Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinicopathological and mutational differences between tumors with multiple metastases and single lung metastasis in colorectal cancer

Cancer metastasis, particularly multiple metastatic cancer, is a significant event that affects patient prognosis. However, single metastasis can be treated by partial resection, although the clinicopathological and molecular profile of single lung metastasis has not been thoroughly elucidated. The present study examined tumor heterogeneity by comparing the mutation status between primary colorectal cancer (CRC) and corresponding metastatic lesions to identify prognostic factors associated with single lung metastasis and multiple metastases. The present study enrolled 31 cases of CRC; 20 cases with multiple metastases and 11 cases with single lung metastasis. Clinicopathologically, all cases with multiple metastases were tubular adenocarcinoma, and 3/11 cases with single metastasis were mucinous adenocarcinoma originating from the left side, the remaining 8 cases were tubular adenocarcinoma from the left side. CRC cases with multiple metastases exhibited more frequent vascular invasion, but not lymphatic invasion, than those with single lung metastasis. Furthermore, CRC with multiple metastases was associated with strong tumor budding (P=0.04). Patients with CRC with multiple metastases had lower recurrence-free survival rates compared with those with single lung metastasis (P=0.02). However, there was no significant difference between these two groups in terms of overall survival rates. A next-generation sequencing cancer hotspot panel was used to analyze a heterochronous multiple metastases case, including brain metastasis. Sanger sequencing, immunohistochemistry and microsatellite instability were examined for all 31 cases to reveal the molecular features. KRAS and TP53 mutation signatures were largely preserved throughout the metastatic events. TP53/APC mutations and overexpression of p53 appeared to be associated with the presence of lymphovascular invasion and strong tumor budding, respectively, although these differences were not statistically significant. Early relapses in patients with CRC could be a sign for eventual multiple metastases, although these may not affect the overall survival of patients with CRC. Considerable mutational changes were seemingly rare during metastatic events in patients with CRC.
Self-Reported Changes in Risk Behaviours of Cardiovascular Diseases among School Adolescents in Nepal: Application of an Integrated Experiential Learning Approach

Cardiovascular diseases (CVDs) account for the largest proportion of all deaths in Nepal (30%). Studies report that CVDs often begin with modifiable lifestyle risk behaviours established during adolescence which manifest later. This study aimed to measure changes in the five mortality-associated CVD risk behaviours (i.e., consumption of tobacco, alcohol, and junk food, physical inactivity, and stress among school adolescents) using an integrative intervention with the experiential learning approach. The study was carried out for 24 weeks (25 credit hours) among 4,225 students from grades 8 to 10 in community schools in seven provinces in Nepal. Pre- and post-intervention in-class self-reported surveys were conducted for assessing change in the students’ aggregate risk behaviours. These percent changes were assessed through bivariate analysis. Key-informant interviews of teachers were conducted to assess their perceptions of the effectiveness of the intervention. At 24 weeks, the percentages of students reporting consumption of cigarettes and smokeless tobacco declined by 25% and 28% respectively, consumption of alcohol declined by 29%, consumption of instant noodles by 11%, and consumption of coke by 43%. The proportion of students reporting ‘going to school by foot every day’ increased by 11%, and those ‘exercising until they sweat’ increased by 29%. The percentage of students who reported feeling that their ‘life has been running as desired’ increased by 16%. Key-informant interviews of the teachers revealed that the intervention had contributed to improved motivation, knowledge, and attitude among students towards mitigating the risk behaviours. These interviews also recommended continuation of the intervention. The sample in this study has shown positive changes in school adolescents’ self-reported aggregate CVD risk behaviours using the experiential learning approach. However, further research should be conducted to explore the sustainability and scaling of these learning modules through the existing non-communicable disease (NCD) school curriculum activities in Nepal.
Variation in species richness, composition and herpetological community structure across a tropical habitat gradient of Palawan Island, Philippines

