Miracle-dz/UniFAR-FTUs huggingface dataset:
Individual tree crown detection and delineation across a woodland using leaf-on and leaf-off imagery from a UAV consumer-grade camera

Abstract. Extraction of information about individual trees using remotely sensed data is essential to supporting ecological and commercial applications in forest environments. Data acquired by consumer-grade cameras onboard unmanned aerial vehicles (UAV) offer an affordable option of high-spatial resolution imagery that can be used to extract forest structural information at a tree level. The aim of this work is to investigate the potential and accuracy of UAV time-series data to automatically detect and delineate tree crowns across an entire woodland. The workflow (presented in a step-by-step manner) involves the construction of a canopy height model (CHM) based on digital elevation models derived from the UAV photogrammetric point clouds. A watershed-based approach is modified to automatically detect and delineate the tree crowns, based on the CHM and the brightness information from the UAV orthomosaics. The accuracy of the proposed method was evaluated by comparing its results against manually delineated tree crowns. The results show an overall accuracy of 63%, where conifer species were more accurately delineated (up to 80%), while broadleaf species returned lower accuracies (<50 % ). Continued research is necessary to improve the confidence of automated individual tree crown detection and delineation, especially over complex forest structures.
Mesh low-pass filters for millimeter-wave applications: is the square capacitive shape optimal?

Abstract. The use of mesh filters for millimeter-wave applications using capacitive and inductive grids is well known, and they are widely used in cosmic microwave background instrumentation. We report on an investigation into whether the capacitive square shape typically used in low-pass filter designs could be improved. The microgenetic algorithm and the finite-difference time-domain, and electromagnetic modeling method were used to look for shape variations to the standard square shape. Any shape changes discovered were then analyzed to establish which variations had the most effect. We show that improvements found using pixelated patterns evolved by the genetic algorithm were somewhat mixed.
Interictal Fast Ripples Are Associated With the Seizure-Generating Lesion in Patients With Dual Pathology

Rationale: Patients with dual pathology have two potentially epileptogenic lesions: One in the hippocampus and one in the neocortex. If epilepsy surgery is considered, stereotactic electroencephalography (SEEG) may reveal which of the lesions is seizure-generating, but frequently, some uncertainty remains. We aimed to investigate whether interictal high-frequency oscillations (HFOs), which are a promising biomarker of epileptogenicity, are associated with the primary focus. Methods: We retrospectively analyzed 16 patients with dual pathology. They were grouped according to their seizure-generating lesion, as suggested by ictal SEEG. An automated detector was applied to identify interictal epileptic spikes, ripples (80–250 Hz), ripples co-occurring with spikes (IES-ripples) and fast ripples (250–500 Hz). We computed a ratio R to obtain an indicator of whether rates were higher in the hippocampal lesion (R close to 1), higher in the neocortical lesion (R close to −1), or more or less similar (R close to 0). Results: Spike and HFO rates were higher in the hippocampal than in the neocortical lesion (p < 0.001), particularly in seizure onset zone channels. Seizures originated exclusively in the hippocampus in 5 patients (group 1), in both lesions in 7 patients (group 2), and exclusively in the neocortex in 4 patients (group 3). We found a significant correlation between the patients' primary focus and the ratio Rfast ripples, i.e., the proportion of interictal fast ripples detected in this lesion (p < 0.05). No such correlation was observed for interictal epileptic spikes (p = 0.69), ripples (p = 0.60), and IES-ripples (p = 0.54). In retrospect, interictal fast ripples would have correctly “predicted” the primary focus in 69% of our patients (p < 0.01). Conclusions: We report a correlation between interictal fast ripple rate and the primary focus, which was not found for epileptic spikes. Fast ripple analysis could provide helpful information for generating a hypothesis on seizure-generating networks, especially in cases with few or no recorded seizures.
Polyfunctional Fc Dependent Activity of Antibodies to Native Trimeric Envelope in HIV Elite Controllers

