C were evaluated. Patients were divided into glycemic control (<7% HbA1c) and without glycemic control (>7% HbA1c). Tests were performed to compare 2 independent groups, Student’s t and U Mann Whitney. Correlation tests and multiple linear regression models were also performed. For statistical analysis, the SPSS v27 program was used. Forty-nine percent of the men had glycemic control (<7% HbA1c). Fatalistic beliefs were negatively correlated with self-care and medication adherence, but not with HbA1c. In multiple linear regression models, fatalistic beliefs were a negative predictor of self-care and medication adherence. In the model for HbA1c, the pessimism subdimension and self-care were the predictors. Fatalistic beliefs negatively affect self-care compliance and medication adherence, while the pessimism subdimension was related to the increase in HbA1c.
Robust Digital Image Steganography Within Coefficient Difference On Integer Haar Wavelet Transform

The development of digital information has lead to increasing demands on information security technology in order to protect the confidentiality of information. Digital steganography is one of technologies that is capable of protecting the information from unauthorized interception. It is due to its capability to hide the embedded of the information without attracting the eavesdropper’s attention. Among digital media, digital image is the most widely used medium for steganography. Discrete Cosine Transform (DCT) is a well known technique in digital image steganography. The use of DCT on small blocks may pose blocking effects and unintended artifacts on the overall image. These disadvantages of DCT can be eliminated by using Discrete Wavelet Transform (DWT) which is more compatible with the Human Visual System (HVS). However the floating point of DWT can causes some loss of information. On the other hand, Integer Wavelet Transform (IWT) represented in finite precision can avoid the problem of floating point precision in DWT. In this paper, the messages are embedded on the 1-level Integer Haar Wavelet Transform (IHWT) using coefficient difference scheme that is adopted from Pixel Value Differencing (PVD). The messages are embedded on the difference values of two adjacent wavelet coefficients. The result shows that the proposed method can easily outperform the existing method that employ IHWT and Pixel Mapping Method (PMM) in term of imperceptibility as well as the maximum capacity.
Temporal and Spectral Observation of Laser-induced THz Radiation at DELTA

Coherent THz pulses caused by a laser-induced density modulation of the electron bunches are routinely produced and observed at DELTA, a 1.5-GeV synchrotron light source operated by the TU Dortmund University. New measurements performed with a fast hot-electron bolometer allow insight into the turn-by-turn evolution of these pulses. Furthermore, first results from a Fourier transform infrared spectrometer, which is currently under commissioning, are presented.
Whole exome data prioritization unveils the hidden weight of Mendelian causes of male infertility. A report from the first Italian cohort

Almost 40% of infertile men cases are classified as idiopathic when tested negative to the current diagnostic routine based on the screening of karyotype, Y chromosome microdeletions and CFTR mutations in men with azoospermia or oligozoospermia. Rare monogenic forms of infertility are not routinely evaluated. In this study we aim to investigate the unknown potential genetic causes in couples with pure male idiopathic infertility by applying variant prioritization to whole exome sequencing (WES) in a cohort of 99 idiopathic Italian patients. The ad-hoc manually curated gene library prioritizes genes already known to be associated with more common and rare syndromic and non-syndromic male infertility forms. Twelve monogenic cases (12.1%) were identified in the whole cohort of patients. Of these, three patients had variants related to mild androgen insensitivity syndrome, two in genes related to hypogonadotropic hypogonadism, and six in genes related to spermatogenic failure, while one patient is mutant in PKD1. These results suggest that NGS combined with our manually curated pipeline for variant prioritization and classification can uncover a considerable number of Mendelian causes of infertility even in a small cohort of patients.
The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning

Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose a greedy algorithm for signed graph partitioning, the "Mutex Watershed". Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.
Evaluation of the chemical composition and biological activities of medicinal plants Alkanna tinctoria and Teucrium polium from Turkey