Information on species richness and community structure is invaluable for guiding conservation and management of biodiversity, but is rarely available in the megadiverse biodiversity conservation hotspot of Philippines – particularly for amphibians and reptiles. This study provides the first report and characterisation of amphibians and reptile communities across primary habitat types of the Victoria-Anepahan Mountain Range on Palawan Island along the western edge of the archipelago. A total of 41 amphibian and reptile species were recorded throughout our sampling sites (n = 27 species) or in targeted habitat searches (14 species). A species richness estimator predicted that 35 species may be present in our sampling sites, suggesting that a significant proportion of secretive species may continue to be unrecorded, especially for reptiles. Higher species richness was found in secondary growth than in mixed-use agricultural areas or even pristine forest. The low species richness recorded from pristine forest types may be due to these forests now being restricted to higher elevations where species diversity has been documented to decrease. Our results also show that complex community structures (species assemblages) are to be equally expected in both secondary growth and pristine forests. Together, our results show how species richness and community assemblages may vary across habitats, highlighting that old growth forest does not always support higher species richness, particularly in high elevations.
Ultrafine particle transport inside an operating room equipped with turbulent diffusers

ABSTRACT The present study numerically and experimentally investigates, for the first time in the literature, the thermo-fluid dynamics and ultrafine particle (UFP) concentration in surgical smoke, with diameters smaller than 100 nm, inside an actual operating room (OR) equipped with a turbulent circular diffuser system. The velocity values and temperature fields measured during the experimental analysis at different points in the OR are in reasonable agreement with the numerical results. For UFP diameters smaller than 70 nm, the particle concentration obtained from the numerical analysis agrees with the experimental data. The numerical results have demonstrated that at the sterile zone, UFP concentration caused by candles smoke is around particles/cm3, which are exposed by the occupants in OR. UFP decay time of 25 and 20 minutes are obtained in current analysis, respectively, in order to evaluate the time needed by the ventilation system to evacuate the particles from the OR.
On split null point and common fixed point problems for multivalued demicontractive mappings

ABSTRACT In this paper, we introduce a new viscosity iterative method for solving the split null point and common fixed point problems for maximal monotone operators and multivalued demicontractive mappings in Hilbert spaces, and we obtain some strong convergence results under some suitable assumptions. As applications, we apply our results to the split feasibility problem and the split minimization problem. The results obtained in this paper generalize and improve the recent ones announced by many authors.
Farmers' perception of coconut mite damage and crop diversification alternatives in the coastal belt of Tanzania

This article analysed farmers' perceptions of the effects of coconut mite in their livelihood and assessed crop diversification as a copping strategy for reduced coconut production. A socio-economic model of farmers' decisions on intercropping as an indicator for overall crop diversity was developed. The study was conducted between November 2009 and March 2010 in five districts in Tanzania, which were selected on the basis of the coconut's economic importance, using structured questionnaires which were administered to 200 household heads. Respondents were categorized in three groups – resource-poor farmers (43% of sample), medium-level farmers (50%) and well-off farmers (7%) according to six criteria. More than 80% of farmers were aware of the negative effects of the coconut mite. The result further indicated that the damaged nuts cause a loss of more than 30% of the cash income from coconut. Intercropping coconut with cassava, maize, cashew nut, sorghum and pineapples were the alternatives used by farmers to cope with declining coconut production caused by coconut mite and lethal yellowing disease. Land ownership and size, income from crops, non-farm income and family size were the main factors that influenced the farmer's decision to diversify crops. Although farmers diversify their cropping systems in order to be self-reliant, there is still a need to promote policies and programmes that will address coconut production constraints such pests and diseases such as rehabilitation of old plantations.
Detection of Low-Frequency and Multi-Stage Attacks in Industrial Internet of Things

