Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows.

To reduce methane (CH4) emissions of dairy cows by animal breeding, CH4 measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH4, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH4 and carbon dioxide production recorded by GreenFeed (GF) and for CH4 and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH4 recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH4 production (grams/day) by GF units and of CH4 concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH4 records were averaged per week. For daily and weekly mean GF CH4 the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH4 the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH4 production and sniffer CH4 concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH4 concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH4 production (grams/day) as recorded with GF.

Cybersecurity Analysis of Wearable Devices: Smartwatches Passive Attack.

Wearable devices are starting to gain popularity, which means that a large portion of the population is starting to acquire these products. This kind of technology comes with a lot of advantages, as it simplifies different tasks people do daily. However, as they recollect sensitive data, they are starting to be targets for cybercriminals. The number of attacks on wearable devices forces manufacturers to improve the security of these devices to protect them. Many vulnerabilities have appeared in communication protocols, specifically Bluetooth. We focus on understanding the Bluetooth protocol and what countermeasures have been applied during their updated versions to solve the most common security problems. We have performed a passive attack on six different smartwatches to discover their vulnerabilities during the pairing process. Furthermore, we have developed a proposal of requirements needed for maximum security of wearable devices, as well as the minimum requirements needed to have a secure pairing process between two devices via Bluetooth.

Packets-to-Prediction: An Unobtrusive Mechanism for Identifying Coarse-Grained Sleep Patterns with WiFi MAC Layer Traffic.

A good night's sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused group, we devised a contactless, unobtrusive mechanism to detect sleep patterns, which, contrary to existing sensor-based solutions, does not require the subject to put on any sensors on the body or buy expensive sleep sensing equipment. We named this mechanism Packets-to-Predictions(P2P) because we leverage the WiFi MAC layer traffic collected in the home and university environments to predict "sleep" and "awake" periods. We first manually established that extracting such patterns is feasible, and then, we trained various machine learning models to identify these patterns automatically. We trained six machine learning models-K nearest neighbors, logistic regression, random forest classifier, support vector classifier, gradient boosting classifier, and multilayer perceptron. K nearest neighbors gave the best performance with 87% train accuracy and 83% test accuracy.

Exploring LoRaWAN Traffic: In-Depth Analysis of IoT Network Communications.

In the past decade, Long-Range Wire-Area Network (LoRaWAN) has emerged as one of the most widely adopted Low Power Wide Area Network (LPWAN) standards. Significant efforts have been devoted to optimizing the operation of this network. However, research in this domain heavily relies on simulations and demands high-quality real-world traffic data. To address this need, we monitored and analyzed LoRaWAN traffic in four European cities, making the obtained data and post-processing scripts publicly available. For monitoring purposes, we developed an open-source sniffer capable of capturing all LoRaWAN communication within the EU868 band. Our analysis discovered significant issues in current LoRaWAN deployments, including violations of fundamental security principles, such as the use of default and exposed encryption keys, potential breaches of spectrum regulations including duty cycle violations, SyncWord issues, and misaligned Class-B beacons. This misalignment can render Class-B unusable, as the beacons cannot be validated. Furthermore, we enhanced Wireshark's LoRaWAN protocol dissector to accurately decode recorded traffic. Additionally, we proposed the passive reception of Class-B beacons as an alternative timebase source for devices operating within LoRaWAN coverage under the assumption that the issue of misaligned beacons can be addressed or mitigated in the future. The identified issues and the published dataset can serve as valuable resources for researchers simulating real-world traffic and for the LoRaWAN Alliance to enhance the standard to facilitate more reliable Class-B communication.

SpaceART SpaceWire Sniffer for Link Monitoring: A Complete Communication Analysis in a Time-Constrained Test Scenario.

