Miracle-dz/UniFAR-FTUs huggingface dataset:
Thiol Redox Regulation of Plant β-Carbonic Anhydrase

β-carbonic anhydrases (βCA) accelerate the equilibrium formation between CO2 and carbonate. Two plant βCA isoforms are targeted to the chloroplast and represent abundant proteins in the range of >1% of chloroplast protein. While their function in gas exchange and photosynthesis is well-characterized in carbon concentrating mechanisms of cyanobacteria and plants with C4-photosynthesis, their function in plants with C3-photosynthesis is less clear. The presence of conserved and surface-exposed cysteinyl residues in the βCA-structure urged to the question whether βCA is subject to redox regulation. Activity measurements revealed reductive activation of βCA1, whereas oxidized βCA1 was inactive. Mutation of cysteinyl residues decreased βCA1 activity, in particular C280S, C167S, C230S, and C257S. High concentrations of dithiothreitol or low amounts of reduced thioredoxins (TRXs) activated oxidized βCA1. TRX-y1 and TRX-y2 most efficiently activated βCA1, followed by TRX-f1 and f2 and NADPH-dependent TRX reductase C (NTRC). High light irradiation did not enhance βCA activity in wildtype Arabidopsis, but surprisingly in βca1 knockout plants, indicating light-dependent regulation. The results assign a role of βCA within the thiol redox regulatory network of the chloroplast.
A homily on signal detection theory.

A lifetime ago, as an undergraduate, I joined a team that developed a new way of thinking about the sensitivity of sensory systems. My teammates were Wilson (Spike) Tanner and John Swets, both now deceased, and we were working at the University of Michigan. I also wish to thank J.C. R. Licklider, J. P. Egan, and Lloyd Jeffress who aided and encouraged that development. I am gratified that what came to be called signal-detection theory (SDT) was so widely accepted and its methods so widely adopted. However, I am somewhat disappointed about how SDT commonly is portrayed, and taught. My reasons are presented here.
Expanding Role of Dopaminergic Inhibition in Hypercapnic Responses of Cultured Rat Carotid Body Cells: Involvement of Type II Glial Cells

Dopamine (DA) is a well-studied neurochemical in the mammalian carotid body (CB), a chemosensory organ involved in O2 and CO2/H+ homeostasis. DA released from receptor (type I) cells during chemostimulation is predominantly inhibitory, acting via pre- and post-synaptic dopamine D2 receptors (D2R) on type I cells and afferent (petrosal) terminals respectively. By contrast, co-released ATP is excitatory at postsynaptic P2X2/3R, though paracrine P2Y2R activation of neighboring glial-like type II cells may boost further ATP release. Here, we tested the hypothesis that DA may also inhibit type II cell function. When applied alone, DA (10 μM) had negligible effects on basal [Ca2+]i in isolated rat type II cells. However, DA strongly inhibited [Ca2+]i elevations (Δ[Ca2+]i) evoked by the P2Y2R agonist UTP (100 μM), an effect opposed by the D2/3R antagonist, sulpiride (1–10 μM). As expected, acute hypercapnia (10% CO2; pH 7.4), or high K+ (30 mM) caused Δ[Ca2+]i in type I cells. However, these stimuli sometimes triggered a secondary, delayed Δ[Ca2+]i in nearby type II cells, attributable to crosstalk involving ATP-P2Y2R interactions. Interestingly sulpiride, or DA store-depletion using reserpine, potentiated both the frequency and magnitude of the secondary Δ[Ca2+]i in type II cells. In functional CB-petrosal neuron cocultures, sulpiride potentiated hypercapnia-induced Δ[Ca2+]i in type I cells, type II cells, and petrosal neurons. Moreover, stimulation of type II cells with UTP could directly evoke Δ[Ca2+]i in nearby petrosal neurons. Thus, dopaminergic inhibition of purinergic signalling in type II cells may help control the integrated sensory output of the CB during hypercapnia.
The Tomato Guanylate-Binding Protein SlGBP1 Enables Fruit Tissue Differentiation by Maintaining Endopolyploid Cells in a Non-Proliferative State