Since ancient times, natural products from various plants have been used for the treatment of different diseases. Alkanna and Teucrium species have been considered medicinal plants and are used popularly around the world. So, investigating the pharmaceutical properties of these plants has been the subject of interest for many researchers. In this work, total phenolic and flavonoid content, antioxydant, antimicrobial activity and in vitro enzyme inhibition properties of Alkanna tinctoria and Teucrium polium ethanol and methanol extracts were investigated. Additionally, GC-MS analyses of oil composition of the plant extracts were performed and 21 different fatty acids were identified in A. tinctoria and 18 in T. polium. Palmitic acid was the major component in A. tinctoria and saturated fatty acids constitutes a large part of the oil composistion. On the other hand, linoleic acid was the major fatty acid in T. polium and unsaturated fatty acids constitutes a large part of the oil composition. According to the results, methanol was more effective than the ethanol for the extraction of bioactive components. Both of the extracts showed a good antioxidant activity and the inhibition of bacteria and yeasts. Slight inhibitory activity of A. tinctoria and protective action of T. polium on enzyme with other results indicate that these plant extracts have potential to control microorganisms which can be promising for food industry.
Spinal cord stimulation for the treatment of peripheral neuropathic pain

E-mail: baalun@ous-hf.no Norwegian National Advisory Unit on Neuropathic Pain and Department of Pain Management and Research Oslo University Hospital He has contributed to the concept and literature search, and to the drafting, revision and approval of the manuscript. Bård Lundeland, senior consultant and specialist in anaesthesiology, and postdoctoral researcher. The author has completed the ICMJE form and declares the following conflicts of interest: He received payment from Abbott for a lecture on radiofrequency ablation for neck and back pain in 2021.
Galaxy populations in the most distant SPT-SZ clusters I. Environmental quenching in massive clusters at 1.4 (cid:46) z (cid:46) 1.7

We present the ﬁrst results from a galaxy population study in the highest redshift galaxy clusters identiﬁed in the 2500deg 2 South Pole Telescope Sunyaev Zel’dovich e ﬀ ect (SPT-SZ) survey, which is sensitive to M 500 (cid:38) 3 × 10 14 M (cid:12) clusters from z ∼ 0 . 2 out to the highest redshifts where such massive structures exist. The cluster selection is to ﬁrst order independent of galaxy properties, making the SPT-SZ sample particularly well suited for cluster galaxy population studies. We carried out a four-band imaging campaign with the Hubble and Spitzer Space Telescopes of the ﬁve z (cid:38) 1 . 4, S / N SZE > 5 clusters, that are among the rarest most massive clusters known at this redshift. All ﬁve clusters show clear overdensities of red galaxies whose colors agree with the initial cluster redshift estimates, although one (SPT-CLJ0607–4448) shows a galaxy concentration much less prominent than the others. The highest redshift cluster in this sample, SPT-CLJ0459–4947 at z ∼ 1 . 72, is the most distant M 500 > 10 14 M (cid:12) cluster discovered thus far through its intracluster medium, and is one of only three known clusters in this mass range at z (cid:38) 1 . 7, regardless of selection. Based on UVJ -like photometric classiﬁcation of quiescent and star-forming galaxies, we ﬁnd that the quiescent fraction in the cluster central regions ( r / r 500 < 0 . 7) is higher than in the ﬁeld at the same redshift, with corresponding environmental quenching e ﬃ ciencies typically in the range ∼ 0 . 5 − 0 . 8 for stellar masses log( M / M (cid:12) ) > 10 . 85. We have explored the impact of emission from star formation on the selection of this sample, concluding that all ﬁve clusters studied here would still have been detected with S / N SZE > 5, even if they had the same quiescent fraction as measured in the ﬁeld. Our results thus point towards an e ﬃ cient suppression of star formation in the central regions of the most massive clusters, occurring already earlier than z ∼ 1 . 5.
> ? ∗ > 0 B ? ∗ > 0 C ? ∗ > 0 DEC Conv = Full-image encoder Conv Conv Conv Conv Conv Conv Convs ASPP # Dense feature extractor Scene understanding modular Ordinal regression Input output

Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring image-level information and hierarchical features from deep convolutional neural networks (DCNNs). These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions. Besides, existing depth estimation networks employ repeated spatial pooling operations, resulting in undesirable low-resolution feature maps. To obtain high-resolution depth maps, skip-connections or multilayer deconvolution networks are required, which complicates network training and consumes much more computations. To eliminate or at least largely reduce these problems, we introduce a spacing-increasing discretization (SID) strategy to discretize depth and recast depth network learning as an ordinal regression problem. By training the network using an ordinary regression loss, our method achieves much higher accuracy and faster convergence in synch. Furthermore, we adopt a multi-scale network structure which avoids unnecessary spatial pooling and captures multi-scale information in parallel. The proposed deep ordinal regression network (DORN) achieves state-of-the-art results on three challenging benchmarks, i.e., KITTI [16], Make3D [49], and NYU Depth v2 [41], and outperforms existing methods by a large margin.
Cardiopulmonary Exercise Testing in Children and Adolescents with Asthma Who Report Symptoms of Exercise-Induced Bronchoconstriction

Patients with asthma often report symptoms of exercise-induced bronchoconstriction. We performed cardiopulmonary exercise testing to establish the cause of exercise limitation in patients with asthma, under treatment, who reported symptoms of exercise-induced bronchoconstriction. Ten of the 42 patients meeting criteria for inclusion in our study (24%) developed exercise-induced bronchoconstriction. Exercise limitation without exercise-induced bronchoconstriction was found in both obese and non-obese patients, suggesting that poor fitness is a problem independent of body habitus. Including cardiopulmonary exercise testing in the management of children with suspected exercise-induced bronchoconstriction would provide a better understanding of the etiology of their symptoms and facilitate more appropriate treatment.
Simultaneous localization and mapping with multimodal probability distributions

Simultaneous Localization and Mapping (SLAM) has focused on noisy but unique data associations resulting in linear Gaussian uncertainty models. However, a unique decision is often not possible using only local information, giving rise to ambiguities that have to be resolved globally during optimization. To solve this problem, the pose graph data structure is extended here by multimodal constraints modeled by mixtures of Gaussians (MoG). Furthermore, optimization methods for this novel formulation are introduced, namely (a) robust iteratively reweighted least squares, and (b) Prefilter Stochastic Gradient Descent (SGD) where a preprocessing step determines globally consistent modes before applying SGD. In addition, a variant of the Prefilter method (b) is introduced in form of (c) Prefilter Levenberg-Marquardt. The methods are compared with traditional state-of-the-art optimization methods including (d) Stochastic Gradient Descent and (e) Levenberg-Marquardt as well as (f) Particle filter SLAM and with (g) an optimal exhaustive algorithm. Experiments show that ambiguities significantly impact state-of-the-art methods, and that the novel Prefilter methods (b) and (c) perform best. This is further substantiated with experiments using real-world data. To this end, a method to generate MoG constraints from a plane-based registration algorithm is introduced and used for 3D SLAM under ambiguities.
Deep Ad-hoc Beamforming

Far-field speech processing is an important and challenging problem. In this paper, we propose \textit{deep ad-hoc beamforming}, a deep-learning-based multichannel speech enhancement framework based on ad-hoc microphone arrays, to address the problem. It contains three novel components. First, it combines \textit{ad-hoc microphone arrays} with deep-learning-based multichannel speech enhancement, which reduces the probability of the occurrence of far-field acoustic environments significantly. Second, it groups the microphones around the speech source to a local microphone array by a supervised channel selection framework based on deep neural networks. Third, it develops a simple time synchronization framework to synchronize the channels that have different time delay. Besides the above novelties and advantages, the proposed model is also trained in a single-channel fashion, so that it can easily employ new development of speech processing techniques. Its test stage is also flexible in incorporating any number of microphones without retraining or modifying the framework. We have developed many implementations of the proposed framework and conducted an extensive experiment in scenarios where the locations of the speech sources are far-field, random, and blind to the microphones. Results on speech enhancement tasks show that our method outperforms its counterpart that works with linear microphone arrays by a considerable margin in both diffuse noise reverberant environments and point source noise reverberant environments.