The increasingly sophisticated cyber attacks have become a serious challenge for Industrial Internet of Things (IIoT), which presents two new characteristics: low frequency and multi-stage. That is, hackers could gain authority to attack industrial equipment/infrastructure gradually in a long interval through lurking, lateral intrusion and privilege escalation. While, the existing Machine Learning (ML) based intrusion detection schemes all require the participation of expert knowledge, so it is difficult to adaptively select an attack interval and a retraining period of the detection model in IIoT, resulting in poor detection performance. To solve above problems, a bidirectional long and short-term memory network with multi-feature layer (B-MLSTM) is designed. Firstly, sequence and stage feature layers are introduced in the model training phase model which can learn the corresponding attack interval from historical data, so that the model can effectively detect attacks with different intervals. Then, a double-layer reverse unit is introduced to update the detection model. By collecting information from test data and making association analysis with historical data, the retraining period is adaptively selected to match the new attack interval. Compared with the previous works, our proposed scheme has a lower false positive rate than existing schemes by at least 46.79%, and the false negative rate is reduced by at least 79.85%, which are carried out on three classic IIoT datasets.
Effect of sex hormones on the gonads of frog larvae (Rana clamitans): Sex inversion in females; stability in males

Among the rertcbrates there is 110 group of animals wliicli have more often or more successfully served in experiments on sex 1*e\yersid than the frogs. Even bclfore fertilization, by allo~viiig the eggs to become ' overripe ' t h~ough prolonged retention in the uteri, a distinct change of tlie sex ratio in favor of tlie males was obtained. Low temperatures applied to larvac at the stage of scx diff'erentiation shift the rat io ternporai4y iii the direction of tlie female, while high temp r a t u i w a t the same stage or even after completion of normal sex differeiitiatioii cause female larvae to ti*ansform into males. After. the discovery of the ses liormoiies it was oiily natural that tlieir possible effect on sex differentiation in tadpoles should be tested. In 1930 we injected estrogenic hormoiies extracted from urine of prepiant women into t! he hi-vae of €taliti claini tans. Although fairly large quantities were admiiii stercd no eflect beyond unspecific degeneration in tlie ovaries could he detected. I I o ~ e ~ e r , in 1936 there appearcd a paper hy Padoa announcing a ' paradosicd ' mascnliiiiziiig effect of an ovarian extract ' Crisballovar. ' First reports 0x1 the effect of pure aridrogcris appeared R year lateiwhen it became evident that testosterone very definitely causes a trwnsformatioii of genetical females into males (Gallien, '37, '38; Witsclii and Crown, ' 3 7 ; Foote, '38; f o r ' hidrd 1)y gi ants from the Nn tioii:il Research Council, ('ominittee for Research ill Prolnlciiis of Re\-. Tlie authors :ire also iiidebtcd to the f i b a Pliariiiaccutical Pioduets , I i ic. f o r the generous supply of sex hormones.
Soil texture and the phenotypic expression of maize (Zea mays L.)

The effect of variation in soil texture on the phenotype of an early maturing downy mildew resistant (DMR-EST) yellow variety of maize (Zea mays L.) was investigated. The four soil types used were of different textural classes, namely clay loam, sandy loam, sandy clay and sandy-clay loam. Five plastic buckets of 5 L size were filled with each soil type, making a total of 20. The buckets were labelled appropriately and arranged in a Completely Randomized Design (CRD) in the screen house of the Cocoa Research Institute of Nigeria (CRIN), Ibadan, Nigeria. Three seeds of maize were sown in each bucket and later thinned to one plant two weeks after planting. Watering was done at an interval of three days. At the blooming stage, leaf length, leaf width, plant height and stem diameter were measured using a tape graduated in centimeters. After harvest, grain weight and grain weight per ear were determined using a weighing balance. Cob length was also measured using a tape graduated in centimeters and the number of grains per ear was counted. Data amassed were subjected to analysis of variance (ANOVA) and means separated using the Least Significant Difference (LSD). The only character that was stable across the soil types is the grain weight. All other characters varied from one soil type to the other
Phase Spectrum of Time-flipped Speech Signals for Robust Spoofing Detection