The induction of polyploid divisions in the tomato gbp1 mutant reveals the role of the guanylate-binding protein in maintaining fruit tissue differentiation. Cell fate maintenance is an integral part of plant cell differentiation and the production of functional cells, tissues, and organs. Fleshy fruit development is characterized by the accumulation of water and solutes in the enlarging cells of parenchymatous tissues. In tomato (Solanum lycopersicum), this process is associated with endoreduplication in mesocarp cells. The mechanisms that preserve this developmental program, once initiated, remain unknown. We show here that analysis of a previously identified tomato ethyl methanesulfonate-induced mutant that exhibits abnormal mesocarp cell differentiation could help elucidate determinants of fruit cell fate maintenance. We identified and validated the causal locus through mapping-by-sequencing and gene editing, respectively, and performed metabolic, cellular, and transcriptomic analyses of the mutant phenotype. The data indicate that disruption of the SlGBP1 gene, encoding GUANYLATE BINDING PROTEIN1, induces early termination of endoreduplication followed by late divisions of polyploid mesocarp cells, which consequently acquire the characteristics of young proliferative cells. This study reveals a crucial role of plant GBPs in the control of cell cycle genes, and thus, in cell fate maintenance. We propose that SlGBP1 acts as an inhibitor of cell division, a function conserved with the human hGBP-1 protein.
META-BASE: A Novel Architecture for Large-Scale Genomic Metadata Integration

The integration of genomic metadata is, at the same time, an important, difficult, and well-recognized challenge. It is important because a wealth of public data repositories is available to drive biological and clinical research; combining information from various heterogeneous and widely dispersed sources is paramount to a number of biological discoveries. It is difficult because the domain is complex and there is no agreement among the various metadata definitions, which refer to different vocabularies and ontologies. It is well-recognized in the bioinformatics community because, in the common practice, repositories are accessed one-by-one, learning their specific metadata definitions as result of long and tedious efforts, and such practice is error-prone. In this paper, we describe META-BASE, an architecture for integrating metadata extracted from a variety of genomic data sources, based upon a structured transformation process. We present a variety of innovative techniques for data extraction, cleaning, normalization and enrichment. We propose a general, open and extensible pipeline that can easily incorporate any number of new data sources, and propose the resulting repository–already integrating several important sources–which is exposed by means of practical user interfaces to respond biological researchers’ needs.
Attention-Based Dropout Layer for Weakly Supervised Single Object Localization and Semantic Segmentation

Both weakly supervised single object localization and semantic segmentation techniques learn an object’s location using only image-level labels. However, these techniques are limited to cover only the most discriminative part of the object and not the entire object. To address this problem, we propose an attention-based dropout layer, which utilizes the attention mechanism to locate the entire object efficiently. To achieve this, we devise two key components, 1) hiding the most discriminative part from the model to capture the entire object, and 2) highlighting the informative region to improve the classification power of the model. These allow the classifier to be maintained with a reasonable accuracy while the entire object is covered. Through extensive experiments, we demonstrate that the proposed method effectively improves the weakly supervised single object localization accuracy, thereby achieving a new state-of-the-art localization accuracy on the CUB-200-2011 and a comparable accuracy existing state-of-the-arts on the ImageNet-1k. The proposed method is also effective in improving the weakly supervised semantic segmentation performance on the Pascal VOC and MS COCO. Furthermore, the proposed method is more efficient than existing techniques in terms of parameter and computation overheads. Additionally, the proposed method can be easily applied in various backbone networks.
Impact of Perception of Green Space for Health Promotion on Willingness to Use Parks and Actual Use among Young Urban Residents

Promoting the use of green space is a fundamental way to improve physical and mental health and to enhance the quality of life of urban residents. In response to increasing demand for green space in cities, the impact of perception of green space for health promotion on willingness to use parks and actual use among young urban residents was investigated in this study. A total of 1135 young residents (ages 18–35) in three cities in China were surveyed by online questionnaire. A group of multiple regression models was constructed to investigate the influencing perception factors of participants’ willingness to use parks and actual use. The results revealed that the young residents’ perception of green space components for health promotion (green space access, types, sizes, plants, water, sensory features, microclimate environments and amenity facilities) had a greater effect on their willingness to use parks and to promote health, while it was less influential with respect to their actual park use behavior (frequency and duration). Among these variables, green space access is a critical concern for willingness to use toward parks. The disparities of perception of green space for health promotion effect on willingness to use a park and actual use provide a better understanding of the psychological factors affecting park use among young residents. The findings also provided some implications for public health policymakers, urban planners and landscape architects in designing parks to encourage visitation by young people.
High-Throughput Sequencing and Expression Analysis Suggest the Involvement of Pseudomonas putida RA-Responsive microRNAs in Growth and Development of Arabidopsis