Antibody dependent (AD) functions such as AD cellular cytotoxicity (ADCC) were associated with lower viral load (VL) in untreated HIV progressors and protection from HIV infection in the modestly protective RV144 HIV vaccine trial. Target cells used to measure ADCC, AD complement deposition (ADCD), and AD cellular trogocytosis (ADCT) have been either HIV envelope (Env) gp120-coated CEM.NKr. CCR5 cells or HIV infected cell cultures. In HIV infected cell cultures, uninfected bystander cells take up gp120 shed from infected cells. Both gp120-coated and gp120+ bystander cells expose CD4 induced (CD4i) epitopes, which are normally hidden in native trimeric Env expressed by genuinely HIV infected cells since Nef and Vpu downmodulate cell surface CD4. Antibody dependent assays using either of these target cells probe for CD4i Abs that are abundant in HIV+ plasma but that do not recognize HIV-infected cells. Here, we examined ADCC, ADCD, and ADCT functions using a target cell line, sorted HIV-infected cell line cells, whose HIV infection frequency nears 100% and that expresses HIV Env in a native trimeric closed conformation. Using sorted HIV-infected cells (siCEM) as targets, we probed the binding and AD functions of anti-gp120/Env Abs in plasma from HIV-infected untreated progressor (UTP, n = 18) and treated (TP, n = 24) subjects, compared to that in Elite controllers (EC, n = 37) and Viral Controllers (VC, n = 16), which are rare subsets of HIV-infected individuals who maintain undetectable or low VL, respectively, without treatment. Gp120-coated beads were used to measure AD cellular phagocytosis. Equivalent concentrations of input IgG in plasma from UTPs, ECs, and VCs supported higher levels of all AD functions tested than plasma from TPs. When AD activities were normalized to the concentration of anti-gp120/Env-specific Abs, between-group differences largely disappeared. This finding suggests that the anti-gp120/Env Abs concentrations and not their potency determined AD functional levels in these assays. Elite controllers did differ from the other groups by having AD functions that were highly polyfunctional and highly correlated with each other. PCR measurement of HIV reservoir size showed that ADCC activity was higher in ECs and VCs with a reservoir size below the limit of detection compared to those having a measurable HIV reservoir size.
Lightweight convolutional neural network for bitemporal SAR image change detection

Abstract. Recently, many convolutional neural networks (CNN) have been successfully employed in bitemporal synthetic aperture radar (SAR) image change detection. However, most of the existing networks are too heavy and occupy a large volume of memory for storage and calculation. Motivated by this, we propose a lightweight neural network to reduce the computational and spatial complexity and facilitate the change detection on an edge device. We replace normal convolutional layers with bottleneck layers that keep the same number of channels between input and output. Next, we employ dilated convolutional kernels with a few non-zero entries that reduce the running time in convolutional operators. Comparing with the conventional convolutional neural network, our lightweight neural network will be more efficient with fewer parameters. We verify our light-weighted neural network on four sets of bitemporal SAR images. The experimental results show that the proposed network can obtain better performance than the conventional CNN and has better model generalization, especially on the challenging datasets with complex scenes.
Snow and cloud detection using a convolutional neural network and low-resolution data from the Electro-L No. 2 Satellite

Abstract. We describe an algorithm based on a convolutional neural network that detects cloud formations and snow cover in satellite images using textures. Herein, multispectral satellite images, received from a multizone scanning instrument used for hydrometeorological support and installed on the Russian satellite Electro-L No. 2, are used as input data. The problem of snow and cloud classification in the absence of a spectral channel in the range of 1.4 to 1.8 μm, which is necessary for their accurate separation, is considered. The developed algorithm can produce cloud and snow cover masks for an area limited by the values of the solar zenith angle in the range of 0 deg to 80 deg for daytime. Algorithm accuracy was evaluated using machine learning metrics and comparing its results with ground truth masks segmented manually by an experienced interpreter. In addition, we compared the resulting masks with a similar cloud mask product from the European Organisation for the Exploitation of Meteorological Satellites based on the data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument installed on the Meteosat-8 satellite. According to the results of this comparison, we conclude that the cloud masks produced by the proposed convolutional neural network-based algorithm have a lower probability of false detection than products based on the SEVIRI data. The proposed algorithm is fully automatic, and it works in any season of the year during the daytime.
IL-7R signalling activates widespread VH and DH gene usage to drive antibody diversity in bone marrow B cells

Generation of the primary antibody repertoire requires V(D)J recombination of hundreds of gene segments in the immunoglobulin heavy chain (Igh) locus. It has been proposed that interleukin-7 receptor (IL-7R) signalling is necessary for Igh recombination, but this has been challenging to partition from the receptor’s role in B cell survival and proliferation. By generating the first detailed description of the Igh repertoire of murine IL-7Rα-/- bone marrow B cells, we demonstrate that IL-7R signalling profoundly influences VH gene selection during VH-to-DJH recombination. We find skewing towards usage of 3’ VH genes during de novo VH-to-DJH recombination that is more severe than the fetal liver (FL) B cell repertoire, and we now show a role for IL-7R signalling in DH-to-JH recombination. Transcriptome and accessibility analyses suggests reduced expression of B lineage-specific transcription factors (TFs) and their targets, and loss of DH and VH antisense transcription in IL-7Rα-/- B cells. These results refute models suggesting that IL-7R signalling is only required for survival and proliferation, and demonstrate a pivotal role in shaping the Igh repertoire by activating underpinning epigenetic mechanisms.
Fungal symbionts of Arctic zooplankton induce mortality and reduce swimming activity

