Transcriptional landscape of a hypoxia response identifies cell-specific pathways for adaptation.

How the HIF-1 (Hypoxia-Inducible) transcription factor drives and coordinates distinct responses to low oxygen across diverse cell types is poorly understood. We present a multi-tissue single-cell gene-expression atlas of the hypoxia response of the nematode . This atlas highlights how cell-type-specific HIF-1 responses overlap and diverge among and within neuronal, intestinal, and muscle tissues.

Deletion of VPS50 protein in mouse brain impairs synaptic function and behavior.

Background: The VPS50 protein functions in synaptic and dense core vesicle acidification, and perturbations of VPS50 function produce behavioral changes in Caenorhabditis elegans. Patients with mutations in VPS50 show severe developmental delay and intellectual disability, characteristics that have been associated with autism spectrum disorders (ASDs). The mechanisms that link VPS50 mutations to ASD are unknown.

A DEAD-box helicase drives the partitioning of a pro-differentiation NAB protein into nuclear foci.

How cells regulate gene expression in a precise spatiotemporal manner during organismal development is a fundamental question in biology. Although the role of transcriptional condensates in gene regulation has been established, little is known about the function and regulation of these molecular assemblies in the context of animal development and physiology. Here we show that the evolutionarily conserved DEAD-box helicase DDX-23 controls cell fate in Caenorhabditis elegans by binding to and facilitating the condensation of MAB-10, the C.

Deletion of VPS50 protein in mice brain impairs synaptic function and behavior.

VPS50, is an accessory protein, involved in the synaptic and dense core vesicle acidification and its alterations produce behavioral changes in C.elegans. Here, we produce the mosaic knock out (mKO) of VPS50 using CRISPR/Cas9 system in both cortical cultured neurons and whole animals to evaluate the effect of VPS50 in regulating mammalian brain function and behavior.

USPoint: Self-Supervised Interest Point Detection and Description for Ultrasound-Probe Motion Estimation During Fine-Adjustment Standard Fetal Plane Finding.

Ultrasound (US)-probe motion estimation is a fundamental problem in automated standard plane locating during obstetric US diagnosis. Most recent existing recent works employ deep neural network (DNN) to regress the probe motion. However, these deep regressionbased methods leverage the DNN to overfit on the specific training data, which is naturally lack of generalization ability for the clinical application.

Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep-learning-based analysis.

Objective: Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan-acquisition order is not prescribed. Using deep-learning-based video analysis, the aim of this study was to develop a deeper understanding of the clinical workflow undertaken by sonographers during second-trimester anomaly scans.

Visual-Assisted Probe Movement Guidance for Obstetric Ultrasound Scanning using Landmark Retrieval.

Automated ultrasound (US)-probe movement guidance is desirable to assist inexperienced human operators during obstetric US scanning. In this paper, we present a new visual-assisted probe movement technique using automated landmark retrieval for assistive obstetric US scanning. In a first step, a set of landmarks is constructed uniformly around a virtual 3D fetal model.

Function and Safety of SlowflowHD Ultrasound Doppler in Obstetrics.

SlowflowHD is a new ultrasound Doppler imaging technology that allows visualization of flow within small blood vessels. In this mode, a proprietary algorithm differentiates between low-speed flow and signals attributed to tissue motion so that microvessel vasculature can be examined. Our objectives were to describe the low-velocity Doppler mode principles, to assess the bone thermal index (TIb) safety parameter in obstetric ultrasound scans and to evaluate adherence to professional guidelines.

Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.

Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed.

Replication stress promotes cell elimination by extrusion.

Cell extrusion is a mechanism of cell elimination that is used by organisms as diverse as sponges, nematodes, insects and mammals. During extrusion, a cell detaches from a layer of surrounding cells while maintaining the continuity of that layer. Vertebrate epithelial tissues primarily eliminate cells by extrusion, and the dysregulation of cell extrusion has been linked to epithelial diseases, including cancer.

Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos.

Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies on the effectiveness of ultrasound training schemes, the role of ultrasound simulation in training, and the effect of introducing scanning guidelines and standards on diagnostic image quality. The Big Data era, and the recent and rapid emergence of machine learning as a more mainstream large-scale data analysis technique, presents a fresh opportunity to study sonography in the field at scale for the first time.

Differentiating Operator Skill during Routine Fetal Ultrasound Scanning using Probe Motion Tracking.

In this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators' personal scanning styles. In this study, probe motion data during routine second-trimester fetal ultrasound scanning was acquired by operators of known experience levels (2 newly-qualified operators and 10 expert operators).

Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound.

We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the world-wide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal.

Spatio-temporal visual attention modelling of standard biometry plane-finding navigation.