Beneficial soil microorganisms largely comprise of plant growth-promoting rhizobacteria (PGPR), which adhere to plant roots and facilitate their growth and development. Pseudomonas putida (RA) strain MTCC5279 is one such PGPR that exhibits several characteristics of plant growth promotion, such as P-solubilization, and siderophores and IAA production. Plant–PGPR interactions are very complex phenomena, and essentially modulate the expression of numerous genes, consequently leading to changes in the physiological, biochemical, cellular and molecular responses of plants. Therefore, in order to understand the molecular bases of plant–PGPR interactions, we carried out the identification of microRNAs from the roots of Arabidopsis upon P. putida RA-inoculation, and analyses of their expression. MicroRNAs (miRNAs) are 20- to 24-nt non-coding small RNAs known to regulate the expression of their target genes. Small RNA sequencing led to the identification of 293 known and 67 putative novel miRNAs, from the control and RA-inoculated libraries. Among these, 15 known miRNAs showed differential expression upon RA-inoculation in comparison to the control, and their expressions were corroborated by stem-loop quantitative real-time PCR. Overall, 28,746 and 6931 mRNAs were expected to be the targets of the known and putative novel miRNAs, respectively, which take part in numerous biological, cellular and molecular processes. An inverse correlation between the expression of RA-responsive miRNAs and their target genes also strengthened the crucial role of RA in developmental regulation. Our results offer insights into the understanding of the RA-mediated modulation of miRNAs and their targets in Arabidopsis, and pave the way for the further exploitation and characterization of candidate RA-responsive miRNA(s) for various crop improvement strategies directed towards plant sustainable growth and development.
Synthesis, characterization, powder X-ray diffraction analysis, thermal stability, antioxidant properties and enzyme inhibitions of M(II)-Schiff base ligand complexes

Abstract The Schiff base ligand ((E)-6-methyl-2-(2,3,4-trimethoxybenzylideneamino)-4,5,6,7-tetrahydrobenzo[b]thiophene-3-carbonitrile) and its cobalt(II) and palladium(II) complexes were successfully prepared. The structure of the compounds was elucidated by various techniques (NMR, FT-IR, powder X-ray diffraction, microanalysis, TGA, magnetic susceptibility, mass spectrometry). The Pd(II) complex showed a square planar geometry and the Co(II) complex had an octahedral geometry. ABTS (2,2-azino-bis 3-ethylbenzothiazloine-6-sulphonic acid), DPPH (1,1-diphenyl-2-picrylhydrazyl), FRAP (ferric-reducing antioxidant power) and CUPRAC (cupric reducing antioxidant capacity) in vitro methods were applied to identify the antioxidant features of the synthesized compounds. In addition, glutathione S-transferase and acetyl/butyryl cholinesterase enzymes were examined for possible inhibition capacities of the complexes. According to the enzyme activity measurements, Ru(II) complex inhibited both GST and BChE enzymes, while Fe(II) complex inhibited only AChE enzyme. Furthermore, the antioxidant activities and enzyme inhibitions of the previously synthesized Fe(II) and Ru(II) complexes of the same ligand were examined to make a comparison of the metal complexes. Communicated by Ramaswamy H. Sarma
Robust Multi-Task Learning With Flexible Manifold Constraint

Multi-Task Learning attempts to explore and mine the sufficient information within multiple related tasks for the better solutions. However, the performance of the existing multi-task approaches would largely degenerate when dealing with the polluted data, i.e., outliers. In this paper, we propose a novel robust multi-task model by incorporating a flexible manifold constraint (FMC-MTL) and a robust loss. Specifically speaking, multi-task subspace is embedded with a relaxed and generalized Stiefel Manifold for considering point-wise correlation and preserving the data structure simultaneously. In addition, a robust loss function is developed to ensure the robustness to outliers by smoothly interpolating between <inline-formula><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>ℓ</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="zhang-ieq1-3007637.gif"/></alternatives></inline-formula>-norm and squared Frobenius norm. Equipped with an efficient algorithm, FMC-MTL serves as a robust solution to tackling the severely polluted data. Moreover, extensive experiments are conducted to verify the superiority of our model. Compared to the state-of-the-art multi-task models, the proposed FMC-MTL model demonstrates remarkable robustness to the contaminated data.
The Platelet Concentrates Therapy: From the Biased Past to the Anticipated Future