In spoofing detection, it is important to capture the attributes related to spoofing attacks from a speech signal. A speech signal has various information such as the speaker, phrase, and environment. When the time sequence of the speech signal is flipped (i.e., time reversal and an additional circular shift), phase spectrum is changed although magnitude spectrum is not changed. It has the effect of data augmentation showing additional attributes in phase spectrum which are not included in magnitude spectrum. We assume that those additional attributes in phase spectrum of time-flipped speeches are related to unseen intraclass conditions. Motivated by our assumption, we propose a method of using the phase spectrum based features from both the original and time-flipped speech signals together. If our assumption stands good, it has the effect of reducing intraclass variances because the previously unseen attributes in magnitude spectrum can be considered in phase spectrum. The additional attributes in phase spectrum are helpful to build more robust spoofing detection systems. The experimental results on ASVspoof 2019 logical and physical access scenarios exhibit significant performance improvements for both scenarios compared to that of the baseline.
A Multi-Target SNR-Progressive Learning Approach to Regression Based Speech Enhancement

We propose a multi-target, signal-to-noise-ratio (SNR)-progressive learning (SNR-PL) framework for regression based speech enhancement (SE). At low SNR levels, it is often not easy to directly learn the complicated regression required in SE. We therefore decompose the original SE problem of mapping noisy to clean speech features, with a large SNR gap, into a series of sub-problems, each with a small SNR increment and presumably easier to learn. In our configurations, each hidden layer of the proposed regression neural network is guided to explicitly learn an intermediate target with a specified but small SNR gain. Tested on both deep neural network (DNN) and long short-term memory (LSTM) architectures, SNR-PL consistently outperforms the conventional “black box” DNN framework in terms of both objective measure superiority and network model compactness. Furthermore, with the best configured LSTM-based SNR-PL model, we often observe that the performance is easily saturated or even degraded when increasing the number of intermediate targets, due to the fact that useful information is lost in dimension reduction when involving more target layers. Accordingly, to address this information loss issue, we explore densely connected networks on top of the LSTM structure where the input and the preceding intermediate targets are concatenated together to learn the next target. Finally, to fully utilize the rich and complementary information of intermediate targets, a simple post-processing strategy is adopted to further improve the performance. Evaluated on the simulation speech data, experimental results in unseen noises cases demonstrate that the proposed approach consistently performs better than the conventional LSTM approach in terms of objective speech enhancement measures for speech intelligibility and quality. Furthermore, when evaluated on real data provided by the CHiME-4 Challenge for automatic speech recognition (ASR) of noisy microphone array speech, we show that the proposed approach with intermediate outputs can directly improve the ASR performance, while the conventional LSTM approach increases the word error rate.
P(l)aying Attention: Multi-modal, multi-temporal music control

The expressive control of sound and music through body movements is well-studied. For some people, body movement is demanding, and although they would prefer to express themselves freely using gestural control, they are unable to use such interfaces without diﬃculty. In this paper, we present the P(l)aying Attention framework for manipulating recorded music to support these people, and to help the therapists that work with them. The aim is to facilitate body awareness, exploration, and expressivity by allowing the manipulation of a pre-recorded ‘ensemble’ through an interpretation of body movement, provided by a machine-learning system trained on physiotherapist assessments and movement data from people with chronic pain. The system considers the nature of a person’s movement (e.g. protective) and oﬀers an interpretation in terms of the joint-groups that are playing a major role in the determination at that point in the movement, and to which attention should perhaps be given (or the opposite at the user’s discretion). Using music to convey the interpretation oﬀers informational (through movement soniﬁcation) and creative (through manipulating the ensemble by movement) possibilities. The approach oﬀers the opportunity to explore movement and music at multiple timescales and under varying musical aesthetics.
The morphology, taxonomy, and molecular phylogeny of Heterocladia and Trigenea (rhodomelaceae, rhodophyta), with delineation of the little‐known tribe Heterocladieae*