The on-board communication standard adopted in current generation space missions of the European Space Agency, and many other agencies as well, is SpaceWire (SpW). In a SpW network, data are exchanged as well-formed packets, whose structure offers low packet overhead and allows developers to tailor their implementation for SpW applications. The development of SpW-compliant devices requires a specific set of test instruments, namely Electrical Ground Support Equipment (EGSE), to verify the correct functionality of SpW units under test. An example of a SpW EGSE is the SpaceART EGSE Emulator, an EGSE for generation and processing of SpW packets for a time-constrained use-case scenario. This EGSE has been developed to address a mission-related SpW communication between a device and an Instrument Control Unit (ICU). It has allowed the generation and processing of specific SpW packets, which cannot be provided by the mostly used general-purpose SpW EGSEs. In this scope, the SpaceART SpW Sniffer, a SpW link analyser for unobtrusive monitoring of SpW link, has been employed to run a comprehensive set of tests and provide further information on the considered scenario. The SpaceART EGSE Emulator-ICU communication has been thoroughly tested through the SpaceART SpW Sniffer, unobtrusively analysing the exchanged data and allowing to assess the compliance with the defined time constraints from an external point of view. The use of the Sniffer has been crucial for testing the on-board communication, providing important support for the success of the mission employing the tested ICU.

A Wi-Fi-Based Passive Indoor Positioning System via Entropy-Enhanced Deployment of Wi-Fi Sniffers.

This study presents a Wi-Fi-based passive indoor positioning system (IPS) that does not require active collaboration from the user or additional interfaces on the device-under-test (DUT). To maximise the accuracy of the IPS, the optimal deployment of Wi-Fi Sniffers in the area of interest is crucial. A modified Genetic Algorithm (GA) with an entropy-enhanced objective function is proposed to optimize the deployment. These Wi-Fi Sniffers are used to scan and collect the DUT's Wi-Fi received signal strength indicators (RSSIs) as Wi-Fi fingerprints, which are then mapped to reference points (RPs) in the physical world. The positioning algorithm utilises a weighted k-nearest neighbourhood (WKNN) method. Automated data collection of RSSI on each RP is achieved using a surveying robot for the Wi-Fi 2.4 GHz and 5 GHz bands. The preliminary results show that using only 20 Wi-Fi Sniffers as features for model training, the offline positioning accuracy is 2.2 m in terms of root mean squared error (RMSE). A proof-of-concept real-time online passive IPS is implemented to show that it is possible to detect the online presence of DUTs and obtain their RSSIs as online fingerprints to estimate their position.

Myxococcus CsgA, Drosophila Sniffer, and human HSD10 are cardiolipin phospholipases.

Myxococcus xanthus development requires CsgA, a member of the short-chain alcohol dehydrogenase (SCAD) family of proteins. We show that CsgA and SocA, a protein that can replace CsgA function in vivo, oxidize the 2'-OH glycerol moiety on cardiolipin and phosphatidylglycerol to produce diacylglycerol (DAG), dihydroxyacetone, and orthophosphate. A lipid extract enriched in DAGs from wild-type cells initiates development and lipid body production in a csgA mutant to bypass the mutational block. This novel phospholipase C-like reaction is widespread. SCADs that prevent neurodegenerative disorders, such as Drosophila Sniffer and human HSD10, oxidize cardiolipin with similar kinetic parameters. HSD10 exhibits a strong preference for cardiolipin with oxidized fatty acids. This activity is inhibited in the presence of the amyloid ß peptide. Three HSD10 variants associated with neurodegenerative disorders are inactive with cardiolipin. We suggest that HSD10 protects humans from reactive oxygen species by removing damaged cardiolipin before it induces apoptosis.

New method of screening for COVID-19 disease using sniffer dogs and scents from axillary sweat samples.