Zooplankton are key intermediaries between microbes and higher trophic levels. Elevated temperatures in the Arctic are expected to increase metabolic rates, which in turn will result in increased rates of zooplankton production and foraging. We postulate that this may also be tied to elevated incidence of fungi parasitism in key zooplankton species. We present data of in-situ fungal parasitism in the key Arctic zooplankton species complex Calanus spp. We found that >10% of Calanus specimens were internally parasitized by fungi that had 100% molecular identity to Penicillium chrysogenum. Experimental studies with two strains of P. chrysogenum and Calanus spp. revealed an increased mortality effect suggesting that fungi decrease the survival of overwintering zooplankton, which may have cascading effects on higher trophic levels that feed on Calanus. Moreover, Calanus demonstrated reduced swimming activity in the presence of P. chyrsogenum, indicating that fungi could affect predator avoidance and vertical migration patterns of zooplankton. Semi-thin sectioning of live zooplankton incubated with P. chrysogenum revealed the presence of hyphae throughout the body, underscoring the parasitic nature of P. chrysogenum in Calanus. We hypothesize that as the Arctic warms, the balance between host and pathogen could shift in favor of pathogen reproduction, resulting in a greater effect of fungal pathogens on zooplankton.
TALE: Transformer-based protein function Annotation with joint sequence–Label Embedding

Motivation Facing the increasing gap between high-throughput sequence data and limited functional insights, computational protein function annotation provides a high-throughput alternative to experimental approaches. However, current methods can have limited applicability while relying on data besides sequences, or lack generalizability to novel sequences, species and functions. Results To overcome aforementioned barriers in applicability and generalizability, we propose a novel deep learning model, named Transformer-based protein function Annotation through joint sequence–Label Embedding (TALE). For generalizbility to novel sequences we use self attention-based transformers to capture global patterns in sequences. For generalizability to unseen or rarely seen functions, we also embed protein function labels (hierarchical GO terms on directed graphs) together with inputs/features (sequences) in a joint latent space. Combining TALE and a sequence similarity-based method, TALE+ outperformed competing methods when only sequence input is available. It even outperformed a state-of-the-art method using network information besides sequence, in two of the three gene ontologies. Furthermore, TALE and TALE+ showed superior generalizability to proteins of low homology and never/rarely annotated novel species or functions compared to training data, revealing deep insights into the protein sequence–function relationship. Ablation studies elucidated contributions of algorithmic components toward the accuracy and the generalizability. Availability The data, source codes and models are available at https://github.com/Shen-Lab/TALE Contact yshen@tamu.edu Supplementary information Supplementary data are available at Bioinformatics online.
A model of bracken (Pteridium aquilinum) growth and the effects of control strategies and changing climate

A mechanistic physiological model for the growth of bracken has been developed which describes its yearly cycle in terms of three compartments: frond biomass, rhizome tissue biomass and rhizome carbohydrate. Difference equations for various processes (including rhizome respiration, tissue senescence, photosynthesis and fluxes between compartments) are presented. These are combined to obtain equations for the three compartments for each stage in the yearly cycle of bracken growth. Collectively, these equations constitute the bracken growth model. The model can be used to predict the effects of different control measures on the biomass present in the stand. 5. As a number of environmental factors are involved in the model, the effects of changes in these variables can be predicted (.)
Beyond Cross-Validation - Accuracy Estimation for Incremental and Active Learning Models

For incremental machine-learning applications it is often important to robustly estimate the system accuracy during training, especially if humans perform the supervised teaching. Cross-validation and interleaved test/train error are here the standard supervised approaches. We propose a novel semi-supervised accuracy estimation approach that clearly outperforms these two methods. We introduce the Configram Estimation (CGEM) approach to predict the accuracy of any classifier that delivers confidences. By calculating classification confidences for unseen samples, it is possible to train an offline regression model, capable of predicting the classifier’s accuracy on novel data in a semi-supervised fashion. We evaluate our method with several diverse classifiers and on analytical and real-world benchmark data sets for both incremental and active learning. The results show that our novel method improves accuracy estimation over standard methods and requires less supervised training data after deployment of the model. We demonstrate the application of our approach to a challenging robot object recognition task, where the human teacher can use our method to judge sufficient training.
TailTimer: an open-source device for automating the rodent tail immersion assay