A morphological, anatomical, and molecular study of the two genera (Heterocladia and Trigenea) and three species of the tribe Heterocladieae (Rhodomelaceae, Ceramiales) is presented. First collections of male and female gametophytes of Heterocladia australis Decaisne and Trigenea umbellata J. Agardh and of tetrasporophytes of the type species of Trigenea, T. australis Sonder, have allowed a much clearer assessment of these taxa from a classical morphological standpoint. Reproductive and vegetative characters of the two Trigenea species are shown to be virtually identical to those of Heterocladia, which differs from Trigenea principally in having both flattened and terete lateral branches, as opposed to exclusively terete axes throughout. As a consequence, we propose to transfer the Trigenea species to the earlier‐named genus Heterocladia as H. caudata L. Phillips, H. ‐G. Choi, G.W. Saunders et Kraft, nom. nov. and H. umbellata ( J. Agardh) L. Phillips, H. ‐G. Choi, G.W. Saunders et Kraft, comb. nov. The close relationship of the three species is supported by molecular data, as nucleotide sequences of the 18S rRNA gene from each are nearly identical. The same sequences from species of eight other rhodomelaceous genera plus those from five outgroup taxa are analyzed to provide grounds for preliminary phylogenetic inferences about the position of the Heterocladieae in the Rhodomelaceae. Both the Heterocladieae and the Rhodomelaceae are monophyletic taxa in our analyses, the Heterocladieae grouping weakly with the Bostrychieae and the problematic Australian endemic genus Sonderella, the latter yet to be assigned to a tribe. Representatives of groups with which Heterocladia has been associated previously, such as the Lophothalieae and Brongniartelleae, appear to be only distantly related, although many more taxa need to be analyzed before the systematic position of the genus becomes clear.
Improving the Accuracy of Automatic Reconstruction of 3D Complex Buildings Models from Airborne Lidar Point Clouds

Due to high requirements of variety of 3D spatial data applications with respect to data amount and quality, automatized, efficient and reliable data acquisition and preprocessing methods are needed. The use of photogrammetry techniques—as well as the light detection and ranging (LiDAR) automatic scanners—are among attractive solutions. However, measurement data are in the form of unorganized point clouds, usually requiring transformation to higher order 3D models based on polygons or polyhedral surfaces, which is not a trivial process. The study presents a newly developed algorithm for correcting 3D point cloud data from airborne LiDAR surveys of regular 3D buildings. The proposed approach assumes the application of a sequence of operations resulting in 3D rasterization, i.e., creation and processing of a 3D regular grid representation of an object, prior to applying a regular Poisson surface reconstruction method. In order to verify the accuracy and quality of reconstructed objects for quantitative comparison with the obtained 3D models, high-quality ground truth models were used in the form of the meshes constructed from photogrammetric measurements and manually made using buildings architectural plans. The presented results show that applying the proposed algorithm positively influences the quality of the results and can be used in combination with existing surface reconstruction methods in order to generate more detailed 3D models from LiDAR scanning.
Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems

Parking Guidance and Information (PGI) systems aim at supporting drivers in finding suitable parking spaces, also by predicting the availability at driver’s Estimated Time of Arrival (ETA), leveraging information about the general parking availability situation. To do these predictions, most of the proposals in the literature dealing with on-street parking need to train a model for each road segment, with significant scalability issues when deploying a city-wide PGI. By investigating a real dataset we found that on-street parking dynamics show a high temporal auto-correlation. In this paper we present a new processing pipeline that exploits these recurring trends to improve the scalability. The proposal includes two steps to reduce both the number of required models and training examples. The effectiveness of the proposed pipeline has been empirically assessed on a real dataset of on-street parking availability from San Francisco (USA). Results show that the proposal is able to provide parking predictions whose accuracy is comparable to state-of-the-art solutions based on one model per road segment, while requiring only a fraction of training costs, thus being more likely scalable to city-wide scenarios.