The ultimate goal of research on platelet concentrates (PCs) is to develop a more predictable PC therapy. Because platelet-rich plasma (PRP), a representative PC, was identified as a possible therapeutic agent for bone augmentation in the field of oral surgery, PRP and its derivative, platelet-rich fibrin (PRF), have been increasingly applied in a regenerative medicine. However, a rise in the rate of recurrence (e.g., in tendon and ligament injuries) and adverse (or nonsignificant) clinical outcomes associated with PC therapy have raised fundamental questions regarding the validity of the therapy. Thus, rigorous evidence obtained from large, high-quality randomized controlled trials must be presented to the concerned regulatory authorities of individual countries or regions. For the approval of the regulatory authorities, clinicians and research investigators should understand the real nature of PCs and PC therapy (i.e., adjuvant therapy), standardize protocols of preparation (e.g., choice of centrifuges and tubes) and clinical application (e.g., evaluation of recipient conditions), design bias-minimized randomized clinical trials, and recognize superfluous brand competitions that delay sound progress. In this review, we retrospect the recent past of PC research, reconfirm our ultimate goals, and discuss what will need to be done in future.
Functional Alterations in the Olfactory Neuronal Circuit Occur before Hippocampal Plasticity Deficits in the P301S Mouse Model of Tauopathy: Implications for Early Diagnosis and Translational Research in Alzheimer’s Disease

Alzheimer’s disease (AD) is characterized by neuronal loss and impaired synaptic transmission, ultimately leading to cognitive deficits. Early in the disease, the olfactory track seems most sensitive to tauopathy, while most plasticity studies focused on the hippocampal circuits. Functional network connectivity (FC) and long-term potentiation (LTP), considered as the plasticity substrate of learning and memory, were longitudinally assessed in mice of the P301S model of tauopathy following the course (time and location) of progressively neurodegenerative pathology (i.e., at 3, 6, and 9 months of age) and in their wild type (WT) littermates. Using in vivo local field potential (LFP) recordings, early (at three months) dampening in the gamma oscillatory activity and impairments in the phase-amplitude theta-gamma coupling (PAC) were found in the olfactory bulb (OB) circuit of P301S mice, which were maintained through the whole course of pathology development. In contrast, LFP oscillatory activity and PAC indices were normal in the entorhinal cortex, hippocampal CA1 and CA3 nuclei. Field excitatory postsynaptic potential (fEPSP) recordings from the Shaffer collateral (SC)-CA1 hippocampal stratum pyramidal revealed a significant altered synaptic LTP response to high-frequency stimulation (HFS): at three months of age, no significant difference between genotypes was found in basal synaptic activity, while signs of a deficit in short term plasticity were revealed by alterations in the fEPSPs. At six months of age, a slight deviance was found in basal synaptic activity and significant differences were observed in the LTP response. The alterations in network oscillations at the OB level and impairments in the functioning of the SC-CA1 pyramidal synapses strongly suggest that the progression of tau pathology elicited a brain area, activity-dependent disturbance in functional synaptic transmission. These findings point to early major alterations of neuronal activity in the OB circuit prior to the disturbance of hippocampal synaptic plasticity, possibly involving tauopathy in the anomalous FC. Further research should determine whether those early deficits in the OB network oscillations and FC are possible mechanisms that potentially promote the emergence of hippocampal synaptic impairments during the progression of tauopathy.
Deep Anatomical Context Feature Learning for Cephalometric Landmark Detection

In the past decade, anatomical context features have been widely used for cephalometric landmark detection and significant progress is still being made. However, most existing methods rely on handcrafted graphical models rather than incorporating anatomical context during training, leading to suboptimal performance. In this study, we present a novel framework that allows a Convolutional Neural Network (CNN) to learn richer anatomical context features during training. Our key idea consists of the Local Feature Perturbator (LFP) and the Anatomical Context loss (AC loss). When training the CNN, the LFP perturbs a cephalometric image based on prior anatomical distribution, forcing the CNN to gaze relevant features more globally. Then AC loss helps the CNN to learn the anatomical context based on spatial relationships between the landmarks. The experimental results demonstrate that the proposed framework makes the CNN learn richer anatomical representation, leading to increased performance. In the performance comparisons, the proposed scheme outperforms state-of-the-art methods on the ISBI 2015 Cephalometric X-ray Image Analysis Challenge.
Bioactive Peptides and Dietary Polyphenols: Two Sides of the Same Coin

The call for health-promoting nutraceuticals and functional foods containing bioactive compounds is growing. Among the great diversity of functional phytochemicals, polyphenols and, more recently, bioactive peptides have stood out as functional compounds. The amount of an ingested nutrient able to reach the bloodstream and exert the biological activity is a critical factor, and is affected by several factors, such as food components and food processing. This can lead to unclaimed interactions and/or reactions between bioactive compounds, which is particularly important for these bioactive compounds, since some polyphenols are widely known for their ability to interact and/or precipitate proteins/peptides. This review focuses on this important topic, addressing how these interactions could affect molecules digestion, absorption, metabolism and (biological)function. At the end, it is evidenced that further research is needed to understand the true effect of polyphenol-bioactive peptide interactions on overall health outcomes.
Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.