BACKGROUND: Early screening for COVID-19 is needed to limit the spread of the virus. The aim of this study is to test if the sniffer dogs can be successfully trained to identify subjects with COVID-19 for 'proof of concept' and 'non-inferiority' against PCR. We are calling this method, Dognosis (DN). METHODS: Four hundred and fifty-nine subjects were included, 256 (Group 'P') were known cases of COVID-19 (PCR positive, some with and some without symptoms) and 203 (Group 'C') were PCR negative and asymptomatic (control). Samples were obtained from the axillary sweat of each subject in a masked fashion. Two dogs trained to detect specific Volatile Organic Compounds for COVID-19 detection were used to test each sample. RESULTS: [DN] turned out positive (+) in all the cases that were PCR positive (100% sensitivity). On the other hand, [DN] turned positive (+) in an average of 12.5 cases (6.2%) that were initially PCR negative (apparent specificity of 93.8%). When the PCR was repeated, true specificity was 97.2%. These parameters varied in subgroups from 100% sensitivity and 99% specificity in symptomatic patients to 100% sensitivity and 93% specificity in asymptomatic patients. CONCLUSION: DN method shows high sensitivity and specificity in screening COVID-19 patients.

COVID-19 sniffer dog experimental training: Which protocol and which implications for reliable sidentification?

The introduction of trained sniffer dogs for COVID-19 detection could be an opportunity, as previously described for other diseases. Dogs could be trained to detect volatile organic compounds (VOCs), the whiff of COVID-19. Dogs involved in the study were three, one male and two females from different breeds, Black German Shepherd, German Shepherd, and Dutch Shepherd. The training was performed using sweat samples from SARS-CoV2 positive patients and from SARS-Cov2 free patients admitted at the University Hospital Campus Bio-medico of Rome. Gauze with sweat was collected in a glass jar with a metal top and put in metal boxes used for dog training. The dog training protocol was performed in two phases: the olfactory conditioning and the olfactory discrimination research. The training planning was focused on the switch moment for the sniffer dog, the moment when the dog was able to identify VOCs specific for COVID-19. At this time, the dog was able to identify VOCs specific for COVID-19 with significant reliability, in terms of the number of correct versus incorrect (p < 0.0001) reporting. In conclusion, this protocol could provide a useful tool for sniffer dogs' training and their introduction in a mass screening context. It could be cheaper and faster than a conventional testing method.

Sensory Perception of Non-Deuterated and Deuterated Organic Compounds.

The chemical background of olfactory perception has been subject of intensive research, but no available model can fully explain the sense of smell. There are also inconsistent results on the role of the isotopology of molecules. In experiments with human subjects it was found that the isotope effect is weak with acetone and D6 -acetone. In contrast, clear differences were observed in the perception of octanoic acid and D15 -octanoic acid. Furthermore, a trained sniffer dog was initially able to distinguish between these isotopologues of octanoic acid. In chromatographic measurements, the respective deuterated molecule showed weaker interaction with a non-polar liquid phase. Quantum chemical calculations give evidence that deuterated octanoic acid binds more strongly to a model receptor than non-deuterated. In contrast, the binding of the non-deuterated molecule is stronger with acetone. The isotope effect is calculated in the framework of statistical mechanics. It results from a complicated interplay between various thermostatistical contributions to the non-covalent free binding energies and it turns out to be very molecule-specific. The vibrational terms including non-classical zero-point energies play about the same role as rotational/translational contributions and are larger than bond length effects for the differential isotope perception of odor for which general rules cannot be derived.

Canine olfactory detection and its relevance to medical detection.

The extraordinary olfactory sense of canines combined with the possibility to learn by operant conditioning enables dogs for their use in medical detection in a wide range of applications. Research on the ability of medical detection dogs for the identification of individuals with infectious or non-infectious diseases has been promising, but compared to the well-established and-accepted use of sniffer dogs by the police, army and customs for substances such as money, explosives or drugs, the deployment of medical detection dogs is still in its infancy. There are several factors to be considered for standardisation prior to deployment of canine scent detection dogs. Individual odours in disease consist of different volatile organic molecules that differ in magnitude, volatility and concentration. Olfaction can be influenced by various parameters like genetics, environmental conditions, age, hydration, nutrition, microbiome, conditioning, training, management factors, diseases and pharmaceuticals. This review discusses current knowledge on the function and importance of canines' olfaction and evaluates its limitations and the potential role of the dog as a biomedical detector for infectious and non-infectious diseases.