We present a novel multi-task neural network called Temporal SonoEyeNet (TSEN) with a primary task to describe the visual navigation process of sonographers by learning to generate visual attention maps of ultrasound images around standard biometry planes of the fetal abdomen, head (trans-ventricular plane) and femur. TSEN has three components: a feature extractor, a temporal attention module (TAM), and an auxiliary video classification module (VCM). A soft dynamic time warping (sDTW) loss function is used to improve visual attention modelling.

Effect of steel slag in recycling waste activated sludge to produce anaerobic granular sludge.

The amount of waste activated sludge (WAS) has grown dramatically in China. WAS is considered as a problematic and hazardous waste, which should be disposed in a safe and sustainable manner. In order to recycle WAS to an anaerobic granular sludge (AnGS) process for anaerobic digestion, Fe powder and steel slags (rusty and clean slags) were used to enhance the granulation process.

Self-Supervised Representation Learning for Ultrasound Video.

Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications. Therefore, there is significant interest in learning representations from unlabelled raw data.

Discovering Salient Anatomical Landmarks by Predicting Human Gaze.

Anatomical landmarks are a crucial prerequisite for many medical imaging tasks. Usually, the set of landmarks for a given task is predefined by experts. The landmark locations for a given image are then annotated manually or via machine learning methods trained on manual annotations.

A new strategy to recover from volatile fatty acid inhibition in anaerobic digestion by photosynthetic bacteria.

The accumulation of volatile fatty acids (VFAs) can decrease reactor pH and inhibit methane-producing process. For the first time, photosynthetic bacteria (PSB) were used to recover from VFAs inhibition (pH 6.0) of an anaerobic digestion system.

Safety Indices of Ultrasound: Adherence to Recommendations and Awareness During Routine Obstetric Ultrasound Scanning.

Purpose:  To analyze bioeffect safety indices and assess how often operators look at these indices during routine obstetric ultrasound.

Materials And Methods:  Automated analysis of prospectively collected data including video recordings of full-length ultrasound scans coupled with operator eye tracking was performed. Using optical recognition, we extracted the Mechanical Index (MI), Thermal Index in soft tissue (TIs), and Thermal Index in bone (TIb) values and ultrasound mode.

Spatio-Temporal Partitioning and Description of Full-Length Routine Fetal Anomaly Ultrasound Scans.

This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2 and 2 + CNN, LSTM, and convolutional LSTM are investigated and compared. The contributions of short-term and long-term temporal changes are studied, and a multi-stream framework analysis is found to achieve the best top-1 accuracy=0.

Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention.

Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable representations of images can be learned without manual annotations by modeling human visual attention. The basis of our analyses is a unique gaze tracking dataset of sonographers performing routine clinical fetal anomaly screenings.

Expected-value bias in routine third-trimester growth scans.

Objectives: Operators performing fetal growth scans are usually aware of the gestational age of the pregnancy, which may lead to expected-value bias when performing biometric measurements. We aimed to evaluate the incidence of expected-value bias in routine fetal growth scans and assess its impact on standard biometric measurements.

Methods: We collected prospectively full-length video recordings of routine ultrasound growth scans coupled with operator eye tracking.

Effect of different initial low pH conditions on biogas production, composition, and shift in the aceticlastic methanogenic population.

Anaerobic digestion (AD) reactors often encounter low pH inhibition during startup and high organic loading periods. The use of a large amount of NaOH in order to raise and buffer the low pH, is reported to be inhibitory to methanogens. In order to address this problem, we acclimatized aceticlastic methanogens to low pH.

Variability of release rate of flame retardants in wastewater treatment plants.

Information on variability is important in the assessment of the releases and potential risks of brominated flame retardants (BRFs) in the environment, but related data are limited. In this study, two release-characterizing parameters, release fraction to final effluent and influent-biosolids transfer coefficient, were used to quantify releases of five BFRs from eight secondary wastewater treatment plants (WWTPs). The five BFRs are recalcitrant, hydrophobic, and low in volatility.

Endoplasmic Reticulum Homeostasis Is Modulated by the Forkhead Transcription Factor FKH-9 During Infection of .

Animals have evolved critical mechanisms to maintain cellular and organismal proteostasis during development, disease, and exposure to environmental stressors. The Unfolded Protein Response (UPR) is a conserved pathway that senses and responds to the accumulation of misfolded proteins in the endoplasmic reticulum (ER) lumen. We have previously demonstrated that the IRE-1-XBP-1 branch of the UPR is required to maintain ER homeostasis during larval development in the presence of pathogenic In this study, we identify loss-of-function mutations in four conserved transcriptional regulators that suppress the larval lethality of mutant animals caused by immune activation in response to infection by pathogenic bacteria: FKH-9, a forkhead family transcription factor; ARID-1, an ARID/Bright domain-containing transcription factor; HCF-1, a transcriptional regulator that associates with histone modifying enzymes; and SIN-3, a subunit of a histone deacetylase complex.