The tail immersion assay is a widely used method for measuring acute thermal pain in a way which is quantifiable and reproducible. It is non-invasive and measures response to a stimulus that may be encountered by an animal in its natural environment. However, tail withdrawal latency data are usually collected manually, and precise temperatures of the water at the time of measurement are most often not recorded. These two factors can reduce the reproducibility of tail immersion assay data. We designed a device, TailTimer, which uses the Raspberry Pi single-board computer and a temperature sensor, to automatically record both tail withdrawal latency and water temperature. The device has a radio frequency identification (RFID) system that can record the ID of animals. Our software recognizes several specific RFID keys as user interface commands, which allows TailTimer to be operated via RFID fobs. We also programmed the device to only allow tests to be conducted when the water is within ± 0.25 °C of the target temperature. Data recorded using the TailTimer device showed a linear relationship between tail withdrawal latency and water temperature when tested between 47 - 50 °C. We also observed a profound effect of water mixing speed on tail withdrawal latency. Our data further revealed significant strain and sex differences, valorizing TailTimer in its ability to detect genetically-determined variations in thermal pain sensitivity. Significance Statement Quantification of tail withdrawal latency in response to thermal pain has essentially remained the same since the method was first introduced decades ago and relies on manual recording of water temperature and tail withdrawal latency. Such manual methods engender relatively substantial variability and are potential contributors to some of the discrepancies present among relevant research. The open source TailTimer device we report here is simple and inexpensive to manufacture. The RFID-based user interface is ergonomic, especially in animal facilities where space is limited and gloves are mandatory. We anticipate that the increased reproducibility of tail withdrawal latency provided by TailTimer will augment its utility in nociception and addiction research.
Population variation in social brain morphology: Links to socioeconomic status and health disparity

ABSTRACT Health disparity across layers of society involves reasons beyond the healthcare system. Socioeconomic status (SES) shapes people’s daily interaction with their social environment and is known to impact various health outcomes. Using generative probabilistic modeling, we investigate health satisfaction and complementary indicators of socioeconomic lifestyle in the human social brain. In a population cohort of ~10,000 UK Biobank participants, our first analysis probed the relationship between health status and subjective social standing (i.e., financial satisfaction). We identified volume effects in participants unhappy with their health in regions of the higher associative cortex, especially the dorsomedial prefrontal cortex (dmPFC) and bilateral temporo-parietal junction (TPJ). Specifically, participants in poor subjective health showed deviations in dmPFC and TPJ volume as a function of financial satisfaction. The second analysis on health status and objective social standing (i.e., household income) revealed volume deviations in regions of the limbic system for individuals feeling unhealthy. In particular, low-SES participants dissatisfied with their health showed deviations in volume distributions in the amygdala and hippocampus bilaterally. Thus, our population-level evidence speaks to the possibility that health status and socioeconomic position have characteristic imprints in social brain differentiation.
A high affinity human monoclonal antibody against Pfs230 binds multiple parasite stages and blocks oocyst formation in mosquitoes

Malaria elimination requires tools that interrupt parasite transmission. Here, we characterized B cell receptor responses among Malian adults vaccinated against the first domain of the cysteine-rich 230kDa gamete surface protein Pfs2301–3 to neutralize sexual stage P. falciparum parasites and halt their further spread. We generated nine Pfs230 human monoclonal antibodies (mAbs). One mAb potently blocked transmission to mosquitoes in a complement-dependent manner and reacted strongly to gamete surface while eight mAbs showed only low or no blocking activity. This study provides a rational basis to improve malaria vaccines and develop therapeutic antibodies for malaria elimination.
The soybean (Glycine max L.) cytokinin oxidase/dehydrogenase multigene family; Identification of natural variations for altered cytokinin content and seed yield

Cytokinins (CKs) play a fundamental role in regulating dynamics of organ source/sink relationships during plant development, including flowering and seed formation stages. As a result, CKs are key drivers of seed yield. The cytokinin oxidase/dehydrogenase (CKX) is one of the critical enzymes responsible for regulating plant CK levels by causing their irreversible degradation. Variation of CKX gene activity is significantly correlated with seed yield in many crop species while in soybean (Glycine max L.), the possible associations between CKX gene family members (GFMs) and yield parameters have not yet been assessed. In this study, seventeen GmCKX GFMs were identified, and natural variations among GmCKX genes were probed among soybean cultivars with varying yield characteristics. The key CKX genes responsible for regulating CK content during seed filling stages of reproductive development were highlighted using comparative phylogenetics, gene expression analysis and CK metabolite profiling. Five of the seventeen identified GmCKX GFMs, showed natural variations in the form of single nucleotide polymorphisms (SNPs). The gene GmCKX14, with high expression during critical seed filling stages, was found to have a non-synonymous mutation (H105Q), on one of the active site residues, Histidine 105, previously reported to be essential for co-factor binding to maintain structural integrity of the enzyme. Soybean lines with this mutation had higher CK content and desired yield characteristics. The potential for marker-assisted selection based on the identified natural variation within GmCKX14, is discussed in the context of hormonal control that can result in higher soybean yield. Key Message Natural variations in soybean cytokinin oxidase/dehydrogenase gene, GmCKX14, with high expression during seed development, were linked to increased sink strength via altered cytokinin profiles in high yielding cultivars.