Sniffer dogs as a screening/diagnostic tool for COVID-19: a proof of concept study.

BACKGROUND: Sniffer dogs are able to detect certain chemical particles and are suggest to be capable of helping diagnose some medical conditions and complications, such as colorectal cancer, melanoma, bladder cancer, and even critical states such as hypoglycemia in diabetic patients. With the global spread of COVID-19 throughout the world and the need to have a real-time screening of the population, especially in crowded places, this study aimed to investigate the applicability of sniffer dogs to carry out such a task. METHODS: Firstly, three male and female dogs from German shepherd (Saray), German black (Kuzhi) and Labrador (Marco) breeds had been intensively trained throughout the classical conditioning method for 7 weeks. They were introduced to human specimens obtained from the throat and pharyngeal secretions of participants who were already reported positive or negative for SARS-COV-2 infection be RT-PCR. Each dog underwent the conditioning process for almost 1000 times. In the meantime another similar condition process was conducted on clothes and masks of COVID-19 patient using another three male and female dogs from Labrador (Lexi), Border gypsy (Sami), and Golden retriever (Zhico) breeds. In verification test for the first three dogs, 80 pharyngeal secretion samples consisting of 26 positive and 54 negative samples from different medical centers who underwent RT-PCR test were in a single-blind method. In the second verification test for the other three dogs, masks and clothes of 50 RT-PCR positive and 70 RT-PCR negative cases from different medical center were used. RESULTS: In verification test using pharyngeal secretion, the sniffer dogs' detection capability was associated with a 65% of sensitivity and 89% of specificity and they amanged to identify 17 out of the 26 positive and 48 out of the 54 true negative samples. In the next verification test using patients' face masks and clothes, 43 out of the 50 positive samples were correctly identified by the dogs. Moreover, out of the 70 negative samples, 65 samples were correctly found to be negative. The sensitivity of this test was as high as 86% and its specificity was 92.9%. In addition, the positive and negative predictive values were 89.6 and 90.3%, respectively. CONCLUSION: Dogs are capable of being trained to identify COVID-19 cases by sniffing their odour, so they can be used as a reliable tool in limited screening.

Rapid safety and quality control during fish shelf-life by using a portable device.

BACKGROUND: Fish consumption is increasing nowadays both because of its positive role for health due to the abundant presence of unsaturated fatty acids and for its use in many new food preparations (e.g. raw fillet used for uncooked sushi and sashimi dishes). The growing food industry and increased demand for the long-term storage and preservation of food have created the need to develop methods that can easily track and preserve food freshness and safety throughout shelf-life (production, storage, shipment, and consumption). While E-nose technologies have already been used and tested for these purposes, scarce information is available in the literature on the feasibility of using other food devices to detect changes in perishable food like fish during shelf-life in order to predict and correctly manage all food storage phases. The aim of the present study was to investigate the potential of Food Sniffer® portable devices to define the quality and safety of salmon fillet and burger (Salmo salar) packaged in modified atmosphere at two refrigerated conditions (4 and 8 °C). RESULTS: An increase in biogenic amines and volatile compounds especially ketones and alcohols were observed, with large amounts at final storage times of 8 °C temperature. CONCLUSION: The Food Sniffer® application was able to anticipate unacceptability conditions of salmon samples also correlated with chemical and microbiological parameters. This could represent a valid support for food industry and retail to manage perishable food commodities preventing possible food risk as well. © 2020 Society of Chemical Industry.

Dancing away from Reform: The Inquest into the Death of Six Patrons of NSW Music Festivals.

In late 2019, the Deputy State Coroner for New South Wales (NSW), Magistrate Harriet Grahame, handed down her findings in relation to the death of six patrons of NSW music festivals. 179 pages in length, the decision represents a comprehensive attempt to understand recent music festival tragedies. The Coroner found that there was compelling evidence to support initiatives such as pill testing, changing the way festivals are policed (including discontinuing the use of sniffer dogs) and enhancing the overall safety of music festivals. The Coroner also questioned whether, in light of the evidence presented to the Inquest, there is a fundamental need to rethink contemporary approaches to drugs and criminalisation. In its response to date, the NSW Government has rejected the idea of pill testing and indicated that it will continue to use sniffer dogs at music festivals as a drug detection strategy. In one of the few recommendations of the Coroner that it has accepted, the NSW Government has agreed to the use of drug amnesty bins and agreed to a trial of less punitive measures of dealing with more minor drug possession offences via the use of Criminal Infringement Notices.

A New Transcutaneous Method for Breast Cancer Detection with Dogs.

We developed a new transcutaneous method for breast cancer detection with dogs: 2 dogs were trained to sniff skin secretion samples on compresses that had been worn overnight by women on their breast, and to recognize a breast cancer sample among 4 samples. During the test, the dogs recognized 90.3% of skin secretion breast cancer samples. This proof-of-concept study opens new avenues for the development of a reliable cancer diagnostic tool integrating olfactory abilities of dogs.

Short communication: Genetic correlations between methane and milk production, conformation, and functional traits.

Livestock produce CH4, contributing to the global warming effect. One of the currently investigated solutions to reduce CH4 production is selective breeding. The goal of this study was to estimate the genetic correlations between CH4 and milk production, conformation, and functional traits used in the selection index for Polish-Holstein cows. In total, 34,429 daily CH4 production observations collected from 483 cows were available, out of which 281 cows were genotyped. The CH4 was measured using a so-called sniffer device installed in an automated milking system. Breeding values for CH4 were estimated with the use of single-step genomic BLUP, and breeding values for remaining traits were obtained from the Polish national genomic evaluation. Genetic correlations between CH4 production and remaining traits were estimated using bivariate analyses. The estimated genetic correlations were in general low. The highest values were estimated for fat yield (0.21), milk yield (0.15), chest width (0.15), size (0.15), dairy strength (0.11), and somatic cell count (0.11). These estimates, as opposed to estimates for the remaining traits, were significantly different from zero.

Stretch-induced Ca2+ independent ATP release in hippocampal astrocytes.

KEY POINTS: Similar to neurons, astrocytes actively participate in synaptic transmission via releasing gliotransmitters. The Ca2+ -dependent release of gliotransmitters includes glutamate and ATP. Following an 'on-cell-like' mechanical stimulus to a single astrocyte, Ca2+ independent single, large, non-quantal, ATP release occurs. Astrocytic ATP release is inhibited by either selective antagonist treatment or genetic knockdown of P2X7 receptor channels. Our work suggests that ATP can be released from astrocytes via two independent pathways in hippocampal astrocytes; in addition to the known Ca2+ -dependent vesicular release, larger non-quantal ATP release depends on P2X7 channels following mechanical stretch. ABSTRACT: Astrocytic ATP release is essential for brain functions such as synaptic long-term potentiation for learning and memory. However, whether and how ATP is released via exocytosis remains hotly debated. All previous studies of non-vesicular ATP release have used indirect assays. By contrast, two recent studies report vesicular ATP release using more direct assays. In the present study, using patch clamped 'ATP-sniffer cells', we re-investigated astrocytic ATP release at single-vesicle resolution in hippocampal astrocytes. Following an 'on-cell-like' mechanical stimulus of a single astrocyte, a Ca2+ independent single large non-quantal ATP release occurred, in contrast to the Ca2+ -dependent multiple small quantal ATP release in a chromaffin cell. The mechanical stimulation-induced ATP release from an astrocyte was inhibited by either exposure to a selective antagonist or genetic knockdown of P2X7 receptor channels. Functional P2X7 channels were expressed in astrocytes in hippocampal brain slices. Thus, in addition to small quantal ATP release, larger non-quantal ATP release depends on P2X7 channels in astrocytes.

Automated acute kidney injury alerts.

Acute kidney injury (AKI) is one of the most common and probably one of the more consequential complications of critical illnesses. Recent information indicates that it is at least partially preventable; however, progress in its prevention, management, and treatment has been hindered by the scarcity of knowledge for effective interventions, inconsistencies in clinical practices, late identification of patients at risk for or with AKI, and limitations of access to best practices for prevention and management of AKI. Growing use of electronic health records has provided a platform for computer science to engage in data mining and processing, not only for early detection of AKI but also for the development of risk-stratification strategies and computer clinical decision-support (CDS) systems. Despite promising perspectives, the literature regarding the impact of AKI electronic alerts and CDS systems has been conflicting. Some studies have reported improvement in care processes and patient outcomes, whereas others have shown no effect on clinical outcomes and yet demonstrated an increase in the use of resources. These discrepancies are thought to be due to multiple factors that may be related to technology, human factors, modes of delivery of information to clinical providers, and level of expectations regarding the impact on patient outcomes. This review appraises the current body of knowledge and provides some outlines regarding research into and clinical aspects of CDS systems for AKI.

Cancer sniffer dogs: how can we translate this peculiarity in laboratory medicine? Results of a pilot study on gastrointestinal cancers.

BACKGROUND: Identification of cancer biomarkers to allow early diagnosis is an urgent need for many types of tumors, whose prognosis strongly depends on the stage of the disease. Canine olfactory testing for detecting cancer is an emerging field of investigation. As an alternative, here we propose to use GC-Olfactometry (GC/O), which enables the speeding up of targeted biomarker identification and analysis. A pilot study was conducted in order to determine odor-active compounds in urine that discriminate patients with gastrointestinal cancers from control samples (healthy people). METHODS: Headspace solid phase microextraction (HS-SPME)-GC/MS and GC-olfactometry (GC/O) analysis were performed on urine samples obtained from gastrointestinal cancer patients and healthy controls. RESULTS: In total, 91 key odor-active compounds were found in the urine samples. Although no odor-active biomarkers present were found in cancer carrier's urine, significant differences were discovered in the odor activities of 11 compounds in the urine of healthy and diseased people. Seven of above mentioned compounds were identified: thiophene, 2-methoxythiophene, dimethyl disulphide, 3-methyl-2-pentanone, 4-(or 5-)methyl-3-hexanone, 4-ethyl guaiacol and phenylacetic acid. The other four compounds remained unknown. CONCLUSIONS: GC/O has a big potential to identify compounds not detectable using untargeted GC/MS approach. This paves the way for further research aimed at improving and validating the performance of this technique so that the identified cancer-associated compounds may be introduced as biomarkers in clinical practice to support early cancer diagnosis.

The use of sniffer dogs for early detection of cancer: a One Health approach.

This review, which is part of the "Currents in One Health" series, describes and evaluates the current research on the utilization of trained medical scent detection, aka "sniffer" dogs for the detection of diseases, with particular emphasis on neoplasia, both within human and veterinary patients. A recent study by the authors that used sniffer dogs to detect differences in saliva from dogs diagnosed with various neoplastic processes compared with healthy control dogs is described. The concept of One Health is explored by the description of previous studies that have utilized sniffer dogs in the detection of human neoplasia (focusing on lung, prostate, and breast cancer) and veterinary neoplasia and demonstrating that further research in this arena can benefit multiple species. Future avenues of research and utilization of these findings are outlined. The companion Currents in One Health by Ungar et al, JAVMA, January 2024, addresses the use of sniffer dogs to detect human COVID-19 infections.